bluuebunny/crossref_metadata_2025_split huggingface dataset:
High performance logistic regression for privacy-preserving genome analysis
Authors: De Cock Martine , Dowsley Rafael , Nascimento Anderson C. A. , Railsback Davis , Shen Jianwei , Todoki Ariel 
Published: January 2021

AbstractBackgroundIn biomedical applications, valuable data is often split between owners who cannot openly share the data because of privacy regulations and concerns. Training machine learning models on the joint data without violating privacy is a major technology challenge that can be addressed by combining techniques from machine learning and cryptography. When collaboratively training machine learning models with the cryptographic technique named secure multi-party computation, the price paid for keeping the data of the owners private is an increase in computational cost and runtime. A careful choice of machine learning techniques, algorithmic and implementation optimizations are a necessity to enable practical secure machine learning over distributed data sets. Such optimizations can be tailored to the kind of data and Machine Learning problem at hand.MethodsOur setup involves secure two-party computation protocols, along with a trusted initializer that distributes correlated randomness to the two computing parties. We use a gradient descent based algorithm for training a logistic regression like model with a clipped ReLu activation function, and we break down the algorithm into corresponding cryptographic protocols. Our main contributions are a new protocol for computing the activation function that requires neither secure comparison protocols nor Yao’s garbled circuits, and a series of cryptographic engineering optimizations to improve the performance.ResultsFor our largest gene expression data set, we train a model that requires over 7 billion secure multiplications; the training completes in about 26.90 s in a local area network. The implementation in this work is a further optimized version of the implementation with which we won first place in Track 4 of the iDASH 2019 secure genome analysis competition.ConclusionsIn this paper, we present a secure logistic regression training protocol and its implementation, with a new subprotocol to securely compute the activation function. To the best of our knowledge, we present the fastest existing secure multi-party computation implementation for training logistic regression models on high dimensional genome data distributed across a local area network.
Old men with prostate cancer have higher risk of Gleason score upgrading and pathological upstaging after initial diagnosis: a systematic review and meta-analysis
Authors: Wang Xiaochuan , Zhang Yu , Ji Zhengguo , Yang Peiqian , Tian Ye 
Published: January 2021

Abstract
Background
To evaluate the predictive performance of age for the risk of Gleason score change and pathologic upstaging.

Evidence acquisition
Ovid MEDLINE, Ovid Embase, and the Cochrane Library were searched from inception until May 2020. Quality of included studies was appraised utilizing the Newcastle-Ottawa Quality Assessment Scale for case-control studies. The publication bias was evaluated by funnel plots and Egger’s tests.

Evidence synthesis
Our search yielded 27 studies with moderate-to-high quality including 84296 patients with mean age of 62.1 years. From biopsy to prostatectomy, upgrading and upstaging occurred in 32.3% and 9.8% of patients, respectively. Upgrading from diagnostic biopsy to confirmatory biopsy was found in 16.8%. Older age was associated with a significant increased risk of upgrading (OR 1.04, 95% CI 1.03–1.05), and similar direction of effect was found in studies focused on upgrading from diagnostic biopsy to confirmatory biopsy (OR 1.06, 95% CI 1.04–1.08). For pathologic upstaging within older men compared with younger, the pooled odds was 1.03 (95% CI 1.01–1.04).

Conclusion
Thorough consideration of age in the context of effect sizes for other factors not only prompts more accurate risk stratification but also helps providers to select optimal therapies for patients with prostate cancer.
Efficacy and tolerability of folate-aminopterin therapy in a rat focal model of multiple sclerosis
Authors: Elo Petri , Li Xiang-Guo , Liljenbäck Heidi , Gardberg Maria , Moisio Olli , Miner Maxwell , Virta Jenni , Saraste Antti , Srinivasarao Madduri , Pugh Michael , Low Philip S. , Knuuti Juhani , Jalkanen Sirpa , Airas Laura , Lu Yingjuan June , Roivainen Anne 
Published: January 2021

AbstractBackgroundActivated macrophages in the experimental model of multiple sclerosis (MS) express folate receptor-β (FR-β), representing a promising target for the treatment of MS. Here, we both evaluated the efficacy of a novel folate-aminopterin construct (EC2319) in a rat focal model of multiple sclerosis (MS) and investigated the utility of68Ga-labeled 1,4,7-triazacyclononane-1,4,7-triacetic acid-conjugated folate (68Ga-FOL) for assessing inflammatory lesions. In addition, we investigated whether FR-β is expressed in the brain of patients with MS.MethodsFocal delayed-type hypersensitivity experimental autoimmune encephalomyelitis (fDTH-EAE) was induced in 40 Lewis rats; 20 healthy Lewis rats were used as controls. Rats were divided into six groups according to the duration of disease (control, acute, or chronic) and intervention (vehicle versus EC2319).68Ga-FOL analyses, histology, and immunofluorescence of the brain were performed to evaluate the efficacy of subcutaneously administered EC2319 on lesion development. Immunofluorescence was used to assess FR-β expression in postmortem brain samples from 5 patients with MS and 5 healthy controls.ResultsImmunofluorescence and histological analyses revealed significant reductions in FR-β expression (P< 0.05) and lesion size (P< 0.01), as well as improved inducible nitric oxide synthase/mannose receptor C type 1 ratios (P< 0.01) in macrophages and microglia during the chronic but not acute phase offDTH-EAE in EC2319-treated rats. The uptake of IV-injected68Ga-FOL in the brain was low and did not differ between the groups, but the in vitro binding of68Ga-FOL was significantly lower in EC2319-treated rats (P< 0.01). FR-β positivity was observed in chronically active lesions and in normal-appearing white matter in MS brain samples.ConclusionsEC2319 was well tolerated and attenuated inflammation and lesion development in a rat model of a chronic progressive form of MS. Human MS patients have FR-β-positive cells in chronically active plaques, which suggests that these results may have translational relevance.
COVID-TRACK: world and USA SARS-COV-2 testing and COVID-19 tracking
Authors: Zohner Ye Emma , Morris Jeffrey S. 
Published: January 2021

Abstract
Background
The COVID-19 pandemic has caused major health and socio-economic disruptions worldwide. Accurate investigation of emerging data is crucial to inform policy makers as they construct viral mitigation strategies. Complications such as variable testing rates and time lags in counting cases, hospitalizations and deaths make it challenging to accurately track and identify true infectious surges from available data, and requires a multi-modal approach that simultaneously considers testing, incidence, hospitalizations, and deaths. Although many websites and applications report a subset of these data, none of them provide graphical displays capable of comparing different states or countries on all these measures as well as various useful quantities derived from them. Here we introduce a freely available dynamic representation tool, COVID-TRACK, that allows the user to simultaneously assess time trends in these measures and compare various states or countries, equipping them with a tool to investigate the potential effects of the different mitigation strategies and timelines used by various jurisdictions.

Findings
COVID-TRACK is a Python based web-application that provides a platform for tracking testing, incidence, hospitalizations, and deaths related to COVID-19 along with various derived quantities. Our application makes the comparison across states in the USA and countries in the world easy to explore, with useful transformation options including per capita, log scale, and/or moving averages. We illustrate its use by assessing various viral trends in the USA and Europe.

Conclusion
The COVID-TRACK web-application is a user-friendly analytical tool to compare data and trends related to the COVID-19 pandemic across areas in the United States and worldwide. Our tracking tool provides a unique platform where trends can be monitored across geographical areas in the coming months to watch how the pandemic waxes and wanes over time at different locations around the USA and the globe.
Forecasting the effectiveness of the DeWorm3 trial in interrupting the transmission of soil-transmitted helminths in three study sites in Benin, India and Malawi
Authors: Truscott James E. , Hardwick Robert J. , Werkman Marleen , Saravanakumar Puthupalayam Kaliappan , Manuel Malathi , Ajjampur Sitara S. R. , Ásbjörnsdóttir Kristjana H. , Khumbo Kalua , Witek-McManus Stefan , Simwanza James , Cottrell Gilles , Houngbégnon Parfait , Ibikounlé Moudachirou , Walson Judd L. , Anderson Roy M. 
Published: January 2021

Abstract
Background
The DeWorm3 project is an ongoing cluster-randomised trial assessing the feasibility of interrupting the transmission of soil-transmitted helminths (STH) through mass drug administration (MDA) using study sites in India, Malawi and Benin. In this article, we describe an approach which uses a combination of statistical and mathematical methods to forecast the outcome of the trial with respect to its stated goal of reducing the prevalence of infection to below 2%.

Methods
Our approach is first to define the local patterns of transmission within each study site, which is achieved by statistical inference of key epidemiological parameters using the baseline epidemiological measures of age-related prevalence and intensity of STH infection which have been collected by the DeWorm3 trials team. We use these inferred parameters to calibrate an individual-based stochastic simulation of the trial at the cluster and study site level, which is subsequently run to forecast the future prevalence of STH infections. The simulator takes into account both the uncertainties in parameter estimation and the variability inherent in epidemiological and demographic processes in the simulator. We interpret the forecast results from our simulation with reference to the stated goal of the DeWorm3 trial, to achieve a target of $$\le 2\%$$

≤
2
%

prevalence at a point 24 months post-cessation of MDA.

Results
Simulated output predicts that the two arms will be distinguishable from each other in all three country sites at the study end point. In India and Malawi, measured prevalence in the intervention arm is below the threshold with a high probability (90% and 95%, respectively), but in Benin the heterogeneity between clusters prevents the arm prevalence from being reduced below the threshold value. At the level of individual study arms within each site, heterogeneity among clusters leads to a very low probability of achieving complete elimination in an intervention arm, yielding a post-study scenario with widespread elimination but a few ‘hot spot’ areas of persisting STH transmission.

Conclusions
Our results suggest that geographical heterogeneities in transmission intensity and worm aggregation have a large impact on the effect of MDA. It is important to accurately assess cluster-level, or even smaller scale, heterogeneities in factors which influence transmission and aggregation for a clearer perspective on projecting the outcomes of MDA control of STH and other neglected tropical diseases.

Graphical Abstract
Correlates of psychological distress and self-rated health among Palestinian citizens of Israel: findings from the health and environment survey (HESPI)
Authors: Khatib Mohammad , Mansbach-Kleinfeld Ivonne , Abu-Kaf Sarah , Ifrah Anneke , Sheikh-Muhammad Ahmad 
Published: January 2021

Abstract
Objectives
Psychological distress is a problem strongly associated with socio-economic conditions. This study aims to assess rates of psychological distress and ‘poor’ self-rated health among Palestinian citizens of Israel, who constitute 21% of the population and nearly 50% live in poverty, and compare their psychological distress scores with those of the general Israeli population.

Design
The Health and Environment Survey among Palestinian citizens of Israel (HESPI-2015), included a representative sample of this minority; 2018 individuals aged ≥18 were interviewed. The questionnaire included socio-demographic and health-related information, the General Health Questionnaire-12, (GHQ-12) and Self-rating of health (SRH).

Results
Subjects with GHQ-12 global scores of ≥17 were considered to have high distress. Low education, female gender, obesity and the presence of chronic diseases were significantly associated with high psychological distress over and above the effect of the other variables. Poor SRH was strongly associated with having a chronic disease and additional risk factors were older age group, low education and high psychological distress. Comparing psychological distress scores of Arabs in Israel with those of the general population showed that 30% of the former were classified as asymptomatic, as compared to 75% in the general population while the proportion of symptomatic or highly symptomatic was 14% in the latter as compared with 45% in the former.

Conclusions
It appears that the burden of poverty, chronic disease and low education in this population, which suffers from multiple stressors, is disproportionate and should be addressed by the authorities, together with concrete plans to improve the education of the younger generations. Clearly, the association between discriminating policies and deprivation with psychological distress is not unique to the case of the Palestinian minority in Israel and therefore this study will allow for the examination and generalization of the current findings to other discriminated and disadvantaged minorities.
Short-term passenger flow forecast for urban rail transit based on multi-source data
Authors: Li Wei , Sui Liying , Zhou Min , Dong Hairong 
Published: January 2021

AbstractShort-term passenger flow prediction in urban rail transit plays an important role because it in-forms decision-making on operation scheduling. However, passenger flow prediction is affected by many factors. This study uses the seasonal autoregressive integrated moving average model (SARIMA) and support vector machines (SVM) to establish a traffic flow prediction model. The model is built using intelligent data provided by a large-scale urban traffic flow warning system, such as accurate passenger flow data, collected using the Internet of things and sensor networks. The model proposed in this paper can adapt to the complexity, nonlinearity, and periodicity of passenger flow in urban rail transit. Test results on a Beijing traffic dataset show that the SARI-MA–SVM model can improve accuracy and reduce errors in traffic prediction. The obtained pre-diction fits well with the measured data. Therefore, the SARIMA–SVM model can fully charac-terize traffic variations and is suitable for passenger flow prediction.
Effectiveness of interventions to reduce household air pollution from solid biomass fuels and improve maternal and child health outcomes in low- and middle-income countries: a systematic review protocol
Authors: Woolley Katherine E. , Dickinson-Craig Emma , Bartington Suzanne E. , Oludotun Tosin , Kirenga Bruce , Mariga Shelton T. , Kabera Telesphore , Coombe April , Pope Francis D. , Singh Ajit , Avis William R. , Day Rosie , Warburton David , Manaseki-Holland Semira , Moore David J. , Thomas G. Neil 
Published: January 2021

Abstract
Background
A variety of public health interventions have been undertaken in low- and middle-income countries (LMICs) to prevent morbidity and mortality associated with household air pollution (HAP) due to cooking, heating and lighting with solid biomass fuels. Pregnant women and children under five are particularly vulnerable to the effects of HAP, due to biological susceptibility and typically higher exposure levels. However, the relative health benefits of interventions to reduce HAP exposure among these groups remain unclear. This systematic review aims to assess, among pregnant women, infants and children (under 5 years) in LMIC settings, the effectiveness of interventions which aim to reduce household air pollutant emissions due to household solid biomass fuel combustion, compared to usual cooking practices, in terms of health outcomes associated with HAP exposure.

Methods
This protocol follows standard systematic review processes and abides by the PRISMA-P reporting guidelines. Searches will be undertaken in MEDLINE, EMBASE, CENTRAL, WHO International Clinical Trials Registry Platform (ICTRP), The Global Index Medicus (GIM), ClinicalTrials.gov and Greenfile, combining terms for pregnant women and children with interventions or policy approaches to reduce HAP from biomass fuels or HAP terms and LMIC countries. Included studies will be those reporting (i) pregnant women and children under 5 years; (ii) fuel transition, structural, educational or policy interventions; and (iii) health events associated with HAP exposure which occur among pregnant women or among children within the perinatal period, infancy and up to 5 years of age. A narrative synthesis will be undertaken for each population-intervention-outcome triad stratified by study design. Clinical and methodological homogeneity within each triad will be used to determine the feasibility for undertaking meta-analyses to give a summary estimate of the effect for each outcome.

Discussion
This systematic review will identify the effectiveness of existing HAP intervention measures in LMIC contexts, with discussion on the context of implementation and adoption, and summarise current literature of relevance to maternal and child health. This assessment reflects the need for HAP interventions which achieve measurable health benefits, which would need to be supported by policies that are socially and economically acceptable in LMIC settings worldwide.

Systematic review registration
PROSPERO CRD42020164998
Bacterial community assemblages in classroom floor dust of 50 public schools in a large city: characterization using 16S rRNA sequences and associations with environmental factors
Authors: Park Ju-Hyeong , Lemons Angela R. , Roseman Jerry , Green Brett J. , Cox-Ganser Jean M. 
Published: January 2021

AbstractCharacterizing indoor microbial communities using molecular methods provides insight into bacterial assemblages present in environments that can influence occupants’ health. We conducted an environmental assessment as part of an epidemiologic study of 50 elementary schools in a large city in the northeastern USA. We vacuumed dust from the edges of the floor in 500 classrooms accounting for 499 processed dust aliquots for 16S Illumina MiSeq sequencing to characterize bacterial assemblages. DNA sequences were organized into operational taxonomic units (OTUs) and identified using a database derived from the National Center for Biotechnology Information. Bacterial diversity and ecological analyses were performed at the genus level. We identified 29 phyla, 57 classes, 148 orders, 320 families, 1193 genera, and 2045 species in 3073 OTUs. The number of genera per school ranged from 470 to 705. The phylum Proteobacteria was richest of all while Firmicutes was most abundant. The most abundant order included Lactobacillales, Spirulinales, and Clostridiales. Halospirulina was the most abundant genus, which has never been reported from any school studies before. Gram-negative bacteria were more abundant and richer (relative abundance = 0.53; 1632 OTUs) than gram-positive bacteria (0.47; 1441). Outdoor environment-associated genera were identified in greater abundance in the classrooms, in contrast to homes where human-associated bacteria are typically more abundant. Effects of school location, degree of water damage, building condition, number of students, air temperature and humidity, floor material, and classroom’s floor level on the bacterial richness or community composition were statistically significant but subtle, indicating relative stability of classroom microbiome from environmental stress. Our study indicates that classroom floor dust had a characteristic bacterial community that is different from typical house dust represented by more gram-positive and human-associated bacteria. Health implications of exposure to the microbiomes in classroom floor dust may be different from those in homes for school staff and students.
Organizational life cycle models: a design perspective
Authors: Mosca Luigi , Gianecchini Martina , Campagnolo Diego 
Published: January 2021

AbstractNew competitive and environmental challenges have fostered renewed attention towards organizational design. This scenario calls for a significant return to organizational design studies that embrace a holistic approach, especially those focusing on the simultaneous interaction of multiple design elements. Organizational life cycle (OLC) models provide a fitting response to this call. In this paper, we review the organizational design characteristics of five seminal OLC models. We show that according to these OLC models, growth in size—which is described as unavoidable—generates business issues that firms are forced to solve by adopting only one possible organizational configuration, here following a deterministic organizational approach. We challenge this approach and propose conceiving of OLC as an evolutionary process, which calls for a variety of equifinal organizational solutions. We conclude by proposing future research avenues.
Loop source transient electromagnetics in an urban noise environment: A case study in Santiago de Chile
Authors: Blanco-Arrué B. , Yogeshwar P. , Tezkan B. , Díaz D. 
Published: January 2021

We have developed a transient electromagnetic (TEM) study in the noisy urban environment of the megacity Santiago de Chile. Our investigation characterizes the electrical conductivity structure of the Santiago Basin down to 300 m depth, providing key information about the sedimentary infill, hydrogeologic aspects, and geomorphological units. In total, 52 TEM soundings were recorded over roughly 900 km2. We identified different transient characteristics and noise patterns, spatially correlated to the investigation area. This step allowed classification of the soundings into three groups: highly distorted data, transients severely affected by cultural electromagnetic noise sources, and good-quality data with low noise levels. Conventional 1D inversion techniques were then used to derive resistivity depth models. The models were interpreted using sedimentary units, alluvial fan, static groundwater level information, and also were verified using borehole data. Based on 1D models, different minimum sedimentary thicknesses are observed, ranging between 50 and 300 m in depth. The average derived sedimentary thickness is approximately 210 m. In addition, seven soundings indicated the basement. These are mostly located toward the periphery of the Santiago Basin. We found a high conductive zone toward the north and a low-conductive area toward the south of the basin. The southern area is characterized by the absence of conductive layers at a depth between 100 and 150 m. This is likely related to the presence of a thick gravel layer and the absence of shallow clay layers acting as aquitards. The resistivity distribution and the sedimentary thickness obtained for this noisy and urban environment are key inputs for groundwater research as the water supply becomes more limited, specifically in central Chile. The overall consistency of the derived subsurface models highlights the suitability of the TEM method for investigating urban noise environments.