Miracle-dz/UniFAR-FTUs huggingface dataset:
Surface Runoff and Lateral Subsurface Flow as a response to Conservation Tillage and Soil-WaterConditions

Surface runoff and lateral subsurface flow were measured on six 0.2 ha plots in South Georgia in order to quantify the effects of conservation tillage. Significant differences in water losses were observed between the conventionally and strip tilled plots. Surface runoff from the conventionally tilled plots exceeded that from the strip tilled plots while subsurface losses were reversed. Surface runoff losses from the conventionally tilled plots exceeded those from the strip tilled plots by 80%. Subsurface losses from the strip tilled plots exceeded those from the conventionally tilled plots by 67%. Overall, a net annual gain of 60 mm of water was observed for the strip till plots.
Rethinking animal attrition in preclinical research: expressing causal mechanisms of selection bias using directed acyclic graphs

Animal attrition in preclinical experiments can introduce bias in the estimation of causal treatment effects, as surviving animals may not be representative of the entire study population. This can compromise the internal validity of the study, despite randomization at the outset. Directed Acyclic Graphs (DAGs) are commonly used tools to transparently visualize assumptions about the causal structure underlying observed data. By illustrating relationships between relevant variables, DAGs enable the detection of even less intuitive biases, and can thereby inform strategies for their mitigation. In this study, we present an illustrative causal model for preclinical stroke research, in which animal attrition induces a specific type of selection bias (i.e., collider stratification bias) due to the interplay of animal welfare, initial disease severity and negative side effects of treatment. Even when the treatment had no causal effect, our simulations revealed substantial bias across different scenarios. We show how researchers can potentially mitigate this bias in the analysis phase, even when only data from surviving animals are available, if knowledge of the underlying causal process that gave rise to the data is available. Collider stratification bias should be a concern in preclinical animal model studies with severe side effects and high post-randomization attrition.
Active Suppression of the Nigrostriatal Pathway during Optogenetic Stimulation Revealed by Simultaneous fPET/fMRI

The dopaminergic system is a central component of the brain’s neurobiological framework, governing motor control, reward responses, and playing an essential role in various brain disorders such as Parkinson’s disease and schizophrenia. Within this complex network, the nigrostriatal pathway represents a critical circuit for dopamine transmission from the substantia nigra to the striatum, a connection that is vital to understanding many of the disease-related dysfunctions. However, stand-alone functional magnetic resonance imaging (fMRI) is unable to study the intricate interplay between brain activation and its molecular underpinnings. In our study, the simultaneous use of [18F]FDG functional positron emission tomography (fPET)/BOLD-fMRI provided a new insight that allowed us to demonstrate an active suppression of the nigrostriatal activity during optogenetic stimulation via presynaptic autoinhibition. Our in vivo observation emphasizes that the observed BOLD signal depression during neuronal stimulation does not correlate with neuronal inactivity, but results from an active suppression of neuronal firing as shown by the high [18F]FDG signal increase. This result not only illustrates the potential of simultaneous fPET/fMRI to understand the molecular mechanisms of brain function but also provides a new perspective on how neurotransmitters such as dopamine influence hemodynamic responses in the brain.
Marker effect p-values for single-step GWAS with the algorithm for proven and young in large genotyped populations

Background Although single-step GBLUP (ssGBLUP) is a breeding value method, single-nucleotide polymorphism (SNP) effects can be backsolved from ssGBLUP genomic estimated breeding values (GEBV), and p-values can be obtained as a measure of estimation certainty. This enables single-step genome-wide association studies (ssGWAS). However, obtaining p-values for ssGWAS relies on the inversion of dense matrices, which poses computational limitations in large genotyped populations. In this study, we present an algorithm to approximate p-values for SNP in ssGWAS with many genotyped animals. The approximation relies on the algorithm for proven and young (APY) and submatrices for core animals. To test that, we first compared SNP p-values obtained with an exact inversion using the genomic relationship matrix (G−1) for 50K genotyped animals to those estimated with an exact inversion using and those obtained with the proposed approximation based on . Then, we compared these results with those obtained with the proposed approximation using 450K genotyped animals. Results The same genomic regions in chromosomes 7 and 20 were identified with p-values obtained with G−1, , and the approximation based on when using 50k genotyped animals and 1.5M in the pedigree. In terms of computational requirements, obtaining p-values with the approximation based on represented a reduction of 38 times in wall-clock time and ten times in memory requirement compared to using the exact inversion with . When the approximation was applied to a population of 450K genotyped animals and 1.8 in the pedigree, apart from the two genomic regions in chromosomes 7 and 20 previously identified with the smaller genotyped population, two new significant regions on chromosomes 6 and 14 were uncovered, indicating an increase in GWAS detection power when including more genotypes in the analyses. The process of obtaining p-value with the approximation and 450K genotyped individuals took 24.5 wall-clock hours and 87.66 GB of memory, which is expected to increase linearly with the addition of noncore genotyped individuals. Conclusions With an algorithm that approximates the prediction error variance of SNP effects based on APY, ssGWAS with p-values for SNP is possible in large genotyped populations. The computational cost of obtaining p-values in ssGWAS is no longer a limitation in extensive populations with many genotyped animals.
Root Ions Fluxes and Osmolarity Changes in Grass Species Differing in Salinity Tolerance

Agricultural areas are increasingly being affected by salt due to irrigation practices and rising levels of salty groundwater. Different plant species have varying degrees of sensitivity to salinity and employ distinct mechanisms to avoid severe damage caused by salt stress. We compared three grass species with different ecological backgrounds, namely Lolium perenne, Festuca rubra, and Puccinellia maritima, in terms of their ability to maintain growth when exposed to salt stress, the extent of Na+-induced K+efflux, and the accumulation of salts in their shoots. Our results demonstrate that the changes in K+and H+fluxes at the root tip induced by NaCl exposure are correlated with the salt tolerance characteristics of these grass species. Specifically, L. perenne exhibited the highest leakage of K+from its roots, the highest accumulation of Na+in its shoots, and the lowest shoot growth under salt stress. On the other hand, P. maritima showed minimal changes in ion fluxes in response to salinity stress. P. maritima maintained the lowest contribution of Na+to the total osmolarity in its shoots and exhibited the least detrimental effect of salt on shoot dry matter. The root cortex including the exodermis and endodermis could be one of the benefit barriers that help defense against salts. In conclusion, root ions fluxes and osmolarity changes in grass species have different salinity tolerance of plants from various habitats. The salt resistance plants restrict leakage of K and exclude Na more effectively. Overall, these results broadened our knowledge of salt resistance in grass species.
Local cattle breeds in Europe : development of policies and strategies for self-sustaining breeds

Which policies and strategies are required to successfully maintain our European Farm Animal Genetic Resources? The European local cattle populations were a target in the EURECA project, co-funded by the European Commission (EU AGRI GENRES 870/04). The general aim was to better understand the state of the breeds, to identify factors that contribute to their success or failure, and to recommend decision-making tools for development of ambitious and sound strategies and policies
Microtubule inner proteins of Plasmodium are essential for transmission of malaria parasites

Microtubule inner proteins, MIPs, are microtubule associated proteins that bind to tubulin from the luminal side. MIPs can be found in axonemes to stabilize flagellar beat or within cytoplasmic microtubules. Plasmodium spp. are the causative agents of malaria that feature different forms across a complex life cycle with both unique and divergent microtubule-based arrays. Here we investigate the role of four MIPs in a rodent malaria parasite for their role in transmission to and from the mosquito. We show by single and double gene deletions that SPM1 and TrxL1, MIPs associated with the subpellicular microtubules are dispensable for transmission from the vertebrate host to the mosquito and back. In contrast, FAP20 and FAP52, MIPs associated with the axonemes of gametes, are essential for transmission to mosquitoes but only if both genes are deleted. In the absence of both, FAP20 and FAP52 the B-tubule of the axoneme partly detaches from the A-tubule resulting in the deficiency of axonemal beating and hence gamete formation and egress. Our data suggest that a high level of redundancy ensures microtubule stability in the transmissive stages of Plasmodium, which is important for parasite transmission.
Remote Sensing Image Super-resolution: Challenges and Approaches (Invited Paper)

Remote sensing has a growing relevance in the modern society with the development of image processing of satellite imagery. However, due to the limitations of the current imaging sensors and the complex atmospheric conditions, we are facing great challenges in the remote sensing applications due to the limited spatial, spectral, radiometric and temporal resolu- tions. Therefore, super-resolution techniques have attracted much attention by which the low quality low resolution remote sensing images are enhanced. In this paper, we discuss the challenges in remote sensing image super-resolution and thereafter review the relevant approaches. More specifically, the different categories of remote sensing techniques, i.e., the learning-based, interpolation based, frequency domain based, and probability based methods, are reviewed and discussed. Furthermore, the super-resolution applications are discussed and insightful comments on future research directions are provided. Index Terms—Super resolution; remote sensing; observation model.
Fast and universal single molecule localization using multi-dimensional point spread functions

The recent development of single molecule imaging techniques has enabled not only high accuracy spatial resolution imaging but also information rich functional imaging. Abundant information of the single molecules can be encoded in its diffraction pattern and be extracted precisely (e.g. 3D position, wavelength, dipole orientation). However, sophisticated high dimensional point spread function (PSF) modeling and analyzing methods have greatly impeded the broad accessibility of these techniques. Here, we present a graphics processing unit (GPU)-based B-spline PSF modeling method which could flexibly model high dimensional PSFs with arbitrary shape without greatly increasing the model parameters. Our B-spline fitter achieves 100 times speed improvement and minimal uncertainty for each dimension, enabling efficient high dimensional single molecule analysis. We demonstrated, both in simulations and experiments, the universality and flexibility of our B-spline fitter to accurately extract the abundant information from different types of high dimensional single molecule data including multicolor PSF (3D + color), multi-channel four-dimensional 4Pi-PSF (3D + interference phase) and five-dimensional vortex PSF (3D + dipole orientation).
Th2 and Th17-Associated Immunopathology Following SARS-CoV-2 Breakthrough Infection in Spike-Vaccinated ACE2-humanized Mice

Vaccines have demonstrated remarkable effectiveness in protecting against COVID-19; however, concerns regarding vaccine-associated enhanced respiratory diseases (VAERD) following breakthrough infections have emerged. Spike protein subunit vaccines for SARS-CoV-2 induce VAERD in hamsters, where aluminum adjuvants promote a Th2-biased immune response, leading to increased type 2 pulmonary inflammation in animals with breakthrough infections. To gain a deeper understanding of the potential risks and the underlying mechanisms of VAERD, we immunized ACE2-humanized mice with SARS-CoV-2 Spike protein adjuvanted with aluminum and CpG-ODN. Subsequently, we exposed them to increasing doses of SARS-CoV-2 to establish a breakthrough infection. The vaccine elicited robust neutralizing antibody responses, reduced viral titers, and enhanced host survival. However, following a breakthrough infection, vaccinated animals exhibited severe pulmonary immunopathology, characterized by a significant perivascular infiltration of eosinophils and CD4+ T cells, along with increased expression of Th2/Th17 cytokines. Intracellular flow cytometric analysis revealed a systemic Th17 inflammatory response, particularly pronounced in the lungs. Our data demonstrate that aluminum/CpG adjuvants induce strong antibody and Th1-associated immunity against COVID-19 but also prime a robust Th2/Th17 inflammatory response, which may contribute to the rapid onset of T cell-mediated pulmonary immunopathology following a breakthrough infection. These findings underscore the necessity for further research to unravel the complexities of VAERD in COVID-19 and to enhance vaccine formulations for broad protection and maximum safety. Significance statement This research investigates the safety and efficacy of a Spike protein subunit vaccine adjuvanted with Alum and CpG in an ACE2-humanized mouse model, simulating SARS-CoV-2 breakthrough infections. The study reveals that despite robust protection against severe COVID-19, vaccinated mice exhibit substantial pulmonary immunopathology, including eosinophilia and enhanced Th2 effector immunity, following breakthrough infections. Surprisingly, the study also uncovers a significant systemic Th17 inflammatory response in vaccinated mice. This research sheds light on the potential risks associated with COVID-19 vaccine breakthrough infections and the need for a comprehensive understanding of vaccine-induced immune responses, emphasizing the importance of ongoing research, surveillance, and careful vaccine development for both protection and safety in the fight against the COVID-19 pandemic.
Representation Exploration and Deep Learning Applied to the Early Detection of Pathological Gambling Risks

The aim of Task 2 (Early Detection of Signs of Pathological Gambling) from the CLEF 2023 eRisk Work-shop is to analyze social media users’ messages for early warning signs of pathological gambling. Given that Pathological Gamblers are a small set compared to the Control group, we propose the utilization of a neural network incorporating a customized loss function to effectively tackle the challenge of class imbalance. In our proposed loss function it is possible to adjust the penalty for false positives and false negatives, increasing the penalty for the critical false negatives. Our proposed solution demonstrates robustness, achieving one of the highest recall rates while maintaining a competitive precision. Furthermore, our system introduces a range of potential variations that warrant further investigation.
PlantCLEF2023: A Bigger Training Dataset Contributes More than Advanced Pretraining Methods for Plant Identification

Plant identification has received a significant improvement in recent years with the development of deep learning. However, large-scale plant identifications still suffer such as the PlantCLEF2022 challenge, and this paper aims to address this issue in the PlantCLEF2023 challenge. On one hand, we extend an effective strategy, transfer learning, resorting to advanced pretrained methods with multi-modal and self-supervised learning. On the other hand, training dataset size and quality are probed and their impacts are compared to one of the advanced pretrained methods. The experimental results suggest that advanced pretrained methods, bigger datasets, and noisy datasets are beneficial to plant identification. Especially, the performance gain from the first one is inferior to the counterparts of the latter two, with the dataset in the PlantCLEF2023 challenge. For example, employing a noisy dataset together receives 0.0236 gains in performance whereas multimodal and self-supervised pretraining contributes 0.00792 gains. We hope that our results highlight the importance of collecting better datasets for plant identification. Our codes are public at https://github.com/xml94/PlantCLEF2022.
A Natural Language Processing Based Risk Prediction Framework for Pathological Gambling

Task 2 of eRisk 2023 shared-tasks challenge at Conference and Labs of the Evaluation Forum (CLEF) was to focus on the early detection of pathological gambling via sequential text processing over social media conversations. The challenge organizers have released different datasets, consisting of social media posts and questionnaires, for all three tasks. The BioNLP research group at the Indian Institute of Science Education and Research Bhopal (IISERB) participated in task 2 of the challenge and submitted five runs that for five different text mining frameworks. The performance of different text classification frameworks and their effectiveness in detecting signs of pathological gambling are demonstrated in this paper. Several classifiers and feature engineering schemes are combined to build individual frameworks. The features from free text are generated following the bag of words model and transformer based embeddings methods. Subsequently, adaptive boosting, logistic regression, support vector machine, and transformer based classifiers were used to identify the signs of pathological gambling from the social media posts. The experimental analysis demonstrates that the support vector machine and adaptive boosting classifier respectively using the entropy and TF-IDF weighting scheme of the bag of words outperforms the other methods on the training set. Furthermore, the adaptive boosting classifier following TF-IDF-based weighting scheme achieves the best precision score among all other submissions of task 2 of erisk 2023. However, the rest of the frameworks could not achieve reasonable performance which needs to be introspected in future.
NailP at eRisk 2023: Search for Symptoms of Depression

Depression is a global health concern with severe consequences for individuals, making its recognition and understanding crucial. Recently, there has been a growing interest in utilizing social media platforms as valuable sources of information to gain insights into individuals’ experiences with depression. Analyzing textual data from diverse user populations enables the identification of common symptoms, triggers, coping mechanisms, and potential warning signs. Researchers have developed algorithms and machine learning models to automate the detection of depressive symptoms in text, facilitating more efficient screening and early intervention. This paper describes the participation of team NailP in the CLEF eRisk 2023 task 1, which focuses on ranking sentences from user writings based on their relevance to symptoms of depression. The goal is to evaluate the sentences and determine their level of relevance to each symptom outlined in the Beck Depression Questionnaire-II. Such participation contributes to the development of effective methods and tools for identifying and predicting potential risks and dangers associated with depression in online environments.
Queen of Swords at Touché 2023: Intra-Multilingual Multi-Target Stance Classification using BERT

Stance classification can be used in various scenarios, such as fake news detection or public opinion measurement. However, little work has been done on stance detection in multilingual data. For this reason, this work uses a multilingual, multi-target, and multi-topic dataset to develop a classifier for detecting stance in such data. The classifier was trained using pre-trained BERT models, with various experiments showing superior performance of a fine-tuned multilingual BERT model with self-training. Since the dataset was unbalanced, with the main label being "in favor", the macro-averaged F1 score was used for measurement. The best performing model achieved a macro-average F1 score of 0.8862 using the same proposals in stance classification for training and testing. The same approach was used to train two classifiers for the CLEF 2023 Touché Lab Task 4 Subtask 1 and 2, using new, previously unseen proposals for testing. However, by using new unseen proposals, the results deteriorated significantly, and in the challenge only a macro F1 score of 0.324 and 0.417 was achieved.
TeamX at CheckThat!-2023: Multilingual and Multimodal Approach for Check-Worthines Detection

Check-worthiness detection is a crucial aspect of the fact-checking pipeline. It aids fact-checkers and journalists by highlighting the claims that necessitate verification. This is especially pertinent in today’s era of social media and varied news channels, where numerous actors voice claims on a range of current affairs topics, including political matters, global warming, and the COVID-19 vaccine. Furthermore, during political events, politicians debate their political agendas and make numerous claims on various subjects. These claims, which often lack factual basis, come in all forms. While some of these claims are important, others are not. Given the time-intensive nature of manual fact-checking, the identification of claims worthy of fact-checking becomes critical. Over the years, there have been research efforts aimed at the automatic detection of such claims. To further this research, in past years, CheckThat! Lab has offered check-worthiness detection tasks on political debates and textual modality social media content. For the first time, CheckThat! Lab offered the task for multimodal content. This study reports our participation in subtask-1A, which consists of Arabic and English. For our experiments, we utilized transformer-based models for both unimodal and multimodal models. The performances of the submitted systems, evaluated using the F1-score on the positive class, were 0 . 671 and 0 . 300 , respectively. Our systems did not rank on the leaderboard as we made late submissions. However, with additional experiments, we achieved 0 . 684 and 0 . 362 for English and Arabic, respectively.