Miracle-dz/UniFAR-FTUs huggingface dataset:
Guidance on harmonised methodologies for human health, animal health and ecological risk assessment of combined exposure to multiple chemicals

Abstract This Guidance document describes harmonised risk assessment methodologies for combined exposure to multiple chemicals for all relevant areas within EFSA's remit, i.e. human health, animal health and ecological areas. First, a short review of the key terms, scientific basis for combined exposure risk assessment and approaches to assessing (eco)toxicology is given, including existing frameworks for these risk assessments. This background was evaluated, resulting in a harmonised framework for risk assessment of combined exposure to multiple chemicals. The framework is based on the risk assessment steps (problem formulation, exposure assessment, hazard identification and characterisation, and risk characterisation including uncertainty analysis), with tiered and stepwise approaches for both whole mixture approaches and component‐based approaches. Specific considerations are given to component‐based approaches including the grouping of chemicals into common assessment groups, the use of dose addition as a default assumption, approaches to integrate evidence of interactions and the refinement of assessment groups. Case studies are annexed in this guidance document to explore the feasibility and spectrum of applications of the proposed methods and approaches for human and animal health and ecological risk assessment. The Scientific Committee considers that this Guidance is fit for purpose for risk assessments of combined exposure to multiple chemicals and should be applied in all relevant areas of EFSA's work. Future work and research are recommended.
Eliminating "systematic survivorship bias" in the attitude of specialists to the significance of investment attractive features of examined objects

Methods for detecting and eliminating false measurements are well known in the theory of metrology. However, the relevant methodology is not adapted to the needs of the qualitative assessment of the impact of the human factor on expert decision-making. The "systematic survivorship bias" refers to the following: when involving experts in conducting examinations, they usually focus on that part of them, where statistically probable agreed opinions are observed based on the results of these examinations. Other experts are considered "marginal", their opinions are discarded and not taken into account, which defines the "systematic survivorship bias", also called the "paradox of information availability". Although the specified "marginality" may be a consequence of the unique experience of conducting examinations or, for example, the use of modern technologies by a specific specialist, little known to the general public. It should be noted that manipulation of statistical data with an orientation only on "successful" cases could be really dangerous, for example, in studies of the human factor in complex ergastic active and organizational management systems, in particular aviation. The rationale and implementation of the algorithm for detecting and eliminating the "systematic survivorship bias" have been given in this paper. m=90 specialists who are usually involved in various examinations by UkrINTEI took part in the research. The actual elimination of the "systematic survivorship bias" occurs after the implementation of a certain number of iterations of the algorithm given in the current work. As a result of iterations of the above-mentioned algorithm, it was established that four subgroups can be distinguished from the initial sample with the number of m=90, with the following numbers: mC=30 people, mH=12 people, mM=11 people, mT=6 people. For the specified subgroups, the consistency of group opinions satisfies the entire spectrum of hypothesis testing criteria established in this paper
Antagonistic effectiveness of Anacardium occidentale leaf extract on lead-acetate exposure-induced hepatorenal toxicity in rats

Lead (Pb) poisoning is an environmental substance that accumulates in the hepato-renal tissue, which is hazardous to health, while Anacardium occidentale L. is a tropical herb used to treat oxidative stress and inflammatory diseases. The aim of this study was to investigate the antagonistic effect of Anacardium occidentale leaf extract on lead acetate exposure-induced hepatorenal toxicity in rats. Thirty-six adult Wistar rats were split into six equal groups (n = 6). Group I served as a control, and groups II and III were administered lead acetate (50 mg/kg) and Anacardium occidentale leaf extract (400 mg/kg), respectively, while rats in groups IV–VI were administered Anacardium occidentale (L) extract (200 mg/kg and 400 mg/kg) and 10 mg/kg of Succimer, respectively, and were then administered lead acetate (50 mg/kg). When compared to the group I, rats administered lead acetate showed an increase in hepatic enzymes, urea, creatinine, MDA, TNF-α, and IL-1β (p < 0.001) levels and decreased levels of SOD, CAT, and GSH, whereas Anacardium occidentale prevented the increase in hepatorenal function parameters, oxidative stress, and inflammatory markers (TNF-α and IL-1β) induced by lead acetate. Rats administered only lead acetate had a marked increase in hepatic Pb concentration, severe hepatic steatosis, and renal glomerulus degeneration. However, treatment with Anacardium occidentale extract and succimer decreases the Pb concentration, oxidative stress, and inflammation, and also reduces histological liver steatosis and glomerular cytoarchitecture deterioration in the kidney. The results of this study revealed that Anacardium occidentale extract protects against lead acetate-induced liver and kidney toxicity by decreasing oxidative stress and inflammation.
ScholarWorks@GVSU ScholarWorks@GVSU

Rhododendron species serve an important ecological and economic role in the mountains of the Eastern Himalayas. Recent changes in this once-pristine landscape have resulted in the classification of many Rhododendron species as threatened or endangered. This study used field surveys and ecological niche modeling to expand existing knowledge about Rhododen-dron species’ distributions in the region, with a view to helping develop strategies for conservation action and possible rein-troductions.
BERT-Based Model for Aspect-Based Sentiment Analysis for Analyzing Arabic Open-Ended Survey Responses: A Case Study

Educational institutions typically gather feedback from beneficiaries through formal surveys. Offering open-ended questions allows students to express their opinions about matters that may not have been measured directly in closed-ended questions. However, responses to open-ended questions are typically overlooked due to the time and effort required. Aspect-based sentiment analysis is used to automate the process of extracting fine-grained information from texts. This study aims to 1) examine the performance of different BERT-based models for aspect term extraction for Arabic text sourced from educational institution surveys; 2) develop a system that automates the ABSA process in a way that will automatically label survey responses. An end-to-end system was developed as a case study to extract aspect terms, identify their polarity, map extracted aspects to their respective categories, and aggregate category polarity. To accomplish this, the models were evaluated using an in-house dataset. The result showed that FAST-LCF-ATEPC, a multilingual checkpoint, outperformed other models including AraBERT, MARBERT, and QARiB, in the aspect-term extraction task, with an F1 score of 0.58. Hence, it was used for aspect-term polarity classification, showing an F1 score of 0.86. Mapping aspects to their respective categories using a predefined list yielded an average F1 score of 0.98. Furthermore, the polarities of the categories were aggregated to summarize the overall polarity for each category. The developed system can support Arabic educational institutions in harnessing valuable information in responses to open-ended survey questions, allowing decision-makers to better allocate resources, and improve facilities, services, and students’ learning experiences.
Testing the SEDD model in sicilian basins

The Sediment Delivery Distributed (SEDD) model combines the universal soil loss equation (USLE) or the revised universal soil loss equation (RUSLE) with a sediment delivery ratio to predict basin sediment yield. The model was applied to seven Sicilian basins, ranging in size from 20 to 213 km 2 . Each basin has a reservoir located at its outlet. The model was applied to each basin using a raster scheme, and a subroutine of Arclnfo software to identify the hydraulic path linking each hillslope cell to the nearest stream cell, and to calculate both the travel time and the sediment delivery ratio of each cell. A procedure for estimating the β coefficient, which appears in the expression for determining the sediment delivery ratio of each cell, was developed. As the sedimentation volume for each of the seven reservoirs was known, it was possible to compare measured values with those calculated by the SEDD model. The agreement between measured sediment yield values and calculated ones confirmed the reliability of the proposed estimation procedure and indicates that it should be possible to apply the SEDD model to ungauged basins.
Impact of biotic and abiotic environmental variables on the toxic potential of the cyanobacterium Microcystis sp.

Toxic cyanobacteria such as Microcystis aeruginosa are a concern in freshwaters all over the world. Laboratory gene expression studies using a novel RT-qPCR assay showed that some climate change-related environmental variables and co-occurring microorganisms can increase its toxigenicity. The link between abiotic and biotic environmental factors and bloom toxicity was also analyzed in field monitoring studies of two recreational lakes.
Effect of Plant Nutrients on Fruit Yield and Quality of Cavendish Banana (Musa sp.) cv. ‘Grand Naine’

A field experiment was conducted to evaluate the effect of plant nutrients on yield and fruit quality of ‘Grand Naine’ banana under alkaline soil conditions for the two cropping seasons 2020-2021 and 2021-2022 at Directorate of Agricultural Research (DoAR), Khajura. The trial was laid out in a randomized complete block design consisting of seven treatments replicated three times having four plants in an experimental plot. Planting of tissue culture banana was done at 2×2 m spacing on 4th July 2020 and suckers selected from the same plant on the same date were taken as the first ratoon crop. Farmyard manure was applied at four, chemical fertilizers at six, and micronutrients at three installments. The pooled results revealed that the longest bunch (114.30 cm), the highest number of hands (10.08) and finger number per bunch (185.80) and second hand (21.50), the heaviest bunch (25.74 kg) and second-hand weight (3.28 kg), the highest finger length (21.08 cm), diameter (3.85 cm) and weight (156.20 g) and bunch yield (64.35 mt/ha), the highest TSS (20.20%), TSS: TA ratio (47.10) and total sugar (12.55%) and the lowest TA (0.43%) were registered in T3 (20 kg FYM, 250:250:350 g NPK along with foliar spray of four types of micronutrients viz. Zn, Fe, Cu and B at 3rd 5th and 7th months of planting. Hence, from the study banana growers are recommended to use FYM, chemical fertilizers along with a foliar spray of micronutrients, at least three times in the vegetative growth stage of bananas for higher yield and improved quality of fruits.
MdSnRK1.1 interacts with MdGLK1 to regulate abscisic acid-mediated chlorophyll accumulation in apple

Abstract Abscisic acid (ABA), as a plant hormone, plays a positive role in leaf chlorosis; however, the underlying molecular mechanism is less known. Our findings provide ABA treatment reduced the chlorophyll accumulation in apple, and Malus × domestica Sucrose Non-fermenting 1-Related Protein Kinase 1.1 (MdSnRK1.1) participates in the process. MdSnRK1.1 interacts with MdGLK1, a GOLDEN2-like transcription factor that orchestrates development of the chloroplast. Furthermore, MdSnRK1.1 affects MdGLK1 protein stability through phosphorylation. We found that Ser468 of MdGLK1 is target site of MdSnRK1.1 phosphorylation. MdSnRK1.1-mediated phosphorylation was critical for MdGLK1 binding to the target gene MdHEMA1 promoters. Collectively, our results demonstrate that ABA activates MdSnRK1.1 to degrade MdGLK1 and inhibit the accumulation of chlorophyll. These findings extend our understanding on how MdSnRK1.1 balances normal growth and hormone response.
Determining the Presence and Size of Shoulder Lesions in Sows Using Computer Vision

Simple Summary Shoulder lesions present a significant welfare concern, particularly among breeding sows. They are frequently linked to decreased mobility and loss of body condition during lactation. This research explores using RGB cameras and the potential of different computer vision methods for detecting and estimating their size. Findings indicate that these techniques hold promise in effectively identifying and quantifying lesion size. This could empower producers to proactively monitor sow welfare, facilitating timely detection and intervention for these lesions. Abstract Shoulder sores predominantly arise in breeding sows and often result in untimely culling. Reported prevalence rates vary significantly, spanning between 5% and 50% depending upon the type of crate flooring inside a farm, the animal’s body condition, or an existing injury that causes lameness. These lesions represent not only a welfare concern but also have an economic impact due to the labor needed for treatment and medication. The objective of this study was to evaluate the use of computer vision techniques in detecting and determining the size of shoulder lesions. A Microsoft Kinect V2 camera captured the top-down depth and RGB images of sows in farrowing crates. The RGB images were collected at a resolution of 1920 × 1080. To ensure the best view of the lesions, images were selected with sows lying on their right and left sides with all legs extended. A total of 824 RGB images from 70 sows with lesions at various stages of development were identified and annotated. Three deep learning-based object detection models, YOLOv5, YOLOv8, and Faster-RCNN, pre-trained with the COCO and ImageNet datasets, were implemented to localize the lesion area. YOLOv5 was the best predictor as it was able to detect lesions with an mAP@0.5 of 0.92. To estimate the lesion area, lesion pixel segmentation was carried out on the localized region using traditional image processing techniques like Otsu’s binarization and adaptive thresholding alongside DL-based segmentation models based on U-Net architecture. In conclusion, this study demonstrates the potential of computer vision techniques in effectively detecting and assessing the size of shoulder lesions in breeding sows, providing a promising avenue for improving sow welfare and reducing economic losses.
Discovery of Lower Cambrian Small Shelly Fossils and Brachiopods from the Lower Vindhyan of Son Valley Central India

R. J. Azmi of the Wadia Institute of Himalayan Geology here reports the discovery of small shelly fossils and brachiopods of lower Cambrian affinity from the topmost Rohtasgarh Limestone at Maihar and Rohtas in the Son Valley. This discovery is of importance as it necessitates a revision in the age of the Vindhyan succession. We welcome a discussion on this subject - Ed.
A Mutually Coupled Stabilization Coil Configuration for Electromagnetic Levitation Melting With Frequency Consideration

A conventional coil configuration of electromagnetic levitation melting (ELM) systems consists of a main coil connected with an anti-loop coil to provide the stability of the levitating workpiece. The reversed current in the anti-loop coil produces a reversed magnetic field that depreciates the total magnetic flux at the workpiece. In many cases, a smaller reversed current is sufficient to provide stabilization for a levitating workpiece. This paper proposes a mutually coupled stabilization coil in place of the anti-loop coil to improve the performance of ELM systems. The proposed stabilization coil system consists of two coils, an isolated upper coil, and a main levitation coil. The two coils are galvanically isolated while magnetically linked. The upper coil current is adjustable through the distance between the coils. This allows the upper coil to operate at a lower current than the main coil current but sufficient for the workpiece stabilization. Simulation results have confirmed that the magnetic flux density at the workpiece is higher in the isolation configuration than in the conventional anti-loop configuration. Experimental results have shown that the proposed system can improve the ELM system performance by reducing the heating time and power consumption of the melting process.
LINC00460 knockdown sensitizes cervical cancer to cisplatin by downregulating TGFBI

The acquired resistance of cancer to cisplatin (DDP) limits the efficacy of chemotherapy. The prognostic value of long noncoding RNA (lncRNA) LINC00460 has been reported in cervical cancer. However, its effect on DDP sensitivity in cervical cancer remains poorly understood. In present study, LINC00460 was screened out through bioinformatics analysis. The expression levels of mRNAs and proteins were measured by reverse transcription‐quantitative PCR (RT‐qPCR) or western blot analysis. The sensitivity to DDP was investigated using an CCK8 assay. Cell apoptosis was determined by flow cytometry. The differentially expressed genes that were associated with the poor prognosis of cervical cancer were screened, and their correlations with LINC00460 expression were explored using Pearson's correlation analysis. Tumor xenograft model was used to assess the effect of LINC00460 knockdown on DDP sensitivity in vivo. The interaction between miR‐338‐3p and LINC00460 or transforming growth factor β‐induced protein (TGFBI) was confirmed by RNA immunoprecipitation (RIP) and luciferase reporter assays. LINC00460 expression was increased in cervical cancer tissues and cells. High expression of LINC00460 was associated with dismal prognosis in cervical cancer patients. Silencing of LINC00460 increased drug sensitivity and induced apoptosis in DDP‐resistant‐cervical cancer cells. LINC00460 knockdown enhanced DDP sensitivity in cervical cancer cells largely by downregulating TGFBI expression. LINC00460 knockdown enhanced the sensitivity of cervical cancer to DDP in vivo, and this effect was partly mediated by the downregulation of TGFBI. LINC00460 positively regulated TGFBI expression, possibly by acting as a sponge of miR‐338‐3p. LINC00460 knockdown contributed to DDP sensitivity of cervical cancer by downregulating TGFBI, providing a novel mechanism underlying the acquisition of DDP sensitivity.
O-Methylheptaphylline from Clausena suffruticosa

O-Methylheptaphylline (1), together with capnolactone (2), were isolated from Clausena suffruticosa. The structure of 1 was established unambiguously by a series of 1D and 2D-NMR analyses. The cytotoxic activity of 1 and 2 was determined against brine shrimp nauplii and the LC50 values recorded were 0.56 μg/mL and 7.94 μg/mL, respectively.
Bridging Interpretability and Performance: Enhanced Machine Learning-Based Prediction of Hematoma Expansion Post-Stroke via Comprehensive Feature Selection

Monitoring and controlling the occurrence of hematoma expansion events after a stroke is a primary clinical focus. The introduction of machine learning (ML) techniques offers intelligent decision support for physicians in this domain. However, for doctors without an ML background, the behavior of a hematoma expansion predictor seems opaque, similar to a “black box. ” Moreover, the vast and diverse set of features typically present in medical data acts as a double-edged sword: while encapsulating rich information with potential value, it also includes redundant details that offer little to predictive utility. Comprehensive feature selection is crucial, but many current state-of-the-art hematoma expansion prediction studies based on ML often overlook this step. In this paper, we propose a methodology tailored for comprehensive feature selection across diverse and abundant medical data features and rigorously evaluate ML models. Through experiments on a real-world post-stroke hematoma expansion prediction dataset, we demonstrate the efficacy of our approach in enhancing the performance of ML predictors. Visualization of the associated feature selection process and results further bolsters physicians’ understanding of the model’s decision-making basis, thereby strengthening its interpretability.
Transfer learning for bearing fault diagnosis: adaptive batch normalization and combined optimization method

Bearings are crucial components in rotating machinery equipment. Bearing fault diagnosis plays a significant role in the maintenance of mechanical equipment. This study aims to enhance the practicality of bearing fault diagnosis to meet real-world engineering requirements. In real industrial environments, the continuously changing operating conditions such as equipment speed and load pose challenges in collecting data for bearing fault diagnosis, as it is challenging to gather data for all operational conditions. This paper proposes a transfer learning approach for bearing fault diagnosis based on adaptive batch normalization (AdaBN) and a combined optimization algorithm. Initially, a ResNet neural network is trained using source domain data. Subsequently, the trained model is transferred to the target domain, where AdaBN is applied to mitigate domain shift issues. Furthermore, a combined optimization algorithm is employed during model training to enhance fault diagnosis accuracy. Experimental validation is conducted using bearing data from the Case Western Reserve University dataset and Northeast Forestry University (NEFU) dataset. Comparison shows that AdaBN and the combined optimization algorithm improve bearing fault diagnosis accuracy effectively. On the NEFU dataset, the diagnostic accuracy exceeds 95%.