text:
Black Sheep Radio
COMMUNITY RADIO FOR VERMONT & NEW HAMPSHIRE
sheepheadani
Sheep happens.
An active participant in the Emergency Alert System, WOOL.fm keeps you informed during local, state, and national emergencies. We've got your back. We'll let you know if something's up.
WOOL.fm is an affiliate of the Pacifica Network, a listener sponsored alternative to commercially driven media. Much of the syndicated content we run originates from other Pacifica affiliates.
As a member, you can have your own weekly noncommercial radio program! Be a DJ, be a talk show host! Our mission to entertain, inform, and elevate the community can be a whole lot of fun.
Have your community-minded event announced on the air! It's a free public service that we offer. See the Mid-River Bulletin Board.
Underwriting is a good outreach solution for your business, organization, cultural event, or occasion! You can reach our listeners 24/7 on the air and support WOOL.fm at the same time.
Donations are tax-deductible. It costs more money than you might think to keep this community resource on the air. Surprisingly, we can't get by on just our good looks and delightful personality.
What's on now? Take a look at our programming schedule where you'll find everything from nuanced talk about political and social issues, to just-plain-fun, to our own hometown DJs.
Listen now! 91.5 FM in the area, or anywhere in the world via internet stream.
Join us! WOOL.fm is commercial free, member owned, volunteer run, and 100% supported by the community... your friends and neighbors. Just us regular people. Together, we're mighty.
Member owned, volunteer run, nonprofit, commercial free WOOL.fm hears you: send surface mail to P.O. Box 110, Bellows Falls VT 05101; phone 802-460-WOOL; or send an email by clicking this message.
William Holtz, President
Mr. Holtz is a licensed architect and a member of the AIA, NHAIA, and NCARB. His more than thirty years of professional practice has included residential and commercial projects in over twenty states and Canada. For more than two decades he has hung his hats at Benson Woodworking Company, a design-build firm in Walpole, NH, as design team leader and co-owner. Evening and weekend pro-bono dabbling has included rebuilding from the Alstead flood (Alstead Gazebo and Kmeic's Garage), a single family residence with Habitat for Humanity (Alstead, NH), and the Orchard Hill Breadworks Pizza Pavilion. His early years at WOOL, before gray hair, were enjoyed co-producing a weekly radio show, "The Generation Gap," with his then-teenage daughter.
Bruce Sterling, Vice President
Mr. Sterling has worked in archaeology for three decades, excavating prehistoric and historic sites in New England, the mid-Atlantic, the desert Southwest, Oregon, and the Aleutian Islands of Alaska. He made early contributions to battlefield archaeology through his work at Antietam Battlefield in Maryland. His archaeological pursuits brought him to Vermont in 1999, where he cares for a house on the Connecticut River with his wife, two children, a dog, and a cat. His obsessive interest in music drove him to join WOOL Radio in 2007 to host, as Border Pete, the show "Border Radio."
Tracy Klonowski, Treasurer
Cheryl Gay-Sherwin, Secretary
Ms. Gay-Sherwin was born in Bellows Falls and raised in North Walpole, where she now resides. A U.S. Air Force Veteran of Desert Storm, she worked at her family's business, Gay's Fuel Service, before pursuing her degree in Communications Disorders. Cheryl is a speech-language pathologist in the Windham Northeast Supervisory Union, serving the needs of local children. She lives with her husband Chris (whose glassblowing studio is also located at 33 Bridge Street), their awesome daughter Cassie, and multiple dogs, cats, chickens, and fish. She is the "C" in The ETC Show on WOOL.
Directors, alphabetically
Melissa Berry-Brown, Director
Ms. Berry-Brown, a resident of Bellows Falls and a lifelong Vermonter, is a classically trained musician and certified teacher, having studied at The New England Conservatory and received her degree in Elementary Education from Simmons College. She currently works in the Information Technology Department for UNFI, the largest distributor of Natural, Organic, and Specialty products in the United States. Ms. Berry-Brown is a published poet, and enjoys baking and doing her weekly radio show, "The Dish."
Dickie Colo, Director
Virginia Driscoll, Director
Miss Driscoll spent half of her life in Connecticut and half of it in Oregon. She has since discovered Bellows Falls and now calls Vermont home. She has over 20 years' experience in the natural food industry, leading to her current job at the largest natural and specialty foods distributor in North America. She is a lapsed actress and a prolific and daring visual artist. After a lifetime of vaguely wishing she could be one of the "cool kids" with her own radio show, it finally happened when co-host Jade Barbee suggested they share the resurrected Mixtape Radio.
Gary Smith, Director
Mr. Smith has been a member of the Board of Directors at WOOL since its founding in 2003. A veteran of the music industry, for 25 years he was owner of Boston's Fort Apache Studios which, as production, management, and recording house worked with artists including the Pixies, Billy Bragg, Radiohead, Belly, Natalie Merchant, Juliana Hatfield, and Uncle Tupelo. In 2012, Smith started The Popolo Restaurant Group with two ex-directors of WOOL, and is its General Manager, helping to put regional growers in contact with consumers in a farm-to-table setting. Smith also promotes concerts and cultural events in the area. Popolo often works in tandem with WOOL. Smith is ex-president of Great Falls Community Broadcasting Company.
WOOL-FM
PO Box 110 (33 Bridge St)
Bellows Falls VT 05101
ph: 802.460.WOOL
feedback@blacksheepradio.org
~ other listening options ~
WOOL-FM, Black Sheep Radio


text:
Un SMSC (sigle pour  ou centre de messagerie SMS) est un composant du réseau de téléphonie mobile permettant de gérer le transfert de messages SMS (textes ou binaires) entre téléphones mobiles.

En particulier, quand un abonné envoie un SMS vers un autre, le téléphone transmet en réalité le SMS vers le SMSC. Le SMSC stocke le message puis le transmet au destinataire lorsque celui-ci est présent sur le réseau (mobile allumé) : Le SMSC fonctionne sur le mode Store and Forward ( en français).

Le SMSC prend en charge la facturation qui doit éventuellement avoir lieu.
Il y a au moins un  (SMSC) par réseau GSM ou UMTS. En pratique il y en a très souvent plusieurs (SFR en a 2 par exemple).

Un SMSC joue également le rôle de « passerelle » entre le réseau IP et le réseau mobile. En  particulier, un serveur peut y accéder par connexion Internet TCP afin d'envoyer des SMS vers des numéros MSISDN de destination.

On parle dans ce cas d'application OTA ().

Un ensemble de protocoles existent pour communiquer entre serveurs et SMSC en TCP/IP : les plus utilisés sont SMPP () et CMG EMI (il y a aussi Nokia CIMD, Sema, CMPP).

Réseau de téléphonie mobile

redpajama_set_name: RedPajamaWikipedia


text:
from flask.ext.wtf import Form
from wtforms import StringField, PasswordField, validators

class LoginForm(Form):
    email_address = StringField('Email Address', [validators.Length(min=6, max=100), validators.DataRequired()])
    password = PasswordField('Password', [validators.InputRequired()])

redpajama_set_name: RedPajamaGithub


text:
Q: How to store a time_t field to MySQL datetime? I have extracted the modification time of a file using the struct stat structure:
long modtime = image_stats.st_mtime;

This returns 1508240128.
Now, I wish to store this value into a MySQL table which has datatype as datetime.
If I store it directly, it fails saying it is not a datetime format.
How do I store it?

A: You can use FROM_UNIXTIME to convert a timestamp into a DATETIME
Query
SELECT FROM_UNIXTIME(1508240128);

Result
FROM_UNIXTIME(1508240128)
---------------------------
2017-10-17 13:35:28

as insert query
Query
INSERT INTO
 [table]
(datetime_column)
VALUES
 (FROM_UNIXTIME(1508240128))

redpajama_set_name: RedPajamaStackExchange


2016 (Freshman): Appeared in 17 games on the season... Recorded nine kills and two assists on the season... Tallied 22 services aces, averaging 0.73 aces per set... Recorded career high 10 aces against Washington Adventist... Notched 26 digs on the season... Averaged season high 4 digs against DeSales, Washington Adventist, and conference competitor Mary Washington this season... Tallied 31.0 points on the season.


It's the anniversary of "Oops!... I Did It Again" so we're throwing a Britney party! Dance to Britney's biggest hits and all your TigerHeat favorites from Top-40, Hip-Hop, EDM, Latin, Throwbacks and more! And a live performance by Adam Cola!.


text:
Sanbo Scoring System, Based on Age and Pre-treatment Hematological Markers, is a Non-invasive and Independent Prognostic Predictor for Patients with Primary Glioblastomas: A Retrospective Multicenter Study
Peng-fei Wang1#, Jianbin Zhang1#, Hong-qing Cai2, Zhe Meng1, Chun-jiang Yu1, Shou-wei Li1, Jing-hai Wan2 , Chang-Xiang Yan1
1. Department of Neurosurgery, Sanbo Brain Hospital, Capital Medical University, China
2. Department of Neurosurgery, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, China
#These authors contributed equally to this work and are co-first authors.
✉ Corresponding authors: Jinghai Wan. Email: wanjinghaicom; Address: 17 Panjiayuan Nanli, Chaoyang District, Beijing, China; Phone: 0086-010-87787338. Chang-Xiang Yan. Email: yancx65828com; Address: Building 1, Ward 6, Xiang Shan Yi Ke Song Road 50, Haidian, Beijing, China; Phone: 0086-010-62856706. More
Wang Pf, Zhang J, Cai Hq, Meng Z, Yu Cj, Li Sw, Wan Jh, Yan CX. Sanbo Scoring System, Based on Age and Pre-treatment Hematological Markers, is a Non-invasive and Independent Prognostic Predictor for Patients with Primary Glioblastomas: A Retrospective Multicenter Study. J Cancer 2019; 10(23):5654-5660. doi:10.7150/jca.33047. Available from https://www.jcancer.org/v10p5654.htm
Various hematological markers are associated with survival in patients with glioblastomas (GBMs), as they reflect inflammation and nutrition status. However, single markers are insufficient for predicting prognosis in GBM, and a comprehensive scoring system is needed. In this study, we developed a simple, inexpensive, and non-invasive scoring system, referred to as the Sanbo Scoring System (SSS), to predict survival in patients with GBMs. Patients with GBM were retrospectively assigned to two independent cohorts at Sanbo Brain Hospital and National Cancer Center/Cancer Hospital. Clinical records, including age, routine blood tests, biochemistry and coagulation examinations, and IDH-1 status, were collected. In total, 274 and 87 patients with GBMs at Sanbo Brain Hospital and National Cancer Center/Cancer Hospital were included as derivation and validation cohorts, retrospectively. We developed the SSS based on data for the derivation cohort, i.e., age, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), albumin-to-globulin ratio (AGR), and fibrinogen levels. These patients were divided into three groups that differed with respect to age, inflammation-nutrition status, and overall survival (p < 0.001), i.e., SSS 0, 1, and 2. NLR, PLR, and fibrinogen levels were lower and AGR was higher in the SSS 2 group than in the other groups, indicating better inflammation and nutrition statuses. Additionally, the longest overall survival was observed in this group. A multivariate analysis showed that SSS was an independent prognostic factor. The validation cohort supported all the results. SSS was a simple, non-invasive, and effective scoring system, and independently predicted survival in GBMs.
Keywords: Glioblastoma, Prognostic factor, Hematological markers, Inflammation, Nutrition
Glioblastomas (GBMs) are the second most common central nervous system tumors and have a poor prognosis [1]. Classification of GBMs is highly important for predicting survival and optimizing therapeutic strategies [2]. Pathology is the only robust and informative method for classification to date, but is invasive and requires a biopsy, at minimum. Plasma cell-free circulating tumor DNA detection is effective for many cancers, but is nearly undetectable in patients with gliomas due to the brain-blood-barrier [3]. Although cerebrospinal fluid is ideal for detecting circulating tumor DNA [4], this method is expensive and technologically demanding. A simple, low-cost, and non-invasive method for classification is needed, especially for patients with newly diagnosed GBMs.
Our results and those of other previous studies have identified several prognostic factors in GBMs, such as the neutrophil-to-lymphocyte ratio (NLR) [5-7], platelet-to-lymphocyte ratio (PLR) [5, 6], albumin-to-globulin ratio (AGR) [8], prognostic nutrition index (PNI) [8], and albumin [9]. These hematological markers reflect the nutritional and inflammatory status in GBMs. Furthermore, combinations of these hematological markers, such as fibrinogen (FIB) and albumin, could better stratify gliomas [10]. These results provide the basis for the development of a scoring system that comprehensively integrates these inflammation and nutrition makers. However, the cutoff values for these prognostic factors, such as NLR [5-7], are not uniform, and it is therefore difficult to apply these factors in clinical practice. In this study, we developed a new scoring system, named the Sanbo Scoring System (SSS), incorporating age, NLR, PLR, AGR, and fibrinogen; this system could be effective for prognostic evaluations of patients with GBMs. To validate our results for SSS, we used clinical data from the National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College. We also set fixed cutoff values for age and hematological markers, with the goal of developing a universal scoring system for application in clinical practice.
Patients with GBMs from the derivation cohort and validation cohort were recruited from the Department of Neurosurgery of Sanbo Brain Hospital and National Cancer Center/Cancer Hospital, and the Chinese Academy of Medical Sciences and Peking Union Medical College, respectively. All the patients had a chemoradiotherapy according to Stupp's protocols [11]. Inclusion criteria were as follows: Patients were first diagnosed of GBMs by pathology [2]. Patients complete routine blood test results, blood biochemical and coagulation results before any treatment. Patients underwent a surgery or at least biopsy with the examination of IDH-1R132H mutations. Exclusion criteria were as follows: Patients suffered from other coexisting diseases, including infection, hematological disorders, autoimmune diseases, renal or hepatic dysfunction. Patients diagnosed with other malignancies. Patients had received preoperatively medical treatment, including glucocorticoid treatment, radiation, and chemotherapy. Written informed consent forms were obtained from patients for sampling and research. The Ethics Committees approved of this study for both the derivation (No. SBNK-2018-003-01) and validation cohort (No. NCC2014G-12).
Blood examination and data collection
Peripheral venous blood samples were obtained before surgery and then conveyed to the Clinical Laboratory Department within 30 min for routine tests. Complete blood counts, blood biochemical indexes, and coagulation parameters were collected for further analyses. In this study, NLR was defined as the ratio of the absolute neutrophil count to the absolute lymphocyte count, PLR was defined as the absolute platelet count divided by the absolute lymphocyte count, and AGR was calculated as the ratio of serum albumin to globulin.
The procedures of IHC was performed as we previously described [12]. The detection of IDH-1R132H mutations by immunohistochemistry was performed as described previously using an anti-IDH-1R132H antibody (working solution, ZM0447, ZSGB-BIO, Beijing, China) as a primary antibody. A cut-off value of 10% was used to evaluate the presence or absence of IDH-1R132H.
All data were analyzed using SPSS 22.0 (IMB Inc., Armonk, New York). Differences in continuous variables between groups were analyzed by unpaired t-tests or a variance analysis, and discrete variables were analyzed by chi-square tests. The Kaplan-Meier method was used to draw an overall survival (OS) curve. A univariate Cox analysis was used to analyze the prognostic significance of variables. A multivariate analysis by the Cox proportional-hazard model was used to determine independent prognostic factors. A two-sided p-value of <0.05 was considered statistically significant.
Patient demographic properties
There were 274 patients in the derivation cohort and 87 patients in the validation cohort. All patients included in the study had pretreatment hematological examinations and survival data. Baseline clinicopathological parameters in the two cohorts were highly similar, including age, gender, NLR, PLR, AGR, FIB, IDH-1 mutations, GTR, chemoradiotherapy, follow-up period, and OS (Table 1).
Development of SSS
SSS was determined by the following five parameters: age at diagnosis (normal, <65 years), NLR (normal, <3.5), PLR (normal, <200), AGR (normal, >1.6), and fibrinogen (normal, <3.36). We divided patients into three groups: those with normal values for five parameters were classified as SSS 2, patients with normal values for three or four parameters were assigned to SSS 1, and others were assigned to SSS 0 (Fig. 1).
Association between SSS and clinicopathological factors
According to our scoring system, age, NLR, PLR, AGR, and fibrinogen levels were significantly greater in SSS 2 than in SSS 0 and 1 (Table 2). The incidence of IDH-1 mutations did not differ among the three groups in the derivation cohort (p = 0.638) or the validation cohort (p = 0.050). However, there were more males in SSS 1 and 2 than in SSS 0 in the derivation cohort (p = 0.007). This difference was not observed in the validation cohort (p = 0.850).
The median follow-up was 12.77 (3.80-48.97) months in the derivation cohort. In total, 186 (67.88%) patients died due to tumor recurrence at the last follow-up, with 50.2% and 13.1% 1- and 2-year survival rates, respectively. The results described above were highly consistent with the results obtained in the validation group. A total of 55 (63.2%) patients died at the last follow-up, and 1- and 2-year survival rates were 57.3% and 14.5%, respectively.
The estimated relative risk (RR) of death was 64.9% lower in SSS 1 and 74.7% lower in SSS 2 than that in SSS 0 (Table 3). Additionally, the RR was significantly lower by 83.3% (70.6-98.0%) in SSS 2 than in SSS 1 (Fig. 2A). In the validation group, the estimated RR was significantly lower, by 65.9% and 69.9%, in SSS 1 and 2 respectively, than in SSS 0 (Table 3). RR was 72.0% (50.5-102.6%) lower in SSS 2 than in SSS 1, but this difference was not significant (p = 0.069, Fig. 2B).
In a univariate analysis, we found that SSS, IDH-1R132H mutations, gross total resection, and complete chemoradiotherapy were significantly associated with a favorable clinical outcome in the derivation group. There was a difference in the validation group, but this difference was small (Table 3). However, a multivariate analysis of the two independent cohorts both showed that SSS, IDH-1R132H mutations and chemoradiotherapy were independent prognostic factors.
Baseline Characteristics
Derivation cohort (n = 274)
Validation cohort (n = 87)
Age (Median, range, years) 54 (15-80) 57 (15-80) 0.641
Women (%) 111 (40.51%) 40 (45.98) 0.368
NLR 3.48 ± 2.87 4.16 ± 3.38 0.067
PLR 150.04 ± 77.38 168.14 ± 93.18 0.072
AGR 1.74 ± 0.36 1.75 ± 0.30 0.863
FIB (g/L) 2.67 ± 0.68 2.84 ± 0.78 0.059
IDH-1 mutation 42 (15.33) 18 (20.69) 0.355
GTR (%) 189 (68.98) 56 (64.37) 0.422
Complete chemoradiotherapy 115 (41.97) 45 (51.72) 0.111
Follow-up period (Median, months) 12.77 (3.80-48.97) 15.23 (2.43-45.97) 0.583
Overall survival (Median, months) 13.13 (11.42 ± 14.84) 15.37 (9.77 ± 20.97) 0.312
# The data of NLR, PLR, AGR and FIB was presented as Mean ± SD.
Association of SSS with clinicopathological factors
Derivation cohort
Validation cohort
SSS 0 (n = 43)
SSS 1 (n = 135)
Age (Mean ± SD) 58.65 ± 13.35 54.34 ± 13.07 46.86 ± 12.00***### 63.21 ± 9.32 53.73 ± 13.53* 43.83 ± 14.52***#
Gender (F/M) 26 / 17 54 / 81 31 / 65 8 / 11 22 / 23 10 /13
NLR 6.94 ± 4.42 3.44 ± 2.21*** 1.99 ± 0.62***### 7.27 ± 4.61 3.88 ± 2.66*** 2.13 ± 0.53***
PLR 236.28 ± 105.30 149.13 ± 67.63*** 112.70 ± 32.80***### 268.77 ± 100.85 151.00 ± 80.29*** 118.53 ± 28.41***
AGR 1.46 ± 0.24 1.64 ± 0.29** 2.00 ± 0.32***### 1.61 ± 0.35 1.74 ± 0.30 1.87 ± 0.21*
FIB (g/L) 3.20 ± 0.75 2.71 ± 0.69*** 2.38 ± 0.46***### 3.50 ± 0.83 2.81 ± 0.69*** 2.33 ± 0.44***#
IDH-1 mutation (%) 5 (11.63) 20 (14.81) 17 (17.71) 1 (5.26) 10 (22.22) 7 (30.43)
*, **, and *** indicated vs SSS 0, p < 0.05, <0.01 and <0.001 respectively.
#, ##, and ### indicated vs SSS 1, p < 0.05, <0.01 and <0.001 respectively.
Calculation of the Sanbo Brain prognostic score
A, Kaplan-Meier survival curve for patients with GBMs according to SSS group in the derivation group. SSS = 0, n = 43; SSS = 1, n = 135; SSS = 2, n = 96. B Survival curve of SSS in validation group. SSS = 0, n = 19; SSS = 1, n = 45; SSS = 2, n = 23.
In this study, we developed and validated, internally and externally, a scoring system for evaluating RR in patients with GBMs. This system, referred to as SSS, showed independent prognostic value with unified cutoff values for continuous variables. The SSS reflected a combined state of nutrition, inflammation, and coagulation in GBMs. Thus, our system can be used to non-invasively and effectively identify patients with a high risk of a shorter OS.
The prognostic significance of hematological markers has recently been established in a variety of cancers. NLR is the most common prognostic marker in GBMs, and its cutoff values range from 4-7 [5-7]. Various cutoff values have also been used for the PLR [5, 6], PNI [8, 13] and red blood cell distribution width [14, 15]. The different cutoff values for these makers can be explained by study heterogeneity, including differences in age, IDH mutations, surgery, and chemoradiotherapy. These results have shown that a single marker is not sufficient to predict survival in patients with GBMs. Additional markers could reflect inflammation, nutrition, and coagulation states simultaneously. For example, a scoring system developed by He et al.[10] based on the combination of plasma fibrinogen and albumin can predict progression-free survival and OS in patients with high-grade gliomas. Another prognostic score simultaneously considering NLR, the lymphocyte-to-monocyte ratio, albumin, and cholesterol is a simple and effective tool for predicting survival in patients with colorectal cancers [16].
Univariate and Multivariate analysis of SSS in GBMs
Univariate analysis
HR (95% CI)
p-val
0 43 Reference < 0.001 0.857 (0.747 - 0.983) 0.027 19 Reference < 0.001 0.783 (0.630 - 0.971) 0.026
1 135 0.649 (0.436 - 0.968) 45 0.659 (0.479 - 0.907)
2 96 0.747 (0.647 - 0.864) 23 0.699 (0.562 - 0.869)
female 111 1.009 (0.750 - 1.358) 0.951 1.014 (0.748 - 1.373) 0.931 40 0.914 (0.534 - 1.536) 0.742 0.914 (0.526 - 1.589) 0.750
male 163 Reference 47 Reference
IDH-1 R132H
Mutation 42 0.589 (0.383 - 0.907) 0.016 0.595 (0.385 - 0.920) 0.020 17 0.499 (0.243 - 1.025) 0.058 0.397 (0.186 - 0.847) 0.017
Wild-type 232 Reference 70 Reference
GTR 189 0.721 (0.532 - 0.978) 0.036 0.763 (0.560 - 1.039) 0.086 56 0.784 (0.444 - 1.383) 0.784 0.714 (0.394 - 1.292) 0.265
non-GTR 85 Reference 31 Reference
Chemoradiotherapy
Complete 159 0.432 (0.323 - 0.578) < 0.001 0.444 (0.330 - 0.597) < 0.001 40 0.268 (0.151 - 0.475) < 0.001 0.240 (0.131 - 0.441) < 0.001
Incomplete 115 Reference 47 Reference
One benefit of our scoring system is that it accounts for age, which is a powerful prognostic factor according to most studies of GBMs [10, 14]. Additionally, SSS was developed based on the nutritional and inflammatory status. The prognostic value of this scoring system was independent of gender, IDH status, surgical resection, and chemoradiotherapy. SSS could be widely applied owing to its unified cutoff values and independent prognostic values.
The components of SSS were age, neutrophils, platelets, lymphocytes, albumin, globulin, and fibrinogen. A younger age was associated with favorable genetic changes, such as IDH mutation and ATRX loss, which were associated with a better clinical outcome in patients with GBMs [17, 18]. Additionally, the cutoff value of 65 years was consistent with that in a previous report [19]. Circulating neutrophils promoted tumor progression by secreting arginase I for immunosuppression and vascular endothelial growth factor for angiogenesis [20]. Blood platelets migrate into the tumor environment and facilitate cancer cell metastasis, invasion, and immune suppression [21]. Lymphocytes protect against tumor progression and prolong OS in patients with GBMs [22, 23]. Consequently, higher NLR and PLR values indicated stronger immune suppression and were associated with a worse OS in GBMs. Lower levels of albumin were induced by tumor necrosis factor (TNF)-α, Interleukin (IL)-1β, and IL-6 [24], and the latter cytokines inhibited T effector cells in GBMs [25]. Globulin was another systemic inflammation marker, and a lower AGR indicated a systemic inflammatory response [26]. Albumin was a marker of nutrition status. There is an active interaction between inflammation and nutrition in cancer [27]. In patients with GBMs, an abnormal body mass index (BMI, kg/m2) indicating a patient is underweight (BMI < 18.5 kg/m2) or overweight (BMI ≥ 30 kg/m2) is associated with a worse OS [28]. Moreover, cholesterol is a positive prognostic marker, with a crucial role in the nutrition status in GBMs [29]. Plasma fibrinogen suppresses anti-tumor immunity by activating neutrophils [20, 30] and inhibiting natural killer cells [31]. Taken together, the components of SSS were indicators of inflammation, immune-suppression, and nutrition status in GBMs.
Our study had several limitations. It was a retrospective study, suggesting the potential for a high recall bias. The external validation was an efficient strategy to account for this limitation. However, future prospective and multicenter studies are needed to confirm these results. Secondly, there were more patients in the derivation cohort than in the validation cohort, and this sample size difference might result in differences in results. For example, we observed a gender difference among the SSS groups, but the same results were not obtained in the validation cohort. Inconsistent NLR results between genders have been reported in GBMs [15, 32]. The scoring system developed by He et al. showed no gender difference in GBMs [10]. Similarly, gender was not a prognostic factor in our study. Accordingly, the inflammation status with respect to gender should be investigated further. Thirdly, the red blood cell distribution width, PNI, albumin, and blood lipids were not included in the SSS. Combinations of these hematological markers might produce a better scoring system. Multidisciplinary approaches, including approaches by researchers in mathematics, clinical laboratories, and medical fields, are needed to create the optimal system. Fourthly, these inflammation markers have diagnostic value for glioma. NLR is positively correlated with glioma grade with a cutoff of 2.59 [32]. Moreover, combinations of these markers, such as NLR and the lymphocyte-monocyte ratio, predict glioma grade and diagnosis with a high sensitivity and specificity [33]. Furthermore, a reduced NLR during concurrent chemoradiotherapy is associated with an improved OS in patients with GBMs [34]. A decreased NLR also predicts a better progression-free survival in patients with advanced cancers treated with PD-1 inhibitors [35]. These results suggest that SSS or another scoring system should be used to determine treatment strategies and may provide additional prognostic value. Lastly, we admitted that there are limitations in regards to the development and statistical analysis of the scoring system. But this scoring showed that combination of these markers together could yield a prognostic indicator in glioblastoma, and encourage future studies to include these factors when building a predictive scoring system.
Pretreatment SSS was an easy and effective scoring system and provided independent prognostic value for patients with GBMs. A low SSS was associated with strong immune suppression and a poor nutrition status. Our results were validated using an independent cohort. This novel system may improve GBM classification, and its effectiveness and diagnostic and prognostic value should be further evaluated.
This work was supported by grants from the National Key Technology Research and Development Program of the Ministry of Science and Technology of China (No. 2014BAI04B01).
1. Ostrom QT, Gittleman H, Liao P, Vecchione-Koval T, Wolinsky Y, Kruchko C. et al. CBTRUS Statistical Report: Primary brain and other central nervous system tumors diagnosed in the United States in 2010-2014. Neuro Oncol. 2017;19:v1-v88
2. Louis DN, Perry A, Reifenberger G, von Deimling A, Figarella-Branger D, Cavenee WK. et al. The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary. Acta neuropathologica. 2016;131:803-20
3. Bettegowda C, Sausen M, Leary RJ, Kinde I, Wang Y, Agrawal N. et al. Detection of circulating tumor DNA in early- and late-stage human malignancies. Sci Transl Med. 2014;6:224ra24
4. De Mattos-Arruda L, Mayor R, Ng CK, Weigelt B, Martinez-Ricarte F, Torrejon D. et al. Cerebrospinal fluid-derived circulating tumour DNA better represents the genomic alterations of brain tumours than plasma. Nat Commun. 2015;6:8839
5. Wang PF, Song HW, Cai HQ, Kong LW, Yao K, Jiang T. et al. Preoperative inflammation markers and IDH mutation status predict glioblastoma patient survival. Oncotarget. 2017
6. Han S, Liu Y, Li Q, Li Z, Hou H, Wu A. Pre-treatment neutrophil-to-lymphocyte ratio is associated with neutrophil and T-cell infiltration and predicts clinical outcome in patients with glioblastoma. BMC Cancer. 2015;15:617
7. Lopes M, Carvalho B, Vaz R, Linhares P. Influence of neutrophil-lymphocyte ratio in prognosis of glioblastoma multiforme. Journal of neuro-oncology. 2018;136:173-80
8. Xu WZ, Li F, Xu ZK, Chen X, Sun B, Cao JW. et al. Preoperative albumin-to-globulin ratio and prognostic nutrition index predict prognosis for glioblastoma. Onco Targets Ther. 2017;10:725-33
9. Borg N, Guilfoyle MR, Greenberg DC, Watts C, Thomson S. Serum albumin and survival in glioblastoma multiforme. Journal of neuro-oncology. 2011;105:77-81
10. He ZQ, Duan H, Ke C, Zhang XH, Guo CC, Al-Nahari F. et al. Evaluation of cumulative prognostic score based on pretreatment plasma fibrinogen and serum albumin levels in patients with newly diagnosed high-grade gliomas. Oncotarget. 2017;8:49605-14
11. Stupp R, Mason WP, van den Bent MJ, Weller M, Fisher B, Taphoorn MJ. et al. Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. The New England journal of medicine. 2005;352:987-96
12. Wang PF, Liu N, Song HW, Yao K, Jiang T, Li SW. et al. IDH-1R132H mutation status in diffuse glioma patients: implications for classification. Oncotarget. 2016;7:31393-400
13. Zhou XW, Dong H, Yang Y, Luo JW, Wang X, Liu YH. et al. Significance of the prognostic nutritional index in patients with glioblastoma: A retrospective study. Clinical neurology and neurosurgery. 2016;151:86-91
14. Auezova R, Ryskeldiev N, Doskaliyev A, Kuanyshev Y, Zhetpisbaev B, Aldiyarova N. et al. Association of preoperative levels of selected blood inflammatory markers with prognosis in gliomas. Onco Targets Ther. 2016;9:6111-7
15. Xu W, Wang D, Zheng X, Ou Q, Huang L. Sex-dependent association of preoperative hematologic markers with glioma grade and progression. Journal of neuro-oncology. 2017
16. Galizia G, Lieto E, Auricchio A, Cardella F, Mabilia A, Podzemny V. et al. Naples Prognostic Score, Based on Nutritional and Inflammatory Status, is an Independent Predictor of Long-term Outcome in Patients Undergoing Surgery for Colorectal Cancer. Dis Colon Rectum. 2017;60:1273-84
17. Yan H, Parsons DW, Jin G, McLendon R, Rasheed BA, Yuan W. et al. IDH1 and IDH2 mutations in gliomas. The New England journal of medicine. 2009;360:765-73
18. Ikemura M, Shibahara J, Mukasa A, Takayanagi S, Aihara K, Saito N. et al. Utility of ATRX immunohistochemistry in diagnosis of adult diffuse gliomas. Histopathology. 2016;69:260-7
19. Bambury RM, Teo MY, Power DG, Yusuf A, Murray S, Battley JE. et al. The association of pre-treatment neutrophil to lymphocyte ratio with overall survival in patients with glioblastoma multiforme. Journal of neuro-oncology. 2013;114:149-54
20. Massara M, Persico P, Bonavita O, Mollica Poeta V, Locati M, Simonelli M. et al. Neutrophils in Gliomas. Front Immunol. 2017;8:1349
21. Menter DG, Kopetz S, Hawk E, Sood AK, Loree JM, Gresele P. et al. Platelet "first responders" in wound response, cancer, and metastasis. Cancer Metastasis Rev. 2017;36:199-213
22. Kmiecik J, Poli A, Brons NH, Waha A, Eide GE, Enger PO. et al. Elevated CD3+ and CD8+ tumor-infiltrating immune cells correlate with prolonged survival in glioblastoma patients despite integrated immunosuppressive mechanisms in the tumor microenvironment and at the systemic level. J Neuroimmunol. 2013;264:71-83
23. Gieryng A, Pszczolkowska D, Walentynowicz KA, Rajan WD, Kaminska B. Immune microenvironment of gliomas. Lab Invest. 2017;97:498-518
24. Chojkier M. Inhibition of albumin synthesis in chronic diseases: molecular mechanisms. J Clin Gastroenterol. 2005;39:S143-6
25. Nduom EK, Weller M, Heimberger AB. Immunosuppressive mechanisms in glioblastoma. Neuro Oncol. 2015;17(Suppl 7):vii9-vii14
26. Niwa N, Matsumoto K, Ide H, Nagata H, Oya M. Prognostic Value of Pretreatment Albumin-to-Globulin Ratio in Patients With Non-Muscle-Invasive Bladder Cancer. Clin Genitourin Cancer. 2018
27. Zitvogel L, Pietrocola F, Kroemer G. Nutrition, inflammation and cancer. Nat Immunol. 2017;18:843-50
28. Siegel EM, Nabors LB, Thompson RC, Olson JJ, Browning JE, Madden MH. et al. Prediagnostic body weight and survival in high grade glioma. Journal of neuro-oncology. 2013;114:79-84
29. Liang R, Li J, Li M, Yang Y, Wang X, Mao Q. et al. Clinical significance of pre-surgical serum lipid levels in patients with glioblastoma. Oncotarget. 2017;8:85940-8
30. Steinbrecher KA, Horowitz NA, Blevins EA, Barney KA, Shaw MA, Harmel-Laws E. et al. Colitis-associated cancer is dependent on the interplay between the hemostatic and inflammatory systems and supported by integrin alpha(M)beta(2) engagement of fibrinogen. Cancer Res. 2010;70:2634-43
31. Degen JL, Palumbo JS. Hemostatic factors, innate immunity and malignancy. Thromb Res. 2012;129(Suppl 1):S1-5
32. Zadora P, Dabrowski W, Czarko K, Smolen A, Kotlinska-Hasiec E, Wiorkowski K. et al. Preoperative neutrophil-lymphocyte count ratio helps predict the grade of glial tumor - a pilot study. Neurol Neurochir Pol. 2015;49:41-4
33. Zheng SH, Huang JL, Chen M, Wang BL, Ou QS, Huang SY. Diagnostic value of preoperative inflammatory markers in patients with glioma: a multicenter cohort study. J Neurosurg. 2017:1-10
34. Mason M, Maurice C, McNamara MG, Tieu MT, Lwin Z, Millar BA. et al. Neutrophil-lymphocyte ratio dynamics during concurrent chemo-radiotherapy for glioblastoma is an independent predictor for overall survival. Journal of neuro-oncology. 2017;132:463-71
35. Moschetta M, Uccello M, Kasenda B, Mak G, McClelland A, Boussios S. et al. Dynamics of Neutrophils-to-Lymphocyte Ratio Predict Outcomes of PD-1/PD-L1 Blockade. BioMed research international. 2017;2017:1506824
Corresponding authors: Jinghai Wan. Email: wanjinghaicom; Address: 17 Panjiayuan Nanli, Chaoyang District, Beijing, China; Phone: 0086-010-87787338. Chang-Xiang Yan. Email: yancx65828com; Address: Building 1, Ward 6, Xiang Shan Yi Ke Song Road 50, Haidian, Beijing, China; Phone: 0086-010-62856706.
Accepted 2019-8-3

redpajama_set_name: RedPajamaCommonCrawl


In the early 1930s, the now legendary Bob Wills created what he called a "Texas fiddle band." It featured him and others on fiddles in addition to just about any other instrument. The band played almost any style of music: blues, country, Mexican, polka, ragtime, swing, cowboy songs, and of course, fiddle tunes. If it made people dance, they played it.
Randy Sabien, a jazz violinist, has tapped into that tradition by including three fiddles (or two fiddles and one cello) in his Fiddlehead Band, featuring drums, bass, guitar, and keyboard rhythm section. With the advantage of 80 years of musical history to tap into, Sabien describes his sound as "Bob Wills meets Art Blakey (be-bop) meets the Allman Brothers (blues rock)." You can expect to hear classic Western swing tunes such as "Maiden's Prayer," an instrumental version of a Grateful Dead song, "Stella Blue," or a swinging Duke Ellington tune, "Mr. Gentle and Mr. Cool." Just like Will's Texas Playboys, Sabien's band will play just about any style that makes people dance, or at least tap their toes.


You, the Non Chinese Users, of course can use it! There Should Be An Alipay Account For You no matter to use the personal page create by Alipay or use the donation API! Unfortunately, the donated RMB for you will be left in the Alipay account forever if there is not any BankCard of you! Caution! That's all but not the end, welcome to use it if you get some ways to get the bucks some time!
使用本插件,使用者有两种方式获取捐赠, 第一种就是使用支付宝的个人收款页面,点此申请. 另外一种就是使用的支付宝的接口.你只需要在后台设置你的支付宝账号即可.在捐赠的时候, 被捐赠主体会显示我的名字, 但是捐助款会直接打到你的支付宝账户中.
郑重声明:本插件的支付宝接口并不存在于插件中, 插件使用方并未以任何形式使用插件接口. 捐赠者通过插件跳转页面传递参数到歪世界的捐赠页面上. 接口的所有权归支付宝所有.插件使用方在歪世界所创建的交易中充当分润人, 交易所有权归歪世界所有!
收费问题:此插件的使用是免费的, 但是由于支付宝的政策-接口使用者需向支付宝支付每笔0.7%-1.2%的手续费, 该费用由插件作者来支付. 此外, 获赠方只能收到每笔捐赠的98%.(当捐赠额小于0.40CNY的时候费率为0), 我插件郑重承诺: 此插件免费, 所有有关费率的调整都会在此页面中说明, 请放心使用! 关于插件的所有问题最终解释权归插件作者所有!
关于捐赠通道的设置, 你可以使用支付宝提供的个人收款页面进行收款, 如果你采用此方式,请点此申请一个支付页面; 或者, 你可以使用支付宝提供的快捷支付服务,你只需要填写你的支付宝Email账号. 总之, 不管你使用何种方式, 你需要设置好相应的必要参数,并选择为付款通道.
关于插件API, 正如你看到的,插件的设置页面最底部有一个链接, 它是本插件的API捐赠的核心部分, 但是它不包含API密钥的任何信息, 目前你可以通过该链接传递三个参数,分别是收款email(必须),博客url(必须),捐赠金额bill(非必须).如果你是开发者,无论是用于任何合法的场合,你都可以使用它获取捐赠.
分情况, 当交易额低于0.40CNY的时候, 不收取任何费用. 其他情况收取2%的费用. 关于这笔费用你不必关心, 最终你能收到每笔捐赠的98%.
不能设置! 支付的接口是插件作者与API签约所获得的. 本插件已郑重声明, 插件使用者不是API使用者, 插件使用者只是充当歪世界 所创建的交易的分润人. 支付页面显示的是签约甲方的名字.
"AlipayDonate" is open source software. The following people have contributed to this plugin.
Translate "AlipayDonate" into your language.


text:
Q: Making Field of view using Raycasts With for loops (Using Unity C#) Code:
        void IdleState ()
    {
    RaycastHit hit;

    for (float i = -ViewWidth; i < ViewWidth; i++)
        {
            float Iterater = i/20;
            if (Physics.Raycast(transform.position, transform.forward + new Vector3(Iterater,0,0), out hit, ViewRange))
             {
                Debug.DrawRay(transform.position,(transform.forward + new Vector3(Iterater,0,0)).normalized * ViewRange, Color.red);
                if (hit.transform.gameObject.layer == LayerMask.NameToLayer("Player"))
                 {
                    FoundPlayer(hit.transform);
                 }
             }
        }

}

Problem: In Unity I am trying to create a Field of view for an enemy AI by drawing multiple raycasts with for loops.  For some reason the raycasts do this:
GIF
I've been trying to fix this for days Please help!
Also the FoundPlayer() function in case you need it:
void FoundPlayer  (Transform DetectedObject)
    {
        float step = TurnSpeed * Time.deltaTime;
        Vector3 Direc = DetectedObject.position - transform.position;
        Vector3 RotMath = Vector3.RotateTowards(transform.forward, Direc,step,0.0f);
        transform.rotation = Quaternion.LookRotation(RotMath);
        Vector3 CurrentRot = transform.eulerAngles;
        transform.rotation = Quaternion.Euler(0,CurrentRot.y,0);
    }

The FoundPlayer() function just rotates the enemy towards the player when one of the raycasts hit it.

A: Regarding Raycast:
transform.Forward is in world-space coordinates, to which you add some fraction of world-space X-axis offset.  What you wanted is to add some fraction of 'local-space' X-axis.  transform.Right is the world-space conversion of local-space X-axis.  Try:
var rayDirection = (transform.Forward + (Vector3.Scale(transform.Right, new Vector3(Iterater, 0, 0))).normalized;

Regarding Fields of View:
If all you want to do is check what objects are within a FOV, start finding everything within a sphere, then filter those objects to what's within appropriate angles from transform.forward:
float ViewRange = 10;
float hHalfFov = 45; // Horizontal Half-Field of View (Degrees)
float vHalfFov = 45; // Vertical Half-Field of View (Degrees)

void IdleState() {

    // Find all colliders within ViewRange
    var hits = Physics.OverlapSphere(transform.position, ViewRange);

    foreach (var hit in hits) {
        if ( hit.gameObject == this.gameObject ) continue; // don't hit self

        // Check FOV
        var direction = (transform.position - hit.transform.position);
        var hDirn = Vector3.ProjectOnPlane(direction, transform.up).normalized; // Project onto transform-relative XY plane to check hFov
        var vDirn = Vector3.ProjectOnPlane(direction, transform.right).normalized; // Project onto transform-relative YZ plane to check vFov

        var hOffset = Vector3.Dot(hDirn, transform.forward) * Mathf.Rad2Deg; // Calculate horizontal angular offset in Degrees
        var vOffset = Vector3.Dot(vDirn, transform.forward) * Mathf.Rad2Deg; // Calculate vertical angular offset in Degrees

        if (hOffset > hHalfFov || vOffset > vHalfFov) continue; // Outside of FOV

        Debug.DrawLine(transform.position, hit.transform.position, Color.red);

        if (hit.transform.gameObject.layer == LayerMask.NameToLayer("Player")) {
            FoundPlayer(hit.transform);
        }
    }
}