bluuebunny/crossref_metadata_2025_split huggingface dataset:
Selection, identification and optimization of lactic acid bacteria with high γ-aminobutyric acid production
Authors: Rehman Ateequr , Benedetto Giulio Di , Bird Julia K. , Dabene Valentina , Vadakumchery Lisa , May Ali , Schyns Ghislain , Sybesma Wilbert , Mak Tim N. 
Published: May 2023

Abstract
Lactic acid bacteria produce γ-aminobutyric acid (GABA) as an acid stress response. GABA is a neurotransmitter that may improve sleep and resilience to mental stress. This study focused on the selection, identification and optimization of a bacterial strain with high GABA production, for development as a probiotic supplement. The scientific literature and an industry database weresearched for probiotics and potential GABA producers. In silico screening was conducted to identify genes involved in GABA production. Subsequently, 17 candidates were screened for in vitro GABA production using thin layer chromatography, which identified three candidate probiotic strains Levilactobacillus brevis DSM 20054, Lactococcus lactis DS75843and Bifidobacterium adolescentis DSM 24849 as producing GABA. Two biosensors capable of detecting GABA were developed: 1. a transcription factor-based biosensor characterized by the interaction with the transcriptional regulator GabR was developed in Corynebacterium glutamicum; and 2. a growth factor-based biosensor was built in Escherichia coli, which used auxotrophic complementation by expressing 4-aminobutyrate transaminase (GABA-T) that transfers the GABA amino group to pyruvate, hereby forming alanine. Consequently, the feasibility of developing a workflow based on co-culture with producer strains and a biosensor was tested. The three GABA producers identified and the biosensors were encapsulated in nanoliter reactors (NLRs) as alginate beads in defined gut-like conditions. The E. coli growth factor-based biosensor was able to detect changes in GABA concentrations in liquid culture and under gut-like conditions. L. brevis and L. lactis were successfully encapsulated in the NLRs and showed growth under miniaturized intestinal conditions.
Gate Engineered Ferroelectric Junctionless BioFET for Label-Free Detection of Biomolecules
Authors: yadav Snehlata , Rewari Sonam , Pandey Rajeshwari 
Published: May 2023

Abstract
A Gate Engineered Ferroelectric Junctionless BioFET is proposed and investigated for label-free detection of various biomolecules. A nanocavity is created by etching a part of the gate oxide material on the top and bottom of the device, which allows biomolecules to get immobilized. The immobilization of biomolecules in the cavity causes changes in electrostatic characteristics such as surface potential, input and output characteristics, transconductance, output conductance, gate capacitance, and cut-off frequency used as sensing metrics. The biosensor is also examined at different biomolecule concentrations, such as -1e12, 0, and 1e12. The transistor's sensitivity is then understood by looking at the fluctuation in threshold voltage, subthreshold swing, and switching ratio. Ferroelectric Junctionless BioFET and Gate Engineered Ferroelectric Junctionless BioFET performances have been compared. It has been found that the Gate Engineered Ferroelectric Junctionless BioFET shows the maximum improvement for protein (1202.4%, 111%, and 565%) and DNA (787.5%, 117.3%, and 600%). For ultrasensitive bio-sensing applications, the Gate Engineered Ferroelectric Junctionless BioFET is shown to be suitable.
Investigation of molecular details of a bacterial cationic amino acid transporter (GkApcT) during arginine transportation using molecular dynamics simulation and umbrella sampling techniques
Authors: Afshinpour Maral , Parsi Parinaz , Mahdiuni Hamid 
Published: May 2023

Abstract

Context: Cationic amino acid transporters (CATs) facilitate arginine transport across membranes and maintain its levels in various tissues and organs, but their overexpression has been associated with severe cancers. A recent study identified the alternating access mechanism and critical residues involved in arginine transportation in a cationic amino acid transporter from Geobacillus kaustophilus (GkApcT). Here, we used molecular dynamics (MD) simulation methods to investigate the transportation mechanism of arginine (Arg) through GkApcT. The results revealed that arginine strongly interacts with specific binding site residues (Thr43, Asp111, Glu115, Lys191, Phe231, Ile234, and Asp237). Based on the umbrella sampling, the main driving force for arginine transport is the polar interactions of the arginine with channel-lining residues. An in-depth description of the dissociation mechanism and binding energy analysis brings valuable insight into the interactions between arginine and transporter residues, facilitating the design of effective CAT inhibitors in cancer cells.Methods The membrane-protein system was constructed by uploading the prokaryotic CAT (PDB ID: 6F34) to the CHARMM-GUI web server. Molecular dynamics simulations were done using the GROMACS package, version 5.1.4 with the CHARMM36 force field and TIP3 water model. The MM-PBSA approach was performed for determining the arginine binding free energy. Further, the hotspot residues were identified through per-residue decomposition analysis. The characteristics of the channel such as bottleneck radius, and channel length were analyzed using the CaverWeb 1.1 webserver. The proton wire inside the transporter was investigated based on the classic Grotthuss mechanism. We also investigated the atomistic details of arginine transportation using the path-based free energy umbrella sampling technique (US).
TorchLens: A Python package for extracting and visualizing hidden activations of PyTorch models
Authors: Taylor JohnMark , Kriegeskorte Nikolaus 
Published: May 2023

Abstract
Deep neural network models (DNNs) are essential to modern AI and provide powerful models of information processing in biological neural networks. Researchers in both neuroscience and engineering are pursuing a better understanding of the internal representations and operations that undergird the successes and failures of DNNs. Neuroscientists additionally evaluate DNNs as models of brain computation by comparing their internal representations to those found in brains. It is therefore essential to have a method to easily and exhaustively extract and characterize the results of the internal operations of any DNN. Many models are implemented in PyTorch, the leading framework for building DNN models. Here we introduce TorchLens, a new open-source Python package for extracting and characterizing hidden-layer activations in PyTorch models. Uniquely among existing approaches to this problem, TorchLens has the following features: (1) it exhaustively extracts the results of all intermediate operations, not just those associated with PyTorch module objects, yielding a full record of every step in the model's computational graph, (2) it provides an intuitive visualization of the model's complete computational graph along with metadata about each computational step in a model's forward pass for further analysis, (3) it contains a built-in validation procedure to algorithmically verify the accuracy of all saved hidden-layer activations, and (4) the approach it uses can be automatically applied to any PyTorch model with no modifications, including models with conditional (if-then) logic in their forward pass, recurrent models, branching models where layer outputs are fed into multiple subsequent layers in parallel, and models with internally generated tensors (e.g., injections of noise). Furthermore, using TorchLens requires minimal additional code, making it easy to incorporate into existing pipelines for model development and analysis, and useful as a pedagogical aid when teaching deep learning concepts. We hope this contribution will help researchers in AI and neuroscience understand the internal representations of DNNs.
Будущее приватности сквозь призму теории постнормальности
Authors: Sukhorolskyi Petro 
Published: May 2023

The theory of postnormal times by Ziauddin Sardar is an attempt to explain the social processes occurring when traditional models, theories, and paradigms seem powerless in the face of looming challenges. Postnormal times are an intermediate period when old ideas, theories, and practices are dying and new ones have not yet been formed, and the whole world seems to be plunged into uncertainty and chaos. Privacy is one of the areas where the situation is considered to be already postnormal. Therefore, the article attempts to outline the current state and prospects related to privacy and personal data protection, based on the methodology developed within the theory of postnormal times.
Complexity, chaos, and contradictions are the main forces propelling postnormal times. All of them are very perceptible in the sphere of privacy. In particular, the outcomes of the major current strategies for the legal protection of personal data are ambiguous and controversial. It relates to the consent of the data subject as one of the main legal grounds for personal data processing, as well as the use of data anonymization and pseudonymization techniques, or the introduction of mechanisms of external risk assessment. In addition, it is argued that in the present world there is a fundamental contradiction between privacy and underlying trends in business and governance, the causes of which are revealed by applying Lawrence Lessig’s theory of four regulators.

Under such conditions, traditional ways of responding to challenges – turning back, increasing control and complexity of measures – not only contribute to normalization, but drive the system even further into a state of postnormalcy. It is accompanied by the exacerbation of identified critical contradictions and revealing of the new ones that increases the likelihood of collapse of the existing system of privacy protection in the near future. Therefore, it is necessary to develop and promote innovative approaches which include both reforms in the field of privacy regulation and efforts to change the external environment.
Design and analysis of experimental adaptive feedback system for active noise control (ANC) in a duct
Authors: Anachkova Maja , Pecioski Damjan , Domazetovska Simona , Shishkovski Dejan 
Published: May 2023

The limitations of passive noise control methods impose a need for new technical solutions to solve the problem of reducing low-frequency noise, which is considered to be a dominant component of noise disturbance. In recent years, the subject of intensive research are the active noise control systems, which have aroused considerable interest and represent a promising solution to the problem of low-frequency noise control. This paper proposes a robust methodology for simplified design and analysis of an experimental active noise control system for real-time control of acoustic environment in a duct. The proposed feedback control model is based on using the LMS algorithm, combined with FxLMS algorithm for estimation and neutralization of the secondary path in the electro-acoustic system. The study shows the potential of the FPGA module and the Real-time module of cRIO from National Instruments, combined with the LabView software environment when applied in adaptive system for active noise control. The reliability and validity of the developed active noise control system is tested for a frequency range of 100 to 1000 [Hz], by measuring the amplitude-time domain in [V] and sound level in [dB]. The comparison of the experimental results shows great efficiency of the system at lower frequency range from 200 to 400 [Hz], where a maximum reduction in sound level achieved at a frequency of 200 [Hz] is 14 [dB] or 17 [%]. A significant sound level reduction is also achieved at both 300 [Hz] and 400 [Hz] which is 12 % or 10 [dB] in both cases. Given the analysis of the challenges and opportunities of the developed active noise control system, recommendations for advancements and future work are proposed.
Enhancing environmental sustainability in eastern Canada's corn agroecosystem with controlled drainage and subsurface irrigation
Authors: Sun Ran , Kulshreshtha Surendra Nath , Crézé Cynthia M. , Madramootoo Chandra A. 
Published: May 2023

Abstract

Water table management through controlled tile drainage and subsurface irrigation (CDSI), retrofitting to conventional tile drainage, has been developed to abate the environmental impacts of irrigation and drainage meanwhile supporting agroecosystems and crop productivity. Since the environmental profile of new technologies is a prerequisite to understanding their socio-economic benefits, a life cycle assessment was conducted to assess the environmental impacts of CDSI on corn production for the 2014 and 2015 growing seasons at St-Emmanuel, south-western Quebec in eastern Canada, compared to the free drainage (FD). Inventory flows of corn production with CDSI and FD were developed using biophysical data from field experiments and public databases. Then, environmental impacts were compared for corn production with CDSI and FD, including climate change, eutrophication potential, acidification potential, and toxicity. The assessment results show the environmental benefits of implementing CDSI, particularly in improving water quality. However, potential synergy and trade-offs of climate change, eutrophication, and acidification impacts from the implementation of CDSI, especially under different climatic conditions, should be further monitored to improve the performance of the technology. Nevertheless, CDSI and associated practices can be adopted as adaptation measures in agricultural water management to support agroecosystems and address the challenges posed by environmental impacts.
Downstream hydrochemistry and irrigation water quality of the Syr Darya, Aral Sea Basin, South Kazakhstan
Authors: Satybaldiyev Bagdat , Ismailov Baimurat , Nurpeisov Nurbek , Kenges Kairat , Snow Daniel D. , Malakar Arindam , Taukebayev Omirzhan , Uralbekov Bolat 
Published: May 2023

Abstract

New hydrochemical measurements from the Syr Darya provide insights into factors affecting the composition and quality of a major freshwater source replenishing the Aral Sea. This river is heavily used for power and irrigation and crosses territories of four Central Asia republics. It is intensely managed, draining several major tributaries, many reservoirs, and numerous irrigation distribution systems and canals. Analysis of seasonal changes in dissolved ion concentrations using geochemical diagrams, elemental ratios, statistical correlation, and equilibrium modeling allowed the characterization of mineral formation processes that control the dissolved chemical composition. Measured water hydrochemistry and composition type differs substantially from previous reports from the upper reaches of the Syr Darya in Kyrgyzstan. Element ratios, statistical correlation, and the presence of NO3- and NO2- suggest that the downstream trend of increasing total dissolved solids (TDS) from Zhetysay city to the Aral Sea in June is controlled by evaporation–crystallization processes, which contribute to the river dissolved load through soil runoff and return irrigation waters following leaching of secondary salts. Downstream sample composition during the growing season shows significant changes in magnesium-to-calcium ratios in the river water. Elevated magnesium levels in Syr Darya waters may pose a problem to sustainable uses for irrigation.
A Study Against Colon Cancer Mechanism of Xanthium sibiricum Herba
Based on Computer Simulation and Bioinformatics
Authors: Qi Ying , Cai Jia-Han , Deng Qiu-Tong , Zeng Yuan-Ning , Wang Qiu-Hong 
Published: May 2023

Introduction:
Cancer is one of the leading causes of death worldwide, accounting for
nearly one in six deaths in 2020. As a folk medicine, Xanthium sibiricum Herba (XSH) has been
used many times in clinical practice for the treatment of various diseases. With the increasing
number of cancer patients, there is a clinical need to find effective anti-cancer drugs.

Aim:
This study aims to explores the bioactivity and the anti-cancer mechanism of XSH.

Methods:
In this study, bioinformatics, network pharmacology, molecular docking, molecular dynamics
simulation techniques, and apoptosis assay were used to explore the bioactivity and the anti-
cancer mechanism of XSH.

Results:
Finally, seven active ingredients in XSH after the screening were obtained, the two most
active compounds were β-sitosterol and aloe-emodin, and good anti-cancer activity of XSH was
predicted.

Discussion:
Four core targets were obtained from the PPI network map, namely Caspase-3
(CASP3), Transcription factor AP-1 (JUN), Myc proto-oncogene protein (MYC), and cellular tumor
antigen p53 (TP53). GO and KEGG analyses showed that the mechanism of XSH anti-cancer
is mainly related to the apoptosis process, and the main signaling pathways are enriched in the p53
signaling pathway, Apoptosis, and MAPK signaling. The molecular docking and molecular dynamics
simulation results showed that CASP3, JUN, MYC, and TP53 had a high affinity with β-
sitosterol and aloe-emodin. Bioinformatics analyses demonstrated the importance of core targets.
Apoptosis assay showed that XSH could significantly promote the apoptosis of cancer cells, and
inhibit their proliferation and migration, especially colon cancer cells.

Conclusion:
This study uncovered the main active components, bioactivities, and potential targets
of XSH, and further revealed the multi-component, multi-target, and multi-pathway mechanism of
XSH for cancer treatment and promoting apoptosis.
Connectivity between Gut Microbiota and Terminal Awakenings in
Alzheimer’s Disease
Authors: Bostancıklıoğlu Mehmet 
Published: May 2023

Abstract:
Memory is empirically described as a brain function that connects the past to the present.
This reductionist approach has focused on memory function within neurons and synapses, leading to
an understanding that memory loss in dementia is caused by irreversible neuronal damage. However,
recent palliative case reports and the Human Connectome Project have challenged the "irreversible"
paradigm by indicating that some demented patients are able to retrieve supposed ‘lost’ memories
and cognitive functions near death. The serotonin-centric hypothesis and the lifelong oligodendrocyte
differentiation capacity may explain terminal awakening symptoms in these patients. Furthermore, an
increased rate of serotonin-secreting and oligodendrocyte precursor cell-triggering gut bacteria near
death temporally correlates with lucid improvements in demented patients. These findings may shift
the context of terminal memory retrieval from a purely neuronal to a systemic idea that bridges terminal
lucidity and gut microbiota. In this review, we take the systemic approach further and point out
a temporal correlation between the gut microbiome and terminal lucid episodes in Alzheimer’s patients.
Predicting Herb-disease Associations Through Graph Convolutional
Network
Authors: Hu Xuan , Lu You , Tian Geng , Bing Pingping , Wang Bing , He Binsheng 
Published: May 2023

Background:
In recent years, herbs have become very popular worldwide as a form of complementary
and alternative medicine (CAM). However, there are many types of herbs and diseases,
whose associations are impossible to be fully revealed. Identifying new therapeutic indications of herbs,
that is drug repositioning, is a critical supplement for new drug development. Considering that exploring
the associations between herbs and diseases by wet-lab techniques is time-consuming and laborious,
there is an urgent need for reliable computational methods to fill this gap.

:
In this study, we first preprocessed the herbs and their indications in the TCM-Suit database, a comprehensive,
accurate, and integrated traditional Chinese medicine database, to obtain the herb-disease association
network. We then proposed a novel model based on a graph convolution network (GCN) to infer
potential new associations between herbs and diseases.

Methods:
In our method, the effective features of herbs and diseases were extracted through multi-layer
GCN, then the layer attention mechanism was introduced to combine the features learned from multiple
GCN layers, and jump connections were added to reduce the over-smoothing phenomenon caused by
multi-layer GCN stacking. Finally, the recovered herb-disease association network was generated by the
bilinear decoder. We applied our model together with four other methods (including SCMFDD, BNNR,
LRMCMDA, and DRHGCN) to predict herb-disease associations. Compared with all other methods,
our model showed the highest area under the receiver operating characteristic curve (AUROC), the area
under the precision-recall curve (AUPRC), as well as the highest recall in the five-fold cross-validation.

Conclusion:
We further used our model to predict the candidate herbs for Alzheimer's disease and
found the compounds mediating herbs and diseases through the herb-compound-gene-disease network.
The relevant literature also confirmed our findings.
Mechanical Properties and Molecular Transport Behavior of NR/Clay and
ENR/Clay Composites
Authors: Reghunath Rakesh , Mathew Aji P. , George Soney C. 
Published: May 2023

Introduction:
Natural rubber and epoxidized natural rubber composites reinforced with
clay have been synthesized in this study using two roll mixing mill.

Objective:
The aim of the research work was to investigate the influence of clay on the overall performance
of NR and ENR based composites.

Methods:
The effect of clay concentration on basic mechanical properties and molecular transport
phenomena of the elastomeric composites was analyzed. Tensile strength and tear strength variation
as a function of clay concentration were studied.

Results:
It was found that as clay concentration increased up to 4 phr, tensile strength improved up
to 22.5 MPa and tear strength up to 32 MPa, beyond which it went down. It was also found that
corresponding to 4 phr clay concentrations, the crosslink density of the composite was at a maximum
of 1.45 gmol/cc.

Conclusion:
As the clay contents are added into the elastomeric matrix system, initially there is a
decrease in the swelling coefficient, but later at higher loading, the swelling coefficient increases
due to the agglomeration of clay particles in the matrix.
Finite Element Analysis of Reinforced Concrete Beams Strengthened with
Hybrid Fiber Reinforced Polymer Systems using ANSYS
Authors: Gurram Kalyani , N. Pannirselvam 
Published: May 2023

Background:
Existing reinforced concrete (RC) structures can deteriorate over time
due to aging, poor construction design, natural disasters, etc. In recent years, fiber-reinforced polymer
(FRP) composite materials are becoming a preferred choice for concrete construction repair
due to their durability, high strength, and corrosion resistance. This study aimed to study and analyze
the properties of the constituent materials to identify any weaknesses and potential improvements.

Methods:
The present study investigated the effectiveness of flexural strengthening of RC beams
using a hybrid grouping of glass-FRP (GFRP) and carbon-FRP (CFRP) unidirectional laminates.
ANSYS finite element analysis (FE) software was used to investigate the failure modes of the
beams and the stress-strain parameters. The impact of adopting two different grades of reinforcing
bars in RC beam modeling was also contrasted in the study.

Results:
Comparisons between the finite element analysis and experimental literature results were
made. Based on the test findings, it could be concluded that retrofitted beams perform better than
non-retrofitted beams. According to experimental results, the HY14 sheet enhanced beam had a
188.46% higher ultimate load than the unenhanced beams.

Conclusion:
Comparing experimental findings to the conclusions of the numerical analysis, a
maximum difference of ultimate load and deflection at mid-span of 3.40% and 4.91%, respectively,
were used to assess the accuracy of the results.