Grid Database Research Articles

Ethically challenging situations in teachers’ work: role of school neighborhood socioeconomic status

Abstract Background Ethically challenging situations are common in the teaching profession and can lead to significant stress. In particular, teachers working in socially disadvantaged areas may face these kinds of situations more often due to conflicts between educational needs and available resources. We investigated the link between school neighborhood socioeconomic status (SES) and the prevalence of ethically challenging situations among teachers. Methods Data were from primary school teachers (n = 1265) participating to the Finnish Public Sector study in 2022. Experiences on ethically challenging situations were assessed with two questions: In your work how often do you feel that you have to 1) reflect on ethically challenging situations and 2) act contrary to rules, norms, or personal values (at least weekly vs. less frequently). Survey data were linked to information on school neighborhood SES obtained from national grid database. Log-binomial regression models were used to analyze the relationship between school neighborhood SES and ethically challenging situations. Results Of the respondents, 61% reported having to reflect on ethically challenging situations, and 18% felt they had to act against rules or personal values at least weekly. Teachers in schools located in the most disadvantaged areas were more likely to feel that they had to act against rules, norms, or values weekly compared to teachers in schools in the most advantaged areas (adjusted RR 1.43, 95% CI 1.01-2.02). No association was observed between reflecting on ethically challenging situations and school neighborhood (adjusted RR 0.98, 95% CI 0.87, 1.11). Conclusions School neighborhood SES appears to be associated with teachers’ possibilities to choose action in ethically challenging situations. Key messages • School neighborhood SES appears to be associated with teachers’ possibilities to choose action in ethically challenging situations. • It is essential to develop strategies to support teachers in managing ethically challenging situations in their work environments.

Catalogue of drought events in peninsular Spanish along 1916–2020 period

We leveraged the most extensive and detailed gridded database of monthly precipitation data across the Spanish mainland (MOPREDAScentury), encompassing 1916–2020 time period, to pinpoint the most severe drought events within this timeframe and analyse their spatio-temporal dynamics. To identify these events, we employed the Standardized Precipitation Index (SPI) at a 12-month timescale. Drought events were identified as periods of at least three months where significantly dry conditions affected 20% or more of the study area, defined as grid cells with SPI values lower than −0.84. Our analysis revealed a total of 40 major drought events. Our catalogue contains detailed information on each episode’s spatial extent, duration, severity, and spatio-temporal dynamics. The analysis of the propagation patterns of the events unveils substantial heterogeneity, implying that droughts stem from diverse atmospheric mechanisms, further influenced by complex local topography. The open-licensed drought database serves as a valuable resource. It not only facilitates exploration of drought onset and evolution mechanisms but also aids in assessing drought impact on agricultural and other socio-economic sectors.

Association of school neighbourhood socioeconomic disadvantage and teaching staff’s risk of violence at work

Aim: The aim of this study was to determine the association between neighbourhood socioeconomic disadvantage and teaching staff’s risk of workplace violence and whether workplace psychosocial resources can act as effect modifiers. Methods: Primary school teaching staff in the six largest cities in Finland responded to a survey in 2018 and were linked to information on school neighbourhood disadvantage obtained from the national grid database ( n = 3984). Results: After adjustment for confounders, staff working in schools located in the most disadvantaged neighbourhoods had a 1.2-fold (95% confidence interval 1.07–1.35) risk of encountering violence or threat of violence compared with staff working in the most advantaged neighbourhoods. The association was less marked in schools with strong support from colleagues (risk ratio 1.14, 95% confidence interval (95% CI) 0.98–1.32 for high support versus 1.23, 95% CI 1.07–1.43 for low/intermediate support), a strong culture of collaboration (1.08, 95% CI 0.93–1.26 versus 1.31, 95% CI 1.12–1.53), high leadership quality (1.12, 95% CI 0.96–1.31 versus 1.29, 95% CI 1.08–1.54), and high organizational justice (1.09, 95% CI 0.91–1.32 versus 1.29, 95% CI 1.09–1.52). Conclusions: The association between school neighbourhood and teaching staff’s risk of violence was weaker in schools with high workplace psychosocial resources, suggesting that targeting these factors might help in minimizing violence at schools, but future intervention studies are needed to confirm or refute this hypothesis.

Luminal and basal subtyping of circulating tumor cells and influence on outcomes in patients with metastatic castration-resistant prostate cancer.

e17034 Background: Luminal and basal subtyping using PAM50 classifier and the prostate cancer classification system (PCS) have been shown to predict outcomes and treatment response in prostate cancer (PCa). However, these genomic classifiers rely on the acquisition of tumor samples, which are usually not available in patients with metastatic castration resistant PCa (mCRPC). Circulating tumor cells (CTC) are tumors cells shed into bloodstream and can serve as surrogates of tumor. In this study, we aimed to subtype circulating tumor cells with PAM50 and PCS classifiers and evaluate their prognostic performance in mCRPC patients. Methods: A CTC RNA assay was developed by combining NanoVelcro microfluidic system for enriching CTCs and Nanostring nCounter system for PAM50 and PCS profiling of CTCs. The PAM50 and PCS genes were refined through a bioinformatic pipeline to ensure the specific profiling of CTCs. The classification and prognostic performance of the refined CTC-PAM50 and CTC-PCS gene panels were validated in GenomeDx GRID database. For the clinical study, 50 blood samples from mCRPC patients before the initiation of androgen receptor signaling inhibitors (ARSI) (n = 34) or taxanes (n = 16) were analyzed with the CTC RNA assay. Kaplan-Meier analysis and log-rank test were used to evaluate the association between CTC-PAM50/PCS subtypes and survival. Results: The refined CTC-PAM50 (36 genes) and CTC-PCS (40 genes) classifiers retained their original classification and prognostic performance in the GenomeDx GRID cohort. In the clinical study, the CTC RNA assay classified the 50 mCRPC patients into luminal A (n = 14), luminal B (n = 22), and basal (n = 14) by CTC-PAM50 classifier; and PCS1 (n = 5), PCS2 (n = 24), and PCS3 (n = 21) by CTC-PCS classifier. Overall, patients assigned as PCS1 subtype had shorter overall survival than other subtypes (hazard ratio [HR] = 4.8). In addition, basal or PCS1 subtype showed worse biochemical progression free survival than other subtypes (ARSI group: Basal: HR = 2.5; PCS1: HR = 6.9. Taxanes group: Basal: HR = 5.0; PCS1: HR = 7.2). Conclusions: Luminal and basal subtyping of CTCs is prognostic and provides a noninvasive method for molecular profiling mCRPC patients.

Do Gridded Weather Datasets Provide High-Quality Data for Agroclimatic Research in Citrus Production in Brazil?

Agrometeorological models are great tools for predicting yields and improving decision-making. High-quality climatic data are essential for using these models. However, most developing countries have low-quality data with low frequency and spatial coverage. In this case, two main options are available: gathering more data in situ, which is expensive, or using gridded data, obtained from several sources. The main objective here was to evaluate the quality of two gridded climatic databases for filling gaps of real weather stations in the context of developing agrometeorological models. Therefore, a comparative analysis of gridded database and INMET data (precipitation and air temperature) was conducted using an agrometeorological model for sweet orange yield estimation. Both gridded databases had high determination and concordance coefficients for maximum and minimum temperatures. However, higher errors and lower confidence coefficients were observed for precipitation data due to their high dispersion. BR-DWGD indicated more accurate results and correlations in all scenarios evaluated in relation to NasaPower, pointing out that BR-DWGD may be better at filling gaps and providing inputs to simulate attainable yield in the Brazilian citrus belt. Nevertheless, due to the BR-DWGD database’s geographical and temporal limitations, NasaPower is still an alternative in some cases. Additionally, when using NasaPower, it is recommended to use a measured precipitation source to improve prediction quality.

Tertiary Gleason Pattern 5 and a High Genomic Classifier Score Better Predicts Lymph Node Invasion at Radical Prostatectomy in 20,441 Intermediate-Risk Disease Patients

<h3>Purpose/Objective(s)</h3> The POP-RT and SSPORT trial showed that pelvic lymph node radiation therapy (PLNRT) can improve prostate or bed-only oncologic outcomes. Grade groups 2 (GG2) or 3 (GG3) and Tertiary Gleason pattern 5 (TP5) are heterogeneous patient groups with varied prognoses. Genomic classifiers (GC) may provide additional information essential to identifying optimal pelvic radiotherapy candidates. We hypothesized that TP5, and a GC, could improve prognostication of lymph node invasion (LNI) after radical prostatectomy (RP) over traditional clinicopathologic risk factors alone. <h3>Materials/Methods</h3> The Decipher GRID database (Veracyte, San Diego, CA) contains de-identified GC testing and pathology results for patients tested between November 2015 and March 2020. Intermediate risk patients with GG2 or GG3 were included in the analysis of continuous GC scores (range 0-1) and GC risk groups (low, intermediate, and high). Multivariable logistic regression (MVA) was used to quantify the relationship between LNI with GC, TP5, and other clinicopathologic features (age, race, extraprostatic extension, seminal vesicle invasion, bladder neck invasion). <h3>Results</h3> 20,441 patients were included (11,934 with GG2 (58.4%) and 8,507 (41.6%) with GG3 disease). Of those with GG2 and GG3 disease, 1,451 (12.2%) and 2,911 (34.2%) had TP5, respectively. Patients with TP5 had a significantly higher GC score (median [interquartile range (IQR)]: 0.39 [0.24-0.61] vs 0.53 [0.32-0.75], p<0.001 for GG2 and median [IQR]: 0.58 [0.35-0.81] vs 0.66 [0.43-0.86, p<0.001 for GG3. In MVA adjusted for clinicopathologic features, TP5 and higher GC score were independently associated with the presence of LNI in both GG2 (TP5: odds ratio [OR] 1.60, 95% CI 1.07-2.38, p=0.02 and GC: OR 1.20 per 0.1 increase, 95% CI 1.11-1.29, p<0.001) and GG3 (TP5: OR 1.47, 95% CI 1.16-1.87, p<0.01 and GC: OR 1.13 per 0.1 increase, 95% CI 1.07-1.19, p<0.001) patients. TP5 patients with GG2 or GG3 and high GC have a 3.8- and 1.8-fold risk of LNI over those with a low GC, respectively. Patients with GC high and GG2 or GG3 have a 2.7- and 1.8-fold risk of LNI over those with or without TP5, respectively (Table 1). <h3>Conclusion</h3> TP5 was associated with higher GC scores and adverse clinicopathologic features. TP5 and higher GC are independently associated with LNI while controlling for other clinicopathologic risk factors, demonstrating their importance in future studies of pelvic RT treatment intensification or de-intensification for these patients.

Mapping urban linguistic diversity with social media and population register data

Globalization, urbanization and international mobility have led to increasingly diverse urban populations. Compared to traditional traits for measuring urban diversity, such as ethnicity and country of origin, the role of language remains underexplored in understanding diversity, interactions between different groups and socio-spatial segregation. In this article, we analyse language use in the Helsinki Metropolitan Area by combining individual-level register data, socio-economic grid database, mobile phone and social media data to understand spatio-temporal patterns of linguistic diversity better. We measured linguistic diversity using metrics developed in the fields of ecology and information theory, and performed spatial clustering and regression analyses to explore the spatio-temporal patterns of linguistic diversity. We found spatial and temporal differences between register and social media data, show that linguistic diversity is influenced by the physical and socio-economic environment, and identified areas where different linguistic groups are likely to interact. Our results provide insights for urban planning and understanding urban diversity through linguistic information. As global urbanization, international migration and refugee flows and climate change drive diverse populations into cities, understanding urban diversity and its implications for urban planning and sustainability become increasingly important.

Python 기반의 제어기 모듈화를 통한 PSS/e 과도 모의 안정성 향상 연구

In this paper, a novel method has been proposed to solve the convergence problem driven by the target user-defined-model under the PSS/e dynamic simulation. The python-language-based control module has been developed to efficiently manage the transient simulation with a large scale of power grid database. Note that the sizes of renewable power plants have grown in such a way that their effects on the power system can no longer be neglected. In order to address these issues, it is imperative to make several power grid database so as to ensure a stable and reliable power grid. So, the objective is to maximize stable dynamic simulation run, while minimizing computing requirements to generate it. The python-based outer controller can be utilized to simulate numerous power grid database. These method can easily manage the dynamic simulation, and it provide a simple and fast simulation without requiring the user-defined-model and frequent power system control parameter change. Application of such methodology can provide higher certainty to the decision making of practical grid design.

MP45-10 DOES PERIOPERATIVE TESTOSTERONE PREDICT POST-PROSTATECTOMY GENOMIC RISK SCORE?

You have accessJournal of UrologyCME1 May 2022MP45-10 DOES PERIOPERATIVE TESTOSTERONE PREDICT POST-PROSTATECTOMY GENOMIC RISK SCORE? Mohammed Shahait, Joseph G. Cheaib, Elai Davicioni, Yang Liu, Ibrahim Abu Ghaida, Ryan Dobbs, Priti Lal, Daniel Lee, and David Lee Mohammed ShahaitMohammed Shahait More articles by this author , Joseph G. CheaibJoseph G. Cheaib More articles by this author , Elai DavicioniElai Davicioni More articles by this author , Yang LiuYang Liu More articles by this author , Ibrahim Abu GhaidaIbrahim Abu Ghaida More articles by this author , Ryan DobbsRyan Dobbs More articles by this author , Priti LalPriti Lal More articles by this author , Daniel LeeDaniel Lee More articles by this author , and David LeeDavid Lee More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002611.10AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: The association between low serum testosterone and high-grade disease, advanced pathologic stage, and biochemical recurrence (BCR) following radical prostatectomy (RP), was observed in several cohorts. The effect of T on the tumor genome is not explored yet. To explore the correlation between perioperative T level on genomic risk score (GRS) in a cohort of men who underwent RP. METHODS: We included Pts who underwent RARP (2013-2018) & have (positive margin, and/or pT3a or higher). The outcome of interest was the GRS: low (<0.45), intermediate (0.45-0.6), & high (>0.6). Pts were categorized into: low (<230), intermediate (230-350), & normal (>350). The associations between serum T level and 188 gene expression-based signatures retrieved from the Decipher GRID database were examined. Secondary outcomes of interest included BCR & receipt of secondary treatment. RESULTS: The median GRS score was lower in the low T group compared to the intermediate & normal T groups (0.38 Vs. 0.52 Vs. 0.53, respectively; p=0.049). However, when Pts were stratified into low-, intermediate-, and high-risk GRS groups, no differences of the categorical GRS groups was observed. A total of 63 (18.6%) Pts developed BCR. There was no difference in BCR-free survival between the three T groups (p=0.9). Pts with low T level had significantly higher odds of receiving secondary treatment (OR: 2.27; 95% CI: 1.14-4.50; p=0.02) as compared to Pts with a normal T level. A total of 43 (of 188) gene expression signatures were associated with T level (p<0.05). 33 signatures were positively associated with serum T levels including 12 signatures involved in DNA repair pathways. CONCLUSIONS: This is the first study to assess the correlation of pre-operative T level on the tumor transcriptome and showed that no clinical correlation between pre-defined GRS groups and T group. Having said that, this study adds to the notion of the limited role of endogenous T on the development of de novo localized Pca. Source of Funding: None © 2022 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 207Issue Supplement 5May 2022Page: e766 Advertisement Copyright & Permissions© 2022 by American Urological Association Education and Research, Inc.MetricsAuthor Information Mohammed Shahait More articles by this author Joseph G. Cheaib More articles by this author Elai Davicioni More articles by this author Yang Liu More articles by this author Ibrahim Abu Ghaida More articles by this author Ryan Dobbs More articles by this author Priti Lal More articles by this author Daniel Lee More articles by this author David Lee More articles by this author Expand All Advertisement PDF DownloadLoading ...

Regional Information Management of Higher Education Based on Network Security and Grey Relational Analysis

The optimal allocation of higher education resources is related to the growth of the regional economy and the development of innovation ability. Therefore, it is essential to deal with the relationship between quality, scale, efficiency, and fairness in the process of regional educational resource allocation. This study uses a method that combines empirical and normative analysis to classify and integrate regional educational data using the grey relational method and to improve the status quo of regional educational resource distribution. The infrastructure and resource-sharing mode of a regional university information resource grid database is designed in combination with the network security method. Various performance indicators are used to evaluate the quality of resource sharing. The assessment of resources sharing using performance indicators can enhance the initiative and enthusiasm of resource sharing among colleges and universities in the region. The feasibility of the scheme is verified through the educational information integration test, which can effectively promote the regional development of higher education and improve the quality of educational services.

Comparative genomic analyses between Asian and Caucasian prostate cancers in an 80,829 patient cohort.

273 Background: The Decipher 22-gene genomic classifier (GC) has been shown to predict for metastasis and survival in predominantly Caucasian and African-American men from Western cohorts. There is however little data on the clinical utility of GC in Asian prostate cancer (PCa). We investigated if GC prognosticates for metastasis-free survival (MFS) in an Asian cohort of localized PCa. Additionally, we performed comparative genomic analyses between our Asian patients and non-Asian cases from a large Decipher GRID database (Veracyte, San Francisco, CA). Methods: We used a cohort of PCa patients who were treated at a single institution from East Asia between 2006 to 2021. Patients underwent active surveillance or radical prostatectomy (RadP) and/or radiotherapy (RT) +/- hormonal therapy (HT). The GC assay (Decipher Biosciences, CA) was performed on diagnostic biopsies, following central review of the Gleason’s grade (GG) and tumor cellularity by an expert GU pathologist. MFS was the primary endpoint for survival analysis. Comparative analyses of 273 gene-signatures were performed against the full and a propensity-score matched (PSM) cohort identified from the Decipher GRID database. Adjusted p-values from Wilcoxon tests were used to select from these signatures. Results: We profiled 126 unique patient tumors, comprising of 19 (15.1%) NCCN-defined low-/favorable intermediate-, 22 (17.5%) unfavorable intermediate-, 34 (27.0%) high-, and 51 (40.5%) very high-risk groups. 24 (19.0%) patients had RadP as primary treatment, 98 (77.8%) had RT +/- HT, while 4 (3.2%) underwent active surveillance or HT alone. Using the GC, 70 (55.6%), and 56 (44.4%) were stratified as low/intermediate- (≤0.6) and high-risk ( &gt; 0.6), respectively. GC high-risk was significantly associated with an inferior MFS than GC low/intermediate-risk ( HR 5.22 [95% CI:1.08–25.3], P = 0.04). Comparison between our Asian and the full unmatched GRID cohort (N = 80,703) revealed a lower proportion of ERG+ PCa (14% vs 41%, P &lt; 0.001) and a higher proportion of PAM50 basal subtypes (41% vs 30%, P &lt; 0.001), as well as a lower T-cell exclusion (median 0.080 vs 0.097; P &lt; 0.001) and angiogenesis (-0.37 vs -0.08; P &lt; 0.001) signature scores. These trends were also observed when comparing with the PSM-subset (N = 630; 5:1 ratio for NCCN, GG, age at diagnosis, and assay quality score). Interestingly, both high angiogenesis and T-cell exclusion signatures were associated with a worse MFS ( HR not available due to no events for low angiogenesis, P = 0.0015 by log-rank test; HR 5.12 [1.03–26.5], P = 0.046, respectively). Conclusions: We validated the Decipher 22-gene GC for prognostication of MFS in a predominantly high-risk PCa cohort. We also identified several gene expression signatures that were significantly different between Asian PCa and other cohorts. These observations may have implications for customizing treatment recommendations to an Asian population.

A Defect-Inspection System Constructed by Applying Autoencoder with Clustered Latent Vectors and Multi-Thresholding Classification

Defect inspection is an important issue in the field of industrial automation. In general, defect-inspection methods can be categorized into supervised and unsupervised methods. When supervised learning is applied to defect inspection, the large variation of defect patterns can make the data coverage incomplete for model training, which can introduce the problem of low detection accuracy. Therefore, this paper focuses on the construction of a defect-inspection system with an unsupervised learning model. Furthermore, few studies have focused on the analysis between the reconstruction error on the normal areas and the repair effect on the defective areas for unsupervised defect-inspection systems. Hence, this paper addresses this important issue. There are four main contributions to this paper. First, we compare the effects of SSIM (Structural Similarity Index Measure) and MSE (Mean Square Error) functions on the reconstruction error. Second, various kinds of Autoencoders are constructed by referring to the Inception architecture in GoogleNet and DEC (Deep Embedded Clustering) module. Third, two-stage model training is proposed to train the Autoencoder models. In the first stage, the Autoencoder models are trained to have basic image-reconstruction capabilities for the normal areas. In the second stage, the DEC algorithm is added to the training of the Autoencoder model to further strengthen feature discrimination and then increase the capability to repair defective areas. Fourth, the multi-thresholding image segmentation method is applied to improve the classification accuracy of normal and defect images. In this study, we focus on the defect inspection on the texture patterns. Therefore, we select the nanofiber image database and carpet and grid images in the MVTec database to conduct experiments. The experimental results show that the accuracy of classifying normal and defect patch nanofiber images is about 86% and the classification accuracy can approach 89% and 98% for carpet and grid datasets in the MVTec database, respectively. It is obvious that our proposed defect-inspection and classification system outperforms the methods in MVTec.

E2E-V2SResNet: Deep residual convolutional neural networks for end-to-end video driven speech synthesis

Speechreading which infers spoken message from a visually detected articulated facial trend is a challenging task. In this paper, we propose an end-to-end ResNet (E2E-ResNet) model for synthesizing speech signals from the silent video of a speaking individual. The model is the convolutional encoder-decoder framework which captures the frames of video and encodes into a latent space of visual features. The outputs of the decoder are spectrograms which are converted into waveforms corresponding to a speech articulated in the input video. The speech waveforms are then fed to a waveform critic used to decide the real or synthesized speech. The experiments show that the proposed E2E-V2SResNet model is apt to synthesize speech with realism and intelligibility/quality for GRID database. To further demonstrate the potentials of the proposed model, we also conduct experiments on the TCD-TIMIT database. We examine the synthesized speech in unseen speakers using three objective metrics use to measure the intelligibility, quality, and word error rate (WER) of the synthesized speech. We show that E2E-V2SResNet model outscores the competing approaches in most metrics on the GRID and TCD-TIMIT databases. By comparing with the baseline, the proposed model achieved 3.077% improvement in speech quality and 2.593% improvement in speech intelligibility.

Diet quality in preschool children and associations with individual eating behavior and neighborhood socioeconomic disadvantage. The STEPS Study

A good quality diet in childhood is important for optimal growth as well as for long-term health. It is not well established how eating behaviors affect overall diet quality in childhood. Moreover, very few studies have considered the association of diet quality and a neighborhood socioeconomic disadvantage in childhood. Our aim was to investigate how diet quality is associated with eating behaviors and neighborhood disadvantage and their interaction in preschool age children in Finland. The participants were from the Steps to Healthy Development Study at age 2 y (n = 780) and 5 y (n = 653). Diet quality was measured with a short questionnaire on habitual food consumption and eating behavior was assessed with the child eating behavior questionnaire to indicate the child's eating style regarding food approach and food avoidance dimensions. Information on neighborhood socioeconomic disadvantage were obtained from the statistics Finland grid database. We found that diet quality was higher at 5 years compared to 2 years of age (p < 0.001). Food approach subscale, enjoyment of food, was positively associated with the diet quality (p < 0.001 for 2 and 5 y) while subscale desire to drink was negatively associated with the diet quality (p = 0.001 for 2 and 5 y). Food avoidance was negatively associated with the diet quality both at 2 and at 5 years of age (p < 0.001). A higher neighborhood disadvantage was negatively associated with the diet quality at the age of 2 years (p = 0.02), but not at the age of 5 years. Eating behavior had similar associations with diet quality both in affluent and deprived neighborhoods. Our results suggest that both the eating behavior and neighborhood disadvantage are, already in the early age, important factors when considering children's diet quality.

Lipreading model based on a two-way convolutional neural network and feature fusion

Lipreading feature extraction is essentially the feature extraction of continuous video frame sequences. A lipreading model based on a two-way convolutional neural network and features is proposed to obtain more reasonable visual-spatial–temporal characteristics. Unlike other lipreading methods based on deep learning, the rank pooling method transforms lip video into a standard RGB image that can be directly input into the convolutional neural network, which effectively reduces the input dimension. In addition, to compensate for the lack of spatial information, the apparent shape and depth features are fused, and then the joint cost function is used to guide the network model learning to obtain more distinguishing features. The experimental results were evaluated on the public GRID database and OuluVS2 database. It shows that the accuracy of the proposed method can reach more than 93%, which validates the effectiveness of the method.

Transcription Factor Signatures May Predict the Prognosis and Status of the Immune Microenvironment of Primary Lower-Grade Gliomas.

AimTranscription factor (TF) in glioma, including proliferation, invasion/migration, and tumor microenvironment, has been receiving increasing attention. However, there are still no systematical analyses based on global TF. Herein, using global TF target gene sets, we comprehensively investigated their relationship with prognosis and potential biological effect in lower-grade glioma (LGG). We aimed to develop a less-biased prognostic model and provide new insight for personalized management of this disease.MethodsTF target gene sets were collected from MSigDB and GRID database followed by ssGSEA calculating normalized enrichment score. Comprehensive survival analysis was combined with Kaplan–Meier and Cox algorithms. Consensus cluster and lasso regression were performed to develop prognostic signatures with validation of ROC and independent external cohort. Approaches of xCell/CIBERSORT/TIMER were involved in analyzing the immune microenvironment. We also correlated identified prognostic signatures with tumor mutational burden (TMB) and m6A genes.ResultsFourteen TFs were significantly screened based on survival. Patients were classified into 2 prognosis-related clusters based on 14-TFs features. The function of differentially expressed TF target genes between Cluster1/2 was enriched mostly on glioma invasion/migration. The prognostic model was trained by 6 out of 14-TFs followed by generating risk-score as an independent prognostic indicator. We found differences between the high/low-risk group in TMB and the immune microenvironment, where the high-risk group represented “hot-tumor”. Besides, 6-TFs were correlated with m6A regulation genes.ConclusionOur findings suggested that the 6-TFs model could be used to predict prognosis and predict the status of the immune microenvironment in LGG.

Directionally dependent Lambertian-equivalent reflectivity (DLER) of the Earth's surface measured by the GOME-2 satellite instruments

Abstract. In this paper we introduce the new concept of directionally dependent Lambertian-equivalent reflectivity (DLER) of the Earth's surface retrieved from satellite observations. This surface DLER describes Lambertian (isotropic) surface reflection which is extended with a dependence on the satellite viewing geometry. We apply this concept to data of the GOME-2 satellite instruments to create a global database of the reflectivity of the Earth's surface, providing surface DLER for 26 wavelength bands between 328 and 772 nm as a function of the satellite viewing angle via a second-degree polynomial parameterisation. The resolution of the database grid is 0.25∘ by 0.25∘, but the real, intrinsic spatial resolution varies over the grid from 1.0∘ by 1.0∘ to 0.5∘ by 0.5∘ down to 0.25∘ by 0.25∘ by applying dynamic gridding techniques. The database is based on more than 10 years (2007–2018) of GOME-2 data from the MetOp-A and MetOp-B satellites. The relation between DLER and bi-directional reflectance distribution function (BRDF) surface reflectance is studied using radiative transfer simulations. For the shorter wavelengths (λ&lt;500 nm), there are significant differences between the two. For instance, at 463 nm the difference can go up to 6 % at 30∘ solar zenith angle. The study also shows that, although DLER and BRDF surface reflectances have different properties, they are comparable for the longer wavelengths (λ&gt;500 nm). Based on this outcome, the GOME-2 surface DLER is compared with MODIS surface BRDF data from MODIS band 1 (centred around 645 nm) using both case studies and global comparisons. The conclusion of this validation is that the GOME-2 DLER compares well to MODIS BRDF data and that it does so much better than the non-directional LER database. The DLER approach for describing surface reflectivity is therefore an important improvement over the standard isotropic (non-directional) LER approaches used in the past. The GOME-2 surface DLER database can be used for the retrieval of atmospheric properties from GOME-2 and from previous satellite instruments like GOME and SCIAMACHY. It will also be used to support retrievals from the future Sentinel-5 UVNS (ultraviolet, visible, near-infrared, and short-wave infrared) satellite instrument.

Neighbourhood characteristics as a predictor of adherence to dietary recommendations: A population-based cohort study of Finnish adults.

Aims: To investigate the association of six-year cumulative level of socioeconomic neighbourhood disadvantage and population density with subsequent adherence to dietary recommendations, controlling for preceding dietary adherence, in adults in Finland. Methods: Population-based Health and Social Support (HeSSup) study participants from four age groups (20–24, 30–34, 40–44 and 50–54 years at baseline in 1998). Data on diet and alcohol consumption were obtained from the 2003 and 2012 surveys and information on neighbourhoods from Statistics Finland Grid database (n = 10,414 men and women). Participants diet was measured as adherence to Nordic Nutrition recommendation (score range 0–100). Neighbourhood disadvantage was measured by median household income, proportion of those with primary education only and unemployment rate, and population density by the number of adult population between years 2007 and 2012. Linear models were used to assess the associations of neighbourhood characteristics with the score for adherence to dietary recommendations in 2012. Results: Cumulative neighbourhood socioeconomic disadvantage was associated with slightly weaker (1.49 (95% confidence interval (CI) −1.89 to −1.09) point decrease in dietary score) adherence while higher population density was associated with better (0.70 (95% CI 0.38−1.01) point increase in dietary score) adherence to dietary recommendations. These associations remained after controlling for prior dietary habits, sociodemographic, chronic cardio-metabolic diseases, and severe life events. Conclusions: These longitudinal findings support the hypothesis that neighbourhood characteristics affect dietary habits.

Non-Intrusive Load Monitoring of Residential Water-Heating Circuit Using Ensemble Machine Learning Techniques

The recent advancement in computational capabilities and deployment of smart meters have caused non-intrusive load monitoring to revive itself as one of the promising techniques of energy monitoring. Toward effective energy monitoring, this paper presents a non-invasive load inference approach assisted by feature selection and ensemble machine learning techniques. For evaluation and validation purposes of the proposed approach, one of the major residential load elements having solid potential toward energy efficiency applications, i.e., water heating, is considered. Moreover, to realize the real-life deployment, digital simulations are carried out on low-sampling real-world load measurements: New Zealand GREEN Grid Database. For said purposes, MATLAB and Python (Scikit-Learn) are used as simulation tools. The employed learning models, i.e., standalone and ensemble, are trained on a single household’s load data and later tested rigorously on a set of diverse households’ load data, to validate the generalization capability of the employed models. This paper presents a comprehensive performance evaluation of the presented approach in the context of event detection, feature selection, and learning models. Based on the presented study and corresponding analysis of the results, it is concluded that the proposed approach generalizes well to the unseen testing data and yields promising results in terms of non-invasive load inference.

Anthropogenic Osmium in Macroalgae from Tokyo Bay Reveals Widespread Contamination from Municipal Solid Waste.

Human activity is influencing the global osmium cycle, driving the Os isotopic composition (187Os/188Os) of the hydrosphere and associated sedimentary material to lower values. Here, we present the Re and Os abundance and isotope systematics of macroalgae, a proxy for seawater, from Tokyo Bay to elucidate the potential sources of anthropogenic Os to the Pacific Ocean. Macroalgae from the Uraga Channel, which connects Tokyo Bay to the Pacific Ocean, record relatively low Os abundances (∼10.1 pg/g) and an 187Os/188Os of ∼0.9, indicative of surface ocean seawater. Contrastingly, macroalgae within the bay closest to central Tokyo record the highest Os abundances (∼22.8 pg/g) and lowest 187Os/188Os values (∼0.47), suggesting contamination from human activity. To determine the source of anthropogenic Os, we have developed the first Os emission inventory, based on the East Asian Air Pollutant Emission Grid database (EAGrid2010). The close relationship (R2 = 0.67 and p-value = <0.05) between Os inventories and macroalgal data suggests that municipal solid waste incinerators (MSWIs) are the dominant source of Os to Tokyo Bay. Projections for Japan estimate that 26-18+38 ng Os/m2/yr is released from MSWI smokestacks, leading to a concentration in precipitation of 26-18+38 fg/g, identifying MSWIs as a major contributor of anthropogenic Os to the hydrological cycle.