text:
namespace System.Net.NetworkInformation
{
    /// Provides Transmission Control Protocol (TCP) statistical data.
    public abstract class TcpStatistics
    {
        /// Gets the number of accepted Transmission Control Protocol (TCP) connection requests.
        public abstract long ConnectionsAccepted { get; }

        /// Gets the number of Transmission Control Protocol (TCP) connection requests made by clients.
        public abstract long ConnectionsInitiated { get; }

        /// Gets the total number of Transmission Control Protocol (TCP) connections established.
        public abstract long CumulativeConnections { get; }

        /// Gets the number of current Transmission Control Protocol (TCP) connections.
        public abstract long CurrentConnections { get; }

        /// Gets the number of Transmission Control Protocol (TCP) errors received.
        public abstract long ErrorsReceived { get; }

        /// Gets the number of failed Transmission Control Protocol (TCP) connection attempts.
        public abstract long FailedConnectionAttempts { get; }

        /// Gets the maximum number of supported Transmission Control Protocol (TCP) connections.
        public abstract long MaximumConnections { get; }

        /// Gets the maximum retransmission time-out value for Transmission Control Protocol (TCP) segments.
        public abstract long MaximumTransmissionTimeout { get; }

        /// Gets the minimum retransmission time-out value for Transmission Control Protocol (TCP) segments.
        public abstract long MinimumTransmissionTimeout { get; }

        /// Gets the number of RST packets recived by Transmission Control Protocol (TCP) connections.
        public abstract long ResetConnections { get; }

        /// Gets the number of Transmission Control Protocol (TCP) segments received.
        public abstract long SegmentsReceived { get; }

        /// Gets the number of Transmission Control Protocol (TCP) segments re-sent.
        public abstract long SegmentsResent { get; }

        /// Gets the number of Transmission Control Protocol (TCP) segments sent.
        public abstract long SegmentsSent { get; }

        /// Gets the number of Transmission Control Protocol (TCP) segments sent with the reset flag set.
        public abstract long ResetsSent { get; }
    }
}

redpajama_set_name: RedPajamaGithub


Adam is the CEO and owner of HRLocker, a cloud based HR platform for progressive companies in the tech and professional service sectors. HRLocker makes managing and recruiting easy and affordable by automating holiday and absentee management, timesheets, employee records and documents, training, CPD, performance management, recruitment and onboarding. He used his knowledge of the domain and hired some really good techies andHRLocker has flourished into a worldwide company that serves clients in over 40 different jurisdictions.
It's his vision to develop HRLocker into the best platform to use to improve the productivity of your workforce, make it easy and fun to use but also to ensure it helps you with the compliance stuff. HRLocker is now focused on developing its platform to address "the work place of the future".
Before HRLocker, Adam coached & facilitated companies through difficult change & systems initiatives including start-up & shut down programmes in the Telecommunications, Software, Energy, Manufacturing and Services Sectors. He also held senior HR Positions in O2 in both Ireland and the UK was head of HR for Esat Digifone.
In addition to Adam's passion for all things people productivity related, he is also a keen surfer and an avid Southampton supporter. Adam loves 'talking to partners and customers and watching his colleagues push the boundaries of the impossible to the possible'.


Asap Home Nurses, Inc was recognized and certified in 2006 by Centers for Medicare & Medicaid Services (CMS) as one of modern home-health care agencies which are scientifically measured and assessed to have high-quality home-health care services for promoting health and improving the quality of life. Asap Home Nurses, Inc is located at One Park Centre, Suite 17 Wadsworth, OH 44281 and can be contacted via phone number (330) 334-1025. Home Health Services being offerred by Asap Home Nurses, Inc includes nursing, physical therapy, occupational therapy, speech pathology, home health aide.


This week on Trapping Across America I am fired up to have Claudie Taylor from NC. Claudie has been trapping for 45 years. Many years catching over 100 otters. His best year was in 2006 when he caught 182 otters. The trapping methods on this DVD have been developed and proven successful for Claudie and those he has trained.
I think this was one of the first videos I bought and I know it opened the world of otter trapping to me. Claudie is one cool dude and I know you will enjoy the interview as much as I did.
Great show. One of the best!! Gonna buy his video today from FnT. Even though we can only get 5 otter in Illinois It is something I really wanna catch.


text:
Hotels near Norfolk State University
Norfolk State University is located in Norfolk, Virginia.
Overview Venues Hotels Events Restaurants
Top Rated Hotels from
The Founders Inn and Spa, Tapestry Collection by Hilton
5641 Indian River Rd Hotel
2 indoor pools, a full-service spa, and a restaurant are available at this hotel. Free WiFi in public areas and free self parking are also provided. Additionally, a fitness center, a bar/lounge, and a snack bar/deli are onsite.
Situated near the airport, this spa hotel is 0.1 mi (0.1 km) from Regent University and within 9 mi (15 km) of Greenbrier Mall and Veterans United Home Loans Amphitheater at Virginia Beach. Virginia Beach Sportsplex is 7.9 mi (12.7 km) away.
Renaissance Portsmouth-Norfolk Waterfront Hotel
425 Water St Hotel
This smoke-free hotel features an indoor pool, a restaurant, and a 24-hour fitness center. WiFi in public areas is free. Other amenities include a bar/lounge, a 24-hour business center, and self parking.
Situated in the historical district, this hotel is within a 5-minute walk of Portsmouth Naval Shipyard Museum and Children's Museum of Virginia. Waterside Festival Marketplace and MacArthur Center are also within 2 mi (3 km). Harbor Park Station is 30 minutes by foot.
Staybridge Suites Chesapeake, an IHG Hotel
709 Woodlake Drive Hotel
An indoor pool, a 24-hour fitness center, and a 24-hour business center are available at this smoke-free hotel. Free buffet breakfast is provided, as well as free WiFi in public areas, free self parking, and a free manager's reception. Other amenities include a spa tub, a business center, and conference space.
Located in the heart of Chesapeake, this hotel is 0.5 mi (0.8 km) from Chesapeake Conference Center and within 3 mi (5 km) of Greenbrier Mall and Chesapeake City Park. Oak Grove Lake Park and Chesapeake Regional Medical Center are also within 6 mi (10 km).
Courtyard by Marriott Chesapeake
1562 Crossways Blvd Hotel
This smoke-free hotel features a restaurant, a 24-hour fitness center, and a bar/lounge. Free WiFi in public areas and free self parking are also provided. Additionally, a snack bar/deli, a 24-hour business center, and a coffee shop/cafe are onsite.
Located in Greenbrier East, this hotel is 0.7 mi (1.2 km) from Greenbrier Mall and within 9 mi (15 km) of Norfolk Scope and Norfolk Premium Outlets. Norfolk State University and Virginia Beach Town Center are also within 12 mi (20 km).
Home2 Suites by Hilton Norfolk Airport
5620 East Princess Anne Road Hotel
Along with an indoor pool, this hotel has a restaurant and a fitness center. Free on-the-go breakfast, free WiFi in public areas, and free self parking are also provided. Other amenities include a bar/lounge, a business center, and laundry facilities.
Situated near the airport, this hotel is 1.2 mi (1.9 km) from Lake Taylor Hospital and within 3 mi (5 km) of Norfolk Premium Outlets and Lake Wright. Gallery of Military Circle and Sentara Leigh Hospital are also within 3 mi (5 km).
Courtyard by Marriott Norfolk Downtown
520 Plume street Hotel
This smoke-free hotel features an indoor pool, a restaurant, and a 24-hour fitness center. WiFi in public areas is free. Additionally, a bar/lounge, a snack bar/deli, and a 24-hour business center are onsite.
Located in Downtown Norfolk, this hotel is steps from MacArthur Center and Douglas MacArthur Memorial. Waterside Festival Marketplace and Wells Theater are also within 10 minutes. MacArthur Square Station and Civic Plaza Station are within a 5-minute walk.
Residence Inn Norfolk Downtown
227 West Brambleton Avenue Hotel
An indoor pool, a fitness center, and a snack bar/deli are available at this smoke-free hotel. Free continental breakfast and free WiFi in public areas are also provided. Additionally, coffee/tea in a common area, a business center, and a meeting room are onsite.
Situated in Norfolk, this hotel is within a 10-minute walk of Chrysler Museum of Art, Norfolk Scope, and The NorVa. Pagoda and Oriental Garden and MacArthur Center are also within 10 minutes. York Street-Free Mason Station is only a 3-minute walk and Monticello Station is 8 minutes.
Hyatt Place Chesapeake
709 Eden Way N Hotel
An indoor pool, a restaurant, and a 24-hour fitness center are available at this smoke-free hotel. Free buffet breakfast is provided, as well as free WiFi in public areas, free self parking, and a free airport shuttle. Other amenities include a bar/lounge, a coffee shop/cafe, and a business center.
Located in Greenbrier East, this hotel is 1.1 mi (1.8 km) from Greenbrier Mall and within 3 mi (5 km) of Oak Grove Lake Park and Chesapeake Regional Medical Center. Chesapeake Conference Center and Chesapeake City Park are also within 3 mi (5 km).
Econo Lodge at Military Circle
865 N Military Hwy Motel
Coffee/tea in a common area, laundry facilities, and a 24-hour front desk are available at this motel. Free continental breakfast, free WiFi in public areas, and free self parking are also provided. Other amenities include gift shops/newsstands and a vending machine.
Located in the heart of Norfolk, this motel is within 1 mi (2 km) of Gallery of Military Circle, Lake Taylor Hospital, and Sentara Leigh Hospital. Virginia Wesleyan University and Norfolk State University are also within 3 mi (5 km). Military Highway Station is 21 minutes by foot.
SureStay Hotel by Best Western Norfolk Little Creek
7940 Shore Drive Hotel
Along with a 24-hour front desk, this hotel has free self parking and free WiFi in public areas.
Situated in Norfolk, this hotel is 0.9 mi (1.4 km) from Norfolk Beaches and within 3 mi (5 km) of Hampton Roads Agricultural Research and Extension Center and Norfolk Botanical Garden. Little Creek Beach and Virginia Wesleyan University are also within 6 mi (10 km).
Find Hotels Near Norfolk State University
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redpajama_set_name: RedPajamaCommonCrawl


text:
« Store at bridgestone arena the store's exterior entrance | LVII state, farm stadium glendale feb 5 super »
Soccer players everybody who society says born philadelphia
Kobe Bryant 7.
These opportunities to get this far and to compete for the Cup are obviously rare, and many players have played many years without ever getting the chance to play in a Stanley Cup Final.
Engram had 116 receiving yards in the opener in Dallas and 113 in Tampa.
Los Angeles Clippers HP Field House, Orlando, ESPN 6 PM • Philadelphia 76ers vs.
Lionel Messi $104 million $72 million 432 million Soccer 4.
You just focus on the next game.
Cristiano Ronaldo 4.
I'm excited to work with them on the Built To Work program.
Alex Morgan 5.
ESPN: Eight more players and two coaches with the Miami Marlins have tested positive for COVID-19, as an outbreak has spread throughout their clubhouse and brought the total of cases in recent days to at least 14.
Indiana Pacers Visa Athletic Center, Orlando 7 PM • Los Angeles Lakers vs.
'Naming the Seattle Kraken demanded steep effort, sweat, research, dreaming, analyzing, listening to fans, respecting the region's natural beauty, sifting through 1-plus names and, no denying it, 20 months to get it right'.
• Stephanie Rudnick, former Head of communications and media relations for XFL, is now handling communications for We Are Angel City, the NWSL expansion create your own football jersey from Natalie Portman, Alexis Ohanian and Serena Williams; and is also a communications advisor for Beautiful Game Group LLC sports investment firm, both via Stephanie Rudnick LLC.
The Marlins postponed their flight home Sunday night after their series finale against the Phillies.
These rumors proved true once Thor bestowed the legendary war hammer, Mjolnir upon the young man.
So things like that go unnoticed now, but long term they know stuff about you.
Houston Rockets The Arena, Orlando ABC 8 PM • Dallas Mavericks vs.
Trout Joins Mays, Aaron, Jeter In Leadoff Spot For Topps.
• Stephanie Rudnick, former Head of communications and media relations for XFL, is now handling communications for We Are Angel City, the NWSL expansion franchise from Natalie Portman, Alexis Ohanian and Serena Williams; and is also a communications advisor for Beautiful Game Group LLC sports investment firm, both via Stephanie Rudnick LLC.
QSR chain Subway has been running a TV spot, with a heavy focus during March Madness games, in which several of its Famous Fans athlete spokespersons talk about Robinson's legacy, followed by a call for consumers to see in theaters.
4 • Super Bowl LIX TBD Feb.
Portland Trail Blazers The Arena, Orlando NBA TV 4 PM • Phoenix Suns vs.
Note: If a player's name is not listed, it is because he opted not to use a message on their jersey.
Absolutely, positively not.
• Due to the recent decision by China's General Administration of Sport that China will not host any international sporting events in 2020 as a result of the COVID-19 pandemic, all WTA tournaments that were scheduled in China on WTA's provisional calendar will not be held.
Phoenix Suns Visa Athletic Center, Orlando 9 PM • Christian McCaffery Carolina Panthers 99 • Derrick Henry Tennessee Titans 93 • Nick Chubb Cleveland Browns 92 • Ezekiel Elliot Dallas Cowboys 92 • Dalvin Cook Minnesota Vikings 91 • Aaron Jones Green Bay Packers 90 • Joe Mixon Cincinnati Bengals 89 • Saquon Barkle New York Giants 89 • Josh Jacobs Las Vegas Raiders 88 • Alvin Kamara New Orleans Saints 88 Highest-Paid Athletes 1.
On if there's anything from his experience that he can use to fool customize your own football gear rookie quarterback: Well, experience is going to help with all the times I've faced rookies, but you just can't go in thinking like, 'Oh, this is what it is.' You just have to go play the game.
Jimmy has been great from the day he got here.
The event generated $170 million in direct, indirect and induced impact derived from spending by visitors to the Dallas Arlington region – including $25 million spent on hotels and accommodations and $8 million in area restaurants – and also generated about $23 million in federal, state and local taxes, according to a study conducted for WWE by the Enigma Research Corp.
In September it was Devin McCourty, and now in October it's Stephon Gilmore.
Pete Alonso, New York Mets 15.
During the two-week period of Phase 3 there were a total of two positive tests among the 6 total tests.
FIFA World Cup $282M 5.
September 12th vs @RealRoyJonesJr on #Triller and PPV #frontlinebattle @TysonLeague.
According to Gillette, submitted videos will be viewed by Urschel, Williams, and Saffold, who will select some of their favorites to review, retweet, and name live on Twitter.
CB: It was like watching a movie.
2 The Top 10 Most Tweeted About Male Athletes in 2019 per Twitter Sports 1.
CB: First of all he was a free agent so he could have done anything that he wanted.
Back to Home Page.
Lionel Messi 3.
This entry was posted on Tuesday, August 11th, 2020 at 2:59 pm and is filed under Uncategorized. You can follow any responses to this entry through the RSS 2.0 feed. Both comments and pings are currently closed.


text:
Missed opportunities for improving practice performance in adult immunizations: a meta-narrative review of the literature
Natalia Loskutova1,
Craig Smail1,
Brian Webster2,
Kemi Ajayi1,
Julie Wood1 &
Jennifer Carroll1,3
BMC Family Practice volume 18, Article number: 108 (2017) Cite this article
We sought to characterize how the term "missed opportunities" is reported in the literature in the context of immunization rates and to assess how missed opportunities can be operationalized.
Peer-reviewed literature searches were conducted in April – May, 2015, to answer: "What methods research studies used to operationalize missed opportunities to vaccinate?" A meta-narrative review methodology was used.
Seven studies met inclusion criteria. The methodologies for quantifying missed opportunities fell into two general categories based on: 1. the number of healthcare encounters per patient without appropriate vaccination services, defined as a number of visits per patient with no vaccination related services (Missed opportunities per patient); 2. vaccination status as "non-vaccinated" among a group of patients who had a healthcare encounter where the vaccination should/could have had happened (Missed opportunities per population).
Our study provided an initial overview of the methods reported in the literature, and concluded that the quantifiable missed opportunity holds promise as a measurable outcome (variable) for research and quality improvement projects aimed to increase adult immunization recommendation and uptake in primary care.
Developing and providing immunizations are two of the greatest public health achievements. Adult immunization rates in US, however, remain well below Healthy People 2020 suggested targets for influenza vaccinations in adults >65 (target 90%), and for pneumococcal disease in adults ≥65 years (target 90%), and in persons at high risk for pneumococcal disease aged 18–64 (target 60%) [1]. Despite scientific advances in vaccine development, morbidity and mortality from preventable infectious diseases persist due to multiple documented barriers. Each year US primary care physicians provide more than 560 million office visits and are in the unique position to administer immunizations to patients of all ages [2]. However, health care encounters in which a person eligible to receive vaccinations is not vaccinated [3] occur in any setting where vaccinations are offered, and these occasions are viewed as missed opportunities to provide adequate vaccination for older adults [4].
It is common in published literature on vaccination to state that missed opportunities to vaccinate exist and likely contribute to the persistently high levels of vaccine-preventable illness and gaps in achieving the population-wide Healthy People 2020 goals. In published literature, however, missed opportunities to immunize are often referred to as an abstract concept that represents the end-result of a patient not being vaccinated due to existing barriers ranging from patient resistance, provider bias, or vaccine unavailability. The term missed opportunities generally is not characterized well and is often used interchangeably with other terms that refer to poor immunization status in individual patients or populations.
Scientific evidence suggests that in order to assess and potentially change a measure, that measure or term needs to be measurable and operationalized. In the social sciences, operationalization is commonly referred to as the process through which abstract concepts are translated into measurable variables [5]. This suggests that missed opportunities could be potentially developed into a measure of immunizations. However, it is unknown if missed opportunities can be systematically measured in a meaningful and repeatable way. In order to address missed opportunities, there is a need to define, quantify, and track missed opportunities. From a population health perspective, operationalizing missed opportunities for immunizations could allow us to identify strategies to reduce the proportion of primary care visits that are viewed as missed opportunities to vaccinate and subsequently to close the gap on vaccine-preventable illness.
The overall goal of this work was to characterize how the term missed opportunities is reported in the literature in the context of immunization rates and to assess whether and how missed opportunities can be operationalized. Specifically, the objectives of this paper are to: 1. characterize the missed opportunities in adult vaccinations in published articles relevant to primary care; 2. synthesize evidence pertaining to operationalization of missed opportunities concept; and 3. explore the potential of operationalized or quantifiable missed opportunities as a measurable outcome (variable) for research and quality improvement projects focused on adult immunizations in primary care.
This is a meta-narrative review study. We selected this method because we sought to assess and synthesize published evidence around operational definitions of missed opportunities for adult vaccinations for which, to the best of our knowledge, no operational definitions currently exist. The meta-narrative review methodology was designed specifically to summarize a research topic with no standard conceptualization (i.e., researchers bring different presumptions and goals to the research) and different paradigms (i.e., potential to explore more complex and diverse questions in one review, the possibility to include studies of different designs, utilizing multiple outcomes, varying systems, and different methodologies). Additionally, the meta-narrative review typically includes two key components: exploration of literature and the synthesis of data, resulting in the meta-narrative maps of the findings that highlight differences and similarities of various paradigms. In contrast, a systematic literature review is designed to summarize and ultimately grade the quality of evidence and the results of available carefully designed clinical trials to evaluate the evidence on the efficacy or effectiveness of healthcare interventions. Since the focus of this review was not on grading the quality of evidence related to immunization interventions but on the methodology for operationalizing a new outcome, the meta-narrative review methodology is a better fit for the objectives of the study. Thus, the meta-narrative review method was selected primarily due to the purpose of the study, the lack of standard definition of missed opportunities in the field. and the above-mentioned characteristics of the meta-narrative review methodology. We followed the meta-narrative review framework proposed by Wong, et al. [6]. We conducted a comprehensive meta-narrative review of the biomedical literature identified in the PubMed database that was published between January, 2000, and May, 2015.
Literature searches
We included studies of any design (i.e., randomized, observational, cross sectional, descriptive, qualitative), size or setting published in English in which adult immunizations was the primary outcome. We also included non-original or summarized literature (i.e., systematic or non-systematic literature reviews, commentaries).
A professional librarian employed full-time at the AAFP Leawood, KS, office, completed the initial literature search in April – May, 2015. To start the search, the following key words were used: immunizations; missed opportunities; vaccination; immunization schedule; and physician practice patterns.
The overall eligibility criteria were as follows:
the study cohort consisted of adults 18 and older
the focus of the study was on adult immunizations
the study was completed in 2000 or later
the study was published in English
Since the term missed opportunities is not a term in the MeSH controlled vocabulary (and hence is not included as a PubMed keyword), we searched the term in both the title and abstract of indexed articles. We also searched with conceptually related MeSH key words. From the results of both approaches, we identified records for title and abstract screening and review. See Additional file 1 for the full search strategy.
Meta-narrative review process
To characterize missed opportunities for adult immunizations, we first reviewed the titles and abstracts of papers retrieved using the aforementioned search criteria and key words. The titles and abstracts of all identified articles were reviewed by two reviewers (NL and CS) and selected for full text review based on the following: the articles should focus on immunizations in adults; the methods and/or results section of the abstracts should include evidence of or indication of use of data for missed opportunities; and/or describe methodology for how the term was operationalized or quantified; and/or have a numerical exemplification of missed opportunities presented in results.
Next, the records were included in the further full text review if they were deemed relevant to the objectives of the study based on full or partial alignment with the inclusion criteria. For those records, full text copies were obtained and included in full review. The articles were excluded if they did not provide a specific definition for missed opportunities or relevant methodology or did not include quantifiable results for missed opportunity in the abstract.
Three individuals (NL, CS, JW) reviewed the full text articles; at least two individuals reviewed each article. Full texts of these articles were reviewed using a template to summarize the study as follows: name (title, authors, date); purpose; population and setting; service or procedure; timeframe; methodology; missed opportunity definition; significant results or observations; methodological advantages and limitations. The articles were included in the results if they had a clear definition of missed opportunities, quantified or operationalized missed opportunities according to their definition or formula, and included a corresponding quantitative measure of missed opportunities in outcomes using data. The articles that originally partially aligned with the guiding review principals that did not provide additional information to satisfy all requirements including a specific definition for missed opportunities, relevant methodology or did not include quantifiable results for missed opportunity in the full text were excluded. Additionally, we reviewed reference lists from eligible studies and conducted additional searches for publications that cited eligible studies. The full texts of these articles were reviewed following the same process as above. The literature search and data extraction were completed in September, 2015.
Literature search and selection results
The literature search for the systematic review identified 80 possibly relevant citations. We included seven studies. The study selection process is described in Fig. 1.
Article selection process
A total of seven articles were included in the final sample. Of the total, two studies focused on influenza vaccinations only, four studies focused on pneumococcal and influenza vaccinations and one study included influenza, pneumococcal and tetanus adult vaccinations. The studies enrolled a combined total of 15,140 participants. The patient populations for the majority of the studies were individuals aged 65 years or older. The patients studied were from a variety of backgrounds including health centers in disadvantaged neighborhoods, Veterans Administration clinics, rural and urban ambulatory practices and hospitals. All studies utilized observational designs - four studies extracted data from retrospective reviews of medical records, one study had direct observations at the time of visit, and three studies incorporated surveys or interview data. Of the total, three studies were conducted in inpatient hospital settings and two were conducted in the ambulatory primary care. The summary of studies is presented in Table 1.
Table 1 Descriptive table summary of the studies that used data derived missed opportunity for clinical or preventative service or procedure. Observational, review and interventional studies in any clinical topic area are included
Missed opportunities to immunize operational methodologies
The studies provided variable detail in how the authors defined and determined missed opportunities. Generally, missed opportunity definitions were defined based on data including either visits (encounters), [7, 8] or a combination of patient vaccination status with encounters [9,10,11,12,13] (Table 1).
The most common definition of missed opportunities was estimated retrospectively; chart reviews or surveys were conducted to determine the vaccination status using reports or records of a past visit to a health care facility (Table 1). For example, Murer, et al. estimated missed opportunities based on the proportion of unvaccinated and amenable patients with at least one office visit between October and December, 2008, derived from the retrospective survey data [10].
In one study, missed opportunities were also estimated based on real-time visit observation and reported as a dichotomous outcome of either no vaccine recommendation (missed opportunity) or vaccine administration at the visit (no missed opportunity) [7].
Five of the seven studies did not take into account patients' willingness or refusal for vaccination when determining missed opportunities [7, 9, 11,12,13]. Nowalk, et al. incorporated patient refusal into their definition of missed opportunities by reporting visits where the vaccine was not discussed, not given, or not offered and refused by the patient. In that study, missed opportunities defined as above were quantified as the number of visits per patient [8]. Maurer, et al. incorporated patient willingness to be vaccinated in the report [10].
Variations in study design additionally included the length of observation period (from one to 5 years) and the type of healthcare encounter, ranging from a visit to a health care provider, a family physician consult, a hospital outpatient department, an emergency room to hospitalizations.
Data synthesis and meta-narrative maps of the common methodological approaches in defining missed opportunities
Here we describe a framework for operationalizing the missed opportunity term based on how the missed opportunity operational methods may be clustered given common methodological characteristics described in the included studies. We synthesized the methodologies identified in the studies and generated the meta-narrative maps of the findings with overall summary of how a "missed opportunity" term may be potentially operationalized (Fig. 2).
Meta-narrative maps of the common methodological approaches in operationalizing missed opportunities
Upon data synthesis, the reported methodologies for quantifying missed opportunities fell into two general categories: 1. Based on the number of healthcare encounters without appropriate vaccination services, defined as a number of visits per patient with no vaccination related services (missed opportunities per patient or Patient-level) 2. Based on vaccination status as "non-vaccinated" among a group of patients who had a health care encounter where the vaccination should/could have had happened presented as proportion of non-vaccinated patients who had an encounter with health care providers (missed opportunities per patient population or Population-level).
From the outcome measure perspective, the studies that used the former patient-level method generated a specific and detailed variable that was characterized by a nominal value per patient presented for a group as a mean and SD, distinctly different form currently used measures of vaccination coverage, i.e., proportion or percentage. The population-level method resulted in a dichotomous variable at the patient level present at the group (population) level as percentage, similar in format to the commonly used vaccination coverage metrics. The population-level missed opportunity measure is, however, distinctly different from the general population metric of vaccine coverage as it assumes the documented health care encounter during a specific time frame, while the vaccination rates do not take in consideration whether the patient actually visited the health care facilities or interacted with the health care.
The results of this study highlighted that missed opportunity is variably defined, indirectly assessed if at all, and does not appear to address or connect in a systematic way to what is known about the barriers to immunization. The results of our study, however, provide initial evidence that the missed opportunity term is operational based on the methodologies identified in several existing studies. Moreover, it can be operationalized or quantified as a measurable outcome (variable) using at least two different approaches: based on the results of a visit and based on patient vaccination status.
The diversity of the studies included in this review resulted in two general approaches for missed opportunity term operationalization – missed opportunities per patient or Patient-level and missed opportunities per patient population or Population-level. Each approach has their unique strengths and limitations. For example, classifying missed opportunities per patient (patient-level) as in Nowalk study may require higher quality data and documentation, needs to take in account circumstances of every visit, and may be labor-intensive [8]. This approach as such may be better suited for prospective, rigorous quality improvement and research studies with the possibility to track and implement changes in real time.
The data for the missed opportunities for patient population (population-level) seem easier to obtain, and the results are usually presented as percentage. However, that percentage in the studies we reviewed can be as high as 99.8% [12] and as such, a measure derived by this method may be prone to a ceiling effect, can only be calculated retrospectively, may not be a good measure for prospective studies, and is not very actionable because of the retrospective nature. This approach may be more suitable for simple quality improvement studies based on past performance and existing data such as Plan-Do-Study-Act (PDSA-based projects, for example. Overall, given the nascent state of research in this area, apparent lack of missed opportunity term standardization, and a limited number of studies published up to date, it is unclear how representative, accurate or comparable the approaches are based on their performance warranting a need for further research.
Despite the lack of standardization, the data synthesis revealed one common feature among all studies included in this review: that regardless of the methodology used, the missed opportunities are clearly associated with a patient encounter. That makes it easier to operationalize the term and to develop appropriate strategies, given that missed opportunities are directly tied to a patient visit with health care providers who have more control of the visits than of some other barriers to vaccinate.
While that leads to higher expectation placed on providers for stronger vaccine recommendations, [14,15,16] the promising aspect of addressing missed opportunities at the time when the visit actually occurs is by utilizing the care team-based strategies such as standing orders, registry-based approaches, and other team- and system- level efficacious intervention [17, 18]. Focusing on one visit at a time for appropriate vaccinations for a visiting patient may be a promising quality improvement approach for increasing overall vaccinations rates among health care utilizers pending further testing.
Though investigating the potential causes of missed opportunities is outside of the scope of this study, it is important to mention that one of the reviewed studies provided initial evidence on the possible causes missed opportunities [7]. The authors of that study (Fontanesi, et al.) identified that a substantial proportion of missed opportunities in the study were explained by a combination of several factors with the most influential being an absence of provider-patient immunization discussion during a visit.
Future studies should further explore and summarize the root causes of missed opportunities and barriers related specifically to missed opportunities as they may differ from the overall barriers to vaccinations. Further missed opportunity term operationalization will help define the currently blurry differences between "immunization barriers" and missed opportunities. We hypothesize that missed opportunities has the potential to serve as a practice performance measure due to a common characteristic identified in our study, namely, missed opportunities are clearly associated with a patient encounter. When a patient is already at the clinic, the number of applicable barriers is reduced, and yet a visit can still result in missed opportunities even when the actual opportunity, i.e., patient had a contact with healthcare provider, existed. From this perspective, missed opportunities can be viewed as a potential way to reduce barriers related to an actual encounter, such as inconsistent and weak provider recommendations, provider bias, patient resistance and clinic inefficiency.
This study also uncovered some of the areas that the future research studies need to address for successful use of operationalized missed opportunities as a measurable outcome (variable) for research and quality improvement projects focused on adult immunizations in primary care.
For example, none of the studies correlated missed opportunities with vaccination rates, were interventional in nature, or provided any normative data or recommendations on how to assess changes in missed opportunities over time. Future investigations addressing lack of standardization around operationalizing and computing missed opportunities could lead to such benefits as saving time and effort by deploying the top-ranked method(s) and design data collection and analysis plans accordingly in research and quality improvement projects. However, in order to compare outcomes of different projects reliably using missed opportunities as an additional vaccination measure and to assess performance and ranking of methods, it is essential to conduct further validation studies of this measure.
Furthermore, it is unclear if vaccination setting is an important contributing factor in the level of missed opportunities as most studies included not only visits to primary care providers but emergency room and hospitalizations, and significant missed opportunities were identified in all settings. Additional variability in how the studies included and defined what constituted a patient visit/encounter needs to be considered in the future as some studies included varying numbers of visits per time period to various settings and encounters where the vaccines were possibly offered but refused by the patient. The studies in this review used various inclusion criteria for what constituted an eligible patient encounter with health care and ranged from scheduled visits with family physicians to hospitalizations. Further standardization of the encounter characteristics will be necessary to improve research and quality improvement methods and accuracy and comparability of results across various efforts. Eligible encounter definition will need to account for several aspects including the type of visits and whether a particular visit is an appropriate opportunity to vaccinate; the type of patients and whether the visiting patient is eligible for certain vaccinations; and the target provider or health care setting and whether the visit can be linked to the provider or the health care site of interest. The development of comparable and validated timeframes for missed opportunities will be useful as the duration of patient encounter observation periods in the absence of rigid vaccination schedules recommended in adults compared to vaccination schedules in children may affect accuracy of estimations in adults.
Furthermore, future studies may need to identify and compare effective interventions and models designed to reduce missed opportunities and ultimately improve vaccination rates among adults.
Strengths and limitations of present study
Only those studies published between January, 2000, and May, 2015, were included in the literature search; therefore, recent studies in-press at time of review or published later were not included. This may in particular reduce the number of studies included in the review that compute missed opportunities using electronic medical record data and other contemporary approaches. Furthermore, we used the term "missed opportunities" as a keyword for the literature search, but it is possible that - owing to the lexical preferences of authors - other terms are in use with the same intended meaning. We performed an exhaustive, wide-ranging literature search of the PubMed database to identify relevant articles matching an extensive list of search terms (Additional file 1). A follow-on literature review was conducted using author citations and bibliographies of articles that passed our selection criteria. Taken together, we are confident that this approach captured all identifiable articles dealing with missed opportunities in adult immunizations published in the identified time-period.
There are several limitations of the present study that may affect generalizability and validity of results. First, the study focused on adult vaccinations only and as a result may not be generalizable to other patient populations. These may include populations distinct from those included in the study (e.g., children or newborns), or sub-groups within the sample who exhibit distinct characteristics (e.g., elderly adults, immunocompromised patients). Additionally, it is possible that studies utilizing quantifiable missed opportunities as an outcome have been published in other areas relevant to different preventative or clinical services.
Lastly, only a small number of studies remained after applying review inclusion criteria which may limit generalizability or inclusiveness of the classifications proposed. However, the data extracted from that small number of studies allowed us to draw initial conclusions that missed opportunity can be operationalized and hold promise as a measurable outcome (variable) for research and quality improvement projects aimed to increase adult immunization recommendation and uptake in primary care. Rather than limitation of the literature search process, these initial results indicate the need for future research in this area.
Our study provided an initial overview of the methods reported in the literature and concluded that the quantifiable missed opportunity holds promise as a measurable outcome (variable) for research and quality improvement projects aimed to increase adult immunization recommendation and uptake in primary care. The preliminary results of our study indicate that the missed opportunities term can be operationalized and point to the need for future studies to test quantifiable missed opportunities as a potentially clinically relevant, actionable, and specific clinical and research outcome.
The Public Health Service Act. In: USC. vol. 42: Anonymous.https://www.healthypeople.gov/2020/topicsobjectives/topic/immunization-and-infectious-diseases/objectives. Accessed 18 Dec 17.
Campos-Outcalt D, Jeffcott-Pera M, Carter-Smith P, Schoof BK, Young HF. Vaccines provided by family physicians. Ann Fam Med. 2010;8(6):507–10.
Immunization Strategies for Healthcare Practices and Providers [http://www.cdc.gov/vaccines/pubs/pinkbook/downloads/strat.pdf]. Accessed 23 June 2017.
Daniels NA, Nguyen TT, Gildengorin G, Perez-Stable EJ. Adult immunization in university-based primary care and specialty practices. J Am Geriatr Soc. 2004;52(6):1007–12.
Social Research Glossary [http://www.qualityresearchinternational.com/socialresearch/operationalisation.htm]. Accessed 23 June 2017.
Wong G, Greenhalgh T, Westhorp G, Buckingham J, Pawson R. RAMESES publication standards: meta-narrative reviews. BMC Med. 2013;11:20.
Fontanesi J, Shefer AM, Fishbein DB, Bennett NM, De Guire M, Kopald D, Holcomb K, Stryker DW, Coleman MS. Operational conditions affecting the vaccination of older adults. Am J Prev Med. 2004;26(4):265–70.
Nowalk MP, Zimmerman RK, Feghali J. Missed opportunities for adult immunization in diverse primary care office settings. Vaccine. 2004;22(25–26):3457–63.
Kyaw MH, Greene CM, Schaffner W, Ray SM, Shapiro M, Barrett NL, Gershman K, Craig AS, Roberson A, Zell ER, et al. Adults with invasive pneumococcal disease: missed opportunities for vaccination. Am J Prev Med. 2006;31(4):286–92.
Maurer J, Harris KM, Lurie N. Reducing missed opportunities to vaccinate adults against influenza: what is realistic? Arch Intern Med. 2009;169(17):1633–4.
Singleton JA, Santibanez TA, Wortley PM. Influenza and pneumococcal vaccination of adults aged > or = 65: racial/ethnic differences. Am J Prev Med. 2005;29(5):412–20.
Skull SA, Andrews RM, Byrnes GB, Kelly HA, Nolan TM, Brown GV, Campbell DA: Missed opportunities to vaccinate a cohort of hospitalised elderly with pneumococcal and influenza vaccines. Vaccine 2007, 25(28):5146-5154.
Weightman NC, Walters A. Missed opportunities in vaccination of patients with subsequent pneumococcal bacteraemia. Br J Gen Pract. 2003;53(492):547–9.
Nowalk MP, Bardella IJ, Zimmerman RK, Shen S. The physician's office: can it influence adult immunization rates? Am J Manag Care. 2004;10(1):13–9.
Smith PJ, Kennedy AM, Wooten K, Gust DA, Pickering LK. Association between health care providers' influence on parents who have concerns about vaccine safety and vaccination coverage. Pediatrics. 2006;118(5):e1287–92.
Sevin AM, Romeo C, Gagne B, Brown NV, Rodis JL. Factors influencing adults' immunization practices: a pilot survey study of a diverse, urban community in central Ohio. BMC Public Health. 2016;16(1):424.
Willis BC, Ndiaye SM, Hopkins DP, Shefer A, Task Force on Community Preventive S. Improving influenza, pneumococcal polysaccharide, and hepatitis B vaccination coverage among adults aged <65 years at high risk: a report on recommendations of the task force on community preventive services. MMWR Recommendations and reports : Morbidity and mortality weekly report Recommendations and reports / Centers for Disease Control. 2005;54(RR-5):1–11.
Gannon M, Qaseem A, Snooks Q, Snow V. Improving adult immunization practices using a team approach in the primary care setting. Am J Public Health. 2012;102(7):e46–52.
We would like to acknowledge the AAFP National Research Network for providing essential expertise, staff and support and Elizabeth Staton for assistance submitting this paper.
This work is supported in part by a research grant from Investigator-Initiated Studies Program of Merck Sharp & Dohme Corp. Merck Sharp & Dohme Corp. was not involved in the study design, data collection, data analysis and interpretation, writing or reporting of this work, and did not have any involvement in the decision to submit this article for publication. The opinions expressed in this paper are those of the authors and do not necessarily represent those of Merck Sharp & Dohme Corp.
Data sharing is not applicable to this article as no datasets were generated or analysed during the current study.
American Academy of Family Physicians National Research Network, 11400 Tomahawk Creek Pkwy, Leawood, KS, 66211, USA
Natalia Loskutova, Craig Smail, Kemi Ajayi, Julie Wood & Jennifer Carroll
Wilmington Health, 1202 Medical Center Drive, Wilmington, NC, 28401, USA
Brian Webster
Department of Family Medicine, University of Colorado School of Medicine, 12631 E. 17th Ave, Aurora, CO, 80045, USA
Jennifer Carroll
Natalia Loskutova
Craig Smail
Kemi Ajayi
NL and CS were involved in all stages of manuscript preparation including obtaining funding and study design; JW and KA were primarily involved in data analysis and interpretation of data, critically revising the paper for important intellectual content, and final editing of the paper. BW was primarily involved in interpretation of data and critically revising the paper for important intellectual content. BW and JC were primarily involved in interpretation of data and critically revising the paper for important intellectual content. All authors have approved the final paper.
Correspondence to Natalia Loskutova.
Not applicable (not human subjects research).
Search Strategy. This lists the search strategy used by the librarian to locate papers for this review. (PDF 52 kb)
Loskutova, N., Smail, C., Webster, B. et al. Missed opportunities for improving practice performance in adult immunizations: a meta-narrative review of the literature. BMC Fam Pract 18, 108 (2017). https://0-doi-org.brum.beds.ac.uk/10.1186/s12875-017-0694-1
Accepted: 05 December 2017
Meta-narrative literature review
Adult immunizations


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Biography Dave started his storytelling with a den of cub scouts to settle the kids down at the end of the evening. Through the past few years Dave has been laying the ground work for expanding into other venues. To this end he has recently been award the Distinguished Toastmasters award from Toastmaster's International.
Repertoire The collection of stories is eclectic ranging from traditional campfire stories, to the retelling of old favorites, to original works and vinyets of going up 'Ten Miles this Side of No Where'.


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You are here: Home / Local History and Genealogy Material / Census Records / Index to 1930 Census / Links to Index in Alphabetical Order / N
Fifteenth Census of the United States (1930) Colorado County, Texas Surnames Beginning With N
Nail, Belle B. (lodger, female, white, 60, widowed, Texas) 14, 22B, 57
Nance, Adam (head, male, black, 38, married, Texas) 8, 2A, 7
Nance, Annie M. (daughter, female, black, 11, single, Texas) 8, 2A, 10
Nance, Fama [Fannie] (mother-in-law, female, black, 64, widowed, Texas) 7, 2B, 59
Nance, George R. (son, male, black, 8, single, Texas) 8, 2A, 11
Nance, Minerva (wife, female, black, 32, married, Texas) 8, 2A, 8
Nance, Tom C. (boarder, male, black, 23, single, Texas) 16, 19A, 40
Nance, Veora (daughter, female, black, 3 4/12, single, Texas) 8, 2A, 12
Nance, Vera L (daughter, female, black, 13, single, Texas) 8, 2A, 9
Nance, William E. (son, male, black, 2 3/12, single, Texas) 8, 2A, 13
Nanes, Lucio (head, male, Hispanic, 33, married, Mexico) 14, 17A, 48
Napolean, Henry (head, male, black, 80, married, Texas) 2, 4A, 6
Napolean, Kittie K. (wife, female, black, 67, married, Texas) 2, 4A, 7
Nardina, Dorothy (granddaughter, female, white, 10, single, Texas) 15, 3A, 29
Nardina, Emma (granddaughter, female, white, 6, single, Texas) 15, 3A, 30
Nardina, Lawrence (grandson, male, white, 13, single, Illinois) 15, 3A, 31
Nashyba, Annie (servant, female, white, 42, single, Czechoslovakia) 5, 18B, 82
Nathan, Annie (wife, female, white, 74, married, Poland) 14, 5A, 6
Nathan, Julius (head, male, white, 76, married, Russia) 14, 5A, 5
Nathan, Louis N. (son, male, white, 19, single, Texas) 14, 8A, 32
Nathan, Meyer D. (head, male, white, 50, married, Texas) 14, 8A, 30
Nathan, Nettie (wife, female, white, 44, married, Texas) 14, 8A, 31
Nathan, Nolan J. (son, male, white, 16, single, Texas) 14, 8A, 33
Nauman, Herman (head, male, white, 30, married, Texas) 3, 2B, 94
Nauman, Tresia L. (wife, female, white, 28, married, Texas) 3, 2B, 95
Naumann, Emma (wife, female, white, 40, married, Texas) 3, 3A, 30
Naumann, Erna (daughter, female, white, 1 2/12, single, Texas) 13, 3B, 91
Naumann, Erna (wife, female, white, 28, married, Texas) 13, 3B, 89
Naumann, Henry (head, male, white, 38, married, Texas) 13, 3B, 88
Naumann, Henry (son, male, white, 2, single, Texas) 13, 3B, 90
Naumann, Ida (wife, female, white, 50, married, Texas) 3, 3A, 36
Naumann, John M. (son, male, white, 9/12, single, Texas) 1, 7B, 85
Naumann, Joyce M. (daughter, female, white, 3, single, Texas) 1, 7B, 84
Naumann, Leana (daughter, female, white, 17, single, Texas) 3, 3A, 37
Naumann, Lee (son, male, white, 21, single, Texas) 3, 3A, 31
Naumann, LeRoy (son, male, white, 8, single, Texas) 1, 7B, 83
Naumann, Mary (wife, female, white, 24, married, Texas) 1, 7B, 82
Naumann, Max (head, male, white, 59, married, Illinois) 3, 3A, 35
Naumann, Morris (head, male, white, 65, married, Germany) 3, 3A, 29
Naumann, Morris, Jr. (head, male, white, 37, married, Texas) 1, 7B, 81
Naumann, Prince (son, male, white, 12, single, Texas) 3, 3A, 39
Naumann, Rosa (daughter, female, white, 15, single, Texas) 3, 3A, 38
Navarro, Antonio (head, male, Hispanic, 38, married, Texas) 13, 2A, 45
Navarro, Benito (son, male, Hispanic, 10, single, Texas) 13, 2A, 47
Navarro, Celolano (head, male, Hispanic, 77, married, Mexico) 16, 13B, 66
Navarro, Claudia (wife, female, Hispanic, 30, married, Texas) 13, 2A, 46
Navarro, David (son, male, Hispanic, 5, single, Texas) 13, 2A, 50
Navarro, Dilano (wife, female, Hispanic, 75, married, Mexico) 16, 13B, 67
Navarro, Dominga (daughter, female, Hispanic, 7, single, Texas) 13, 2A, 49
Navarro, Juan (son, male, Hispanic, 22, single, Texas) 16, 13B, 68
Navarro, Juan (son, male, Hispanic, 9, single, Texas) 13, 2A, 48
Navarro, Ramon (son, male, Hispanic, 3, single, Texas) 13, 2B, 51
Neal, Andrew (head, male, white, 26, married, Texas) 1, 11A, 33
Neal, Annie (sister, female, white, 54, single, Texas) 10, 6B, 98
Neal, Ben F. (head, male, white, 54, single, Texas) 10, 6B, 97
Neal, Dora M. (wife, female, white, 23, married, Texas) 1, 11A, 34
Neal, Joyce L. (daughter, female, white, 2/12, single, Texas) 1, 11A, 37
Neal, Margie M. (daughter, female, white, 5, single, Texas) 1, 11A, 35
Neal, Melvin L. (son, male, white, 3 1/4, single, Texas) 1, 11A, 36
Needham, Franklin (father-in-law, male, white, 76, widowed, Texas) 5, 3A, 16
Needham, Lee (head, male, white, 40, widowed, Texas) 16, 23B, 100
Needham, Lee O. Jr. (son, male, white, 4 11/12, single, Texas) 16, 24A, 1
Neely, George L. (head, male, white, 62, married, Mississippi) 5, 3B, 86
Neely, Missouri A. (wife, female, white, 63, married, Mississippi) 5, 3B, 87
Neely, Otis (wife, male, white, 33, single, Lavaca) 5, 3B, 88
Neiser, Emil (brother-in-law, male, white, 22, single, Texas) 7, 6A, 20
Neiser, Joe (brother-in-law, male, white, 21, single, Texas) 7, 6A, 21
Neiser, Pauline (mother-in-law, female, white, 49, widowed, Texas) 7, 6A, 18
Neiser, Pauline (sister-in-law, female, white, 16, single, Texas) 7, 6A, 19
Neiswander, Homer T. (lodger, male, white, 44, single, Kansas) 14, 12B, 88
Nelesquez, Jose (head, male, Hispanic, 30, married, Texas) 16, 5A, 34
Nelesquez, Julia (sister-in-law, female, Hispanic, 25, married, Mexico) 16, 5A, 37
Nelesquez, Lupe (sister, female, Hispanic, 14, single, Texas) 16, 5A, 36
Nelesquez, Ynes (sister, female, Hispanic, 16, single, Texas) 16, 5A, 35
Nelms, Jack (boarder, male, black, 40, single, Texas) 16, 7A, 22
Nelson, Agnes (wife, female, white, 47, married, Texas) 13, 3B, 60
Nelson, Alberta (wife, female, black, 23, married, Texas) 3, 5B, 95
Nelson, Alberta (wife, female, black, 26, married, Texas) 8, 9A, 24
Nelson, Anton (head, male, white, 50, married, Texas) 13, 3B, 59
Nelson, Clarence (roomer, male, black, 19, single, Texas) 8, 11A, 33
Nelson, Colman (head, male, black, 34, married, Texas) 8, 9A, 23
Nelson, Ella L. (daughter, female, black, 9, single, Texas) 8, 9A, 25
Nelson, Ernest (son, male, black, 7, single, Texas) 7, 2A, 40
Nelson, Felix (head, male, black, 29, married, Texas) 7, 2A, 38
Nelson, Garland (grandson, male, black, 8, single, Texas) 11, 7A, 43
Nelson, Henry (son, male, white, 17, single, Texas) 13, 3B, 62
Nelson, J. Dean (son, male, white, 8, single, Texas) 12, 2B, 54
Nelson, Jamie (wife, female, black, 36, married, Texas) 3, 4B, 59
Nelson, John (head, male, black, 59, married, Texas) 2, 10A, 1
Nelson, Magdeline (mother-in-law, female, white, 74, widowed, Texas) 12, 5A, 41
Nelson, Maria (boarder, female, white, 16, single, Texas) 7, 11B, 68
Nelson, Mary (wife, female, black, 24, married, Texas) 7, 2A, 39
Nelson, Millan (son, male, white, 2, single, Texas) 12, 2B, 56
Nelson, Minnie (mother-in-law, female, white, 75, widowed, Texas) 3, 9A, 5
Nelson, Mollie (daughter, female, black, 17, single, Texas) 8, 10B, 77
Nelson, Mollie (head, female, black, 60, widowed, Texas) 8, 10B, 76
Nelson, Rachael (wife, female, black, 58, married, Texas) 2, 10A, 2
Nelson, Robert (head, male, black, 28, married, Texas) 3, 5B, 94
Nelson, Rubylean (daughter, female, white, 5, single, Texas) 12, 2B, 55
Nelson, Tecla (wife, female, white, 32, married, Texas) 12, 2B, 52
Nelson, Thelma (daughter, female, black, 2, single, Texas) 8, 9A, 27
Nelson, Venus (head, male, black, 85, single, Texas) 7, 9A, 17
Nelson, Vernell (daughter, female, white, 2/12, single, Texas) 12, 2B, 57
Nelson, William (son, male, white, 9, single, Texas) 12, 2B, 53
Nelson, Willie (grandson, male, black, 5, single, Texas) 11, 7A, 44
Nelson, Willie (head, male, black, 42, married, Texas) 3, 4B, 58
Nelson, Willie (head, male, white, 34, married, Texas) 12, 2B, 51
Nelson, Willie (son, male, white, 18, single, Texas) 13, 3B, 61
Nelson, Young C. (son, male, black, 5, single, Texas) 8, 9A, 26
Nenicka, Bertha (sister, female, white, 49, single, Texas) 9, 11A, 22
Nenicka, Johana (sister, female, white, 53, single, Texas) 9, 11A, 21
Nenicka, John (head, male, white, 51, single, Texas) 9, 11A, 20
Nesbitt, Della (wife, female, white, 61, married, Texas) 1, 3B, 76
Nesbitt, Laurie D. (daughter, female, white, 27, single, Texas) 1, 3B, 78
Nesbitt, Lee (daughter, female, white, 29, single, Texas) 1, 3B, 77
Nesbitt, William L. (head, male, white, 61, married, Texas) 1, 3B, 75
Nesby, Berneice (daughter, female, black, 1, single, Texas) 6, 10B, 79
Nesby, Gladys (daughter, female, black, 3, single, Texas) 6, 10B, 78
Nesby, Harry (head, male, black, 49, married, Texas) 6, 10B, 72
Nesby, Harry, Jr. (son, male, black, 6, single, Texas) 6, 10B, 77
Nesby, Hattie (wife, female, black, 31, married, Texas) 6, 10B, 73
Nesby, Maryan (daughter, female, black, 10, single, Texas) 6, 10B, 75
Nesby, Pearline (daughter, female, black, 12, single, Texas) 6, 10B, 74
Nesby, Ruben (son, male, black, 8, single, Texas) 6, 10B, 76
Neschyba [Neshyba], Annie (wife, female, white, 34, married, Texas) 10, 9B, 81
Neschyba [Neshyba], Charlie (head, male, white, 47, married, Czechoslovakia) 10, 9B, 80
Neschyba [Neshyba], Josie (daughter, female, white, 8, single, Texas) 10, 9B, 83
Neschyba [Neshyba], Lydia (daughter, female, white, 10, single, Texas) 10, 9B, 82
Neuendorff, Anton (head, male, white, 31, married, Texas) 12, 5A, 18
Neuendorff, August (head, male, white, 42, married, Texas) 12, 3A, 44
Neuendorff, Augusta (wife, female, white, 70, married, Texas) 12, 5A, 47
Neuendorff, Bernice (daughter, female, white, 14, single, Texas) 12, 3A, 46
Neuendorff, Carnistine (daughter, female, white, 2, single, Texas) 12, 3A, 50
Neuendorff, Charles (son, male, white, 7, single, Texas) 12, 6B, 95
Neuendorff, Dennis (son, male, white, 7, single, Texas) 12, 5A, 39
Neuendorff, Dora (wife, female, white, 37, married, Texas) 12, 3A, 45
Neuendorff, Dorothy (daughter, female, white, 5, single, Texas) 12, 3A, 48
Neuendorff, Drau (son, male, white, 7, single, Texas) 12, 4A, 41
Neuendorff, Ed (head, male, white, 36, widowed, Texas) 12, 4A, 40
Neuendorff, Ellis (daughter, female, white, 11, single, Texas) 12, 3A, 47
Neuendorff, Emily (wife, female, white, 36, married, Texas) 12, 6B, 92
Neuendorff, Eunice (daughter, female, white, 8, single, Texas) 12, 6B, 94
Neuendorff, Hattie (wife, female, white, 32, married, Texas) 12, 5A, 38
Neuendorff, Jeanette (daughter, female, white, 4, single, Texas) 12, 3A, 49
Neuendorff, Jeraldine (daughter, female, white, 6, single, Texas) 12, 3A, 42
Neuendorff, Jesse (head, male, white, 37, married, Texas) 12, 3A, 39
Neuendorff, John (head, male, white, 45, married, Texas) 12, 5A, 37
Neuendorff, Joline (wife, female, white, 27, married, Texas) 12, 5A, 19
Neuendorff, Joy W. (daughter, female, white, 5, single, Texas) 12, 4A, 42
Neuendorff, Leeland (son, male, white, 7/12, single, Texas) 12, 5A, 22
Neuendorff, Lloyd (son, male, white, 4, single, Texas) 12, 5A, 21
Neuendorff, Lois (daughter, female, white, 4, single, Texas) 12, 5A, 40
Neuendorff, Luella (wife, female, white, 33, married, Texas) 12, 3A, 40
Neuendorff, Melvin (son, male, white, 6, single, Texas) 12, 5A, 20
Neuendorff, Nita (daughter, female, white, 10, single, Texas) 12, 3A, 41
Neuendorff, Odel (daughter, female, white, 2/12, single, Texas) 12, 3A, 43
Neuendorff, Peter (head, male, white, 40, married, Texas) 12, 6B, 91
Neuendorff, Viola (daughter, female, white, 10, single, Texas) 12, 6B, 93
Neuendorff, Weldon (son, male, white, 3 9/12, single, Texas) 12, 6B, 96
Neumann, Angelena (daughter, female, white, 16, single, Texas) 10, 4B, 74
Neumann, Kermit (son, male, white, 11, single, Texas) 10, 4B, 76
Neumann, Lurgene (daughter, female, white, 6, single, Texas) 10, 4B, 77
Neumann, Mathilda (wife, female, white, 37, married, Texas) 10, 4B, 72
Neumann, Mina (mother-in-law, female, white, 70, widowed, Germany) 10, 4A, 12
Neumann, Vernise (daughter, female, white, 17, single, Texas) 10, 4B, 73
Neumann, Willard (son, male, white, 14, single, Texas) 10, 4B, 75
Neumann, Willie F. (head, male, white, 50, married, Texas) 10, 4B, 71
Newsom, Malcom E. (head, male, white, 58, single, Texas) 8, 6B, 57
Newton, Forrest F. (son, male, white, 2 1/6, single, Texas) 14, 16A, 7
Newton, Helena (wife, female, white, 40, married, Missouri) 14, 16A, 4
Newton, Horabe (son, male, black, 14, single, Texas) 9, 1B, 77
Newton, Ida M. (daughter, female, white, 8, single, Arkansas) 14, 16A, 6
Newton, Irene (wife, female, white, 30, married, Kansas) 14, 15B, 98
Newton, Lula M. (daughter, female, black, 9, single, Texas) 9, 1B, 78
Newton, Mamie (wife, female, black, 29, married, Texas) 9, 1B, 76
Newton, Odis D. (son, male, white, 2/12, single, Texas) 14, 16A, 8
Newton, Pink (head, male, black, 34, married, Texas) 9, 1B, 75
Newton, Ralph E. (son, male, white, 1, single, Kansas) 14, 15B, 100
Newton, Vernon D. (head, male, white, 30, married, Missouri) 14, 15B, 97
Newton, Vernon D., Jr. (son, male, white, 3 5/6, single, Kansas) 14, 15B, 99
Newton, Wade N. (son, male, white, 11, single, Missouri) 14, 16A, 5
Newton, William F. (head, male, white, 37, married, Missouri) 14, 16A, 3
Ney, Betty K. (daughter, female, white, 11, single, Texas) 14, 3B, 76
Ney, Jack J. (son, male, white, 7, single, Texas) 14, 3B, 78
Ney, Kate L. (wife, female, white, 40, married, Texas) 14, 3B, 73
Ney, Kathryn (wife, female, white, 46, married, Texas) 14, 16A, 30
Ney, Kitte B. (daughter, female, white, 18, single, Texas) 14, 3B, 74
Ney, Lucille K. (daughter, female, white, 21, single, Texas) 14, 16A, 32
Ney, Marshal N. (son, male, white, 23, single, Texas) 14, 16A, 31
Ney, Merlin N. (son, male, white, 14, single, Texas) 14, 3B, 75
Ney, Nicaleus C. (head, male, white, 48, married, Texas) 14, 16A, 29
Ney, Richard B. (head, male, white, 44, married, Texas) 14, 3B, 72
Ney, Richard R. (son, male, white, 11, single, Texas) 14, 3B, 77
Nicewander, Dan A. (son, male, white, 10, single, Texas) 5, 2A, 46
Nicewander, Elaine M. (daughter, female, white, 15, single, Texas) 5, 2A, 45
Nicewander, Ethel M. (wife, female, white, 35, married, Missouri) 5, 2A, 43
Nicewander, Leonard (son, male, white, 17, single, Texas) 5, 2A, 44
Nicewander, Merritt (head, male, white, 45, married, Illinois) 5, 2A, 42
Nichida, Alice (daughter, female, white, 38, widowed, Texas) 16, 23A, 12
Nichols, Anna (daughter, female, white, 22, single, Texas) 5, 10B, 61
Nichols, Arthur A. (head, male, white, 56, married, Illinois) 1, 13A, 39
Nichols, Ernest (head, male, black, 20, married, Texas) 5, 12A, 9
Nichols, Estoria (wife, female, black, 23, married, Texas) 5, 12A, 2
Nichols, Henry (head, male, black, 60, married, Texas) 16, 14A, 30
Nichols, Herbert M. (son, male, white, 27, single, Texas) 1, 13A, 41
Nichols, Irly (son, male, white, 17, single, Texas) 5, 10B, 62
Nichols, Jack (lodger, male, white, 31, single, Texas) 7, 13A, 35
Nichols, Leroy (son, male, black, 3 3/12, single, Texas) 5, 12A, 19
Nichols, Margarite (wife, female, black, 19, married, Texas) 5, 12A, 10
Nichols, Martha (head, female, white, 56, widowed, Texas) 5, 10B, 60
Nichols, Oliver (son, male, white, 13, single, Texas) 5, 10B, 63
Nichols, Rachel (wife, female, black, 54, married, Texas) 16, 14A, 31
Nichols, Richard (head, male, black, 29, married, Texas) 5, 12A, 1
Nichols, Rosie M. (daughter, female, black, 4 1/12, single, Texas) 5, 12A, 3
Nichols, Tracy M. (daughter, female, white, 15, single, Texas) 1, 13A, 42
Nichols, Tracy S. (wife, female, white, 53, married, Texas) 1, 13A, 40
Nichols, Wady (head, male, white, 32, widowed, Texas) 5, 7B, 100
Nickel, Alice (wife, female, white, 46, married, Texas) 9, 1B, 91
Nickel, Henry L. (son, male, white, 8, single, Texas) 9, 1B, 94
Nickel, Joe (head, male, white, 52, married, Texas) 9, 1B, 90
Nickel, Laurence (son, male, white, 12, single, Texas) 9, 1B, 92
Nickel, Maybell (daughter, female, white, 10, single, Texas) 9, 1B, 93
Niemeyer, Aline (daughter, female, white, 12, single, Texas) 9, 11B, 76
Niemeyer, Annie (wife, female, white, 39, married, Texas) 9, 11B, 69
Niemeyer, Carry (mother, female, white, 67, widowed, Germany) 9, 11B, 71
Niemeyer, Erwin (son, male, white, 14, single, Texas) 9, 11B, 75
Niemeyer, Esther (daughter, female, white, 8, single, Texas) 9, 11B, 77
Niemeyer, Helen D. (daughter, female, white, 10/12, single, Texas) 9, 11B, 80
Niemeyer, Henry (head, male, white, 39, married, Texas) 9, 11B, 68
Niemeyer, Leona M. (daughter, female, white, 6, single, Texas) 9, 11B, 78
Niemeyer, Leroy (son, male, white, 2 7/12, single, Texas) 9, 11B, 79
Niemeyer, Lillian (daughter, female, white, 16, single, Texas) 9, 11B, 74
Niemeyer, Owen (son, male, white, 12, single, Texas) 9, 11B, 70
Niemeyer, William (head, male, white, 41, married, Texas) 9, 11B, 72
Niemeyer, William (head, male, white, 63, widowed, Missouri) 4, 2B, 95
Niemeyer, William, Jr. (son, male, white, 18, single, Texas) 4, 2B, 96
Niltsch, John (head, male, white, 82, married, Austria) 9, 2B, 88
Niltsch, Mary (wife, female, white, 75, married, Austria) 9, 2B, 89
Nispel, Clyde W (head, male, white, 28, married, Nebraska) 14, 17A, 7
Nispel, Erma M (wife, female, white, 26, married, Missouri) 14, 17A, 8
Nitchman, Alice (wife, female, white, 24, married, Texas) 7, 2A, 9
Nitchman, Henry (head, male, white, 34, married, Texas) 7, 2A, 8
Nitchmann, Frank (head, male, white, 60, widowed, Austria) 7, 7A, 46
Nitchmann, Lillie (sister-in-law, female, white, 22, single, Texas) 7, 10B, 99
Nitchmann, Selma (daughter, female, white, 36, single, Texas) 7, 7A, 47
Nitschman, Anna (wife, female, white, 55, married, Austria) 6, 6A, 37
Nitschman, Henry (head, male, white, 53, married, Austria) 6, 6A, 36
Nitschman, Hugo (son, male, white, 20, single, Texas) 6, 6A, 39
Nitschman, Selma (daughter, female, white, 19, single, Texas) 6, 6A, 38
Nitschmann, Caroline (wife, female, white, 27, married, Texas) 9, 13A, 44
Nitschmann, Clara M. (daughter, female, white, 1 3/12, single, Texas) 9, 13A, 46
Nitschmann, Henry (head, male, white, 30, married, Texas) 9, 13A, 43
Nitschmann, Hilbert (son, male, white, 5, single, Texas) 9, 13A, 45
Nohavitz, Annie (daughter, female, white, 6, single, Texas) 9, 7A, 9
Nohavitz, Cassue M. (daughter, female, white, 1 3/12, single, Texas) 9, 7A, 11
Nohavitz, Cyril (son, male, white, 15, single, Texas) 9, 7A, 4
Nohavitz, Dorothy (daughter, female, white, 8, single, Texas) 9, 7A, 8
Nohavitz, Edward (head, male, white, 39, married, Texas) 9, 7A, 2
Nohavitz, Edward, Jr. (son, male, white, 13, single, Texas) 9, 7A, 5
Nohavitz, Howard (son, male, white, 3, single, Texas) 9, 7A, 10
Nohavitz, Martha (daughter, female, white, 12, single, Texas) 9, 7A, 6
Nohavitz, Mary (daughter, female, white, 10, single, Texas) 9, 7A, 7
Nohavitz, Paulina (wife, female, white, 38, married, Texas) 9, 7A, 3
Nohavitza, Adolf (head, male, white, 21, married, Texas) 8, 6A, 36
Nohavitza, Martha (wife, female, white, 22, married, Texas) 8, 6A, 37
Norman, Arthur L. (son, male, black, unknown, single, Texas) 1, 17A, 25
Norman, Belle (head, female, black, 80, widowed, Texas) 1, 18A, 11
Norman, Ceola (daughter, female, black, 5, single, Texas) 1, 17B, 75
Norman, Cleve (head, male, black, 45, married, Texas) 1, 17B, 68
Norman, Eliza (wife, female, black, 53, married, Texas) 14, 21B, 68
Norman, Henry (head, male, black, 39, married, Texas) 1, 17A, 22
Norman, Henry (son, male, black, 1 1/2, single, Texas) 1, 17B, 76
Norman, Ida L. (daughter, female, black, 12, single, Texas) 1, 17B, 72
Norman, John (head, male, black, 55, married, Texas) 14, 21B, 67
Norman, Julia (daughter, female, black, 8, single, Texas) 1, 17B, 73
Norman, Julia (granddaughter, female, black, 11, single, Texas) 1, 18A, 12
Norman, Lawrence (lodger, male, black, 20, single, Texas) 1, 19B, 94
Norman, Lawrence (son, male, black, 30, single, Texas) 1, 17A, 11
Norman, Lena (wife, female, black, 29, married, Texas) 1, 17A, 23
Norman, Lizzie (wife, female, black, 36, married, Texas) 1, 17B, 69
Norman, Lucile (daughter, female, black, 14, single, Texas) 1, 17B, 71
Norman, Lucious (son, male, black, 16, single, Texas) 1, 17B, 70
Norman, Natalie (daughter, female, black, 18, single, Texas) 16, 18B, 79
Norman, Octavia (mother-in-law, female, black, 68, widowed, Texas) 3, 6A, 31
Norman, Percilla (daughter, female, black, 6, single, Texas) 1, 17B, 74
Norman, Richmon (head, male, black, 87, widowed, Texas) 1, 16B, 92
Norman, Richmon (son, male, black, 3 1/6, single, Texas) 1, 17A, 24
Norris, Alberta (wife, female, black, 32, married, Texas) 16, 15B, 57
Norris, Alice W. (head, female, white, 70, widowed, Texas) 14, 9B, 53
Norris, Constance (daughter, female, black, 14, single, Texas) 16, 15B, 54
Norris, John L. (head, male, white, 39, married, Missouri) 14, 3B, 65
Norris, John L., Jr. (son, male, white, 11, single, Michigan) 14, 3B, 67
Norris, Mary (wife, female, black, 58, married, Texas) 16, 15B, 53
Norris, Oddie (wife, female, white, 34, married, N. Carolina) 14, 3B, 66
Norris, T. A. (head, male, black, 62, married, Texas) 16, 15B, 52
Norris, Vance (son, male, white, 9, single, Oklahoma) 14, 3B, 68
Norris, Washington (head, male, black, 60, married, Texas) 16, 15B, 56
Northcut, J.C. (head, male, white, 30, married, Texas) 10, 2A, 23
Northcut, Johnnie M. (wife, female, white, 33, married, Texas) 10, 2A, 24
Northflest, Cynthia (wife, female, black, 53, married, Texas) 16, 14B, 94
Northflest, Julius (head, male, black, 53, married, Texas) 16, 14B, 99
Northrup, James, Jr. (son, male, white, 10, single, Texas) 1, 10B, 98
Northrup, Mary K. (daughter, W white, 9, single, Texas) 1, 10B, 99
Northrup, Sunbeam A. (daughter, female, white, 12, single, Texas) 1, 10B, 97
Northrup, Sunbeam W. (head, female, white, 36, divorced, Indiana) 1, 10B, 96
Northrup, Yvonne (daughter, female, white, 6, single, Texas) 1, 10B, 100
Norway, [illegible] (granddaughter, female, black, 15, single, Texas) 3, 6A, 48
Norway, Alla (wife, female, black, 32, married, Texas) 3, 1B, 85
Norway, Charlie (head, male, black, 32, married, Texas) 2, 7A, 33
Norway, Frankie (wife, female, black, 32, married, Texas) 2, 7A, 34
Norway, Robert (head, male, black, 39, married, Texas) 3, 1B, 84
Norway, Robert (son, male, black, 17, single, Texas) 3, 1B, 86
Norway, Will (brother-in-law, male, black, 20, single, Texas) 13, 1B, 52
Norway, William (roomer, male, black, 55, widowed, Texas) 11, 2A, 9
Noska, Adolph (head, male, white, 33, married, Texas) 10, 8B, 97
Noska, Adolph (son, male, white, 8, single, Texas) 10, 7A, 47
Noska, Agatha (daughter, female, white, 7, single, Texas) 10, 9A, 1
Noska, Albert (head, male, white, 21, single, Texas) 10, 9B, 77
Noska, Albert (son, male, white, 4 6/12, single, Texas) 10, 10A, 4
Noska, Anna (wife, female, white, 64, married, Czechoslovakia) 10, 7A, 42
Noska, Bertha (daughter, female, white, 16, single, Texas) 10, 11A, 9
Noska, Bohumil (son, male, white, 23, single, Texas) 10, 11A, 7
Noska, Celestine (daughter, female, white, 12, single, Texas) 10, 1B, 84
Noska, Clements (son, male, white, 7, single, Texas) 10, 10B, 87
Noska, Eddie (son, male, white, 26, single, Texas) 10, 11A, 6
Noska, Elenora (wife, female, white, 27, married, Texas) 10, 10B, 86
Noska, Elo (son, male, white, 19, single, Texas) 10, 11A, 8
Noska, Emilie (step-daughter, female, white, 40, single, Texas) 10, 7A, 44
Noska, Emilie (wife, female, white, 22, married, Texas) 11, 1A, 49
Noska, Erna (daughter, female, white, 3, single, Texas) 10, 10B, 90
Noska, Evelyn (daughter, female, white, 11, single, Texas) 10, 11A, 10
Noska, Francis (head, female, white, 45, divorced, Texas) 10, 11A, 5
Noska, Frank (head, male, white, 46, married, Texas) 10, 1B, 80
Noska, Frank (son, male, white, 20, single, Texas) 10, 1B, 82
Noska, Ida (wife, female, white, 33, married, Texas) 10, 7A, 46
Noska, Joe (head, male, white, 43, married, Texas) 10, 10A, 1
Noska, John (head, male, white, 38, married, Texas) 10, 7A, 45
Noska, John (head, male, white, 66, married, Czechoslovakia) 10, 7A, 41
Noska, Karoline (son, male, white, 18, single, Texas) 10, 1B, 83
Noska, Lee J. (son, male, white, 2 9/12, single, Texas) 11, 1B, 51
Noska, Lee Roy (son, male, white, 6, single, Texas) 10, 10B, 88
Noska, Leona (daughter, female, white, 11/12, single, Texas) 11, 1B, 52
Noska, Leopold (son, male, white, 0/12, single, Texas) 10, 7B, 51
Noska, Lilly (daughter, female, white, 3 1/12, single, Texas) 10, 7A, 50
Noska, Ludwig R. (son, male, white, 4 7/12, single, Texas) 11, 1A, 50
Noska, Martha (daughter, female, white, 15, single, Texas) 10, 10A, 3
Noska, Mina (wife, female, white, 45, married, Texas) 10, 1B, 81
Noska, Paul (son, male, white, 5, single, Texas) 10, 7A, 49
Noska, Peter (son, male, white, 7, single, Texas) 10, 7A, 48
Noska, Raymond (son, male, white, 2 11/12, single, Texas) 10, 9A, 3
Noska, Regina (daughter, female, white, 11, single, Texas) 10, 8B, 99
Noska, Rosie (daughter, female, white, 16, single, Texas) 10, 7A, 43
Noska, Rudolph (head, male, white, 30, married, Texas) 11, 1A, 48
Noska, Rudolph (son, male, white, 9, single, Texas) 10, 8B, 100
Noska, Sophie (wife, female, white, 30, married, Texas) 10, 8B, 98
Noska, Tonny (daughter, female, white, 9, single, Texas) 10, 1B, 85
Noska, Veronica (daughter, female, white, 5, single, Texas) 10, 9A, 2
Noska, Wilhmina (wife, female, white, 46, married, Texas) 10, 10A, 2
Noska, Willie (head, male, white, 26, married, Kansas) 10, 10B, 85
Noska, Willie (son, male, white, 5, single, Texas) 10, 10B, 89
Nugent, Ophia F. (lodger, female, white, 58, married, Canada) 14, 22A, 42
Nugent, William J. (lodger, male, white, 59, married, Massachusetts) 14, 22A, 41
Null, Ida (daughter, female, black, 31, single, Texas) 16, 8B, 68
Nunlay, Hattie (wife, female, black, 38, married, Texas) 9, 4A, 9
Nunlay, Willie (head, male, black, 43, married, Texas) 9, 4A, 8
Nunn, Ben (father, male, black, 83, widowed, Texas) 9, 3A, 8
Nunn, Clara (wife, female, black, 39, married, Texas) 9, 3A, 7
Nunn, Dalthular (wife, female, black, 32, married, Texas) 9, 11B, 62
Nunn, Frank (head, male, black, 22, married, Texas) 9, 11B, 61
Nunn, Frank (son, male, black, 11/12, single, Texas) 9, 11B, 67
Nunn, Ida (wife, female, black, 53, married, Texas) 9, 4B, 57
Nunn, Joseph (grandson, male, black, 8, single, Texas) 9, 2B, 66
Nunn, Lillian (granddaughter, female, black, 6, single, Texas) 9, 2B, 67
Nunn, Malinda (head, female, black, 70, married, Texas) 7, 4B, 88
Nunn, Melvin (son, male, black, 18, single, Texas) 9, 4B, 59
Nunn, Myrtle (daughter, female, black, 3 2/12, single, Texas) 9, 11B, 66
Nunn, Ora B. (wife, female, black, 17, married, Texas) 9, 3B, 57
Nunn, Pearl (daughter, female, black, 19, single, Texas) 9, 4B, 58
Nunn, Robert L. (son, male, black, 10/12, single, Texas) 9, 3B, 58
Nunn, Theodore (head, male, black, 23, married, Texas) 9, 3B, 56
Nunn, Velma (daughter, female, black, 9, single, Texas) 9, 11B, 64
Nunn, Wilford (head, male, black, 44, married, Texas) 9, 3A, 6
Nunn, William (head, male, black, 57, married, Texas) 9, 4B, 56
Nunn, William (son, male, black, 6, single, Texas) 9, 11B, 65
Nunn, Woodrow (son, male, black, 11, single, Texas) 9, 11B, 63
Nussbaum, Henry (head, male, white, 59, married, Poland) 14, 4B, 53
Nussbaum, Pauline (wife, female, white, 61, married, Austria) 1, 4B, 81
Nussbaum, Rosa (wife, female, white, 57, married, Germany) 14, 4B, 54
Nussbaum, Simon (head, male, white, 63, married, Texas) 1, 4B, 80


Art 84 is excited to be participating for the 2nd year in the EAST Austin Studio Tour. Join us at 84 Waller Street (two blocks south of Cesar Chavezn Street) for lots great music, dancing, and art! Artists Stops #544-555.
Special Exhibit: Many works will be displayed at the show including pieces by each artist highlighting Art 84's Dance + Music + Art theme for EAST 2018.


One of the main articles in October 2017's issue of Castings SA featured a story on how Atlantis Foundries have embarked on a process that will pave the way to becoming a Smart Foundry by embracing the Fourth Industrial Revolution or Industry 4.0 that it is more commonly known as. The project at Atlantis Foundries aims to combine various technologies available to gather and analyse process data, with the aim of improving product quality and cost efficiency.
One might say that this is how any business should be run and that should have been the basic principle you should adopt when setting up a business. After all we are in business to make profits and the only way you can do that is having a quality product and/or service that is not outstripped by the costs. Attracting clients and keeping them happy by delivering does play a role as well!
The first thing to understand about Industry 4.0 is it is not one technology but a combination of modern technologies combined to create a 'Smart factory'. The modern technologies include advanced robotics and artificial intelligence, sophisticated sensors, cloud computing, the Internet of Things, data capture and analytics, digital fabrication (including 3D printing), software-as-a-service and other new marketing models, smartphones and other mobile devices, platforms that use algorithms and the embedding of all these elements in an interoperable global value chain, shared by many companies from many countries.
At first this sounds extreme but when you start to look at the possibilities it is easy to see how these technologies can become real game-changers. In a previous column I even questioned the concept as being one of hype or, as I asked, does it add real value?
Industry 4.0 is a German led initiative and the train of thought is to create smarter, more efficient manufacturing through the use of Smart factories. The big question is how can we utilise these new technologies within the foundry industry and what are the benefits? This means change lies ahead for all companies – including foundries.
Atlantis Foundries' management must be congratulated on being one of the first foundries in South Africa, probably even in the world, to begin implementing Industry 4.0. The basic building blocks for such a concept are robotics, process instrumentation, and the tracking of components using RFID and other software applications. With all the data available and it being traceable to individual castings, the door has opened to enable the use of Artificial Intelligence for process control and inspection of components.
Industry 4.0 and the Internet of Things (one cannot operate without the other) are certainly taking hold. The integral role technology now plays across industries will transform how companies operate in the future. It has been hailed as the fourth industrial revolution, in the course of which physical and virtual worlds connect. Tomorrow's interconnected factories will offer new opportunities for optimisation and will reduce energy consumption and waste at all stages of production, to name a few.
The article on Atlantis Foundries has attracted many views on the Castings SA website, has been read worldwide and even received an accolade from a German foundry Internet platform that awarded Atlantis Foundries as their Foundry of the Week. And it also appears as the number one on the Google rankings for the search term 'Foundries embracing the Fourth Industrial Revolution'.
The great publicity that the Atlantis Foundries article has created for the South African foundry industry is what this magazine is all about. I am pleased to say that another forward thinking foundry in South Africa has approached me to run an article on them because they too are implementing the concept. But this time the foundry is a pressure die casting foundry. I look forward to running the article in a future issue and reminding the rest of the world that South Africa is open for business despite the many obstacles that are thrown at us.


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Ravyn Lenae: HYPNOS Album Review
When Ravyn Lenae emerged on Chicago's R&B scene in 2015, she was a sensory architect. Her vocals have been eclectic: a singular neo-soul model that channeled Erykah Badu's nasal coolness and a falsetto that carried whispers of FKA twigs. Between 2015 and 2017, Lenae launched two EPs, exploring a mode of different R&B that integrated components of dream pop, bounce, and soul. Between opening excursions for Noname and SZA, Lenae seemingly discovered her groove on 2018's Steve Lacy-produced EP Crush, a group of daring electro-funk bed room bangers. She appeared unstoppable, releasing a lot in a short while interval that it was exhilarating to see what she had lined up subsequent. Apart from a number of teases, nonetheless, Lenae went radio silent for the higher a part of 4 years.
Her debut full-length, HYPNOS, arrives as a mature reintroduction, a love-stained, moody transport that flies via Lenae's world with a featherlight cadence. Lenae surveys the latest historical past of soul, various R&B, and even Afrobeats with precision. However most significantly, her debut showcases her ascendant vocal prowess as she strikes throughout her big selection with ease. Lenae's transcendental poise establishes her as a resonant voice in R&B.
The rapid enchantment of HYPNOS is simply how tantalizing Lenae's preparations are amid acquainted soundscapes. On earlier songs like "Sticky" or "Free Room," Lenae tapped into her greater register, however right here Lenae's soprano has turn out to be a gravitational drive. On the Kaytranada-produced "Xtasy," Lenae's sultry and blithe singing washes over the beat like water beading off a automotive. "Lullabye," a kiss-off to a former lover, strikes via melismatic harmonies like clockwork. "I hope she retains you heat at night time/That is our lullabye," she croons, however you already know she's going to be OK simply by her composure.
For somebody already lauded for her singing, Lenae's vocal approach on HYPNOS is the work of a perfectionist. Her sound has blossomed right into a potpourri of the R&B feminine icons of the final three many years, however particularly of the '90s and early '00s. The obvious comparability right here is Aaliyah, however Lenae's vocal composition honors many on HYPNOS, pulling from Brandy's "vocal bible" riffs and Solange's visionary harmonies, Kelela's outré artistry, and Future's Youngster's lullabying melodies. "Venom," a seething funkadelic synth-led monitor, feels caught between one thing off OutKast's Stankonia and Brandy's "What About Us?". "Why do you play me for a idiot?" Lenae asks, earlier than descending into harmonies that unravel like these on Solange's "Rise." On "Cameo," Lenae brings again Lacy and frequent collaborator Luke Titus for a cool opener that remembers the synth bass sound of Herbie Hancock's "Chameleon." The influences are by no means distracting a lot as they're twinkling, enjoyable, and thoroughly blended.
Sounding like another person is certainly not the restrict to Lenae's inventive yield; she is as confident as she is exploratory. Reasonably than replicating a nostalgia that's turn out to be commonplace, she has earnestly studied these forebears and utilized their strategies to her personal model of sentimental and intense music. "Gentle Me Up," the album's attractive, slow-burning centerpiece, creates a moodboard of R&B references, and digs deep into the thrill of sexual exploration: "Come inside/Present me you're the chief/Switchin' sides/Make me a believer," she begs. Lenae's weightless falsetto and stimulated writing make it a feminine modern to D'Angelo's "Untitled (How Does It Really feel)."


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Disease trends
Biogen Inc
Sinopharm
Key forecasts
Coronavirus and other pandemics
US healthcare reform
Pharma under pressure
Health access in emerging markets
Ageing populations and tight budgets
Japan pharma: R&D price tag
September 3rd 2013 | Japan | Pharmaceuticals and biotechnology | Multiple companies
FROM THE ECONOMIST INTELLIGENCE UNIT
Proposals to increase reimbursement prices for innovative drugs face stiff resistance in Japan.
Japan, like most pharma-producing countries, has to tread a careful line between supporting its pharma industry and keeping pharma spending low. Pharma companies have long complained that the country's drug reimbursement rules focus too squarely on price-cuts. Now, as part of his economic expansion programme, Prime Minister Shinzo Abe wants to shift the balance towards innovation. Japan's finance ministry is not happy.
The key to the dispute lies with the Council for Regulatory Reform (CRR). In January, Mr Abe asked the CRR to find ways to slash red tape and create a regulatory environment to stimulate productivity and economic growth. The CRR has come back with a recommendation that the government raises the reimbursement prices it pays for innovative medicines that have been developed in Japan. Only by providing proper rewards for innovation can Japan attract more research and development (R&D) spending in pharma, it argued.
Japan's pharma companies are heavy spenders on R&D, at least as a proportion of their revenues, but much of that spending goes abroad. In 2005 new laws lifted long-held restrictions on how much manufacturing Japanese companies could outsource. Since then, many Japanese drug makers have gone global, buying biotech and other companies abroad and establishing international networks of R&D centres. Eisai, for example, has research operations in the US, UK, India and Singapore¯as well as Japan. This has allowed them to tap foreign scientific knowledge and experience, but it also means they have missed the opportunity to build up a sizeable Japanese R&D base.
The CRR claims that one reason for this decision to base R&D operations abroad is Japan's tight drug reimbursement system, which allows the government to use its leverage to secure low prices from manufacturers. Indeed, the pharma industry has long been moaning that prices are unfairly low, despite various reforms since the 1990s. The CRR wants Japan to follow the example of other pharma-producing countries, notably the US, where R&D-based pharma companies are effectively allowed a premium on pricing to encourage innovation.
The CRR has also recommended that, to speed up the availability of new medicines, the government should make it easier for companies to predict the prices they will be able to get for new drugs that are still in the R&D pipeline. And it has called for innovative treatments that are under tentative and conditional marketing approval to be reimbursed under National Health Insurance.
The Prime Minister has reportedly backed the proposals. His office has handed the reform recommendations to the Ministry of Health, Labour and Welfare (MHLW), which will review the proposed measures with the Central Social Insurance Medical Council. If all goes well, then the measures should be incorporated into the reimbursement system before the next round of price revisions next April.
Cost implications
Before then, though, the proposed changes will need to be approved by the finance ministry, which is reported to be unhappy at the cost implications during a period of worldwide austerity. Officials there have pointed out that Japan has already set up a programme in 2010 that gives premium reimbursement price to innovative medicines. They argue that this system has been used mostly by foreign drug makers, not by Japanese companies, and has therefore had little impact on domestic R&D.
Arguably, this merely demonstrates the need to design the premium pricing incentive better. The CRR has, however, made a concession to those concerned over costs. Under the current pricing rules, the reimbursement price for a branded drug falls every two years during its patent period. The price then falls by a further 4-6% in the first biannual review following the launch of competing generic products. The CRR has recommended that this final price-cut is deeper, with the percentage depending on the product.
Mr Abe's bigger argument, though, is that the changes the CRR is proposing will eventually bring down the price of innovative medicines, by easing domestic supplies. He is also hopeful that, with drug prices under pressure worldwide, Japan's decision to buck the global trend would attract huge investor interest. Nevertheless, the proposed changes face some stiff resistance over the next few months. Some members of the Drug Pricing Subcommittee of CSIMC, which has for many years been in charge of reimbursement prices, are also said to be opposed to the idea of pricing premiums. Which body¯the CRR or the CSIM¯carries the greatest influence in terms of final outcome remains unclear.
Amgen takes over Japan joint venture
Astellas acquires US cancer drug developer for up to US$405m
Astellas acquires UK gene therapy company


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Q: Bash: Execute command WITH ARGUMENTS in new terminal So i want to open a new terminal in bash and execute a command with arguments.
As long as I only take something like ls as command it works fine, but when I take something like route -n , so a command with arguments, it doesnt work.
The code:
gnome-terminal --window-with-profile=Bash -e whoami  #WORKS
gnome-terminal --window-with-profile=Bash -e route -n #DOESNT WORK
I already tried putting "" around the command and all that but it still doesnt work

A: You can start a new terminal with a command using the following:
gnome-terminal --window-with-profile=Bash -- \
    bash -c "<command>"

To continue the terminal with the normal bash profile, add exec bash:
gnome-terminal --window-with-profile=Bash -- \
    bash -c "<command>; exec bash"

Here's how to create a Here document and pass it as the command:
cmd="$(printf '%s\n' 'wc -w <<-EOF
  First line of Here document.
  Second line.
  The output of this command will be '15'.
EOF' 'exec bash')"

xterm -e bash -c "${cmd}"

To open a new terminal and run an initial command with a script, add the following in a script:
nohup xterm -e bash -c "$(printf '%s\nexec bash' "$*")" &>/dev/null &

When $* is quoted, it expands the arguments to a single word, with each separated by the first character of IFS. nohup and &>/dev/null & are used only to allow the terminal to run in the background.

A: Try this:
gnome-terminal --window-with-profile=Bash -e 'bash -c "route -n; read"'

The final read prevents the window from closing after execution of the previous commands. It will close when you press a key.
If you want to experience headaches, you can try with more quote nesting:
gnome-terminal --window-with-profile=Bash \
  -e 'bash -c "route -n; read -p '"'Press a key...'"'"'

(In the following examples there is no final read. Let's suppose we fixed that in the profile.)
If you want to print an empty line and enjoy multi-level escaping too:
gnome-terminal --window-with-profile=Bash \
  -e 'bash -c "printf \\\\n; route -n"'

The same, with another quoting style:
gnome-terminal --window-with-profile=Bash \
  -e 'bash -c '\''printf "\n"; route -n'\'

Variables are expanded in double quotes, not single quotes, so if you want them expanded you need to ensure that the outermost quotes are double:
command='printf "\n"; route -n'
gnome-terminal --window-with-profile=Bash \
  -e "bash -c '$command'"

Quoting can become really complex. When you need something more advanced that a simple couple of commands, it is advisable to write an independent shell script with all the readable, parametrized code you need, save it somewhere, say /home/user/bin/mycommand, and then invoke it simply as
gnome-terminal --window-with-profile=Bash -e /home/user/bin/mycommand

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