Miracle-dz/UniFAR-FTUs huggingface dataset:
Pedigree analysis in the mhorr gazelle (Nanger dama mhorr): Genetic variability evolution of the captive population

Abstract Breeding programs have an essential role in the recovery of threatened populations through optimal genetic management and mating strategies. The dama gazelle (Nanger dama) is a North African ungulate listed as critically endangered. The mhorr subspecies is extinct in the wild and currently survives thanks to the creation in 1971 of an ex situ breeding program. The aim of the present study was to assess the evolution of genetic variability in this mhorr gazelle captive population, as well as the mating strategy used in two reference populations studied (Almeria and Europe). The entire pedigree, with 2739 animals, was analyzed to measure demographic characters, pedigree completeness level, probability of gene origin, level of relatedness and genetic structure of the population. The population size has been progressively increasing, with up to 264 individuals alive in Europe at the time of the study. The average number of equivalent complete generations was 5.55. The effective number of founders and ancestors was both 3, and the founder genome equivalent was 1.99. The genetic contributions of the four main ancestors were unbalanced. The average values of inbreeding and average relatedness for the whole pedigree were, respectively, 28.34% and 50.14%. The effective population size was 8.7 by individual increase in inbreeding and 9.8 by individual increase in coancestry. F‐statistics evidenced a very small level of population subdivision (F ST = 0.033370). The mating strategy used, based on the minimum coancestry of the individuals, has minimized the losses of genetic variability and helped to balance the genetic contributions between ancestors. The strategy also avoided large subdivisions within the population and the appearance of new bottlenecks. This study shows how pedigree analysis can both be used to determine the genetic variability of the population and to assess the influence of the mating strategy used in the breeding program on such variability.
Hypoxemic Respiratory Failure and Coccidioidomycosis-Associated Acute Respiratory Distress Syndrome

Abstract Background Severe coccidioidomycosis presenting with respiratory failure is an uncommon manifestation of disease. Current knowledge of this condition is limited to case reports and small case series. Methods A retrospective multicenter review of patients with coccidioidomycosis-associated acute respiratory distress syndrome (CA-ARDS) was conducted. It assessed clinical and laboratory variables at the time of presentation, reviewed the treatment course, and compared this cohort with a national database of patients with noncoccidioidomycosis ARDS. Survivors and nonsurvivors of coccidioidomycosis were also compared to determine prognostic factors. Results In this study, CA-ARDS (n = 54) was most common in males, those of Hispanic ethnicity, and those with concurrent diabetes mellitus. As compared with the PETAL network database (Prevention and Early Treatment of Acute Lung Injury; n = 1006), patients with coccidioidomycosis were younger, had fewer comorbid conditions, and were less acidemic. The 90-day mortality was 15.4% for patients with coccidioidomycosis, as opposed to 42.6% (P < .0001) for patients with noncoccidioidomycosis ARDS. Patients with coccidioidomycosis who died, as compared with those who survived, were older, had higher APACHE II scores (Acute Physiology and Chronic Health Evaluation), and did not receive corticosteroid therapy. Conclusions CA-ARDS is an uncommon but morbid manifestation of infection. When compared with a national database, the overall mortality appears favorable vs other causes of ARDS. Patients with CA-ARDS had a low overall mortality but required prolonged antifungal therapy. The utility of corticosteroids in this condition remains unconfirmed.
Using temporal MODIS data to detect paddy rice in Red River Delta

Information on the area and spatial distribution of paddy rice fields is needed for food security, management of water resources, and estimation of Methan emission as well. MODIS remote sensing data including visible bands, near infrared band and short wave infrared band is foundation of calculating vegetation indices such as NDVI, EVI and LSWI. These remote sensing indices are very sensitive and strongly correlative to physiological status of plant, they are useful means for detecting and mapping paddy rice. This paper focus on an algorithm that uses time series of these vegetation indices to identify paddy rice areas based on sensivity of LSWI to the increased surface moisture during the period of flooding and rice transplanting.
PM-CNN: microbiome status recognition and disease detection model based on phylogeny and multi-path neural network

Abstract Motivation The human microbiome, found throughout various body parts, plays a crucial role in health dynamics and disease development. Recent research has highlighted microbiome disparities between patients with different diseases and healthy individuals, suggesting the microbiome’s potential in recognizing health states. Traditionally, microbiome-based status classification relies on pre-trained machine learning (ML) models. However, most ML methods overlook microbial relationships, limiting model performance. Results To address this gap, we propose PM-CNN (Phylogenetic Multi-path Convolutional Neural Network), a novel phylogeny-based neural network model for multi-status classification and disease detection using microbiome data. PM-CNN organizes microbes based on their phylogenetic relationships and extracts features using a multi-path convolutional neural network. An ensemble learning method then fuses these features to make accurate classification decisions. We applied PM-CNN to human microbiome data for status and disease detection, demonstrating its significant superiority over existing ML models. These results provide a robust foundation for microbiome-based state recognition and disease prediction in future research and applications. Availability and implementation PM-CNN software is available at https://github.com/qdu-bioinfo/PM_CNN.
The Effect of Tirzepatide on Weight, Lipid Metabolism and Blood Pressure in Overweight/Obese Patients with Type 2 Diabetes Mellitus: A Systematic Review and Meta-Analysis

Aim To explore the effects of Tirzepatide (TZP), a new hypoglycemic drug, on weight, blood lipids and blood pressure in overweight/obese patients with type 2 diabetes mellitus (T2DM). Methods Relevant studies investigating the influence of TZP therapy on weight, lipid profiles and blood pressure in overweight/obese T2DM patients were selected from the PubMed, Embase, Web of Science and Cochrane databases from establishment until November 2022. A systematic review and meta-analysis were conducted to evaluate the effect of TZP on weight, blood lipids and blood pressure in overweight/obese patients with T2DM. Results Eight randomized controlled trials (RCTs), comprising 7491 patients with T2DM, were included in the meta-analysis. Results showed that compared with the glucagon-like peptide-1 receptor agonist (GLP-1RA), insulin, and placebo groups, body weight, triglycerides (TG), very low-density lipoprotein cholesterol (VLDL-C), total cholesterol (TC), systolic blood pressure (SBP), diastolic blood pressure (DBP), fasting blood glucose (FBG), and glycosylated hemoglobin (HbA1c) levels were significantly decreased in the TZP-treated groups, while high-density lipoprotein cholesterol (HDL-C) levels increased. With the gradual increase of TZP doses, the proportions of T2DM patients with weight loss >5% gradually increased. The 10 mg and 15 mg TZP doses had a stronger effect on the levels of TG, VLDL-C, and HDL-C. Moreover, the reduction in SBP levels in the 15 mg TZP-treated group was more pronounced than those in the 10 mg and 5 mg TZP-treated groups [MD=−2.07, 95% CI (−2.52, −1.63) and MD=−3.14, 95% CI (−4.42, −1.87)]. Compared with GLP-1RA, insulin, and placebo groups, the proportions of patients with HbA1c<7% in 10mg and 15mg TZP-treated groups were significantly higher than in the 5mg TZP-treated group [OR=1.53, 95% CI (1.25, 1.8)], OR=1.7, 95% CI (1.15, 2.50)]. There was no significant difference regarding the risk of adverse reactions.
Preasymptotic Approaches to Upscaling of Diffusion Limited Transport of Solutes

by Nicole Lyn Sund We assess the importance of accounting for preasymptotic velocity correlation in upscaling solute transport in a variety of situations. We compare upscaled predictions of the Spatial Markov model which accounts for correlation and upscaled predictions of models which do not account for correlation to high resolution simulations of both reactive and conservative transport. We find that for conservative transport in steady flows velocity correlation plays a major role and cannot be neglected at preasymptotic length scales, yet in unsteady flows this correlation is weak and can be neglected. For reactive transport, we find that velocity correlation is even more important, as accounting for it allows us to accurately model the amount of reactions that take place in the system. Additionally, we find the minimum length scale necessary to use the Spatial Markov model and propose guidelines for the model’s use. Dedicated to my mother, Lynette, for her support and patience and to my daughter, Kiley, for her encouragement and motivation.
Assessing the impacts of banana bacterial wilt disease on banana (Musa spp.) productivity and livelihoods of Ugandan farm households.

Banana Xanthomonas wilt disease (BXW, caused by Xanthomonas campestris pv) was discovered simultaneously in 2001 in Uganda and the DR Congo, and in 2002 in neighboring Rwanda. In Uganda, the disease was first seen in the central region where ABB bananas (Musa spp.) dominate. Subsequently, the disease spread into other regions in Uganda and into Kenya and Tanzania. A collaborative study was carried out in four districts in Central Uganda to establish the factors contributing to the epidemic and to assess the impact of the disease on banana yield. On average, 33% of the total banana mats were infected with BXW between 2001 and 2004. None of the banana cultivars were resistant to the disease. However, East African highland cultivars (AAA-EAHB genome) were significantly less affected than cultivars with AAB and ABB genomes. The results showed that a participatory development communication campaign against the disease had substantial impact on reducing disease incidence. Compared to pre-infection levels, the total banana yield loss due to BXW infection was estimated at 30-52% between 2001 and 2004, hence a reduction in the amount of bananas harvested by farm households. This in turn had negative livelihood impacts. Consumption of own produced bananas, sale of bananas, prices received, and farm and total household incomes were substantially lower between BXW infected farm households than their noninfected counterparts. For affected farm households that have little or no opportunities outside banana production, other interventions, in addition to the educational campaign on controlling the disease, will be required.
Influence of host phylogeny and water physicochemistry on microbial assemblages of the fish skin microbiome

Abstract The skin of fish contains a diverse microbiota that has symbiotic functions with the host, facilitating pathogen exclusion, immune system priming, and nutrient degradation. The composition of fish skin microbiomes varies across species and in response to a variety of stressors, however, there has been no systematic analysis across these studies to evaluate how these factors shape fish skin microbiomes. Here, we examined 1922 fish skin microbiomes from 36 studies that included 98 species and nine rearing conditions to investigate associations between fish skin microbiome, fish species, and water physiochemical factors. Proteobacteria, particularly the class Gammaproteobacteria, were present in all marine and freshwater fish skin microbiomes. Acinetobacter, Aeromonas, Ralstonia, Sphingomonas and Flavobacterium were the most abundant genera within freshwater fish skin microbiomes, and Alteromonas, Photobacterium, Pseudoalteromonas, Psychrobacter and Vibrio were the most abundant in saltwater fish. Our results show that different culturing (rearing) environments have a small but significant effect on the skin bacterial community compositions. Water temperature, pH, dissolved oxygen concentration, and salinity significantly correlated with differences in beta-diversity but not necessarily alpha-diversity. To improve study comparability on fish skin microbiomes, we provide recommendations for approaches to the analyses of sequencing data and improve study reproducibility.
Improvement of Mouse Preantral Follicle Survival and Development following Co-Culture with Ovarian Parenchyma Cell Suspension

Background: The parallel and continued improvements in both infertility treatment and the management of malignancy cases have brought to the forefront the potential for fertility preservation. Using ovarian follicular resources can effectively improve reproductive capacity and prevent infertility. The primary aim of this research was to try to generate an appropriate in vivo environment for the growth of the mouse follicles. Hence, the possible effects of the ovarian parenchyma cell suspension were explored on the growth and maturation of preantral follicles in vitro. Materials and Methods: In this experimental study, ovarian parenchymal cells were mechanically dissociated from preantral follicles of 12-14 days-old NMRI mice and then divided into 5 experimental groups (G1: Control, G2: Fresh follicle with fresh parenchyma cell suspension, G3: Vitrified-warmed follicle with fresh parenchyma cell suspension, G4: Fresh follicle with frozen-thawed parenchyma cell suspension, and G5: Vitrified-warmed follicle with frozenthawed parenchyma cell suspension). The diameter of the follicles and immature oocytes, viability, antrum formation, resumption of meiosis, in vitro fertilization (IVF), and Gdf9, Bmp6, and Bmp15 gene expression were examined on different periods. Results: The diameter of the follicles and the oocytes on days 4 and 8, as well as the survival rate of the follicles up to day 12, were significantly higher in G2 and G4 compared to the Ctrl group (G1: 73.66%, G2:87.99%, G3: 82.70%, G4: 94.37%, and G5: 78.59%). Expression of growth marker genes for G3, and G5 groups was significantly higher than other groups, which indicated the protective effects of parenchyma cell suspension on follicles damaged by vitrification solutions. Conclusion: The growth, survival, and maturation of preantral follicles could be enhanced by co-culturing them with ovarian parenchyma cells. Further studies are needed to optimize the conditions for a successful parenchyma cell suspension-induced in vitro maturation (IVM) to occur in infertility clinics.
UAS, sensors, and data processing in agroforestry: a review towards practical applications

ABSTRACT The aim of this study is twofold: first, to present a survey of the actual and most advanced methods related to the use of unmanned aerial systems (UASs) that emerged in the past few years due to the technological advancements that allowed the miniaturization of components, leading to the availability of small-sized unmanned aerial vehicles (UAVs) equipped with Global Navigation Satellite Systems (GNSS) and high quality and cost-effective sensors; second, to advice the target audience – mostly farmers and foresters – how to choose the appropriate UAV and imaging sensor, as well as suitable approaches to get the expected and needed results of using technological tools to extract valuable information about agroforestry systems and its dynamics, according to their parcels’ size and crop’s types.Following this goal, this work goes beyond a survey regarding UAS and their applications, already made by several authors. It also provides recommendations on how to choose both the best sensor and UAV, in according with the required application. Moreover, it presents what can be done with the acquired sensors’ data through theuse of methods, procedures, algorithms and arithmetic operations. Finally, some recent applications in the agroforestry research area are presented, regarding the main goal of each analysed studies, the used UAV, sensors, and the data processing stage to reach conclusions.
Cloud Kitchen: Using Planning-based Composite AI to Optimize Food Delivery Process

The global food delivery market provides many opportunities for AI-based services that can improve the efficiency of feeding the world. This paper presents the Cloud Kitchen platform as a decision-making tool for restaurants with food delivery and a simulator to evaluate the impact of the decisions. The platform consists of a Technology-Specific Bridge (TSB) that provides an interface for communicating with restaurants or the simulator. TSB uses a PDDL model to represent decisions embedded in the Unified Planning Framework (UPF). Decision-making, which concerns allocating customers' orders to vehicles and deciding in which order the customers will be served (for each vehicle), is done via a Vehicle Routing Problem with Time Windows (VRPTW), an efficient tool for this problem. We show that decisions made by our platform can improve customer satisfaction by reducing the number of delayed deliveries using a real-world historical dataset.
Policy Learning for Off-Dynamics RL with Deficient Support

Reinforcement Learning (RL) can effectively learn complex policies. However, learning these policies often demands extensive trial-and-error interactions with the environment. In many real-world scenarios, this approach is not practical due to the high costs of data collection and safety concerns. As a result, a common strategy is to transfer a policy trained in a low-cost, rapid source simulator to a real-world target environment. However, this process poses challenges. Simulators, no matter how advanced, cannot perfectly replicate the intricacies of the real world, leading to dynamics discrepancies between the source and target environments. Past research posited that the source domain must encompass all possible target transitions, a condition we term full support. However, expecting full support is often unrealistic, especially in scenarios where significant dynamics discrepancies arise. In this paper, our emphasis shifts to addressing large dynamics mismatch adaptation. We move away from the stringent full support condition of earlier research, focusing instead on crafting an effective policy for the target domain. Our proposed approach is simple but effective. It is anchored in the central concepts of the skewing and extension of source support towards target support to mitigate support deficiencies. Through comprehensive testing on a varied set of benchmarks, our method's efficacy stands out, showcasing notable improvements over previous techniques.
Theoretical Understanding of Learning from Adversarial Perturbations

It is not fully understood why adversarial examples can deceive neural networks and transfer between different networks. To elucidate this, several studies have hypothesized that adversarial perturbations, while appearing as noises, contain class features. This is supported by empirical evidence showing that networks trained on mislabeled adversarial examples can still generalize well to correctly labeled test samples. However, a theoretical understanding of how perturbations include class features and contribute to generalization is limited. In this study, we provide a theoretical framework for understanding learning from perturbations using a one-hidden-layer network trained on mutually orthogonal samples. Our results highlight that various adversarial perturbations, even perturbations of a few pixels, contain sufficient class features for generalization. Moreover, we reveal that the decision boundary when learning from perturbations matches that from standard samples except for specific regions under mild conditions. The code is available at https://github.com/s-kumano/learning-from-adversarial-perturbations.
Determining classes for generalized $\psi$-estimators

We prove that the values of a generalized $\psi$-estimator (introduced by Barczy and P\'ales in 2022) on samples of arbitrary length but having only two different observations uniquely determine the values of the estimator on any sample of arbitrary length without any restriction on the number of different observations. In other words, samples of arbitrary length but having only two different observations form a determining class for generalized $\psi$-estimators. We also obtain a similar statement for the comparison of generalized $\psi$-estimators using comparative functions, and, as a corollary of this result, we derive the Schweitzer's inequality (also called Kantorovich's inequality).
Trading off Consistency and Dimensionality of Convex Surrogates for the Mode

In multiclass classification over $n$ outcomes, the outcomes must be embedded into the reals with dimension at least $n-1$ in order to design a consistent surrogate loss that leads to the"correct"classification, regardless of the data distribution. For large $n$, such as in information retrieval and structured prediction tasks, optimizing a surrogate in $n-1$ dimensions is often intractable. We investigate ways to trade off surrogate loss dimension, the number of problem instances, and restricting the region of consistency in the simplex for multiclass classification. Following past work, we examine an intuitive embedding procedure that maps outcomes into the vertices of convex polytopes in a low-dimensional surrogate space. We show that full-dimensional subsets of the simplex exist around each point mass distribution for which consistency holds, but also, with less than $n-1$ dimensions, there exist distributions for which a phenomenon called hallucination occurs, which is when the optimal report under the surrogate loss is an outcome with zero probability. Looking towards application, we derive a result to check if consistency holds under a given polytope embedding and low-noise assumption, providing insight into when to use a particular embedding. We provide examples of embedding $n = 2^{d}$ outcomes into the $d$-dimensional unit cube and $n = d! $ outcomes into the $d$-dimensional permutahedron under low-noise assumptions. Finally, we demonstrate that with multiple problem instances, we can learn the mode with $\frac{n}{2}$ dimensions over the whole simplex.