bluuebunny/crossref_metadata_2025 huggingface dataset:
The Lives Behind the Mesoamerican Archaeology Collection at the Museum of Vancouver
Authors: Tremain Cara Grace
Published: January 2021

The origin of the Museum of Vancouver in British Columbia, Canada, dates to the late nineteenth century—only a few years after the city was formally incorporated. Initially intended to showcase curios and items of interest, among the early donations were objects from various Mesoamerican cultures. Over the years the Mesoamerican archaeology collection grew not from a deliberate collecting strategy, but from chance donations by residents of the city. Not long after the re-focus to a civic museum the collection ceased to grow, and today the collection is consigned to the museum’s storage area. Despite not being of immediate relevance to the museum’s mission, the history of the collection reveals a fascinating insight into the lives of past Vancouver residents and demonstrates the utility of investigating the provenance of previously little-known collections.
Where Have All the Data Gone? A Critical Reflection on Academic Digital Research in the Post-API Age
Authors: Tromble Rebekah
Published: January 2021

In the wake of the 2018 Facebook–Cambridge Analytica scandal, social media companies began restricting academic researchers’ access to the easiest, most reliable means of systematic data collection via their application programming interfaces (APIs). Although these restrictions have been decried widely by digital researchers, in this essay, I argue that relatively little has changed. The underlying relationship between researchers, the platforms, and digital data remains largely the same. The platforms and their APIs have always been proprietary black boxes, never intended for scholarly use. Even when researchers could mine data seemingly endlessly, we rarely knew what type or quality of data were at hand. Moreover, the largesse of the API era allowed many researchers to conduct their work with little regard for the rigor, ethics, or focus on societal value, we should expect from scholarly inquiry. In other words, our digital research processes and output have not always occupied the high ground. Rather than viewing 2018 and Cambridge Analytica as a profound disjuncture and loss, I suggest that digital researchers need to take a more critical look at how our community collected and analyzed data when it still seemed so plentiful, and use these reflections to inform our approaches going forward.
Impact of Patient Load on the Quality of Electronic Medical Record Documentation
Authors: Jacob Aasems , Raj Rishi , Alagusundaramoorthy Sayee , Wei Jing , Wu Jianrong , Eng Margaret
Published: January 2021

Objective:

American College of Graduate Medical Education (ACGME) recommends ongoing care of 10 patients per resident however its implication is unclear. We hypothesized EMR quality to vary based on patient load and call status.

Methods:

We conducted a double-blind, single-center, retrospective observational study between 2017 and 2019 to investigate the quality and accuracy of resident documentation using the Responsible Electronic Documentation (RED) Checklist, a validated scoring system.

Results:

A total of 234 independent charts were analyzed and 80 met scoring criteria. Average patients per residents was 4, 9.1, 7.2, and 5.5 on “call” day (D0), “post-call” day (D1), “mid-call” day (D2), and “pre-call” day (D3), respectively. Mean RED checklist scores were 68.1%, 57%, 68.6%, and 72.1% on the above call status. The difference in score between D3 and D1 was statistically significant ( P = .00042). There was a negative correlation between score and number of patients per resident ( r = −0.286, P = .010).

Conclusion:

EMR documentation quality is directly impacted by patient load and resident call status with the lowest documentation quality on post-call day, correlating with patient load.
Mapping and Assessing Green Entrepreneurial Performance: Evidence from a Vertically Integrated Organic Beverages Supply Chain
Authors: Adel Heba Mohamed
Published: January 2021

The purpose of this article is to use strategy mapping (SM) and balanced scorecard (BSC) in portraying and assessing green entrepreneurial supply chain management (GESCM) performance of a vertically integrated entrepreneurial supply chain in an Egyptian emerging market of organic beverages industry. Based on thematic literature review and qualitative research (in-depth interviews with experts, focus groups, observations, and reports’ analysis), multi-item measurement quantitative BSC scale and strategy map were proposed to measure and communicate the GESCM performance of a hierarchical chain with multiple nodes and cross-functional green practices. This article conceptually contributes to the active debate on contemporary GESCM performance measurement. It integrates the literature of green production and operations management, strategic management and entrepreneurial supply chain management (ESCM) to investigate the unexplored context of GESCM research. It empirically assesses GESCM performance of an attractive network using BSC and maps its strategy with its multidisciplinary environment-friendly practices. The GESCM performance was measured in terms of four integrated perspectives (innovative and proactive processes, learning and growth, financial gains and supply chain stakeholders’ delight). Results showed that BSC and SM are effective and efficient tools in managing the performance of a successful GESC in an emerging market. Royal Herbs’ BSC key performance indicators (KPIs), which were used in assessing a GESCM strategy, indicate improvements along the BSC’s four perspectives. Results give green entrepreneurs/manufacturers in an emerging market as Egypt a comprehensive view on how they can effectively assess/improve their GESCM performance. It draws attention to the importance of investing in GESCM practices for maintaining sustainable development in a clean business environment. It helps practitioners, academics and policymakers in Egypt to bridge the gap between theory and practice regarding GESCM performance. From an interdisciplinary perspective, it provides insights on improving the ESCM performance of the industry without harming its natural environment.
Multi-hazard parametric catastrophe bond trigger design for subduction earthquakes and tsunamis
Authors: Goda Katsuichiro
Published: January 2021

This study presents trigger design methods and performance evaluations of multi-hazard parametric catastrophe bonds for mega-thrust subduction earthquakes and tsunamis. The catastrophe bonds serve as alternative disaster risk financing tools for insurers and reinsurers as well as municipalities and governments. Two types of parametric catastrophe bond trigger are investigated. A scenario-based method utilizes available earthquake source-based information, such as magnitude and location, whereas a station-intensity-based method can be implemented when seismic and tsunami hazard monitoring systems are in place in a region. The case study results, focusing on wooden buildings in Miyagi Prefecture, indicate that the station-intensity-based trigger methods outperform the scenario-based trigger methods significantly. Incorporating seismic and tsunami hazard information from multiple recording stations results in smaller trigger errors. The station-intensity-based methods are applicable to building portfolios at both municipality levels and regional levels.
Temperament and character of patients with alcohol toxicity during COVID − 19 pandemic
Authors: Estedlal Ali Reza , Mani Arash , Vardanjani Hossein Molavi , Kamali Mahsa , Zarei Leila , Heydari Seyed Taghi , Lankarani Kamran Bagheri
Published: January 2021

Background

Corona virus epidemic may be acts as a stressor or trauma that affects both physical health and mental health. People exhibited various reactive behaviors to confront with this stressful situation. In Iran, one of the common motives for alcohol consumption is to scape problems and cope with stresses. It has been shown that personality factors influence alcohol consumption, since they are associated with drinking motives. The main purpose of this study was to investigate the correlation between temperament and character and alcohol abuse.

Methods

This cross-section study was conducted on 135 alcohol intoxicated patients admitted to emergency room in March 2020 and 255 participants who were randomly selected from public in Shiraz. A questionnaire consisted of TCI (Temperament and character inventory) and several questions about COVID-19 pandemic. It was completed by a trained interviewer using the matched answer technique. Demographic factors were self-reported.

Results

Among the alcohol intoxicated group, 117 (86.7%) were males and 18(13.3%) were females. The mean age of the participants was 32.43 ± 10.81 years. Among control group, 99 (38.8%) were males and 156 (61.2%) were females. The mean age of control group was 33.12 ± 14.77 years. Alcohol toxicity was mostly observed among the young males (male/female ratio was 6.45). According to temperament and character index, mean scores of novelty seeking, harm avoidant, and self-transcendence were higher in the alcohol toxicity group than normal population (P < 0.01). Mean scores of reward dependent, cooperativeness, and self-directedness were higher in normal population than the alcohol toxicity group (P < 0.001). The mean score of persistence was not significantly different between methanol intoxicated and normal population groups (P = 0.718).

Conclusion

Alcohol intoxicated patients had higher scores of novelty-seeking and self-transcendence and lower scores of reward-dependency scores, cooperativeness and self-directedness. These scores are associated with higher likelihood of personality disorders.
Topical co‐administration of zoledronate with recombinant human bone morphogenetic protein-2 can induce and maintain bone formation in the bone marrow environment
Authors: Ueyama Hideki , Ohta Yoichi , Imai Yuuki , Suzuki Akinobu , Sugama Ryo , Minoda Yukihide , Takaoka Kunio , Nakamura Hiroaki
Published: January 2021

Background

Bone morphogenetic proteins (BMPs) induce osteogenesis in various environments. However, when BMPs are used alone in the bone marrow environment, the maintenance of new bone formation is difficult owing to vigorous bone resorption. This is because BMPs stimulate the differentiation of not only osteoblast precursor cells but also osteoclast precursor cells. The present study aimed to induce and maintain new bone formation using the topical co-administration of recombinant human BMP-2 (rh-BMP-2) and zoledronate (ZOL) on beta-tricalcium phosphate (β-TCP) composite.

Methods

β-TCP columns were impregnated with both rh-BMP-2 (30 µg) and ZOL (5 µg), rh-BMP-2 alone, or ZOL alone, and implanted into the left femur canal of New Zealand white rabbits (n = 56). The implanted β-TCP columns were harvested and evaluated at 3 and 6 weeks after implantation. These harvested β-TCP columns were evaluated radiologically using plane radiograph, and histologically using haematoxylin/eosin (H&E) and Masson’s trichrome (MT) staining. In addition, micro-computed tomography (CT) was performed for qualitative analysis of bone formation in each group (n = 7).

Results

Tissue sections stained with H&E and MT dyes revealed that new bone formation inside the β-TCP composite was significantly greater in those impregnated with both rh-BMP-2 and ZOL than in those from the other experimental groups at 3 and 6 weeks after implantations (p < 0.05). Micro-CT data also demonstrated that the bone volume and the bone mineral density inside the β-TCP columns were significantly greater in those impregnated with both rh-BMP-2 and ZOL than in those from the other experimental groups at 3 and 6 weeks after implantations (p < 0.05).

Conclusions

The topical co-administration of both rh-BMP-2 and ZOL on β-TCP composite promoted and maintained newly formed bone structure in the bone marrow environment.
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

Background

In 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.

Methods

Our 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.

Results

For 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.

Conclusions

In 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

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

Background

Activated 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.

Methods

Focal 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.

Results

Immunofluorescence 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.

Conclusions

EC2319 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

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

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

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

Short-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

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

Characterizing 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

New 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.
An lp-space matching pursuit algorithm and its application to robust seismic data denoising via time-domain Radon transforms
Authors: Li Ji , Sacchi Mauricio D.
Published: January 2021

Sparse solutions of linear systems of equations are essential in many applications of seismic data processing. These systems arise in many denoising algorithms, such as those that use Radon transforms. We have developed a robust matching pursuit (RMP) algorithm for the retrieval of sparse Radon domain coefficients. The algorithm is robust to outliers and, hence, is applicable for seismic data deblending. The classic matching pursuit (MP) algorithm is often adopted to approximate data by a small number of basis functions. It performs effectively for data contaminated with well-behaved, typically Gaussian, random noise. However, MP tends to identify the wrong basis functions when the data are contaminated by erratic noise such as source interference encountered in common-receiver and common-channel gathers of simultaneous source surveys. Incorporating an [Formula: see text] space inner product into the MP algorithm significantly increases its robustness to erratic signals. Deblending experiments with synthetic and field data examples indicate a significant signal-to-noise ratio improvement when one adopts a Radon denoiser computed via our RMP solver. We determine in detail the steps required to implement our [Formula: see text] space RMP algorithm when the basis functions are not given in an explicit form, as is the case with the time-domain Radon transform.
An analytic signal-based accurate time-domain viscoacoustic wave equation from the constant-Q theory
Authors: Liu Hongwei , Luo Yi
Published: January 2021

We have introduced a concise time-domain wave equation to accurately simulate wave propagation in viscoacoustic media. The central idea behind this work is to dismiss the negative frequency components from a time-domain signal by converting the signal to its analytic format. The negative frequency components of any analytic signal are always zero because we can construct the viscoacoustic wave equation to honor the relaxation property of the medium for positive frequencies only. The newly proposed complex-valued wave equation (CWE) represents the wavefield with its analytic signal, whose real part is the desired physical wavefield, whereas the imaginary part is the Hilbert transform of the real component. Specifically, this CWE is accurate for weakly and strongly attenuating media in terms of the dissipation and dispersion, and the attenuation is precisely linear with respect to the frequencies. The CWE easily and flexibly models dispersion-only, dissipation-only, or dispersion-plus-dissipation seismic waves. We have verified these CWEs by comparing the results with analytical solutions, and we achieved nearly perfect matches. When extending to heterogeneous media, the results are consistent with those computed from the nonstationary operator-based Fourier integral method and from the standard linear solid equations.
A comparison of ray-based traveltime and waveform inversion results for depth velocity model building in complex fold and thrust belts
Authors: Soni Alok K. , Plessix Rene-Edouard , Huiskes Mark J.
Published: January 2021

In onshore fold and thrust belt geology, seismic wave propagation leads to complex waveforms due to large lateral variations in the earth parameters and multiple scattering effects. Moreover, due to acquisition difficulties in mountainous terrain, data coverage can be limited. Although waveform inversion should help in imaging in this context, finding a sufficiently accurate initial model parameter is challenging. Efficient ray-based traveltime inversions could be used when we focus on the early arrivals. However, they rely on a high-frequency assumption that may not be justified in this context. Indeed, significant interferences may occur within the first Fresnel zone. To evaluate the potential challenges, we have inverted 3D passive and 2D active data sets recorded over a fold and thrust belt region in Albania. We compare ray-based high-frequency traveltime and finite-frequency waveform inversion results obtained by inverting local earthquake events and first arrivals from 2D active seismic data. We consider only acoustic propagation. The two results differ significantly. The ray-based traveltime inversion we used does not always give a velocity model that can be used as an initial model for waveform inversion. This could be due to a bias toward the initial model when the data coverage is limited because the inversions have a nonzero null space, or it could be a limitation of the ray-based traveltime inversion that assumes the phases of the picked events are linear in frequency over the considered frequency band.
High-frequency localized elastic full-waveform inversion for time-lapse seismic surveys
Authors: Yuan Shihao , Fuji Nobuaki , Singh Satish C.
Published: January 2021

Seismic full-waveform inversion (FWI) is a powerful method used to estimate the elastic properties of the subsurface. To mitigate the nonlinearity and cycle-skipping problems, in a hierarchical manner, one first inverts the low-frequency content to determine long- and medium-wavelength structures and then increases the frequency content to obtain detailed information. However, the inversion of higher frequencies can be computationally very expensive, especially when the target of interest, such as oil/gas reservoirs and axial melt lens, is at a great depth, far away from source and receiver arrays. To address this problem, we have developed a localized FWI algorithm in which iterative modeling is performed locally, allowing us to extend inversions for higher frequencies with little computation effort. Our method is particularly useful for time-lapse seismic, where the changes in elastic parameters are local due to fluid extraction and injection in the subsurface. In our method, the sources and receivers are extrapolated to a region close to the target area, allowing forward modeling and inversion to be performed locally after low-frequency full-model inversion for the background model, which by nature only represents long- to medium-wavelength features. Numerical tests show that the inversion of low-frequency data for the overburden is sufficient to provide an accurate high-frequency estimation of elastic parameters of the target region.
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.
Uncertainty quantification in fault detection using convolutional neural networks
Authors: Feng Runhai , Grana Dario , Balling Niels
Published: January 2021

Segmentation of faults based on seismic images is an important step in reservoir characterization. With the recent developments of deep-learning methods and the availability of massive computing power, automatic interpretation of seismic faults has become possible. The likelihood of occurrence for a fault can be quantified using a sigmoid function. Our goal is to quantify the fault model uncertainty that is generally not captured by deep-learning tools. We have used the dropout approach, a regularization technique to prevent overfitting and coadaptation in hidden units, to approximate the Bayesian inference and estimate the principled uncertainty over functions. Particularly, the variance of the learned model has been decomposed into aleatoric and epistemic parts. Our method is applied to a real data set from the Netherlands F3 block with two different dropout ratios in convolutional neural networks. The aleatoric uncertainty is irreducible because it relates to the stochastic dependency within the input observations. As the number of Monte Carlo realizations increases, the epistemic uncertainty asymptotically converges and the model standard deviation decreases because the variability of the model parameters is better simulated or explained with a larger sample size. This analysis can quantify the confidence to use fault predictions with less uncertainty. In addition, the analysis suggests where more training data are needed to reduce the uncertainty in low-confidence regions.
Neurons with dendrites can perform linearly separable computations with low resolution synaptic weights
Authors: Cazé Romain D. , Stimberg Marcel
Published: January 2021

In theory, neurons modelled as single layer perceptrons can implement all linearly separable computations. In practice, however, these computations may require arbitrarily precise synaptic weights. This is a strong constraint since both biological neurons and their artificial counterparts have to cope with limited precision. Here, we explore how non-linear processing in dendrites helps overcome this constraint. We start by finding a class of computations which requires increasing precision with the number of inputs in a perceptron and show that it can be implemented without this constraint in a neuron with sub-linear dendritic subunits. Then, we complement this analytical study by a simulation of a biophysical neuron model with two passive dendrites and a soma, and show that it can implement this computation. This work demonstrates a new role of dendrites in neural computation: by distributing the computation across independent subunits, the same computation can be performed more efficiently with less precise tuning of the synaptic weights. This work not only offers new insight into the importance of dendrites for biological neurons, but also paves the way for new, more efficient architectures of artificial neuromorphic chips.
Fundamentals of Philosophy
Authors: Lipskiy Boris , Gusev Stanislav , Tul'chinskiy Grigoriy , Markov Boris
Published: January 2021

"The textbook consists of five sections, each of which is devoted to one of the fundamental areas of philosophical knowledge. The first section describes the problem of the emergence of philosophy as a field of scientific knowledge and its main objectives; the second identifies the problems of the knowability of the world and examines the main forms of organization of knowledge; the third section is devoted to the problem of man and his place in the world; the fourth section concerns the analysis of social relations from family to political; the fifth section discusses the main ideas of the philosophy of history. It is designed for the level of basic training of students of secondary vocational education institutions, written in simple language, includes numerous examples from history, mythology, ethnography and art. Each section contains individual and group questions and tasks focused on both self-control and checking the depth of understanding of the educational material.
Meets the requirements of the federal state educational standards of secondary vocational education of the latest generation.
For students and teachers, as well as anyone interested in philosophy."
Theory of automatic control. Linear, continuous systems
Authors: Seslavin Andrey
Published: January 2021

The textbook presents the basics of the classical theory of automatic control, based on mathematical models of real systems, given in the form of systems of linear differential equations with constant coefficients. Methods based on Laplace and Fourier transforms, stability, controllability, and observability theory, as well as directed graph theory and linear algebra are used. Meets the requirements of the federal state educational standards of higher education of the latest generation. For students of higher educational institutions studying in the areas of training and specialties 15.00.00 "Mechanical Engineering", 27.00.00 "Management in technical systems".
Economy
Authors: Reznik Semen , Mebaduri Zurab , Duhanina Elena
Published: January 2021

The textbook provides the basics of economic knowledge, as well as discusses technologies that allow junior students to actively participate in the educational process and practical activities, to get a prestigious job while still studying at the university. Special attention is paid to the effective use of their abilities and time, career management. For junior students of higher educational institutions studying in the areas of "Management", "Personnel Management" and "Economics".