bluuebunny/crossref_metadata_2025_split huggingface dataset:
Co-culture of postnatal mouse spinal cord and skeletal muscle explants as an experimental model of neuromuscular interactions
Authors: Mikhailova Mariya M. , Klein Olga I. , Patsaev Timofey D. , Panteleyev Andrey A. 
Published: August 2024

Abstract

The intercommunication between nerves and muscles plays an important role in the functioning of our body, and its failure leads to severe neuromuscular disorders such as spinal muscular atrophy, amyotrophic lateral sclerosis etc. Understanding the cellular and molecular mechanisms underlying nerve-muscle interactions and mediating their mutual influence is an integral part of strategies aimed at the cure of neuromuscular diseases. Here, we propose a novel ex vivo experimental model for the spinal cord (SC) and skeletal muscle interactions which for the first time utilizes only fully formed postnatal tissues. The model represents an organotypic co-culture comprising a longitudinal slice of the mouse SC and an extensor digitorum longus (EDL) muscle explant placed in the “damage zone” of transversally dissected SC. Using this model we have shown that SC tissue stimulates muscle contractions, affects AChR distribution on muscle surface and directs cell migration from the muscle tissue. In turn, EDL muscles stimulate the growth of SC-derived neurites. Thus, our organotypic model allows to assess the mutual influence of neurons and muscles in an nearly natural setting which maintains the architecture and cellular composition of intact tissues. Therefore, this model may provide an effective platform for studying molecular and cellular mechanisms linked to defective neuro-muscular interactions in associated pathologies.
Pathogenicity of tick-derived lymphocytic choriomeningitis virus in BALB/c mice
Authors: Liu Ziyan , Liang Xiaojie , Li Liang , Liu Ning , Wang Zedong , Wei Feng 
Published: August 2024

Abstract

Background Lymphocytic choriomeningitis virus (LCMV) is a zoonotic pathogen primarily transmitted by rodents. Recently, we identified LCMV in ticks in northeastern China; however, the pathogenicity of the virus in mice remains unknown.Results Here, we examined the tick-derived LCMV strain JX14 by inoculating BALB/c mice with 3.5×105 PFU of virus. The mice infected with LCMV displayed clinical manifestations including unkempt fur, anorexia, depression, and oliguria, which subsequently resolved by 10 days post infection (dpi) leading to survival of the infection. Viral RNA was detectable in throat and anal swabs during the early phase of infection and exhibited proliferation in Vero cells. Viral RNA was detected in the blood and various organs, with detectable levels persisting for up to 6 months specifically in the heart. Pathological lesions were identified in most organs within 5 dpi except for the kidneys and testicles. Elevated production of pro-inflammatory cytokines, including interleukin (IL)-5, IL-6, keratinocyte chemoattractant (KC), and interferon gamma (IFN-γ), was observed during the early stages of infection.Conclusions This study provides crucial insights into the pathogenic characteristics of LCMV strain JX14, isolated from ticks in northeastern China, thereby offering a valuable in vivo research tool for investigating immunological responses induced by chronic viral infections.
Climate change and socioeconomic drivers equally influence PM 2.5 : 23-year analysis using geographically and temporally weighted regression model
Authors: Rehman Saqib , Usmani Bilal , Ahmed Muhammad , Naz Shumaila , Ahmed Zafar , Fatmi Zafar 
Published: August 2024

Abstract

Background: Fine particulate matter (PM <2.5µm), key air pollution indicator, affects not just health but also climate and vice versa. Identifying its drivers in megacities is vital for designing interventions. Few studies have explored the variations in PM2.5 and its causes across time and location, including climate and socioeconomic factors.
Method: We adopted a geographically and temporally weighted regression model (GTWR) to explore the direction and strength of the influences of climate change and socioeconomic variables on the concentration of PM2.5 in one of the highest polluted megacities (Lahore), covering the period 1998 to 2020.
Result: Lahore's PM2.5 concentration jumped from an average of 63.4 to 87.5 µg/m3 over 23 years. Among climatic factors, higher windspeed has a strong negative effect (coefficient range: -0.75 to -0.25) and humidity has moderate variable negative to no influence (coefficient range: -0.5 to 0.0) on PM2.5 in the city. Temperature and rainfall had a small mixed (positive and negative) influence, depending on specific locations in the city on PM2.5 (coefficient range: -0.25 to 0.25). Low winter windspeed was the key driver of increased PM2.5. Among socioeconomic factors, road density overall positive association (coefficient range: 0.9 to 0.7), land use has positive and negative association (coefficient range: 0.9 to -0.9), population density primarily has positive association (-0.4 to 0.9), FDI has positive association (coefficient range: 0.7 to 0.9) and GDP has positive association (coefficient range: 0.5 to 0.8) with PM2.5.
Conclusion: Lahore's rising PM2.5 is linked to local emissions due to socioeconomic factors, urban changes, and climatic factors over 23 years. Climate change and socioeconomic factors like windspeed, GDP, and population density all play a role. This model can explain historical and spatial PM2.5 drivers, aiding in designing solutions for Lahore.
Translocation and dissipation of seven indicator polychlorinated biphenyls from contrast soils cultivated with different root vegetables
Authors: Kosnar Zdenek , Tlustos Pavel 
Published: August 2024

Abstract

The behaviour of 7 polychlorinated biphenyls (PCBs) in agricultural Chernozem and Fluvisol soils from the Czech Republic planted with radishes, onions and carrots was studied. Only the total biomass carrot yield was significantly (P < 0.05) reduced when compared to the control biomass yield. All tested PCBs, especially congener PCB 28, were able to accumulate in slightly higher amounts in the radish surface than in the onion one. The highest PCB content was observed in the surface layer of carrots, which could be related to the agrochemical properties of the soil, vegetation period and more intensive contact of carrot roots with PCBs in a bulk soil. The translocation factors of PCBs were lower than 1, indicating a poor ability for PCB uptake from soil. The highest relative PCB removal from soil was observed in acidic Fluvisol, accounting for 27.2%, with a maximum of 2% plant contribution to soil PCB dissipation. Non-significant (P < 0.05) PCB removal from vegetable-planted soils indicated that root vegetables were not able to enhance the autochthonous soil microorganisms to degrade PCBs by releasing exudates in the rhizosphere of vegetables. Bioaccumulation PCB factors derived from the cultivation of examined root vegetables in Chernozem and Fluvisol soils contaminated with PCBs at nearly 1500 µg/kg dw can be considered as low and negligible.
Development of high-resolution 2D-LIF temperature system: application on a non-isothermal turbulent jet
Authors: Helfer Vincent , Cariteau Benjamin , Fillion Philippe , Toutant Adrien 
Published: August 2024

Abstract

Laser-induced fluorescence (LIF) visualization techniques are used to quantify a wide range of aqueous flows, whether monophasic or two-phase. Depending on the resolution of the cameras used, these techniques generate a large amount of data that can be used to further study the behavior of a flow. The techniques use a laser sheet to stimulate fluorescent dyes seeded in the flow, resulting in fields of local measurements, depending on the thickness of the laser sheet, and offering a variety of possibilities for comparison with different digital resolution tools. Specifically, this paper presents a two-dye LIF (2D-LIF) temperature measurement system that exploits the variation of the photo-luminescence properties of two different fluorescent dyes with temperature, allowing quantitative measurement from the recorded fluorescence intensity. The system consists of high-resolution cameras (65 Mpxl) and a pulsed Nd-YAG laser, providing instantaneous and spatially resolved measurements. The present study describes the development of the 2D-LIF system and explains the choice of experimental setups for the most accurate and precise measurements. It then applies the system to measure the temperatures of several non-isothermal and turbulent jets. Time-averaged measurements are presented according to the physical model proposed by Morton et al. (1956), calibrated here with Particle Image Velocimetry (PIV) measurements. Radial turbulent temperature fluctuation distributions are estimated and compared in accordance with the literature.
A Research on a Method of Calculating Pixel Coordinates of Projectile Explosion Position Based on MCF-U-net Explosive Firelight Image Segmentation Algorithm
Authors: Liang Shaopeng , Li Hanshan 
Published: August 2024

Abstract

Aiming at the difficulties existing in the test of projectile explosion position of long-range artillery, the paper proposes an image recognition method of projectile explosion fire based on MCF-U-net network, which mainly optimises and improves the original U-net network from the perspectives of model lightweighting and the accuracy of recognition of weak fire information in the beginning stage of the explosion. In addition, after segmenting and recognising the projectile explosion fire image, we base on the spatial characteristics of the downward dive of the firelight from a projectile near-ground explosion, proposing a method of calculating the pixel coordinates of the projectile explosion position based on the judgement criterion of the initial stage of the projectile explosion in consecutive multi-frame frequency and the threshold-constrained top-mean method of the firelight. By setting a threshold to define the initial and the mutation phase of explosion, the approximation of the pixel coordinates of the projectile explosion position is based on a process of successive frame-by-frame analysis of the projectile explosion fire between this two phases. Through experimental comparison and analysis, it is found that the proposed recognition algorithm of projectile explosion firelight can reach a mIoU of 81.39%, which provides a basis for the projectile fuzing control detonation judgement.
Assessment of MALDI-TOF MS for arthropod identification based on exuviaes
Authors: Bouledroua Rym , Diarra Adama Zan , Amalvict Remy , Berenger Jean-Michel , Benakhla Ahmed , Parola Philippe , ALMERAS Lionel 
Published: August 2024

Abstract

Background.
MALDI-TOF MS is an innovative tool for identifying both hematophagous and non-hematophagous arthropods at various life stages. However, identification by MALDI-TOF MS requires currently, euthanizing of the specimen, hindering further phenotypic tests. All arthropods have a common factor which is the molting of their exoskeletons, called exuviae. This phenomenon is indispensable for their growth and metamorphosis, and can evidence past arthropod presence.
Objective.
This study assessed the performance of MALDI-TOF MS biotyping for arthropod identification using exuviae from nine distinct laboratory-reared species (Aedes aegypti, Anopheles coluzzii, Cimex lectularius, C. hemipterus, Pediculus humanus corporis, Triatoma infestans, Rhodnius prolixus, Supella longipalpa and Blatta germanica) and compared it efficiency with molecular biology approach.
Results.
Molecular analysis showed low DNA quantity in exuviae (n = 108) across species, resulting low success of COI, 16s, and 18s amplification (50.0%), depending on the species and sequencing (10.2%). The establishment of exuviae protocol for MS submission, yielded MS spectra of high reproducibility and specificity per species. After upgrading home made reference MS database with exuviae spectra, query with remaining spectra revealed that 100% of samples were correctly identified, with 85.8% (278/324) exceeding the threshold score value for reliable identification.
Conclusion.
MALDI-TOF MS shown it high efficiency to identify various arthropod species based on their exuviae. This approach is a groundbreaking development in the field of entomology underlining that MALDI-TOF outperformed traditional methods of exuviae identification, including morphological and molecular tools. It allows also to prevent specimen sacrifice which could be used for complementary analyses.
SkillAR: Omnipresent In-Situ Feedback for Motor Skill Training using AR
Authors: Diller Florian , Henkel Nico , Scheuermann Gerik , Wiebel Alexander 
Published: August 2024

Abstract

We present a novel technique called SkillAR to display augmented reality feedback for motor skill learning on a head-mounted display (HMD). SkillAR allows the user to consider movement corrections independent of the head position. Therefore, the user can receive motor feedback comfortably without risking an incorrect exercise performance.
Head-mounted displays represent versatile technologies for providing motor feedback regarding skill training. In contrast to room-mounted displays, HMDs are easily portable and wearable. That allows for in-situ feedback in many situations where this would otherwise not be possible. However, the spatial positioning of the 3D feedback is not trivial. On the one hand, the user needs to understand the relation between the body and suggested correction in space. On the other hand, certain exercises demand a specific head positioning to minimize errors and injuries. Depending on the exercise and the type of feedback, these two aspects can be highly conflicting. The paper at hand presents a solution for augmented reality headsets, that provides continuous and omnipresent motor feedback comfortably while facilitating a correct exercise performance.
In a user study we verify that SkillAR, given the apparent major advantages for user health and usability, is not disadvantageous compared to conventional feedback methods found in the literature regarding identification and execution time as well as identification accuracy. Furthermore, we found that the users could identify the feedback on the HMD more accurately.
How well do climate modes explain precipitationvariability?
Authors: Hobeichi Sanaa , Abramowitz Gab , Gupta Alex Sen , Taschetto Andréa , Richardson Doug , Rampal Neelesh , Ayat Hooman , Alexander Lisa , Pitman Andy 
Published: August 2024

Abstract

Large-scale modes of climate variability, such as the El Niño-Southern Oscillation, North Atlantic Oscillation, and Indian Ocean Dipole, show significant regional correlations with seasonal weather conditions, and are routinely forecast by meteorological agencies attempting to anticipate seasonal precipitation patterns. Here, we use machine learning together with more traditional approaches to quantify how much precipitation variability can be explained by large-scale modes of variability, and to understand the degree to which these modes interact non-linearly. We find that the relationship between climate modes and precipitation is predominantly non-linear. In some regions and seasons climate modes can explain up to 80% of precipitation variability. However, variability explained is below 10% for more than half of the land surface, and only 1% of the land shows values above 50%. This outcome provides a clear rationale to limit expectations of predictability from modes of variability in all but a few select regions and seasons.
Individual differences in prosocial learning are represented in the hippocampal dorsal CA1
Authors: Scheggia Diego , Greca Filippo La , Zianni Elisa , Coccia Giulia , Cerchiaro Ilaria , Gusmini Michela , Castoldi Carlo , Maggioni Davide , Silva Bianca , Gardoni Fabrizio , DiLuca Monica 
Published: August 2024

Abstract

Learning from others is among the main expressions of humans’ social nature. Evolution has favored this capacity, which is conserved across mammals, for its environmental advantages and benefits for the species compared to trial-and-error learning. Animals can learn about danger by observing their conspecifics, but if or how they acquire prosocial behaviors is not understood. Here, we found that mice by observing a demonstrator learned to take actions that benefit others, which were goal-directed and flexible. We found that the hippocampal dorsal CA1 (dCA1) activity is required to learn a given observed prosocial behavior. Chemogenetic silencing of the dCA1 impaired the acquisition of a social preference in mice with high prosocial tendency. Fiber-photometry recording revealed individual differences in dCA1 activity patterns during observation that closely tracked the observer's later prosocial or selfish behavioral propensities. Optogenetic manipulations demonstrated that the dCA1 is key in acquiring observed social behaviors and can orient a mouse’s actions toward prosocial or selfish choices in future interactions. Our study provides a model of social transmission of knowledge that guides prosocial behavior in mice that can be relevant for studying neuropsychiatric and neurodevelopmental disorders, in which the ability to learn from others’ actions is often compromised.
Sustained Freshening of Arabian Sea High Salinity Water Induced by Extreme Tropical Cyclone Precipitation
Authors: Thoppil Prasad 
Published: August 2024

Abstract

In the northern Arabian Sea, high salinity levels are primarily sustained by year-round evaporation, driving the convective formation of Arabian Sea High Salinity Water (ASHSW) during the winter monsoon (November – February). Although precipitation has largely been discounted as a critical controlling mechanism for winter convection, recent years have seen a notable increase in extreme cyclones over the Arabian Sea, particularly in post-monsoon cyclones (September – December) since 2014. However, the extent to which these cyclone-induced freshwater inputs disrupt the region's freshwater balance (evaporation – precipitation) and impact ASHSW formation remains unclear. Here, we present observational evidence supported by a suite of model simulation experiments, revealing a significant weakening in ASHSW formation triggered and sustained by extreme tropical cyclones. The addition of freshwater reduces the density of high-salinity water, augmenting stratification and disrupting the convective sinking process, ultimately limiting the depth of convective mixing. This strengthened stratification stabilizes the water column, exacerbating warming trends and destabilizing the freshwater balance between the Arabian Sea and the Bay of Bengal. These findings underscore the profound implications of extreme cyclone-induced freshwater inputs.
Shear Strain Control of Multiferroic Magnetic Ordering for Giant Ferromagnetism
Authors: Zhang Linxing , Xi Guoqiang , Fang Yue-Wen , Zheng Dongxing , Xu Shuai , Li Hangren , Tu Jie , Zhu Fangyuan , Liu Xudong , Liu Xiuqiao , Zhang Xixiang , Jin Kuijuan , Tian Jianjun 
Published: August 2024

Abstract

The magnetic ordering of perovskite ferroelectric oxides is essential for enhancing their stability and minimizing energy losses in magnetoelectric devices. However, inducing a transition from a magnetically disordered state to an ordered one remains a formidable challenge. Here, we propose a chemical sulfurization method that significantly bolsters the magnetic ordering of multiferroic super-tetragonal phase BiFeO3 thin film, thereby enhancing the magnetic properties. The sulfured films exhibit a robust magnetic transition temperature of 586 K. The remanent magnetization increases approximately 1.6 times in the out-of-plane direction and an impressive 62 times in the in-plane direction. Additionally, the magnetic easy axis transitions from the out-of-plane to the in-plane direction. The X-ray absorption spectroscopy and atomic scale investigation reveal a reconfiguration of the local electronic hybridization states in the film. The sulfur-induced shear strain is identified as the catalyst for a shift in the Fe–O hybridization, from the pyramid-like geometry of FeO5 to the octahedral arrangement of FeO6. This transformation is deemed the root cause of the observed magnetic transition in the films. This sulfur-induced strategy for electronic hybridization reconfiguration is expected to break new ground, offering innovative methodologies for modulating perovskite oxides, two-dimensional ferroelectric films, and other ferromagnetic functional thin films.
Spectral Similarity Based Multiscale Spatial-Spectral Preprocessing Framework for Hyperspectral Image Classification
Authors: AKYÜREK Hasan Ali , KOÇER Barış 
Published: August 2024

Abstract

Hyperspectral imaging represents an advanced technology that offers an extensive array of spectral data concerning various materials. Each pixel within a hyperspectral image encompasses reflectance or transmittance values spanning a spectrum of wavelengths, thereby constructing a spectral signature or spectral curve. Despite the high spectral resolution inherent in hyperspectral images, their spatial resolution frequently remains limited, resulting in a mixture of spectral information within the spectral signatures. This situation presents a significant obstacle to achieving precise hyperspectral image classification, given that both spectral and spatial information play pivotal roles in this endeavor. In the present investigation, a novel spectral-spatial preprocessing strategy is introduced, employing a multiscale filtering technique based on spectral similarity to enhance the accuracy of hyperspectral image classification. The methodology entails performing a neighborhood operation for each target pixel vector, predicated on their spectral resemblance. This operation assigns higher priority to more similar pixels within the neighborhood window to establish the new spectral curve of the pixel of interest. The resultant spectral curves effectively amalgamate both spatial and spectral information and are subsequently utilized during the classification process instead of the original spectral curves. The study incorporates established spectral similarity metrics alongside an innovative metric grounded in Fréchet distance to calculate spectral similarities. The outcomes derived from these metrics are juxtaposed to assess their efficacy in ameliorating the accuracy of hyperspectral image classification. Moreover, the classification performance is evaluated utilizing kernel extreme learning machine and support vector classifiers across four distinct hyperspectral image datasets. The findings underscore that, particularly when confronted with constraints related to small sample sizes, the proposed spectral-spatial preprocessing technique markedly enhances the classification accuracy of hyperspectral images.
Penentuan Portofolio Saham Optimal Menggunakan Metode Markowitz Sebagai Dasar Keputusan Investasi
Authors: Nisardi Muhammad Rifki , Husain Hartina , Kusnaeni Kusnaeni , Resky Aprizal 
Published: August 2024

A stock portfolio is a combination of two or more securities invested over a specific period and under certain conditions. This study analyzes the combination of stocks that can be formed into an optimal portfolio using the Markowitz method. The Markowitz method is employed to maximize returns and minimize the risks associated with a portfolio. This method uses a mathematical formulation that allows for adjustments based on risk tolerance levels and expected returns to achieve an optimal portfolio. The data used in this study comprises the monthly closing prices of stocks from five selected companies, namely ICBP, BBCA, TLKM, BBNI, and INCO, for the period from June 2019 to December 2022. The findings indicate a recommended portfolio with the lowest risk, known as the Minimum Variance Portfolio (MVP). The MVP comprises the following proportions: ICBP 36.10%, BBCA 36.28%, TLKM 17.84%, INCO 8.39%, and BBNI 1.39%. This combination of stock proportions yields an expected return of 8.58% and a portfolio risk of 21.52%.Keywords: Markowitz method, Minimum Variance Portfolio, Portfolio Optimization.