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Recently, the Netherlands has shifted toward more welfare-friendly broiler production systems using slower-growing broiler breeds. Early post-hatch feeding (EF) is a dietary strategy that is currently used in commercial broiler production to modulate the gut microbiota and improve performance and welfare. However, there is a knowledge gap in how both breed and EF and their interplay affect gut microbiota composition and diversity, inflammatory status, and broiler behavior. Therefore, the aim of this study was to investigate the effects of breed (fast vs. slower-growing), EF, and their interaction on jejunum microbiota, inflammation, and behavior of broiler chickens. The study included a total of 416 Ross 308 and 416 Hubbard JA757 day-old male broiler chickens, observed until they were 37 days and 51 days old, respectively. Within each breed, one-half of the chickens received EF and the other half did not. A total of two chickens per pen were euthanized at two time points, that is, target body weight (BW) of 200 g and 2.5 kg, and jejunum samples were collected. The jejunum content samples (N = 96) were analyzed for their microbiota, whereas the jejunum tissue (N = 96) was used for the detection of mRNA levels of cytokines (IL-17, IL-22, and IFNγ). Two behavioral tests were performed to assess fear responses: (1) a novel environment test at a target BW of 200 g and (2) a tonic immobility test at a target BW of 2.5 kg. Breed affected the microbiota at a target BW of 2.5 kg (p = 0.04). A breed × EF interaction (p = 0.02) was present for IFNγ at a target BW of 200 g. During the novel environment test, Ross 308 chickens exhibited a shorter latency to vocalize and a higher number of vocalizations compared to Hubbard JA757 chickens (p

Este trabalho objetivou estimar a exigência nutricional de triptofano digestível para codornas japonesas na fase inicial de postura, além de verificar seus efeitos sobre a qualidade dos ovos, o peso dos órgãos, a contagem diferencial leucocitária e a relação heterófilo:linfócito. O delineamento experimental adotado foi o inteiramente casualizado, apresentando cinco tratamentos (0,14; 0,19; 0,24; 0,29 e 0,34%) e cinco repetições, resultando em 25 unidades experimentais, com 12 codornas por unidade experimental, totalizando 300 aves no período de 42 a 126 dias de idade. Considerando os resultados, estimou-se o nível de 0,14% de triptofano digestível para a fase de postura. O peso de oviduto apresentou efeito quadrático, possibilitando estimar as exigências de 0,24% de triptofano digestível. Os heterófilos e basófilos apresentaram efeito linear decrescente, os linfócitos e monócitos efeito linear crescente e para a relação heterófilo:linfócito foi observado efeito quadrático, possibilitando estimar as exigências de 0,31% de triptofano digestív. No entanto, para esta fase (63 a 126 dias de idade), com base no desempenho, o nível de 0,14% de triptofano digestível foi o mais indicado, relação triptofano:lisina digestível de 13% e consumo de 34,67 mg / ave/ dia de triptofano digestível. Desta forma, a redução do nível de triptofano na dieta convencional não afetará o desempenho das aves.

# Background and Purpose The comprehensive treatment for an athlete who sustains a complete posterior cruciate ligament (PCL) injury remains unclear. The purpose of this case report is to describe the effects of an isokinetic exercise program on muscle performance and physical function in a professional soccer player with a PCL injury. # Study Design Case Report # Case Description A 23-year-old male professional soccer player injured his right knee (non-dominant) during a soccer match, with magnetic resonance imaging confirming a complete PCL rupture. The athlete completed 23 sessions of isokinetic treatment over nine weeks to improve physical function and muscle performance. The concentric mode was used to evaluate quadriceps and hamstrings performance isokinetically at angular velocities of 60 °/s, 120 °/s, and 300 °/s. The LEFS questionnaire was used to evaluate physical function. The assessment occurred before starting the treatment and at the end of nine weeks. An athlete with similar anthropometric characteristics was invited to participate and serve as a control, in order to better understand the athlete’s assessment results. # Outcomes After nine weeks, peak torque normalized to body mass (PT/BM) improved to 4.0 N.m/kg for knee extensors (control: 3.6 Nm/kg) and 2.3 N.m/kg for knee flexors (control: 1.9 Nm/kg) at 60 °/s. This increase in PT was reflected in the hamstrings-to-quadriceps (H:Q) ratio post-treatment (57 %). At the end of treatment, the athlete returned to sports activities with 98.7 % restored physical function as measured by the LEFS (Score: 79 points). # Discussion The results demonstrated that the isokinetic treatment improved knee functional capacity, with increased PT/BM for knee extensors and flexors and enhanced torque-holding capacity, indicating improved muscle performance. # Level of evidence 5

Identifying plantation lines in aerial images of agricultural landscapes is re-quired for many automatic farming processes. Deep learning-based networks are among the most prominent methods to learn such patterns and extract this type of information from diverse imagery conditions. However, even state-of-the-art methods may stumble in complex plantation patterns. Here, we propose a deep learning approach based on graphs to detect plantation lines in UAV-based RGB imagery, presenting a challenging scenario containing spaced plants. The first module of our method extracts a feature map throughout the backbone, which consists of the initial layers of the VGG16. This feature map is used as an input to the Knowledge Estimation Module (KEM), organized in three concatenated branches for detecting 1) the plant positions, 2) the plantation lines, and 3) the displacement vectors between the plants. A graph modeling is applied considering each plant position on the image as vertices, and edges are formed between two vertices (i.e. plants). Finally, the edge is classified as pertaining to a certain plantation line based on three probabilities (higher than 0.5): i) in visual features obtained from the backbone; ii) a chance that the edge pixels belong to a line, from the KEM step; and iii) an alignment of the displacement vectors with the edge, also from the KEM step. Experiments were conducted initially in corn plantations with different growth stages and patterns with aerial RGB imagery to present the advantages of adopting each module. We assessed the generalization capability in the other two cultures (orange and eucalyptus) datasets. The proposed method was compared against state-of-the-art deep learning methods and achieved superior performance with a significant margin considering all three datasets. This approach is useful in extracting lines with spaced plantation patterns and could be implemented in scenarios where plantation gaps occur, generating lines with few-to-no interruptions.

Aldehyde dehydrogenase 1A3 (ALDH1A3) is a cancer stem cell marker that promotes metastasis. Triple‐negative breast cancer (TNBC) progression has been linked to ALDH1A3‐induced gene expression changes. To investigate the mechanism of ALDH1A3‐mediated breast cancer metastasis, we assessed the effect of ALDH1A3 on the expression of proteases and the regulators of proteases that degrade the extracellular matrix, a process that is essential for invasion and metastasis. This revealed that ALDH1A3 regulates the plasminogen activation pathway; it increased the levels and activity of tissue plasminogen activator (tPA) and urokinase plasminogen activator (uPA). This resulted in a corresponding increase in the activity of serine protease plasmin, the enzymatic product of tPA and uPA. The ALDH1A3 product all‐trans‐retinoic acid similarly increased tPA and plasmin activity. The increased invasion of TNBC cells by ALDH1A3 was plasminogen‐dependent. In patient tumours, ALDH1A3 and tPA are co‐expressed and their combined expression correlated with the TNBC subtype, high tumour grade and recurrent metastatic disease. Knockdown of tPA in TNBC cells inhibited plasmin generation and lymph node metastasis. These results identify the ALDH1A3–tPA–plasmin axis as a key contributor to breast cancer progression.

Robustness in remote sensing image captioning is crucial for real-world applications. However, most of the research focuses on improving the performance of single captioning algorithms, either by introducing novel feature processing units or metatasks that indirectly improve the captioning performance. Despite indisputable improvements in performance, we argue that relying on the output of a single model can be critical, especially when data scarcity limits the generalization capability of the trained algorithms. Focusing on the advantages of ensembles for improving robustness, we propose different ways to select or generate a single most coherent caption from a set of predictions made by different captioning algorithms. The disjunction between the two phases of prediction and selection/generation provides high flexibility for inserting different captioning algorithms, each with its peculiarities and strengths. In this context, based on neural natural language processing tools, our approach can be considered as an additional fusion block that enables higher robustness with a contained complexity burden.

Unmanned Aerial Vehicles (UAVs) play a crucial role in exploring unpredictable terrains, such as accident sites and search zones. These robots require autonomous navigation and precise path planning to operate safely and efficiently in dynamic environments. UAVs must balance navigation accuracy and energy efficiency constraints during autonomous operations. This paper introduces an innovative boundary-driven mapping strategy for UAV exploration in unknown environments. The study proposes a novel approach for boundary extraction using deep learning and presents a decision-making methodology to enable the UAV to select the most optimal frontier for exploration based on deep learning-derived information. The primary objective is to efficiently expand the unknown map. The research demonstrates a significant reduction in exploration time within a simulated environment, achieving better performance compared to other methodologies documented in the literature. The results highlight the effectiveness and efficiency of the proposed strategy in terms of mapping exploration.

Crop segmentation, the process of identifying and delineating agricultural fields or specific crops within an image, plays a crucial role in precision agriculture, enabling farmers and public managers to make informed decisions regarding crop health, yield estimation, and resource allocation in Midwest Brazil. The crops (corn) in this region are being damaged by wild pigs and other diseases. For the quantification of corn fields, this paper applies novel computer-vision techniques and a new dataset of corn imagery composed of 1416 256 × 256 images and corresponding labels. We flew nine drone missions and classified wild pig damage in ten orthomosaics in different stages of growth using semi-automatic digitizing and deep-learning techniques. The period of crop-development analysis will range from early sprouting to the start of the drying phase. The objective of segmentation is to transform or simplify the representation of an image, making it more meaningful and easier to interpret. For the objective class, corn achieved an IoU of 77.92%, and for background 83.25%, using DeepLabV3+ architecture, 78.81% for corn, and 83.73% for background using SegFormer architecture. For the objective class, the accuracy metrics were achieved at 86.88% and for background 91.41% using DeepLabV3+, 88.14% for the objective, and 91.15% for background using SegFormer.

Abstract Drugs causing ferroptosis, iron-mediated cell death, represent promising tools for cancer treatment. While exploring the effect of these drugs on breast cancer (BC), we found that a ferroptosis-inducing drug erastin dramatically inhibits tumorigenicity of human BC cells in mice but when used at a concentration known to effectively kill other cell types only modestly reduces such growth in 2D monolayer culture. BCs grow in vivo as 3D masses, and we found that ferroptosis inducers erastin and sulfasalazine inhibit growth of multiple human BC cell lines in 3D culture significantly stronger than in 2D culture. To understand the mechanism of this differential effect, we found that ferroptosis inducers upregulate mRNAs encoding multiple direct and indirect autophagy stimulators, such as ATG16L2, ATG9A, ATG4D, GABARAP, SQSTM/p62, SEC23A and BAX, in tumor cells growing in 2D but not in 3D culture. Furthermore, these drugs promoted autophagy of tumor cells growing in a 2D but not in a 3D manner. We observed that pharmacological inhibition of autophagy-stimulating protein kinase ULK1 or RNA interference-mediated knockdown of autophagy mediator ATG12 significantly sensitized tumor cells to erastin treatment in 2D culture. We also found that ferroptosis-promoting treatments upregulate heme oxygenase-1 (HO-1) in BC cells. HO-1 increases cellular free iron pool and can potentially promote ferroptosis. Indeed, we observed that HO-1 knockdown by RNA interference reversed the effect of ferroptosis inducers on BC cell 3D growth. Hence, the effect of these drugs on such growth is mediated by HO-1. In summary, autophagy triggered by ferroptosis-promoting drugs reduces their ability to kill BC growing in a 2D manner. This protection mechanism is inhibited in BC cells growing as a 3D mass, and ferroptosis-promoting drugs kill such cells more effectively. Moreover, this death is mediated by HO-1. Thus, ferroptosis induction represents a promising strategy for blocking 3D BC growth.

In the context of hydroelectric plants, this article emphasizes the imperative of robust monitoring strategies. The utilization of fiber-optic sensors (FOSs) emerges as a promising approach due to their efficient optical transmission, minimal signal attenuation, and resistance to electromagnetic interference. These optical sensors have demonstrated success in diverse structures, including bridges and nuclear plants, especially in challenging environments. This article culminates with the depiction of the development of an array of sensors featuring Fiber Bragg Gratings (FBGs). This array is designed to measure deformation and temperature in protective grids surrounding the turbines at the Santo Antônio Hydroelectric Plant. Implemented in a real-world scenario, the device identifies deformation peaks, indicative of water flow obstructions, thereby contributing significantly to the safety and operational efficiency of the plant.

Angiogenesis is a normal physiological process that also contributes to diabetic retinopathy-related complications and facilitates tumor metastasis by promoting the hematogenic dissemination of malignant cells from solid tumors. Here, we investigated the in vitro, ex vivo, and in vivo anti-angiogenic activity of phloridzin docosahexaenoate (PZ-DHA), a novel ω-3 fatty acid ester of a flavonoid precursor. Human umbilical vein endothelial cells (HUVEC) and human dermal microvascular endothelial cells (HMVEC) treated with a sub-cytotoxic concentration of PZ-DHA to assess in vitro anti-angiogenic activity showed impaired tubule formation on a Matrigel matrix. Ex vivo angiogenesis was measured using rat thoracic aortas, which exhibited reduced vessel sprouting and tubule formation in the presence of PZ-DHA. Female BALB/c mice bearing VEGF165- and basic fibroblast growth factor-containing Matrigel plugs showed a significant reduction in blood vessel development following PZ-DHA treatment. PZ-DHA inhibited HUVEC and HMVEC proliferation, as well as the migration of HUVECs in gap closure and trans-well cell migration assays. PZ-DHA inhibited upstream and downstream components of the Akt pathway and vascular endothelial growth factor (VEGF165)-induced overexpression of small molecular Rho GTPases in HUVECs, suggesting a decrease in actin cytoskeletal-mediated stress fiber formation and migration. Taken together, these findings reveal the potential of combined food biomolecules in PZ-DHA to inhibit angiogenesis.

Abstract Objectives The immune response in children elicited by SARS‐CoV‐2 Omicron infection alone or in combination with COVID‐19 vaccination (hybrid immunity) is poorly understood. We examined the humoral and cellular immune response following SARS‐CoV‐2 Omicron infection in unvaccinated children and children who were previously vaccinated with COVID‐19 mRNA vaccine. Methods Participants were recruited as part of a household cohort study conducted during the Omicron predominant wave (Jan to July 2022) in Victoria, Australia. Blood samples were collected at 1, 3, 6 and 12 months following COVID‐19 diagnosis. Humoral immune responses to SARS‐CoV‐2 Spike proteins from Wuhan, Omicron BA.1, BA.4/5 and JN.1, as well as cellular immune responses to Wuhan and BA.1 were assessed. Results A total of 43 children and 113 samples were included in the analysis. Following Omicron infection, unvaccinated children generated low antibody responses but elicited Spike‐specific CD4 and CD8 T‐cell responses. In contrast, vaccinated children infected with the Omicron variant mounted robust humoral and cellular immune responses to both ancestral strain and Omicron subvariants. Hybrid immunity persisted for at least 6 months post infection, with cellular immune memory characterised by the generation of Spike‐specific polyfunctional CD8 T‐cell responses. Conclusion SARS‐CoV‐2 hybrid immunity in children is characterised by persisting SARS‐CoV‐2 antibodies and robust CD4 and CD8 T‐cell activation and polyfunctional responses. Our findings contribute to understanding hybrid immunity in children and may have implications regarding COVID‐19 vaccination and SARS‐CoV‐2 re‐infections.

Abstract The use of uncrewed aerial vehicle to map the environment increased significantly in the last decade enabling a finer assessment of the land cover. However, creating accurate maps of the environment is still a complex and costly task. Deep learning (DL) is a new generation of artificial neural network research that, combined with remote sensing techniques, allows a refined understanding of our environment and can help to solve challenging land cover mapping issues. This research focuses on the vegetation segmentation of kettle holes. Kettle holes are small, pond‐like, depressional wetlands. Quantifying the vegetation present in this environment is essential to assess the biodiversity and the health of the ecosystem. A machine learning workflow has been developed, integrating a superpixel segmentation algorithm to build a robust dataset, which is followed by a set of DL architectures to classify 10 plant classes present in kettle holes. The best architecture for this task was Xception, which achieved an average F1‐score of 85% in the segmentation of the species. The application of solely 318 samples per class enabled a successful mapping in the complex wetland environment, indicating an important direction for future health assessments in such landscapes.

In Brazil’s wholesale electricity market, long-term contract prices are negotiated between power generators and large consumers. Unlike traditional markets, pricing is not driven by market forces but rather determined by complex computational models known as Hydrothermal Dispatch Optimization Models. These models calculate the Difference Settlement Price (DSP), serving as the short-term market price for electricity. The Brazilian market is divided into four interconnected submarkets: the Southeast, Northeast, North, and South. This study fills an existing research gap by examining the multifractality of these submarkets by applying Multifractal Detrended Fluctuation Analysis over a deseasonalized price return time series. Specifically, it aims to characterize the multifractal features of electricity prices, identify the underlying causes of this multifractality, and assess market efficiency indices over time. Our analysis of historical electricity prices revealed that all submarkets demonstrated anti-persistent behavior–also known as mean-reversion–and multifractality. This finding aligns with similar observations in global markets. The South submarket displayed the highest level of multifractality and the lowest market efficiency. Conversely, the North submarket had the lowest multifractality and the highest efficiency. Through sliding-window analysis, we investigated temporal variations in the Hurst exponent and Long Memory Magnitude, an index to compute market inefficiency. We found consistent anti-persistent behavior across all submarkets, with the South submarket showing greater volatility in its inefficiency index. While preliminary and requiring further in-depth analysis and consideration of other factors, these findings offer valuable insights for decision-makers and regulators pursuing new market arrangements to boost efficiency.

Abstract Dairy calves, including surplus calves, are typically separated from their dam within hours of birth. The aim of this study was to assess the welfare impacts of raising surplus calves destined for veal with their dam for 2 or 4 weeks until transport. Surplus calves from one dairy farm were separated from their dam at birth (n = 39) or kept with the dam (n = 37) until transport to the veal farm at either 2 (n = 50) or 4 (n = 26) weeks of age, with abrupt separation for dam-reared calves. Calf measures of body weight, health, immunity, haematology and behaviour were recorded at the dairy and veal farms. Dam-reared calves had higher body weights in weeks 3, 4 and 5 at the DF, as well as at arrival at the veal farm, but by slaughter this advantage was lost. More dam-reared calves had fever in week 3 and showed signs of disease in week 5 at the dairy farm. Dam-reared calves did not differ in IgG, IgA or IgM levels but had higher counts of white blood cells, which could reflect a higher pathogen exposure rather than improved immunity. Dam-reared calves displayed more fear towards humans in a human approach test at 5 and 7 weeks after arrival at the veal farm, and more frequent social behaviours at the veal farm at 9 and 16 weeks of age. In conclusion, it seems that there may be both advantages and disadvantages to keeping veal calves with the dam in terms of welfare in the current system.

Introdução/objetivos: Reação adversa à doação é definida como uma resposta não intencional do doador associada à coleta de sangue. Apesar do avanço tecnológico e dos cuidados dispensados para a proteção ao doador, reações adversas ou efeitos desagradáveis à coleta de sangue podem ocorrer. Essas reações são classificadas pelos serviços hemoterápicos quanto ao tempo de ocorrência e gravidade, como leves, moderadas e graves, dependendo do quadro clínico de sinais e sintomas apresentado pelo doador podendo ser classificadas como imediatas ou tardias, podendo ser manifestadas até 14 dias após a doação. Este estudo objetivou identificar as principais manifestações clínicas das reações adversas à doação de sangue total no hemonúcleo em Belo Horizonte, Minas Gerais. Material e métodos:: Trata-se de um estudo descritivo, retrospectivo e documental, realizado com base na estatística mensal de doações de sangue via sistema informatizado da instituição, ocorridas entre o período definido de janeiro de 2019 a dezembro de 2022. Para coleta dos dados foi utilizada uma planilha com as variáveis pertinentes ao alcance do objetivo deste estudo para posterior análise. Resultados:: Durante o período de 01 de janeiro de 2019 a 31 dezembro de 2022, foram realizadas 88.026 coletas. Dessas coletas, 84.109 (95,5%) ocorreram sem nenhuma intercorrência e 3917 (4,65%) dos doadores apresentaram alguma intercorrência no ato da doação ou ao término, sendo a maioria composta pelas mulheres (67%). Dentre as intercorrências identificadas: 62 doadores (1,5%) apresentaram hematoma ou dor no braço puncionado, 291 doadores (7,4%) necessitaram da segunda coleta, 483 (12,3%.) apresentaram interrupção de fluxo durante a coleta. Em relação a intercorrências clínicas, o total de 279 (7,1%) doadores apresentaram intercorrências clínicas leves e apenas 14 (0,35%) doadores apresentaram intercorrências clínicas moderadas. A interrupção da doação ocorreu em apenas 6 (0,15%) dos doadores. Foram identificados 494 doadores com risco de queda no período avaliado. Discussão: Após a coleta e interpretação dos dados é possível traçar as intercorrências mais comuns ao ato de doação de sangue. Constatou-se uma baixa ocorrência de reações adversas em relação à proporção de doadores. Observa-se o predomínio de reações adversas de grau leve e na população feminina, condizente com os dados de literatura. Conclusão:: Embora a maioria dos doadores não apresentem nenhuma reação adversa durante ou após a coleta, alguns doadores podem apresentar reações inesperadas. Fatores preditores às reações adversas como mulheres jovens, doadores com idade inferior a 19 anos, peso próximo do limite 50 Kg, alimentação inadequada, histórico de reação adversa em doação anterior, histórico de desmaio, primeira doação e pressão arterial em patamares inferiores (ex.:100/60 mmHg ou inferior), taquicardia, fadiga devem ser identificados no momento da triagem clínica e sinalizado à equipe da coleta . Dessa forma, é necessário que todos os profissionais envolvidos estejam capacitados para o reconhecimento e atendimento das intercorrências visando a segurança e bem estar do doador.