bluuebunny/crossref_metadata_2025_split huggingface dataset:
Accelerating Inhibitor Discovery for Multiple
SARS-CoV-2 Targets with a Single,
Sequence-Guided Deep Generative Framework
Authors: Chenthamarakshan Vijil , Hoffman Samuel , Owen C. David , Lukacik Petra , Strain-damerell Claire , Fearon Daren , Malla Tika , Tumber Anthony , Schofield Christopher , Duyvesteyn Helen , Ddejnirattisai Wanwisa , Carrique Loic , Walter Thomas , Screaton Gavin , Matviyuk Tetiana , Mojsilovic Aleksandra , Crain Jason , Walsh Martin , Stuart David , Das Payel 
Published: May 2022

Abstract
The COVID-19 pandemic has highlighted the urgency for developing more efficient molecular discovery pathways. As exhaustive
exploration of the vast chemical space is infeasible, discovering novel inhibitor molecules for emerging drug-target proteins
is challenging, particularly for targets with unknown structure or ligands. We demonstrate the broad utility of a single deep
generative framework toward discovering novel drug-like inhibitor molecules against two distinct SARS-CoV-2 targets — the
main protease (Mpro) and the receptor binding domain (RBD) of the spike protein. To perform target-aware design, the
framework employs a target sequence-conditioned sampling of novel molecules from a generative model. Micromolar-level
in vitro inhibition was observed for two candidates (out of four synthesized) for each target. The most potent spike RBD
inhibitor also emerged as a rare non-covalent antiviral with broad-spectrum activity against several SARS-CoV-2 variants in live
virus neutralization assays. These results show that a broadly deployable machine intelligence framework can accelerate hit
discovery across different emerging drug-targets.
A Machine Learning Analysis of Impact of the Covid-19 Pandemic on Alcohol Consumption Habit Changes Among Healthcare Workers in the U.S.
Authors: Rezapour Mostafa 
Published: May 2022

Abstract
In this paper, we discuss the impact of the Covid-19 pandemic on alcohol consumption habit changes among healthcare workers in the United States. We utilize multiple supervised and unsupervised machine learning methods and models such as Decision Trees, Logistic Regression, Naive Bayes classifier, k-Nearest Neighbors, Support Vector Machines, Multilayer perceptron, Random Forests, XGBoost, CatBoost, LightGBM, Synthetic Minority Oversampling, Chi-Squared Test and mutual information method on a mental health survey data obtained from the University of Michigan Inter-University Consortium for Political and Social Research to find out relationships between COVID-19 related negative effects and alcohol consumption habit changes among healthcare workers. Our findings suggest that COVID-19-related school closures, COVID-19-related work schedule changes and COVID-related news exposure may lead to an increase in alcohol use among healthcare workers in the United States.
COVID-19: Monitoring of patients’ laboratory data during a 90-day length of hospital stay
Authors: Perraud Eunice Bobô de Carvalho , Oliveira Breno Douglas Dantas , Meireles Lilia Sousa Martins Alcantara , Moreira Thainá Bastos Mangueira , Loureiro Vinicius Lopes , Rocha Hermano Alexandre Lima , Martins Filadélfia Passos Rodrigues 
Published: May 2022

Abstract
Background
In March 2020, COVID-19 was declared a pandemic. In this study, patients were selected through the chest HRCT diagnosis. Laboratory tests such as blood count, fibrinogen, D-dimer, AST, ALT, troponin, LDH, albumin, CRP, electrolytes and ferritin were analyzed.
Aims
To monitor the laboratory evolution of COVID-19 in patients during hospitalization.
Method
Of 115 patients, 93 were selected and analyzed every three days for 90 days. Data were analyzed using the IBM, SPSS, Statistics for Windows, 23.0 software.
Results
34.4% were female, and 65.6% male; Hct and Hb dropped after the 13th day of hospitalization; leukocyte levels remained up to 15,000, increasing to > 20,000 after the 55th day of hospitalization; lymphopenia occurred in 10 to 15% of the patients, with increased eosinophils; platelet levels decreased at the end of hospitalization. Fibrinogen, D-dimer, LDH, CRP and ferritin levels increased throughout the hospitalization period. Urea and creatinine increased slightly from the 30th day onwards. There were no alterations in PTA, troponin, chlorine, potassium and albumin levels, with a decrease throughout the hospitalization period.
Conclusion
Knowledge of the behavior of laboratory tests together with the disease evolution give support to the clinical and therapeutic management of COVID-19.
The social penalty paid by teetotallers
Authors: Walker Benjamin , Munford Luke 
Published: May 2022

Abstract
Background: Recent trends suggest that the proportion of individuals in Britain who do not drink alcohol is increasing. Despite this, British culture is still fairly dominated by alcohol as a cultural norm, and as an instigator/focus of social activities. Little is known about the effects of stopping drinking alcohol on social outcomes, which are important factors that also help protect health.Aims: Examine if drinking alcohol has a causal effect on an individual’s social life.Methods:We use data from 11,631 individuals in the UK Household Longitudinal Study (UKHLS) and adopt the outcome-wide longitudinal framework detailed by VanderWeele et al. (2020) to examine the causal relationship between alcohol consumption and social outcomes. Our two exposures are a binary variable for drinking and a categorical variable for frequency of drinking. We look at five binary social outcomes; being able to visit friends when feels like it, feeling isolated, lacking companionship, feeling left out, and feeling lonely. We control for past values of the outcome and exposure, and many covariates. We report E-values, the minimum strength of association that an unmeasured confounder would need to have with both the treatment and the outcome to fully explain away our treatment-outcome association.Results:We find that being a drinker has a most likely causal protective effect against not visiting friends when feels like it with an odds ratio (OR) of 0.71 (E -value: 2.18) and against feeling isolated often with an OR of 0.71 (E-value: 2.18). We also find evidence that it is not simply about drinking vs. non-drinking, and that the frequency of drinking matters. Drinking monthly or less had no statistically significant effect on social outcomes relative to not drinking, while drinking 2-3 times a week had the biggest protective effect against adverse social outcomes, including against not visiting friends when you feel like it with an OR of 0.59 (E-value: 2.81).Conclusions:Our results suggest a positive relationship between drinking and social interaction, and that this relationship may be causal. This could be due to alcohol being the norm for socialization in the UK disadvantaging teetotallers.
The Effect of Cash Transfer on Maternal and Child Health Outcomes: A Systematic Review in Sub-Saharan Africa
Authors: Ngamasana Emery L , Bowling Jessamyn 
Published: May 2022

Abstract
Background: Evidence-based policies are critical to navigate the time post-COVID-19. Improving maternal and child health is core to any recovery strategy. Cash Transfer programs can improve maternal and child health outcomes in low- and middle-income countries. However, studies assessing the effectiveness of these programs on maternal and child health outcomes, beyond service utilization, remain inconclusive. This synthesis fills a gap in the existing literature on the effectiveness of these programs in improving mothers and children’s health outcomes beyond service utilization and suggests a framework for reporting such health outcomes. We focused on Sub-Saharan Africa because of substantial operational differences between regions, and the need for maternal and child health advancement in this region.Methods: This review searched PubMed Central and Google Scholar, and supplemented with a backward citation search, for studies conducted in Sub-Saharan Africa using a combination of MeSH terms and key words for the period between 2000 – 2021. The review included only studies on cash transfer programs that reported health outcomes beyond service utilization among women of reproductive age and children below 18 years old. Findings: The synthesis pulled data from 17 articles conducted in 6 Sub-Saharan African countries. All studies reported health outcomes measures other than service utilization, and programs targeted women of reproductive age and children under 18 years of age. Of the 17 articles, 1 reported measures of mortality, 9 reported measures of functional status; 3 reported subjective measures of well-being, and 4 reported behavioral health outcomes. Across all categories of reported measures, evidence emerges that cash transfer programs are associated with improved health outcomes (e.g., improved infant and child survival, reduced incidence of illnesses, improved cognitive and motor development, improved general health, delayed sexual debut, lower transactional sex, etc.).Conclusion: Cash Transfer programs are effective, with great potential to improve maternal and child health outcomes in Sub-Saharan African countries. However, further research is needed to address implementation challenges, which include data collection, and program management.
An association study of IL2RA polymorphisms with cerebral palsy in a Chinese population
Authors: Qiao Yimeng , Wang Yangong , Xu Yiran , Zhang Jin , Su Yu , Cheng Ye , Bi Dan , Song Juan , Xia Lei , Li Ming , Zhang Xiaoli , Zhu Dengna , Wang Ting , Ding Jian , Wang Xiaoyang , Zhu Changlian , Xing Qinghe 
Published: May 2022

Abstract
Background
Cerebral palsy (CP), the most common physical disability of childhood, is a nonprogressive movement disorder syndrome. Eighty percent of cases are considered idiopathic without a clear cause. Evidence has shown that cytokine abnormalities are widely thought to contribute to CP.
Methods
An association between IL2RA (rs12244380, rs2025345, rs12722561, rs4749926, rs2104286 and rs706778) and CP was investigated using a case-control method based on 782 CP cases and 778 controls. The allele, genotype and haplotype frequencies of SNPs were assessed using the SHEsis program. Subgroup analyses based on complications and clinical subtypes were also conducted.
Results
Globally, no differences in genotype or allele frequencies for any SNPs remained significant after Bonferroni correction between patients and controls, except rs706778, which deviated from Hardy-Weinberg equilibrium and was excluded from further analyses. However, subgroup analysis revealed a significant association of rs2025345 with spastic tetraplegia (P genotype = 0.048 after correction) and rs12722561 with CP accompanied by global developmental delay (P allele = 0.045 after correction), even after Bonferroni correction.
Conclusions
These findings indicated that genetic variations in IL2RA are significantly associated with CP susceptibility in the Chinese Han population, suggesting that IL2RA is likely involved in the pathogenesis of CP. Further investigation with a larger sample size in a multiethnic population is needed to confirm the association.
Methods for assessing the epistemic uncertainty captured in ground-motion models
Authors: Bustos Guillermo Aldama , Douglas John , Strasser Fleur , Daví Manuela , MacGregor Alice 
Published: May 2022

Abstract
A key task when developing a ground-motion model (GMM) is to demonstrate that it captures an appropriate level of epistemic uncertainty. This is true whether multiple ground motion prediction equations (GMPEs) are used or a backbone approach is followed. The GMM developed for a seismic hazard assessment for the site of a UK new-build nuclear power plant is used as an example to discuss complementary approaches to assess epistemic uncertainty. Firstly, trellis plots showing the various percentiles of the GMM are examined for relevant magnitudes, distances and structural periods to search for evidence of “pinching”, where the percentiles narrow excessively. Secondly, Sammon’s maps, including GMPEs that were excluded from the logic tree, are examined to check the spread of the GMPEs for relevant magnitudes and distances in a single plot. Thirdly, contour plots of the standard deviation of the logarithms of predicted ground motions from each branch of the logic tree (σµ) are compared with plots drawn for other relevant hazard studies. Fourthly, uncertainties implied by a backbone GMM derived using the Campbell (2003)’s hybrid stochastic empirical method are compared to those of the proposed multi-GMPE GMM. Finally, the spread of the percentile of hazard curves resulting from implementing the GMM are examined for different return periods to check whether any bands of lower uncertainty in ground-motion space resulted in bands of lower uncertainty in hazard space. These five approaches enabled a systematic assessment of the level of uncertainty captured by the proposed GMM.
WITHDRAWN: Identifying and analyzing the relationship between attractive spots in Tehran at the regional level: based on Twitter data and human mobility
Authors: Dorostkar Ehsan , Najarsadeghi Mahsa 
Published: May 2022

Abstract
Cities attractive spots affect human mobility. Understanding the pattern of human mobility and its stimulant factors for planning and designing cities is very important. The cyberspace as well as Twitter social media with capabilities such as record users life events with accurate time and location data reflect human mobility in the city. The purpose of this study is to identify and analyze attractive urban spots based on the content and location data of residents in Tehran. Also the question is what is the connection rate of Twitter social media with attractive urban spots based on the behavior and performance of residents in the city? First, the data are collected through the Twitter API. The trend analysis method, the period and the changes made are analyzed and then the regression of the correlation between the rates of tweets extracted with transportation (trip production and absorption) is evaluated and is calculated, and a GWA spatial weighted regression model map for the city of Tehran is presented. Finally, it was found that Twitter's social media could play a role in human mobility and used as an important variable in field studies and analytical studies from urban spaces.
Significant Atmospheric CO2 Uptake by Antarctic Polynyas
Authors: Rhee Tae , Zappa Christopher , Kwon Young 
Published: May 2022

Abstract
The Southern Ocean plays a pivotal role in controlling the most critical greenhouse gas, carbon dioxide (CO2), in the atmosphere. However, insufficient observations, particularly in the Antarctic Sea Ice Zone (ASIZ; south of 60°S), hinders accurate estimation of the Southern Ocean sink strength of atmospheric CO2. Here, we show that Antarctic polynyas play a significant role in the CO2 budget of the Southern Ocean based on the 5-year continuous observations at the Korean Antarctic Station, Jang Bogo, in the Terra Nova Bay (TNB) polynya, Ross Sea, Antarctica and on the compilation of shipboard observations in the major Antarctic polynyas. The seasonal variation of the fugacity of dissolved CO2 (fCO2) between them shows in accordance, which was dominated by biological and physical processes in different seasons. Antarctic polynyas absorb atmospheric CO2 at a rate of 9 to 14 Tg C a-1, that is comparable to or more than the amount outgassed in the ASIZ, concentrating in the summer season from December to March. As the summer drawdown of fCO2 in the polynya is dominantly driven by phytoplankton blooms which is in turn maintained by the supply of iron from circumpolar deep water and glacier melt water, we expect this CO2 sink in the Antarctic polynyas to be enhanced in the future, resulting in a negative feedback with future climate change.
Novel insights on association and reactivity of Bispectral Index, frontal electromyogram, and autonomic responses in nociception- sedation monitoring of critical care patients
Authors: Stewart Juhani A. , Särkelä Mika O. K. , Wennervirta Johanna , Vakkuri Anne P. 
Published: May 2022

Abstract
Background: Assessing nociception and sedation in mechanically ventilated patients in the ICU is challenging, with few reliable methods available for continuous monitoring. Measurable cardiovascular and neurophysiological variables, such as frontal EEG, frontal EMG, and blood pressure, are potential methods for sedation and nociception monitoring. The hypothesis of this explorative study is that the aforementioned variables predict the level of sedation, as described by the Richmond Agitation-Sedation score (RASS).Methods: Thirty adult postoperative ICU patients on mechanical ventilation and receiving intravenous sedation, excluding patients with primary neurological disorders, head injury, or need for continuous neuromuscular blockage. Bispectral Index (BIS), EMG power (EMG), EMG-derived Responsiveness Index (RI), and averaged blood pressure variability (ARV) were tested against RASS measurements. The aforementioned variables together with blood pressure and Surgical Pleth Index (SPI) were explored before and after painful stimuli (for example bronchoscopy, or pleural puncture) at varying RASS levels, to test variable responsiveness.Results: BIS, EMG, and RI predicted RASS levels with a prediction probability (PK) of 0.776 for BIS, 0.761 for EMG, and 0.763 for RI. In addition, BIS, EMG, and ARV demonstrated responsiveness to painful stimuli during deep sedation (RASS score ≤-3). Conclusion: Variables derived from EEG and EMG are associated with sedation levels, as described by the RASS score. Furthermore, these variables, along with ARV, react with consistency to painful stimuli during deep sedation (RASS -5 to -3), offering novel tools for nociception-sedation monitoring of mechanically ventilated ICU patients requiring deep sedation.
The theoretical model of straightness error in gun drilling of small diameter and long deep hole
Authors: Zhang Ying , Liang Li , Xie Chen , Lou Jiacheng 
Published: May 2022

Abstract
Deep-hole drilling (depth-diameter ratio > 10) accounts for about 40% of hole processing. As an important hole processing technology, the gun drill has a complex structure and weak relative stiffness, which makes it difficult to avoid the straightness deviation of the hole. In addition, the current theoretical model of straightness deviation is inconsistent with the actual working condition of long and deep hole gun drilling, so the prediction of straightness error is still a difficulty in deep hole machining. Based on the analysis of influencing factors of straightness deviation of gun drilling, this paper establishes the theoretical model of the straightness error of the middle four supports by using the Euler-Bernoulli beam theory and reveals the relationship between the bit deflection and the deviation of the hole axis. In this paper, the influence of support clearance, support spacing, support stiffness, axial force, and drill pipe stiffness on straightness error is analyzed through simulation and experiments. The method to reduce straightness deviation by controlling support clearance is proposed and verified by experiments.
Long Variation in Voltage and Supply Interruption in a Distribution System
Authors: Amoo Abdullahi Lanre , Okelola Muniru , Said Dalila 
Published: May 2022

Abstract
The paper examines a distribution system field data for the assessment of voltage variation and supply interruption. The under voltage has severe impacts on the consumer loads working conditions causing overloading and high transmission losses. The under-voltage limit has been set for voltage below 0.9p.u. The measurement was conducted in the control room for the duration of one week for logging interval of 1-min using Fluke VR1710 that monitors single-phase voltage quality of power network. Long duration variations are considered to be present when the limits are exceeded for greater than 1 minute time lapse. In this paper, the lowest interruption duration recorded was 20 minutes while the number of under-voltage was 41 occurrences. A simple mathematical analysis of power flow deviation was determined for correlation with the frequency disturbances recorded by the equipment. Both measurements and simulation results show that frequency rapidly changes in the network. The lowest and highest frequency vary between (48.7665 Hz to 51.196 Hz) and (48.843 Hz to 51.263 Hz) respectively. Thus, voltage and frequency disturbance statistics vindicate the voltage variation in the site to be within the ±10% limits but the overall performance of frequency monitored in the site violated the operational limits of ± 0.2 Hz (i.e. 49.8 Hz to 50.2 Hz) and ± 0.5 Hz (i.e. 49.5 Hz to 50.5 Hz) for the statutory regulatory. The paper would provide repository data as point of reference for Electric power sector reform act No.6 2005.
GraphRXN: A Novel Representation for Reaction Prediction
Authors: Li Baiqing , Su Shimin , Zhu Chan , Lin Jie , Hu Xinyue , Su Lebin , Yu Zhunzhun , Liao Kuangbiao , Chen Hongming 
Published: May 2022

Abstract
In recent years, it has been seen that artificial intelligence (AI) starts to bring revolutionary changes to chemical synthesis. However, the lack of suitable ways of representing chemical reactions and the scarceness of reaction data has limited the wider application of AI to reaction prediction. Here, we introduce a novel reaction representation, GraphRXN, for reaction prediction. It utilizes a universal graph-based neural network framework to encode chemical reactions by directly taking two-dimension reaction structures as inputs. The GraphRXN model was evaluated by three publically available chemical reaction datasets and gave on-par or superior results compared with other baseline models. To further evaluate the effectiveness of GraphRXN, wet-lab experiments were carried out for the purpose of generating reaction data. GraphRXN model was then built on high-throughput experimentation data and a decent accuracy (R2 of 0.713) was obtained on our in-house data validation. This highlights that the GraphRXN model can be deployed in an integrated workflow which combines robotics and AI technologies for forward reaction prediction.
Effect of Inflammatory Markers on the Pathologic Complete Response in the Neoadjuvan Treatment of Her-2 Positive Local Advanced Breast Cancer
Authors: Gulmez Ahmet , Harputluoglu Hakan 
Published: May 2022

Abstract
There are various effective combinations of therapy in the neoadjuvant treatment of human epidermal growth factor receptor 2 (Her-2) positive breast cancer (BC). One of the best-known of these treatments is the dual blockade with pertuzumab and trastuzumab, which is an anti-Her-2 treatment added to chemotherapy. The aim of this study was to retrospectively investigate the rate of pathological complete response (pCR) and markers predicting this response for trastuzumab and pertuzumab-containing regimens compared to trastuzumab-containing regimens for stage II to III HER-2 positive BC. In this study, we retrospectively analyzed the data of patients with Her-2 positive advanced BC who were treated with trastuzumab or trastuzumab and pertuzumab in addition to neoadjuvant chemotherapy. Patients were classified according to their pre-treatment clinical stages, hormone receptor status, Her-2 immuno-histochemical score, ki-67 index, neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio (PLR), and prognostic nutritional index (PNI) ratio. The cut-off values of these parameters were calculated by performing a Roc-Curve analysis. Patients with and without pCR with neoadjuvant therapy were evaluated. For NLR, it was 1.9 ± 0.85 in patients with pCR and 2.4 ± 0.83 in patients without pCR. For PLR it was 112 ± 53.3 and 129.4 ± 36.2, respectively. For PNI, it was 51.6 ± 5.8 and 48.1 ± 5.9. Low NLR and PLR as well as high PNI were significantly associated with pCR. In addition, the pCR of patients with Hormone Receptor negativity and dual Her-2 blockade was statistically significant. This is the first study in the literature to show that prognostic scores such as NLR, PLR, and PNI have statistically significant benefits on pCR in patients treated with the neoadjuvant dual bloc