bluuebunny/crossref_metadata_2025_split huggingface dataset:
PSnpBind-ML: predicting the effect of binding site mutations on protein-ligand binding affinity
Authors: Ammar Ammar , Cavill Rachel , Evelo Chris , Willighagen Egon 
Published: March 2023

AbstractProtein mutations, especially those which occur in the binding site, play an important role in inter-individual drug response and may alter binding affinity and thus impact the drug’s efficacy and side effects. Unfortunately, large-scale experimental screening of ligand-binding against protein variants is still time-consuming and expensive. Alternatively, in silico approaches can play a role in guiding those experiments. Methods ranging from computationally cheaper machine learning (ML) to the more expensive molecular dynamics have been applied to accurately predict the mutation effects. However, these effects have been mostly studied on limited and small datasets, while ideally a large dataset of binding affinity changes due to binding site mutations is needed. In this work, we used the PSnpBind database with six hundred thousand docking experiments to train a machine learning model predicting protein-ligand binding affinity for both wild-type proteins and their variants with a single-point mutation in the binding site. A numerical representation of the protein, binding site, mutation, and ligand information was encoded using 256 features, half of them were manually selected based on domain knowledge. A machine learning approach composed of two regression models is proposed, the first predicting wild-type protein-ligand binding affinity while the second predicting the mutated protein-ligand binding affinity. The best performing models reported an RMSE value within 0.5 $$-$$
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0.6 kcal/mol-1 on an independent test set with an R2 value of 0.87 $$-$$
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0.90. We report an improvement in the prediction performance compared to several reported models developed for protein-ligand binding affinity prediction. The obtained models can be used as a complementary method in early-stage drug discovery. They can be applied to rapidly obtain a better overview of the ligand binding affinity changes across protein variants carried by people in the population and narrow down the search space where more time-demanding methods can be used to identify potential leads that achieve a better affinity for all protein variants.
A protein-based subunit vaccine with biological adjuvants provides effective protection against Pasteurella multocida in pigs
Authors: Wu Min-Chia , Wu Hsing-Chieh , Lee Jai-Wei , Chang Wan-Chen , Chu Chun-Yen 
Published: March 2023

AbstractStreptococcus suis (S. suis) and Pasteurella multocida (P. multocida) are pathogens that can cause zoonotic diseases. P. multocida toxin (PMT) is an important virulence factor that causes atrophic rhinitis in pigs. Suilysin (Sly) is an extracellular protein of S. suis and has been shown to be a potential adjuvant. Previous studies have indicated that subunit vaccines containing several fragments of PMT as antigens are safer than traditional inactivated or live-attenuated vaccines. However, protein-based vaccines need strong adjuvants to enhance their immunogenicity. In this study, recombinant PMT-NC (rPMT-NC) protein antigen was formulated with either recombinant Sly (rSly) or CpG oligodeoxynucleotides (CpG) as the adjuvant. The immune responses elicited by these vaccines and the protective efficacy after challenge with live P. multocida were evaluated in piglets. In the dose-dependent test, piglets immunized with the low dose (100 µg) of rSly had increased antigen-specific total IgG, interferon (IFN)-γ gene expression, and CD4+ and CD8+ T-cell populations. Compared to piglets in the commercial (Al-gel) adjuvant and the control groups (p < 0.05), piglets in the biological adjuvant groups showed significantly reduced turbinate atrophy, nasal distortion, and lung lesion scores after challenge with P. multocida serotype A. Vaccines containing rSly or CpG adjuvant enhanced humoral and cellular immune responses and protection against P. multocida. This combination of a protein-based antigen formulated with a biological adjuvant showed synergistic and protective effects against atrophic rhinitis and has potential to be developed as part of a bivalent vaccine.
A cost-effective machine learning-based method for preeclampsia risk assessment and driver genes discovery
Authors: Wang Hao , Zhang Zhaoyue , Li Haicheng , Li Jinzhao , Li Hanshuang , Liu Mingzhu , Liang Pengfei , Xi Qilemuge , Xing Yongqiang , Yang Lei , Zuo Yongchun 
Published: March 2023

Abstract
Background
The placenta, as a unique exchange organ between mother and fetus, is essential for successful human pregnancy and fetal health. Preeclampsia (PE) caused by placental dysfunction contributes to both maternal and infant morbidity and mortality. Accurate identification of PE patients plays a vital role in the formulation of treatment plans. However, the traditional clinical methods of PE have a high misdiagnosis rate.

Results
Here, we first designed a computational biology method that used single-cell transcriptome (scRNA-seq) of healthy pregnancy (38 wk) and early-onset PE (28–32 wk) to identify pathological cell subpopulations and predict PE risk. Based on machine learning methods and feature selection techniques, we observed that the Tuning ReliefF (TURF) score hybrid with XGBoost (TURF_XGB) achieved optimal performance, with 92.61% accuracy and 92.46% recall for classifying nine cell subpopulations of healthy placentas. Biological landscapes of placenta heterogeneity could be mapped by the 110 marker genes screened by TURF_XGB, which revealed the superiority of the TURF feature mining. Moreover, we processed the PE dataset with LASSO to obtain 497 biomarkers. Integration analysis of the above two gene sets revealed that dendritic cells were closely associated with early-onset PE, and C1QB and C1QC might drive preeclampsia by mediating inflammation. In addition, an ensemble model-based risk stratification card was developed to classify preeclampsia patients, and its area under the receiver operating characteristic curve (AUC) could reach 0.99. For broader accessibility, we designed an accessible online web server (http://bioinfor.imu.edu.cn/placenta).

Conclusion
Single-cell transcriptome-based preeclampsia risk assessment using an ensemble machine learning framework is a valuable asset for clinical decision-making. C1QB and C1QC may be involved in the development and progression of early-onset PE by affecting the complement and coagulation cascades pathway that mediate inflammation, which has important implications for better understanding the pathogenesis of PE.
Effects of 12 mg vs. 6 mg dexamethasone on thromboembolism and bleeding in patients with critical COVID-19 - a post hoc analysis of the randomized, blinded COVID STEROID 2 trial
Authors: Jonmarker Sandra , Alarcón Felix , Litorell Jacob , Granholm Anders , Alm Eva Joelsson , Chew Michelle , Russell Lene , Weihe Sarah , Madsen Emilie Kabel , Meier Nick , Leistner Jens Wolfgang , Mårtensson Johan , Hollenberg Jacob , Perner Anders , Kjær Maj-Brit Nørregaard , Munch Marie Warrer , Dahlberg Martin , Cronhjort Maria , Wahlin Rebecka Rubenson 
Published: March 2023

AbstractBackgroundThromboembolism is more common in patients with critical COVID-19 than in other critically ill patients, and inflammation has been proposed as a possible mechanism. The aim of this study was to investigate if 12 mg vs. 6 mg dexamethasone daily reduced the composite outcome of death or thromboembolism in patients with critical COVID-19.MethodsUsing additional data on thromboembolism and bleeding we did a post hoc analysis of Swedish and Danish intensive care unit patients enrolled in the blinded randomized COVID STEROID 2 trial comparing 12 mg vs. 6 mg dexamethasone daily for up to 10 days. The primary outcome was a composite outcome of death or thromboembolism during intensive care. Secondary outcomes were thromboembolism, major bleeding, and any bleeding during intensive care.ResultsWe included 357 patients. Whilst in intensive care, 53 patients (29%) in the 12 mg group and 53 patients (30%) in the 6 mg group met the primary outcome with an unadjusted absolute risk difference of − 0.5% (95% CI − 10 to 9.5%, p = 1.00) and an adjusted OR of 0.93 (CI 95% 0.58 to 1.49, p = 0.77). We found no firm evidence of differences in any of the secondary outcomes.ConclusionsAmong patients with critical COVID-19, 12 mg vs. 6 mg dexamethasone daily did not result in a statistically significant difference in the composite outcome of death or thromboembolism. However, uncertainty remains due to the limited number of patients.
Physiological and molecular mechanisms of insect appendage regeneration
Authors: Zhong Jiru , Jing Andi , Zheng Shaojuan , Li Sheng , Zhang Xiaoshuai , Ren Chonghua 
Published: March 2023

AbstractRegeneration, as a fascinating scientific field, refers to the ability of animals replacing lost tissue or body parts. Many metazoan organisms have been reported with the regeneration phenomena, but showing evolutionarily variable abilities. As the most diverse metazoan taxon, hundreds of insects show strong appendage regeneration ability. The regeneration process and ability are dependent on many factors, including macroscopic physiological conditions and microscopic molecular mechanisms. This article reviews research progress on the physiological conditions and internal underlying mechanisms controlling appendage regeneration in insects.
The Neumann problem for a class of generalized Kirchhoff-type potential systems
Authors: Chems Eddine Nabil , Repovš Dušan D. 
Published: March 2023

AbstractIn this paper, we are concerned with the Neumann problem for a class of quasilinear stationary Kirchhoff-type potential systems, which involves general variable exponents elliptic operators with critical growth and real positive parameter. We show that the problem has at least one solution, which converges to zero in the norm of the space as the real positive parameter tends to infinity, via combining the truncation technique, variational method, and the concentration–compactness principle for variable exponent under suitable assumptions on the nonlinearities.
OpenStackDP: a scalable network security framework for SDN-based OpenStack cloud infrastructure
Authors: Krishnan Prabhakar , Jain Kurunandan , Aldweesh Amjad , Prabu P. , Buyya Rajkumar 
Published: March 2023

AbstractNetwork Intrusion Detection Systems (NIDS) and firewalls are the de facto solutions in the modern cloud to detect cyberattacks and minimize potential hazards for tenant networks. Most of the existing firewalls, perimeter security, and middlebox solutions are built on static rules/signatures or simple rule matching, making them inflexible, susceptible to bugs, and difficult to introduce new services. This paper aims to improve network management in OpenStack Clouds by taking advantage of the combination of software-defined networking (SDN), Network Function Virtualization (NFV), and machine learning/artificial intelligence (ML/AI) and for making networks more predictable, reliable, and secure. Artificial intelligence is being used to monitor the behavior of the virtual machines and applications running in the OpenStack SDN cloud so that when any issues or degradations are noticed, the decision can be quickly made on how to handle that issue, being able to analyze data in motion, starting at the edge. The OpenStackDP framework comprises lightweight monitoring, anomaly-detecting intelligent sensors embedded in the data plane, a threat analytics engine based on ML/AI algorithms running inside switch hardware/network co-processor, and defensive actions deployed as virtual network functions (VNFs). This network data plane-based architecture makes high-speed threat detection and rapid response possible and enables a much higher degree of security. We have built the framework with advanced streaming analytics technologies, algorithms, and machine learning to draw knowledge from this data that is in motion before the malicious traffic goes to the tenant compute nodes or long-term data store. Cloud providers and users will benefit from improved Quality-of-Services (QoS) and faster recovery from cyber-attacks and compromised switches. The multi-phase collaborative anomaly detection scheme demonstrates an accuracy of 99.81%, average latencies of 0.27 ms, and response speed within 9 s. The simulations and analysis show that the OpenStackDP network analytics framework substantially secures and outperforms prior SDN-based OpenStack solutions for Cloud architectures.
Association between cognitive impairment and motor dysfunction among patients with multiple sclerosis: a cross-sectional study
Authors: Alharthi Hanadi Matar , Almurdi Muneera Mohammed 
Published: March 2023

Abstract
Background
Previous studies have shown that there is a relationship between cognitive impairment (CI) and motor dysfunction (MD) in neurological diseases, such as Alzheimer’s and Parkinson’s disease. However, there whether CI and MD are associated in patients with multiple sclerosis (MS) is unknown. Here we studied the association between CI and MD in patients with MS and examined if muscle weakness or incoordination, balance impairment, gait abnormalities, and/or increased fall risk are indicators of CI in patients with MS.

Methods
Seventy patients with MS were included in this cross-sectional study. Cognitive impairment was assessed using the Montreal Cognitive Assessment Scale (MoCA), muscle strength using a hand-held dynamometer, and balance, gait, and fall risk assessment using the Tinetti scale. Motor coordination was assessed using the timed rapid alternating movement test for the upper extremity and the timed alternate heel-to-knee test for the lower extremity.

Results
There was a significant association between CI and motor coordination, balance, gait, and risk of fall (p < 0.005) but not muscle strength. Stepwise multiple linear regression showed that 22.7% of the variance in the MoCA was predicted by the fall risk and incoordination of the upper extremities in the MS population.

Conclusions
CI is significantly associated with motor incoordination, balance impairment, gait abnormality, and increased fall risk. Furthermore, the risk of fall and upper extremity incoordination appeared to be best indicators of CI in patients with MS.
Considering the lip print patterns of Ibo and Hausa Ethnic groups of Nigeria: checking the wave of ethnically driven terrorism
Authors: Uzomba G. C. , Obijindu C. A. , Ezemagu U. K. 
Published: March 2023

Abstract
Introduction
Lip print of an individual is distinct and could be a useful form of evidence to identify the ethnicity of a terrorist.

Objectives
The study analyzed the distribution of lip print patterns of two major ethnic groups in Nigeria; Ibo and Hausa, to develop a strategic plan to check the wave of ethnically driven terrorism in Nigeria, carried out by groups such as Boko Haram and Indigeneous People of Biafra (IPOB).

Materials and methods
The study comprised 800 participants of Ibo and Hausa ethnic groups (400 males and 400 females). The study adopted a digital method of lip print analysis and followed the guidelines outlined by the Institute of Medicine (IOM) for anthropometric measurements. The lip was classified, using Tsuchihashi and Suzuki method of classification.

Results
The predominant lip print patterns of Ibo were Type I with complete vertical groove and Type III with intersect of groove for male and Type III for female. Type I’ with partial length groove was the predominant pattern for both male and female Hausa. The lip width and height of female Ibo were longer than that of the Hausa counterpart (P < 0.05), but none of the anthropometric variables could predict the lip print pattern.

Conclusion
The lip size and print could aid forensic investigation, though genetic diversity and heterogeneity of ethnic groups in Nigeria, especially that of Ibo, could setback use of lip print pattern to identify the ethnicity of an unknown individual in Nigeria to help determine the terrorist group to which they may belong.
Selection processes of Arctic seasonal glacier snowpack bacterial communities
Authors: Keuschnig Christoph , Vogel Timothy M. , Barbaro Elena , Spolaor Andrea , Koziol Krystyna , Björkman Mats P. , Zdanowicz Christian , Gallet Jean-Charles , Luks Bartłomiej , Layton Rose , Larose Catherine 
Published: March 2023

AbstractBackgroundArctic snowpack microbial communities are continually subject to dynamic chemical and microbial input from the atmosphere. As such, the factors that contribute to structuring their microbial communities are complex and have yet to be completely resolved. These snowpack communities can be used to evaluate whether they fit niche-based or neutral assembly theories.MethodsWe sampled snow from 22 glacier sites on 7 glaciers across Svalbard in April during the maximum snow accumulation period and prior to the melt period to evaluate the factors that drive snowpack metataxonomy. These snowpacks were seasonal, accumulating in early winter on bare ice and firn and completely melting out in autumn. Using a Bayesian fitting strategy to evaluate Hubbell’s Unified Neutral Theory of Biodiversity at multiple sites, we tested for neutrality and defined immigration rates at different taxonomic levels. Bacterial abundance and diversity were measured and the amount of potential ice-nucleating bacteria was calculated. The chemical composition (anions, cations, organic acids) and particulate impurity load (elemental and organic carbon) of the winter and spring snowpack were also characterized. We used these data in addition to geographical information to assess possible niche-based effects on snow microbial communities using multivariate and variable partitioning analysis.ResultsWhile certain taxonomic signals were found to fit the neutral assembly model, clear evidence of niche-based selection was observed at most sites. Inorganic chemistry was not linked directly to diversity, but helped to identify predominant colonization sources and predict microbial abundance, which was tightly linked to sea spray. Organic acids were the most significant predictors of microbial diversity. At low organic acid concentrations, the snow microbial structure represented the seeding community closely, and evolved away from it at higher organic acid concentrations, with concomitant increases in bacterial numbers.ConclusionsThese results indicate that environmental selection plays a significant role in structuring snow microbial communities and that future studies should focus on activity and growth.
Using strain-resolved analysis to identify contamination in metagenomics data
Authors: Lou Yue Clare , Hoff Jordan , Olm Matthew R. , West-Roberts Jacob , Diamond Spencer , Firek Brian A. , Morowitz Michael J. , Banfield Jillian F. 
Published: March 2023

Abstract
Background
Metagenomics analyses can be negatively impacted by DNA contamination. While external sources of contamination such as DNA extraction kits have been widely reported and investigated, contamination originating within the study itself remains underreported.

Results
Here, we applied high-resolution strain-resolved analyses to identify contamination in two large-scale clinical metagenomics datasets. By mapping strain sharing to DNA extraction plates, we identified well-to-well contamination in both negative controls and biological samples in one dataset. Such contamination is more likely to occur among samples that are on the same or adjacent columns or rows of the extraction plate than samples that are far apart. Our strain-resolved workflow also reveals the presence of externally derived contamination, primarily in the other dataset. Overall, in both datasets, contamination is more significant in samples with lower biomass.

Conclusion
Our work demonstrates that genome-resolved strain tracking, with its essentially genome-wide nucleotide-level resolution, can be used to detect contamination in sequencing-based microbiome studies. Our results underscore the value of strain-specific methods to detect contamination and the critical importance of looking for contamination beyond negative and positive controls.
Congenital hydrocephalus: new Mendelian mutations and evidence for oligogenic inheritance
Authors: Jacquemin Valerie , Versbraegen Nassim , Duerinckx Sarah , Massart Annick , Soblet Julie , Perazzolo Camille , Deconinck Nicolas , Brischoux-Boucher Elise , De Leener Anne , Revencu Nicole , Janssens Sandra , Moorgat Stèphanie , Blaumeiser Bettina , Avela Kristiina , Touraine Renaud , Abou Jaoude Imad , Keymolen Kathelijn , Saugier-Veber Pascale , Lenaerts Tom , Abramowicz Marc , Pirson Isabelle 
Published: March 2023

Abstract
Background
Congenital hydrocephalus is characterized by ventriculomegaly, defined as a dilatation of cerebral ventricles, and thought to be due to impaired cerebrospinal fluid (CSF) homeostasis. Primary congenital hydrocephalus is a subset of cases with prenatal onset and absence of another primary cause, e.g., brain hemorrhage. Published series report a Mendelian cause in only a minority of cases. In this study, we analyzed exome data of PCH patients in search of novel causal genes and addressed the possibility of an underlying oligogenic mode of inheritance for PCH.

Materials and methods
We sequenced the exome in 28 unrelated probands with PCH, 12 of whom from families with at least two affected siblings and 9 of whom consanguineous, thereby increasing the contribution of genetic causes. Patient exome data were first analyzed for rare (MAF < 0.005) transmitted or de novo variants. Population stratification of unrelated PCH patients and controls was determined by principle component analysis, and outliers identified using Mahalanobis distance 5% as cutoff. Patient and control exome data for genes biologically related to cilia (SYScilia database) were analyzed by mutation burden test.

Results
In 18% of probands, we identify a causal (pathogenic or likely pathogenic) variant of a known hydrocephalus gene, including genes for postnatal, syndromic hydrocephalus, not previously reported in isolated PCH. In a further 11%, we identify mutations in novel candidate genes. Through mutation burden tests, we demonstrate a significant burden of genetic variants in genes coding for proteins of the primary cilium in PCH patients compared to controls.

Conclusion
Our study confirms the low contribution of Mendelian mutations in PCH and reports PCH as a phenotypic presentation of some known genes known for syndromic, postnatal hydrocephalus. Furthermore, this study identifies novel Mendelian candidate genes, and provides evidence for oligogenic inheritance implicating primary cilia in PCH.
Investigating the correlation between perceived stress and health anxiety with obsessive–compulsive disorder and quality of life during COVID-19 pandemic
Authors: Homayuni Atefeh 
Published: March 2023

AbstractBackground and aimsThe present study aimed to investigate the correlation between perceived stress and health anxiety with obsessive–compulsive symptoms and quality of life during COVID-19 pandemic.MethodsThis cross-sectional study was performed in the general public in Isfahan and Bandar Abbas. 559 citizens were selected by convenience sampling. An online questionnaire was used to collect the data, which consisted of: short health anxiety inventory, perceived stress scale, world health organization quality of life questionnaire and Padua inventory. Data analysis was performed using SPSS-24 and Amos-21.ResultsThere were significant positive correlations between health anxiety and perceived stress (r = 0/338), obsessive–compulsive symptoms and perceived stress (r = 0/16), obsessive–compulsive symptoms and health anxiety (r = 0/344). Also there were significant negative correlations between obsessive–compulsive symptoms and quality of life (r = − 0/21), health anxiety and quality of life (r = − 0/366), perceived stress and quality of life (r = − 0/715).Conclusionhealth anxiety and perceived stress during COVID-19 affect the obsessive–compulsive symptoms and quality of life. Therefore, it is recommended to pay attention to these psychological disorders during this global crisis and take actions to prevent and treat them.