In the realm of computer vision, semantic segmentation is the task of recognizing objects in images at the pixel level. This is done by performing a classification of each pixel. The task is complex and requires sophisticated skills and knowledge about the context to identify objects’ boundaries. The importance of semantic segmentation in many domains is undisputed. In medical diagnostics, it simplifies the early detection of pathologies, thus mitigating the possible consequences. In this work, we provide a review of the literature on deep ensemble learning models for polyp segmentation and we develop new ensembles based on convolutional neural networks and transformers. The development of an effective ensemble entails ensuring diversity between its components. To this end, we combine different models (HarDNet-MSEG, Polyp-PVT, and HSNet) trained with different data augmentation techniques, optimization methods, and learning rates, which we experimentally demonstrate to be useful to form a better ensemble. Most importantly, we introduce a new method to obtain the segmentation mask which is more suitable for combining transformers in an ensemble. In our extensive experimental evaluation, the proposed ensembles exhibit state-of-the-art performance.

A single paragraph of about 200 words maximum. For research articles, abstracts should give a pertinent overview of the work. We strongly encourage authors to use the following style of structured abstracts, but without headings: (1) Background: place the question addressed in a broad context and highlight the purpose of the study; (2) Methods: describe briefly the main methods or treatments applied; (3) Results: summarize the article’s main findings; (4) Conclusions: indicate the main conclusions or interpretations. The abstract should be an objective representation of the article, it must not contain results which are not presented and substantiated in the main text and should not exaggerate the main conclusions.

Objective A trained T1 class Convolutional Neural Network (CNN) model will be used to examine its ability to successfully identify motor imagery when fed pre-processed electroencephalography (EEG) data. In theory, and if the model has been trained accurately, it should be able to identify a class and label it accordingly. The CNN model will then be restored and used to try and identify the same class of motor imagery data using much smaller sampled data in an attempt to simulate live data. Approach PyCharm, a Python platform, will be used to house and process the CNN. The raw data used for the training of the CNN will be sourced from the PhysioBank website. The EEG signal data will then be pre-processed using Brainstorm software that is a toolbox used in conjunction with MATLAB. The sample data used to validate and test the trained CNN, will be also be extracted from Brainstorm but in a much smaller size compared to the training data which is comprised of thousands of images. The sample size would be comparable to a person wearing a Brain Computer Interface (BCI), offering approximately 20 seconds of motor imagery signal data. Results The raw EEG data was successfully extracted and pre-processed. The deep learning model was trained using the extracted image data along with their corresponding labels. After training, it was able to accurately identify the T1 class label at 100 percent. The python coding was then modified to restore the trained model and feed it test sample data in which it was found to recognise 6 out of 10 lines of T1 signal image data. The result suggested that the initial training of the model required a different, more varying approach, so that it would be able to detect varying sample signal image data. The outcome of which could mean that the model could be used in applications for multiple patients wearing the same BCI hardware to control a device or interface.

Recent progress in microfabrication technique allowed the rapid development of neural implants. They are getting categorized as effective tools for clinical practice, especially to treat traumatic and neurodegenerative disorders. Microelectrode arrays already have been used in numerous neural interface devices. Basically, almost all neural implants have been developed based on BCI (Brain Computer Interface) system. When BCI system falls under invasive technique, it is referred as BMI or Brain Machine Interface. BMIs hold promises for neurorehabilitation of motor and sensory function, cognitive state evaluation and treatment of neurological chaos. A directed overview of the field of neural implants is discussed in this article. The aim of this review is to give a brief introduction of neural prosthetics as well as their exciting applications in treating neurological disorders and a deep discussion on their functionality are mentioned. BCI system and their different types, their functionality, their pros and cons, how other neural implants developed, and their present status have been covered. Different possibilities and possible future of deep brain stimulation (DBS), Neuralink, motor and sensory neural prosthetics are further discussed.

The use of Attitude and Heading Reference Systems (AHRS) for orientation estimation is now common practice in a wide range of applications, e.g., robotics and human motion tracking, aerial vehicles and aerospace, gaming and virtual reality, indoor pedestrian navigation and maritime navigation. The integration of the high-rate measurements can provide very accurate estimates, but these can suffer from errors accumulation due to the sensors drift over longer time scales. To overcome this issue, inertial sensors are typically combined with additional sensors and techniques. As an example, camera-based solutions have drawn a large attention by the community, thanks to their low-costs and easy hardware setup; moreover, impressive results have been demonstrated in the context of Deep Learning. This work presents the preliminary results obtained by DOES , a supportive Deep Learning method specifically designed for maritime navigation, which aims at improving the roll and pitch estimations obtained by common AHRS. DOES recovers these estimations through the analysis of the frames acquired by a low-cost camera pointing the horizon at sea. The training has been performed on the novel ROPIS dataset, presented in the context of this work, acquired using the FrameWO application developed for the scope. Promising results encourage to test other network backbones and to further expand the dataset, improving the accuracy of the results and the range of applications of the method.

Steel is one of the essential materials in the world's civilization. It is essential to produce many products such as pipelines, mechanical elements in machines, vehicles, profiles, and beam sections for buildings in many industries. Until the '50s of the 20th century, steel products required a complex process known as ingot casting; for years, steelmakers focused on developing and simplifying this process. The result was the con-tinuous casting process (CCP); it is the most productive method to produce steel. The CCP allows producing significant volumes of steel sections without interruption and is more productive than the formal ingot casting process. The CCP begins by transferring the liquid steel from the steel-ladle to a tundish. This tundish or vessel distributes the liquid steel, by flowing through its volume, to one or more strands having wa-ter-cooled copper molds. The mold is the primary cooling system, PCS, solidifying a steel shell to withstand a liquid core and its friction forces with the mold wall. Further down the mold, the rolls drive the steel section in the SCS. Here the steel section is cooled, solidifying the remaining liquid core, by sprays placed in every cooling segment all around the billet and along the curved section of the machine. Finally, the steel strand goes towards a horizontal-straight free-spray zone, losing heat by radiation mechanism, where the billet cools down further to total solidification. A moving torch cutting-scissor splits the billet to the desired length at the end of this heat-radiant zone.

Adhesion systems are very important in robots for infrastructure inspection (especially in vertical walls). They present the challenge of optimizing the ratio vacuum/power consumption in battery-powered robots. In this paper a CFD (Computer Fluid Dynamics) modelling and optimization process of a robot adhesion system is carried out to determine the best performing configuration in terms of vacuum and power consumption. Analytical and numerical models were developed to predict the behaviour of the system for different configurations. The models were validated, using test rig measurements, by calibrating an arbitrary defined inlet height that simulates the leakage flow. Then, different geometric parameters were varied to determine the best performing configuration based on the vacuum/power consumption ratio value. The model presented in the paper was capable of predicting the behaviour of the system for different configurations, with a margin of error of 15% for the vacuum prediction and a 25% for the motor power calculation. Finally, the model was used to optimize parameters of the system, like the number of blades of the impeller. The adhesion system was conceived for the modular autonomous climbing legged robot ROMERIN.

Auto checkout has received more and more attention in recent years and this system automatically generates a shopping bill by identifying the picture of the products purchased by the customers. However, the system is challenged by the domain adaptation problem, where each image of the training set contains only one commodity, whereas the test set is a collection of multiple commodities. The existing solution to this problem is to resynthesize the training images to enhance the training set. Then the composite images are rendered using CycleGAN to make the image distribution of the training set and the test set more similar. However, we find that the detection boxes given by the ground truth of the common dataset contain a large part of the background area, the area will affect the training process as noise. To solve this problem, we propose a mask data priming method. Specifically, we redo the large scale Retail Product Checkout (RPC) dataset and add segmentation annotation information to each item in the training set image based on the original dataset using pixel-level annotation. Secondly, a new network structure is proposed in which we train the network using joint learning of detectors and counters, and fine-tune the detection network by filtering out suitable images from the test set. Experiments on the RPC dataset have shown that our method yields better results. we used an approach that reached 81.87% compared to 56.68% for the baseline approach which demonstrates that pixel-level information helps to improve the detection results of the network.

Spike shape and morphometric characteristics are among the key characteristics of cultivated cereals associated with their productivity. Identification of the genes controlling these traits requires morphometric data harvesting and analysis for numerous plants, which is automatable using technologies of digital image analysis. A method for wheat spike morphometry utilizing 2D image analysis is proposed. Digital images are acquired in two variants: a spike on a table (one projection) or fixed with a clip (four projections). The method identifies spike and awns in the image and estimates their quantitative characteristics (area in image, length, width, circularity, etc.). Models of sections, quadrilaterals, and radial model are proposed for describing spike shape. Parameters of these models are used to predict spike shape type (spelt, normal, or compact) by machine learning. The mean error in spike density prediction for the images in one projection is 4.61 (~18%) versus 3.33 (~13%) for the parameters obtained using four projections. F1 measure in automated spike classification into three types is 0.78 using logistic regression (one projection) and 0.85 using random forest method (four projections). The proposed method is implemented in Java; examples of images and user guide are available at http://wheatdb.org/werecognizer.

Heat emitters constitute the primary devices used in space heating and cover a fundamental role in the energy efficient use of buildings. In the search for an optimized design, heating devices should be compared with a benchmark emitter with maximum heat emission efficiency. However, such an ideal heater still needs to be defined. In this paper we perform an analysis of heat transfer in a European reference room, considering room side effects of thermal radiation and computing the induced operative temperature both analytically and numerically. By means of functional optimization, we analyse trends such as the variation of operative temperature with radiator panel dimensions, finding optimal configurations. In order to make our definitions as general as possible, we address panel radiators, convectors, underfloor (UFH) and ceiling heater. We obtain analytical formulas for the operative temperature induced by panel radiators and identify the 10-type as our ideal radiator, while the UFH provides the best performance overall. Regarding specifically UFH and ceiling heaters, we find optimal sizes that identify the according ideal emitters. The analytical method and quantitative results reported in this paper can be generalized and adopted in most studies concerning the efficiency of different heat emitter types in building enclosures.

We consider a random dynamical system arising in a model of associative memory. This system can be seen as a small (stochastic and deterministic) perturbation of a determinstic system having two weak attractors which are destroyed after the perturbation. We show, with a computer aided proof, that the system has a kind of chaotic itineracy. Typical orbits are globally chaotic, while they spend relatively long time visiting attractor's ruins.

The aim of the present study is to investigate both the performance and preferences of males and females Computer Science (CS) graduates. In order to attain the above goal, a quantitative case study was conducted regarding 89 degrees, acquired from 2006 to 2012, from the Department of Computer Science and Technology, University of Peloponnese, Greece. The analysis of the data revealed that in terms of performance, no significant differences between the mean grades of males and females exist, in almost most of the courses included in the curriculum of the aforementioned CS department. Any statistically significant differences in performances were present in almost equal number of courses in favor of males and females. It seems also, that females performed better in the courses they selected more than males. Regarding preferences, in CS courses, it seems that gender differences are existent. Males preferred more than females did core programming courses and advanced topics of Software Systems, computer networks, computer engineering, robotics and mathematics, whereas females preferred more the study of algorithms and security issues, computer fractals, data management, computer architecture, and mobile communication. In addition, females preferred courses in reference with humanities and social sciences, CS terminology, and career opportunities. Yet, females did not select any of programming lab-based courses, computer engineering, computer network issues and robotics.

This paper aims to argue the fundamental significance of education in addressing notable gaps in the constitutive, performance, and evaluation criteria for corporate social responsibility (CSR) and endeavours to showcase the complementarity between education, human security, sustainable human development, and the pursuit of CSR as an ideal normative paradigm. An abstractive approach to studying CSR engages with corporate philanthropy, which has not been helpful outside traditional CSR paradigms without looking at key dynamics that can robustly underpin successful CSR. It relies on the systematic analysis of written data sources to theoretically explore the central significance, and complementarity between human security, education, and human dignity and how they can orchestrate a holistic CSR paradigm. In 1994, the UNDP in formulating the human security doctrine recognised the syncretic correlation between human security, education, and sustainable human development as fundamental elements that should underpin CSR. This paper contends that these are necessary intrinsic components of human dignity as the central factor that justifies a multi-dimensional and proactive approach to CSR. Despite the criticisms against human security, this paper identifies it as an important concept that could instrumentally orchestrate a model of CSR that uniformly mainstreams sustainable human development and human dignity that leverages education which has routinely and traditionally been seen as a constitutive component of CSR. It reveals how a holistic path to CSR can reinvigorate the central pillars of human security and human dignity using education as a springboard and makes a compelling case for supporting explicit references to education in company law with appropriate human security informed constitutive, performance, and evaluation criteria which are absent in existing legally orchestrated CSR.

This paper presents a multi-year research study conducted at the University of Toledo, aimed at improving human-machine teaming (HMT) methods and technologies. With the incorporation of artificial intelligence (AI) into 21st-century machines, collaboration between humans and machines has become highly complicated for real-time applications. The penetration of intelligent and synthetic assistants (IA/SA) in virtually every field has opened a path for the new area of HMT. When it comes to crucial tasks such as patient treatment and care, defense, and industrial production, the use of non-standardized HMT technologies may pose a risk to human lives as well as up to billions of taxpayer dollars. A thorough literature survey revealed that there are not many established standards or benchmarks for HMT. In this paper, we aim to address the lack of HMT design and development standards. We propose a method to design an HMT based on a generalized architecture. This design includes the development of an intelligent collaborative system and human team. Followed by processes and metrics to test and validate the proposed model, we present a novel human-in-the-loop (HIL) simulation method using two controlled HMT scenarios; first, emergency care provider (ECP) training, and second, patient treatment by an experienced medic. Both scenarios include humans processing visual data and performing actions that represent real-world applications while responding to a Voice-Based Synthetic Assistant (VBSA) as a collaborator that keeps track of actions. As part of these HMT simulation studies, the impact of each parameter related to machines, humans, and HMT (such as errors made by humans and by the VBSA) is presented from the perspective of performance, rules, roles, and operation limitations. In addition, based on the analysis of results from two case studies, we presented guidelines for designing future HMT. Our conclusions indicate that the proposed HIL can be used to perform standardization studies in the pursuit of developing techniques for benchmarking HMT that can be used in critical situations.

Carukia barnesi (Cb), Malo kingi (Mk) and Chironex fleckeri (Cf) are dangerous Australian box jellyfish species that provoke distinct and not well understood envenomation syndromes. Specifically, Cb and Mk are small, rare and able to induce a systemic syndrome of generalised muscle pain and catecholamine excess termed “Irukandji syndrome”; Cf has been widely regarded as one of the most venomous organisms in the animal kingdom causing severe sting site pain combined with potentially lethal cardiotoxicity. Building on past studies of major chirodropid and carybdeid species venoms, this study compared the utility of various cubozoan specific antibody reagents to better define the relationships between venom proteins from both exemplar Irukandji species (Cb and Mk) and the archetype C. fleckeri box jellyfish. With the aid of commercial ovine derived Cf-specific antivenom, mouse antibodies reactive to Cb and Mk and rabbit antibodies specific to two Cf toxins (CfTX-1 and 2), as well as human sera, the cross-reactivity of jellyfish species-specific polyclonal antibodies against these three cubozoan venoms was investigated. Immunoblot assays revealed distinc levels of immune recognition across the three species, indicating that Mk specific reagents may bind both Irukandji and Cf venoms. Irukandji venom appears to be antigenic with the exception of a few proteins in the range of 43/46 kDa maybe homologous to CfTX-1 and 2. The implications of such antibody binding for future antivenom development require further investigation.

Background: Artificial intelligence can help improve the quality of healthcare by analyzing vast amounts of data and providing more effective and personalized treatment plans. Researchers are working on developing AI-powered solutions that can help improve the outcomes of patients. Objective: To explore the potential of AI in improving healthcare outcomes and patient experience. Results: The study suggests that AI can improve healthcare efficiency and patient outcomes but cannot fully replace human healthcare professionals. AI can assist healthcare professionals in their work, leading to better resource utilization and improved patient care. However, there is still a need for human healthcare professionals to oversee AI systems and provide empathy and personalized care to patients. Conclusion: While there is immense potential for AI in healthcare, it is not yet feasible to replace human healthcare workers. Instead, it should be viewed as a tool that can help improve the efficiency and effectiveness of human healthcare.

This research explores the role of transparency and traceability in the agricultural food supply chain. By utilizing blockchain's decentralized and secure features, stakeholders can improve information sharing from farm to consumer, transparency and traceability issues. Real-world examples illustrate blockchain's successful application in agriculture, boosting transparency, efficiency, and trust and create immutable records, enhance visibility, and provide reliable data, while combating fraud and promoting sustainability. It also examines blockchain's impact on food safety through IoT and smart contracts, highlighting successful implementations in supply chain management, the research underscores blockchain's potential to build consumer trust in food safety, quality, and origin.

The genus Aeromonas is widely distributed in aquatic environments. It is frequent as a fish pathogen, but has also been described associated to human infections. Most human cases are caused by A. caviae, A. veronii biovar sobria, and A. hydrophila, though A. dhakensis is becoming increasingly important. Transmission happens primarily by ingestion or contact with aquatic collections, or by consumption of contaminated food, especially of aquatic origin. Increasing resistance has been described in Aeromonas to penicillins, including their combinations with classical b-lactamase inhibitors, cephalosporins and carbapenems. Among the b-lactam antibiotics, only the 4th generation cephalosporins remain almost uniformly active. In addition, the simultaneous presence of resistance genes to 3rd generation cephalosporins and carbapenems in the same isolates is increasing. Recently, the presence of mobile genes linked to colistin resistance has also been described, in some cases with resistance percentages higher than 30%. Most likely, this evolution of colistin resistance is linked to its use in aquaculture, and, together with the evolution of b-lactam resistance, may be turning this genus into a reservoir of resistance genes that may affect other species much more frequently linked to human infections, such as Enterobacterales.

The vibrant landscape of Malaysia serves as a dynamic backdrop for the convergence of artificial intelligence (AI) and social science, reshaping knowledge and inquiry amidst tradition and modernity. With Malaysia leading a technological renaissance driven by digital transformation and innovation, the ethical implications of AI-driven decision-making in public policy and governance emerge as pressing research problems. Addressing concerns such as algorithmic bias and discrimination requires interdisciplinary collaboration to ensure transparency, accountability, and fairness. Exploring questions regarding the distribution of welfare benefits and strategies for enhancing transparency and accountability in urban planning underscores the need for contextually relevant frameworks and stakeholder engagement. Through comprehensive research objectives, including investigating algorithmic biases and analyzing their social implications, scholars aim to contribute to a deeper understanding of the complex interplay between AI technology, social dynamics, and public policy. As Malaysia navigates the challenges and opportunities presented by AI deployment, prioritizing interdisciplinary collaborations, longitudinal studies, and capacity-building efforts will be essential for shaping a future that upholds principles of social justice and collective well-being.

Human papillomavirus (HPV) can potentially compromise both unassisted conception and success rates with assisted reproductive technology (ART). We aimed to evaluate the rates of HPV vaccination among women presenting for fertility care. This was a prospective study analyzing a self-reported intake form completed by all new patients presenting to a fertility clinic. The primary outcome was HPV vaccination status. Among the 523 new patients included in this study 172 (32.9%) were fully vaccinated, 237 (45.3%) were unvaccinated, and 95 (18.2%) patients didn’t respond. There was a significant age difference between fully vaccinated patients (33.1 years) compared to unvaccinated patients (37.4 years) p<0.001 and a significant difference in gravidity between fully vaccinated patients (average 0.65) and unvaccinated patients (average 1.11) p=0.0005. No significant association was found between HPV vaccination status and history of abnormal Pap smears or relationship status (single vs. married). Less than half of patients presenting to our fertility center were vaccinated against HPV, despite being eligible to receive the vaccine. Younger patients with lower gravidity were more likely to be vaccinated against HPV than older patients with higher gravidity. Reproductive Endocrinologists have the opportunity to recommend HPV vaccination for eligible patients presenting for fertility care.

Early marriage is a phenomenon that has an impact on various aspects of life which then affects human development. This study aims to conduct a literature review on the implications of early marriage on human development. The method used in this study is the SLR (Systematic Literature Review) method. Data collection was carried out by documenting all literature that met the research inclusion requirements. The literature obtained as many as 25 articles from the Google Scholer database with the help of Harzing's Publish or Perish application. From this literature study, it was found that the phenomenon of early marriage has implications for the human development index based on health indicators and life expectancy, education, and welfare. Another finding is that women are the most disadvantaged from the impact of early marriage. Things that can be done to deal with the phenomenon and impact of early marriage include formal and non-formal education, strengthening access to health and education services, creating empowerment programs, and strengthening regulations and legal protection.

With the emergence of new VR/AR technologies, the question arises of how they can be used in human-swarm interaction. Where are the limitations and boundaries of conventional interfaces, and how can VR/AR overcome them? To find out, a user study was conducted. The subjects were able to interact with an autonomously acting swarm of drones using both a conventional desktop interface (Desktop-HSI) and a newly developed VR interface (VR-HSI).The resulting assessments are used as a basis for determining the extent to which this new technology can be applied in human-swarm interaction. The technical basis was an already tested simulation software that was expanded with a network interface to allow interacting with a drone swarm using connected HSIs (Human-Swarm Interfaces). Standardized communication was emphasized to enable the development of HSIs with different requirements. Specifically for this work, a VR-HSI was developed with Unity and a desktop-HSI with Java and their performance regarding human-swarm interaction was evaluated.

Prostate cancer contributes to cancer-related deaths globally, and the etiology of this disease is not yet fully understood. While Human Papillomavirus (HPV) has been associated with several types of cancer, including cervical, anal, and oropharyngeal cancer, studies investigating the relationship between HPV and prostate cancer have shown mixed results. This systematic review aimed to evaluate the causative association between HPV and prostate cancer using Bradford Hill's criteria. A comprehensive search of PubMed was conducted, and 60 out of 482 studies were included in the review. The included studies were evaluated based on nine Bradford Hill criteria, and information on the identification and transmission of the virus and potential oncogenic mechanisms was also extracted. The strength of association criterion was not met, and other criteria, such as consistency and coherence, were not fulfilled. However, biological plausibility was supported, and potential oncogenic mechanisms were identified. While some studies have reported the presence of HPV in prostate cancer tissues, the overall quality of evidence remains low, and the association between HPV and prostate cancer is weak. Nevertheless, the prostate is a potential reservoir for the transmission of HPV, and the HPV E6 and E7 oncoproteins and inflammation are likely to be involved in any oncogenic mechanisms. Further studies with a higher level of evidence are needed to establish a definitive link between HPV and prostate cancer.

Human bocavirus (HBoV) is an emerging virus that has been detected worldwide that could be associated with cases of acute gastroenteritis (AGE). However, its contribution to AGE has not been elucidated. This study aimed to describe the frequency, clinical features, and HBoV genotypes circulation in children up to 5 years with or without AGE symptoms in Acre, Northern Brazil. A total of 480 stool samples were collected between January and December 2012. Fecal samples were used for extraction, nested PCR amplification, and sequencing for genotyping. Statistical analysis was applied to verify the association between epidemiological and clinical characteristics. Overall HBoV-positivity was 10% (48/480), being HBoV-positivity rates of 8.4% (19/226) and 11.4% (29/254) recorded among diarrheic and non-diarrheic children, respectively. The most affected age group was between 7 and 24 months (50%). HBoV infection was more frequent in children living in urban areas (85.4%), using water from the public network (56.2%), and living with adequate sewage facilities (50%). Co-infection with other enteric viruses was 16.7% (8/48) and the most prevalent coinfection was RVA+ HBoV (50%, 4/8). HBoV‐1 was the most frequent species detected, responsible for 43.8% (21/48) of cases, followed by HBoV-3 (29.2%, 14/48) and HBoV-2 (25%, 12/48). In the present study, HBoV infections are not associated with AGE, as most HBoV cases belonged to the non-diarrheal group without AGE symptoms. Future studies are warranted to determine the role of HBoV in causing acute diarrhea disease.

With the advent of Neural Machine Translation, the more the achievement of human-machine parity is claimed at WMT, the more we come to ask ourselves if their evaluation environment can be trusted. In this paper, we argue that the low quality of the source test set of the news track at WMT may lead to an overrated human parity claim. First of all, we report nine types of so-called technical contaminants in the data set, originated from an absence of meticulous inspection after web-crawling. Our empirical findings show that when they are corrected, about 5% of the segments that have previously achieved a human parity claim turn out to be statistically invalid. Such a tendency gets evident when the contaminated sentences are solely concerned. To the best of our knowledge, it is the first attempt to question the “source” side of the test set as a potential cause of the overclaim of human parity. We cast evidence for such phenomenon that according to sentence-level TER scores, those trivial errors change a good part of system translations. We conclude that to overlook it would be a mistake, especially when it comes to an NMT evaluation.

Is human capital heterogeneity a decisive factor for Chinese enterprises to invest in Africa? Based on the Outward Foreign Direct Investment (OFDI） data of Chinese enterprises in 37 African countries from 2007 to 2017, this paper using the FGLS model, is the empirical analysis of the mechanism and effect of the human capital heterogeneity of host countries on OFDI location selection. The human capital heterogeneity can be divided into four dimensions: scale, quality, cost and structure, which can be measured by health level, education level, wage level, child dependency ratio and old-age dependency ratio. The results show that: (1) the host country's human capital scale and child dependency ratio structure have a significant positive impact on decision-making for OFDI; (2) the cost of human capital and the structure of old-age dependency ratio are negatively correlated with the inflow of OFDI; (3) different from the existing conclusions, the quality of human capital will inhibit the inflow of OFDI in the sample period; (4) the extended test shows that the quality of human capital has a significant positive impact on OFDI decision-making. The results of robustness test are reliable. Finally, according to the conclusion of this paper, policy recommendations are put forward.

Human milk (HM) is the gold standard for feeding infants but has been associated with slower growth in preterm infants compared with preterm formula. This systematic review and meta-analysis summarises the post-1990 literature to examine the effect of HM feeding on growth during the neonatal admission of preterm infants with birth weight ≤1500g and/or born ≤28 weeks’ gestation. Medline, PubMed, CINAHL and Scopus were searched, and comparisons grouped as: exclusive human milk (EHM) vs exclusive preterm formula (EPTF), any HM vs EPTF and higher vs lower doses of HM. We selected studies that used fortified HM and compared that with a PTF; studies comparing unfortified HM and term formula were excluded. Experimental and observational studies were pooled separately. The GRADE system was used to evaluate risk of bias and certainty of evidence. Forty-four studies were included with 37 (n =9,963 infants) included in the meta-analyses. In general, due to poor quality studies, evidence of the effect of any HM feeds or higher versus lower doses of HM was inconclusive. There was a possible effect that lower doses of HM compared with higher doses of HM improved weight gain during the hospital admission, and separately, a possible effect of increased head circumference growth in infants fed EPTF vs any HM. The clinical significance of this is unclear. There was insufficient evidence to determine the effects of an exclusive HM diet on any outcomes.

Human factors are the things that go wrong in the interactions between a team of people and a system of technology. This is part of a broader transdisciplinary field called engineering psychology, which as the name suggests, draws from both engineering and psychology. Many, if not most, catastrophic accidents involve a socio-technical interaction, i.e. are not solely due to technology failure. Hence, there is a need to consider human factors in the development or deployment of any technical system. This article is about the human factors involved in an aviation accident in New Zealand between a Yak and a cherry picker. The types of human error are identified, and the barrier bowtie method is used to represent them. The analysis gives different insights into the accident compared to the formal accident report, and better represents the human error characteristics.

OBJECTIVES: Most of human papilomavirus (HPV) infections are “cleared” by the immune system, however, in cases of immune system suppression infections could lead to development of malignancies. The aim of this study was to find out the frequency of HR-HPV infection in early period after renal transplantation in Latvian recipients receiving immunosuppressive therapy and to follow the progression of the infection up to one year. METHODS: 43 female renal recipients (median age of 48 IQR= 39-58) and 79 practically healthy female individuals (median age of 48 IQR= 42-57) as a control group were enrolled in this investigation. For the detection of HPV infection patients' samples (blood and vaginal swabs) where collected two weeks after transplantation with following collection of six months and one year. Different polymerase chain reactions for HR-HPV genomic sequences detection and commercial ELISA kit for detection of anti-HPV IgG antibodies were used. RESULTS: In this study we show that frequency rate of HR-HPV infection has increased by the one year after transplantation from early stage of immumosuppressive therapy (from 24% to 36%). Also increase of HR-HPV load was detected over the time, showing the highest median viral load at sixth month after transplantation. CONCLUSIONS: From the obtained data follows that it is very important to carefully monitor patients receiving immunosuppression therapy on progression of HR-HPV. In the case of this viral infection presence, immunosuppressive therapy must be attentively adjusted to avoid the HR-HPV infection rapid progression with the subsequent development of CIN or cervical cancer.

Using the potent tools of information geometry (IG), the current research analyses the information geometrics of the five-dimensional HTBFC Manifold. By enabling a unique revolutionary analysis for the performance of Human-Trust Based Feedback Control (HTBFC), the integration of information geometry into HTBFC facilitates the relativistic analysis of the five-dimensional HTBFC manifold. Both the two- and three-dimensional manifold subcases are investigated. This work provides a ground-breaking approach to IG unification of Human- Machine Interactions (HMIs). Some potential applications of HTBFC to robotics are presented.Conclusions are provided, along with some emerging open problems and recommendations for future research.

Quantitative polymerase chain reaction (qPCR) is the gold standard approach for detecting a variety of pathogens either via probe-based or SYBR green detection chemistry. Generally, probe-based detection is more specific than SYBR green chemistry. This report describes work done in using a FAM probe targeting a specific segment of the human KRAS gene for detecting presence and relative abundance of the gene in human gastric CloTest samples. Results indicate ineffective detection of the human KRAS gene in samples that are shown to harbor the gene through a similar qPCR assay using SYBR green detection chemistry. Such observations point to inability of the designed FAM probe to bind to target segment of the human KRAS gene that suggests a region of rapid genomic evolution that require close surveillance in the public health community. Given its rapid genomic evolution, this probe region may be an important region in the human KRAS gene playing critical functional roles in either molecular recognition or other structural functions. Overall, human KRAS gene may be undergoing rapid genomic evolution, and qPCR probes targeting specific segment of the gene may be rendered ineffective in short time clinically, thereby, making SYBR green qPCR assay a more reliable choice.

Digital enabled manufacturing systems require high level of automation for fast and low-cost production but should also present flexibility and adaptiveness to varying and dynamic conditions in their environment, including the presence of human beings; However, this presence of workers in the shared workspace with robots decreases the productivity as the robot is not aware about the human position and intention which leads to concerns about the human safety. This issue is addressed in this work by designing a reliable safety monitoring system for collaborative robots (Cobots). The main idea here is, to significantly enhance safety by a combination of recognition of human actions using visual perception and at the same time interpreting physical human-robot contact by tactile perception. Two datasets containing contact and vision data are collected by using different volunteers. The action recognition system classifies human actions using the skeleton representation of the latter when entering the shared workspace and the contact detection system distinguishes between intentional and incidental interactions if a physical contact between human and cobot takes place. Two different deep learning networks are used for human action recognition and contact detection which in combination, are expected to lead to the enhancement of human safety and an increase of the level of cobot perception about human intentions. The results show a promising path for future AI-driven solutions in safe and productive human–robot collaboration (HRC) in industrial automation.

Effective pain management in laboratory animals is crucial for both animal welfare and the reliability of scientific research. We retrospectively examined the effects of carprofen as post-operative analgesia in Sprague Dawley rats following stereotactic surgery. Our data indicate that administering carprofen twice daily (5 mg/kg), as currently recommended by GV-SOLAS, led to adverse effects such as reduced food and water intake, disrupted fecal excretion, and abdominal bloating consistent with peritonitis. Continued administration exacerbated these symptoms, with post-mortem findings of intestinal obstructions and ulcers. However, when the frequency was reduced to once daily, such adverse symptoms were not observed. These results are based on incidental data collected from various neuroscientific experiments, resulting in small and uneven sample groups across various experimental cohorts. The inherent imbalances in these groups present challenges for statistical interpretation. While the findings suggest that less frequent carprofen use may reduce adverse effects, the surgical interventions and concurrent use of other drugs in these experiments likely exacerbated these outcomes. Further investigation into the interactions between carprofen, surgical stress, and other perioperative factors is needed to refine analgesia protocols in laboratory animals. Despite these limitations, these observations contribute to understanding analgesia protocols and may assist in improving animal welfare practices.

The study objective is to evaluate the reliability of the Göttingen Mini-pig (G-MP) as a large animal model for preclinical studies addressing articular cartilage treatment procedures. Second this work compared the G-MP to the domestic pig (DP) in terms of surgical anatomy, postoperative management, and follow-up period possibilities and challenges. Six G-MP and four DP underwent a two-stage surgical procedure including cartilage harvesting using a superolateral approach followed by cartilage implantation using a medial parapatellar tendon approach. The superolateral approach exposed 11% (SD ± 5) of the entire trochlea in G-MP and 20% in the DP model. The medial parapatellar tendon approach in G-MP exposed 63% (SD ± 4) of the trochlear surface and exposed 34% (SD ± 13) of the medial femoral condyle, allowing 4 trochlear lesions of 6mm of diameter and one to the medial condyle in 4 out of 6 G-MP and in all DPs. Cartilage thickness was under 1mm in G-MP compared to more than 2mm in DP. Weight-progression was + 4 kg/week in DP and + 0.2 kg/week for G-MP. The G-MP is a feasible model for cartilage research, with adequate access to the joint via the dual approach. Thus enabling 4-5 lesions of 6mm, however a thinner cartilage in the G-MP needs to be considered.

Microsatellite instability (MSI) is a crucial feature in cancer biology, yet its prevalence and significance in canine cancers remain largely unexplored. This study conducted a comprehensive analysis of MSI across 10 distinct canine cancer histotypes using whole-exome sequencing data from 692 tumor-normal sample pairs. MSI was detected in 64% of tumors, with prevalence varying significantly among cancer types. B-cell lymphomas exhibited the highest MSI burden, contrasting with human studies. A novel "MSI-burden" score was developed, correlating significantly with tumor mutational burden. MSI-high (MSI-H) tumors showed elevated somatic mutation counts compared to MSI-low and microsatellite stable tumors. The study identified 3,632 recurrent MSI-affected genomic regions across cancer types. Notably, seven of the ten cancer types exhibited MSI-H tumors, with prevalence ranging from 1.5% in melanomas to 37% in B-cell lymphomas. These findings highlight the potential importance of MSI in canine cancer biology and suggest opportunities for targeted therapies, particularly immunotherapies. The high prevalence of MSI in canine cancers, especially in B-cell lymphomas, warrants further investigation into its mechanistic role and potential as a biomarker for prognosis and treatment response

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by impairments in social interaction and communication, anxiety, hyperactivity, and restricted interest for specific subjects. In addition to the genetic factors, multiple environmental factors have been related to the development of ASD. Animal models can serve as crucial tools for understanding the complexity of ASD and identifying potential therapeutic strategies. In this study, a chemical model of ASD has been developed in zebrafish by exposing embryos to valproic acid (VPA) from 4 to 48 hours post-fertilization. After the exposure, embryos were transferred to fish water and the effects on behavior and neurotransmitters profile were analyzed at both larval and adult stages. Larvae from VPA-treated embryos showed hyperactivity and decreased visual and vibrational escape responses, as well as an altered neurotransmitters profile, with increased glutamate and decreased acetylcholine and norepinephrine levels. Adults from VPA-treated embryos exhibited impaired social behavior characterized by larger shoal sizes and a decreased interest for their conspecifics. Neurotransmitter analysis revealed a significant decrease in dopamine and GABA levels in the brain. These results support the interest of this model in the study of ASD.

Buruli ulcer is a chronic ulcerative disease of skin and subcutaneous tissues caused by infection with Mycobacterium ulcerans. Although Australian possums are known to be susceptible to Buruli ulcer, many aspects of the disease in possums, including welfare impacts, remain largely unreported. Severe clinical Buruli ulcer was identified in four common ringtail possums (Pseudocheirus peregrinus) from Melbourne, Victoria. All four possums were euthanised due to welfare concerns and poor prognosis, based on the presence of deep ulcerative lesions to paws, some of which exposed carpal or tarsal bones. These lesions were considered likely to cause partial or complete loss of limb function, which would severely affect the welfare and survival of these wild arboreal animals. Histologically, there was severe ulcerative necrotising pyogranulomatous dermatitis, panniculitis, and myositis, with intralesional acid-fast bacteria. M. ulcerans was detected by real-time PCR in all swabs and biological tissues collected from all four cases. Buruli ulcer may be an important and under-recognised cause of poor possum welfare in endemic areas. In addition to severe cutaneous lesions, infection extends to underlying bones and joints, and the systemic distribution of M. ulcerans throughout all major tissues and internal organs, as observed in these four cases, may further impact the health and fitness of infected possums. Further research is needed to investigate the epidemiology, pathogenesis and impacts of Buruli ulcer in possums, and to inform the design of interventions that may protect the health and welfare of possums and other affected animal species.

Disease outbreaks can severely affect populations in the wild. However, their ecological consequences are poorly understood. Here, we used a multi-site capture-recapture model to investigate the impacts of a yellow fever outbreak on the endangered golden lion tamarin Leontopithecus rosalia, in the Atlantic forest, Brazil. We show that the annual adult survival rate severely declined in 2017-2018, coinciding with the outbreak period. Simultaneously, dispersal patterns changed temporarily, with a slight reduction of dispersal between groups of the same forest fragment from 3.6 to 2.8%, and a strong increase of dispersal between forest fragments, from 0.4 to 4.3%. Those results suggest that disease transmission potential was low, especially between forest fragments and advocate for a better integration of host movements and landscape configuration when evaluating species response to vector-borne diseases

Lower urinary tract dysfunction, such as incontinence or urinary retention, is one of the leading consequences of neurological diseases. This significantly impacts the quality of life for those affected, with implications extending not only to humans but also to clinical veterinary care. Having motor and sensory fibers, the pudendal nerve is an optimal candidate for neuromodulation therapies using bidirectional intraneural prostheses, paving the way towards the restoration of a more physiological urination cycle: bladder state can be detected from recorded neural signals, then an electrical current can be injected to the nerve based on the bladder real-time need. To develop such prostheses and investigate this novel approach, animal studies are still required since the morphology of the target nerve is fundamental to optimize the prosthesis design. This study aims to describe the porcine pudendal nerve as a model for neuromodulation studies aiming at restoring lower urinary tract dysfunction. Five male farm pigs were involved in the study. First, a surgical procedure to access the porcine pudendal nerve without muscle resection was developed. Then, an intraneural interface was implanted to confirm the presence of fibers innervating the external urethral sphincter by measuring its electromyographic activity. Finally, the morpho-physiology of the porcine pudendal nerve at the level of surgical exposure was described by using histological and immunohistochemical characterization. This analysis confirmed the nerve fasciculate nature and the presence of mixed fibers with a spatial and functional organization. These achievements pave the way for further pudendal neuromodulation studies by using a clinically relevant animal model with the potential for translating the findings into clinical applications.

Trypanosomosis is a global animal issue, causing significant economic losses, particularly in Africa. In Spain, only one pathogenic species, Trypanosoma evansi, has been identified so far. It was first detected in a dromedary camel in the Canary Islands in 1997. Since then, numerous cases of the disease, known as surra, have been diagnosed, prompting various studies and efforts in control and surveillance. Given the lack of a comprehensive database that consolidates the most relevant data in this area, the development of a national atlas, with focus on the Canary Islands, to incorporate all available information on T. evansi in Spain became a necessity. For the development of the atlas, a repository was constructed, encompassing a range of datasets and documents spanning from 1997 to 2022. Information from each source, and in particular georeferenced locations and results of blood tests on animals, were extracted and integrated into a comprehensive database. A total of 31 sources were analysed, providing a total of 99 georeferenced locations and 12,433 animal samples. Out of these samples, 601 (mostly from dromedaries) were found to be positive for T. evansi. The Card Agglutination Test for T. evansi (CATT/T. evansi), a serological test, was the most commonly used diagnostic method, and it showed higher prevalence for all tested animal species. Positive cases were mainly concentrated in the Canary Islands, specifically in the eastern islands, with isolated cases found in the province of Alicante (Iberian Peninsula). This atlas provides an overview of the history and occurrence of surra in Spain, and it represents a valuable tool for future control initiatives and for research. Still, the need for more studies remains, especially for further testing of potential hosts other than camelids and for the examination of their potential transmission vectors.

Chloroquine (CQ) and its derivate hydroxychloroquine (HCQ), the compounds with recognized ability to suppress autophagy, have been tested in experimental works and in clinical trials as adjuvant therapy for the treatment of cancers of different origin to increase the efficacy of cytotoxic agents. Such strategy can be effective to overcome the resistance to standard chemotherapy or anti-angiogenic therapy. This review presents the results of combined application of CQ/HCQ with conventional chemotherapy drugs (doxorubicin, paclitaxel, platinum-based compounds, gemcitabine, tyrosine kinases and PI3K/Akt/mTOR inhibitors, and other agents) for the treatment of different malignancies obtained in experiments on cultured cancer cells and on animal xenograft models, with a few examples of clinical trials. The effects of such approach on viability of cancer cells and tumor growth, as well as autophagy-dependent and independent molecular mechanisms underlying cellular responses of cancer cells to CQ/HCQ are summarized. Although the majority of experimental studies in vitro and in vivo have shown that CQ/HCQ can effectively sensitize the cancer cells to cytotoxic agents and increase the potential of chemotherapy, the results of clinical trials are often inconsistent. Although pharmacological suppression of autophagy remains a promising tool for increasing the efficacy of standard chemotherapy, the development of more specific compounds is required.

Hepatocellular carcinoma (HCC) poses a significant global health concern, with its incidence steadily increasing. The development of HCC is a multifaceted, multi-step process involving alterations in various signaling cascades. In recent years, significant progress has been made in understanding the molecular signaling pathways that play central roles in hepatocarcinogenesis. In particular, the EGFR/PI3K/Akt/mTOR signaling pathway in HCC has garnered renewed attention from both basic and clinical researchers. Preclinical studies in vitro and in vivo have shown the effectiveness of targeting the key components of this signaling pathway in human HCC cells. Thus, targeting these signaling pathways with small molecule inhibitors holds promise as a potential therapeutic option for patients with HCC. In this review, we will explore recent advancements in understanding the role of the EGFR/PI3K/Akt/mTOR signaling pathway in HCC and assess the effectiveness of targeting this signaling cascade as a potential strategy for HCC therapy based on preclinical studies

The use of metal oxide nanoparticles as an alternative antimicrobial agent has gained attention due to the increasing problem of antibiotic resistance. Understanding its properties and potential benefits can contribute to the development of more effective and sustainable treatments in veterinary medicine. The aim of this study was to characterize TiO2-NP formulations and evaluate their antibacterial and wound healing abilities. The diameters and zeta potentials were determined using the Zetasizer in conjunction with dynamic light scattering. The agar-well diffusion method, time-kill kinetic assay and crystal violet assay were used to evaluate their antimicrobial activities. Wound healing assays were conducted both in-vitro and in-vivo. The study demonstrated that TiO2-NP formulations exhibit significant antimicrobial properties against various bacterial strains such as S. aureus and E. coli. No measurable E. coli growth was observed within a 15-mins period following exposure to TiO2-NP formulations. The TiO2-NP formation can improve wound healing by enhancing cell migration and collagen formation in both in-vitro and in-vivo conditions. In summary, our study suggests that TiO2-NP has the potential for use as an antimicrobial agent for animal wound treatment due to its ability to suppress bacterial growth and biofilm formation, as well as to enhance wound healing.

Pesticides are chemical products are used to protect crops to prevent or eliminate undesirable organisms. Pesticides can contaminate continental waters, air, soil and food products. The presence of pesticide residues must therefore be monitored in water and food using sensitive, reliable and broad-spectrum analytical methods, as there is a huge variety of pesticides. Biosensors are one of the screening methods with great potential for detecting residues below the maximum residue limits. The different sensing principles of enzymatic biosensors for pesticide analysis will be described. This review highlights the great potential of colorimetric, enzymatic biosensors for detecting pesticide residues in water and food. Moreover the development of dual colorimetric and fluorimetric sensors during the past years is presented. These methods are most often based on the enzyme acetylcholinesterase, whose activity is inhibited by pesticides. The different approaches used in the development of these biosensors will be detailed, based on different chemical substrates, fluorophores and nanomaterials. The advantages and disadvantages of each approach will be discussed. Finally, the development prospects for improving these biosensors will be presented.

The pond slider (Trachemys scripta) was designated as an invasive alien species in South Korea in 2001. Although the prevention and control of invasive species have received considerable atten-tion worldwide, studies on the current status and effective management of T. scripta in South Korea are lacking. This study aimed to elucidate the status of domestic reuse of T. scripta and provide effective management suggestions. Analysis of the National Institute of Ecology's “Na-tionwide Survey of Non-native Species in Korea” conducted from 2015 to 2022 confirmed the habitats of 1,440 T. scripta individuals at 295 sites. T. scripta is the most frequently observed among invasive turtles identified in the Korean Peninsula. To determine the reuse of T. scripta, 13 tradi-tional markets were surveyed; five markets were confirmed to have T. scripta. Although the exact distribution route is unknown, individuals living or abandoned in the region might be resold in the market. T. scripta is mostly sold for ritual purposes. Moreover, T. scripta accounted for 31% of freshwater turtles abandoned between 2019 and 2022. Their continuous reuse has been confirmed 22 years after their designation as an invasive alien species. Therefore, effective and fundamental management measures for T. scripta are required.

Animal welfare assessment protocols are important for identifying welfare problems in conservation programs. This study aimed to develop a baseline welfare protocol for the assessment of welfare in huemul (Hippocamelus bisulcus) in conservation centers. This protocol is based on the Welfare Quality® (WQ) framework for cattle, considering the available literature on the behaviours, ecology, conservation plans, captive management of this species, and welfare assessment protocols for other ungulate species. As a result, the protocol was specifically developed for huemuls and included four principles, 12 criteria, and 23 animal- and resource-based indicators. The 12 criteria of the WQ protocol were reduced to nine, and three new criteria were added because they were both feasible and essential for welfare assessment in captive huemuls. This protocol is mainly intended to identify welfare problems in endangered species in the context of conservation centers (reproduction, rescue, rehabilitation, or treatment centers). Thus, aggregation of different measures to obtain a global score has not been proposed. However, a scoring system that assigns a value on a 0-2 scale (0= No welfare concern; 1= welfare concern; 2= urgent welfare concern) is proposed for each category. Although further research is still needed to fully validate the protocol, this is the first development of a completed protocol for huemul welfare assessment.

The effect of defatted Hermetia illucens meal (HIM) dietary inclusion on growth performance, stress indicators and gut histological traits of Sparus aurata was studied. For 131 days, 312 fish were fed with one basal diet, containing fish meal as animal protein source, and three diets containing 25%, 35% and 50% HIM as a partial replacement for fishmeal. On all fish (26 fish per tank, 3 replicate tanks per diet, 78 fish per diet) the growth performance were calculated. At the end of the trial, on a subsample of 72 specimens (6 fish per tank, 3 replicate tanks per diet, 18 fish per diet), stress parameters were determined on blood samples and gut histological tract investigated. Insect meal inclusion did not affect (p > 0.05) growth performance, blood parameters, length and width of villi and goblet cell count of the posterior gut tract while, those of the anterior gut tract while increased (p < 0.05). The histological examination of the intestinal sections showed in fish fed the HIM25 and HIM50 diets, more frequent and evident morphological changes; instead, there were no substantial differences between HIM0 and HIM35 groups. In conclusion, the HIM35 was the most tolerated formulation by fish.

High-resolution GPS/GSM dataloggers provide spatial information of the highest quality, which outperform previous tracking methods, such as Argos telemetry or conventional VHF ground-tracking. As a result, this has improved our knowledge of home-range behavior and spatial ecology of many species, including large raptors. In this paper, we use high-resolution GPS/GSM dataloggers to assess the home-range size and the role of sex, season (breeding or non-breeding season), and breeding status (reproductive or non-reproductive individuals) on the space use of Bonelli’s eagle (Aquila fasciata). To this end, 51 territorial individuals (25 females and 26 males) were equipped with GPS/GSM transmitters and were tracked over 7 years (2015–2021) in eastern Spain. Overall, we recorded 4,791,080 fixes that were analyzed through kernel density methods (50%, 75%, and 95% fixed kernels). The average individual home-range size according to the 95%, 75%, and 50% kernels was 54.84 ± 20.78 km², 24.30 ± 10.18 km², and 11.17 ± 4.90 km², respectively. Overall, the home-range size of individuals occupying the same territory was similar, mainly due to the cooperative hunting behavior exhibited by the species. We did not find interannual differences in the home-range size (95% fixed kernel) of the majority of individuals, showing a strong territorial fidelity of the breeding pairs. In general, females’ home-range size was slightly smaller than males’ size due to the decrease in activity in the breeding season as a result of laying, incubation, and chick attendance at nests. No seasonal variation in the 95% kernel was found, but it was found in the 75% and 50% kernels. In regard to the breeding status, higher home-range size was recorded in the non-reproductive individuals. Moreover, we found a low neighbor overlap among the territories (4.18% ± 3.06%), which evidences a high level of intraspecific competition in the Bonelli’s eagle. Finally, this study highlights the advantages of the use of accurate telemetry information to improve our understanding of the spatial ecology of the endangered Bonelli’s eagle, which ultimately will serve to better inform management actions for its conservation.

A new variant of the SARS-CoV-2 Omicron BA.5 virus has displaced all previous variants of the virus around the world. Preliminary assessment of the effectiveness of drugs for the prevention and treatment of COVID-19 requires the availability of infection models in animals. In this study, we characterize the infection model SARS-CoV-2 Omicron BA.5 and its progeny sublineage BE.1 in hACE2-transgenic mice and in Syrian hamsters. Both sublineages turned out to be pathogenic for animals – the challenged animals showed weight loss, a high level of viral load and acute inflammation in the lungs. Part of BA.5-infected mice died after virus challenge, indicating that this virus variant is more pathogenic than the previous BA.1 variant but less pathogenic than Wuhan variant.

Early disease detection in livestock allows for target treatment decreasing antibiotics use and allow advancements in precision veterinary medicine. MicroRNA (miRNA) -driven signaling cascades play a crucial role in the context of farm animal disease diagnostics and prediction, and their proper understanding remains a challenge. In livestock farm animals, only a small number of miRNAs have been fully validated with respect to disease conditions and physiological or behavioral traits. Low abundance of miRNAs in blood and bodily fluids, along with a small number of nucleotides, makes detection and discrimination tedious and challenging task in. miRNAs usually are homologous, owing to which detection specificity becomes next to impossible when screening for multiple miRNAs in the same analyte sample. Hence, a concurrent, multiplexing, approach becomes crucial for the development of on-farm point-of-care based detection systems. Comprehensive screening methods demand broad dynamic range and enhanced specificity. For on-farm handheld platform development, the ability to screen for multiple varieties of miRNA is essential. In this review paper, I provide an overview of the recent developments of miRNA sensing and the current bottlenecks in the realization of the sensors for detecting miRNAS as target analyte for various livestock disease detection applications. Due to the nascent stages of this research, the possibilities of exploiting miRNAs as a biomarker opens up ways to move from reactive to predictive possibilities in diseases detection in the modern digital livestock farming.

As a stimulus signal, coded electrical signals can control the motion behavior of animals, which has been widely used in the field of animal robots. In current research, most of the stimulus signals used by researchers are traditional waveform, such as square waves. To enrich the stimulus waveform, a wireless animal robot stimulation system based on neuronal electrical signal characteristics is presented in this paper. The stimulator uses the CC1101 wireless module to control animal behavior through brain stimulation. The LabVIEW-based graphical user interface(GUI) can manipulate brain stimulation remotely while the stimulator powered by battery. Additionally, the spikes of animals have been simulated by this system through Direct Digital Synthesizer(DDS) algorithm. The GUI enable users to customize the combination of these analog spike signals. The recombined signals are sent to the stimulator through CC1101 as stimulus signals. In vivo experiments conducted on five pigeons verified the efficacy of the stimulation mechanism. The analog spike signal with an amplitude of 3-5V successfully caused the pigeon’s turning behavior. The feasibility of the analog spike signals as stimulus signals was successfully verified. Increased the diversity of stimulus waveform in the field of animal robots.

Increasing rates of infection by antibiotic resistant bacteria have led to a resurgence of interest in bacteriophage (phage) therapy. Several phage therapy studies in animals and humans have been completed over the last two decades. We conducted a systematic review of safety and toxicity data associated with phage therapy in both animals and humans reported in English-language publications from 2008 – 2021. Overall, 69 publications met our eligibility criteria including 20 animal studies, 35 clinical case reports or case series, and 14 clinical trials. After summarizing safety and toxicity data from these publications, we discuss potential approaches to optimizing safety and toxicity monitoring with the therapeutic use of phage moving forward. In our systematic review of the literature, we found few, but no serious, adverse events associated with phage therapy. Comprehensive and standardized reporting of potential toxicities associated with phage therapy has generally been lacking in the published literature. Structured safety and tolerability endpoints are necessary when phages are administered as anti-infective therapeutics.

Higher-risk myelodysplastic syndrome (HR-MDS) has a poor prognosis in the absence of efficient therapy. The evaluation of new therapies in animal models of HR-MDS is hampered by the absence of accurate in vivo biomarkers of the disease. In this study we compared [18F]Fluoro-desoxyglucose Positron Emission Tomography (FDG-PET) and [18F]Fluoro-thymidine (FLT)-PET imaging for disease follow-up in a triple transgenic MMTVtTA/TetoBCL-2/MRP8NRASD12 mouse model of HR-MDS. Normal control FVB/N mice (G1,n=9) and HR-MDS mice (G2,n=12) underwent both FDG- and FLT-PET procedures at 2-day intervals, on a dedicated small animal device. Blood cell counting, BCL-2 and Mac-1hi/Gr-1lo expression measurements in blood were performed before each PET procedure. Visually, PET images of G2 mice demonstrated homogeneous FDG uptake in the whole skeleton similar to that observed in G1 mice, and abnormal FLT hot spots in bone marrow not observed in G1 mice. The intensity of FLT hot spots in bone marrow was higher in 3-months old G2 mice than in 2-months old G2 mice, concordant with a higher percentage of cells expressing Mac-1hi/Gr-1lo and lower platelets counts. We conclude that FLT-PET/CT imaging is a more valuable surrogate non-invasive quantitative marker of HR-MDS bone marrow involvement than FDG-PET/CT in our mouse model of HR-MDS.

Mate choice that is based on behavioural traits is a common feature in the animal kingdom. Using the Trinidadian guppy, a species with mutual mate choice, we investigated whether males use female swimming activity – a behavioural trait known to differ consistently among individuals in many species – as a trait relevant for their mate choice. In a first experiment, we assessed male and female activity in an open field test alone (two repeated measures) and afterwards in heterosexual pairs (two repeated measures). In these pairs, we simultaneously assessed males’ mating efforts by counting number of sexual behaviours (courtship displays and copulation). Male and female guppies showed consistent individual differences in their swimming activity when tested both alone and in a pair, and these differences were maintained across both test situations. When controlling for male swimming behaviour and both male and female body size, males performed more courtship displays towards females with higher swimming activity. In a second experiment, we tested for a directional male preference for swimming activity by presenting males video animations of low and high active females in a dichotomous choice test. In congruence with experiment 1, we found males to spend significantly more time in association with the high active female stimulus. Both experiments thus point towards a directional male preference for higher activity levels in females. We discuss the adaptive significance of this preference as activity patterns might indicate individual female quality, health or reproductive state while, mechanistically, females that are more active might be more detectable to males as well.

The study aimed to develop standards for a welfare assessment protocol, by validating potential categorisation thresholds for assessment of beef farms in various beef cow-calf production systems in Namibia. Forty measures combined from a New Zealand-based protocol plus Namibia-specific measures, were applied on 55 beef farms (17 commercial farms, 20 semi-commercial and 18 communal village farms) during pregnancy testing, and a questionnaire guided interview. The categorised measures on a 3-point welfare score of 0: good 1: marginal and 2: poor/unacceptable welfare were subsequently compared with derivation of thresholds based upon the poorest 15% and best 50% of herds for each measure. Overall combined thresholds of continuous measures across the 3 farm types, showed 10/22 measures that posed welfare compromise across Namibia, where commercial farms had 4/22 measures and semi-commercial and communal village farms had 12/22 and 11/22 respectively with high thresholds. Most measures-imposed thresholds were retained because of significant importance to welfare of animals and preventiveness of the traits, while leniency was given to adjust good feeding and mortality measures to signify periods of drought. Handling measures (fearful, falling/lying) and abrasions thresholds were adjusted to reflect the temporary stress caused by infrequent cattle handling, and faulty yard designs/design and possible cattle breed influence on handling. Hence, the country needs prioritised investigation of underlying contributing factors and remediation to reduce the high thresholds.

In this review, we address issues that relate to the rapid “Warp Speed” development of vaccines to counter the COVID-19 pandemic. We review the antibody response that is triggered by SARS-CoV-2 infection of humans, and how it may inform vaccine research. The isolation and properties of neutralizing monoclonal antibodies from COVID-19 patients provide additional information on what vaccines should try to elicit. The nature and longevity of the antibody response to coronaviruses are relevant to the potency and duration of vaccine-induced immunity. We summarize the immunogenicity of leading vaccine candidates tested to date in animals and humans, and discuss the outcome and interpretation of virus-challenge experiments in animals. By far the most immunogenic vaccine candidates for antibody responses are recombinant proteins, which are not included in the initial wave of “Warp Speed” immunogens. A substantial concern for SARS-CoV-2 vaccines is adverse events, which we review by considering what was seen in studies of SARS-CoV-1 and MERS-CoV vaccines. We conclude by outlining the possible outcomes of the “Warp Speed” vaccine program, which range from the hoped-for rapid success to a catastrophic adverse influence on vaccine uptake generally.

The interaction between endocannabinoid (eCB) system with in key brain structures such as hippocampus, amygdala and prefrontal cortex and sleep deprivation (SD)-induced psychosis has been less studied. The present hypothesis revolves around the question whether altered chemical dynamics within the eCB system with the resultant impact on cannabinoid receptors in key cortical hubs would impact SD-induced psychosis-like symptoms. Having this investigated research is expected to pave the path towards identifying newer drug targets namely for schizophrenia.

Rift Valley Fever virus (RVFV) is a zoonotic arbovirus of the Phenuiviridae family. Infection causes abortions in pregnant animals, high mortality in neonate animals and mild to severe symptoms in both people and animals. There is currently an ongoing effort to produce safe and efficacious veterinary vaccines against RVFV in livestock to protect against both primary infection in animals and zoonotic infections in people. To test the efficacy of these vaccines it is essential to have a reliable challenge model in relevant target species, including ruminants. We evaluated two goats breeds (Nubian and LaMancha), three routes of inoculation (intranasal, mosquito-primed subcutaneous and subcutaneous) using an infectious dose of 10⁷ pfu/ml, a virus strain from the 2006-07 Kenyan/Sudan outbreak and compared the effect of using virus stocks produced in either mammalian or mosquito cells. Our results demonstrated that Nubian goats achieved the highest levels and longer duration of viremia. In the Nubian goats, all three routes of inoculation were equally efficient at producing clinical signs, consistent viremia (peak viremia: 1.2x10³ - 1.0x10⁵ pfu/ml serum), nasal and oral shedding of viral RNA (1.5x10¹ – 8x10⁶ genome copies/swab), a systemic infection of tissues, and robust antibody responses. The Nubian goat breed and a needle-free intranasal inoculation technique could both be utilized in future vaccine and challenge studies.

Recent advancements in Intelligent Tutoring Systems (ITS), driven by Artificial Intelligence (AI), have attracted substantial research interest, particularly in the domain of computer programming education. Given the diversity in learners' backgrounds, cognitive abilities, and learning paces, the development of personalized tutoring strategies to support effective attainment of learning objectives has become a critical challenge. This paper introduces PerFuSIT (Personalized Fuzzy Logic Strategies for Intelligent Tutoring of Programming), an innovative fuzzy logic-based module designed to select the most appropriate tutoring strategy from five available options, based on individual learner characteristics. The available strategies include revisiting previous content, progressing to the next topic, providing supplementary materials, assigning additional exercises, or advising the learner to take a break. PerFuSIT's decision-making process incorporates a range of learner-specific parameters, such as performance metrics, error types, indicators of carelessness, frequency of help requests, and the time required to complete tasks. Embedded within the traditional ITS framework, PerFuSIT introduces a sophisticated reasoning mechanism for dynamically determining the optimal instructional approach. Experimental evaluations demonstrate that PerFuSIT significantly enhances learner performance and improves the overall efficacy of interactions with the ITS. The findings highlight the potential of fuzzy logic to optimize adaptive tutoring strategies by customizing instruction to individual learners' strengths and weaknesses, thereby providing more effective and personalized educational support in programming instruction.

Deep Reinforcement Learning has achieved state-of-the-art results in the field of autonomous driving, especially in video game simulations. Trained agents were able to beat the best players achieving supremacy in the field. However, these agents often take in their neural network input the geometry of the track during the learning process. Our approach is to create a learning procedure for autonomous driving agents that is not environment-dependent by design to extend Deep Reinforcement Learning to racing games without racetrack information. Our work builds upon the notorious game Trackmania Nations United Forever, an open access 3D realistic Formula 1 simulation game that offers a high degree of customization. We use the Proximal Policy Optimization algorithm, using hybrid raw pixel data and physics sensors vectors as agent observations. The training track is a custom-made circuit carefully crafted from all available blocks in the game. The training track is considered like a standard dataset in machine learning and thus split in several segments in which the agent is randomly put into. We also created two regularization reward functions for the car’s speed and position to increase robustness of the agent in new scenarios.

The behavior of low concentrations of methanol (0.5 and 1.0 wt%) as a promoter for hydrate formation in seawater or pure water metastable solutions of methane was investigated using the classical molecular dynamics method at moderate temperature and pressure. The influence of methanol on the dynamics of the re-arrangement of the hydrogen bond network in seawater and pure water solutions of methane was studied by calculating the order parameters of the tetrahedral environment and intermolecular torsion angles for water molecules, as well as by calculating the number of hydrogen bonds, hydrate and hydrate-like cavities. It was found that hydrate nucleation can be considered a collective process in which the rate of hydrate growth is faster in systems with low concentrations of methanol and confident hydrate growth begins earlier in a metastable solution without sea salt with a small amount of methanol than in systems without methanol.

We explore the integration of smart pixel light modulators (SPLMs) into bidirectional optical neural networks (BONNs), highlighting their advantages over traditional spatial light modulators (SLMs). SPLMs enhance BONN performance by enabling faster light modulation in both directions, significantly increasing the refresh rate of neural network weights to hundreds of megahertz, thus facilitating the practical implementation of the backpropagation algorithm and two-mirror-like BONN structures. The architecture of an SPLM-based BONN (SPBONN) features bidirectional modulation, simplifying hardware with electrical fan-in and fan-out. Scalability limitations of the projection system restrict input and output array sizes but achieve high throughput, up to 4.3 × 10¹⁷ MAC/s with 100 layers. Energy assessments showed that the SPLM array, despite its higher power consumption compared to the SLM array, is manageable via effective heat dissipation. Smart pixels with programmable memory in the SPBONN provide a cost-effective solution for expanding network node size and overcoming scalability limitations without the need for additional hardware.

Bacillus subtilis is a model organism used to study molecular processes in Gram-positive bacteria. Sigma factor B, which associates with RNA polymerase, is one of the transcriptional regulators involved in the cell’s response to environmental stress. Experiments have proven that the amounts of free σB (SigB) are controlled by a system of anti- (RsbW) and anti-anti-sigma (RsbV) factors expressed from the same operon as SigB. Moreover, the phosphorylation state of RsbV is controlled by phosphatases RsbP and RsbU, which directly dephosphorylates RsbV. A set of chemical equations describing the network controlling the levels of free SigB was converted to a set of differential equations quantifying the dynamics of the network. The solution of these equations allowed the simulation of the kinetic behavior of the network and its components under real conditions. In this study, the time series of protein expression measured by mass spectrometry were utilized to investigate the role of the phosphatases RsbU/RsbP in transmitting the environmental signal. Additionally, the influence of kinetic constants and the amounts of other network components on the functioning of the network was investigated. A comparison with the same simulation performed using transcriptomic dataset showed that while the time series between the proteomic and transcriptomic datasets) are not correlated, the results are the same. This indicates that when modeling is done within one dataset, it does not matter whether the data come from the mRNA or protein level. In summary, the computational results based on experimental data provide quantitative insight into the functioning of the SigB-dependent circuit and offer a template for the quantitative study of similar systems.

In high-risk railway construction, personal protective equipment monitoring is critical but challenging due to small and frequently obstructed targets. We propose YOLO-EA, an innovative model that enhances safety measure detection by integrating ECA into its backbone's convolutional layers, improving discernment of minuscule objects like hardhats. YOLO-EA further refines target recognition under occlusion by replacing GIoU with EIoU loss. YOLO-EA's effectiveness was empirically substantiated using a dataset derived from real-world railway construction site surveillance footage. It outperforms YOLOv5, achieving 98.9% precision and 94.7% recall, up 2.5% and 0.5% respectively, while maintaining real-time performance at 70.774 fps. This highly efficient and precise YOLO-EA holds great promise for practical application in intricate construction scenarios, enforcing stringent safety compliance during complex railway construction projects.

Through the refinement of approaches to applying visual augmentation to virtual environments, the possibilities of utilising this process in robotics continues to be explored. However these endeavours have only yielded a collaborative relationship between the two technologies, where the augmented reality (AR) component is separate from the robot functionality. Regarding telepresence robots, research has shown that their perceived usefulness wanes in comparison to that of video conferencing tools as they do not offer a level of immersion and modified visual experience to justify their use. Therefore this paper contributes a novel virtually augmented robot (VAR) model that embeds AR functionality into telepresence, through the use of augmentation and facial recognition sub-models and algorithms. Experimental results show that using pulse width modulation (PWM), duty cycles, digital signals and accelerometer angles provides an approach for stabilising robot motion and the transference of human to robot head movement. The results also show the benefit of having a robot model with intrinsic AR functionality, as it offers a greater level of immersion and visual control of an environment.

The purpose of this study is to ​​explore the use of a custom Transformer model in brain-computer interfaces (BCIs) that translate the neural activity present when an individual with limited verbal and fine-motor skills attempts to handwrite. As found in previous studies, Transformers have performed better than recurrent neural networks (RNNs) in translation tasks which are similar to its usage here in decoding neural signals into intended handwritten text. Due to the known benefits of a Transformer, the hypothesis was that the Transformer would show promise in the context of a BCI through the recorded metrics. The neural signals of a tetraplegic individual, when they attempted to handwrite, were provided by existing research. Four trials were conducted using data with or without augmentation which the model used when training to separately minimize training loss and validation loss. When comparing the results of a BCI with the implementation of a Transformer model with the original RNN BCI (the original data source), the Transformer model performed less favorably across all four trials. Although the results do not indicate that the Transformer model currently outperforms an RNN BCI, it is important to note that further testing of the model's capabilities (such as training it with a larger and more preferable dataset and/or for longer, comparing training times between the RNN and Transformer, and/or seeing how the Transformer is improved with an offline autocorrect feature) is necessary before determining whether Transformers can enhance communication in this manner.

Pedagogic Conversational Agents (PCAs) are interactive systems that engage the student in a dialogue to teach some domain. They can have the role of teacher, student, or companion, and adopt several shapes. In our previous work, a significant increase of students’ performance when learning programming was found when using PCAs in the teacher role. However, it is not common to find PCAs used in the classrooms. In this paper, it is explored whether teachers would accept PCAs to teach programming better if co-designed with them. An experiment with 35 pre-service Primary Education teachers was carried out during the 2021/2022 academic year to co-design a robotic PCA to teach programming. The experience validates the idea that involving the teachers in the design of the PCA facilitates that they integrate this technology in their classrooms. 97% of the pre-service teachers answered in a survey that they believed that the robot PCA could help children to learn programming and 80% answered that they would like to use them in their classrooms.

Track fasteners play a pivotal role in infrastructure inspection for high-speed rail. Yet, images taken by drones often capture shadows cast by electrical towers flanking the high-speed rail tracks. These shadows can hinder the visibility of the track fasteners, thereby impacting detection efficiency and accuracy considerably. The present paper introduces an end-to-end shadow removal algorithm, rooted in generative adversarial network training. The comprehensive network framework is segmented into three sub-networks: pseudo-mask generation, shadow removal, and result refinement. We have integrated a Fourier convolutional residual module to bolster the feature extraction capability of the generator network. This integration ensures the network retains a global receptive field, even in its more superficial layers. By employing an overall weighted loss function, we enhance the quality of the images produced without shadows. Further, a perceptual loss function has been incorporated to retain the structural information of objects, setting the stage for subsequent defect detection. Our results highlight that Pse-ShadowNet adeptly eradicates fastener shadows while maintaining vital visual features, including object position, structure, texture, edges, and other key visual elements. Consequently, the reconstructed images are detailed and showcase superior image quality.

Developmental hazard evaluation is an important part of assessing chemical risks during pregnancy. Toxicological outcomes from prenatal testing in pregnant animals result from complex chemical-biological interactions, and while New Approach Methods (NAMs) based on in vitro bioactivity profiles of human cells offer promising alternatives to animal testing, most of these assays lack cellular positional information, physical constraints, and regional organization of the intact embryo. Here, we engineered a fully computable model of the embryonic disc in the compucell3d.org modeling environment to simulate epithelial-mesenchymal transition of epiblast cells and self-organization of mesodermal domains (chordamesoderm, paraxial, lateral plate, posterior/extraembryonic). Cell fate in the model is determined by an autonomous homeobox (HOX) clock driven by morphogenetic signals (e.g., FGF, WNT, ATRA, CDX). Executing the model renders a quantitative cell-level computation of mesodermal subpopulations and consequences of perturbation based on known embryogeny. For example, synthetic perturbation of the control network rendered altered phenotypes (cybermorphs) mirroring experimental mouse embryology, with 50% reductions in FGF4, FGF8 and BMP4 signaling resulting in 86%, 98% and 59% reductions, respectively in the posterior mesodermal population, while ATRA exposure also resulted in a 78% decrease in this population. This model enables integration of in vitro chemical bioactivity data for specific molecular targets with known embryology to test mechanistic veracity and quantitative prediction of altered development.

A single paragraph of about 200 words maximum. Electroencephalography (EEG) accumulates the electrical activities of human brain. It is an easy and cost effective tool to characterize motor imagery (MI) task used in brain computer interface (BCI) implementation. The MI task is represented by short time trial of multichannel EEG. In this paper, the raw EEG is decomposed into a finite set of narrowband signals obtained from individual EEG channels using Fourier transformation based bandpass filter. Each of the subband signals represents narrowband rhythmic components which characterize the brain activities related to motor imagery. The subband signals are arranged to extent the dimension of EEG trial in spatial domain. The spatial features are extracted from the set of extended trials using common spatial pattern (CSP). An optimum number of features are used to classify the motor imagery tasks represented by EEG trials. Artificial neural network is used to classify MI tasks. The performance of the proposed method is evaluated using two publicly available benchmark datasets. The experimental results show that it performs better than the recently developed algorithms.

With the advent of deep learning, significant progress has been made in low-light image enhancement methods. However, deep learning requires enormous paired training data, which is challenging to capture in real-world scenarios.To address this limitation, this paper presents a novel unsupervised low-light image enhancement method, which first introduces the frequency domain features of images in low-light image enhancement tasks. Our work is inspired by imagining a digital image as a spatially varying metaphoric “field of light”, then subjecting the influence of physical processes such as diffraction and coherent detection back onto the original image space via a frequency-domain to spatial-domain transformation (inverse Fourier transform). However, the mathematical model created by this physical process still requires complex manual tuning of the parameters for different scene conditions to achieve the best adjustment. Therefore, we proposed a dual-branch convolution network to estimate pixel-wise and high-order spatial interactions for dynamic range adjustment of the frequency feature of the given low-light image. Guided by the frequency feature from the “field of light” and parameter estimation networks, our method enables dynamic enhancement of low-light images. Extensive experiments have shown that our method performs well compared to state-of-the-art unsupervised, and its performance approximates the level of the state-of-the-art supervised methods qualitatively and quantitatively. At the same time, the light network structure design allows the proposed method to have an extremely fast inference speed(nearly 150 FPS on an NVIDIA 3090 Ti GPU for an image of size 600×400×3 ). Furthermore, the potential benefits of our method to object detection in the dark are discussed.

The use of mathematical models of physiological systems in medicine has allowed the development of diagnostic, treatment, and medical educational tools, but their application for predictive, preventive, and personalized purposes is restricted by their complexity. Although there are strategies that reduce the complexity of applying models by fitting techniques, they focus on a single instant of time, neglecting the effect of the system's temporal evolution. The aim of this work is to propose a dynamic fitting strategy of physiological models with large number of parameters and a constrained amount of experimental data, focused on obtaining better predictions based on the system's temporal trend and useful to predict future states. It was applied in a cardiorespiratory model as a case study. Experimental data from a longitudinal study of healthy adult subjects under aerobic exercise were used for fitting and validation. The model predictions obtained at steady-state using the proposed strategy and the nominal values of the parameters were compared. The best results corresponded mostly to the proposed strategy, mainly regarding the overall prediction error. The results evidenced the usefulness of the dynamic fitting strategy, highlighting its use for predictive, preventive, and personalized applications.

An extensive review of literature simulations of polymer melts is given, considering results that test aspects of the Rouse model in the melt. We focus on the mean-square amplitudes < (X_p(0))^{2} > and time correlation functions < X_p(0) X_p(t) > of the Rouse modes $X_p(t)$. Contrary to the Rouse model: (i) Mean-square Rouse mode amplitudes < (X_p(0))^2> do not scale as sin^{-2}(p \pi/2N), N being the number of beads in the polymer. For small p (say, p <= 3) < (X_p(0))^2> scales with p as p^{-2}$; for larger p it scales as p^{-3}. (ii) Rouse mode time correlation functions < X_p(t) X_p(0) > do not decay with time as exponentials; they instead decay as stretched exponentials exp(-a t^b)$. b depends on p, typically with a minimum near N/2 or N/4. (iii) Polymer bead displacements are not described by independent Gaussian random processes. (iv) For p not equal to q, < X_p(t) X_{q}(0) > is sometimes non-zero. (v) The response of a polymer coil to a shear flow is a rotation, not the affine deformation predicted by Rouse. Simulations thus conclusively demonstrate that the Rouse model is invalid in polymer melts. We also briefly consider the Kirkwood-Riseman polymer model.

Background Bone fracture fixation surgery is one of the most commonly performed surgical procedures in the orthopedic field. However, fracture healing complications occur frequently, and the choice of the most optimal surgical approach often remains challenging. In the last years, computational tools have been developed with the aim to assist preoperative planning procedures of bone fracture fixation surgery. Objectives The aims of this review are (1) to provide a comprehensive overview of the state-of-the-art in computer-assisted preoperative planning of bone fracture fixation surgery, (2) to assess the clinical feasibility of the existing virtual planning approaches, and (3) to assess their clinical efficacy in terms of clinical outcomes as compared to conventional planning methods. Methods A literature search was performed in the MEDLINE-PubMed, Ovid-EMBASE, Ovid-EMCARE, Web of Science, and Cochrane libraries to identify articles reporting on the clinical use of computer-assisted preoperative planning of bone fracture fixation. Results 79 articles were included to provide an overview of the state-of-the art in virtual planning. While patient-specific geometrical model construction, virtual bone fracture reduction, and virtual fixation planning are routinely applied in virtual planning, biomechanical analysis is rarely included in the planning framework. 21 of the included studies were used to assess the feasibility and efficacy of computer-assisted planning methods. The reported total mean planning duration ranged from 22 to 258 minutes in different studies. Computer-assisted planning resulted in reduced operation time (Standardized Mean Difference (SMD): -2.19; 95% Confidence Interval (CI): -2.87, -1.50), less blood loss (SMD: -1.99; 95% CI: -2.75, -1.24), decreased frequency of fluoroscopy (SMD: -2.18; 95% CI: -2.74, -1.61), shortened fracture healing times (SMD:-0.51; 95% CI: -0.97, -0.05) and less postoperative complications (Risk Ratio (RR): 0.64, 95% CI: 0.46, 0.90). No significant differences were found in hospitalization duration. Some studies reported improvements in reduction quality and functional outcomes but these results were not pooled for meta-analysis, since the reported outcome measures were too heterogeneous. Conclusions Current computer-assisted planning approaches are feasible to be used in clinical practice and have been shown to improve clinical outcomes. Including biomechanical analysis into the framework has the potential to further improve clinical outcome.

Objective tools to track medication adherence are lacking. A tool to monitor pill intake that can be implemented in mHealth apps without the need for additional devices was developed. We propose a pill intake detection tool that uses digital image processing to analyze images of a blister to detect the presence of pills. The tool uses the circular Hough transform as a feature extraction technique and is therefore primarily useful for the detection of pills with a round shape. This pill detection tool is composed of two steps. First, the registration of a full blister and storing of reference values in a local database. Second, the detection and classification of taken and remaining pills in similar blisters, to determine the actual number of untaken pills. In the registration of round pills in full blisters, 100% of pills in gray blisters or blisters with a transparent cover were successfully detected. In counting of untaken pills in partially opened blisters, 95.2% of remaining and 95.1% of taken pills were detected in gray blisters, while 88.2% of remaining and 80.8% of taken pills were detected in blisters with a transparent cover. The proposed tool provides promising results for the detection of round pills. However, the classification of taken and remaining pills need to be further improved, in particular for the detection of pills with non-oval shapes.

Computational neuroscience combines mathematics, computer science models, and neurosciences for theorizing, investigating, and simulating neural systems involved in the development, structure, physiology, and cognitive abilities of the brain. Computational models constitute a major stake in translational neuroscience: the analytical understanding of these models seems fundamental to consider a translation towards clinical applications. Method: We propose a minimal typology of computational models, which allows distinguishing between more realistic models (e.g., mechanistic models) and pragmatic models (e.g., phenomenological models). Result: Understanding the translational aspects of computational models goes far beyond the intrinsic characteristics of models. First, we assume that a computational model is rarely uniquely mechanistic or phenomenological. Idealization seems necessary because of i) the researcher’s perspectives on the phenomena and the purposes of the study (i.e., by the relativity of the model); ii) The complexity of reality across different levels and therefore the nature and number of dimensions required to consider a phenomenon. Especially, the use of models goes far beyond their function, and requires considering external characteristics rooted in path dependence, interdisciplinarity, and pluralism in neurosciences. Conclusion: The unreasonable use of computational models, which are highly complex and subject to a shift in their initial function, could be limited by bringing to light such factors.

The chemical graph theory is a subfield of mathematical chemistry which applies classic graph theory to chemical entities and phenomena. Chemical graphs are main data structures to represent chemical structures in cheminformatics. Computable properties of graphs lay the foundation for (quantitative) structure activity and structure property predictions - a core discipline of cheminformatics. It has a historic relevance for natural sciences, such as chemistry, biochemistry and biology, and is in the heart of modern disciplines, such as cheminformatics and bioinformatics. This review first covers the history of chemical graph theory, then provides an overview of its various techniques and applications for CASE, and finally summarises modern tools using chemical graph theory for CASE.

Long-term continuous time series of SO2 emissions are considered critical elements of both volcano monitoring and basic research into processes within magmatic systems. One highly successful framework for computing these fluxes involves reconstructing a representative time-averaged SO2 plume from which to estimate the SO2 source flux. Previous methods within this framework have used ancillary wind datasets from reanalysis or numerical weather prediction (NWP) to construct the mean plume and then again as a constrained parameter in the fitting. Additionally, traditional SO2 datasets from ultraviolet (UV) sensors lack altitude information which must be assumed to correctly calibrate the SO2 data and to capture the appropriate NWP wind level which can be a significant source of error. We have made novel modifications to this framework which do not rely on prior knowledge of the winds and therefore do not inherit errors associated with NWP winds. To perform the plume rotation, we modify a rudimentary computer vision algorithm designed for object detection in medical imaging to detect plume-like objects in gridded SO2 data. We then fit a solution to the general time-averaged dispersion of SO2 from a point source. We demonstrate these techniques using SO2 data generated by a newly developed probabilistic layer height and column loading algorithm designed for the Cross-track Infrared Sounder (CrIS), a hyperspectral infrared sensor aboard the Joint Polar Satellite System’s Suomi-NPP and NOAA-20 satellites. This SO2 data source is best suited to flux estimates at high-latitude volcanoes and at low-latitude, but high-altitude volcanoes. Of particular importance, IR SO2 data can fill an important data gap in the UV-based record: estimating SO2 emissions from high-latitude volcanoes through the polar winters when there is insufficient solar backscatter for UV sensors to be used.

The objective of this paper is to assess the potential for potable water savings due to rainwater use in a precast concrete factory in southern Brazil. The economic feasibility and the rainwater quality were also assessed. The current water consumption, future water demand and rainwater demand in the factory were estimated. The future demand considered was two times higher than the current water consumption since there are plans to increase the production. Three scenarios were then simulated using the computer programme Netuno. The ideal rainwater tank capacity, the potential for potable water savings and the economic feasibility analysis for each scenario were estimated. Samples of rainwater were collected in the factory and tested for quality for manufacturing precast concrete. For a rainwater tank capacity equal to 25,000 litres, the potential for potable water savings for the first scenario was 55.4%, but the first scenario was considered economically unfeasible. For the same tank capacity, the second and third scenarios presented viable results regarding potable water savings and payback. As for the rainwater quality, it was proven to be adequate for manufacturing precast concrete. The main conclusion is that rainwater can be used to manufacture precast concrete in the factory studied herein.

The article describes a classification solution for amber stones. The problem of classifying amber is known for a long time among jewelers and artisans of amber art. Existing solutions can classify amber pieces according to color, but a need to classify by shape and texture is not satisfied up to now. The proposed solution is capable of classifying the gemstones according to a shape. Amber can be considered as a specific object since the form is difficult to define unambiguously. Data for amber experiments was gathered from amber art craftsmen. In the proposed solution amber form can be classified into 10 different classes (7 classes chosen during the experiment).

Ancient numismatics, that is, the study of ancient currencies (predominantly coins), is an interesting domain for the application of computer vision and machine learning, and has been receiving an increasing amount of attention in recent years. Notwithstanding the number of articles published on the topic, the variety of different methodological approaches described, and the mounting realisation that the relevant problems in the field are most challenging indeed, all research to date has entirely ignored one specific, readily accessible modality: colour. Invariably, colour is discarded and images of coins treated as being greyscale. The present article is the first one to question this decision (and indeed, it is a decision). We discuss the reasons behind the said choice, present a case why it ought to be reexamined, and in turn investigate the issue for the first time in the published literature. Specifically, we propose two new colour-based representations specifically designed with the aim of being applied to ancient coin analysis, and argue why it is sensible to employ them in the first stages of the classification process as a means of drastically reducing the initially enormous number of classes involved in type matching ancient coins (tens of thousands, just for Ancient Roman Imperial coins). Furthermore, we introduce a new data set collected with the specific aim of denomination-based categorisation of ancient coins, where we hypothesised colour could be of potential use, and evaluate the proposed representations. Lastly, we report surprisingly successful performances which goes further than confirming our hypothesis—rather, they convincingly demonstrate a much higher relevant information content carried by colour than even we expected. Thus we trust that our findings will be noted by others in the field and that more attention and further research will be devoted to the use of colour in automatic ancient coin analysis.

We propose a computer-generated hologram method for near-eye virtual reality and augmented reality 3D display. A 3D object located near the holographic plane is projected onto a projection plane to obtain a plurality of projected images with different angles. The hologram is calculated by superposition of projected image convolution with a specific PSF through the interference of reference wave with object wave. Holographic 3D display systems with LED as illumination, 4f optical filtering system and lens as eyepiece for near-eye VR display and HOE as combiner for near-eye AR display are designed. The results show that the proposed method is about 38 times faster than the conventional point cloud method. The proposed method is flexible to produce speckle noise free high-quality VR and AR images with efficient focus and defocus capabilities.

The beautiful and stable posture is essential for ballet. Especially the pirouette which dancers balance themselves on their one leg is one of the most impressive postures. During the pirouette motion, loads from the rotation and the body weight are concentrated to the one leg. Proper ballet shoes can reduce the muscle burden and improve the stability. Four types of shoes; bare feet, running shoes, pointe shoes, and dance sneakers are analyzed. The motion capture system and SIMM are used for the analysis of muscle burden and stability during pirouette. The major 6 muscles in the leg (beceps femoris long head, beceps femoris short head, soleus, rectus femoris, gastrocnemius medialis, and gastrocnemius lateralis) are analyzed, and the effect of rotating velocity is considered. Dance sneakers are outstanding for improving the stability and lessening the muscle burdens in all conditions for beginners. That comes from the design feature of the divided shoe soles and protecting the ankle of the axis leg. With the results and analysis, the direction for design optimization of ballet shoes is suggested. Consequently, this research is about the verification of sports equipment using computer simulation with the musculoskeletal model from a scientific viewpoint of biomechanics.

As the energy efficiency demands for future buildings become increasingly stringent, preliminary assessments of energy consumption are mandatory. These are possible only through numerical simulations, whose reliability crucially depends on boundary conditions. We therefore investigate their role in numerical estimates for the usage of geothermal energy, performing annual simulations of transient heat transfer for a building employing a geothermal heat pump plant and energy piles. Starting from actual measurements, we solve the heat equations in 2D and 3D using COMSOL Multiphysics and IDA-ICE, and discover a negligible impact of the multiregional ground surface boundary conditions. Moreover, we verify that the thermal mass of the soil medium induces a small vertical temperature gradient on the piles surface. We also find a roughly constant temperature on each horizontal cross-section, with nearly identical values if the average temperature is integrated over the full plane or evaluated at one single point. Calculating the yearly heating need for an entire building we then show that the chosen upper boundary condition affects the energy balance dramatically. Using directly the pipes’ outlet temperature induces a 54% overestimation of the heat flux, while the exact ground surface temperature above the piles reduces the error to 0.03%.

The efficiency of data sorting algorithms is the key aspect which determines the speed of data processing and searching. The best known efficiency of sorting algorithm has been Log (N) if there are N terms. All of the well-known sorting algorithms use various techniques to sort data. The basis for most of these are comparing the data terms with each other. In this manuscript, we are introducing a new approach for sorting data. This method is postulated to have the highest efficiency ever achieved by any of the sorting algorithms. We achieve this by sorting data without comparing the data terms. Or achieving results of data comparison without comparing the terms explicitly.

Life on Earth evolved to accommodate the biochemical and biophysical boundary conditions of the planet millions of years ago. The former includes nutrients, water, and ability to synthesize other needed chemicals. The latter include the 1 g gravity of the planet, radiation, and the geomagnetic field (GMF)of the planet. How complex life forms have accommodated the GMF is not known in detail considering that Homo sapiens evolved a neuro system and a cardiovascular system that develops electromagnetic fields as part of their functioning. In addition, many proteins and physiologic processes utilize Fe ions, an ion that exhibits magnetic properties. Thus, complex organisms such as current humans generate magnetic fields, contain significant quantities of the Fe ion, and also respond to exogenous-supplied static and electromagnetic fields. Whether humans can store information in self-generated magnetic fields remains to be determined. Given the current body of literature, it remains somewhat unclear how Homo sapiens use exogenous magnetic fields to regulate function, and what can happen if the boundary condition of the GMF of Earth is no longer exerting an effect. Proposed deep space flights to destinations such as Mars will provide some insights as space flight could not have been anticipated by evolution. The results of such space flight “experiments” will provide new insights in the role of magnetic fields on human functioning. The outcomes may also have implications for those on Earth as magnetic field influences may play a role in developmental conditions such as autism and senescence-associated conditions such as dementia when integration of the biological and biophysical aspects of humans of humans are required for effective functioning. This review will discuss the involvement of magnetic fields on various normal and disturbed processes in humans while on Earth and then further discuss potential outcomes when the GMF is no longer available to impact host systems. Thus, the GMF of the Earth has been present throughout evolution but details of its role in human functions still remain to be elucidated.

Pluripotent stem cells (PSCs) hold many potential research and clinical benefits due to their ability to differentiate into nearly every cell type in the body. They are often used as model systems for early stages of ontogenesis to better understand key developmental pathways, and for drug screening. However, in order to fully realise the potential of PSCs and their translational applications, a deeper understanding of developmental pathways, especially in humans, is required. Several signalling molecules play important roles during development and are required for proper differentiation of PSCs. The concentration and timing of signal activation is important, with perturbations resulting in improper development and/or pathology. Bone morphogenetic protein (BMP) is one such key signalling molecule involved in the specification and differentiation of various cell types and tissues in the human body including tooth and otic development. In this review, we describe the role of BMP signalling and its regulation, the consequences of BMP dysregulation in disease and differentiation, and how PSCs can be used to investigate the effects of BMP modulation during development, mainly focusing on otic development. Finally, we emphasise the unique role of BMP4 in otic specification and how refined knowledge in controlling its regulation could lead to the generation of more robust and reproducible human PSC-derived otic organoids for research and translational applications.

Fortified human milk is the first choice for preterm infants. Although individualized fortification is recommended, the optimal method for this population remains uncertain. We conducted a comparative study assessing the growth effects of adjusted (AF) and targeted fortification (TF) in extremely low birth weight (ELBW) infants. This single-center, randomized, controlled clinical trial was conducted at a tertiary neonatal unit in Spain. Eligible participants were premature infants with a birthweight <1000g exclusively fed with human milk. A total of 38 patients were enrolled, 15 of them randomised to AF group and 23 to TF group. AF was based on BUN concentration and TF on human milk analysis. The primary outcome was weight gain velocity (g/kg/day).No significant differences were found in weight gain velocity at 28 days, at 36 weeks of postmenstrual age, at discharge nor during intervention. Protein intake was significantly higher in AF group (5.02 g/kg/day vs 4.48 g/kg/day, p= 0.001). No differences were found in lipids, carbohydrates and energy intake; neither in weight z score change between the different time points; nor in length and head circumference growth. Both AF and TF are comparable methods of fortification and provide appropriate growth rate in ELBW infants.

The task of human action recognition (HAR) based on skeleton data is a challenging yet crucial technique owing to its wide-ranging applications in numerous domains, including patient monitoring, security surveillance, and observation of human-machine interactions. While numerous algorithms have been proposed in an attempt to distinguish between a myriad of activities, most practical applications necessitate highly accurate detection of specific activity types. This study proposes a novel and highly accurate spatiotemporal graph autoencoder network for HAR based on skeleton data. Furthermore, an extensive investigation was conducted employing diverse modalities. To this end, a spatiotemporal graph autoencoder was constructed to automatically learn both spatial and temporal patterns from human skeleton datasets. The powerful graph convolutional network, designated as GA-GCN, developed in this study, notably outperforms the majority of existing state-of-the-art methods when evaluated on two common datasets, namely NTU RGB+D and NTU RGB+D 120. On the first dataset, the proposed approach achieved accuracies of 92.3\% and 96.8\% for the cross-subject and cross-view evaluations, respectively. On the more challenging NTU RGB+D 120 dataset, GA-GCN attained accuracies of 88.8\% and 90.4\% for the cross-subject and cross-set evaluations, respectively.

Artificial intelligence’s rapid advancement has enabled various applications, including intelligent video surveillance systems, assisted living, and human-computer interaction. These applications often require one core task: video-based human action recognition. Research in human video-based human action recognition is vast and ongoing, making it difficult to assess the full scope of available methods and current trends. This survey provides an in-depth exploration of the vision-based human action recognition field, comprehensively offering the available techniques and their evolution, highlighting the cutting-edge ideas driving its development. We also analyze the most used keywords in research papers over the past years to identify trends and predict possible future directions. Hence, this concise survey helps researchers understand the breadth of existing approaches, evaluate current research trends, and stay up-to-date on potential developments.

The neuroglial extracellular matrix (ECM) provides critical support and physiological cues for the proper growth, differentiation, and function of neuronal cells in the brain. However, in most in vitro settings that study neural physiology, cells are grown as monolayers on stiff surfaces that maximize adhesion and proliferation, and therefore lack the physiological cues that ECM in native neuronal tissues provides. Macromolecular crowding (MMC) is a biophysical phenomenon based on the principle of excluded volume that can be harnessed to induce native ECM deposition by cells in culture. Here, we show that MMC using two species of Ficoll with vitamin C supplementation significantly boosts deposition of relevant brain ECM by cultured human astrocytes. Dopaminergic neurons co-cultured on this astrocyte-ECM bed prepared under MMC treatment showed longer and denser neuronal extensions, a higher number of pre ad post synaptic contacts, and increased physiological activity as evidenced by higher frequency calcium oscillation, compared to standard co-culture conditions. When the pharmacological activity of various compounds was tested on MMC-treated co-cultures, their responses were enhanced, and for apomorphine, a D2-receptor agonist, it was inverted in comparison to control cell culture conditions, thus emulating responses observed in in vivo settings. These results indicate that macromolecular crowding can harness the ECM-building potential of human astrocytes in vitro forming an ultra-flat 3D microenvironment that makes neural cultures more physiological and pharmacological relevant.

We assessed how close human perceptions of landscape modification matched a multivariate index based on remotely sensed data of the same locations. Using a Human Footprint (HF) map of the continental U.S. (scaled 0-100), we created three series of aerial images, each with ten images distributed evenly across the 10 deciles of HF score. Using a web-based survey, 290 members of the global public ranked the images in one series based on their perception of the degree of human modification. Respondents also reported age, sex, and country. The degree of correspondence between rankings by respondents and by HF score was high, an average of 1.29 units of difference out of a maximum possible of 5.0. Differences among respondents were not explained by age, sex, or general geographic location. These results suggest that human perception of relative landscape modification conforms closely with the relative ranking made by a multivariate, analytical index.

Overconsumption of an unbalanced diet and overweight, leading to insulin resistance, dyslipidemia, hepatic steatosis, cardiovascular diseases and obesity, is nowadays a worldwide health issue, with different patterns of metabolic disturbances in the young and adult popula-tions, in both gender. Preclinical models that closely mimic the gradual weight gain that occurs in humans upon consumption of a “western-type” diet (WD) are of great interest. In particular, the diet-induced obesity (DIO) model in the C57BL/6J mouse sub-strain is often used in biomed-ical research, considering that it is prone to metabolic disorders and obesity, and is widely used to generate genetically modified mice. This study aimed to phenotypically characterize the C57BL/6J mouse strain fed a WD-like diet, using high-frequency ultrasound imaging (HFUS) as a complementary tool to longitudinally monitor morphofunctional changes in the liver, heart and kidney associated with DIO. Long term WD feeding increased mice body weight (BW), liver/BW ratio and body condition score (BCS), serum transaminases, glucose and insulin, as well as Homeostasis model assessment (HOMA) indexes, and caused dyslipidemia and insulin resistance. According to the 3Rs, ultrasound imaging has enabled a comprehensive study of metabolic syndrome in mice, in vivo and non-invasive, improving the identification of early stages of diseases.

Sick sinus syndrome (SSS) is a disease of sinus rhythm abnormalities. This study aimed to establish an SSS model by injecting 3% NaOH into the jugular vein(JV). Sprague-Dawley rats were randomly divided into sham, 3% NaOH, and 10% NaOH groups. Anesthetized rats underwent electrocardiogram recording before right JV exposure. Rats in the experimental groups received NaOH injections at 0.01 ml/s, while the sham group did not. Model success was evaluated after one hour. Heart rate(HR) and rhythm were documented at 1 hour, 1 week, and 2 weeks post-molding, including HE staining, membrane ion transcription and expression, and calcium ion content test. In the 3% NaOH group, the model establishment rate was 87%, and the mortality rate was 13%, whereas in the 10% NaOH group, they were 57% and 43% respectively (P < 0.01). The HR of the 3% NaOH group decreased at multiple time points post-molding (P < 0.001), presenting as arrhythmias such as sinus bradycardia. The JV sustained less damage, with necrosis of P cells, changes in membrane ion channels, and reduced calcium ion distribution. In conclusion, JV injection of 3% NaOH can establish the SSS model and benefit animals.

Appropriate animal models of human diseases are a cornerstone in the advancement of science and medicine. To create animal models of neuropsychiatric and neurobehavioral diseases such as ASD necessitates the development of sufficient neurobehavioral measuring tools to translate human behavior to expected measurable behavioral features in animals. Indeed, at least in rodents, adequate neurobehavioral and neurological tests have been developed. Since ASD is characterized by a number of specific behavioral trends with significant severity, animal models of autistic-like behavior have to demonstrate the specific characteristic features, namely impaired social interactions, and communication, restricted, repetitive behavioral patterns with association to several additional impairments such as somatosensory, motor and memory impairments. Thus, an appropriate model must show behavioral impairment of a minimal number of neurobehavioral characteristics using an adequate number of behavioral tests. The proper animal models enable the study of ASD like-behavior from the etiologic, pathogenetic and therapeutic aspects. From the etiologic aspects, models have been developed by the use of immunogenic substances like polyinosinic-polycytidylic acid (PolyIC), Lipopolysaccharide (LPS) and propionic acid or other well-documented immunogens or pathogens, like mycobacterium tuberculosis. Another approach is the use of chemicals like valproic acid, Polychlorinated biphenyls (PCBs), organophosphate pesticides - chlorpyrifos (CPF) and others. These substances were administered either prenatally, generally after the period of major organogenesis, or, especially in rodents, during the early postnatal life. In addition, using modern genetic manipulation methods, genetic models have been created of almost all human genetic diseases that are manifested by autistic like behavior (i.e. fragile X, Rett syndrome, SHANK gene mutation Neuroligin genes and others). Ideally, we should not only evaluate the different behavioral modes affected by the ASD-like behavior but also assess the severity of the behavioral deviations by an appropriate scoring system, as applied to humans.

Australia has the highest small-mammal extinction rate in the world, and there are ample of reasons that have led to the higher extinction rates such as introduction of feral species, wildfires, climate change and uneven population distribution. All these factors instil a dire need to protect the species in captive environment. Stress has a major role to play in maintaining an adequate welfare conditions for wildlife, hence quantification of the stress levels in captive animals help us understand the effectiveness of the methods used to house the animals. In this study, we assessed the stress levels of captive marsupials namely Rufous bettongs (Aepyprymnus rufescens) (n=9), Long-nosed potoroo (Potorous tridactylus) (n=4) and Greater bilby (Macrotis lagotis) (n=5) by measuring the faecal cortisol metabolites using the Enzyme-Immuno assay (EIA) (R4866). We carried out a laboratory validation of the EIA for the Rufous bettongs and long-nosed potoroo by demonstrating parallelism between the pooled faecal samples and the standard curve. We also used the iterative baseline approach to calculate the baseline levels and detect peak concentrations of FCM in all the studied species. No significant differences were found in the FCM and baseline levels in the potoroos. Results highlighted a significant difference (p &lt; 0.05) in the individual FCM and baseline levels of the bettongs. Additionally, significant differences (p &lt; 0.05) were found in the FCM and baseline levels between male and female bettongs. In case of bilbies, the FCM and baseline levels showed no significant differences, however the male and female baseline levels were significantly different (p &lt; 0.05). Our study provides the baseline and peak FCM concentrations in Long-nosed potoroos and Rufous bettongs under captive management. Future studies can use the iterative baseline approach and validated EIA to detect any possible variations in the FCM concentration and baseline values in captive marsupials created due to factors like sex, grouping, and housing conditions.

The main challenge in milk production has been to maintain a focus on efficient processes that enhance production outcomes, while aligning with animal welfare and sustainability and being valued by society. As an alternative to improve cow welfare in production and provide better handling of the waste generated by the activity, the system called compost-bedded pack barn (CBP) is an alternative that has been widely adopted in countries with temperate climates and higher milk production. This CBP has been attracting global interest, including from countries with tropical and subtropical climates, such as Brazil, where many producers have started to use it due to the response in terms of milk productivity. A CBP can be designed either in a) an open facility with natural ventilation or positive pressure ventilation system or b) totally closed facilities, equipped with negative pressure ventilation systems and permanent thermal control. The latter system is being implemented in Brazil, despite insufficient knowledge about its efficiency. The objective of this study was, through a review, to gather and describe the most recent information on the use of open and closed CBP systems for dairy cattle housing, mainly how it is applied in tropical climate regions. To achieve the proposed objective, this review study included the following topics related to CBPs: (i) implementation; (ii) bedding; (iii) general construction and architectural characteristics; iv) ambient thermal conditioning. Knowledge gaps and directions for future research are also identified.

When wildlife and motor vehicles collide, the result for the animals is often death (roadkill). A commercial roadkill Virtual Fence (VF) mitigation device (iPTE Traffic Solutions) was used in a field trial to test its effectiveness along a 4.9-km segment of road on Bruny Island, Tasmania. A total of 585 days of monitoring of roadkill by species was conducted, with six sections alternatively switched on or off, according to a variation of a Crossover and Multiple Before-After-Control-Impact (MBACI) experimental designs that divided monitoring into “off-on” then “on-off” blocks of periods with each “on” period followed by a 16 d “wash-out” period. For the six sections over the four periods that exclude wash-outs, the 24 aggregated values of daily counts of roadkill of Tasmanian pademelons (Thylogale billardierii), that gave a total count of 222, were modelled. Other species were killed in insufficient numbers for analysis to be informative. The statistical analysis exploited the MBACI design to estimate the VF effect using a log-odds ratio parameter (LORP) while accounting for local spatio-temporal effects. Both versions of the analysis, either averaging over the three spatial replicates (paired sections) or two temporal replicates (blocks) gave no statistically significant effect of the VF judged as a LORP estimate not sufficiently below zero. Corresponding percentage reduction estimates of 9% and 16% were derived from the LORP estimates and given parameter uncertainty estimates the power to detect as significant a LORP corresponding to a 50% reduction in rate were 0.5 and 0.6, respectively. This study confirms results from a similar field trial carried out in 2018 also in southern Tasmania and adds to the evidence that this VF is likely to give if anything only a minor reduction in roadkill for a commonly killed species for which researchers have the best chance of detecting any practically useful reduction.

Due to the increasing risk of extreme events caused by climate change (i.e. floods, fires, hurricanes) or wars, European veterinary public health may need some improvement. Utilizing a mix of qualitative (participatory observation) and quantitative methods (Internet mining), we analyzed the Greek media responses to the millennial flood in Thessaly (September 2023), focusing on animal health (including wild, companion animals and livestock) and public sentiment towards epizootic/epidemic threats. The study revealed a gap in crisis management plans regarding veterinary-related issues, emphasizing the need for comprehensive emergency response strategies. Our findings show how: (i) lay-referral system is projecting perception of epidemic threats into the population; (ii) emotional load of images of animal carcasses is misused by media creators aiming for big audience; (iii) pets owners are creating on-line communities for searching and treatment of their pets. Our results stress the importance of integrating crisis communication in consecutive phases of the discourse, such as (i) weather change; (ii) acute flood; (iii) recovery; (iv) outbreaks, into veterinary practices to better prepare for such disasters.

Animal trypanosomiasis (AT) is a complex of veterinary diseases known under various names such as nagana, surra, dourine and mal de caderas, depending on the country, the infecting trypanosome species and the host. AT is caused by parasites of the genus Trypanosoma, and the main species infecting domesticated animals are T. brucei brucei, T. b. rhodesiense, T. congolense, T. vivax, T. evansi and T. equiperdum. AT transmission, again depending on species, is through tsetse flies or common Stomoxys and tabanid flies or through copulation. Therefore, the geographical spread of all forms of AT together is not restricted to the habitat of a single vector like the tsetse fly and currently includes almost all of Africa, and most of South America and Asia. The disease is a threat to millions of companion and farm animals in these regions, creating a financial burden in the billions of dollars to developing economies as well as serious impacts on livestock rearing and food production. Despite the scale of these impacts, control of AT is neglected and under-resourced, with diagnosis and treatments being woefully inadequate and not improving for decades. As a result, neither the incidence of the disease, nor the effectiveness of treatment is documented in most endemic countries, although it is clear that there are serious issues of resistance to the few old drugs that are available. In this review we particularly look at the drugs, their application to the various forms of AT, and their mechanisms of action and resistance. We also discuss the spread of veterinary trypanocide resistance and its drivers, and highlight current and future strategies to combat it.