The present essay illustrates the methodological and theoretical premises of an emerging research area carrying out both ethological and (bio)ethical implications: the ethology of the freed animal (EFA). Unlike existing ethological fields, EFA focuses neither on non human (NH) animals in natural conditions of freedom in their own environment, nor on NH animals kept in conditions of “captivity”. Rather, EFA consists of a comparative study of NH animals that are released from a condition of more or less abusive captivity and instead relocated in an environment more appropriate to their species-specific and individual characteristics and inclinations. Ideal places for this study are contexts like “Animal sanctuaries” and parks/reserves provided with a camp or station for researchers, where a previously-captive NH animal can be reintroduced in his/her natural habitat. Even though EFA exists already, as a de facto practice of the specialized and/or volunteer personnel running sanctuaries and parks, the field still lacks a recognizable scholarly paradigm, and it is yet to be acknowledged at institutional/academic level. By consequence, one important aim for creating a field like this lies in the establishment of an active interaction between the two parties involved (researchers and sanctuaries/parks operators).

Virtual Reality (VR) is increasingly used in neuropsychological assessments due to its ability to simulate real-world environments. This study aimed to develop and evaluate the Trail Making Test in VR (TMT-VR) and investigate the effects of different interaction modes and gaming skills on cognitive performance. A total of 71 young female and male adults (aged 18-35) with high and low gaming skills participated in this study. Participants completed the TMT-VR using three interaction modes: eye-tracking, head movement, and controller. Performance metrics included task completion time and accuracy. User experience, usability, and acceptability of TMT-VR were also examined. Results showed that both eye-tracking and head movement modes significantly outperformed the controller in terms of task completion time and accuracy. No significant differences were found between eye-tracking and head movement modes. Gaming skills did not significantly influence task performance using any interaction mode. Participants reported high usability, acceptability, and user experience for the TMT-VR. Eye-tracking facilitated higher accuracy, while head movement higher speed. The TMT-VR offers high usability and user experience and is well accepted by the participants. The findings suggest that VR-based assessments can effectively measure cognitive performance without being influenced by prior gaming skills, indicating potential applicability for diverse populations.

The human mandible's cancellous bone, which is characterized by its unique porosity and directional sensitivity to external forces, is crucial for sustaining biting stress. Traditional CAD models fail to fully represent bone's anisotropic structure and thus depend on simple isotropic assumptions. For our research, we use the latest versions of nTopology and Creo Parametric software to make biomimetic Voronoi lattice models that accurately reflect the complex geometry and mechanical properties of trabecular bone. The porosity of human cancellous bone is accurately modeled in this work using biomimetic Voronoi lattice models. The porosities range from 70% to 95%, which can be achieved by changing the pore sizes to 1.0 mm, 1.5 mm, 2.0 mm, and 2.5 mm. Finite element analysis (FEA) was used to examine the displacements, stresses, and strains acting on dental implants with a buttress thread, abutment, retaining screw, and biting load surface. The results show that the Voronoi model accurately depicts the complex anatomy of the trabecular bone in the human jaw, compared to standard solid block models. The ideal pore size for biomimetic Voronoi lattice trabecular bone models is 2 mm, taking into account both the von Mises stress distribution over the dental implant, screw retention, cortical bone, cancellous bone, and micromotions. This pore size displayed balanced performance by successfully matching natural bone's mechanical characteristics. Advanced finite element analysis (FEA) improves biomechanical understanding of how bones and implants interact by creating more accurate models of biological problems and dynamic loading situations. This makes biomechanical engineering better.

It is widely recommended to group house male laboratory mice because they are ‘social animals’, but male mice do not naturally share territories and aggression can be a serious welfare problem. Even without aggression, not all animals within a group will be in a state of positive welfare. Rather, many male mice may be negatively affected by the stress of repeated social defeat and subordination, raising concerns about welfare and also research validity. However, individual housing may not be an appropriate solution, given the welfare implications associated with no social contact. An essential question is whether it is in the best welfare interests of male mice to be group- or singly-housed. This review explores the likely impacts, positive and negative, of both housing conditions, presents results of a survey of current practice and awareness of mouse behaviour, and includes recommendations for good practice and future research. We conclude that whether group- or single-housing is better (or less worse) in any situation is highly context-dependent according to several factors including strain, age, social position, life experiences, and housing and husbandry protocols. It is important to recognise this and evaluate what is preferable from animal welfare and ethical perspectives in each case.

The proliferation of intelligent monitoring devices has led to the widespread adoption of background extraction technology across a multitude of domains, including intelligent transportation, video surveillance, human-computer interaction, and medical diagnosis. In this work, the model employs a multi-layer convolutional neural network structure, which enables the extraction and fusion of scene features from different scales in a layer-by-layer manner. This approach facilitates the comprehensive capture of complex scene information. The convolutional network comprises multiple layers, with each layer responsible for extracting features at a specific scale. The shallower layers capture more detailed feature information, whereas the deeper layers focus on more global scene features. The strategy of multi-scale feature fusion allows the model to fully capture multi-level and multi-dimensional information in the context of large-scale scenes. Furthermore, the incorporation of an attention mechanism enables the model to adaptively allocate attention to salient regions, thereby enhancing its capacity to discern intricate scenes by assigning greater significance to pivotal features. The experimental results demonstrate that the proposed method is effective in practice, as evidenced by its performance on public datasets.

This study explores the integration of computer vision with topology optimization for additive manufacturing, with a focus on maximizing eigenfrequency in a design domain. Utilizing cus-tom-developed photogrammetry software, high-resolution images are processed to generate de-tailed 3D models, subsequently converted to STL files with precision. Adaptive meshing in COMSOL Multiphysics, controlled through a MATLAB API, ensures optimal mesh resolution. Prioritizing resource conservation in extraterrestrial environments, the original volume is reduced by 50% while preserving structural integrity. The design domain undergoes rigorous topology optimization in MATLAB, supported by COMSOL's advanced FEM simulation. The optimized design exhibits a 57% performance improvement and a 50% weight reduction, maintaining desired vibration characteristics, validating the efficacy of the modifications. Moreover, the case with an eccentric mass shows a significant 64% increase in eigenfrequency.

The knowledge of what separates us genetically from our less-evolved relatives is crucial for gaining new biomedical insights about the human-chimpanzee relatedness for the use of appropriate stand-in towards the development of new treatments and diagnostic aids for various ailments. Although the genomes of humans and chimpanzees share 99% similarity, significant differences exist between the two species in their non-coding intronic regions. However, no work has been carried out in the aspects of target prediction concerning the ‘predicted homology’ in their microRNA sequences. Non-coding miRNAs which are post-transcriptional regulators of development, differentiation, growth, and metabolism, harboring the intronic regions may be crucial for expanding the horizons of our understanding. In this study, we proposed to perform the target prediction for the human-chimp miRNA homologs in the PHEX gene of the human X chromosome using various computational tools and databases. We identified a total of 1296 human miRNAs, 46, 957 gene targets, and 30, 563 targets of human and homologous chimp miRNAs respectively. Furthermore, we analysed gene interacting networks to identify the top interacting targets in both the species. Finally, we interpreted the biological importance of top-interacting miRNAs and their targets. The results demonstrated varying levels of multiplicity and cooperativity between the predicted miRNAs and target genes in the two genera. Such miRNAs may be responsible for the dysregulation of gene expression in several signaling pathways.

We aimed to assess whether play behavior can be used as a welfare indicator for farmed spotted pacas (Cuniculus paca) by examining its association with other positive welfare markers including affiliative behavior and low-amplitude vocalizations. We submitted six groups of spotted pacas (one male/two females per group) (N=18) to an ABA experimental design (A1/A2: without ball; B: with three boomer balls). Play behavior occurred only during phase B (mean=35.5s, SE=6.4). Spotted pacas spent more time in affiliative and exploratory behaviors, while they spent less time engaging in agonistic interactions during phase B than in both control phases (A1 and A2) (P<0.05). Moreover, spotted pacas emitted more low-amplitude bark vocalizations during phase B than during either control phase (P<0.05), which have previously been shown to indicate a positive affective state and low arousal level. Because the expression of play was associated with a decrease in aggression, an increase in affiliative behavior, and an increase in low-amplitude barking, indicating thus a positive state, we suggest that play behavior can be used as a non-invasive indicator of positive welfare in this species.

The human capital index is a multivariate variable used to measure the assessment of human resource productivity in the future. Human capital has a connection with economic development. Moreover, the study looks to analyses the relationship between Human Capital and Economic Growth indicators such as HCI and GDP. Human Capital relates to the development of individual skills to make them function more profitable for the national economy. The study was aimed at investigating the effect of population, the number of people engaged and the average annual hours worked by engaged persons on the human capital index. The analysis was in SPSS for the findings. Correlation tests were used for analysis. The study revealed that population, the number of people engaged and the average annual hours worked by engaged persons had a significant positive effect on the human capital index.

Engineering of quantum computers requires reliable characterization of qubits, quantum operations, and even the entire hardware. Quantum tomography is an indispensable framework in quantum characterization, verification, and validation (QCVV), which has been widely accepted by researchers. According to the tomographic target, quantum tomography can be categorized into quantum state tomography (QST), quantum process tomography (QPT), gate set tomography (GST), process tensor tomography (PTT), and instrument set tomography (IST). Standard quantum tomography toolkits generally consist of basic linear inverse methods and statistical maximum likelihood estimation (MLE) based methods. Furthermore, the performance of standard methods, including effectiveness and efficiency, has been further developed by exploiting Bayesian estimation, neural networks, matrix completion techniques, etc. In this review, we introduce the fundamental quantum tomography techniques, including QST, QPT, GST, PTT, and IST. We first introduce the details of basic linear inverse methods. Then, the framework of MLE methods with constraints is summarized. Finally, we briefly introduce recent further research in developing the performance of tomography. This review provides a primary getting-start in developing quantum tomography, which promotes quantum computer development.

The global meat industry has grown substantially, producing 357.39 million tons in 2021, with poultry and pork comprising nearly 73% of this total. However, contamination by mycotoxins, such as zearalenone (ZEA) and fumonisin B1 (FB1), presents a major issue, as these toxins resist common preservation methods. This study explores the potential of bioprotective microorganisms in mycotoxin degradation and fungal control within the animal food production chain, a sector facing significant challenges due to fungal contamination. In this study, 23 bacterial and yeast strains were tested for their ability to degrade ZEA and FB1, and to inhibit mycotoxigenic fungi. Four bacterial strains were highly effective in degrading ZEA: Bacillus amyloliquefaciens MLB3, Bacillus subtilis MLB2, Bacillus velezensis CL197, and Streptomyces griseus CECT 3276. However, no strain achieved satisfactory FB1 degradation. The strains also displayed antifungal activity, inhibiting up to 100% of fungi growth in solid media co-culture tests. Simulated swine and poultry digestion demonstrated complete ZEA degradation after 2 hours of incubation. Metabolite analysis revealed that low-toxicity conjugates were formed. These findings suggest that these bacteria hold significant promise for biotechnological applications in animal production, helping to reduce mycotoxin contamination, improve food safety, and protect both animal and human health.

The purpose of this disquisition is to analyze bullfighting, its advances and setbacks at a global and local level in terms of animal law. It was developed as a descriptive documentary research with a non-experimental design, using the observation and interpretation of documents related to the subject, from the global level, to the local, the national, specific to the regions and communities, being interpreted through the hermeneutic method. In the analysis, results were reached such as: every day more countries are willing to assume that bulls, because they are living beings that feel, are being attended to from the legal and the ethical, propitiating changes in the way of thinking of people in terms of wanting to consider bullfighting, a sport, art and hobby considered cultural heritage of many countries. For this reason, different actions have been generated from surveys, to define the acceptance or not of these events. It is concluded that there are important advances in this regard, as bullfighting is being prohibited in different parts of the world, taking into account the protection that protects animals, but the laws do not establish specific prohibition in this regard worldwide.

In the wake of Portugal's pivotal shift to animal welfare legislation in 2018, where the no-kill policy for population control came into effect, animal shelters have emerged as critical sanctuaries for stray or unwanted animals. Recognising the critical role that adopter preferences play in the successful placement of shelter animals and the pressing need for shelters to expedite adoptions to liberate valuable space to accommodate more animals in need of refuge, this study seeks to investigate the intricate matrix of factors guiding adopters' choices. A descriptive cross-sectional study was performed with a convenience sample of the Portuguese population to assess their preferences and opinions about pet adoption. An online questionnaire was completed by 415 participants from February to October 2021. Among participants selecting attributes for pet adoption, the top preferences were behaviour (52.8%), size (49.6%), vaccination and deworming status (44.3%), and age (36.1%). Conversely, breed (7.2%), sex (6.3%), and animal colour (5.1%) were the least prioritised. Combinations were established based on the most selected attributes. Certain participant characteristics, such as education level and pet ownership history, exhibited distinct preferences in animal attributes. Portuguese people show a marked preference for shelter adoption, prioritising behaviour, size, and health over breed and colour. This insight allows shelters to allocate resources effectively. While promising, the trend towards shelter adoption needs adaptive strategies to align with evolving adopter preferences.

Zoonoses are diseases transmitted from (vertebrate) animals to humans. Control and prevention of these diseases require an appropriate way to measure health for prudent and well-balanced decisions in public health. We propose a framework that aims to explore, understand and open up a conversation about the non-monetary value of animals through environmental and normative ethics. As an example of its application, participants can choose what they are willing to give in exchange for curing an animal in hypothetical scenarios selecting a human health condition to suffer, the amount of money, and lifetime as a tradeoff. We believe that considering animals beyond their monetary value in public health decisions will contribute to a more rigorous assessment of the burden of zoonotic diseases, among other health decisions. This method might help us complement the existing metrics in health, adding more comprehensive values for animal and human health for the “One Health” approach.

Adolescence represents a critical period for the programming of future adult behaviours. Neurogenesis is particularly active during adolescence, with increased number of granule cells and increased hippocampal volume both in animals and humans. Among the factors which can affect neurogenesis during adolescence, stress is considered a major one. Indeed, adolescence is known to be a particularly stressful period in life, with some adolescents suffering from mood disorders and anxiety. While there is increasing interest on the neurogenic changes occurring during the adolescent period, evidence is sparse. We conducted a systematic review summarising changes in hippocampal neurogenesis, neuroplasticity and hippocampal-dependent cognitive functions and behavioural outcomes in stress-induced adolescent animal models of depression, and investigating long-term stress effects on the same outcomes assessing the same animals in adulthood. Overall, the results show a significant reduction in hippocampal cell proliferation, and a concomitant increase in depressive-like behaviours in adolescent animals exposed to stress challenges, however reduction in the number of surviving neurons was accompanied by no changes in both cognition and behaviour. Studies also observed altered neuroplasticity, including a stress-induced decrease in markers of pre- and post-synaptic plasticity, dendritic spine length and density, and long-term potentiation. These changes in neuroplasticity were accompanied by cognitive impairments and depressive-like behaviours. Overall, some of the negative effects observed during adolescence, especially on cell proliferation, neuroplasticity, cognition and behaviour either persisted or worsened during adulthood. Interestingly, treatment during adolescence with antidepressants, glutamate receptor inhibitors, glucocorticoid antagonists, or a healthy diet consisting of omega-3 fatty acids and vitamin A, were able to reverse or prevent these detrimental effects. Future research should aim to investigate the translational impact of these preclinical findings, developing novel tools for the measurement of hippocampal neurogenesis directly in depressed adolescents, and subsequently assessing neurogenic changes in response to stress as well as pharmacological and non-pharmacological interventions.

Mycobacterium ulcerans is the causative agent of the Buruli ulcer, also known, in Australia, as Daintree ulcer or Bairnsdale ulcer. This destructive skin disease is characterized by extensive and painless necrosis of the skin and soft tissue with the formation of large ulcers, commonly on the leg or arm. To date, 33 countries with tropical, subtropical and temperate climates in Africa, the Americas, Asia and the Western Pacific have reported cases of Buruli Ulcer. The disease is rarely fatal, although it may lead to permanent disability and/ or disfigurement if not treated appropriately or in time. It is the third most common mycobacterial infection in the world after tuberculosis and leprosy. The precise mode of transmission of M. ulcerans is yet to be elucidated. Nevertheless, it is possible that the mode of transmission varies with different geographical areas and epidemiological settings. The knowledge about the possible route of transmission and potential animal reservoir of M. ulcerans is poorly understood and still remains patchy. We conducted a systematic review with selected key words on PubMed and INFORMIT databases to aggregate available published data on animal reservoirs of M. ulcerans. After certain inclusion and exclusion criteria, a total of 17 studies were included in the review. A variety of animals, e.g rodents, shrews, possums (ringtail and brush tail), horses, dogs, alpacas, koalas and Indian flap-shelled turtles have been recorded as being infected with M. ulcerans around the world. The majority of studies included in this review identified animal reservoirs, either aquatic or terrestrial, as predisposing for the emergence and reemergence of M. ulcerans infection. Taken together, the selected studies in this systematic review and discussed so far, it is clear that exotic wildlife, aquatic animals and native mammals play a significant role as reservoirs for M. ulcerans.

In this paper, we present a novel approach for managing the file system in Linux using a voice assistant. Our system allows users to perform file system operations such as creating directories, renaming files, and deleting files by issuing voice commands. We develop a voice assistant using Python libraries and integrate it with the file system in Linux. The voice assistant is capable of understanding natural language and executing commands based on the user's voice inputs. We conduct experiments to evaluate the performance of the system and demonstrate that our approach is effective and efficient in managing the file system using voice commands. Our system can enhance the accessibility and usability of the file system in Linux for individuals with disabilities or those who prefer a hands-free approach to file management.

Chatbots are intelligent conversational computer systems designed to mimic human conversation to enable automated online guidance and support. The increased benefits of chatbots led to their wide adoption by many industries in order to provide virtual assistance to customers. Chatbots utilise methods and algorithms from two Artificial Intelligence domains: Natural Language Processing and Machine Learning. However, there are many challenges and limitations in their application. In this survey we review recent advances on chatbots, where Artificial Intelligence and Natural Language processing are used. We highlight the main challenges and limitations of current work and make recommendations for future research investigation

In the livestock industry, maintaining the health of animals is very important, and the use of technology has greatly helped this. One of the existing challenges has been that most of the available sensors and electronic processing devices are designed to fit the human body, and the use of these devices for livestock faces limitations due to the difference in the structure of the body and skin of animals and humans. Reliable solutions tailored for animal husbandry can be achieved by modifying the physical and software structure of existing devices. In this article, we have presented the current situation, by reviewing the research done and the existing technology in the field of animal husbandry. The aim is to obtain comprehensive parameters for monitoring livestock health by leveraging available information and exploring new methods based on the Internet of Things (IOT), Artificial Neural Networks (ANN), and Artificial Intelligence (AI). We take a proactive approach by utilizing available technologies based on sound-based diagnostic techniques. Initially, we propose utilizing piezoelectricity and amplifying received sound using a diaphragm attached to a collar. Subsequently, we discuss the results of relevant tests that can be conducted using the phonocardiography system. For the next phase of research and designing a more advanced livestock health monitoring system, we suggest employing an ultrasonic system to measure heart rate and blood pressure for the next phase of research and designing a more advanced livestock health monitoring system. This system, coupled with a piezoelectric system to make electric energy from the pulse of the veins, could be subcutaneously implanted for continuous monitoring

Disrupted nutrient recycling is a significant problem for Europe, while phosphorus and nitrogen are wasted instead of being used for plant nutrition. Mineral phosphate is critical raw material, which contains environmentally hazardous elements such as cadmium and uranium. Therefore, phosphorus recovery from agricultural by-product streams is critically important key priority. Phosphorus recovery from food grade animal bone by-products have been applied researched since 2002 with objective driven evolution progress towards specialized pyrolysis processing technology and animal bone char product (ABC) developments in economical industrial scale. Different animal bone by-products tested under different conditions at 400 kg/h throughput capacity in the continuously operated 3R zero emission autothermal carbonization system. The different material core treatment temperatures (between >300°C and <850°C) were combined with different residence times under industrial productive processing conditions. It has been industrial demonstrated that material core treatment temperature <850°C with 20 minutes residence time is necessary to achieve high quality ABC with useful agronomic value. The output ABC product having concentrated >30% phosphorus pentoxide (P2O5) and specific quality innovative fertilizer for agronomical efficient organic and low input farming applications as functional organic fertilizer, soil improver, growing medium and/or fertilizing product blend with high mineral phosphate fertiliser replacement value.

This study presents a novel approach combining computer vision with topology optimization for additive manufacturing. Using specialized photogrammetry software, we capture high-resolution images of the design domain to create precise 3D models through detailed scanning. These models are converted into STL file format and remeshed using an adaptive algorithm within COMSOL 5.3 Multiphysics, managed by a custom MATLAB 2023. This integration allows optimal mesh resolution and accuracy in analyses. We applied this method to design a concrete pillar for 3D printing, aiming for a 75% volume reduction to enhance material efficiency and stability, crucial for extraterrestrial environments. The design, scanned with a 360-degree camera array, informed the MATLAB-based topology optimization process. Combining MATLAB’s optimization algorithms with COMSOL’s meshing and finite element solver solver, we explored material-efficient configurations. The results show significant volume reduction, particularly in the central design area, optimizing material use while maintaining structural stability. The optimization algorithm demonstrated rapid, stable convergence to near-optimal solutions within about 20 iterations, highlighting the method’s efficiency and robustness in complex design optimization.

Experiential learning, defined as the result, source, or context of knowledge obtained from direct experience, is increasingly bolstered by technology and human-computer interaction (HCI) principles and protocols. This article will describe how technology-based human-computer interaction (HCI) has been used in experiments to enhance direct knowledge experience. In this paper, immersive technologies (such as Virtual Reality, Augmented Reality, and haptics) will be described based on cognitive learning theory, empirical research and practical experience gained from implementing cutting-edge technology and maximizing HCI principles in constructive learning. Additionally, this article will describe how AI can be used to create individualized learning environments according to a student's needs in the future. While cost, technical issues and specialised trainings are the obstacle to deliver personalized learning experiences especially to economically disadvantaged students, technological change opportunities are projected, and these technologies will take educational outcomes to the next level.

The genus Flavivirus includes 53 members and poses a big threat to human health among worldwide. Diseases caused by flavivirus infections can be broadly classified into hemorrhagic diseases (Dengue, Yellow fever, etc) and neurological diseases (West Nile disease, Zika disease, etc). Many flaviviruses still lack specific treatments and effective vaccines. In this context, it becomes particularly important to thoroughly characterize the pathogenesis of flavivirus infection and develop animal models. Therefore, animal models that can accurately simulate human diseases are essential for evaluating the effectiveness of therapeutic drugs and preventive vaccines. In this paper, we provide a comprehensive review of the currently available animal models that support flavivirus infection and exhibit clinical manifestations related to flavivirus infection. Additionally, we discuss the strengths and limitations of each animal model, aiming to enhance researchers understanding of the use of animal models in flavivirus research. This insight is intended to facilitate further studies into the pathogenesis of flavivirus, as well as the development of antiviral agents and vaccines.

Orthobunyaviruses (order Bunyavirales, family Peribunyaviridae) in the Simbu serogroup have been responsible for widespread epidemics of congenital disease in ruminants. Australia has a national program to monitor arboviruses of veterinary importance. While monitoring for Akabane virus, a novel orthobunyavirus was detected. To inform the priority that should be given to this detection, a scoping review was undertaken to 1) Characterise the associated disease presentations and establish which of the Simbu group viruses are of veterinary importance; 2) Examine the diagnostic assays that have undergone development and validation for this group of viruses, and 3) Describe the methods used to monitor the distribution of these viruses. Two search strategies identified 224 peer-reviewed publications for 33 viruses in the serogroup. Viruses in this group may cause severe animal health impacts, but only those phylogenetically arranged in clade B are associated with animal disease. Six viruses (Akabane, Schmallenberg, Aino, Shuni, Peaton, and Shamonda) were associated with congenital malformations, neurological signs, and reproductive disease. Diagnostic test interpretation is complicated by cross-reactivity, the timing of foetal immunocompetence and sample type. Serological testing in surveys remains a mainstay of the methods used to monitor the distribution of SGVs. Due to significant differences in survey designs, only broad mean seroprevalence estimates could be provided. Further research is required to characterise the SGV detected and determine the disease risk posed by novel orthobunyaviruses and how they could challenge current diagnostic and surveillance capabilities.

In China, 65 types of venomous snakes exist, with the Chinese Cobra Naja atra being prominent and a major cause of snakebites in humans. Furthermore, N. atra is a protected animal in some areas, as it has been listed as vulnerable by the International Union for Conservation of Nature. Recently, due to the medical value of snake venoms, venomics has experienced growing research interest. In particular, genomic resources are crucial for understanding the molecular mechanisms of venom production. Here, we report a highly continuous genome assembly of N. atra, based on a snake sample from Huangshan, Anhui, China. The size of this genome is 1.67 Gb, while its repeat content constitutes 37.8% of the genome. A total of 26,432 functional genes were annotated. This data provides an essential resource for studying venom production in N. atra. It may also provide guidance for the protection of this species.

The king ratsnake (Elaphe carinata) of the genus Elaphe is a common large non-venomous snake that is widely distributed in Southeast and East Asia, and is an economically important farmed snake species. As a non-venomous snake, the king snake that is predatory on venomous snakes such as cobras and pit vipers. The immune mechanisms of which has been unclear. Despite their economic and research importance, genomic resources which will benefit studies in toxicology, phylogeography and immunogenetics are lacking. In this study, we use single-tube long fragment read (stLFR) sequencing to display the first complete genome of a King ratsnake from Huangshan City, Anhui province in China. The genome size is 1.56GB with a scaffold N50 of 6.53M, the total length of the genome is approximately 621Mb, and the repeat content is 38.90%. Additionally, we predicted 22,339 protein-coding genes, of which 22,065 had functional annotations. Our genome is a potentially useful addition to those currently available for snakes.

The severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), etiological agent of the novel coronavirus disease 2019 (COVID-19), has spread since December 2019, resulting in massive health and economic crisis worldwide. While efforts to stop the pandemic are crucial, collecting epidemiological data to help manage current and future pandemics will be important. In addition to humans, serological and molecular based studies have demonstrated SARS CoV-2 exposure in several wild, domestic and farmed animals. For examples Shriner and the team showed serologically an exposure of 40% to the white deer living in close proximity to urban centers. Additional reports have also emerged of susceptibility of animal’s species like cats, ferrets, raccoon dogs, cynomolgus macaques, rhesus macaques, white-tailed deer, rabbits, Egyptian fruit bats, and Syrian hamsters to SARS-CoV-2 infection.. It’s worth emphasizing that these reports are based on experimental data mostly derived from Europe, USA, South America and parts of Asia. In limited instances natural infections of SARS-CoV-2 have been reported in pet dogs, cats, tigers, lions, snow leopards, pumas, gorillas at zoos and farmed mink and ferrets. The presence of the virus in animal species and an understanding of whether these are natural or recent human to animal transmissions is important. It’s possible that such transmission could passage the virus or subject the virus to a different immunological pressure thereby helping with the development of viral variants in addition to being a host for future reservoirs of the virus. In Kenya SARS-CoV-2 was first detected on March 12th 2020 from imported human cases of persons who had travelled from the United States. This was followed by detection of imported cases majorly from China, Sweden and United Kingdom. Later infections were confirmed in Nairobi and Mombasa suggesting further cases of disease importations through the major ports of entry. However, no comparable data on animal exposure have hitherto been generated in Kenya. To address this key concern, we focused on three objectives; 1) development of a robust antibody ELISA based on crude SARS-CoV-2 lysate. 2) SARS-CoV-2 serology of domestic animals in Kenya. 3) Corroboration of the crude lysate based seroprevalence data and a commercial ELISA kit based on the Spike receptor binding domain (RBD) antigen. Our sample set included camel sera (both pre- & post outbreak sera), as well as sera from cats and dogs collected at the peak of the pandemic. Our results using the ELISA based on crude SARS-CoV-2 lysate indicated SARS-CoV-2 antibodies in camels (71%, N=145), cats 11% (N=16) and dogs (81%, N=36) with varying titer levels. These findings were comparable to those obtained using the commercial ELISA kit based on the spike RBD antigens. In summary, the data warrants two key conclusions: (i) we have demonstrated that the crude lysate ELISA allows for SARS-CoV-2 antibody detection, and given its potential to offer robust detection could be applied for initial mass screening (ii) although the current study cannot disentangle the relative contributions of antigenic cross-reactivity, pre-pandemic exposure to SARS-CoV-2 or human-animal transmission, it nonetheless demonstrates for the first time the prevalence of SARS-CoV-2 like antibodies in domestic and wild animals in Kenya. Our findings set the scene for further research into the prevalence of SARS-CoV-2 in domestic and wild animals to understand their potential epidemiological implications.

Melanins are phenolic biopolymers synthesised by most of the living organism mainly for photoprotection or surviving in harsh conditions. Melanin is localised in different areas or complexed with different other biomolecules when observed from animals to microbes. This makes the melanin extraction procedure different in animal, plant and microbial tissues. Basically, the alkali-acid extraction is used in most protocols of which slight variations are there depending on the tissue used. This review will try to compile melanin extraction procedures from different cells and tissues ranging from animals to bacteria.

Background: Previously, our laboratory has provided evidence that pre-administration of the antioxidant, lipoic acid covalently bonded to various naturally occurring antioxidants, enhanced neuroprotective capacity compared to the administration of lipoic acid on its own. The naturally occurring compound scopoletin, a coumarin derivative, has been shown in various in vitro studies to have both antioxidant and anti-inflammatory mechanism of actions. To date, the effect of scopoletin on neuronal cell death in an in vivo model of ischemia or ischemia-reperfusion has not been investigated. Therefore, the present investigation was designed to determine if scopoletin on its own, or a co-drug consisting of lipoic acid and scopoletin covalent bond, named UPEI-400, would be capable of demonstrating a similar neuroprotective efficacy. Methods: Using a rodent model of stroke in male rats (anesthetized with Inactin®; 100 mg/kg, iv), the middle cerebral artery was permanently occluded for 6 hours (pMCAO), or in separate animals, occluded for 30 min followed by 5.5 hrs of reperfusion (ischemia/reperfusion; I/R). Results: Pre-administration of either scopoletin or UPEI-400 significantly decreased infarct volume in the I/R model (p<0.05), but not in the pMCAO model of stroke. However, UPEI-400 was ~1000 times more potent as compared to scopoletin on its own. The optimal dose of UPEI-400 was then injected during the occlusion and at several time points during reperfusion and significant neuroprotection was observed for up to 150 mins following the start of reperfusion (p<0.05). Conclusion: The data suggest that synthetic combination of scopoletin with lipoic acid (UPEI-400) is a more effective neuroprotectant that either compound on their own. Also, since UPEI-400 was only effective in a model of I/R, it is possible that it may act to enhance neuronal antioxidant capacity and/or upregulate anti-inflammatory pathways to prevent the neuronal cell death.

As businesses increasingly adopt advanced technologies to enhance productivity and safety, the integration of human-robot collaboration (HRC) emerges as a transformative approach. This paper explores the role of GPU-accelerated computer vision and generative AI in optimizing HRC within business environments. By leveraging the computational power of GPUs, real-time data processing, and advanced machine learning algorithms, robots can better interpret complex visual cues and adapt to dynamic workspaces. Generative AI further enables the design of intelligent robotic systems capable of anticipating human actions, optimizing task allocation, and ensuring safety through predictive modeling. This research highlights the potential of these technologies to improve operational efficiency, reduce human error, and create safer, more adaptable workplaces. The implications of such advancements are discussed, providing insights into the future of HRC in various industries.

Some of the barriers preventing Virtual Reality (VR) from being widely adopted are the cost and unfamiliarity of VR systems. Here we propose that in many cases the specialized controllers shipped with most VR head-mounted displays can be replaced by a regular smartphone, cutting the cost of the system, and allowing users to interact in VR using a device they are already familiar with. To achieve this, we developed SmartVR Pointer, an approach that uses smartphones as a replacement for the specialized controllers for two essential operations in VR: selection and navigation by teleporting. In SmartVR Pointer a camera mounted on the head-mounted display (HMD) is tilted downwards so that it points to where the user will naturally be holding their phone in front of them. SmartVR Pointer supports three selection modalities: tracker-based,gaze-based, and combined/hybrid. In the tracker-based SmartVR pointer selection we use image-based tracking to track a QR code displayed on the phone screen and them map the phone’s position to a pointer shown within the field of view of the camera in the Virtual Environment. In the gaze-based selection modality the user places the pointer using their gaze and taps on the phone for selection. The combined technique is a hybrid between head-gaze-based interaction in VR and smartphone-based Augmented Reality. It allows the user to control a VR pointer that behaves like a mouse pointer by moving their smartphone to select objects within the virtual environment, and to interact with the selected objects using the smartphone’s touch screen. The touchscreen is used for selection and dragging. SmartVR Pointer is simple and requires no calibration and no complex hardware assembly or disassembly. We demonstrate successful interactive applications of SmartVR Pointer in a VR environment with a demo where the user navigates in the virtual environment using teleportation points on the floor and then solves a Tetris-style key-and-lock challenge.

Integrating voice assistant technology can potentially enhance accessibility and user interaction in web-based platforms for geospatial data visualisation and processing. This paper presents an open-source development incorporating a voice virtual assistant into the BStreams web application platform, designed specifically for visualising geospatial data. The study aimed to mitigate the observed limitations of virtual assistants in understanding the semantics and context of user commands in the geospatial domain. A discourse framework specialised for geospatial data was developed using a structured prototype methodology. In addition, a survey assisted in identifying commonly used English terminologies for geospatial data interaction. The questionnaire results were subsequently assessed using the ChatGPT natural language processing model. The application used open-source frameworks, including Web Speech API, Leaflet, Mapbox geocoding, and JavaScript. This investigation indicates that voice virtual assistants can potentially enhance human-computer interaction for geospatial data visualisation and processing. However, challenges related to natural language understanding, accents, and domain knowledge were raised. The paper advocates for further research to refine the integration of voice technology in this domain.

Human papillomavirus (HPV) is the most sexually transmitted infection associated with the development of cervical cancer. This study investigated cervical HPV prevalence, characteristics and distribution according to age and human immunodeficiency virus (HIV) among women attending public community health facility in the Eastern Cape Province of South Africa. A total of 325 participants (aged 18 to 60) visiting a community health facility for any reason were recruited. Cervical HPV infection was detected using the Seegene Anyplex™ II HPV28 assay. Overall HPV prevalence was 65.2% (95% CI: 59.9–70.2%), with the highest prevalence of 80.9% (95% CI: 67.2–89.8%) observed in the 18–25 years age group and lowest prevalence of 46.3% (95% CI: 35.8–57.1%) in 46–60 years of age. HR-HPV infection was found to decrease with increasing age among women who were HPV infected by any type (p=0.02), but not when stratified according to HIV status. A proportion of 12.9% (42/325) were infected with one or more HPV types covered by the Cervarix® HPV vaccine (HPV-16 and/or -18), 18.8% (61/325) by those covered by Gardasil®4 (HPV-6, -11, -16 and/or -18) and 42.2% (137/325) by those covered by Gardasil®9 (HPV-6, -11, -16, -18, -31, -33, -45, -52 and/or -58). Alpha-9 HPV species were the most dominant species (40.6%, 132/325), followed by alpha-7 species (29.8%, 97/325). In the overall population, alpha-9 HPV species were always part of the most dominant HPV alpha species co-infection, which were alpha-9 and -7 species (17.5%, 57/325), alpha-9 and -5 species (9.8%, 32/325), and alpha-9 and -6 species (9.5%, 31/325). High overall HPV, HR-HPV, and alpha-9 species prevalence were observed among women attending the public health facility. These findings contribute to the limited HPV distribution data among Eastern Cape women, which could be used to improve HPV-related policy and assess the effectiveness of the HPV vaccination.

Chan and colleagues in their paper titled “Human origins in a southern African palaeo-wetland and first migrations” (https://www.nature.com/articles/s41586-019-1714-1) report 198 novel whole mitochondrial DNA (mtDNA) sequences and infer that ‘anatomically modern humans’ originated in the Makgadikgadi–Okavango palaeo-wetland of southern Africa around 200 thousand years ago. This claim relies on weakly informative data. In addition to flawed logic and questionable assumptions, the authors surprisingly disregard recent evidence and debate on human origins in Africa. As a result, the emphatic and high profile conclusions of the paper are unjustified.

The human virome is an area of increasing interest with relation to human health and disease. It has been demonstrated to alter in concert with the bacterial microbiome in early life and was also found to be different in patients with certain diseases such as inflammatory bowel disease. However, all virome analyses are hampered by a lack of annotated representative database sequences, often referred to as the ‘viral dark matter’. Here we provide the first description of the gut DNA virome in elderly individuals (>65 years old) as well as the description of novel bacteriophages not present in current reference databases. Diversity analysis comparing elderly persons from different residence locations (community living vs long term care facilities) did not reveal any difference in their virome diversity profiles despite the reported differences at the bacteriome level. An abundance of Microviridae of the subfamily Gokushovirinae were present in the faeces of elderly individuals. Several novel members of the order Caudovirales were also characterized and annotated. Assignment of host bacteria to detected viral genomes was attempted using a combination of CRISPR spacers, tRNA genes and a probabilistic approach. Further characterization of the viral dark matter is necessary for developing tools and expanding databases to study the human virome. This study focused on the virome of an aging human cohort with the goal of illuminating part of the viral dark matter.

Human-Robot Interaction (HRI) for collaborative robots has become an active research topic recently. Collaborative robots assist the human workers in their tasks and improve their efficiency. But the worker should also feel safe and comfortable while interacting with the robot. In this paper, we propose a human-following motion planning and control scheme for a collaborative robot which supplies the necessary parts and tools to a worker in an assembly process in a factory. In our proposed scheme, a 3-D sensing system is employed to measure the skeletal data of the worker. At each sampling time of the sensing system, an optimal delivery position is estimated using the real-time worker data. At the same time, the future positions of the worker are predicted as probabilistic distributions. A Model Predictive Control (MPC) based trajectory planner is used to calculate a robot trajectory that supplies the required parts and tools to the worker and follows the predicted future positions of the worker. We have installed our proposed scheme in a collaborative robot system with a 2-DOF planar manipulator. Experimental results show that the proposed scheme enables the robot to provide anytime assistance to a worker who is moving around in the workspace while ensuring the safety and comfort of the worker.

Porcine reproductive and respiratory syndrome virus (PRRSV) infections cause significant economic losses to swine producers every year. Aerosols containing infectious PRRSV are an important route of transmission, and proper treatment of air could mitigate the airborne spread of the virus within and between barns. Previous bioaerosol studies focused on the microbiology of PRRSV aerosols; thus, the current study addressed the engineering aspects of virus aerosolization and collection. Specific objectives were to (1) build and test a virus aerosolization system, (2) achieve a uniform and repeatable aerosol generation and collection throughout all replicates, (3) identify and minimize sources of variation, (4) verify that the collection system (impingers) performed similarly. The system for virus aerosolization was built and tested (Obj. 1). The uniform airflow distribution was confirmed using a physical tracer (<12% relative standard deviation) for all treatments and sound engineering control of flow rates (Obj. 2). Theoretical uncertainty analyses and mass balance calculations showed <3% loss of air mass flow rate between the inlet and outlet (Obj. 3). A comparison of TCID₅₀ values among impinger fluids showed no statistical difference between any two of the three trials (p-value = 0.148, 0.357, 0.846) (Obj. 4). These results showed that the readiness of the system for research on virus aerosolization and treatment (e.g., by ultraviolet light), as well as its potential use for research on other types of airborne pathogens and their mitigation on a laboratory scale.

Porous coatings on prosthetic implants encourage implant fixation. Enhanced fixation may be achieved using a magneto-active porous coating that can deform elastically in vivo on application of an external magnetic field, straining in-growing bone. Such coating, made of 444 ferritic stainless steel fibres, was previously characterised in terms of its mechanical and cellular responses. In this work, co-cultures of human osteoblasts and endothelial cells were seeded into a novel fibrin-based hydrogel embedded in a 444 ferritic stainless steel fibre network. Albumin was successfully incorporated into fibrin hydrogels improving the specific permeability and the diffusion of fluorescently-tagged dextrans without affecting their Young’s modulus. The beneficial effect of albumin was demonstrated by upregulation of osteogenic and angiogenic gene expression. Furthermore, mineralisation, extracellular matrix production and formation of vessel-like structures were enhanced in albumin-enriched fibrin hydrogels compared to fibrin hydrogels. Collectively, the results indicate that the albumin-enriched fibrin hydrogel is a promising bio-matrix for bone tissue engineering and orthopaedic applications.

Arrhythmogenic cardiomyopathy (AC) is a hereditary cardiac disorder characterized by the gradual replacement of cardiomyocytes with fibrous and adipose tissue, leading to ventricular wall thinning, chamber dilation, arrhythmias, and sudden cardiac death. Despite advances in treatment, disease management remains challenging. Animal models, particularly mice and zebrafish, have become invaluable tools for understanding AC's pathophysiology and testing potential therapies. Mice models, although useful for scientific research, cannot fully replicate the complexity of the human AC. However, they have provided valuable insights into gene involvement, signalling pathways, and disease progression. Zebrafish offer a promising alternative to mammalian models, despite the phylogenetic distance, due to their economic and genetic advantages. By combining animal models with in vitro studies, researchers can comprehensively understand AC, paving the way for more effective treatments and interventions for patients and improving their quality of life and prognosis.

Nanotechnology has become a popular topic in agricultural and animal research, generating sig-nificant global interest. Nanoparticles (NPs)-enabled agriculture and animal husbandry (AHs) products have three main principles: crop production, protection, and nutrient enrichment. Re-search on this topic has grown exponentially in recent years, with an increasing number of peer-reviewed literature and patents on NPs-enabled agriculture and AH products. This study examines and collects data on the various applications of NPs-enabled agricultural products for crop production, protection, and livestock improvement from the core nanotechnological database. The search returned several types of NPs and products used in agriculture and AH, countries that utilize these products at a commercial level, and analysis of these data by phyton, R-statistical software. Our findings show remarkable growth of nanotechnology in agriculture, AH, and re-search fields for future sustainability goals in the era of climate change

Blood-based biomarkers for early detection of recurrence have a significant clinical impact on the improved management of breast cancer patients, as cancer recurrence results in poor prognosis. This study aimed to investigate if the measurement of blood endocan in breast cancer patients with high ESM1 expression could be an effective tool to detect postoperative recurrence compared with existing tumor markers. Blood was collected before and after the tumor resection from the mouse models of breast cancer, and endocan levels were measured while visualizing metastatic recurrence with noninvasive luminescence imaging. In clinical settings, blood was withdrawn from 16 breast cancer patients before and after the tumor resection, and the effect of lumpectomy on blood endocan level was evaluated. Additionally, the blood endocan from 20 patients diagnosed with postoperative recurrence was measured, and their positivity rate for endocan was compared with that for serum carcinoembryonic antigen (CEA) or cancer antigen 15-3 (CA15-3). Our preclinical and clinical experiments revealed that blood endocan levels reflected tumor burden. Furthermore, over 60% of patients suffering from postoperative recurrence who tested negative for CEA or CA15-3 were positive for endocan. Our results support the clinical significance of endocan in breast cancer patients for detecting breast cancer recurrence.

Henipaviruses are single-stranded RNA viruses that have been shown to be virulent in several species including humans, pigs, horses, and rodents. Isolated nearly 30 years ago, these viruses have been shown to be of particular concern to public health, as at least two members (Nipah and Hendra viruses) are highly virulent, as well as zoonotic, and are thus classified as BSL4 pathogens. Although only 5 members of this genus have been isolated and characterized, metagenomics analysis using animal fluids and tissues has demonstrated the existence of other novel henipaviruses, suggesting a far greater degree of phylogenetic diversity than currently known. Using a variety of molecular biology techniques, it has been shown that these viruses exhibit varying degrees of tropism, on a species, organ/tissue, and cellular level. This review will attempt to provide a general overview of our current understanding of henipaviruses, with particular emphasis on viral tropism.

The study of the currently known >3,000 species of snakes can provide valuable insights into the evolution of their genomes. Deinagkistrodon acutus, also known as Sharp-nosed Pit Viper, one hundred-pacer viper or five-pacer viper, is a venomous snake with significant economic, medicinal and scientific importance. Widely distributed in southeastern China and South-East Asia, D. acutus has been primarily studied for its venom. Here, we employed next-generation sequencing to assemble and annotate a highly continuous genome of D. acutus. The genome size is 1.46 Gb; its scaffold N50 length is 6.21 Mb, the repeat content is 42.81%, and 24,402 functional genes were annotated. This study helps to further understand and utilize D. acutus and its venom at the genetic level.

Vitamin E is a group of eight compounds divided into two subgroups: four tocopherols (α-, β-, γ-, and δ-) and four tocotrienols (α-, β-, γ-, and δ-). α-Tocopherol is considered the most biologically active form. This review paper summarizes the current knowledge and historical progression of vitamin E research, with particular emphasis on its role in animal nutrition. Early studies in the 1920s identified vitamin E’s role in preventing neonatal mortality in rats. Over the following decades, scientists discovered the compound’s chemical structure and its importance for the immune system, skin health, anti-inflammatory properties, hormonal balance, and other bodily functions. Production of vitamin E products has evolved, starting with isolating the vitamin from natural sources and advancing to synthesizing it with greater efficiency and standardized potency units. Early animal studies focused on reproductive health and growth disorders, but further research demonstrated the importance of vitamin E in preventing encephalomalacia in domestic fowl and muscular dystrophy in rabbits and other animals. Today, vitamin E is commonly used as a feed additive to meet animals’ requirements and assure immune function and overall health. Despite much knowledge on vitamin E’s role in animal nutrition, many questions remain unanswered, and current research aims to determine optimal supplementation levels, interactions with other nutrients, impacts on gene expression, cell signaling and communication, and effects of supranutritional dosages in livestock. Vitamin E has a long and fascinating history in scientific research, with significant advancements in understanding its chemical structure and biological activity. In animal nutrition, vitamin E continues to play an important role in improving animal health, and ongoing research will continue to expand our understanding of its benefits and mechanisms of action.

The use of electronic means to support tasks such as pastoralism is a way of intelligently optimizing that activity. As any autonomous system, it requires human intervention in case of failure, and therefore it needs an autonomous mechanism that draws the attention of the human operator whenever the system or the animals evolve to undesired conditions. The present work progresses an existing alarm system, used in the SheepIT gateway, which can monitor the behavior of animals and equipment, warning human supervisors of the occurrence of unwanted events and the need for intervention. Concretely, given the lack of coverage of Internet access in rural areas, the system was integrated with a satellite interface to guarantee communication and the timely delivery of alarm messages. The paper compares the overall networking performance of the satellite link, against a Wi-Fi laboratorial baseline.

Leptospirosis is an important zoonotic disease that is transmitted worldwide through infected small mammals such as rodents. In Malaysia, there is paucity of information on the animal reservoirs that are responsible for leptospirosis transmission, with only few studies focusing on leptospirosis risk in recreational areas. Therefore, in this study, we characterized the species composition and the prevalence of pathogenic Leptospira spp. in non-volant small mammals of Hutan Lipur Sekayu, Terengganu. We performed ten trapping sessions totaling 3,000 trapping efforts between September 2019 and October 2020. Kidney samples from captured individuals were extracted for the PCR detection of pathogenic Leptospira spp. Overall, we captured 45 individuals from 8 species (1.56% successful trapping effort), with 9 individuals testing positive for pathogenic Leptospira, that is 20% (n = 9/45) prevalence rate. Rattus tiomanicus (n = 22) was the most dominant captured species and was found to harbour the highest positive individual with pathogenic Leptospira (44.4%, n = 4/9). Despite the low successful trapping effort in this study, the result shows that the non-volant small mammals of Hutan Lipur Sekayu are capable of maintaining and transmitting pathogenic Leptospira, thus making this recreational area a potential infestation ground for leptospirosis.

Choanoflagellates, the closest living relatives of animals, have the potential to reveal the genetic and cell biological foundations of complex multicellular development in animals. Here we describe the history of research on the choanoflagellate Salpingoeca rosetta. From its original isolation in 2000 to the establishment of CRISPR-mediated genome editing in 2020, S. rosetta provides an instructive case study in the establishment of a new model organism.

Campylobacter is one of the major foodborne pathogens of concern in its growing trend of antimicrobial resistance. C. jejuni and C. coli are the major causative agents, with C. jejuni contributing to most of the cases in approximately 90% in the world. Infection is transmitted to humans due to consumption of contaminated food and water. Campylobacteriosis caused by C. jejuni is commonly presented with severe diarrhoea, abdominal pain, fever, headache, nausea, and vomiting with some extreme cases resulting in Guillain–Barré syndrome (GBS) and acute flaccid paralysis. Symptoms are severe in cases of children below 5 years, elderly and individuals who are immunocompromised. The infection is usually sporadic, and self-limiting and thus does not require antibiotics for treatment. Still, the antimicrobial resistance in Campylobacter is a major concern because of the transmission of resistance from animal sources to humans. This review highlights the recent epidemiology, geographical impact, resistance mechanisms, spread of Campylobacter spp. and the strategies to control the transmission of Campylobacter from veterinary sources and its antimicrobial resistance.

Here we review and describe a set of research priorities to meet present and future challenges posed to farmed animal production that build on progress, successes and resources from the Functional Annotation of ANimal Genomes (FAANG) project.

Fullerene molecules are composed of carbon in forms of a hollow sphere, ellipsoid, or tube. Fullerenes have attracted considerable attention in different fields of science since their discovery in 1985. The unique carbon cage structure of fullerene provides immense scope for derivatization, rendering potential for various industrial applications. The prospective applications of fullerenes thus have led to assorted fullerene derivatives. The unique chemical structure also provides ease for fullerene to be synthesized through various kinds of conjugating techniques, where fullerene can be located either on the backbone or the branch chain. Here in this review, we have compiled the toxicity and biosafety aspects of fullerene in aquatic organisms. The frequent use of fullerene is likely to come in contact and interact with the aquatic environment and aquatic organisms. According to the current understanding, waterborne exposure to fullerene-based nanomaterials indeed triggers toxicities at cellular, organic, molecular as well as neurobehavioral levels.

We evaluated progress towards animal biodiversity research in Georgia, a key area in the Caucasus biodiversity hotspot. By reviewing recently (1990-2018) published articles in all areas of animal diversity research, we unmasked the trends in biodiversity inventory, ecological and biogeographical studies, and conservation issues in Georgia. We concluded that species inventory and biodiversity research in Georgia has significantly increased during the last ten years, however the rate and extent of investigation is far from satisfactory. Major gaps remain in all branches of animal diversity research in Georgia, and consequently existing knowledge is inadequate to address modern challenges related to species and ecosystem conservation. We urge local governmental authorities and international scientific societies to support development of stronger research facilities and cultivate interest in biodiversity inventory and research in Georgia as an important step towards maintaining globally important biodiversity in the Caucasus.

Fear can sometimes paralyze us, and it can sometimes be exciting; for some people, fear is so crippling it can significantly mix up their lifes! We understand a little bit about how the brain acts when we are afraid, mainly by studying the brains of animals. Recently, surprising findings were made using a humble animal, the zebrafish – a small aquarium fish that in the past has helped scientists figure out how our organs develop. Zebrafish are useful because they develop quickly, reproduce richly, and have brains which are similar to ours. They also produce what we call an “alarm substance” that alerts shoalmates when one of them has been injured; when they smell this substance in the water they act as if they are very scared. When this happens, they release serotonin in their brains, a neurotransmitter that acts as a light switch, making them less afraid but more cautious – as if trying to figure out if a predator is there or not. Hopefully, finding more about how the zebrafish brains process this serotonin signal can help scientists develop better treatments for mental disorders that are associated with fear.

Little is known about public knowledge of meat chicken production and how it influences attitudes to birds’ welfare and consumer behaviour. We interviewed 506 members of the public in SE Queensland, Australia, to investigate this. Knowledge was assessed from 15 questions, and low scores were supported by respondents’ self-assessed report of low knowledge levels and agreement that their knowledge was insufficient to form an opinion about which chicken products to purchase. Older respondents and single people without children were most knowledgeable. There was uncertainty about whether chicken welfare was adequate, particularly in those with little knowledge. There was also evidence that lack of empathy towards chickens related to lack of knowledge, since those that thought it very acceptable that some birds are inadequately stunned at slaughter had low knowledge scores. More knowledgeable respondents ate chicken more frequently and were less likely to buy products with accredited labelling. Approximately half of the respondents thought the welfare of the chicken to be more important than the cost. It is concluded that the public’s knowledge has an important connection to their attitudes and consumption of meat chickens. Respondents with little knowledge demonstrated that they had both lack of empathy and intolerance to religious slaughter practices.

Automation in agriculture is transforming the management of field tasks. This article focuses on identifying the needs and preliminary requirements for designing an autonomous robot for fruit harvesting. These robotic systems integrate advanced machine vision technologies and neural networks to accurately detect ripe fruits and obstacles, thereby optimizing harvesting efficiency. Additionally, the use of proximity and touch sensors is emphasized, ensuring careful and safe handling of fruits to prevent damage during harvesting. The technical specifications of these robots, including traction, speed, and their ability to operate on slopes, are evaluated to ensure adaptability to different types of crops and ground conditions. Perception systems, utilizing advanced cameras and sensors, provide essential data for crop inspection and route planning. Through a thorough analysis of operational demands, environmental conditions, and technical specifications, this article aims to establish a solid foundation for guiding the development and construction of an efficient and viable harvesting robot. The prospects and potential of fruit-harvesting robots to transform agriculture are highlighted, underscoring the importance of ongoing research and development to overcome current challenges and enhance the feasibility and effectiveness of these robotic systems.

The Mixture of Experts (MoE) architecture has evolved as a powerful and versatile approach for improving the performance and efficiency of deep learning models. This survey aims to provide the fundamental principles of MoE in details, a paradigm that harnesses the collective power of multiple specialized "expert" models working in concert to tackle complex tasks.Our exploration is a thorough analysis of the core components that constitute the MoE framework. We begin by dissecting the routing mechanism, a crucial element responsible for dynamically assigning input data to the most appropriate expert models. This routing process is pivotal in ensuring that each expert's specialized knowledge is optimally utilized. The survey places significant emphasis on expert specialization, a key feature that sets MoE apart from traditional architectures. We examine various strategies for developing and training specialized experts, exploring how this specialization enables MoE models to effectively handle diverse and multifaceted problems.Load balancing, another critical aspect of MoE systems, receives thorough attention. We discuss techniques for efficiently distributing computational resources among experts, ensuring optimal model performance while managing hardware constraints. This section provides insights into the delicate balance between model capacity and computational efficiency. Furthermore, we provide an in-depth discussion of the expert models themselves, examining their architectural designs, training methodologies, and how they interact within the larger MoE framework. This includes an analysis of different types of experts, from simple neural networks to more complex, task-specific architectures. The survey also navigates through diverse research avenues within the MoE landscape, highlighting recent advancements and innovative applications across various domains of machine learning. We pay particular attention to the burgeoning use of MoE in two rapidly evolving fields: computer vision and large language model (LLM) scaling. By providing this comprehensive overview, our survey aims to offer researchers and practitioners a deep understanding of MoE's capabilities, current applications, and potential future directions in the ever-evolving landscape of deep learning.The Mixture of Experts (MoE) architecture has evolved as a powerful and versatile approach for improving the performance and efficiency of deep learning models. This survey aims to provide the fundamental principles of MoE in details, a paradigm that harnesses the collective power of multiple specialized "expert" models working in concert to tackle complex tasks.Our exploration is a thorough analysis of the core components that constitute the MoE framework. We begin by dissecting the routing mechanism, a crucial element responsible for dynamically assigning input data to the most appropriate expert models. This routing process is pivotal in ensuring that each expert's specialized knowledge is optimally utilized. The survey places significant emphasis on expert specialization, a key feature that sets MoE apart from traditional architectures. We examine various strategies for developing and training specialized experts, exploring how this specialization enables MoE models to effectively handle diverse and multifaceted problems.Load balancing, another critical aspect of MoE systems, receives thorough attention. We discuss techniques for efficiently distributing computational resources among experts, ensuring optimal model performance while managing hardware constraints. This section provides insights into the delicate balance between model capacity and computational efficiency. Furthermore, we provide an in-depth discussion of the expert models themselves, examining their architectural designs, training methodologies, and how they interact within the larger MoE framework. This includes an analysis of different types of experts, from simple neural networks to more complex, task-specific architectures. The survey also navigates through diverse research avenues within the MoE landscape, highlighting recent advancements and innovative applications across various domains of machine learning. We pay particular attention to the burgeoning use of MoE in two rapidly evolving fields: computer vision and large language model (LLM) scaling. By providing this comprehensive overview, our survey aims to offer researchers and practitioners a deep understanding of MoE's capabilities, current applications, and potential future directions in the ever-evolving landscape of deep learning.

The computer program "Histropy" is an interactive Python program for the quantification of selected features of two-dimensional (2D) images/patterns (in either JPG/JPEG, PNG, GIF, BMP, or baseline TIF/TIFF formats) by means of calculations based on the pixel intensities in this data, their histograms, and user-selected sections of those histograms. The histograms of these images display pixel-intensity values along the x-axis (of a 2D Cartesian plot), with the frequency of each intensity value within the image represented along the y-axis. The images need to be of 8-bit or 16-bit information depth and can be of arbitrary size. (Up to 1024 pixels maximum on both sides is recommended as larger images tend to significantly slow the program’s performance). Histropy generates an image’s histogram surrounded by a graphical user interface that allows one to select any range of image-pixel intensity levels, i.e. sections along the histograms’ x-axis, using either the computer mouse or numerical text entries. The program subsequently calculates the (so-called Monkey Model) Shannon entropy and root-mean-square contrast for the selected section and displays them as part of what we call a "histogram-workspace-plot." To support the visual identification of small peaks in the histograms, the user can switch between a linear and log-base-10 display scale for the y-axis of the histograms. Pixel intensity data from different images can be overlaid onto the same histogram-workspace-plot for visual comparisons. The visual outputs of the program can be saved as histogram-workspace-plots in the PNG format for future usage. The source code of the program and a brief user manual are published in the supporting materials as well as on GitHub . Instead of taking only 2D images as inputs, the program’s functionality could be extended by a few lines of code to other potential uses employing data tables with one or two dimensions in the CSV format.

Livestock marketplaces consist of infrastructure and rules through which information is exchanged and transactions are completed. Livestock trading incurs significant transaction costs, beyond just commission fees paid to marketplace owners (e.g., se-lecting, loading and transporting animals). In addition, animals being transported, held and transacted in a market setting increases the likelihood of injury or disease. The Covid-19 pandemic contributed to large-scale changes across typically physical marketplaces as stay-at-home orders and travel restrictions were invoked. Livestock marketplaces pivoted online rapidly, facilitated by the deployment of video feeds, al-lowing buyers to view/purchase animals via mobile phone. This shift offered the promise of mitigating many of the transaction costs associated with a fully physical marketplace. This study evaluated how computer vision (CV) approaches can con-tribute to the evolution of livestock marketplaces. It used livestock video data for 240 cattle (bulls) weighing 320-740 Kg from 16 different breeds to elicit additional trade-relevant data (livestock weight) and can be considered as an intermediary step to better understand how video data can be used to further digitalize livestock trade, price formation and transaction completion via technology solutions. The dataset comes from three sales held on different dates in a single mart location, which allows a consistent camera setup throughout. CV technologies were employed to solve the problem of cattle weight estimation and a distinctive achievement lies in curating a dataset cap-turing cattle in motion within the context of a cattle auction thereby injecting realism and complexity into the CV weight estimation task. Employing advanced object detection algorithms, the study created an intricate data pipeline to generate an appropriate modelling dataset and devised new multi-channel architectures for deeper analysis of diverse image inputs. The significance of sizable datasets and the challenge of man-aging inherent input variability for accurate CV regression is highlighted. Top-down view predictions demonstrate comparatively better performance, although failing to achieve benchmark precision. Challenges arise from camera angle variations, roaming behaviors, and breed diversity. Likewise, side-view predictions face challenges due to increased variability and lack of standardization. Despite this, these predictions offer insights into the value of incorporating Region of Interest (ROI) coordinates and data augmentation for enhanced performance. The study concludes with a multi-input ar-chitecture combining top-down and side-view predictions, demonstrating moderate success highlighting potential synergy between both perspectives. The central con-clusion resonates strongly – using standardized and multi-view images encompassing controlled movement and consistent poses could bolster accurate CV weight estima-tion.

The term artificial intelligence refers to the field of computer science that deals with the design and implementation of computer systems that mimic elements of human behavior that imply basic intelligence: learning, adaptability, inference, contextual understanding, and problem solving among others. Artificial intelligence is a point of intersection between multiple sciences such as computer science, psychology, philosophy, neurology, linguistics and engineering science, with the aim of synthesizing intelligent behavior, with elements of reasoning, learning and adaptation to the environment, while it is usually applied to specially made machines or computers.

The aim of this study was to evaluate the clinical and radiographic outcomes of the All-on-4 technique in fully edentulous maxilla using R2GATE software for flapless, computer-guided surgery and immediate loading. Ten adult patients with fully edentulous maxilla, aged between 35 and 60 years, were included in the study between April 2021 and April 2022. The surgical procedure was performed under local anesthesia and followed the All-on-4 approach with immediate loading of implants using a screw-retained provisional acrylic resin prosthesis on the same day. Final prosthesis was delivered four months after the surgery. Clinical and radiographic outcomes were evaluated at four- and twelve-months post-operation. The study reported a 95% implant survival rate using the All-on-4 technique. The mean marginal bone level was recorded at 0.35 mm and 0.66 mm during the 4 and 12-month follow-up periods, respectively. Fixed provisional prosthesis fracture, abutment, and prosthesis screw loosening were the most frequent mechanical complications reported. Within the limitations of this study, the use of computer-guided surgery in the All-on-4 technique seemed to be a promising treatment option with high implant survival rates that may help reduce post-surgical discomfort and mechanical complications in the rehabilitation of the edentulous maxilla.

Background: Electrical conductivity of trabecular bone at 100 kHz was recently reported as a good predictor of bone volume fraction. However, to quantify its relationship with the free water (or physiological solution) content and between the conductivities of its constituents, is still unclear. Methods: In this contribution, in silico models inspired by microCT images of trabecular bovine samples were used to build realistic geometries. Finite Element Method was applied to solve the electrical problem and to robustly fit the conductivity of the constituents with literature data. The obtained effective electrical conductivity was compared to Bruggeman three media mixture model: physiological solution, bone marrow and bone matrix. Results: The values for physiological solution plus bone marrow and bone matrix that captured better the bone volume fraction in the two media Finite Element model were: σps+bm = 298.4 mS/m and σb = 21.0 mS/m, respectively. Additionally, relative good results were obtained by a three media Bruggeman mixture model with σbm= 103 mS/m, σb= 21.0 mS/m, and σps= 1200 mS/m. Simple linear relationships between the proportions of constituents depending on bone volume fraction were tested. Degree of anisotropy and fractal dimension do not show detectable changes in effective conductivity. Conclusions: These results provided some useful findings for simulation purposes: first, a higher value of electrical conductivity of bone marrow has to be used in order to obtain similar values to those experimental published data. Second, anisotropy is not detectable by conductivity measurements for small trabecular samples (cube 5 mm). Finally, the simulations presented here showed a relatively good fitting of the mixture Bruggeman model which potentially would allow both, accounting for the free water content and re-scaling the model for whole bone electrical simulations.

Regular inspections during construction work verify that the work completed is consistent with the plans and specifications and ensure that it is within the planned time and budget. This requires frequent physical site observations to independently measure and verify the completion percentage of the construction progress performed over periods of time. The current computer vision-based (CV) techniques for the measurement of as-built elements, predominantly use 3D laser scanning or 3D Photogrammetry modelling to determine the geometrical properties of as-built elements on construction sites. Both techniques require data acquisition from several positions and angles to generate sufficient information about the element’s coordinates making the deployment of these techniques on dynamic construction project sites a challenging task. In this paper, we propose a pipeline for automating the measurement of as-built components using artificial intelligence (AI) and computer vision (CV) techniques. The pipeline requires a single image obtained with a stereo-camera system to measure the size of selected objects or as-built components. We demonstrate our approach by measuring the size of concrete walls and columns. The novelty of this work is attributed to the fully automated CV-based method for measuring any given element using a single image only. The proposed solution is suitable for use in measuring the sizes of as-built components of built assets. It has the potential to be further developed and integrated with BIM models for use on construction projects for progress monitoring.

To solve two key problems in the identification of people who are infected with COVID-19: the first problem is that the identification accuracy is not high enough. The second problem is that present identification method such as nucleic acid testing is expensive in many countries. Methods: So, I decided to design a fast identification method for COVID-19 patients which is based on deep learning. After the model (CoughNet) learns more than 6,000 cough spectrograms of both COVID-19 patients and normal people, the accuracy rate of identification of COVID-19 patients and normal people is higher than 99% in the test set. Structure: This paper is mainly divided into three parts: the first part introduces the main background and research status of the research; The second part introduces the research methods; The third part introduces the specific process of the experiment.

Susceptible, infective, recovered, and hospitalized/isolated individuals are placed on the cells of a nxn square lattice, where each cell is occupied by a single individual or is vacant. At discrete time units (typically one day each) all susceptibles and infectives execute a random movement and when a coincidence of the two types occurs the susceptible is converted to infective status according to some probability. Infectives are labelled by the number of days since originally infected. At each time increment the age label of the infectives is increased by one unit. When the label reaches a number like 15 or 20 days the susceptibles recover with some probability or become isolated/hospitalized. Upon reaching some age the latter types either recover or die. Probabilities for the movements and conversions from one status to another are implemented by random numbergeneration. Simulations were carried out to investigate the effect of several probability and age parameters, the size of population (proportional to nxn) and density (related to fraction of occupied cells), and the size of the movements. Mid-term gradual conversion of susceptibles to isolated was explored as intervention policy.

The growth of urban population, combined with an increase of extreme events due to climate changes call for a better understanding and representation of urban floods. Rainfall and infiltration are two important factors that affect the watershed response to a given precipitation event. In this paper, we evaluate the influence of the representation of infiltration and spatially variable rainfall on the computer simulation of the floods that affected the city of Hull, UK in June 2007. This work compares a uniform rainfall with one generated using Kriging with External Drift and a constant infiltration equal to the soil hydraulic conductivity with a neglected infiltration. The results of the four simulations are then compared with the flood extents observed by public authorities. It results that the computer model is able the reproduce the general dynamic of the flood and identify the main inundated areas. We found that neglecting the infiltration induce a better representation of this flood event. Furthermore, the use of radar rainfall results in an accuracy similar to the one obtained with a constant rainfall. This study indicates that when the spatial resolution of the rainfall data is low compared to the catchment size and the precipitation distribution is uniform, the spatial variability of the rainfall might not add significant information.

As the global population rapidly ages with longer life expectancy and declining birth rates, the need for healthcare services and caregivers for older adults is increasing. Current research envisions addressing this shortage by introducing domestic service robots to assist with daily activities. The successful integration of robots as domestic service providers in our lives requires them to possess efficient manipulation capabilities, provide effective physical assistance, and have adaptive control frameworks that enable them to develop social understanding during human-robot interaction. In this context, human factors, especially quantifiable ones, represent a necessary component. The objective of this paper is to conduct an unbiased review encompassing studies on human factors studied in research involving physical interactions and strong manipulation capabilities. We identified the prevalent human factors in physical human-robot interaction (pHRI), noted the factors typically addressed together and determined the frequently utilized assessment approaches. Additionally, we gathered and categorized proposed quantification approaches based on measurable data for each human factor. We also formed a map of common contexts and applications addressed in pHRI for a comprehensive understanding and easier navigation of the field. We found out that most of the studies in direct pHRI (when there is direct physical contact) focus on social behaviors with belief being the most commonly addressed human factor type. Task collaboration is moderately investigated, while physical assistance is rarely studied. In contrast, indirect pHRI (when the physical contact is mediated via a third item) studies often involve industrial settings, with physical ergonomics being the most frequently investigated human factor. More research is needed on human factors in direct and indirect physical assistance applications, including studies that combine physical social behaviors with physical assistance tasks. We also found that while the predominant approach in most studies involves the use of questionnaires as the main method of quantification, there is a recent trend that seeks to address quantification approaches based on measurable data.

Of the roughly 20,000 canonical human protein sequences, as of September 15, 2020, 6,937 proteins have had their full or partial, medium- to high-resolution structures determined by x-ray crystallography or other methods. Which of these proteins dominate the Protein Data Bank (the PDB) and why? In this paper, we list the 273 top human protein structures based on the number of their PDB entries. This set of proteins accounts for more than 40% of all available human PDB entries and represent past trends as well as current status for protein structural biology. We briefly discuss the relationship which some of the prominent protein structures have with protein research as a whole and mention their relevance to human diseases. The top-10 soluble and membrane proteins are all well-known (most of their first structures being deposited more than 30 years ago). Overall, there is no dramatic change in recent trends in the PDB. Remarkably, the number of structure depositions has grown nearly exponentially over the last 10 or more years (with a doubling time of 7 yrs for proteins from all organisms). Growth in human protein structures is slightly faster (at 5.9 yrs, while E.Coli and Mouse+Rat protein structures accumulate more slowly, Zebrafish protein structures are growing most, at a doubling every 3.7 years, albeit starting from only approx. 100 structure entries in 2010). The information may be informative to senior scientists but also inspire researchers who are new to protein science, providing the year 2020 snap-shot for the state of protein structural biology.

It has been a challenge to visualize in nature some concepts from abstract subjects such as mathematics and philosophy. Irrationality is certainly among the least comprehensive. This essay aimed to propose that irrationality is simply an illusion resulting from the human inability to grasp the full nature of reality, rather than actual continuity flaws in the fabric of existence. There are three major arguments: (1) human senses have limitations and these are likely to be extended to the intellect, (2) there are no observable existential discontinuities in everyday experience, and (3) some rational phenomena have irrational components. These arguments point towards the existence of a physical existence of irrational quantities or distances, visible to the human eye but not fully comprehended through arithmetic abstraction.

The human virome is an area of increasing interest with relation to human health and disease. It has been demonstrated to alter in concert with the bacterial microbiome in early life and was also found to be different in patients with certain diseases such as inflammatory bowel disease. However, all virome analyses are hampered by a lack of annotated representative database sequences, often referred to as the ‘viral dark matter’. Here we provide the first description of the gut DNA virome in elderly individuals (>65 years old) as well as the description of novel bacteriophages not present in current reference databases. Diversity analysis comparing elderly persons from different residence locations (community living vs long term care facilities) did not reveal any difference in their virome diversity profiles despite the reported differences at the bacteriome level. An abundance of Microviridae of the subfamily Gokushovirinae were present in the faeces of elderly individuals. Several novel members of the order Caudovirales were also characterized and annotated. Assignment of host bacteria to detected viral genomes was attempted using a combination of CRISPR spacers, tRNA genes and a probabilistic approach. Further characterization of the viral dark matter is necessary for developing tools and expanding databases to study the human virome. This study focused on the virome of an aging human cohort with the goal of illuminating part of the viral dark matter.

Propolis, or bee glue, is a complex mixture of resins from plant buds and flowers, wax, pollen and bee saliva. It also contains various vitamins, essential oils, mineral salts, trace elements, hormones and enzymes. It has been found that propolis possesses antimicrobial, antiviral and anti-inflammatory properties. We have studied the antiviral activity of six extracts of Bulgarian propolis collected from six districts of Bulgaria. The study was conducted against structurally different viruses: Human coronavirus strain OC-43 (HCoV OC-43) and Human respiratory syncytial virus type 5 (HRSV-5) (enveloped RNA viruses), Human herpes simplex virus type 1 (HSV-1) (enveloped DNA virus), Human rhinovirus type 14 (HRV-14) (non-enveloped RNA virus) and Human adenovirus type 2 (HadV-2) (non-enveloped DNA virus). The influence of the extracts on the internal replicative cycle of viruses was determined using the cytopathic effect (CPE) inhibition test. The virucidal capacity, their influence on the adsorption stage and their protective effect on uninfected cells were also determined by the end-point dilution method. Residual viral infectivity in propolis samples was compared to untreated controls and Δlgs were calculated. When administered during viral replication, most propolis extracts demonstrated the strongest activity against HCoV OC-43. The influence of propolis extracts on the viability of extracellular virions was expressed to a different degree in the various viruses studied, and the effect was significantly stronger in those with an envelope. Almost all extracts significantly inhibited the step of herpes virus adsorption to the host cell, and some of them showed a protective effect on healthy cells before they were subjected to herpes infection. Our results enlarge the knowledge about the action of propolis and could open new perspectives for its application in viral infections treatment.

Living systems are intricate repositories of biomolecules, where the interactions between these molecules play an important role in maintaining homeostasis. Interruptions in these interactions can lead to diseases, underscoring the significance of biomolecular interactions and their molecular basis. HUGENICS (Human Genetic Database) is a comprehensive biological database that focuses on molecular interactions between human and microbial proteins, particularly emphasizing protein-protein interactions (PPIs). This database offers insights into disease pathways by elucidating the interplay between microbial surface/secretory proteins and human disease-related proteins. This white paper highlights the significance, scope, and potential applications of HUGENICS in advancing biomedical research and improving human health.

Given the broad and intense use of plastic, society is being increasingly affected by its degradation and by-products, particularly by microplastics (MPs) -fragments smaller than 5 mm in size-, and nanoplastics (NPs) -with sizes less than 1 µm. MPs and NPs may enter the body primarily through inhalation, consumption, and skin contact. Once ingested, MPs can penetrate tissues, deviating to other parts of the body, and hence potentially affecting important cellular pathways such as nonconforming chemokine receptors that control the communication between the fetus and the mother. Consequently, the potential health harm induced by MP internalization is a major issue, evidenced by multiple studies demonstrating harmful consequences in diverse animal models and human cells. Here, an overview of the various modes of exposure to MPs and NPs is presented, including inhalation, placental transfer, ingestion, breastmilk consumption, and skin absorption, as well as placental and fetal toxicity due to plastic particles based on animal and in vitro studies. Though MPs in our environment are becoming more recognized, their developmental toxicity is still scarcely known. Besides negatively affecting pregnancy, MPs and NPs have been shown to potentially harm the developing fetus, given their ability to cross the placental barrier. Still, considerable gaps remain in our understanding of the dispersion and toxicity of these particles in the environment, as well as the precise types of NPs and MPs bearing the greatest dangers. As a result, we advocate for larger-scale epidemiological investigations, the development of novel approaches for measuring NP and MP exposures, and the necessity of understanding the toxicity of various kinds of NPs to guide future research efforts.

Economic, social, and environmental sustainability are the results of efforts aiming to improve all aspects of milk production, respecting animal welfare and improving herd health. An epidemi-ological study was designed to assess the role of contagious pathogens (S. aureus and S.agalactiae) in a cohort of 120 dairy herds located in the southern regions of Italy. Milk quality was assessed using certified methods, and the prevalence of mastitis pathogens in bulk tank milk was determined using quantitative polymerase chain reaction. Welfare scores were assessed using a scoring card that have more than 100 items, including animal-based measurements. Statistical analyses were performed using general lineal model and logistic regression procedures. The results showed that S. aureus had a significant negative effect on the amount of milk components delivered to the dairy plant, and on the level of welfare. Whereas, the presence of S.agalactiae did not show any significant association. The major risk factors associated with the presence of S.aureus were also identified to help prioritise control programs. These results support the “One Sustainability” approach im-plying that an increase of animal productivity is related to the improvement of animal health and welfare and potentially leading to the mitigation of greenhouse gas emissions.Economic, social, and environmental sustainability are the results of efforts aiming to improve all aspects of milk production, respecting animal welfare and improving herd health. An epidemi-ological study was designed to assess the role of contagious pathogens (S. aureus and S.agalactiae) in a cohort of 120 dairy herds located in the southern regions of Italy. Milk quality was assessed using certified methods, and the prevalence of mastitis pathogens in bulk tank milk was determined using quantitative polymerase chain reaction. Welfare scores were assessed using a scoring card that have more than 100 items, including animal-based measurements. Statistical analyses were performed using general lineal model and logistic regression procedures. The results showed that S. aureus had a significant negative effect on the amount of milk components delivered to the dairy plant, and on the level of welfare. Whereas, the presence of S.agalactiae did not show any significant association. The major risk factors associated with the presence of S.aureus were also identified to help prioritise control programs. These results support the “One Sustainability” approach im-plying that an increase of animal productivity is related to the improvement of animal health and welfare and potentially leading to the mitigation of greenhouse gas emissions.

COVID-19, caused by SARS-CoV-2, was first identified in China in 2019. The exact origin of the disease remains uncertain, with theories suggesting it may have emerged from animals or accidentally leaked from a research laboratory. If the hypothesis that COVID-19 originated from a research laboratory is accurate, it is crucial to investigate potential pathways for pathogens to escape from biocontainment research animal facilities (BRAFs). We examine key biosafety issues associated with BRAFs, including inadequate decontamination procedures for water and experimental samples, handling high biosafety level pathogens in lower level laboratories, risks of animal bites and sharps injuries, contamination of bedding and enrichment materials, and improper management and transportation of biohazard samples. Additionally, we discuss the role of veterinarians in African research animal facilities and the challenges they encounter in maintaining biocontainment standards. We emphasize the importance of routine monitoring of effluent water to detect possible disease outbreaks. We recommend a thorough investigation of the COVID-19 pandemic to identify potential sources of pathogen release from BRAFs, which could serve as hotspots for future disease outbreaks. Findings from such investigations will inform the development of policies aimed at safeguarding human populations from future pandemics and preventing BRAFs from becoming sources of infectious disease outbreaks.

Latin America and the Caribbean offer the most favorable natural and social conditions for the emergence of cattle ranching that regenerate and maintains the ecosystem services on which life on Earth depends, that is energy efficient and that contributes to meeting fundamental human food needs. Unlike other major global producers, cattle farming in Latin America and the Caribbean is fundamentally based on pasture. Therein lies both its strength (due to the regenerative potential of well-managed pastures) and its vulnerability (due to the pervasive link between deforestation and pasture formation, especially in the Amazon, where 40% of the Brazilian herd is concentrated). The diversification of pastures (today marked by impressive monotony, with brachiaria accounting for 85% of the species in Brazil), the introduction of legumes and tree plantations within them, and the proper management of herds, open the way to the possibility of drastically reducing the area currently occupied by the activity (especially in the Amazon) while maintaining the supply of its products. Agricultural research has disseminated forms of moderate intensification of cattle ranching, which represent a low opportunity cost of animal feed and, at the same time, regeneration of ecosystem services that have so far been destroyed by the prevailing methods of farming.

Livestock farming plays a crucial role in agriculture and is important in meeting people's needs for a better life in the new era. However, due to China's "double carbon" target, the development of animal husbandry has been affected by the level of carbon emissions. To study the spatial dis-tribution and dynamic evolution of livestock carbon emissions in Shandong Province, we used panel data from 16 prefecture-level cities in Shandong Province from 2001 to 2022 to determine the spatial differentiation of livestock carbon emissions in Shandong Province and their dynamic evolution by combining spatial and non-parametric estimation methods based on livestock carbon emissions and livestock emission intensity measured by city. The results of the study show that the distribution of carbon emissions from animal husbandry in Shandong Province is in a "dis-persed-aggregated" pattern, and the intensity of carbon emissions shows a gradual downward trend, with a ladder-like distribution characterized by high in the west and low in the east. Through the analysis of the dynamic evolution of nuclear density, we observe that the regional differences of animal husbandry carbon emissions in Shandong Province are expanding, showing the characteristics of polarization. The difference of carbon emissions in the peninsula is gradually narrowing, and the phenomenon of polarization is weakening. The carbon emissions of animal husbandry in the central and western regions of Shandong are polarized.

The article presents comparative results from 5 categories of animals (4 samples originating from pigs, 2 from cows, 1 from chicken, 1 from ostriches, 1 from horses, samples quantified with A, B, C, D, E, F, G, H, I). These samples were collected from large, medium and small (family) farms. The results were validated by the biogas production generated by the respective samples, using the Automatic Methane Potential Test System (AMPTS II) bio-processor. The largest amounts of biogas (2,464.5 Nml, 2,262.0 Nml) were recorded for the samples from pig farms (Sample B and Sample D), whereas the lowest values were obtained in the case of Sample A (414.1 Nml) and Sample C (558.3 Nml). The heavy metals analyzed in the article are Zn, Cu, Fe, Mn, Ca, K, As. The results corroborate that the C/N ratio can also vary to values lower than 20-30, without affecting the biogas production process, and the heavy metal content must be known especially for the full management of the wastes. All final data presented in the article are based on repeated, triplicate experiments, with less as 2% deviation between data.

Introduction: Overuse of antibiotics has been increasing in recent years and is currently the most prescribed drug in the paediatric age. It is estimated that 72% of patients in the neonatal intensive care unit (NICU) are treated with antibiotics. One of the effects observed with their use is the alteration of the microbiota, which is associated with metabolic disorders including obesity. In this study, the aim was to determine in newborn rats, whether most commonly prescribed antibiotics in Mexican NICUs have a differential effect on weight gain, development of adiposity, and modification of carbohydrate and lipid metabolism. Methods: This is an experimental study in newborn rats, comparing Ampicillin/Meropenem (Access/Watch groups) 100 mg/g every 12 h /10 mg/g every 12 h (each of these two antibiotics was adjusted to the required dosages based on weight); Cefotaxime 200 mg/g every 24 h; (Watch group) and Amikacin 15 mg/g every 24 h (Access group) versus Saline Solution as Control. Each antibiotic was intraperitoneal administered daily for 5 days in males (to avoid gender differences) to 14 newborn rats in each group. A comparison of anthropometric and biochemical parameters registered on day 28 was made using ANOVA analysis. Differences were considered if p < 0.05.Results: Amikacin was the antibiotic with the greatest effect on anthropometric measurements and LDL-cholesterol; as well as Cefotaxime on Glucose and triglycerides, while Meropenem plus Ampicillin showed the weakest effect on measured parameters. Conclusion: The administration of antibiotics in the neonatal stage has repercussions on the body composition of adult rats, as well as on lipids and carbohydrates serum levels. Future studies might evaluate the toxicity of antibiotics on the immature neonatal organs.

(1) Background: the popularity of rabbits has increased during the last decades and became the third common companion animals in EU. Their participation in animal assisted interventions (AAI) is growing. It is highly important to ensure the well-being of the animals in AAI. Whereas the needs and the advantages of people involved in AAI are becoming more and more evident, the needs of animals are not clearly determined, it is required not to affect the animals negatively. Those animals who are used for AAI purpose need toneed to be transported regulrarly; (2) Methods: the rabbits stress – caused by transportation – were measured by non-invasive faecaesl cortisol metabolite (FCM) analysis. 18 animals were involved in the study and experienced a 30 minutes transportation every second day duringfor two weeks (total 6 times) while 128 samples were collected; (3) Results: rabbits –could handle the transportation procedure by the first and second time but subsequently the stress hormone metabolites in faeces samples increased significantly regardless of the offered treatments (hay, carrot and apple) during the carriage. (4) Conclusions: those owners who use rabbits for Animal Assisted Intervention purpose need to take into account that transportation itself is a stressful experience for the animals.

Pasture-based production systems are predominant in major beef-producing countries, however, these systems lack validated protocols to assess animal welfare under commercial conditions. The objective of this study was to validate an animal welfare protocol for fattening Zebu cattle farms in tropical pasture systems. The initial protocol was developed with the participation of producers, professionals, the general public, and the Colombian health authority, through workshops with a participatory approach and collaborative knowledge management. The validation was carried out in 24 pasture-based commercial Zebu cattle farms in the middle Magdalena region of Colombia. Visits were made with an average duration of 2.5 hours, which included the evaluation of 788 fattening cattle. The protocol evaluated animal-based, resource-based, and livestock management indicators through a questionnaire-guided interview to evaluate cattle handling and health, animal-based measurements, and documentation management. A protocol validation process was carried out by selecting indicators that remained unchanged, adjusting those that were feasible to implement, and eliminating inadequate or unnecessary indicators. The application of the protocol demonstrated that there are valid and feasible measures to include in the evaluation protocols of pasture-based fattening systems. Likewise, the active participation of producers is crucial to achieving a greater commitment to the implementation of this protocol.

In the dairy cattle sector, the evaluation of the effects induced by heat stress is still one of the most impactful and investigated aspects as it is strongly connected to both sustainability of the production and animal welfare. On the other hand, more recently, the possibility of collecting large dataset made available by the increasing technology diffusion is paving the way for the application of advanced numerical techniques based on machine learning or big data approaches. In this scenario driven by rapid change there could be the risk of dispersing the relevant information represented by the physiological animal component, which should maintain the central role in the development of numerical models and tools. In the light of this, the present literature review aims to consolidate and synthesize existing research on the physiological consequences of heat stress in dairy cattle. The present review provides, in a single document, an overview as complete as possible of the heat stress-induced responses in dairy cattle with the intent of filling the existing research gap for extracting the veterinary knowledge present in the literature and make it available to those who are developing numerical models and tools.

Numerous studies have shown that quinolones residues in the foods exceeding the MRL is harmful to the human health. There are some limitations in the existing methods for the residues, so we developed a new limit test method (TLC-SERS) to rapidly determine following residues: enrofloxacin(A), ciprofloxacin (B), ofloxacin(C), fleroxacin(D), sparfloxacin(E), enoxacin(F), gatifloxacin(G) and nadifloxacin(H). The residues can be preliminarily separated by TLC. Labeling the tested compounds’ position on the thin layer plate by their relative Rf under 254nm ultraviolet, and an appropriate amount of nanometer silver solution should be added to the position. The silver on the plate should be irradiated with 532nm laser to obtain SERS of the compounds. The results showed a significant differences of the 8 quinolones’ SERS; the LOD of the H, A, D, E, C, G, F and B were orderly 9.0, 12.6, 8.0, 19.0, 8.0, 8.4, 9.0 and 12.6 ng/mL, and the RSD≤4.9% for SERS of each quinolones.The limit test results of the 20 samples was consistent with that determined by the UPLC–MS/MS. The above results indicate that the TLC-SERS is specific, sensitive, stable, and accurate, providing a new reference for the rapid limit test of harmful residues in foods.

The identification and quantitative determination of wool and fine animal fibers are of great interest in the textile field because of significant price differences between them and common adulterations in raw and processed textiles. Since animal fibers have remarkable similarities in their chemical and physical characteristics, specific identification methods have been studied and proposed following advances in analytical technologies. The identification methods of wool and fine animal fibers are reviewed in this paper and the results of relevant studies are listed and summarized, starting from classical microscopy methods which are still used today not only in Small to Medium Enterprises but also in large industries, research studies and quality control laboratories. Particular attention has been paid to image analysis, Nir spectroscopy and proteomics which constitute the most promising technologies of quality control in the manufacturing and trading of luxury textiles and can find application in forensic science and archeology.

The ongoing emergence of SARS-CoV-2 virus variants remains a source of concern because it is accompanied by the potential for increased virulence as well as evasion of immunity. Here we show that, although having an almost identical spike gene sequence as another Omicron variant (BA.5.2.1), a BA.4 isolate lacked all the typical disease characteristics of other isolates seen in the Golden Syrian hamster model despite replicating almost as effectively. Animals infected with BA.4 had similar viral shedding profiles to that seen with BA.5.2.1 (up to day 6 post infection) but they all failed to lose weight or present with any other significant clinical signs. We hypothesize that this lack of detectable signs of disease during infection with BA.4 was due to a small (nine nucleotide) deletion (∆686-694) in the viral genome (ORF1ab) responsible for production of non-structural protein 1 which resulted in the loss of three amino acids (aa 141-143).

The Golden Syrian hamster (Mesocricetus auratus) is now commonly used in preclinical research for the study of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and the assessment of vaccines, drugs and therapeutics. Here we show that hamsters inoculated via the intranasal route with the same infectious virus dose of prototypical SARS-CoV-2 administered in a different volume present with different clinical signs, weight loss and viral shedding, with a reduced volume resulting in reduced severity of disease similar to that obtained by a 500-fold reduction in challenge dose. The tissue burden of virus and the severity of pulmonary pathology were also significantly affected by different challenge inoculum volumes. These findings suggest that direct comparison between the severity of SARS-CoV-2 variants or studies assessing the efficacy of treatments determined by hamster studies cannot be made unless both the challenge dose and inoculation volume are matched when using the intranasal route. Additionally, analysis of sub-genomic and total genomic RNA PCR data demonstrated no link between sub-genomic and live viral titres and that sub-genomic analyses do not provide any information beyond that provided by more sensitive total genomic PCR.

The number of animals bred, raised, and slaughtered each year is on the rise, resulting in increasing impacts to welfare. Farmed animals are also becoming more diverse, ranging from pigs to bees. The diversity and number of species farmed invites questions about how best to allocate currently limited resources towards safeguarding and improving welfare. This is of the utmost concern to animal welfare funders and effective altruism advocates, who are responsible for targeting the areas most likely to cause harm. For example, is tail docking worse for pigs than beak trimming is for chickens in terms of their pain, suffering, and general experience? Or are the welfare impacts equal? Answering these questions requires making an interspecies welfare comparison; a judgment about how good or bad different species fare relative to one another. Here, we outline and discuss an empirically-based methodology that aims to improve our ability to make interspecies welfare comparisons by investigating welfare range, which refers to how good or bad animals can fare. We begin our proposal with a theory of welfare. We operationalize that theory of welfare by identifying metrics that are defensible proxies for measuring welfare, including cognitive, affective, behavioral, and neuro-biological measures. We assign differential weights to those proxies that reflect their evidential value for the determinants of welfare, such as the “Delphi'' structured deliberation method with a panel of experts. Then we review the evidence and score its quality to ascertain whether a particular taxa may possess the proxies in question to construct a taxa-level welfare range profile. Finally, we use a Monte Carlo simulation to generate an overall estimate of comparative welfare range relative to our hypothetical index species - humans. Interspecies welfare comparisons will help facilitate empirically informed decision-making to streamline the allocation of resources and to ultimately better prioritize and improve animal welfare.

In order to analyse asymmetries between hemimandibles, a sample of 24 mandibles from ferrets was studied by means of geometric morphometric methods, using a set of 3 landmarks and 14 semilandmarks, on the lateral aspect. Results showed that both size and shape played a significative role in mandibular asymmetry. For shape, there appeared significative fluctuating and directional asymmetries, with an especially high level for this latter. Landmarks corresponding to muscular attachments showed greater landmark asymmetry. This it is supported the hypothesis of a chewing side preference, e.g., a mastication-related driver for mandibular shape asymmetry.

Medical and translational scientific research requires the use of animal models as an initial approach to the study of new therapies and treatments, but when the objective is an exploration of translational potentialities, classical models fail to adequately mimic problems in humans. Among the larger animal models that have been explored more intensely in recent decades, small ruminants, namely sheep and goats, have emerged as excellent options. The main advantages associated to the use of these animals in research works are related to their anatomy and dimensions very similar to those of humans in most physiological systems, in addition to their low maintenance and feeding costs, tendency to be docile, long life expectancies and few ethical complications raised in society. The most obvious disadvantages are the significant differences in some systems such as the gastrointestinal, and the reduced amount of data that limits the comparison between works and the validation of the characterization essays. Despite everything, recently these species have been increasingly used as animal models for diseases in different systems, and the results obtained open doors for their more frequent and advantageous use in the future. The purpose of this review is to summarize the general principles related to the use of small ruminants as animal models, with focus on regenerative medicine, to group the most relevant works and results published recently and to highlight the potentials for the near future in medical research.

A novel coronavirus has been reported as the causative pathogen of the Coronavirus disease 2019 (COVID-19) outbreak Wuhan city, China in December 2019. Due to the rapid spreading of COVID-19 worldwide, it has been announced as a pandemic by the World Health Organization (WHO). Hospitalized patients in Wuhan are associated with the Huanan seafood wholesale market where live animals, such as poultry, bats, snakes, frogs, rabbits, marmots, and hedgehogs are sold in that market which suggests a possible zoonotic infection. Therefore, it is essential to identify the potential animal reservoir, and the possibility of infection for other animal species. This short review aims to provide an overview on the relation between severe acute respiratory syndrome coronavirus‐2 (SARS‐CoV‐2) infection and animals.

In the digital age, social media platforms have become a significant source of both information and misinformation, presenting challenges and opportunities for threat intelligence. Natural Language Processing (NLP) has emerged as a powerful tool for extracting actionable insights from the vast amounts of unstructured text generated on these platforms. This paper explores the application of NLP techniques to enhance social media threat intelligence, focusing on methodologies for detecting and analyzing threats such as disinformation, cyberbullying, and extremist content. We examine various NLP approaches, including sentiment analysis, topic modeling, and entity recognition, and their effectiveness in identifying and mitigating potential risks. The paper also addresses the challenges associated with processing social media data, such as dealing with slang, context, and multilingual content. By leveraging NLP, organizations can improve their ability to monitor and respond to emerging threats in real-time, ultimately enhancing their overall security posture. The findings suggest that while NLP offers significant benefits, it must be complemented by human expertise and ethical considerations to ensure accurate and responsible threat assessment.

The integration of the Internet of Things (IoT) and Distributed Computer Systems (DCS) is transforming business models across industries. IoT devices enable real-time monitoring of equipment and processes, reducing downtime and enhancing efficiency. In the case, manufacturing companies use IoT sensors to monitor machinery, predict failures, and schedule maintenance. Also, automation via IoT, reduces manual intervention, resulting in boosted productivity in smart factories and automated supply chains. IoT devices generate thus vast amounts of data, which businesses analyze to gain insights into customer behavior, operational inefficiencies, and market trends. In turn, distributed computer systems process this data, providing actionable insights and enabling advanced analytics and machine learning for future trend predictions. While, IoT facilitates personalized products and services by collecting data on customer preferences and usage patterns, enhancing satisfaction and loyalty, IoT devices support new customer interactions, like wearable health devices, and enable subscription-based and pay-per-use models in transportation and utilities. Conversely, real-time monitoring enhances security, as distributed systems quickly respond to threats, ensuring operational safety. It also aids regulatory compliance by providing accurate operational data. In this way, this study, through a Bibliometric Literature Review (LRSB) of 91 screened pieces of literature, aims at ascertaining to what extent the aforementioned capacities enhance business models. The study concludes that those systems leverage businesses, promoting competitive edge, continuous innovation, and adaptability to market dynamics. A review of 91 academic documents from the Scopus database highlights these impacts.

This article explores the efficiency of various clustering methods for image segmentation under different luminosity conditions. Image segmentation plays a crucial role in computer vision appli-cations, and clustering algorithms are commonly used for this purpose. The search for an adaptive clustering mechanism aims to ensure maximum symmetry of real objects with objects/segments in their digital representations. However, clustering methods performance can fluctuate with varying lighting conditions during image capture. Therefore, we assess the performance of several clustering algorithms — including k-means, K-Medoids, fuzzy c-means, Possibilistic C-Means, Gus-tafson-Kessel, Entropy-based Fuzzy, Riddler-Calvard, Kohonen Self-Organizing Maps, and Meanshift — across images captured under different illumination conditions. Additionally, we develop an adaptive image segmentation system utilizing empirical data. Conducted experiments highlight varied performance among clustering methods under different luminosity conditions. This research enhance better understanding of luminosity's impact on image segmentation and aiding in method selection for diverse lighting scenarios.

We introduce a novel microscopic image dataset augmented with segmentation and detection labels specifically designed for microplastic analysis in sewage environments. Recognizing the increasing concern over microplastics — particles of synthetic polymers smaller than 5mm — and their detrimental effects on marine ecosystems and human health, our research focuses on enhancing detection and analytical methodologies through advanced computer vision and deep learning techniques. The dataset comprises high-resolution microscopic images of microplastics collected from sewage, meticulously labeled for both segmentation and detection tasks, aiming to facilitate accurate and efficient identification and quantification of microplastic pollution. In addition to dataset development, we present example deep learning models optimized for segmentation and detection of microplastics within complex sewage samples. The models demonstrate significant potential in automating the analysis of microplastic contamination, offering a scalable solution to environmental monitoring challenges. Furthermore, we ensure the accessibility and reproducibility of our research by making the dataset and model codes publicly available, accompanied by detailed documentation on GitHub and LabelBox. The dataset and example deep learning models are publicly available at the following GitHub link. https://anonymous.4open.science/r/Microplastics-in-Sewage-1BEF

Knowledge is not merely the characteristic of certain pieces of information being deemed true, believed and justifiable, but rather a process. The sentences that reaches the status of knowledge forms a universe that provides an interpretation of the reality one aims to describe. Language exchanges information through communication and the justification, if concludes successfully with respect to a sentence, it elevates the information to the status of knowledge. We provide a model where we define the fundamental elements of communication and grammar. As a result, knowledge must be \open\ (everyone must be able to acquire it), \free\ (everyone must be able to justifiy) and \tidy\ (everyone must be able to retrace the justification process in all its parts recursively). Ultimately, the development of knowledge processing, referred to as \emph{knowmatic}, represents the evolution of the information technologies (\it).

Skin cancer is a global health concern with increasing prevalence, necessitating effective early detection and classification systems. Transfer learning has emerged as a powerful tool in this domain, enhancing diagnostic accuracy. Attention mechanisms, which selectively focus on pertinent image features, play a pivotal role in transfer learning. While previous studies have highlighted their effectiveness, their applicability across different models and datasets remains understudied. This empirical study explores the impact of various attention mechanisms on the performance of transfer learning models for skin cancer detection and classification. Using five transfer learning models (DenseNet121, InceptionV3, MobileNet, VGG16, and Xception) and six attention mechanisms (Channel Attention, Global Context Attention, Guided Attention, Nonlocal Attention, Positional Attention, and Spatial Attention), we conducted 105 experiments across three datasets. Traditional metrics (accuracy, precision, recall, and f1-score) were employed for empirical validation. The results reveal a nuanced relationship between attention mechanisms, transfer learning models, and datasets. Overall, attention mechanisms exhibit the potential to enhance skin cancer classification. Spatial and channel attention mechanisms consistently outperform others, offering simplicity and effectiveness. Model-specific selection of attention mechanisms is crucial, with a trade-off between model complexity and performance evident. This study provides insights into developing efficient skin cancer classification models utilizing attention mechanisms.

Image analysis is widely applied in plant science for phenotyping and monitoring botanic and agricultural species. Although a lot of software is available, tools integrating image analysis and statistical assessment of seedling growth in large groups of plants are limited or absent, and do not cover the needs of the researchers. In this study, we developed Morley, a free, open-source graphical user interface written in Python. Morley automates the following workflow: (1) group-wise analysis of a few thousand seedlings from multiple images; (2) recognition of seeds, shoots and roots in seedling images; (3) calculation of shoot and root lengths and surface areas, (4) evaluation of statistically significant differences between plant groups, (5) calculation of germination rates, (6) visualization and interpretation. Morley is designed for laboratory studies of biotic effects on seedling growth, when molecular mechanisms underlying morphometric changes are analyzed. Performance was tested using cultivars of T. aestivum, P. sativum on seedlings of up to 1 week old. Accuracy of the measured morphometric parameters was comparable with the ones obtained using ImageJ and manual measurements. Dose-dependent laboratory tests for germination affected by new bioactive compounds and fertilizers, assuming extraction of seedlings from a substrate and/or dissection are among the suggested applications.

Existing one-shot neural architecture search (NAS) methods have to conduct a search over a giant super-net, which leads to the huge computational cost. To reduce such cost, in this paper, we propose a method, called FTSO, to divide the whole architecture search into two sub-steps. Specifically, in the first step, we only search for the topology, and in the second step, we search for the operators. FTSO not only reduces NAS’s search time from days to 0.68 seconds, but also significantly improves the found architecture's accuracy. Our extensive experiments on ImageNet show that within 18 seconds, FTSO can achieve a 76.4% testing accuracy, 1.5% higher than the SOTA, PC-DARTS. In addition, FTSO can reach a 97.77% testing accuracy, 0.27% higher than the SOTA, with nearly 100% (99.8%) search time saved, when searching on CIFAR10.

The increasing use of deep learning techniques to manipulate images and videos, commonly referred to as "deepfakes," is making more and more challenging to differentiate between real and fake content. While various deepfake detection systems have been developed, they often struggle to detect deepfakes in real-world situations. In particular, these methods are often unable to effectively distinguish images or videos when these are modified using novel techniques which have not been used in the training set. In this study, we carry out an analysis of different deep learning architectures in an attempt to understand which is more capable of better generalizing the concept of deepfake. According to our results, it appears that Convolutional Neural Networks (CNNs) seem to be more capable of storing specific anomalies and thus excel in cases of datasets with a limited number of elements and manipulation methodologies. The Vision Transformer, conversely, is more effective when trained with more varied datasets, achieving more outstanding generalization capabilities than the other methods analysed. Finally, the Swin Transformer appears to be a good alternative for using an attention-based method in a more limited data regime. All the analyzed architectures seem to have a different way to look at deepfakes but since in a real-world environment, the generalization capability is essential, based on the carried out experiments the Vision Transformer seems to provide superior performances.

This study presents review of 7580 papers in 13 academic journals, published from January 2015 to January 2020. After a detailed analysis of all papers, 46 papers were further selected showing research on a student population aged between 6 and 15 years old. In order for the paper to be included in the research, the condition was that the paper deals with teaching at least one of the following content areas: programming languages, game design, computer thinking (CT), algorithmic thinking and robotics programming. This study shows the representation of the listed content area in reviewed papers for the specified time period as well as a detailed analysis of the selected papers. Available data about study, participants and education level, country (first author), learning domain, teaching tools, research questions, hypothesis, pre/post-test results, interviews, control groups, course duration, research design, previous experience, project or grant and re-search purpose in detected papers were analysed. In addition, impact of studying some of the listed content areas on student learning performance, motivation, attitude and perception were investigated.

Nowadays, emerging technologies have changed the places of work through computers and ICT tools, which have revolutionized teaching and learning environments in different ways. In spite of the fact that computers as ICT tools have become part and progressively instrument for teachers and instructors used in teaching and learning, but most educators can't incorporate them into their teaching and learning process, which results in students being ill-equipped or lacking some necessary skills in the world of work, which leads to low performance and poor handling of tools whose lead to low production. To tackle this issue, it is essential to develop the technical and vocational education and training (TVET) system by determining the quality of technical and vocational education (TVE) teachers. In this paper, the literature concerning the competence required by TVET teachers towards computer-related instructional tech-nology for classroom teaching and learning was examined through the technological pedagogical content knowledge (TPACK) model. Sixty (60) questionnaires were ad-ministered to TVE teachers within six technical colleges of education in north-eastern Nigeria. The data was quantitatively analyzed using traditional linear models, namely multilinear regression (MLR) and artificial intelligence (AI) models, namely artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANFIS), which were developed using MATLAB 9.3 (R2019a), while the classical linear MLR model was developed using SPSS software. The results from our classical study indicated that TVE teachers are competent in computer and some instructional technology usage and show a high correlation between competence and teaching experience and a lower correlation between competence and gender. The goodness of fit shows a good fit of the model. Future studies should examine the application of other linear and non-linear AI techniques.

The number of AI applications in education is growing every day. One recent AI application in the educational sector is Chatbot technology, which is used to support teaching and administrative tasks. This document presents the design and implementation of a Chatbot called Tashi-Bot that helps applicants and university students to obtain information from an educational institution about certain academic and administrative processes. Among these are processes related to well-being, tuition, costs, admission, and other services. In order to design the Chatbot, an analysis of the state of the art, methodologies, and suitable tools was carried out, and a survey was conducted to discover the needs of users and their preferences in the use of a Chatbot for this specific purpose. Tashi-Bot was implemented on the SnatchBot platform and later deployed on a Telegram channel. In its evaluation, a final survey was carried out to check on the satisfaction of the users. The results suggest that Tashi-Bot could help applicants and university students to find information on academic and administrative processes with great certainty and without the need for human interaction. Tashi-Bot can be found at: https://web.telegram.org/#/im?p=@TashiE_Bot..