Are animal models necessary? Exploring (dis)advantages and alternativesAna Isabel Guimarães1,2airsaguimaraes@gmail.com1Pharmacology and Neurobiology Laboratory of the Immunophysiology and Pharmacology Department and2Center for Drug Discovery and Innovative Medicines (MedInUP), School of Medicine and Biomedical Sciences(ICBAS), University of Porto, Portugal

Runner up in the FENS Care Writing Competition - Literary Category

This is a mentoring statement submitted in context of the FENS-Kavli Network of Excellence Mentoring Prize evaluation process.

The misfolding and aggregation of TAR DNA binding protein-43 (TDP-43), leading to the formation of cytoplasmic inclusions, emerge as a key pathological feature in a spectrum of neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS) and frontotemporal lobar dementia (FTLD). TDP-43 shuttles between the nucleus and cytoplasm but forms nuclear bodies (NBs) in response to stress. These NBs partially colocalize with nuclear speckles and paraspeckles that sequester RNAs and proteins, thereby regulating many cellular functions. The laboratory of Steven Brown has recently found that the non-POU domain-containing octamer-binding protein (NONO), a component of paraspeckles, forms novel nuclear speckle-like structures in mouse cortical neurons in response to stress and sleep deprivation. These findings suggest the possibility of a functional link between NONO and TDP-43, potentially contributing to TDP-43 proteinopathy. Here, we demonstrate that loss of function in the Drosophila homolog of NONO, no on or off transient A (NonA), exacerbates pathological phenotypes caused by TDP-43 gain of function, leading to locomotor defects and life span shortening. These results provide supporting evidence for the functional link between NONO and TDP-43 and lay the foundation for dissecting underlying mechanisms.

INTRODUCTIONInternal hernias are primarily caused by the intestine stretching through a mesenteric defect or peritoneum defect (1). They could be acquired or congenital. A rare congenital defect called a para duodenal hernia (PDH) is caused by a malrotation of the midgut (2). PDHs make up 53% of all internal hernias, making them the most frequent type (3). Because of the variability of the clinical signs, PDHs are challenging to identify (4). PDH may start as acute blockage or recurrent abdominal pain symptoms (43%), or it may remain asymptomatic for the duration of the patient’s life. Of all internal hernias, between 10 and 50 percent are found after unrelated abdominal surgeries, imaging tests, or autopsies (5). An abdominal computed tomography scan performed before surgery is typically diagnostic, yet Frequently, the diagnosis is made during surgery. Both minimally invasive laparoscopic procedures and conventional open approaches can be used to undertake surgical treatments. Here, we report on an adult man’s successful laparoscopic repair of a left PDH, as well as the preoperative radiological diagnostic.

How calcifications guide the diagnosis : A case of Gorlin’s cyst

Accurately simulating hydrological conditions is a significant challenge for hydrologists globally, particularly in capturing patterns of high and low streamflow due to the ongoing impacts of climate change and human activities on water resource availability. To address this challenge, this study proposes an innovative integrated approach that combines the Soil and Water Assessment Tool (SWAT), Stationary Wavelet Transform (SWT), and interpretable machine learning models, specifically Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. The hydrological and meteorological features generated by SWAT were preprocessed using SWT and then used as inputs for the CNN-LSTM model. During the testing period, the SWAT-SWT-CNN-LSTM model achieved R 2 and NSE values of 0.90 and 0.88, respectively, outperforming other machine learning models (e.g., Support Vector Machine, Random Forest, LSTM) as well as the calibrated SWAT model. Additionally, it effectively reduced the underestimation of high streamflow and the overestimation of low streamflow, with deviations in both categories maintained within 1.3%. From a machine learning perspective, solar radiation and percolation volume are identified as key factors influencing local streamflow, while precipitation shows the highest sensitivity to streamflow variation. Even with a reduced sliding window length, the model’s R 2 and NSE during the training and testing periods remained above 0.88 and 0.80, respectively, demonstrating exceptional stability. Furthermore, the SWAT-CNN-LSTM comparative experiment showed that SWT effectively mitigates the overfitting issue in machine learning. This study highlights that the organic integration of SWAT, SWT, CNN, and LSTM, along with the application of interpretable methods such as Individual Conditional Expectation (ICE), Partial Dependence Plots (PDP), and Shapley Additive Explanations (SHAP), not only enhances model performance significantly but also increases the credibility of machine learning results, paving a valuable new pathway for long-term streamflow simulations in watersheds.

Parasite occurrence and infection estimates vary through time and space, making understanding the underlying drivers highly complex. Comparative studies based on empirical data must consider the factors of variation involved in estimating infection metrics in natural populations to make appropriate and reliable comparisons. Using a multi-scale approach, we explored the sources of variation in the estimation of infection prevalence, focusing on black spot disease in littoral freshwater fish communities sampled across 15 lakes in Québec, Canada. Our results show that infection prevalence is spatially heterogeneous across the landscape with evidence of infection hotspots and coldspots. Method-related sampling biases led to significant variations in prevalence estimates and spatial patterns of disease occurrence. Our results also indicated that low sampling efforts tend to overestimate the prevalence of infection in the landscape, and that the sampling effort required to estimate an accurate infection prevalence depends on the sampling method employed. Physico-chemical characteristics of the sites and local fish community structure were found to be the best drivers of infection at smaller spatial scales. Furthermore, our results suggest dilution effects due to obstruction and compatibility barriers limit the survival of the free-living cercaria parasite lifestage. Several relationships between infection prevalence and environmental drivers revealed non-linearity, suggesting complex interactions. Examining infection prevalence data at various spatial scales revealed method-induced biases, sampling effort effect and environment driven relationships underscoring the importance of context-dependencies and scale-dependencies in studies on host-parasite interactions.

Background: Mycoplasma pneumoniae (MP) is a common pathogen for respiratory infections in children. Previous studies have reported respiratory tract microbial disturbances associated with MP infection (MPI); however, since the COVID-19 pandemic, respiratory virome data in school-aged children with MPI remains insufficient. This study aims to explore the changes in the respiratory virome caused by MPI after the COVID-19 pandemic to enrich local epidemiological data. Methods: Clinical samples from 70 children with MPI (70 throat swab samples and 70 bronchoalveolar lavage fluid (BALF) samples) and 78 healthy controls (78 throat swab samples) were analyzed using viral metagenomics. Virus reads were calculated and normalized using MEGAN.6, followed by statistical analysis. Results: Principal Coordinate Analysis (PCoA) showed that viral community diversity is a significant difference between disease cohorts and healthy controls. After MPI, the number of virus species in the upper respiratory tract (URT) increased obviously, and the abundance of families Poxviridae, Retroviridae, and Iridoviridae, which infect vertebrates, rose evidently, particularly the species BeAn 58085 virus (BAV). Meanwhile, phage alterations in the disease cohorts were predominantly characterized by increased Myoviridae and Ackermannviridae families and decreased Siphoviridae and Salasmaviridae families ( p<0.01). In addition, some new viruses, such as rhinovirus, respirovirus, dependoparvovirus, and a novel gemykibvirus, were also detected in the BALF of the disease cohort. Conclusions: This cross-sectional research highlighted the respiratory virome characteristics of school-aged children with MPI after the COVID-19 outbreak and provided important epidemiological information. Further investigation into the impact of various microorganisms on diseases will aid in developing clinical treatment strategies.

In this study, Team Rookies presents a deep learning approach for the Capsule Vision 2024 Challenge, utilizing a fine-tuned Swin Transformer model for multi-class abnormality classification in video capsule endoscopy images. The dataset comprised over 50,000 frames from three public sources and one private dataset, labeled across ten gastrointestinal classes, including Angioectasia, Bleeding, Erosion, and more. Our model achieved an overall accuracy of 93% on the validation set, with class-wise precision ranging from 0.65 to 0.99 and F1-scores between 0.50 and 0.98. Notably, classes such as Ulcer and Worms achieved F1-scores of 0.96 and 0.97, respectively, while Erythema and Erosion recorded lower F1-scores, indicating areas for improvement. These results underscore the potential of Swin Transformer-based architectures in enhancing the automated detection of gastrointestinal conditions, thereby facilitating early diagnosis and reducing manual review time in clinical practice.As participants in the Capsule Vision 2024 Challenge, we fully complied with the competition’s rules as outlined in the official guidelines. Our AI model development was based exclusively on the datasets provided in the official release. We are proud to have achieved 12th rank in the challenge, showcasing the effectiveness of our approach and its competitive performance. Our code is available on GitHub.

Agriculture continues to be one of the world’s main sources of income and provides great environmental, territorial and social value. However, frost is a recurring problem for farmers each year, representing a significant threat to agricultural production. In a matter of hours, temperatures below the freezing point can result in the loss of nearly the entire crop from a producer. In this article, we have analyzed and compared the application of a set of machine learning algorithms to predict the occurrence of frost events in the next 24 hours. The prediction process covers several challenges, such as data capture, processing, extracting each relevant parameter and finally building different prediction models to compared their performance. Furthermore, we have employed the Synthetic Minority Oversampling Technique (SMOTE) methodology to address the issue of imbalanced datasets, given the natural scarcity of frost events during the data sampling period. Our results show that among the machine learning algorithms we compared, the most efficient in terms of Recall score is K-Nearest Neighbor (KNN), while using the Area Under Curve (AUC) criteria, the highest score belongs to the Extra Trees al- gorithm, with 0.9909. Moreover, by applying the SMOTE balancing process, the AUC score of our models increased 13%, while the Recall score increased from 55% to 82%.

Note: Silicon nanowires are one of the best examples of semiconductor nanostructures that can be made as a single crystal with a small diameter of 9 to 0 nm.The electromagnetic nature of nanoparticles in magnetic materials, the molecules and atoms that make them  have electromagnetic properties. In other words, elements such as iron, cobalt, nickel and their alloys  are attracted by magnets.  It is called magnetic material. The classification of electromagnetic materials is done based on the magnetic receptivity (magnetization ability of the material). Based on this, materials are classified into three groups: ferromagnetism, paramagnetism, and diamagnetism.

This article develops a duality principle applicable to originally non-convex primal variational formulations. More specifically, we establish a convex (in fact concave) dual variational formulation for a model in micro-magnetism. The results are obtained through basic tools of functional analysis, calculus of variations, duality and optimization theory in infinite dimensional spaces. We emphasize such a convex dual formulation obtained may be applied to a large class of similar models in the calculus of variations, including models in elasticity and phase transition.

To explore the kinetic behavior of lumpy skin disease, we study a stochastic SEITR model with bilinear incidence. In this paper, we introduce a lognormal Ornstein-Uhlenbeck process as a random effect of infectious disease epidemics. In contrast to existing stochastic modeling approaches, this paper illustrates that the lognormal Ornstein-Uhlenbeck process is a reasonable assumption both in a biological sense and mathematically. We obtain two thresholds R 0 S and R 0 E . R 0 S is used to reflect the persistence and stationary distribution of infectious diseases, and R 0 E is used to ensure exponential extinction of diseases.

The demand for efficient processing capabilities in language models continues to grow, driven by the ever-increasing complexity of language tasks and the vast amounts of data involved. Existing techniques often struggle to balance computational efficiency with model performance, leading to a pressing need for innovative solutions that address these challenges. The introduction of Dynamic Context Utilization (DCU) represents a significant advancement in token optimization, enabling adaptive weighting of token relevance within attention mechanisms to enhance inference speed while concurrently reducing token redundancy. Empirical evaluations demonstrate that the implementation of DCU leads to substantial improvements in processing efficiency without sacrificing accuracy, thereby offering a promising direction for optimizing future model architectures. This research highlights the potential of DCU as a scalable framework to alleviate computational constraints associated with large-scale language applications, ultimately contributing to more efficient and effective language understanding systems.

Among the most cutting-edge techniques explored in the field of optimization algorithms, the MOANA stands out as a very promising innovation. MOANA is an extension of Ant Nesting Algorithm (ANA) which is capable of solving difficult multi-objective optimization problems. It is inspired by Leptothorax ants; specifically, the way these ants distribute the collected food items among their colonies. This paper describes the design and use of MOANA, which is designed to solve complex problems using the inherent intelligence of natural ant colonies while addressing conflicting objectives. The paper offers an overview of MOANA's framework and then discusses the benchmark testing carried out in the subsequent step on ZDT functions and CEC 2019 Multimodal multi-objective functions. Moreover, some empirical case studies contribute to the understanding of the applicability and effectiveness of MOANA. The results demonstrate that MOANA's outperforms existing multi-objective optimization algorithms such as NSGA-III, MOPSO, and modern approaches like multi-objective fitnessdependent optimizer MOFDO. MOANA not only excels in efficiently exploring solution trade-offs but also sets a new standard in in the field of optimization research. By offering robust solutions across diverse application domains, MOANA provides valuable insights and advancements for both academia and industry.

In order to improve the online monitoring capability of electromagnetic transformers, particle swarm optimization algorithm is combined with BP neural network, and L2 regularization term is introduced to prevent overfitting, in order to enhance the online monitoring and fault detection capability of electromagnetic transformers. The newly designed system includes data acquisition, data processing, improved neural network models, decision support, and user interface. The particle swarm optimization algorithm is used to optimize the weights and thresholds of the BP neural network to improve its predictive performance. The BP neural network adopts a multi-layer feedforward neural network structure and trains network parameters through backpropagation algorithm. The study also established a mathematical model for electromagnetic transformers, and trained a neural network model by simulating fault signals. The experimental results show that the proposed improved algorithm can effectively identify various types of faults with an accuracy rate of up to 100%, and has high diagnostic accuracy and practical value.

jabbrv-ltwa-all.ldf jabbrv-ltwa-en.ldf Summary: A high voltage gain inverter is essential requirements in high voltage applications. Z-source inverter (ZSI) is widely used for such a kind of applications. However, as the time goes on, new applications needed more boosting voltage from limited magnitude of voltage source. Hence effort is still continuing to increase the gain of ZSI but using optimum number of components. In most recently some ultra-gain topologies has been published in literature. The gain of these ultra-gain topologies is highest in contemporary times. However still there is a possibility to increase the voltage gain using same number of components that are already used in the ultra-gain topologies. In this manuscript, a topology has been proposed which provide higher voltage gain among the latest ultra-gain topologies. The proposed topology uses an active switch in the impedance network along with the components. The mathematical model along with the simulink and hardware model has been presented in the manuscript.

Abstract: Arteriosclerosis (AS) is a chronic, inflammatory disease closely associated with the development of various cardiovascular diseases and poses a significant threat to human life and health. Programmed Cell Death (PCD) is precisely regulated by multiple genes and pathways, playing a critical role in maintaining cellular homeostasis. Recent evidence underscores the crucial role of endothelial cells (ECs), vascular smooth muscle cells (VSMCs), and macrophages-mediated PCD, encompassing apoptosis, autophagy, pyroptosis, ferroptosis, necroptosis, cuproptosis, and NETosis, in the pathogenesis of AS. Natural products from plants, exhibiting anti-AS properties, have attracted considerable research interest. A wealth of evidence suggests that these natural products, by modulating PCD through various mechanisms, hold great promise in the prevention and treatment of AS. Therefore, this review synthesizes information on the common types of PCD in ECs, VSMCs, and macrophages, and examines how cell-mediated PCD influences different stages of AS. Additionally, highlight recent discoveries in the roles and molecular mechanisms of natural products in modulating PCD to mitigate AS. Drawing on these insights, we underscore the significance of natural drugs in treating AS by targeting PCD, potentially providing a foundation for developing novel anti-AS drugs from natural sources.

jabbrv-ltwa-all.ldf jabbrv-ltwa-en.ldf Infective endocarditis (IE) is the inflammation of the endocardium, the inner lining of the heart, most commonly caused by gram-positive bacterial infections Staphilococci and Streptococci. The incidence of IE is approximately 5 per 100,000 people annually. Despite advancements in antimicrobial therapy and surgical interventions, the prognosis for IE remains poor. This report discusses a rare case of infective endocarditis caused by uncommon pathogen Serratia marcescens, a gram-negative bacterium. (9)

Galliformes are one of the most rapidly declining groups of bird species in Europe. The black grouse belongs to species closely related to the types of habitats that are disappearing due to environmental changes caused by man, climate crisis, and an increase in the number of predator species. While the populations of this species in Northern and North-Eastern Europe are still relatively stable, in Central and Western Europe the black grouse is declining very quickly. For example, in Poland, there has been an approximately 100-fold decrease in its population over the last 50 years. However, there is a difference between the rate of decline in black grouse numbers in Central European lowlands and mountain refuges - e.g. the Alps and the Carpathians. The European mountains, still offering habitats shaped by relatively severe climate, may soon be the only type of habitat for this species to survive in this part of the continent. Our study aimed to indicate the main environmental factors determining the occurrence of the species in a mountain refuge, on the southwestern border of this species' continuous range. Based on a comprehensive model containing data on land cover by vegetation, topography, and human disturbance, we assessed environmental factors that shape the probability of black grouse occurrence in one of its last refuges in Europe. Our results reveal a trend for black grouse to prefer habitats of an early succession stage, and those can only persist in specific climatic conditions, or thanks to active protection. Detailed knowledge of the habitat choice of an endangered species is valuable data necessary to avoid the fragmentation of remaining patches of its habitat, to assess the state of the environment in times of climate crisis, and to protect its features that ensure and increase the survival of vulnerable species, such as black grouse.

jabbrv-ltwa-all.ldf jabbrv-ltwa-en.ldf Despite high population densities, valuable plant communities persist in various isolated habitat fragments in many cities around the world. These include several sand grasslands in Budapest, which are notable for their species richness and the presence of rare, protected species such as Hippophaë rhamnoides. Since 2006, efforts to restore natural open sandy grassland habitats have been ongoing within the Újpest Sea buckthorn Nature Conservation Area. The objective of these initiatives is to reduce the prevalence of invasive woody species and to preserve the fragmented sandy grassland ecosystems. The objective of our research was to evaluate the efficacy of conserving the habitat of Hippophaë rhamnoides and restoring natural sandy vegetation over a 15-year period. Furthermore, the objective was to ascertain which Festuca species are dominant in the area. The impact of these interventions was evaluated through the analysis of coenological data across 10 quadrats per plot, with each sample plot representing a different year of shrub removal. By employing a systematic approach to habitat management, over 40% of the protected area has been successfully converted into grassland. In the undisturbed central region, where natural open sandy grasslands have always existed, the dominant grass species is Festuca vaginata. However, in areas where shrubs have been eradicated, Festuca pseudovaginata and Festuca tomanii have emerged as the dominant species. Keywords: Festuca pseudovaginata, Festuca vaginata, Festuca tomanii, habitat restoration, Hippophaë rhamnoides, sandy vegetation

Infective endocarditis is not uncommon. Complications of endocarditis may be related to hemodynamic instability through valve destruction and ensuing congestive heart failure. Embolization of vegetations is a known complication as well and may occur at any time, however there are certain high-risk features that predispose to distal embolization. This included vegetation size and causative organism especially with left heart valve endocarditis. Central nervous system or peripheral embolism to different parts of the body, may occur frequently and may be subclinical or overt. Embolization down the coronary arteries may cause myocardial ischemia, however, sudden cardiac death from infective endocarditis embolizing down a coronary artery is an extremely rare occurrence. The true incidence is not known, but in a recent study, 30 of 6000 (0.5%) cases of sudden cardiac death had a diagnosis of infective endocarditis as cause of death, with most cases being diagnosed post-mortem. Here, we present an unusual case of vegetation on the aortic valve that embolized down the left main coronary artery resulting in death.

Bicuspid aortic valve (BAV) malformations and rupture of the aortic valve chordae tendineae can independently cause aortic regurgitation; however, their coexistence in a single patient is rare. This article reports on a 35-year-old male patient who presented with sudden breathing difficulty, and transthoracic echocardiography revealed only prolapse of the right coronary cusp of the aortic valve with severe regurgitation. This indicates the necessity of adopting a new diagnostic approach, namely transesophageal echocardiography, to clearly identify the disease etiology. The final diagnosis was a combination of partially fused bicuspid aortic valve (BAV) malformation and ruptured aortic valve chordae tendineae.