bluuebunny/crossref_metadata_2025_split huggingface dataset:
Heat stress, labour productivity and adaptation in Europe—a regional and occupational analysis
Authors: Szewczyk Wojciech , Mongelli Ignazio , Ciscar Juan-Carlos 
Published: September 2021

Abstract
Labour exposure to heat stress driven by climate change will increase significantly with the rising global temperatures. Under heat stress, workers have to reduce work intensity and take longer breaks from work to prevent occupational illness and injuries. This study explores how the global warming and the resulting increase in heat stress can directly affect productivity of workers and indirectly impact upon the broader economy. Occupational statistics for 269 European regions are combined with daily heat stress indicator derived from a set of high-resolution climate scenarios. The novel approach, enabled by using a set of published exposure-response functions, assesses workers productivity losses differentiated by occupations. A macro econometric model of the European economy is then used to asses implications of change in productivity in monetary terms. The study finds that, compared to nowadays, productivity of labour can be 1.6% lower in Europe in 2080s, with a clear geographical gradient showing that southern and eastern regions are much more affected (e.g. up to 5.4% productivity loss in Greece). Furthermore, regions where the dominant occupations have relative lower earnings would also experience higher productivity loses. The analysis also focuses on the potential role of adaptation to reduce the productivity and economic losses, via air conditioning and a preliminary assessment of the potential of wearable robotics, which can reduce damages by 30%–40%.
Optimization for Injection Moulding process parameters towards Warpage and Shrinkage of HDPE-PBI composites
Authors: Ramesh M. , Panneerselvam K. 
Published: September 2021

Abstract
Injection moulding is a high-quality process that is preferred in modern manufacturing. Due to the defects in HDPE polymer composites the parameter’s had to be adjusted based on the necessity. Therefore, this study of parameters is to improve the quality characteristics of the injection moulding of HDPE-PBI composite samples. As the composition of HDPE composites varies, the quality aspects that must be controlled are shrinkage and warpage. The parameters that are considered as factors are Melting Temperature (°C), Injection pressure (MPa) and cooling time (sec). Using the Taguchi Optimization method of L9 Orthogonal array, the injection moulding process was performed and found that the injection pressure of 65 MPa and temperature of 180-185 °C showed better results. The influence of parameters was studied through this research using Analysis of Variance (ANNOVA).
Treatment of Dye Waste Water using Moving Bed Biofilm Reactor and Granular Activated Carbon with Neem Leaf Powder
Authors: Sasivarman B. , Dharani S. , Jothika A. , Mohana Priya D. 
Published: September 2021

Abstract
In recent days, the waste productions from industries are increased and direct discharge of wastes causes various pollution to the environment. Wastewater treatment in textile and dye industry mainly involves treatment of highly colored wastewater containing variety of dyes in different concentrations. The dyeing industries using different types of pigments and dyes of various concentration during their several production steps. These industries discharge a wastewater which contains organic substance, high colour with high COD and low concentration of BOD. This dye waste water are directly discharge in to the streams and thus contaminate the groundwater. To treat the wastewater, which is discharge from the dyeing industries a simple physio-chemical treatment is not sufficient for the removal of pollutants. So that a biological treatment followed with a physio-chemical treatment have been required. In this study, the experiment was carried out to reduce the organic substance, high colour, COD, BOD, and Heavy metals from dye wastewater using the laboratory scale Moving Bed Biofilm Reactor followed with Granular activated Carbon bed (GAC with NLP-MBBR) with Neem leaf powder. A various thick granular activated carbon bed was used for adsorption technique and for biological treatment of wastewater. The GAC with 8cm thick is layered followed by Ennore sand with 3 grades with 4cm thick equally. Then the MBBR was operated continuously with various retention times. The polypropylene type bio balls were used for microorganism attachment in MBBR. The outlet water from the MBBR are treated using membrane process. Membrane process using moving membranes reduces the pH, organic contents, heavy metals, COD, BOD, turbidity etc., The final effluent water is suitable for Irrigation.
Polyelectrolyte Complex for Drug Delivery in Biomedical Applications: A Review
Authors: Jagtap Pratik , Patil Kiran , Dhatrak Pankaj 
Published: September 2021

Abstract
There are so many significant developments made in drug delivery system (DDS) in which Polymer DDS has reached great developments. The main motive of this paper is to focus on DDS developing by Polyelectrolytes. Polymers have achieved much importance in DDS. Polyelectrolytes shows an exceptional category of polymer compounds containing of opposite charged polyions which are mainly cationic or anionic charges; they are hydrophilic in nature because of presence of opposite charge, can be used in various of applications in DDS. Polyelectrolytes (PE) exhibits various types of reactive groups that permits easy, adaptable modification with functional agents such as targeting molecules. Further, the oppositely charged nature exist in polyelectrolytes been used to transport macromolecules such as DNA and nanoparticles. Significance of smart polyelectrolyte is growing day by day from last several year. In the last several years, polyelectrolytes representing promising means for targeted based drug delivery and gene treatment. In the present paper, polyelectrolyte, polyelectrolyte complexes, deposition method for multilayer system, features, characterization of polyelectrolytes, and applications are reviewed.
Effect of Zirconium Dibromide and Aluminum Oxide Hybrid Particle Reinforcement on the Mechanical, Microstructure and Wear Properties of AA6063 Surface Metal Matrix Composite
Authors: Raja R. , Jannet Sabitha , Joy Christo , Johnson Jeffin 
Published: September 2021

Abstract
In the present study Aluminum 6063 as the matrix and with Zirconium dibromide and Aluminum oxide as the reinforcement particle Metal Matrix surface composites were fabricated by friction stir processing. By the addition of reinforcements such as Zirconium Dibromide and Aluminum Oxide used as the strengthening agents during FSP process certainly made the MMC more wear resistant and harder than the base matrix. The effect of varying reinforcement percentage on the Mechanical wear and microstructure properties were studied. The Micro hardness showed an increasing trend in the nugget zone. The wear rate decrease with increase in volume percentage of hybrid reinforcement. FESEM images of the samples displayed uniform distribution of the particle in the matrix. And also reiterated the formation of hybrid particles.
Basic properties of 𝑋 for which the space 𝐶_{𝑝}(𝑋) is distinguished
Authors: Ka̧kol Jerzy , Leiderman Arkady 
Published: September 2021

In our paper [Proc. Amer. Math. Soc. Ser. B 8 (2021), pp. 86–99] we showed that a Tychonoff spaceXXis aΔ\Delta-space (in the sense of R. W. Knight [Trans. Amer. Math. Soc. 339 (1993), pp. 45–60], G. M. Reed [Fund. Math. 110 (1980), pp. 145–152]) if and only if the locally convex spaceCp(X)C_{p}(X)is distinguished. Continuing this research, we investigate whether the classΔ\DeltaofΔ\Delta-spaces is invariant under the basic topological operations.

We prove that ifX∈ΔX \in \Deltaandφ:X→Y\varphi :X \to Yis a continuous surjection such thatφ(F)\varphi (F)is anFσF_{\sigma }-set inYYfor every closed setF⊂XF \subset X, then alsoY∈ΔY\in \Delta. As a consequence, ifXXis a countable union of closed subspacesXiX_isuch that eachXi∈ΔX_i\in \Delta, then alsoX∈ΔX\in \Delta. In particular,σ\sigma-product of any family of scattered Eberlein compact spaces is aΔ\Delta-space and the product of aΔ\Delta-space with a countable space is aΔ\Delta-space. Our results give answers to several open problems posed by us [Proc. Amer. Math. Soc. Ser. B 8 (2021), pp. 86–99].

LetT:Cp(X)⟶Cp(Y)T:C_p(X) \longrightarrow C_p(Y)be a continuous linear surjection. We observe thatTTadmits an extension to a linear continuous operatorT^\widehat {T}fromRX\mathbb {R}^XontoRY\mathbb {R}^Yand deduce thatYYis aΔ\Delta-space wheneverXXis. Similarly, assuming thatXXandYYare metrizable spaces, we show thatYYis aQQ-set wheneverXXis.

Making use of obtained results, we provide a very short proof for the claim that every compactΔ\Delta-space has countable tightness. As a consequence, under Proper Forcing Axiom every compactΔ\Delta-space is sequential.

In the article we pose a dozen open questions.
Cycle Threshold Probability Score for Immediate and Sensitive Detection of B.1.351 SARS-CoV-2 Lineage
Authors: De Smet Dieter , Vanhee Merijn , Maes Brigitte , Swaerts Koen , De Jaeger Peter , Maelegheer Karel , Van Hoecke Frederik , Martens Geert Antoine 
Published: September 2021

Abstract

Background
Detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern associated with immune escape is important to safeguard vaccination efficacy. We describe the potential of delayed N gene amplification in the Allplex SARS-CoV-2 Assay (Seegene) for screening of the B.1.351 (20H/501.V2, variant of concern 2 [VOC.V2], South African SARS-CoV-2 variant) lineage.

Methods
In a study cohort of 397 consecutive polymerase chain reaction–positive samples genotyped by whole-genome sequencing, amplification curves of E/N/S-RdRP targets indicated delayedN vs E gene amplification characteristic of B.1.351. Logistic regression was used to calculate a VOC.V2 probability score that was evaluated as a separate screening test in an independent validation cohort vs sequencing.

Results
B.1.351 showed a proportionally delayed amplification of the N vs E gene. In logistic regression, only N and E gene cycle thresholds independently contributed to B.1.351 prediction, allowing calculation of a VOC.V2 probability score with an area under the curve of 0.94. At an optimal dichotomous cutoff point of 0.12, the VOC.V2 probability score achieved 98.7% sensitivity at 79.9% specificity, resulting in a negative predictive value (NPV) of 99.6% and a positive predictive value of 54.6%. The probability of B.1.351 increased with an increasing VOC.V2 probability score, achieving a likelihood ratio of 12.01 above 0.5. A near-maximal NPV was confirmed in 153 consecutive validation samples.

Conclusions
Delayed N vs E gene amplification in the Allplex SARS-CoV-2 Assay can be used for fast and highly sensitive screening of B.1.351.
Differential RNA methylation using multivariate statistical methods
Authors: Ayyala Deepak Nag , Lin Jianan , Ouyang Zhengqing 
Published: September 2021

Abstract

Motivation
m6A methylation is a highly prevalent post-transcriptional modification in eukaryotes. MeRIP-seq or m6A-seq, which comprises immunoprecipitation of methylation fragments , is the most common method for measuring methylation signals. Existing computational tools for analyzing MeRIP-seq data sets and identifying differentially methylated genes/regions are not most optimal. They either ignore the sparsity or dependence structure of the methylation signals within a gene/region. Modeling the methylation signals using univariate distributions could also lead to high type I error rates and low sensitivity. In this paper, we propose using mean vector testing (MVT) procedures for testing differential methylation of RNA at the gene level. MVTs use a distribution-free test statistic with proven ability to control type I error even for extremely small sample sizes. We performed a comprehensive simulation study comparing the MVTs to existing MeRIP-seq data analysis tools. Comparative analysis of existing MeRIP-seq data sets is presented to illustrate the advantage of using MVTs.

Results
Mean vector testing procedures are observed to control type I error rate and achieve high power for detecting differential RNA methylation using m6A-seq data. Results from two data sets indicate that the genes detected identified as having different m6A methylation patterns have high functional relevance to the study conditions.

Availability
The dimer software package for differential RNA methylation analysis is freely available at https://github.com/ouyang-lab/DIMER.

Supplementary information
Supplementary data are available at Briefings in Bioinformatics online.
Improving cancer driver gene identification using multi-task learning on graph convolutional network
Authors: Peng Wei , Tang Qi , Dai Wei , Chen Tielin 
Published: September 2021

Abstract
Cancer is thought to be caused by the accumulation of driver genetic mutations. Therefore, identifying cancer driver genes plays a crucial role in understanding the molecular mechanism of cancer and developing precision therapies and biomarkers. In this work, we propose a Multi-Task learning method, called MTGCN, based on the Graph Convolutional Network to identify cancer driver genes. First, we augment gene features by introducing their features on the protein-protein interaction (PPI) network. After that, the multi-task learning framework propagates and aggregates nodes and graph features from input to next layer to learn node embedding features, simultaneously optimizing the node prediction task and the link prediction task. Finally, we use a Bayesian task weight learner to balance the two tasks automatically. The outputs of MTGCN assign each gene a probability of being a cancer driver gene. Our method and the other four existing methods are applied to predict cancer drivers for pan-cancer and some single cancer types. The experimental results show that our model shows outstanding performance compared with the state-of-the-art methods in terms of the area under the Receiver Operating Characteristic (ROC) curves and the area under the precision-recall curves.
The MTGCN is freely available via https://github.com/weiba/MTGCN.
A deep learning-based approach to model anomalous diffusion of membrane proteins: the case of the nicotinic acetylcholine receptor
Authors: Maizón Héctor Buena , Barrantes Francisco J 
Published: September 2021

Abstract
We present a concatenated deep-learning multiple neural network system for the analysis of single-molecule trajectories. We apply this machine learning-based analysis to characterize the translational diffusion of the nicotinic acetylcholine receptor at the plasma membrane, experimentally interrogated using superresolution optical microscopy. The receptor protein displays a heterogeneous diffusion behavior that goes beyond the ensemble level, with individual trajectories exhibiting more than one diffusive state, requiring the optimization of the neural networks through a hyperparameter analysis for different numbers of steps and durations, especially for short trajectories (&lt;50 steps) where the accuracy of the models is most sensitive to localization errors. We next use the statistical models to test for Brownian, continuous-time random walk and fractional Brownian motion, and introduce and implement an additional, two-state model combining Brownian walks and obstructed diffusion mechanisms, enabling us to partition the two-state trajectories into segments, each of which is independently subjected to multiple analysis. The concatenated multi-network system evaluates and selects those physical models that most accurately describe the receptor’s translational diffusion. We show that the two-state Brownian-obstructed diffusion model can account for the experimentally observed anomalous diffusion (mostly subdiffusive) of the population and the heterogeneous single-molecule behavior, accurately describing the majority (72.5 to 88.7% for α-bungarotoxin-labeled receptor and between 73.5 and 90.3% for antibody-labeled molecules) of the experimentally observed trajectories, with only ~15% of the trajectories fitting to the fractional Brownian motion model.
RNA Solutions: Synthesizing Information to Support Transcriptomics (RNASSIST)
Authors: Chen Yi-Pei , Ferguson Laura B , Salem Nihal A , Zheng George , Mayfield R Dayne , Eslami Mohammed 
Published: September 2021

Abstract

Motivation
Transcriptomics is a common approach to identify changes in gene expression induced by a disease state. Standard transcriptomic analyses consider differentially expressed genes (DEGs) as indicative of disease states so only a few genes would be treated as signals when the effect size is small, such as in brain tissue. For tissue with small effect sizes, if the DEGs do not belong to a pathway known to be involved in the disease, there would be little left in the transcriptome for researchers to follow up with.

Results
We developed RNA Solutions: Synthesizing Information to Support Transcriptomics (RNASSIST), a new approach to identify hidden signals in transcriptomic data by linking differential expression and co-expression networks using machine learning. We applied our approach to RNA-seq data of post-mortem brains that compared the Alcohol Use Disorder (AUD) group with the control group. Many of the candidate genes are not differentially expressed so would likely be ignored by standard transcriptomic analysis pipelines. Through multiple validation strategies, we concluded that these RNASSIST-identified genes likely play a significant role in AUD.

Availability and implementation
The RNASSIST algorithm is available at https://github.com/netrias/rnassist and both the software and the data used in RNASSIST are available at https://figshare.com/articles/software/RNAssist_Software_and_Data/16617250.

Supplementary information
Supplementary data are available at Bioinformatics online.
This is how it Feels: Activating Lived Experience in the Penal Voluntary Sector
Authors: Buck Gillian , Tomczak Philippa , Quinn Kaitlyn 
Published: September 2021

Abstract
Increasing calls for ‘nothing about us without us’ envision marginalized people as valuable and necessary contributors to policies and practices affecting them. In this paper, we examine what this type of inclusion feels like for criminalized people who share their lived experiences in penal voluntary sector organizations. Focus groups conducted in England and Scotland illustrated how this work was experienced as both safe, inclusionary and rewarding and exclusionary, shame-provoking and precarious. We highlight how these tensions of ‘user involvement’ impact criminalized individuals and compound wider inequalities within this sector. The individual, emotional and structural implications of activating lived experience, therefore, require careful consideration. We consider how the penal voluntary sector might more meaningfully and supportively engage criminalized individuals in service design and delivery. These considerations are significant for broader criminal justice and social service provision seeking to meaningfully involve those with lived experience.
Spatial-temporal evolution and diversification inSisyrinchium(Iridaceae) with emphasis on abiotic drivers
Authors: Thode Verônica A , Dellanhese Inácio Camila , Eggers Lilian , Reginato Marcelo , Souza-Chies Tatiana T 
Published: September 2021

AbstractEvolutionary and ecological processes that influenced the assembly of the New World flora are best understood through investigation of spatio-temporal processes of specific lineages, but some groups still lack a historical overview. Here, we produced a well-sampled dated tree, reconstructed ancestral ranges and performed diversification analyses for Sisyrinchium (Iridaceae) to elucidate its evolution in the Americas. Eight molecular markers and samples representing its full geographical range and morphological diversity were used to estimate divergence times with a Bayesian relaxed clock with secondary calibrations. Ancestral range reconstruction under likelihood methods and diversification analyses were performed. Sisyrinchium originated in a broad range including the Andes and Mesoamerica in the Mid-Miocene. Diversification at high elevations occurred in the early diverging lineages, which feature the highest extinction rates. Increase in diversification rate was detected during the Pliocene/Pleistocene after the colonization of lower elevations. Later sympatric speciation in south-eastern Brazil was followed by movements to other regions, including a long-dispersal event to North America. Higher extinction rates were followed by movements to lower elevations, with periods of accelerated Andean orogeny and global temperature decrease. Our results indicate that palaeoclimate and changes in elevational range influenced diversification in Sisyrinchium.
Neuronal Oscillatory Signatures in the Developing Mouse Visual Cortex After Short-Term Monocular Deprivation
Authors: Malik Anju , Eldaly Abdelrahman B M , Chen Ke , Chan Leanne Lai-Hang 
Published: September 2021

Abstract
Development and maturation in cortical networks depend on neuronal activity. For stabilization and pruning of connections, synchronized oscillations play a crucial role. A fundamental mechanism that enables coordinated activity during brain functioning is formed of synchronized neuronal oscillations in low- (delta and theta) and high- (gamma) frequency bands. The relationship between neural synchrony, cognition, and the perceptual process has been widely studied, but any possible role of neural synchrony in amblyopia has been less explored. We hypothesized that monocular deprivation (MD) during early postnatal life would lead to changes in neuronal activity that would be demonstrated by changes in phase-amplitude coupling (PAC) and altered power in specific oscillatory frequency. Our results demonstrate that functional connectivity in the visual cortex is altered by MD during adolescence. The amplitude of high-frequency oscillations is modulated by the phase of low-frequency oscillations. Demonstration of enhanced delta–gamma and theta–gamma PAC indicates that our results are relevant for a broad range of nested oscillatory markers. These markers are inherent to neuronal processing and are consistent with the hypothesized increase in the intrinsic coupling that arises from neural oscillatory phase alignment. Our results reveal distinct frequency bands exhibit altered power and coherence variations modulated by experience-driven plasticity.