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The consumption of energy-dense diets has contributed to an increase in the prevalence of obesity and its comorbidities worldwide. The adoption of unhealthy feeding habits often occurs at early age, prompting the early onset of metabolic disease with unknown consequences for reproductive function later in life. Recently, evidence has emerged regarding the intergenerational and transgenerational effects of high-fat diets (HFD) on sperm parameters and testicular metabolism. Hereby, we study the impact of high-fat feeding male mice (F 0 ) on the testicular metabolome and function of their sons (F 1 ) and grandsons (F 2 ). Testicular content of metabolites related to insulin resistance, cell membrane remodeling, nutritional support and antioxidative stress (leucine, acetate, glycine, glutamine, inosine) were altered in sons and grandsons of mice fed with HFD, comparing to descendants of chow-fed mice. Sperm counts were lower in the grandsons of mice fed with HFD, even if transient. Sperm quality was correlated to testicular metabolite content in all generations. Principal Component Analysis of sperm parameters and testicular metabolites revealed an HFD-related phenotype, especially in the diet-challenged generation and their grandsons. Ancestral HFD, even if transient, causes transgenerational “inherited metabolic memory” in the testicular tissue, characterized by changes in testicular metabolome and function.

Given the continuous increase in the global population, the food manufacturers are advocated to either intensify the use of cropland or expand the farmland, making land cover and land usage dynamics mapping vital in the area of remote sensing. In this regard, identifying and classifying a high-resolution satellite imagery scene is a prime challenge. Several approaches have been proposed either by using static rule-based thresholds (with limitation of diversity) or neural network (with data-dependent limitations). This paper adopts the inductive approach to learning from surface reflectances. A manually labeled Sentinel-2 dataset was used to build a Machine Learning (ML) model for scene classification, distinguishing six classes (Water, Shadow, Cirrus, Cloud, Snow, and Other). This models was accessed and further compared to the European Space Agency (ESA) Sen2Cor package. The proposed ML model presents a Micro-F1 value of 0.84, a considerable improvement when compared to the Sen2Cor corresponding performance of 0.59. Focusing on the problem of optical satellite image scene classification, the main research contributions of this paper are: (a) an extended manually labeled Sentinel-2 database adding surface reflectance values to an existing dataset; (b) an ensemble-based and a Neural-Network-based ML models; (c) an evaluation of model sensitivity, biasness, and diverse ability in classifying multiple classes over different geographic Sentinel-2 imagery, and finally, (d) the benchmarking of the ML approach against the Sen2Cor package.

Hormonal regulation is essential to spermatogenesis. Sertoli cells (SCs) have functions that reach far beyond the physical support of germ cells, as they are responsible for creating the adequate ionic and metabolic environment for germ cell development. Thus, much attention has been given to the metabolic functioning of SCs. During spermatogenesis, germ cells are provided with suitable metabolic substrates, in a set of events mediated by SCs. Multiple signaling cascades regulate SC function and several of these signaling pathways are hormone-dependent and cell-specific. Within the seminiferous tubules, only SCs possess receptors for some hormones rendering them major targets for the hormonal signaling that regulates spermatogenesis. Although the mechanisms by which SCs fulfill their own and germ cells metabolic needs are mostly studied in vitro, SC metabolism is unquestionably a regulation point for germ cell development and the hormonal control of these processes is required for a normal spermatogenesis.

Sertoli cells provide nutritional support for germ cells by secreting nutrients or metabolic intermediates, such as amino acids, carbohydrates, lipids, vitamins, and metal ions. Here, the authors discuss the importance of Sertoli cell metabolism in the formation of the mature spermatozoa, and the regulation of this metabolism, which could have a direct influence on male fertility. Male factor infertility is increasing in developed countries, and several factors linked to lifestyle have been shown to negatively affect spermatogenesis. Sertoli cells are pivotal to spermatogenesis, providing nutritional support to germ cells throughout their development. Sertoli cells display atypical features in their cellular metabolism; they can metabolize various substrates, preferentially glucose, the majority of which is converted to lactate and not oxidized via the tricarboxylic acid cycle. Why Sertoli cells preferentially export lactate for germ cells is not entirely understood. However, lactate is utilized as the main energy substrate by developing germ cells and has an antiapoptotic effect on these cells. Several biochemical mechanisms contribute to the modulation of lactate secretion by Sertoli cells. These include the transport of glucose through the plasma membrane, mediated by glucose transporters; the interconversion of pyruvate to lactate by lactate dehydrogenase; and the release of lactate mediated by monocarboxylate transporters. Several factors that modulate Sertoli cell metabolism have been identified, including sex steroid hormones, which are crucial for maintenance of energy homeostasis, influencing the metabolic balance of the whole body. In fact, energy status is essential for normal reproductive function, since the reproductive axis has the capacity to respond to metabolic cues. Key Points Sertoli cells have multiple roles in germ cell development, ranging from physical support and immunoprotection to the supply of nutrients and other factors Germ cells have specific metabolic needs, which change during their development into spermatozoa, rendering them dependent on the nurturing provided by Sertoli cells Sertoli cells utilize a number of different substrates (including glucose and fatty acids) and pathways to fulfill their metabolic requirements, as well as those of developing germ cells A number of hormones and factors, such as follicle-stimulating hormone, insulin, insulin growth factor-I, epidermal growth factor, paracrine factor P-Mod-S, tri-iodothyronine, basic fibroblast growth factor, cytokines, carnitine, AMP-activated protein kinase, arachidonic acid and sex steroid hormones, are known to be metabolic modulators of Sertoli cells Metabolic status is central to the regulation of the energy demands of the reproductive system, and extreme metabolic disorder conditions (such as obesity) are deleterious to reproductive function The reproductive axis (hypothalamus–pituitary–testis axis) is exceptionally sensitive to energetic imbalance and disturbances of this axis severely affect Sertoli cells functions

This work investigates the use of LLMs to enhance automation in software testing, with a particular focus on generating high-quality, context-aware test scripts from natural language descriptions, while addressing both text-to-code and text+code-to-code generation tasks. The Codestral Mamba model was fine-tuned by proposing a way to integrate LoRA matrices into its architecture, enabling efficient domain-specific adaptation and positioning Mamba as a viable alternative to Transformer-based models. The model was trained and evaluated on two benchmark datasets: CONCODE/CodeXGLUE and the proprietary TestCase2Code dataset. Through structured prompt engineering, the system was optimized to generate syntactically valid and semantically meaningful code for test cases. Experimental results demonstrate that the proposed methodology successfully enables the automatic generation of code-based test cases using large language models. In addition, this work reports secondary benefits, including improvements in test coverage, automation efficiency, and defect detection when compared to traditional manual approaches. The integration of LLMs into the software testing pipeline also showed potential for reducing time and cost while enhancing developer productivity and software quality. The findings suggest that LLM-driven approaches can be effectively aligned with continuous integration and deployment workflows. This work contributes to the growing body of research on AI-assisted software engineering and offers practical insights into the capabilities and limitations of current LLM technologies for testing automation.

Prediabetes (PrDM) is a prodromal stage of diabetes mellitus (DM) with an increasing prevalence worldwide. During DM progression, individuals gradually develop complications in various organs. However, lungs are suggested to be affected later than other organs, such as the eyes, heart or brain. In this work, we studied the effects of PrDM on male Wistar rats’ lungs and whether the regular consumption of white tea (WTEA) for 2 months contributes to the improvement of the antioxidant profile of this tissue, namely through improved activity of the first line defense antioxidant enzymes, the total antioxidant capacity and the damages caused in proteins, lipids and histone H2A. Our data shows that PrDM induced a decrease in lung superoxide dismutase and glutathione peroxidase activities and histone H2A levels and an increase in protein nitration and lipid peroxidation. Remarkably, the regular WTEA intake improved lung antioxidant enzymes activity and total antioxidant capacity and re-established the values of protein nitration, lipid peroxidation and histone H2A. Overall, this is the first time that lung is reported as a major target for PrDM. Moreover, it is also the first report showing that WTEA possesses relevant chemical properties against PrDM-induced lung dysfunction.

The digital world is very dynamic. The ability to timely identify possible vendor migration trends or customer loss risks is very important in cloud-based services. This work describes a churn risk prediction system and how it can be applied to guide cloud service providers for recommending adjustments in the service subscription level, both to promote rational resource consumption and to avoid CSP customer loss. A training dataset was built from real data about the customer, the subscribed service and its usage history, and it was used in a supervised machine-learning approach for prediction. Classification models were built and evaluated based on multilayer neural networks, AdaBoost and random forest algorithms. From the experiments with our dataset, the best results for a churn prediction were obtained with a random forest-based model, with 64 estimators, having 0.988 accuracy and 0.997 AUC value.

Obesogens have been identified as a significant factor associated with increasing obesity rates, particularly in developed countries. Substances with obesogenic traits are prevalent in consumer products, including certain pharmaceuticals. Specific classes of pharmaceuticals have been recognized for their ability to induce weight gain, often accompanied by hormonal alterations that can adversely impact male fertility. Indeed, research has supplied evidence underscoring the crucial role of obesogens and therapeutic agents in the normal functioning of the male reproductive system. Notably, sperm count and various semen parameters have been closely linked to a range of environmental and nutritional factors, including chemicals and pharmacological agents exhibiting obesogenic properties. This review aimed to explore studies focused on analyzing male fertility parameters, delving into the intricacies of sperm quality, and elucidating the direct and adverse effects that pharmacological agents may have on these aspects.

The current scenario of male infertility is not yet fully elucidated; however, there is increasing evidence that it is associated with the widespread exposure to endocrine-disrupting chemicals (EDCs), and in particular to obesogens. These compounds interfere with hormones involved in the regulation of metabolism and are associated with weight gain, being also able to change the functioning of the male reproductive axis and, consequently, the testicular physiology and metabolism that are pivotal for spermatogenesis. The disruption of these tightly regulated metabolic pathways leads to adverse reproductive outcomes. The permanent exposure to obesogens has raised serious health concerns. Evidence suggests that obesogens are one of the leading causes of the marked decline of male fertility and key players in shaping the future health outcomes not only for those who are directly exposed but also for upcoming generations. In addition to the changes that lead to inefficient functioning of the male gametes, obesogens induce alterations that are “imprinted” on the genes of the male gametes, establishing a link between generations and contributing to the transmission of defects. Unveiling the molecular mechanisms by which obesogens induce toxicity that may end-up in epigenetic modifications is imperative. This review describes and discusses the suggested molecular targets and potential mechanisms for obesogenic–disrupting chemicals and the subsequent effects on male reproductive health.

Automatic speech recognition (ASR), commonly known as speech-to-text, is the process of transcribing audio recordings into text, i.e., transforming speech into the respective sequence of words. This paper presents a deep learning ASR system optimization and evaluation for the European Portuguese language. We present a pipeline composed of several stages for data acquisition, analysis, pre-processing, model creation, and evaluation. A transfer learning approach is proposed considering an English language-optimized model as starting point; a target composed of European Portuguese; and the contribution to the transfer process by a source from a different domain consisting of a multiple-variant Portuguese language dataset, essentially composed of Brazilian Portuguese. A domain adaptation was investigated between European Portuguese and mixed (mostly Brazilian) Portuguese. The proposed optimization evaluation used the NVIDIA NeMo framework implementing the QuartzNet15×5 architecture based on 1D time-channel separable convolutions. Following this transfer learning data-centric approach, the model was optimized, achieving a state-of-the-art word error rate (WER) of 0.0503.