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Acute ischemic stroke occurs when a thrombus obstructs a cerebral artery, leading to sub-optimal blood perfusion to brain tissue. A recently developed, preventive treatment is the endovascular stroke treatment (EVT), which is a minimally invasive procedure, involving the use of stent-retrievers and/or aspiration catheters. Despite its increasing use, many critical factors of EVT are not well understood. In this respect, in vitro, and in silico studies have the great potential to help us deepen our understanding of the procedure, perform further device and procedural optimization, and help in clinical training. This review paper provides an overview of the previous in vitro and in silico evaluations of EVT treatments, with a special emphasis on the four main aspects of the adopted experimental and numerical set-ups: vessel, thrombus, device, and procedural settings., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

To investigate whether the polymerase chain reaction (PCR) on the BI gene of Toxoplasma gondii could contribute to the diagnosis of cerebral toxoplasmosis in patients with AIDS, we retrospectively tested CSF samples from 20 patients with AIDS suspected of having cerebral toxoplasmosis for the presence of T. gondii. Suspicion of cerebral toxoplasmosis was based on accepted criteria. Nine patients with AIDS with IgG antibodies to T. gondii but who were not suspected of having cerebral toxoplasmosis and four patients with AIDS seronegative for T. gondii served as negative control patients. T. gondii was demonstrated by PCR in the CSF from 13 of the 20 patients with AIDS suspected of having cerebral toxoplasmosis but was not demonstrated in the CSF samples from the nine control patients seropositive for T. gondii and the four control patients seronegative for T. gondii. The data were statistically evaluated. This study shows the value of PCR for the detection of T. gondii in CSF for the diagnosis of cerebral toxoplasmosis in patients with AIDS. [ABSTRACT FROM PUBLISHER]

The aim of the present study was to determine the in-vitro susceptibility of Toxoplasma gondii to dihydrofolate rcductase inhibitors and sulphonamides alone, and in combination. It was found that pyrimethamine had the most potent anti-Toxoplasma activity, while sulphadiazine, sulphamethoxazole and sul-phametrole were approximately equally effective, but only at a high concentration. The 50% inhibitory concentration of sulphamethoxazole was determined using different fixed concentrations of pyrimethamine or trimethoprim. It was found that a ten-fold increment of the concentration of pyrimethamine, reduced the IC50 of sulphamethoxazole 1000 times. The influence of trimethoprim on the IC50 of sulphamethoxazole was less concentration dependent. Addition of dihydrofolate or tetrahydrofolate did not influence the IC50 of pyrimethamine. In conclusion, the present dose recommendations for sulphonamides, when combined with pyrimethamine, may be unnecessarily high. There may be a justification for dose reduction of sulphonamides in order to prevent side effects. [ABSTRACT FROM PUBLISHER]

An aneurysm is a disease condition, which is due to the pathological weakening of an arterial wall. These aneurysms are often found in various branch points and bifurcations of an artery in the cerebral circulation. Most aneurysms come to medical attention, either due to brain hemorrhages caused by rupture or found unruptured. To consider surgically invasive treatment modalities, clinicians need scientific methods such as, hemodynamic analysis to assess rupture risk. The arterial wall loses its structural integrity when wall shear stress (WSS) and other hemodynamic parameters exceed a certain threshold. In the present study, numerical simulations are carried out for unruptured middle cerebral artery (MCA) aneurysms. Three distinct representative sizes are chosen from a larger patient pool of 26 MCA aneurysms. Logically, these aneurysms represent three growth stages of any patient with similar anatomical structure. Simulations are performed to compare the three growth phases (with different aspect ratios) of an aneurysm and correlate their hemodynamic parameters. Simulations with patient specific boundary conditions reveal that, aneurysms with a higher aspect ratio (AR) correspond to an attendant decrease in both time-averaged wall shear stress (TAWSS) and spatial wall shear stress gradients (WSSG). Smaller MCAs were observed to have higher positive wall shear stress divergence (WSSD), exemplifying the tensile nature of arterial wall stretching. Present study identifies positive wall shear stress divergence (PWSSD) to be a potential biomarker for evaluating the growth of an aneurysm. [ABSTRACT FROM AUTHOR]

Microscopic tests, molecular dynamics (MD) simulation, and two-piece healing (TPH) approaches were employed to investigate the interfacial intrinsic healing behavior and mechanisms of graphene-modified asphalt (GMA) binders at different aging levels. A vacuum layer 10-Å thick was injected between two molecules of asphalt binder to resemble a pattern of cracks. The mean squared displacement (MSD), molecular diffusion coefficient (D), and surface free energy (SFE) of asphalt molecules were utilized to evaluate the micro intrinsic healing behavior of asphalt binders. The macro intrinsic healing capacity of asphalt binders was determined using the intrinsic healing function [Rh(t)] developed from the TPH test. The GMA binder had a higher healing efficiency than the base binder in the short-term healing condition according to the density of the MD simulations, the SFE of the sessile drop method, and the initial healing rate (R0) of the TPH experiment. GMA binder had higher potential for instantaneous healing than the base binder because of its greater SFE in general. According to an analysis of parameters MSD, D , and Rh(t) quantified by MD simulations and TPH tests, the GMA binder had a greater capacity for long-term healing than the base binder after a long-term aging process. An R2 of 0.87 was obtained from the regression analysis of R0 against SFE, and an R2 of 0.93 was obtained from the regression analysis of Rh(3,600)−R0 against D. The experimental findings at the macroscopic scale and the simulation outcomes at the microscopic level were mutually supportive. [ABSTRACT FROM AUTHOR]

Lymph node metastasis, the initial step in distant metastasis, represents a primary contributor to mortality in patients diagnosed with oral squamous cell carcinoma (OSCC). However, the underlying mechanisms of lymph node metastasis in OSCC remain incompletely understood. Here, the transcriptomes of 56 383 single cells derived from paired tissues of six OSCC patients are analyzed. This study founds that CXCR4+ epithelial cells, identified as highly malignant disseminated tumor cells (DTCs), exhibited a propensity for lymph node metastasis. Importantly, a distinct subset of tumor‐associated macrophages (TAMs) characterized by exclusive expression of phosphoprotein 1 (SPP1) is discovered. These TAMs may remodel the metastatic lymph node microenvironment by potentially activating fibroblasts and promoting T cell exhaustion through SPP1‐CD44 and CD155‐CD226 ligand‐receptor interactions, thereby facilitating colonization and proliferation of disseminated tumor cells. The research advanced the mechanistic understanding of metastatic tumor microenvironment (TME) and provided a foundation for the development of personalized treatments for OSCC patients with metastasis. [ABSTRACT FROM AUTHOR]

Copyright of Van Veterinary Journal is the property of University of Yuzuncu Yil, Faculty of Veterinary Medicine and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Rotavirus species A (RVA) is a major cause of acute viral gastroenteritis in young humans and diverse animal species. The study of the genetic characteristics of RVAs that infect rabbits (Oryctolagus cuniculus) (lapine strain [LRV]) has been limited, and, to date, the most common and epidemiologically important combinations of G/P genotypes in rabbits have been reported to be G3 P[14] and G3 P[22]. In this study, a rotavirus species A detected from an outbreak of enteritis in a Mexican commercial rabbitry was genotypically characterized. Based on sequence and phylogenetic analysis of the VP7 and VP4 genes, the strain identified in this study (C-3/15) demonstrated a G3 P[8] genotype of rotavirus, which had not previously been reported in rabbits. Moreover, both genes were closely related to human, not lapine, rotaviruses. The G3 genotype has been reported in a wide variety of hosts, including humans and rabbits, whereas the P[8] genotype has only been reported in humans. Because this combination of genotypes has never been identified in rabbits, it is proposed that the finding presented here is possibly the result of an interspecies transmission event. This is the first work to study the molecular characteristics of rotaviruses in rabbits in Mexico, as well as the identification of human G3 and P[8] genotypes in a rabbit with enteric disease. [ABSTRACT FROM AUTHOR]

Group A rotaviruses (RVAs) are the main etiological agent of infantile diarrhea in both humans and animals worldwide. A limited number of studies have investigated the molecular characteristics of RVA strains in stool specimens of rabbits, with only a few lapine RVA strains isolated and (partially) characterized to date. The most common G/P-genotype combinations found in rabbits are G3P[14] and G3P[22]. In this study a RVA strain was isolated from the small intestine of a 9-week-old rabbit from an infected laboratory rabbit colony. The RVA strain RVA/Rabbit-tc/NLD/K1130027/2011/G6P[11] was shown to possess the typical bovine G6 and P[11] genotypes. The complete genome of this unusual lapine strain was sequenced and characterized. Phylogenetic analyses of all 11 gene segments revealed the following genotype constellation: G6-P[11]-I2-R2-C2-M2-A13-N2-T6-E2-H3. The VP1, VP2, VP3, VP6, NSP2 and NSP4 genes all belonged to DS-1-like genotype 2, but clustered more closely to bovine RVA strains than to lapine RVA strains. The NSP1 genotype A13 is typically associated with bovine RVAs, while the NSP3 genotype T6 and the NSP5 genotype H3 have been found in a wide variety of species. However, the isolated strain clustered within bovine(-like) T6 and H3 subclusters. Overall, the data indicate that the RVA strain is most closely related to bovine-like RVA strains and most likely represents a direct interspecies transmission from a cow to a rabbit. Altogether, these findings indicate that a RVA strain with an entirely bovine genome constellation was able to infect and spread in a laboratory rabbit colony., (Copyright © 2013 Elsevier B.V. All rights reserved.)

The family Picornaviridae contains well-known human pathogens (e.g., poliovirus, coxsackievirus, rhinovirus, and parechovirus). In addition, this family contains a number of viruses that infect animals, including members of the genus Cardiovirus such as Encephalomyocarditis virus (EMCV) and Theiler's murine encephalomyelits virus (TMEV). The latter are important murine pathogens that cause myocarditis, type 1 diabetes and chronic inflammation in the brains, mimicking multiple sclerosis. Recently, a new picornavirus was isolated from humans, named Saffold virus (SAFV). The virus is genetically related to Theiler's virus and classified as a new species in the genus Cardiovirus, which until the discovery of SAFV did not contain human viruses. By analogy with the rodent cardioviruses, SAFV may be a relevant new human pathogen. Thus far, SAFVs have sporadically been detected by molecular techniques in respiratory and fecal specimens, but the epidemiology and clinical significance remained unclear. Here we describe the first cultivated SAFV type 3 (SAFV-3) isolate, its growth characteristics, full-length sequence, and epidemiology. Unlike the previously isolated SAFV-1 and -2 viruses, SAFV-3 showed efficient growth in several cell lines with a clear cytopathic effect. The latter allowed us to conduct a large-scale serological survey by a virus-neutralization assay. This survey showed that infection by SAFV-3 occurs early in life (>75% positive at 24 months) and that the seroprevalence reaches >90% in older children and adults. Neutralizing antibodies were found in serum samples collected in several countries in Europe, Africa, and Asia. In conclusion, this study describes the first cultivated SAFV-3 isolate, its full-length sequence, and epidemiology. SAFV-3 is a highly common and widespread human virus causing infection in early childhood. This finding has important implications for understanding the impact of these ubiquitous viruses and their possible role in acute and/or chronic disease.

With the rapid development of entity recognition technology, animal recognition has gradually become essential in modern society, supporting labour‐intensive agriculture and animal husbandry tasks. Severe problems such as maintaining biodiversity can also benefit from animal identification technology. However, certain invasive recognition systems have resulted in permanent harm to animals, while noninvasive identification methods also exhibit certain drawbacks. This paper conducts a systematic literature review (SLR), presenting a comprehensive overview of various animal recognition technologies and their applications. Specifically, it examines methodologies such as deep learning, image processing and acoustic analysis used for different animal characteristics and identification purposes. The contribution of machine learning to animal feature extraction is highlighted, emphasising its significance for animal taxonomy and wild species monitoring. Additionally, this review addresses the challenges and limitations of current technologies, including data scarcity, model accuracy and computational requirements, and suggests opportunities for future research to overcome these obstacles. [ABSTRACT FROM AUTHOR]

Simple Summary: Accurate detection of wildlife, particularly small and hidden animals, is crucial for conservation efforts. Traditional image-based methods often struggle in complex environments. This study introduces a novel approach that combines image and text data to improve detection accuracy. By incorporating textual information about animal characteristics and leveraging a Concept Enhancement Module (CEM), our model can better understand and locate animals, even in challenging conditions. Experimental results demonstrate a significant improvement in detection accuracy, achieving an average precision of 95.8% on a challenging wildlife dataset. Compared to existing multimodal target detection algorithms, this model achieved at least a 25% improvement in AP and excelled in detecting small targets of certain species, significantly surpassing existing multimodal target detection model benchmarks. This represents a substantial improvement compared to existing state-of-the-art methods. Our multimodal approach offers a promising solution for enhancing wildlife monitoring and conservation efforts. Accurate and efficient wildlife monitoring is essential for conservation efforts. Traditional image-based methods often struggle to detect small, occluded, or camouflaged animals due to the challenges posed by complex natural environments. To overcome these limitations, an innovative multimodal target detection framework is proposed in this study, which integrates textual information from an animal knowledge base as supplementary features to enhance detection performance. First, a concept enhancement module was developed, employing a cross-attention mechanism to fuse features based on the correlation between textual and image features, thereby obtaining enhanced image features. Secondly, a feature normalization module was developed, amplifying cosine similarity and introducing learnable parameters to continuously weight and transform image features, further enhancing their expressive power in the feature space. Rigorous experimental validation on a specialized dataset provided by the research team at Northwest A&F University demonstrates that our multimodal model achieved a 0.3% improvement in precision over single-modal methods. Compared to existing multimodal target detection algorithms, this model achieved at least a 25% improvement in AP and excelled in detecting small targets of certain species, significantly surpassing existing multimodal target detection model benchmarks. This study offers a multimodal target detection model integrating textual and image information for the conservation of rare and endangered wildlife, providing strong evidence and new perspectives for research in this field. [ABSTRACT FROM AUTHOR]

Accurate livestock counts are essential for effective pastureland management. High spatial resolution remote sensing, coupled with deep learning, has shown promising results in livestock detection. However, challenges persist, particularly when the targets are small and in a heterogeneous environment, such as those in African rangelands. This study evaluated nine state-of-the-art object detection models, four variants each from YOLOv5 and YOLOv8, and Faster R-CNN, for detecting cattle in 10 cm resolution aerial RGB imagery in Kenya. The experiment involved 1039 images with 9641 labels for training from sites with varying land cover characteristics. The trained models were evaluated on 277 images and 2642 labels in the test dataset, and their performance was compared using Precision, Recall, and Average Precision (AP0.5–0.95). The results indicated that reduced spatial resolution, dense shrub cover, and shadows diminish the model's ability to distinguish cattle from the background. The YOLOv8m architecture achieved the best AP0.5–0.95 accuracy of 39.6% with Precision and Recall of 91.0% and 83.4%, respectively. Despite its superior performance, YOLOv8m had the highest counting error of −8%. By contrast, YOLOv5m with AP0.5–0.95 of 39.3% attained the most accurate cattle count with RMSE of 1.3 and R2 of 0.98 for variable cattle herd densities. These results highlight that a model with high AP0.5–0.95 detection accuracy may struggle with counting cattle accurately. Nevertheless, these findings suggest the potential to upscale aerial-imagery-trained object detection models to satellite imagery for conducting cattle censuses over large areas. In addition, accurate cattle counts will support sustainable pastureland management by ensuring stock numbers do not exceed the forage available for grazing, thereby mitigating overgrazing. [ABSTRACT FROM AUTHOR]

Simple Summary: Accurate identification of individual Amur tigers is vital for their conservation, as it helps us understand their population and distribution. Existing identification methods often fall short in accuracy, and our study focuses on creating a more accurate method for identifying individual Amur tigers using advanced deep learning techniques. We improved an existing neural network model called InceptionResNetV2 by adding features like dropout layers and dual-attention mechanisms to better capture the unique stripe patterns of each tiger and reduce errors during training. We tested our model on a large dataset of tiger images and found it to be highly effective, achieving an average recognition accuracy of over 95% for different body parts, with left stripes reaching the highest 99.37%. This method significantly outperforms previous models and provides a reliable tool for wildlife researchers and conservationists to monitor and protect Amur tigers. By improving the ability to track individual tigers, our research offers practical benefits for preserving this endangered species and enhancing wildlife management practices globally. Accurate and intelligent identification of rare and endangered individuals of flagship wildlife species, such as Amur tiger (Panthera tigris altaica), is crucial for understanding population structure and distribution, thereby facilitating targeted conservation measures. However, many mathematical modeling methods, including deep learning models, often yield unsatisfactory results. This paper proposes an individual recognition method for Amur tigers based on an improved InceptionResNetV2 model. Initially, the YOLOv5 model is employed to automatically detect and segment facial, left stripe, and right stripe areas from images of 107 individual Amur tigers, achieving a high average classification accuracy of 97.3%. By introducing a dropout layer and a dual-attention mechanism, we enhance the InceptionResNetV2 model to better capture the stripe features of individual tigers at various granularities and reduce overfitting during training. Experimental results demonstrate that our model outperforms other classic models, offering optimal recognition accuracy and ideal loss changes. The average recognition accuracy for different body part features is 95.36%, with left stripes achieving a peak accuracy of 99.37%. These results highlight the model's excellent recognition capabilities. Our research provides a valuable and practical approach to the individual identification of rare and endangered animals, offering significant potential for improving conservation efforts. [ABSTRACT FROM AUTHOR]

With traditional survey methods such as ground-based counting, camera trapping, and aerial surveys, monitoring wild deer in Nepal's Chitwan National Park is challenging due to the dense tall vegetation that often conceals them. However, the thermal signatures of wild deer contrast sharply against the cooler background, facilitating detection via thermal imaging. This study explores the use of Unmanned Aerial Vehicles (UAVs) equipped with thermal cameras to monitor wild deer. A large volume of images can be captured, where wild animals appear as small objects. Reviewing these images manually is labor-intensive and time-consuming. To address this, we developed an object detection model using modified Faster R-CNN that automatically identifies small deer objects in the thermal images. Instead of VGG 16, the Feature Pyramid Network and Residual Neural Network (ResNet152) were employed to enhance feature extraction from these images, constructing multi-scale feature maps that enrich the feature information for small object detection. Customized anchor boxes were also designed to handle the wide variation in object scale and aspect ratios. To improve species identification accuracy for small Regions of Interest, a multi-scale aggregation method was proposed, which fuses features from multiple feature maps via Multi-scale RoIAlign pooling. The model proposed in this paper was evaluated by the COCO metrics. The experimental results obtained for the detection of deer and other animals in UAV thermal images with the resolution of 640 × 512 , showing mean Average Precision of 92.3% for all objects, 78.9% for small objects, 94.6% for medium objects, and 95.8% for large objects. This research provides a valuable means for detecting small objects in thermal images and contributes significantly to the field of wildlife monitoring. [ABSTRACT FROM AUTHOR]

Acute ischemic stroke occurs when a blood clot occludes a cerebral artery. Mechanical interventions, primarily stent retrievers and aspiration thrombectomy, are used currently for removing the occluding clot and restoring blood flow. Aspiration involves using a long catheter to traverse the cerebral vasculature to reach the blood clot, followed by application of suction through the catheter bore. Aspiration is also used in conjunction with other techniques such as stent retrievers and balloon guide catheters. Despite the wide use of aspiration, our physical understanding of the process and the causes of the failure of aspiration to retrieve cerebral clots in certain scenarios is not well understood. Experimental and computational studies can help develop the capability to provide deeper insights into the procedure and enable development of new devices and more effective treatment methods. We recapitulate the aspiration-based thrombectomy techniques in clinical practice and provide a perspective of existing engineering methods for aspiration. We articulate the current knowledge gap in the understanding of aspiration and highlight possible directions for future engineering studies to bridge this gap, help clinical translation of engineering studies, and develop new patient-specific stroke therapy. [ABSTRACT FROM AUTHOR]

Monitoring population trends is pivotal to effective wildlife conservation and management. However, wildlife managers often face many challenges when analyzing time series of census data due to heterogeneities in sampling methodology, strategy, or frequency. We present a three‐step method for modeling trends from time series of count data obtained through multiple census methods (aerial or ground census and expert estimates). First, we design a heuristic for constructing credible intervals for all types of animal counts including those which come with no precision measure. Then, we define conversion factors for rendering aerial and ground counts comparable and provide values for broad classes of animals from an extant series of parallel aerial and ground censuses. Lastly, we construct a Bayesian model that takes the reconciled counts as input and estimates the relative growth rates between successive dates while accounting for their precisions. Importantly, we bound the rate of increase to account for the demographic potential of a species. We propose a flow chart for constructing credible intervals for various types of animal counts. We provide estimates of conversion factors for 5 broad classes of species. We describe the Bayesian model for calculating trends, annual rates of population increase, and the associated credible intervals. We develop a bespoke R CRAN package, popbayes, for implementing all the calculations that take the raw counts as input. It produces consistent and reliable estimates of population trends and annual rates of increase. Several examples from real populations of large African mammals illustrate the different features of our method. The approach is well‐suited for analyzing population trends for heterogeneous time series and allows a principled use of all the available historical census data. The method is general and flexible and applicable to various other animal species besides African large mammals. It can readily be adapted to test predictions of various hypotheses about drivers of rates of population increase. [ABSTRACT FROM AUTHOR]

Counting animal populations is fundamental to understand ecological processes. Counts make it possible to estimate the size of an animal population at specific points in time, which is essential information for understanding demographic change. However, in the case of large populations, counts are time‐consuming, particularly if carried out manually. Here, we took advantage of convolutional neural networks (CNN) to count the total number of nest‐entrances in 222 photographs covering the largest known Psittaciformes (Aves) colony in the world. We conducted our study at the largest Burrowing Parrot Cyanoliseus patagonus colony, located on a cliff facing the Atlantic Ocean in the vicinity of El Cóndor village, in north‐eastern Patagonia, Argentina. We also aimed to investigate the distribution of nest‐entrances along the cliff with the colony. For this, we used three CNN architectures, U‐Net, ResUnet, and DeepLabv3. The U‐Net architecture showed the best performance, counting a mean of 59,842 Burrowing Parrot nest‐entrances across the colony, with a mean absolute error of 2.7 nest‐entrances over the testing patches, measured as the difference between actual and predicted counts per patch. Compared to a previous study conducted at El Cóndor colony more than 20 years ago, the CNN architectures also detected noteworthy differences in the distribution of the nest‐entrances along the cliff. We show that the strong changes observed in the distribution of nest‐entrances are a measurable effect of a long record of human‐induced disturbance to the Burrowing Parrot colony at El Cóndor. Given the paramount importance of the Burrowing Parrot colony at El Cóndor, which concentrates 71% of the world's population of this species, we advocate that it is imperative to reduce such a degree of disturbance before the parrots reach the limit of their capacity of adaptation. [ABSTRACT FROM AUTHOR]

The 'landscape of fear' concept offers valuable insights into wildlife behaviour, yet its practical integration into habitat management for conservation remains underexplored. In this study, conducted in the subtropical monsoon grasslands of Bardia National Park, Nepal, we aimed to bridge this gap through a multi‐year, landscape‐scale experimental investigation in Bardia National Park, Nepal. The park has the highest density of tigers (with an estimated density of ~7 individuals per 100 km2) in Nepal, allowing us to understand the effect of habitat management on predation risk and resource availability especially for three cervid species: chital (Axis axis), swamp deer (Rucervus duvaucelii) and hog deer (Axis porcinus). We used plots with varying mowing frequency (0–4 times per year), size (ranging from small: 49 m2 to large: 3600 m2) and artificial fertilisation type (none, phosphorus, nitrogen) to assess the trade‐offs between probable predation risk and resources for these cervid species, which constitute primary prey for tigers in Nepal. Our results showed distinct responses of these deer to perceived predation risk within grassland habitats. Notably, these deer exhibited heightened use of larger plots, indicative of a perceived sense of safety, as evidenced by the higher occurrence of pellet groups in the larger plots (mean = 0.1 pellet groups m−2 in 3600 m2 plots vs. 0.07 in 400 m2 and 0.05 in 49 m2 plots). Furthermore, the level of use by the deer was significantly higher in larger plots that received mowing and fertilisation treatments compared to smaller plots subjected to similar treatments. Of particular interest is the observation that chital and swamp deer exhibited greater utilisation of the centre (core) areas within the larger plots (mean = 0.21 pellet groups m−2 at the centre vs. 0.13 at the edge) despite the edge (periphery) also provided attractive resources to these deer. In contrast, hog deer did not display any discernible reaction to the experimental treatments, suggesting potential species‐specific variations in response to perceived predation risk arising from management interventions. Our findings emphasise the importance of a sense of security as a primary determinant of habitat selection for medium‐sized deer within managed grassland environments. These insights carry practical implications for park managers, providing a nuanced understanding of integrating the 'landscape of fear' into habitat management strategies. This study emphasises that the 'landscape of fear' concept can and should be integrated into habitat management to maintain delicate predator–prey dynamics within ecosystems. [ABSTRACT FROM AUTHOR]

The 2023 RSPCA/UFAW/IAT Rodent Welfare Group Meeting report discusses advancements in rodent welfare, focusing on improvements in cage technology, enrichment practices, and handling methods. Presentations emphasized the importance of providing adequate space, enrichment, and gentle handling to enhance animal welfare. The report also explores the transition to environmental health monitoring as an alternative to sentinel rodents, with evidence supporting its effectiveness. It addresses the psychological well-being of Animal Technicians in biomedical research and advocates for better support and recognition for these workers. The text highlights the increasing acceptance of EHM in laboratory settings, offering tips for a smooth transition and outlining the benefits of this approach, such as improved compliance with the 3Rs, accuracy, cost-effectiveness, and reduced emotional strain. Additional resources and case studies are available on the North American 3Rs Collaborative website for those interested in implementing EHM. [Extracted from the article]

The following metabolites of sulfadiazine (S) were isolated from monkey urine by preparative HPLC: 5-hydroxysulfadiazine (5OH), 4-hydroxysulfadiazine (4OH) and the glucuronide (5OHgluc) and sulfate conjugate of 5OH (5OHsulf). The compounds were identified by NMR, mass and infrared spectrometry and hydrolysis by beta-glucuronidase. The analysis of S, the hydroxymetabolites (4OH, 5OH) and conjugates N4-acetylsulfadiazine (N4), 5OHgluc and 5OHsulf in human and monkey plasma and urine samples was performed using reversed-phase gradient HPLC with UV detection. In plasma, S and N4 could be detected in high concentrations, whereas the other metabolites were present in only minute concentrations. In urine, S, the metabolites and conjugates were present. The limit of quantification of the compounds in plasma varies between 0.2 and 0.6 microgram/ml (S 0.31, N4 0.40, 4OH 0.20, 5OH 0.37, 5OHgluc 0.33 and 5OHsulf 0.57 microgram/ml). In urine it varies between 0.6 and 1.1 micrograms/ml (S 0.75, N4 0.80, 4OH 0.60, 5OH 0.80, 5OHgluc 0.80 and 5OHsulf 1.1 micrograms/ml). The method was applied to studies with healthy human subjects and Rhesus monkeys. The metabolites 5OH, 5OHgluc and 5OHsulf were present in Rhesus monkey and not in man. Preliminary results of studies of metabolism and pharmacokinetics in Rhesus monkey and man are presented.

This paper describes a rhesus monkey model to study congenital transmission and the prenatal diagnosis of Toxoplasma gondii infection. Transmission to the fetus was investigated after maternal infection in the second (Day 90) or third trimester (Day 130) of pregnancy. A parasitemia was induced and lasted for about 10 days as proven by mouse inoculation and nested PCR on whole blood samples and peripheral blood mononuclear cells. Transmission of T. gondii was proven in 4 of 9 (44%) fetuses in the second trimester and in 3 of 9 fetuses in the third trimester of gestation. Six of the 7 proven fetal infections could be diagnosed antenatally. Postnatally, 4 additional fetuses that most likely were congenitally infected were found, although this could not formally be proven. The mothers of these 4 fetuses were infected in the third trimester. If we accept this group as being antenatally infected, an overall transmission rate is found of 11 of 18 cases (61%). This rate is similar to that found in humans. This is the first description of a monkey model for congenital toxoplasmosis, that is suitable to study the approach of antenatal diagnosis and the effectivity of treatment, with obvious relevance for Toxoplasma infections in humans.

Ischemic stroke, a significant threat to human life and health, refers to a class of conditions where brain tissue damage is induced following decreased cerebral blood flow. The incidence of ischemic stroke has been steadily increasing globally, and its disease mechanisms are highly complex and involve a multitude of biological mechanisms at various scales from genes all the way to the human body system that can affect the stroke onset, progression, treatment, and prognosis. To complement conventional experimental research methods, computational systems biology modeling can integrate and describe the pathogenic mechanisms of ischemic stroke across multiple biological scales and help identify emergent modulatory principles that drive disease progression and recovery. In addition, by running virtual experiments and trials in computers, these models can efficiently predict and evaluate outcomes of different treatment methods and thereby assist clinical decision-making. In this review, we summarize the current research and application of systems-level computational modeling in the field of ischemic stroke from the multiscale mechanism-based, physics-based and omics-based perspectives and discuss how modeling-driven research frameworks can deliver insights for future stroke research and drug development. [ABSTRACT FROM AUTHOR]

In Wire Arc Additive Manufacturing (WAAM), variable layer heights enable the non-parallel or non-planar slicing of parts. In researching variable layer heights, this paper documents printing strategies for a reference geometry whose key features are non-orthogonal growth and unsupported overhangs. The complexity of 3D printing with welding requires parameter optimization to control the deposition of molten material. Thus, 3D printing with welding requires the precise deposition of molten material. Currently, there is no standard solution for the customization of process parameters and intelligent collection of data from sensors. To address this gap in technology, this research develops an Internet of Things (IoT)-enabled, distributed communication protocol to control process parameters, synchronize commands, and integrate device data. To intelligently collect sensor information, this research creates a query-able database during pre-planning and production. This contributes to fundamental research in WAAM by documenting strategies for printing variable layer heights, the customization of control parameters, and the collection of data through a WAAM Information Model (WIM). [ABSTRACT FROM AUTHOR]

Simple Summary: Species recognition is a key part of understanding biodiversity and can help us to better conserve and manage biodiversity. Traditional species recognition methods require large amounts of image data to train the recognition model, but obtaining image data of rare and endangered species is a challenge. However, Contrastive Language–Image Pre-training (CLIP), a generalized artificial intelligence model, can perform classification by calculating the similarity between images and text without the need for training data. Taking advantage of this and considering the unique geographic distribution pattern of species, we propose a CLIP-based species recognition method that can recognize species based on geographic distribution knowledge. This study is the first to combine geographic distribution knowledge with species recognition, which can lead to more effective recognition of rare and endangered species. Species recognition is a crucial part of understanding the abundance and distribution of various organisms and is important for biodiversity conservation and management. Traditional vision-based deep learning-driven species recognition requires large amounts of well-labeled, high-quality image data, the collection of which is challenging for rare and endangered species. In addition, recognition methods designed based on specific species have poor generalization ability and are difficult to adapt to new species recognition scenarios. To address these issues, zero-shot species recognition based on Contrastive Language–Image Pre-training (CLIP) has become a research hotspot. However, previous studies have primarily utilized visual descriptive information and taxonomic information of species to improve zero-shot recognition performance, and the use of geographic distribution characteristics of species to improve zero-shot recognition performance has not been explored. To fill this gap, we proposed a CLIP-driven zero-shot species recognition method that incorporates knowledge of the geographic distribution of species. First, we designed three prompts based on the species geographic distribution statistical data. Then, the latitude and longitude coordinate information attached to each image in the species dataset was converted into addresses, and they were integrated together to form the geographical distribution knowledge of each species. Finally, species recognition results were derived by calculating the similarity after acquiring features by the trained CLIP image encoder and text encoder. We conducted extensive experiments on multiple species datasets from the iNaturalist 2021 dataset, where the zero-shot recognition accuracies of mammals, mollusks, reptiles, amphibians, birds, and insects were 44.96%, 15.27%, 17.51%, 9.47%, 28.35%, and 7.03%, an improvement of 2.07%, 0.48%, 0.35%, 1.12%, 1.64%, and 0.61%, respectively, as compared to CLIP with default prompt. The experimental results show that the fusion of geographic distribution statistical data can effectively improve the performance of zero-shot species recognition, which provides a new way to utilize species domain knowledge. [ABSTRACT FROM AUTHOR]

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This work reports the results of an experimental and modeling study on dual-frequency Ar–NH3 dielectric barrier discharges (DBDs) exhibiting the α – γ transition. A combination of space- and time-resolved optical absorption and emission spectroscopy is used to record spatio-temporal mappings of the Ar metastable number density, Ar 750.4 nm line emission intensity, and electron–Ar bremsstrahlung continuum emission intensity. With the increase of the radio frequency (RF) voltage amplitude in a 50 kHz–5 MHz DBD, maximum populations of Ar excited species (1s and 2p states, linked to the population of high-energy electrons) observed in the γ mode decrease and appear earlier in the low-frequency cycle. On the other hand, the density of the bulk electrons, monitored from the continuum emission intensity, increases, with a more prominent rise in the RF- α mode than in the γ regime. Such behaviors are consistent with the predictions of 1D fluid model and results from a decrease of the gas voltage required for self-maintenance of the cathode sheath in the γ breakdown. [ABSTRACT FROM AUTHOR]