Miracle-dz/UniFAR-FTUs huggingface dataset:
Bactericidal efficacy of plasma-activated water against Vibrio parahaemolyticus on Litopenaeus vannamei

In this study, the antimicrobial mechanism of plasma-activated water (PAW) against Vibrio parahaemolyticus and the effectiveness of PAW in artificially contaminated Litopenaeus vannamei were investigated. The results demonstrated a significant reduction (p < 0.05) in viable counts of V. parahaemolyticus with increasing plasma discharge time (5, 10, 20, and 30 min) and PAW immersion time (3, 5, 10, 20, and 30 s). Specifically, the count of V. parahaemolyticus decreased by 2.1, 2.7, 3.3, and 4.4 log CFU/mL after exposed to PAW 5, PAW 10, PAW 20, and PAW 30 for 30 s, respectively. Significant cell surface wrinkling, accompanied by notable nucleic acid and protein leakage were observed after treatment with PAW. The permeability of the inner and outer cell membranes was significantly increased (p < 0.05), along with an increase in electrical conductivity (p < 0.05). The reactive oxygen species (ROS) within V. parahaemolyticus cells were significantly increased (p < 0.05), while superoxide dismutase (SOD) activity, and the relative expression of the ompW, emrD, and luxS genes were significantly decreased (p < 0.05). A reduction number of 1.3, 1.8, 2.1, and 2.2 log CFU/g of V. parahaemolyticus in artificially contaminated L. vannamei was obtained with PAW for 5 min. The study elucidated that PAW could destroy cell membranes, leading to cell death. The findings would strengthen strategies for V. parahaemolyticus control and provide a potential application of PAW for preserving aquatic products.
Recent Advances in Fluorescent Probes for Cancer Biomarker Detection

Many important biological species have been identified as cancer biomarkers and are gradually becoming reliable targets for early diagnosis and late therapeutic evaluation of cancer. However, accurate quantitative detection of cancer biomarkers remains challenging due to the complexity of biological systems and the diversity of cancer development. Fluorescent probes have been extensively utilized for identifying biological substances due to their notable benefits of being non-invasive, quickly responsive, highly sensitive and selective, allowing real-time visualization, and easily modifiable. This review critiques fluorescent probes used for detecting and imaging cancer biomarkers over the last five years. Focuses are made on the design strategies of small-molecule and nano-sized fluorescent probes, the construction methods of fluorescence sensing and imaging platforms, and their further applications in detection of multiple biomarkers, including enzymes, reactive oxygen species, reactive sulfur species, and microenvironments. This review aims to guide the design and development of excellent cancer diagnostic fluorescent probes, and promote the broad application of fluorescence analysis in early cancer diagnosis.
Habitat suitability models for two species of forest raptors in Catalonia. Methodological consequences related with different scales and data sources

Species distribution along a territory is a function of different environmental variables that changes in space and time. In this communication we use GIS based information from different sources and at different scales (1x1 km 2 and 10x10 km 2 ) for elaborating habitat suitability models of two forest raptors species (Buteo buteo and Accipiter gentilis) along a north-south gradient in Cataluna. The area of study has an extent of 7000 km 2 . Forest raptors species presence/absence data on 679 1x1 km 2 grid cells of the Catalan Breeding Bird Atlas, and Spanish Forest Map at a scale 1:50,000 are the main information sources. Logistic regression methods have been essayed for comparison across different scales. Unexpectedly, preliminary results show no relation between variables like land use type or diversity of habitats and raptors presence at 1x 1 km 2 , and this relationship is only significant for Buteo buteo at 10 x 10 km 2
SMMF: a self-attention-based multi-parametric MRI feature fusion framework for the diagnosis of bladder cancer grading

Background Multi-parametric magnetic resonance imaging (MP-MRI) may provide comprehensive information for graded diagnosis of bladder cancer (BCa). Nevertheless, existing methods ignore the complex correlation between these MRI sequences, failing to provide adequate information. Therefore, the main objective of this study is to enhance feature fusion and extract comprehensive features from MP-MRI using deep learning methods to achieve an accurate diagnosis of BCa grading. Methods In this study, a self-attention-based MP-MRI feature fusion framework (SMMF) is proposed to enhance the performance of the model by extracting and fusing features of both T2-weighted imaging (T2WI) and dynamic contrast-enhanced imaging (DCE) sequences. A new multiscale attention (MA) model is designed to embed into the neural network (CNN) end to further extract rich features from T2WI and DCE. Finally, a self-attention feature fusion strategy (SAFF) was used to effectively capture and fuse the common and complementary features of patients’ MP-MRIs. Results In a clinically collected sample of 138 BCa patients, the SMMF network demonstrated superior performance compared to the existing deep learning-based bladder cancer grading model, with accuracy, F1 value, and AUC values of 0.9488, 0.9426, and 0.9459, respectively. Conclusion Our proposed SMMF framework combined with MP-MRI information can accurately predict the pathological grading of BCa and can better assist physicians in diagnosing BCa.
Changes of chlorophyll a fluorescence parameters in water limited maize IBM population

Drought is the second most important cause of yield loss after low soil fertility. Aim of this study was to investigate changes in chlorophyll a fluorescence parameters of maize subjected to drought stress during tasseling. Results have shown that all selected parameters in drought stressed maize differ significantly from control. Negative effects can be placed in three groups: inactivation of reaction centers turning them into heat sinks (decrease in RC/ABS, increase in DIo/RC), impairment of OEC (oxygen evolving center) (increase in F300), decrease in electron transport efficiency and related problems in QA (primary quinonce acceptor) and plastoquinone oxidation.
Assessment of Common Factors Associated with Droplet Digital PCR (ddPCR) Quantification of Paratrichodorus allius in Soil

This research investigated the factors associated with the quantitative detection of Paratrichodorus allius in soil using droplet digital PCR (ddPCR). Small-sized nematodes exhibited significantly lower DNA quantities compared to their medium and large counterparts. Soil pre-treatments (room temperature drying and 37 °C oven-drying) demonstrated no substantial impact on ddPCR detection, and soil storage (0–3 months at 4 °C) exhibited negligible alterations in DNA quantities. A commercial DNA purification kit improved the resulting quality of ddPCR, albeit at the cost of a notable reduction in DNA quantity. Upon assessing the impact of inhibitors from soil extracts, a higher inhibitor concentration (5%) influenced ddPCR amplification efficiency. Incorporating bovine serum albumin (BSA) (0.2 μg/μL or 0.4 μg/μL) into the ddPCR setup mitigated the issue. In brief, while ddPCR exhibits minimal sensitivity to soil pre-treatments and storage, higher concentrations of PCR inhibitors and the DNA purification process can influence the results. Despite ddPCR’s capability to detect nematodes of all sizes, quantification may not precisely reflect soil population. Incorporating BSA into the ddPCR setup enhances both detection and quantification capacities. This study represents the first comprehensive investigation of its kind for plant-parasitic nematodes, providing crucial insights for application of ddPCR in nematode diagnosis directly from the soil DNA.
Modelling and automated calibration of a general multi‐projective camera

Recently, multi‐projective cameras (MPCs), often based on frame‐mounted multiple cameras with a small baseline and arbitrary overlap, have found a remarkable place in geomatics and vision‐based applications. This paper outlines the geometric calibration of a general MPC by presenting a mathematical model that describes its unknown generic geometry. A modified bundle block adjustment is employed to calibrate an industrial‐level 360° non‐metric camera. The structure of any MPC can be retrieved as a calibration set of relative and interior orientation parameters (as well as the pose of the MPC shots) using a calibration room which has been accurately determined by close range photogrammetry. To demonstrate the efficiency and precision of the model, a Panono camera (an MPC with 36 individual cameras) was calibrated. After the adjustment, sub‐pixel image residuals and acceptable object‐space errors were observed.
Distributed Cooperative Driving Strategy for Connected Automated Vehicles at Unsignalized Intersections Based on Monte Carlo Method

One of the most important goals of cooperative driving is to control connected automated vehicles (CAVs) passing through conflict areas safely and efficiently without traffic signals. As a typical application scenario, allocating right-of-way reasonably at unsignalized intersections can effectively avoid collisions and reduce traffic delays. Proposed here is a new cooperative driving strategy for CAVs at unsignalized intersections based on distributed Monte Carlo tree search (MCTS). A task-area partition framework is also proposed to decompose the mission of cooperative driving into three main tasks: vehicle information sharing, passing order optimization, and trajectory control. Based on the schedule tree of the vehicle passing order, the root parallelization of MCTS combined with the majority voting rule is used to explore as many feasible passing orders (leaf nodes) as possible in a distributed way and find a nearly global-optimal passing order within the limited planning time. The aim is for CAVs to perform proper trajectory adjustments based on the obtained passing order to minimize traffic delays while making the slightest acceleration adjustments. A coupled simulation platform integrating SUMO and Python is developed to construct the unsignalized intersection scenarios and generate the proposed distributed cooperative driving strategy. Comparative analysis with conventional driving strategies demonstrates that the proposed strategy significantly enhances efficiency, safety, comfort, and emission, aligning well with innovative and environmentally friendly urban mobility aspirations.
A Dynamic Collision Risk Assessment Model for the Traffic Flow on Expressways in Urban Agglomerations in North China

Expressways in urban agglomerations are important in connecting cities, thus attracting great attention from researchers in the expressways risk assessment. However, there is a lack of safety assessment models suitable for the characteristics of expressways in Chinese urban agglomerations, and the nature and mode of dynamic risks on Chinese highways are still unclear. Therefore, this study adopts the Adaptive Neural Fuzzy Inference System (ANFIS) and the method of decision tree, combined with data from the Beijing section of the Beijing Harbin Expressway, to model the risk of accident-prone highways in urban agglomerations. To determine the optimal model, we evaluated the model’s bias at different time intervals. In addition, key factors affecting highway safety were analyzed, providing scientific support for the risk prevention of highways in urban agglomerations in China.
The benefits of purple sweet potato waste flour in the ratio of maintaining the quality of Lohmann brown chicken eggs

Lohmann brown chickens are laying hens as a source of egg supply. However, eggs are easily damaged if stored, it is necessary to improve egg quality, one of which can be done by utilizing purple sweet potato waste. This research aims to determine the effect of using purple sweet potato waste flour in rations on the quality of Lohmann brown chicken eggs stored for 5 weeks, carried out in Candiksuma Village, Melaya District, Jembrana Regency Bali for 12 weeks. The design used was a completely randomized design (CRD) consisting of 4 treatments and 4 replications. The treatment consisted of rations without purple sweet potato waste flour as a control (A), and rations containing 5% purple sweet potato waste flour (B), 10% (C), and 15% (D). Each experimental unit consisted of 10 chickens aged 65 weeks. After one month of treatment, 96 eggs were taken to be stored. The variables observed were egg weight, egg pH, egg specific gravity, egg yolk color, eggshell thickness, egg white percentage, egg yolk percentage, egg white index, egg yolk index, and haugh units (HU). The research results showed that giving purple sweet potato waste flour did not reduce egg quality in the variables of egg weight, egg pH, egg specific gravity, egg yolk percentage, egg white index, and egg yolk index during 5 weeks of storage. A decrease in egg quality occurs in egg yolk color, eggshell thickness, egg white percentage, and Haugh Units (HU). It can be concluded that providing purple sweet potato waste flour in the ration can maintain the quality of Lohmann brown chicken eggs stored for 5 weeks.
22q11 deletion syndrome: current perspective

Chromosome 22q11 is characterized by the presence of chromosome-specific low-copy repeats or segmental duplications. This region of the chromosome is very unstable and susceptible to mutations. The misalignment of low-copy repeats during nonallelic homologous recombination leads to the deletion of the 22q11.2 region, which results in 22q11 deletion syndrome (22q11DS). The 22q11.2 deletion is associated with a wide variety of phenotypes. The term 22q11DS is an umbrella term that is used to encompass all 22q11.2 deletion-associated phenotypes. The haploinsufficiency of genes located at 22q11.2 affects the early morphogenesis of the pharyngeal arches, heart, skeleton, and brain. TBX1 is the most important gene for 22q11DS. This syndrome can ultimately affect many organs or systems; therefore, it has a very wide phenotypic spectrum. An increasing amount of information is available related to the pathogenesis, clinical phenotypes, and management of this syndrome in recent years. This review summarizes the current clinical and genetic status related to 22q11DS.
Reaction Norm of Embryo Growth Rate Dependent On Incubation Temperature in the Olive Ridley Sea Turtle, Lepidochelys Olivacea, From Pacific Central America

Olive ridleys (Lepidochelys olivacea) and loggerheads (Caretta caretta) are two closely phylogenetically related sea turtles that nest in very different thermal habitat. Olive ridleys nest in pan-tropical beaches whereas loggerheads nest in more temperate beaches. In the context of climate change, the temperature in temperate beaches will increase much more than for tropical beach due to buffering effect of air humidity in the later. We have determined the thermal reaction norm for embryonic growth in both species using field records of incubation temperatures and incubation length from loggerheads in Western Mediterranean Sea or olive ridleys from Pacific coast of Guatemala. We show that the optimum temperature for the growth of embryos is lower for loggerheads than for olive ridleys. This makes loggerhead turtles particularly sensitive to increase of beach temperature as it is expected due to effect of global warming in temperature regions. Furthermore, olive ridleys are more resilient to increase of temperatures and should not suffer from sublethal incubation temperatures. DOI : COMING SOON Corresponding author: Marc Girondot, Laboratoire Écologie, Systématique et Évolution (UMR8079), Faculté des Sciences d’Orsay, Université Paris-Sud, 91405 Orsay, France Phone +33 6 20 18 22 16, Fax: +33 1 69 15 56 96. Email: marc.girondot@u-psud.fr Running title: Reaction norm of embryo growth rate
Effects of Meteorological Factors on Asthma Hospital Visits in Haikou City, China

The effects of meteorological conditions on asthma in Haikou, a tropical city in China, are still unclear. This study aimed to determine the relationships between meteorological factors and the number of asthma hospital visits in Haikou. A Poisson generalized additive model combined with a distributed lag nonlinear model is used to model the nonlinear exposure–response relationship between the daily mean temperature and asthma hospital visits. The daily mean pressure and air quality are used as covariates and simultaneously control the mixed effects of holiday effects, weekend effects, and long-term trends. The results indicate that there is a significant statistical relationship between the daily mean temperature and asthma hospital visits, which shows an inverted J-shaped relationship. When the daily mean temperature is below the reference value (29.3 °C), the number of asthma patients increases considerably, and there is a marked lag in the prevalence of asthma. The longest lag is 9 days, and the most pronounced impact of the daily mean temperature on the number of asthma hospital visits can be found when the lag time is 1–4 days. When the daily mean temperature is 10 °C, the cumulative effect of the relative risk of asthma is 2.204, an increase of 120.4% (95% CI 1.294–3.755). If the daily mean temperature is below the 2.5th percentile value (14.8 °C), the relative risk significantly increases by more than 5.3% (95% CI 1.000–1.110), and the longest lasting impact time is 5 days. This indicates that increases in asthma hospital visits in Haikou, China, are significantly correlated with low-temperature weather. We suggest that preventive measures for asthma should take low-temperature weather into account. Additionally, we also found that extremely high temperatures have a certain impact on the increase in asthma hospital visits, but that the correlation is not significant.
ANALYSIS OF SPATIAL REQUIREMENT FOR CONFINEMENT CENTRE TO SUPPORT THE NEEDS OF WOMEN WITH POSTPARTUM DEPRESSION

Women during the postpartum period are vulnerable to the problems of mental health. Having manifold emotional, behavioural, and physiological changes in their lives, postpartum women experience negative impacts on their thought, feeling, behaviours, and relationships with others. Some women need special rehabilitative spaces to reduce the negative impacts of postpartum depression. Architectural solutions with designated spatial provisions may help postpartum women to curb the negativity during their confinement stage. Hence, this research intends to analyse the spatial requirement of confinement facilities based on precedent studies that support the psychological and physical needs of postpartum depression women. This research was conducted using a qualitative approach, where precedent studies of postpartum confinement centres were analysed and compared via table research to generate spatial requirements for the effective rehabilitation of postpartum depression women. Researchers conducted interview sessions with experts to substantiate the finding of earlier comparisons. Researchers hoped that the finding of this research could outline the optimum spatial requirements for the design of a confinement centre to support the psychological and physical needs of postpartum depression women, especially those in Malaysia. The research would also serve as a platform for an effective architectural solution for reducing the negative impacts of postpartum depression among women.
Drowning in the Information Flood: Machine-Learning-Based Relevance Classification of Flood-Related Tweets for Disaster Management

In the early stages of a disaster caused by a natural hazard (e.g., flood), the amount of available and useful information is low. To fill this informational gap, emergency responders are increasingly using data from geo-social media to gain insights from eyewitnesses to build a better understanding of the situation and design effective responses. However, filtering relevant content for this purpose poses a challenge. This work thus presents a comparison of different machine learning models (Naïve Bayes, Random Forest, Support Vector Machine, Convolutional Neural Networks, BERT) for semantic relevance classification of flood-related, German-language Tweets. For this, we relied on a four-category training data set created with the help of experts from human aid organisations. We identified fine-tuned BERT as the most suitable model, averaging a precision of 71% with most of the misclassifications occurring across similar classes. We thus demonstrate that our methodology helps in identifying relevant information for more efficient disaster management.
The Effect of Financial Development on Economic Growth; Incorporation with Impact of the COVID-19 Pandemic: Evidence from Asian Frontier Financial Markets

This study aimed to investigate how financial development affects economic growth and explore the relationship between financial development and economic growth. The sample considered for the study were Asian frontier financial markets (Sri Lanka, Bangladesh, Pakistan, and Vietnam). The considered period ranged from 1992 to 2021. This study uses a quantitative approach, and the research logic is deductive because the hypotheses are made in the study and the results are assessed. Panel regression is used as the main statistical analysis model. This study concludes that financial development significantly impacts economic growth in the Asian frontier financial markets. The results show both negative and positive figures in individual independent variables. But when those results are considered combined, it offers a positive impact. In addition to the main analysis, another analysis was done to find the impact of the COVID-19 pandemic on the economic growth in Asian frontier financial markets. The results show that the COVID-19 pandemic has had a significant negative impact on the economic growth of the sample selected. The significance of this study is to analyze the impact over 30 years and measure the impact of the COVID-19 pandemic simultaneously.
Impact of reflecting land surface on radiation environment over Hornsund, Spitsbergen – a model study for cloudless skies

: This paper addresses the influence of land topography and cover on 3D radiative effects under cloudless skies in the Hornsund area, Spitsbergen, Svalbard. The authors used Monte Carlo simulations of solar radiation transfer over a heterogeneous surface to study the impact of a non-uniform surface on: (1) the spatial distribution of irradiance transmittance at the fjord surface under cloudless skies; (2) the spectral shortwave aerosol radiative forcing at the fjord surface; (3) normalized nadir radiance at the Top Of the Atmosphere (TOA) over the fjord. The modelled transmittances and radiances over the fjord are compared to the transmittances and radiances over the open ocean under the same conditions. The dependence of the 3D radiative effects on aerosol optical thickness, aerosol type, surface albedo distribution, solar azimuth and zenith angle and spectral channel is discussed. The analysis was done for channels 3 (459–479 nm) and 2 (841–876 nm) of the MODIS radiometer. In the simulations a flat water surface was assumed. The study shows that snow-covered land surrounding the fjord strongly modifies the radiation environment over the fjord surface. The enhancement of the mean irradiance transmittance over the fjord with respect to the open ocean is up to 0.06 for channel 3. The enhancement exceeds 0.11 in the vicinity of sunlit cliffs. The influence of the snow-covered land on the TOA radiance over the fjord in channel 3 is comparable to the impact of an increase in aerosol optical thickness of over 100%, and in lateral fjords of up to several hundred percent. The increase in TOA radiance is wavelength dependent. These effects may affect retrievals of aerosol optical thickness.