Intrauterine Blood Plasma Platelet-Therapy Mitigates Persistent Breeding-Induced Endometritis, Reduces Uterine Infections, and Improves Embryo Recovery in Mares

Microorganisms, including pathogenic or opportunistic bacteria and fungi, may gain access to the uterus during breeding, and infectious endometritis plays a major role in equine subfertility. This study aimed to assess the post-breeding inflammatory response, endometrial culture, and embryo recovery of mares susceptible to persistent breeding-induced endometritis (PBIE) treated with plasma-rich (PRP) or -poor (PPP) plasma. Mares (n = 12) susceptible to PBIE had three cycles randomly assigned to receive intrauterine infusions of lactate ringer solution (LRS, control), or autologous PRP or PPP pre- (−48 and −24 h) and post-breeding (6 and 24 h). Mares were bred with fresh semen from one stallion. Intrauterine fluid accumulation (IUF) and endometrial neutrophils were assessed every 24 h up to 96 h post-breeding. Uterine cytokines (Ilβ, IL6, CXCL8, and IL10) were evaluated before (0 h), 6, and 24 h post-breeding, and endometrial culture three and nine days after breed. Embryo flushing was performed 8 days post-ovulation. Data were analyzed with mixed model, Tukey’s post-hoc test, and multivariate regression. PRP treatment reduced endometrial neutrophils, post-breeding IUF, and pro-inflammatory cytokines when compared to control-assigned cycles, but not significantly different than PPP. Controls had a significantly higher percentage of positive bacterial cultures (33%) in comparison to PRP-assigned cycles (0%), whereas cycles treated with PPP were not significantly different from the other groups (25%). The PRP-assigned cycles had significantly greater embryo recovery rates (83%) than the control (33%), though not significantly different than PPP (60%). Plasma infusion reduced the duration and intensity of the post-breeding inflammatory response and improved embryo recovery in mares susceptible to PBIE. Platelets incrementally downregulate PBIE and appear to have a dose-dependent antimicrobial property

Neural Implicit Surface Evolution using Differential Equations

This work investigates the use of smooth neural networks for modeling dynamic variations of implicit surfaces under partial differential equations (PDE). For this purpose, it extends the representation of neural implicit surfaces to the space-time $\mathbb{R}^3\times \mathbb{R}$, which opens up mechanisms for \textbf{continuous} geometric transformations. Examples include evolving an initial condition surface towards general vector fields, smoothing and sharpening using the mean curvature equation, and interpolations of initial conditions regularized by specific differential equations. The network training considers two constraints. A data term is responsible for fitting the PDE's initial condition to the corresponding time instant, usually $\mathbb{R}^3 \times \{0\}$. Then, a PDE term forces the network to approximate a solution of the underlying equation, \textbf{without any supervision}. The network can also be initialized based on previously trained initial conditions resulting in faster convergence when compared with the standard approach

INTOXICAÇÃO POR NITRATO/NITRITO EM BOVINOS: RELATO DE CASO

Introdução: A intoxicação por nitrato/nitrito é uma importante enfermidade na bovinocultura brasileira. A fonte mais comum de intoxicação de bovinos por nitrato é por meio da ingestão de gramíneas com níveis altos desse composto levando a quadros de metahemoglobinemia, o que pode causar dispneia, aborto, andar cambaleante, ataxia, tremores musculares, taquipneia, ranger de dentes,  salivação e óbito, dependendo da quantidade que foi ingerida. A tendência de acúmulo de nitrato nas plantas está relacionada com excesso de adubação, baixa incidência solar e períodos de seca seguidos por período chuvoso. Objetivo: relatar a ocorrência de um surto de intoxicação por nitrato/nitrito em bovinos. Método: Para o diagnóstico foram coletados dados epidemiológicos, e na necropsia, foram coletados fragmentos de órgãos em formalina 10%, processados rotineiramente, corados com coloração de hematoxilina e eosina e foram examinados por microscopia óptica. Resultados: O caso clínico ocorreu logo após o início de um período chuvoso posterior à algumas semanas de estiagem.  O lote era composto de aproximadamente 30 bovinos de 1 ano de idade, e após o acesso dos bovinos a pastagem iniciaram em poucos minutos manifestação de dispneia e mucosas cianóticas à amarronzadas. Quatro bovinos do lote adoeceram, destes, um morreu, enquanto os demais foram tratados com azul de metileno 1% com melhora completa do quadro clínico. Na necropsia, as mucosas e órgãos viscerais estavam amarronzados e sangue não coagulável vermelho escuro. Conclusão: Um fator importante para a ocorrência da intoxicação foi o período prolongado de estiagem seguido de chuva, pois nesses casos, as plantas absorvem rapidamente altos níveis de nitrato. Os sinais clínicos e achados post mortem estão de acordo com o que foi descrito anteriormente na literatura. Além disso, a reversão do quadro clínico diante da aplicação do tratamento, também contribuiu para o diagnóstico definitivo

Geology and mineral resources of S?o Jo?o do Para?so (RJ) region.

A regi?o de S?o Jo?o do Para?so (noroeste do Rio de Janeiro) representa um importante p?lo gerador de rochas ornamentais e de revestimento. Atualmente, s?o escassos os trabalhos que retratam as fei??es de campo, aspectos petrol?gicos e recursos minerais desta regi?o. O objetivo do presente trabalho ? apresentar os aspectos geol?gicos e recursos minerais baseados no levantamento de campo realizado e an?lises minerais por meio de estereomicrosc?pio, microsc?pio e refrat?metro. Na regi?o afloram granitoides diatex?ticos leucocr?ticos (com composi??o de plagiocl?sio, quartzo, K-feldspato, granada e biotita), m?rmores de composi??o calc?tica, paragnaisses de composi??es variadas, al?m de in?meros corpos pegmat?ticos. Tais litotipos apresentam grande potencial para o uso como rochas ornamentais e, no caso dos pegmatitos, como fonte de minerais gema, especialmente de berilo em sua variedade ?gua marinha, e de turmalina negra (shorlita).The region of S?o Jo?o do Para?so (northwest of Rio de Janeiro) is an important pole generating ornamental rocks. Currently, few works show the field features, petrological aspects and mineral resources this region. The objective of the present work is to present the geological aspects and mineral resources based on the field survey and mineral analyzes using a stereomicroscope, microscope and refractometer. In the region are exposed leucocratic diatextic granitoids (with plagioclase, quartz, K-feldspar, garnet and biotite), marbles of calcitic composition, paragnaisses of varied compositions, besides numerous pegmatitic bodies. Such lithotypes present great potential for use as ornamental rocks and, in the case of pegmatites, as a source of gem minerals, especially beryl in its aquamarine variety, and black tourmaline (shorlite)

A framework for enhancing industrial soft sensor learning models

Refinery industrial processes are very complex with nonlinear dynamics resulting from varying feedstock characteristics and also from changes in product prioritization. Along these processes, there are key properties of intermediate compounds that must be monitored and controlled since they directly affect the quality of the end products commercialized by these manufacturers. However, most of these properties can only be measured through time-consuming and expensive laboratory analysis, which is impossible to obtain in high frequencies, as required to properly monitor them. In this sense, developing soft sensors is the most common way to obtain high-frequency estimations for these measurements, helping advanced control systems to establish the correct setpoints for temperatures, pressures, and other sensors along the refining process, controlling the quality of end products. Since the amount of labeled data is scarce, most academic research has focused on employing semi- supervised learning strategies to develop machine learning (ML) models as soft sensors. Our research, on the other hand, goes in another direction. We aim to elaborate a framework that leverages the knowledge of domain experts and employs data augmentation techniques to build an enhanced fully labeled dataset that could be fed to any supervised ML algorithm to generate a quality soft sensor. We applied our framework together with Automated ML to train a model capable of predicting a specific key property associated with the production of Naphtha compounds in a refinery: the ASTM 95% distillation temperature of the Heavy Naphtha. Although our framework is model agnostic, we opted by using Automated ML for the optimization strategy, since it applies a diverse set of models to the dataset, reducing the bias of utilizing a single optimization algorithm. We evaluated the proposed framework on a case study carried out in an industrial refinery in Brazil, where the previous model in production for estimating the ASTM 95% distillation temperature of the Heavy Naphtha was based entirely on the physicochemical knowledge of the process. By adopting our framework with Automated ML, we were capable of improving the R2 score by 120%. The resulting ML model is currently operating in real-time inside the refinery, leading to significant economic gains