In recent years, new therapies have been developed based on molecules that target mo-lecular mechanisms involved in both the initiation and maintenance of the oncogenic process. Among these molecules are the poly (ADP-ribose) polymerase 1 (PARP1) inhibitors. PARP1 has emerged as a target with great therapeutic potential for some tumor types, drawing attention to this enzyme and resulting in many small molecule inhibitors of its enzymatic activity. Thus, many PARP inhibitors are currently in clinical trials for the treatment of homologous recombina-tion (HR)-deficient tumors, BRCA-related cancers, taking advantage of synthetic lethality. In ad-dition, several novel cellular functions unrelated to its role in DNA repair have been described, including post-translational modification of transcription factors, or acting through pro-tein-protein interactions as a co-activator or co-repressor of transcription. Previously, we report-ed that this enzyme may play a key role as a transcriptional co-activator of an important compo-nent of cell cycle regulation, the transcription factor E2F1. Here, we show that PARP inhibitors, which interfere with its activity in cell cycle regulation, do so without affecting its enzymatic function.

The survival rate of extracorporeal life support (ECLS) remains overall at 60 %. Research and development is slow, partly due to the lack of sophisticated experimental models. This publication introduces a dedicated rodent oxygenator (“RatOx”) and shows preliminary in vitro classification tests. The RatOx has an adaptable fiber module size for various rodent models. Gas transfer performance over the fiber module for different blood flows and fiber module sizes were tested according to DIN EN ISO 7199.At the maximum possible amount of effective fiber surface and a blood flow of 100 ml/min, the oxygenator performance has been tested to a maximum of 6.27 ml O2/min and 8.2 ml CO2/min, respectively. The priming volume for this the largest fiber module is 5.4 ml, while the smallest possible configuration with a single fiber mat layer has 1.1 ml of priming volume. The novel RatOx ECLS system has been evaluated in-vitro to comply to a high degree with all predefined functionality criteria for rodent sized animal models. We intend for the RatOx to become a standard testing platform for scientific studies on ECLS therapy and technology.

The global threat of COVID-19 has led to the increasing use of metabolomics to study SARS-CoV-2 infection in humans and animals. Despite this, understanding SARS-CoV-2's metabolome during infection remains difficult and incomplete. Here, metabolic responses were characterized from sampled nasal washes collected from an asymptomatic ferret model (n = 20) at different time points before and after the SARS-CoV-2 challenge using an LC-MS-based metabolomics approach. Multivariate analysis of the nasal wash metabolome data resulted in several statistically significant features being observed. Despite no effects of gender or interaction between gender and time on the time course of SARS-CoV-2 infection, 16 metabolites were significantly different at every time point post-infection. Among these altered metabolites, the relative abundance of taurine was elevated post infection which could be an indication of hepatotoxicity, while the accumulation of sialic acids could indicate SARS-CoV-2 invasion. The pathway analysis identified several pathways influenced by SARS-CoV-2 infection. Of these, sugar, glycan, and amino acid metabolisms were the key altered pathways in the upper respiratory channel during infection. These findings provide some new insights into the progression of SARS-CoV-2 infection in ferrets at the metabolic level which could be useful for the development of early clinical diagnosis tools and new or repurposed drug therapies.

Deep learning dominates automated animal activity recognition (AAR) tasks due to the high performance on large-scale datasets. However, constructing centralised data across diverse farms raises data privacy issues. Federated learning (FL) provides a distributed learning solution to train a shared model by coordinating multiple farms (clients) without sharing their private data. Whereas directly applying FL to AAR tasks often faces two challenges: client-drift during local training and gradient conflicts during global aggregation. In this study, we develop a novel FL framework called FedAAR to achieve AAR with sensor data. Specifically, we devise a prototype-guided local update module to alleviate the client-drift issue, which introduces global prototypes as shared knowledge to force clients to learn consistent features. To reduce gradient conflicts between clients, we design a gradient refinement-based aggregation module to eliminate conflicting components between client gradients during global aggregation, thereby improving the agreement between clients. Experiments are conducted on a public dataset to verify FedAAR’s effectiveness, which consists of 87,621 2-s motion data. The results demonstrate that FedAAR outperforms state-of-the-arts, with precision (75.23%), recall (75.17%), F1-score (74.70%), and accuracy (88.88%), respectively. The ablation experiments also show FedAAR’s robustness against various factors (i.e., different data sizes, communication frequency, and client numbers).

Waste recovery is an important aspect towards human and environmental health protection. Unfortunately, proper food waste management is among the serious challenges in the field of solid waste management worldwide. Therefore, it is of great importance to conduct studies towards achieving efficient and cost-effective approaches for food waste management. This study investigated the potential of recovering food waste through maggots’ production as animal feed. The influence of fly attractant application on maggot production was also investigated. The study also investigated the potential of maggot production for waste recovery and reduction. Four different types of food waste (starch food leftovers, rotten bananas and peels, rotten pineapple and peels, and rotten oranges) were used in the investigation process. From the results, it was observed that the application of fly attractants had a significant effect on the production of maggots as determined by the weights after harvesting. Average weight of 94 g/kg of maggot was achieved from banana materials with an application of fly attractant during the 8th day of the cultivation; which is equivalent to a 32.4% increase from the same day when the material was cultured without applying fly attractant. Also, from the starch materials, about 77 g/kg of maggot weight was achieved; which is a 54.6% increase from the same day and the same material but without application of fly attractant. Moreover, the relative dry weight reduction in the trials varied from 52.5% to 82.4%.

Animal welfare is not only an ethically important consideration in good animal husbandry, but can also have a significant effect on an animal’s productivity. The aim of this paper is to show that a reduction in animal welfare, in the form of increased stress, can be identified in pigs from frontal images of the animals. We train a Convolutional Neural Network (CNN) using a leave-one-out design and show that it is able to discriminate between stressed and unstressed pigs with an accuracy of >90% in unseen animals. Grad-CAM is used to identify the animal regions used, and these support those used in manual assessments such as the Pig Grimace Scale. This innovative work paves the way for further work examining both positive and negative welfare states with a view to the development of an automated system that can be used in precision livestock farming to improve animal welfare.

COVID-19 is a contagious respiratory disease that is causing significant global morbidity and mortality. Understanding the impact of a SARS-CoV-2 infection on the host metabolism is still in its infancy but of great importance. Herein, we investigated the metabolic response during viral shedding and post-shedding in an asymptomatic SARS-CoV-2 ferret model (n=6) challenged with two SARS-CoV-2 isolates. Virological and metabolic analyses were performed on (minimally invasive) collected oral swabs, rectal swabs, and nasal washes. Fragments of SARS-CoV-2 RNA were only found in the nasal wash samples in four of the six ferrets, and in the samples collected 3 to 9 days post-infection (referred to as viral shedding). Central carbon metabolism metabolites were analyzed during viral shedding and post-shedding periods using a dynamic MRM (dMRM) database and method. Subsequent untargeted metabolomics and lipidomics of the same samples were performed using an LC-QToF-MS methodology, building upon the identified differentiated central carbon metabolism metabolites. Multivariate analysis of the acquired data identified 29 significant metabolites and three lipids that were subjected to pathway enrichment and impact analysis. The presence of viral shedding coincided with the challenge dose administered and significant changes in the citric acid cycle, purine metabolism, and pentose phosphate pathways, amongst others, in the host nasal wash samples. An elevated immune response in the host was also observed between the two isolates studied. These results support other reported metabolomic-based findings found in clinical observational studies and indicate the utility of metabolomics applied to ferrets for further COVID-19 research that advances early diagnosis of asymptomatic and mild clinical COVID-19 infections, in addition to assessing the effectiveness of new or re-purposed drug therapies.

Anthropomorphism is a natural tendency in humans, but it is also influenced by many characteristics of the observer (the human) and the observed entity (here, the animal species). This study asked participants to complete an online questionnaire about three videos showing epimeletic behaviours in three animal species. In the videos, an individual (a sparrow, an elephant and a macaque, respectively) displayed behaviours towards an inanimate conspecific that suddenly regained consciousness at the end of the footage. A fourth video showed a robot dog being kicked by an engineer to demonstrate its stability. Each video was followed by a series of questions designed to evaluate the degree of anthropomorphism of participants, from mentaphobia (no attribution of intentions and beliefs, whatever the animal species) to full anthropomorphism (full attribution of intentions and beliefs by animals, to the same extent as in humans) and to measure how far the participants had correctly assessed each situation in terms of biological reality (current scientific knowledge of each species). There is a negative correlation (about 61%) between the mental states attributed to animals by humans to animals and the real capability of animals. The heterogeneity of responses proved that humans display different forms of anthropomorphism, from rejecting all emotional or intentional states in animals to considering animals to show the same intentions as humans. However, the scores participants attributed to animals differed according to the species shown in the video and to human sociodemographic characteristics. Understanding the potential usefulness of these factors can lead to better relationships with animals and encourage a positive view of human-robot interactions. Indeed, reflective or critical anthropomorphism can increase our humanity.

In this study, we asked participants to answer an online questionnaire about videos showing animal epimeletic behaviours: an individual (a sparrow, an elephant and a macaque) displayed behaviours towards an inanimate conspecific who suddenly got back to conscious at the end of the footage. A fourth video showed a dog-robot kicked by an engineer to demonstrate its stability. After each video, questions were asked to score the degree of anthropomorphism of participants, from mentophobia (no attribution whatever the species) to full anthropomorphism and to measure how close participants are to biological reality (actual scientific knowledge). A first important result is that there is a negative correlation (about 61%) between the anthropomorphism score (AS) and the biological reality one (BRS) showing a wrong statement. The heterogeneity of responses proved that all levels of anthropomorphism are covered from mentaphobia to full anthropomorphism. However, the scores participants attributed to animals differ according to the species shown in the video and to human characteristics. Understanding how one can play with these factors can conduct to better relationships with animals as encourage human-robot interactions. Finally, such reflective anthropomorphism can lead to an increase of human empathy and sociality, finally increasing our humanity.

Preclinical animal studies provide valuable opportunities to better understand human diseases and contribute to major advances in medicine. This review provides a comprehensive overview of ocular parameters in humans and selected animals, with a focus on the ocular surface, detailing species differences in ocular surface anatomy, physiology, tear film dynamics and tear film composition. We describe major pitfalls that tremendously limit the translational potential of traditional laboratory animals (ie., rabbits, mice and rats) in ophthalmic research, and highlight the benefits of integrating companion dogs with clinical analogues to human diseases into preclinical pharmacology studies. This One Health approach can help accelerate and improve the framework in which ophthalmic research is translated to the human clinic. Studies can be conducted in canine subjects with naturally occurring or non-invasively induced ocular surface disorders (eg., dry eye disease, conjunctivitis), reviewed herein, and tear fluid can be easily retrieved from canine eyes for various bioanalytical purposes. In this review, we discuss common tear collection methods, including capillary tubes and Schirmer tear strips, and provide guidelines for tear sampling and extraction to improve the reliability of analyte quantification (drugs, proteins, others).

Duchenne muscular dystrophy (DMD) is a fatal X-linked recessive neuromuscular disorder most commonly caused by mutations disrupting the reading frame of the dystrophin (DMD) gene. DMD codes for dystrophin, which is critical for maintaining the integrity of muscle cell membranes. Without dystrophin, muscle cells receive heightened mechanical stress, becoming more susceptible to damage. An active body of research continues to explore therapeutic treatments for DMD as well as to further our understanding of the disease. These efforts rely on having reliable animal models that accurately recapitulate disease presentation in humans. While current animal models of DMD have served this purpose quite well, each comes with their own limitations. To help overcome this, clustered regularly interspaced short palindromic repeats (CRISPR)-based technology has been extremely useful in creating novel animal models for DMD. This review focuses on animal models developed for DMD that have been created using CRISPR, their advantages and disadvantages as well as their applications in the DMD field.

Two cases of cutaneous nocardiosis in a cat and in a dog have been described. Diagnosis was made on basis of direct microscopy and cultural mycological analysis. Phenotypic characteristics of two indigenous Nocardia strains are presented. The strain isolated from cat was presumably identified as N. asteroids. There have been no reports of suchlike cases in pets in Russia so far.

Eastern (EEEV) and Venezuelan (VEEV) equine encephalitis viruses (EEVs) are related, (+)ssRNA arboviruses that can cause severe, sometimes fatal, encephalitis in humans. EEVs are highly infectious when aerosolized, raising concerns for potential use as biological weapons. No licensed medical countermeasures exist; given the severity/rarity of natural EEV infections, efficacy studies require animal models. Cynomolgus macaques exposed to EEV aerosols develop fever, encephalitis, and other clinical signs similar to humans. Fever is nonspecific for encephalitis in macaques. Electrocardiography (ECG) metrics may predict onset, severity, or outcome of EEV-attributable disease. Macaques were implanted with thermometry/ECG radiotransmitters and exposed to aerosolized EEV. Data was collected continuously, and repeated-measures ANOVA and frequency-spectrum analyses identified differences between courses of illness and between pre-exposure and post-exposure states. EEEV-infected macaques manifested widened QRS-intervals in severely ill subjects post-exposure. Moreover, QT-intervals and RR-intervals decreased during the febrile period. VEEV-infected macaques suffered decreased QT-intervals and RR-intervals with fever onset. Frequency-spectrum analyses revealed differences in the fundamental frequencies of multiple metrics in the post-exposure and febrile periods compared to baseline and confirmed circadian dysfunction. Heart rate variability (HRV) analyses revealed diminished variability post-exposure. These analyses support using ECG data alongside fever and clinical laboratory findings for evaluating medical countermeasure efficacy.

Numerous investigations have suggested that seismic source parameters and properties of the surrounding medium harbor valuable information regarding alterations in stress fields and medium properties at the focal depth. Monitoring the spatial and temporal evolution of these parameters can yield insights into variations in stress fields or medium properties within the seismogenic zone, offering a critical avenue to overcome the Earth's inaccessible nature. In recent years, modern seismic parameters such as seismic moment, focal mechanism solution, source stress drop, corner frequency, radiated seismic energy, rupture radius, and apparent stress have been increasingly used to characterize source characteristics. The Sichuan region is situated on the southeastern edge of the Qinghai-Tibet Plateau and is known for its strong eastward extrusion and structural deformation. The main fault zones in the area include the Xianshuihe Fault, Anninghe Zemuhe Fault, and Longmenshan Fault. This paper estimates source parameters of small to medium-sized earthquakes (ML 1.5-5.2) in the main fault zones and adjacent areas in Sichuan. A total of 4,310 earthquake stress drop measurements from 2019 to 2023 were analyzed to create a stress distribution image along the fault. This earthquake parameters training catalog provides insight into how coseismic stresses change and their relationship with other geophysical factors, as well as their spatial and temporal evolution.

Researchers are exploring solutions to meet the growing demand for protein due to the expected increase in global population by 2050. Interest in alternative protein sources like insects has risen, driven by concerns about environmental impact and the need for sustainable food production. This study aimed to develop and evaluate the physicochemical properties of soy protein-based burger enriched with insect protein from Alphitobius diaperinus. Three formulations were developed: a control (B0), and burgers with 5% (B5) and 10% (B10) insect protein - Whole Buffalo Powder (WBP). Results showed that adding insect protein decreased the burger analogue's pH. There was observed a clear trend of increasing total lipids and SFA, and decreasing MUFA and PUFA, as the WBP concentration increases from 0% to 10%. No significant differences with increasing WBP concentration in the protein content of the burger analogue, as well as the cooking yield, were noted. The WBP addition had a notable effect on the color change, especially decrease in brightness (L*). It was shown that as the WBP concentration increased, there were no significant differences in the texture profile of the burger analogues. The formulation with 5% WBP concentration was the most acceptable in sensory analysis.

This study introduces a low-cost solution provided by a specialized source measure unit (SMU), offering an alternative to conventional source meter units. Through precise IV curve measurements for graphene-channel FETs, the SMU demonstrates its capability in accurately characterizing these devices with minimal noise and high accuracy. This cost-effective solution presents a promising avenue for researchers and developers seeking reliable tools for sensor development and characterization, providing valuable insights into the behavior of graphene-channel FETs and the effect of surface illumination without the need for expensive equipment. Additionally, the SMU was validated with known passive and active components, along with probe station integration for microscale connection, ensuring its effectiveness in capturing accurate measurements. The SMU’s focus on collecting IV curves, coupled with its ability to identify device defects, such as Schottky junctions, contributes to its utility in quality testing for semiconductor devices. Its low-cost nature makes it accessible for various research endeavors, enabling efficient data collection and analysis for graphene-based sensor applications.

The escalation of mining activities in the karst regions of Guizhou Province has heightened the occurrence of water-inrush incidents in deep coal mines. This study focused on water-inrush phenomena within the Xinhua mining area of Jinsha County, Guizhou Province, aiming to investigate the sources of these incidents. The findings indicated that the overlying limestone of the Changxing Formation in the coal seam served as a vulnerable aquifer under certain conditions, leading to water inrushes. The analysis of the spatiotemporal distribution patterns of water-inrush incidents at the working face indicated that previous mining operations damaged the shallow Changxing Formation limestone, resulting in the accumulation of goaf water and the formation of numerous mining-induced fractures. These fractures served as rapid conduits for water inrushes from both atmospheric precipitation and underground sources at the deep working face. The examination of surface water and mine water quality demonstrated that both exhibited similar characteristics, predominantly featuring bicarbonate, sulfate, and sodium compositions. Investigation into the relationship between mine water inflow and atmospheric precipitation established that atmospheric precipitation influenced the mine water supply cycle, with a replenishment period of ~10 months during the operational phase of the Jinyuan Coal Mine and about one month post-closure. The fractures induced by mining activities within the Changxing Formation limestone facilitated water flow, with atmospheric precipitation serving as the primary water source for the mine. This study offered a valuable scientific foundation for addressing water-related damage resulting from atmospheric precipitation in mines susceptible to water inrushes under analogous hydrogeological conditions.

Infrared small target detection (ISTD) plays a crucial role in both civilian and military applications. The detection of small targets against dense cluttered backgrounds remains a challenging task, requiring the collaboration of false alarm source elimination and target detection. Existing approaches primarily focus on modeling targets while often neglecting false alarm sources. To address this limitation, we propose a Target and False Alarm Collaborative Detection Network to leverage the information provided by false alarm sources and the background. Firstly, we introduce a False Alarm Source Estimation Block (FEB) that estimates potential interferences present in the background by extracting features on multiple scales and employing stepwise upsampling for feature fusion. Subsequently, we propose a framework that employs multiple FEBs to eliminate false alarm sources across multiple scales. Finally, a Target Segmentation Block (TSB) is introduced to accurately segment the targets to produce the final detection result. Experiments on public datasets demonstrate that, compared to other methods, our model achieves high accuracy in segmenting targets while being able to extract false alarm sources which can be used for further studies.

This study employs the FLACS code to analyze hydrogen leakage, vapor dispersion, and subsequent explosions. Utilizing pseudo-source models, a liquid pool model, and a hybrid model combining both, we investigate dispersion processes for varying leak mass flow rates (0.225 kg/s and 0.73 kg/s) in a large open space. We also evaluate explosion hazards based on overpressure and impulse effects on humans. The computational results, compared with experimental data, demonstrated reasonable hydrogen vapor cloud concentration predictions, especially aligned with the wind direction. For higher mass flow rate of 0.73 kg/s, the pseudo-source model and hybrid model proved appropriate, while the liquid pool model was more suitable for lower mass flow rate of 0.225 kg/s. Regarding explosion analyses using overpressure-impulse diagram, higher mass flow rates leaded to potentially fatal overpressure and impulse effects on humans. However, lower mass flow rates may cause severe eardrum damage at the maximum overpressure point.

Aiming at the difficult problem of broadband oscillation localization in power systems, the intel-ligent localization method of oscillation source based on the digital twin is proposed and the os-cillation source localization system based on the digital twin is constructed. Firstly, a digital twin-based oscillation source localization method and system architecture are proposed. Fur-thermore, an intelligent positioning method of oscillation source based on data-driven and mechanism fusion is proposed. It includes three steps: oscillation signal preprocessing, oscillation modal analysis and oscillation source localization. For the oscillation signal preprocessing, the generative adversarial imputation network is used to repair the missing samples, and the su-per-resolution technique is used to realize the super-resolution measurement of broadband os-cillation. In the oscillation modal analysis, the spectrum of the oscillation signal is extracted using the fast Fourier transform. To accurately locate the oscillation source, the branch potential energy is used as input to the data-driven model, such as LSTM and CNN. Finally, an oscillation source localization system is developed based on the digital twin workshop CloudPSS-XStudio, which can locate the oscillation source quickly and accurately.

It is a challenging and meaningful task to carry out drone-based livestock monitoring in high-altitude and cold regions. The purpose of AI is to execute automated tasks and to solve practical problems in actual applications by combining the software technology with the hardware carrier to create integrated advanced devices. Only in this way, the maximum value of AI could be realized. In this paper, a real-time tracking system with dynamic target tracking ability is proposed. It is developed based on the tracking-by-detection architecture using YOLOv7 and DeepSORT algorithms for target detection and tracking, respectively. To address the existing problems of the DeepSORT algorithm, the following two optimizations are made: (1) Optical flow is used to compensate the Kalman filter for improvement of the prediction accuracy; (2) A low-confidence trajectory filtering method is adopted to reduce the influence of unreliable detection on target tracking. In addition, an visual servo controller for the UAV is designed to enable the automated tracking task. Finally, the system is tested using the Tibetan yaks living in the Tibetan Plateau as the tracking targets, and the results reveal the real-time multiple tracking ability and the ideal visual servo effect of the proposed system.

In a context of exponential demographic growth, the imbalance between human resources and public health problems is impelling us to envision other solutions to the difficulties faced in the diagnosis, prevention and large-scale management of the most common diseases. Cardiovascular diseases represent the leading cause of morbidity and mortality worldwide. A large-scale screening program would make it possible to promptly identify patients with high cardiovascular risk in order to manage them adequately. Optical coherence tomography-angiography (OCT-A), as a window into the state of the cardiovascular system, is a rapid, reliable, and reproducible imaging examination that enables prompt identification of at-risk patients through the use of automated classification models. One challenge that limits the development of computer-aided diagnostic programs is the small number of open-source OCT-A acquisitions available. To facilitate the development of such models, we have assembled a set of images of the retinal microvascular system from 499 patients. It consists of 814 angiocubes as well as 2005 en face images. Angiocubes were captured with a swept-source OCT-A device of patients with varying overall cardiovascular risk. To the best of our knowledge, our dataset, RASTA, is the only publicly available dataset comprising such a variety of images from healthy and at-risk patients. This dataset will enable the development of generalizable models for screening of cardiovascular diseases from OCT-A retinal images.

Electrical impedance tomography (EIT) can monitor the real-time hemodynamic state of a conscious and spontaneously breathing patient noninvasively. However, cardiac volume signal (CVS) extracted from EIT images has a small amplitude and is sensitive to motion artifacts (MAs). This study aimed to develop a new algorithm to reduce MAs from the CVS for more accurate heart rate (HR) and cardiac output (CO) monitoring in patients undergoing hemodialysis based on the source consistency between the electrocardiogram (ECG) and the CVS of heartbeats. Two signals were measured at different locations on the body through independent instruments and electrodes, but the frequency and phase were matched when no MAs. As the number of motions per hour (MI) increased over 30, the proposed algorithm had a correlation of 0.83 and a precision of 1.65 beats per minute (BPM) compared to the conventional statical algorithm of a correlation of 0.56 and a precision of 4.04 BPM. For CO monitoring, the precision and upper limit of the mean ∆CO were 3.41 and 2.82 liters per minute (LPM), respectively, compared to 4.05 and 3.82 LPM of the statistical algorithm. The developed algorithm could reduce MAs and improve HR/CO monitoring accuracy and reliability, particularly in high-motion environments.

With the widespread application of Voltage Source Converter (VSC) in the power system with high penetration of renewable resources, the problem of wideband oscillations caused by the interaction between the power converters and the power grid has drawn large attention. This paper presents a new methodology for simplifying the mathematical derivations of the impedance model of VSC. Using the new simplified model, the stability and the spectrum characteristic of the performance of the VSC can be clearly analyzed. The proposed second-order d-q impedance model of the grid connected VSC at presence of PLL, not only mathematically proves that the wideband oscillatory modes always occurs in conjugate pairs, but also deliver a clear vision of the physical essence of the wideband oscillations. The classic modal analysis is executed in the paper to demonstrate the stability and frequency spectral characteristics of the wideband oscillations in the VSC integrated system. The impact of control parameters of the VSC on the wideband oscillatory stability is thouroughly analyzed using the results of the modal analysis. The methodology can be considered a powerful tool for the control design of VSC in the modern power system.

Voltage Source Inverters (VSI) are the integral part of Electrical Vehicles (EV) to enhance the reliability of supply power to critical loads in vehicle to load (V2L) applications. Inherent properties of sliding mode control (SMC) makes it one of the best vailable options to achieve desired voltage quality under variable load conditions. Intrinsic characteristic of robustness associated with SMC is achieved generally at the cost of unwanted chattering along the sliding surface. To manage this compromise better, optimal selection of sliding surface coefficient is applied with proposed composite exponential reaching law (C-ERL). The novelty of proposed C-ERL is associated with the intelligent mix of exponential, power and difference functions blended with rotating sliding surface selection (RSS) technique for three phase two level VSI. Moreover, proposed reaching law along with power rate exponential reaching law(PRERL), enhanced exponential reaching law(EERL) and repeatitive reaching law(RRL) are implemented on two level three phase VSI under variable load condtions. Comparative analysis of which strongly advocates the authenticity and effectiveness of proposed reaching law in achieving well regulated output voltage, with high level of robustness, reduced chattering and low %THD.

The eutrophication caused by excessive total nitrogen(TN) and total phosphorus(TP) emissions has been widely concerned by the whole society. Studies have revealed the relationship between land use and TN and TP, but the relationship between land use compound topographic position and TP and TN was seldom studied. Therefore, Spearman correlation and redundancy analyses were used to reveal the relationship between land use compound topographic position and TN and TP based on the monthly data of 28 water quality sampling sites and the land use data of 2013 and 2016 in the lakes of Guizhou Plateau. The results show that the nutritional state of the HBA watershed is medium. The trophic level index (TLI) value and TN concentration were high during flood, while TP concentration was high in dry period. The TN concentration in the tributaries was higher than that in the reservoir area. Construction and valley were the sources of the pollution, whereas forest land and gentle slope were the sink. According to the”source-sink”effect, the optimal zoning of land use was completed, and the urban land pollution govern area should be strengthened firstly. This paper can provide decision support for water environment management and sustainable development decision-making.

Seaports are well known as the medium that has evolved into the central link between sea and land for complex marine activities. The growth in maritime logistics especially necessitates a large volume of energy supply to maintain the operation of sea trade, resulting in an imbalance between the generation and demand sides. Future projections for three major concerns show an increase in load demand, cost of operation, and environmental issues. In order to overcome these problems, integrating microgrids as an innovative technology in the seaport power system appears to be a vital strategy. It is believed that microgrids enhance the seaport operation by providing sustainable, environmentally friendly, and cost-effective energy. Despite the fact that microgrids are well established and widely used in a variety of operations on land, their incorporation into the seaport is still limited. The involvement of a variety of heavy loads such as all-electric ships, cranes, cold ironing, and buildings infrastructure makes it a complicated arrangement task in several aspects, which necessitate further research and leave space for improvement. In this paper, an overview of the seaport microgrids in terms of their concepts, requirements, and operation management is presented. It provides the perspectives of integrating the microgrid concept into a seaport from both shore side and seaside as a smart initiative for the green ports vision. Future research directions are discussed towards the development of more efficient marine power system.

Tree resin, which in addition to its widespread use in industry is also a macroergic component which has not yet been used for energy purposes. The main goal of this work is to determine the energy content of the resin of spruce, pine and larch and wood components – pulp and turpentine. The combustion heats of resin (MJ/kg) from each timber was determined calorimetrically. The energy values of tree resin (>38.0 MJ.kg-1) were 2.2 and 2.4 times higher than that of bleached and unbleached cellulose, the highest value was recorded for the turpentine (>39.0 MJ.kg-1). The best way of resin tapping is the America method, providing 5 kg resin ha-1 yr-1. The resin quantity tapped was raised at least 3 times applying stimulant, its production cost compared to other feedstocks was the cheapest. The tree resin can be applied as a good means to mitigate global warming and consequently dampen climate change. One tonne of tree resin burned instead of coal spares the atmosphere by 5.0 Mt CO2 since it is environmentally neutral. As resin heating values approach to the lower end of ones of liquid hydrocarbon fuels, greater efforts are needed to examine the most efficient energy use of resin.

Learning disorders (LD) are diagnosed in children impaired in the academic skills of reading, writing and/or mathematics. Children with LD usually show a slower resting-state electroencephalogram (EEG), with EEG patterns corresponding to a neurodevelopmental lag. LD-children also show a consistent cognitive impairment in working memory (WM), including an abnormal task-related EEG with an overall slower EEG activity of more delta and theta power, and less gamma activity in posterior sites; task-related EEG patterns considered indices of an inefficient neural resource management. Neurofeedback (NFB) treatments aimed at normalizing the resting-state EEG of LD-children have shown improvements in cognitive-behavioral indices and diminished EEG abnormalities. Given the typical findings of a WM impairment in LD-children; we aimed to explore the effects of a NFB treatment in the WM of children with LD, by analyzing the WM-related EEG power-spectrum. We recruited 18 children with LD (8-10 years old). They performed a Sternberg-type WM-task synchronized with an EEG of 19 leads (10-20 system) twice in pre-post treatment conditions. They went through either 30 sessions of a NFB treatment (NFB-group, n= 10); or through 30 sessions of a placebo-sham treatment (Sham-group, n= 8). We analyzed the before-after treatment group differences for the behavioral performance and the WM-related power-spectrum. The NFB group showed faster response times in the WM-task post-treatment. They also showed an increased gamma power at posterior sites and a decreased beta power. We explain these findings in terms of NFB improving the maintenance of memory representations coupled with a reduction of anxiety.

Since the recent legalization of medical and recreational use of cannabis (Cannabis sativa L.) in many regions worldwide, there has been high demand for research to improve yield and quality. With the paucity of scientific literature on the topic, this study investigated the relationships between light intensity (LI) and photosynthesis, inflorescence yield, and inflorescence quality of cannabis grown in an indoor environment. After growing vegetatively for 2 weeks under a canopy-level photosynthetic photon flux density (PPFD) of ≈ 425 μmol·m-2·s-1 and an 18-h light/6-h dark photoperiod, plants were grown for 12 weeks in a 12-h light/12-h dark ‘flowering’ photoperiod under canopy-level PPFDs ranging from 120 to 1800 μmol·m-2·s-1 provided by light emitting diodes. Leaf light response curves varied both with localized (i.e., leaf-level) PPFD and temporally, throughout the flowering cycle. Therefore, it was concluded that the leaf light response is not a reliable predictor of whole- plant responses to LI, particularly crop yield. This may be especially evident given that dry inflorescence yield increased linearly with increasing canopy-level PPFD up to 1800 μmol·m-2·s-1, while leaf-level photosynthesis saturated well below 1800 μmol·m-2·s-1. The density of the apical inflorescence and harvest index also increased linearly with increasing LI, resulting in higher-quality marketable tissues and less superfluous tissue to dispose of. There were no LI treatment effects on cannabinoid potency, while there were minor LI treatment effects on terpene potency. Commercial cannabis growers can use these light response models to determine the optimum LI for their production environment to achieve the best economic return; balancing input costs with the commercial value of their cannabis products.

The study explored the contribution of ICT-based information sources to market participation among smallholder livestock farmers. Use of ICTs is considered paramount for providing smallholder farmers with required market information, in order to reduce market asymmetries. A Double Hurdle regression was utilized to analyze data collected from 150 smallholder livestock farmers in the study area. The results show that while use of ICT-based market information sources significantly influenced market participation, the effect of using ICT-based information sources on intensity of market participation was not significant. Other variables shown to influence both market participation and the intensity of market participation were age, additional income and membership of farmer cooperatives. This suggests the need to also consider other associated factors in the application of interventions which utilize ICT-based information sources in achieving planned market interventions.

Although sources of seston are much more complicated in lakes compared to marines, the influences of different source on the spatiotemporal variations in seston stoichiometry are still underexplored, especially in large eutrophic floodplain lakes. Here, we investigated the seston stoichiometry across a typical large eutrophic floodplain lake (Lake Taihu) over one year. In addition, we used the n-alkanes proxies for source estimation which are more robust than other elemental indicators (e.g. C: N ratios). Throughout the study, the average value of C: N: P ratio of 143: 19: 1 across Lake Taihu was higher than the classical Redfield ratio, but closed to the synthesized data from other lakes. Generally, seston C: N ratios varied the least across all environments, but C: P and N: P ratios varied widely and shown a significant seasonal pattern with lower ratios of N: P and C: P during senescence seasons and higher ratios in the growing seasons. This seasonal change was mainly associated with the shift from terrestrial-derived seston to algal-derived seston as, the significant lower ratios of terrestrial-derived seston from surrounding agricultural watershed. Spatially, the mean ratios of each site were similar, except relative high values of C: P and N: P ratios in the algal dominated area. Statistically, the predictive power of environmental variables was strongly improved by adding n-alkanes proxies. However, apart from sources indicators, particulate phosphorus (PP) contents also partly explained the spatiotemporal variations in stoichiometric ratios. Nevertheless, the mechanisms behind the dynamics of PP could be totally different and source-specific. This study highlights the priority of using n-alkanes proxies as tools to identify the source of seston which is essential to interpret the spatiotemporal variations in seston stoichiometric ratios among eutrophic floodplain lakes.

The quality of digital elevation models (DEMs) is inevitably affected by the limitations of the imaging modes and the generation methods. One effective way to solve this problem is to merge the available datasets through data fusion. In this paper, a fusion-based global DEM dataset (82°S-82°N) is introduced, which we refer to as GSDEM-30. This is a 30-m DEM mainly reconstructed from the unfilled SRTM1, AW3D30, and ASTER GDEM v2 datasets combining the multi-source and multi-scale fusion techniques. A comprehensive evaluation of the GSDEM-30 data, as well as the 30-m ASTER GDEM v2 and AW3D30 DEM, was presented. Global ICESat GLAS data and the local National Elevation Dataset (NED) were used as the reference for the vertical accuracy validation, while GlobeLand30 was introduced for the landscape analysis. Furthermore, we employed the maximum slope approach to detect the potential artefacts in the DEMs. The results show that the GDEM data are seriously affected by noise and artefacts. With the advantage of the multiple datasets and the refined post-processing, the GSDEM-30 are contaminated with fewer anomalies than both ASTER GDEM and AW3D30. The fusion techniques used can also be applied to the reconstruction of other fused DEM datasets.

A focal plane array (FPA) detector was used for hyperspectral imaging in the infrared (IR) spectral region using thermal and synchrotron light sources. FPA Fourier-transform IR (FTIR) imaging microspectroscopy will be able to monitor real time changes at specific absorption bands when combined with high brightness synchrotron source. In this study, several types of samples with unique structural motifs were selected and used for assessing the capability of the FPA-FTIR imaging technique. It was shown that the time required for polariscopy at IR wavelengths can be substantially reduced by the FPA-FTIR imaging approach. By using natural and laser fabricated polymers with sub-wavelength features, alignment of absorbing molecular dipoles was revealed as well as higher order patterns (laser fabricated structures). Micro-spectroscopy of absorber orientation reveals alignment patterns even when they are not spatially resolved.

In Hawaiʻi, the transition from customary subsistence flooded taro agroecosystems, which regulate stream discharge rate trapping sediment and nutrients, to a plantation-style economy (c. the 1840s) led to nearshore sediment deposition - smothering coral reefs and destroying adjacent coastal fisheries and customary fishpond mariculture. To mitigate sediment transport, Rhizophora mangle was introduced in estuaries across Hawai’i (c. 1902) further altering fishpond ecosystems. Here, we examine the impact of cultural restoration between 2012-2018 at Heʻeia Fishpond, a 600-800-year-old walled fishpond. Fishpond water quality was assessed by calculating water exchange rates, residence times, salinity distribution, and abundance of microbial indicators prior to and after restoration. We hypothesized that R. mangle removal and concomitant reconstruction of sluice gates would increase mixing and decrease bacterial indicator abundance in the fishpond. We find that Heʻeia Fishpond’s physical environment is primarily tidally driven; wind forcing and river flux are secondary drivers. Post-restoration, two gates in the northeastern region account for >80% of relative flux in the fishpond. Increase in exchange rates during spring and neap tide and shorter minimum water residence time corresponded with the reconstruction of a partially obstructed 56 m gap together with the installation of an additional sluice gate in the fishpond wall. Lower mean salinities post-restoration suggests increased freshwater influx due to R. mangle removal. Spatial distribution of microbial bio-indicator species inversely correlated with salinity. Average abundance of Enterococcus and Bacteroidales did not significantly change after restoration efforts, however, average abundance of a biomarker specific to birds nesting in the mangroves decreased significantly after restoration. This study demonstrates the positive impact of biocultural restoration regimes on water flushing and water quality parameters, encouraging the prospect of revitalizing this and other culturally and economically significant sites for sustainable aquaculture in the future.

This study reports multivariate statistical techniques applied including cluster analysis to evaluate and classify the river pollution level in Taiwan, and principal component analysis-multiple linear regression (PCA-MLR) to identify the possible pollution source. Water quality and heavy metal monitoring data from Taiwan Environmental Protection Administration (EPA) was evaluated for 14 rivers in the four regions of Taiwan. The Erren River was classified as the most polluted River in Taiwan. Biochemical oxygen demand, ammonia, and total phosphate concentration in this river were the highest of the 14 rivers evaluated. In addition, heavy metal levels of the following rivers exceeded the Taiwan EPA standard limit: lead - in the Dongshan, Jhuoshuei, and Xinhuwei Rivers; copper - in the Dahan, Laojie, and Erren Rivers; and manganese - in all rivers. Water pollution in the Erren River was estimated to originate 72% from industrial sources, 16% from domestic black water, and 12% from natural sources and runoff from other tributaries. Our research showed that PCA-MLR and the cluster analysis model accomplished our study objectives and will be helpful tools to evaluate water quality in rivers and we suggest that the continuous monitoring should be conducted to monitor water pollution from anthropogenic activities.

In table tennis competitions the rule violation judgment with the greatest controversy is the height of the ball serve. This is because inaccuracy in the ball height judgment, which results in erroneous judgment, is unavoidable. Thus, we designed an automatic image judgment auxiliary system for table tennis ball height during service in this study. We used a high-speed camera to record the ball toss in the table tennis service. The designed algorithm architecture can automatically search for the ball and the position of the hand action under low light source conditions. The algorithm is mainly divided into hue-saturation-value color space processing and morphology processing using Hough transform to search for the circular ball. Experiment result shows that color segmentation can successfully and accurately determine the ball position under low light conditions. The morphology method can find the position of the hand and help determine the moment when the ball leaves the hand during the service ball toss. Finally, the actual size of the target is used to estimate the actual distance unit represented by the image pixel.

The air is not the same as thousands and hundreds of years ago. In the air, suspended particles originate from natural phenomena like dust storms or volcanic activities, as well as anthropogenic pollutants such as fuel engine exhaust and everyday activities at home. The total particles in the air can be classified by sizes, such as PM10, PM2.5 or ultrafine particles. However, there are many other important factors in addition to the particle size, influencing the particle behavior and affecting our health. The surface area, chemical and biological composition, aspect ratio, and the charge are all factors characteristic of particles. OoC microfluidic chips are very useful for the pollutant toxicity measurements on various body tissues. A better understanding of pollutants will help to trace these to the potential sources. The data from the on-the-ground and satellite monitoring can be integrated into models, helping to predict and prevent pollution exposure.

The research field of energy system analysis is dealing with increasingly complex energy systems and their respective challenges. Moreover, the requirement for open science has become a focal point of public interest. Both drivers have triggered the development of a broad range of (open) energy models and frameworks in recent years. However, there are hardly any approaches on how to evaluate these tools in terms of their capabilities to tackle energy system modelling challenges. This paper describes a first step towards a flexible evaluation of software to model energy systems. We propose a qualitative approach as an useful supplementary to existing model fact sheets and transparency checklists. We demonstrate the applicability by evaluating the newly developed “Open Energy Modelling Framework” with respect to existing challenges in energy system modelling. The case study results highlight that challenges related to complexity and scientific standards can be tackled to a large extent while the challenges of model utilization and interdisciplinary modelling are only tackled partially. However, the challenge of uncertainty remains for the most part unaddressed at present. Advantages of the evaluation approach lie in its simplicity, flexibility and transferability to other tools. Disadvantages mostly stem from its qualitative nature. Our analysis reveals that some challenges in the field of energy system modelling cannot be addressed by a software as they are on meta level like model result communication and interdisciplinary modelling.

In this paper, we consider a fourth-order suspension bridge equation with nonlinear damping term |u_t|^m-2u_t and source term |u|^p-2u. We give necessary and sufficient condition for global existence and energy decay results without considering the relation between m and p. Moreover, when p>m, we give sufficient condition for finite time blow-up of solutions. The lower bound of the blow-up time T_max is also established. It worth to mention that our obtained results extend the recent results of Wang (J. Math. Anal. Appl., 2014) to the nonlinear damping case.

Epilepsy is a non-communicable neurological disease characterized by abnormal brain activity with conceivable peripheral implications. Few studies examine the role of peripheral and brain inflammation in the relationship between seizure predisposition and nonalcoholic fatty liver disease. Severe early-life infections leading to sepsis results in increased inflammation that can aggravate epilepsy and hepatic damage progression both associated with increased odds for hospital admissions in epileptic patients. Here, we examined the effect of early LPS challenge (1 mg/kg, i.p. at post-natal day3, PND3) in inducing hepatic damage in a genetic model of young epileptic WAG/Rij rats (PND45), evaluating the intra- and inter-gender differences on systemic and liver inflammation, hepatic lipid dysmetabolism and oxidative damage associated to mitochondrial functional impairment. First, epileptic rats exposed to LPS, regardless of gender, displayed increased serum hepatic enzymes and altered lipid profile. Early LPS challenge induced a major inflammatory and immune response in male epileptic rats than females in both serum and liver, increasing pro-inflammatory cytokines and hepatic immune cell recruitment. Conversely, LPS-insulted females showed a marked alteration in hepatic metabolic and lipid profile and the reduction in mitochondrial fatty acid oxidation. The two different gender-related mechanisms of LPS-induced liver injury converge in mitochondrial oxidative damage with intensified ROS production in both sexes, that notably induced a compensatory increase in antioxidant defense only in females. Our study with a translational potential points out that early postnatal infections predispose epileptic rats to develop or worsen hepatic disorders in a sex-dependent manner, amplifying inflammation, lipid dysmetabolism, and mitochondrial impairment.

Animals are distinguished by their complex developmental programs, in which transcription factors play essential roles in coordinating the regulation of the genome. Thus, it has been hypothesized that animal origins involved the evolution of increasingly complex transcriptional regulatory mechanisms that permit greater spatiotemporal control over gene expression. Here, we revisit this hypothesis in light of new genomic and functional data from diverse phylogenetically-relevant taxa, including early branching animals (sponges, ctenophores) and close relatives of animals (choanoflagellates, filastereans, ichthyosporeans). We argue that many of the mechanisms posited to explain animal transcriptional complexity, such as large increases in transcription factor numbers, new transcription factor families, and distal enhancers, did not factor significantly into animal origins. Instead, we propose that the re-purposing of pre-existing transcriptional regulatory modules through cis-regulatory evolution and gene duplication events, combined with new protein-protein interactions among TFs, may have been fundamental to the transcriptional regulatory architecture of the first animals.

Regular weight measurement is important in fattening geese to assess their health status. Failure to gain weight may indicate a potential illness. Standard weight gain analysis involves direct contact with the animal, which can cause stress to the animal, resulting in overall negative impacts on animal welfare. The focus of this study was to design a smart solution for monitoring weight changes in the breeding of farm animals. The proposed IoT system with a weighing device equipped with RFID technology for animal registration aimed to minimise the negative aspects associated with measuring in contact with humans. The designed system aims to incorporate modern approaches in animal husbandry and utilise the acquired data for the potential development of breeding approaches for various animal breeds. The system consisted of three main components: a data acquisition system, a weighing system with RFID, and an environmental monitoring system. In this study, the RFID system accuracy for detecting geese in the weighing system environment was assessed. The entire system evaluation yielded a sensitivity of 93.87%, specificity of 99.94%, accuracy of 99.85%, and precision of 95.09%. Regression analysis revealed a good correlation between observed feeding and RFID registrations with a determination coefficient of R2 = 0.9813.

Here, we critically evaluate the communication and integrative mechanisms in ctenophores or comb jellies, the descendants of the earliest animal lineage. Ctenophores convergently evolved their neuro-muscular systems compared to other animals. Besides the canonical neural organization with distinct synapses, recent 3D electron microscopy reconstruction revealed unique syncytial-type connectivity in some neuronal populations, providing additional support for the independent origins of neurons in ctenophores. Furthermore, it was proposed that non-synaptic transmission is the distinct characteristic of ctenophores, in contrast to other metazoans and Cajal’s neuronal doctrine, highlighting Golgi’s reticular theory. However, the astonishing diversity and higher information capacity of classical synapses and volume transmission indicate that chemical signaling is the hallmark of neural and other integrative systems regardless of their origins.

A form of dementia distinct from healthy cognitive aging, Alzheimer's disease (AD) is a complex multi-stage disease that currently afflicts over 50 million people worldwide. Unfortunately, previous therapeutic strategies developed from murine models emulating different aspects of AD pathogenesis have failed. Consequently, researchers are now developing models that express several aspects of pathogenesis that better reflect the clinical situation in humans. As such, this review seeks to provide insight regarding current applications of mammalian models in AD research by addressing recent developments and characterizations of prominent transgenic models and their contributions to pathogenesis as well as discuss the advantages, limitations, and application of emerging models (hAβ-KI) that better capture genetic heterogeneity and mixed pathologies observed in the clinical situation.

During the preclinical research process, multiple factors can be difficult to implement without the careful consideration and planning of each step. As research has become more advanced with the use of increasingly complex technology, animal models have also become essential for understanding the potential impact of devices, drug therapies, and surgical techniques on humans before clinical trials are conducted. The use of an in vivo animal model is a key and necessary step in the progression of preclinical research studies that will lead to future medical inventions and innovation. Here, we describe the three phases of effectively designing a preclinical research protocol: the research, preprocedural planning, and experimental phases. Furthermore, we provide researchers with guidance through these phases and discuss important considerations.

Antimicrobial resistance (AMR) remains a challenge in Kenya, while the extent remains unknown. To assess the knowledge, cultural beliefs, practices, and behavioral patterns among multisectoral stakeholders in Kenya. The cross-sectional survey was conducted in August 2021 among farmers, animal health service providers and AMR researchers. Regional digital data collection tool developed by FAO was shared and responses obtained through mail, phone calls and direct interviews. Descriptive and inferential analysis were conducted. Antimicrobials were mostly sourced from agro-veterinary shops and from veterinary professionals. Farmers, often implement self-treatment and reported overuse, unnecessary use, and sometimes fail to complete the dosage in livestock. More farmers reported to have heard about antibiotics as compared to antimicrobials, mostly from friends and radio program, however only 9.2% could correctly differentiate the two. Animal Health Service Providers (AHSP) were the source of information to farmers regarding AMR. AHSPs mainly relied on suppliers and distributors for information about antibiotics. Both farmers and AHSPs treated viral infections with antibiotics. One Health Partners (OHPs) had higher knowledge and largely favorable attitudes towards AMR. Up to 72.7% of OHPs from training institutions had AMR included in training curriculum however, they were optimistic livestock farmers and government were less concerned about AMR. Gaps in knowledge and practice on Antimicrobial Stewardship (AMS) were observed in all categories of stakeholders. Given the documented knowledge-practice gap, innovative solutions are needed for both AHSPs and farmers to promote good antimicrobial stewardship practices and to mitigate burdens of AMR. Outcomes of this research should deepen the understanding of critical information and trigger behavioral change in usage and stewardship of antimicrobials.

Crimean-Congo hemorrhagic fever virus (CCHFV), a member of the Nairoviridae family and Bunyavirales order, is transmitted to humans via tick bites or contact with the blood of infected animals. It can cause severe symptoms, including hemorrhagic fever, with a mortality rate between 5 to 30%. CCHFV is classified as a high-priority pathogen by the World Health Organization (WHO) due to its high fatality rate and the absence of effective medical countermeasures. CCHFV is endemic in several regions across the world, including Africa, Europe, the Middle East, and Asia, and has the potential for global spread. The emergence of the disease in new areas, as well as the presence of the tick vector in countries without reported cases, emphasizes the need for preventive measures should be taken. In the past, the lack of a suitable animal model susceptible to CCHFV infection has been a major obstacle in the development of vaccines and treatments. However, recent advances in biotechnology and the availability of suitable animal models have significantly expedited the development of vaccines against CCHF. These advancements have not only contributed to an enhanced understanding of the pathogenesis of CCHF but have also facilitated the evaluation of potential vaccine candidates. This review outlines of the immune response to CCHFV and animal models utilized for the study of CCHFV and highlights the progress made in CCHFV vaccine studies. Despite remarkable advancements in vaccine development for CCHFV, it remains crucial to prioritize continued research, collaboration, and investment in this field.

Fungi constitute a diverse group with highly positive and negative impacts in different envi-ronments, having several natural roles and beneficial applications in human life, but also caus-ing several concerns. Fungi can affect human health directly, but also indirectly by being detri-mental for animal and plant health influencing food safety and security. Climate changes are al-so affecting fungal distribution, prevalence, and their impact on different settings. Searching for sustainable solutions to deal with these issues is challenging due to the complexity of the inter-actions among fungi and agriculture, animal production, environment, and human health. In this way, the “One Health” approach may be useful to get some answers since it recognizes that human health is closely connected to animal and plant health, as well as to the shared environ-ment. This review aims to explore and correlate each of those factors influencing human health in this “One Health” perspective.

The risk of 'zoonotic spillovers,' or the transmission of viruses from animals to humans, has been raised by climate change and the devastation of nature, as infectious disease experts have long warned. Even as the world works to stop the spread of the currently unfolding pandemic of coronavirus disease 2019 (COVID-19) and the breakout of monkeypox virus (MPXV), a new animal virus, the Langya henipavirus (LayV), has been discovered in humans in Eastern China. The scientists say there is little danger of the virus spreading among humans, but it shares genetic material with Hendra virus and Nipah virus, two other henipaviruses that infect humans and cause life-threatening respiratory diseases. Humans infected with LayV can expect to experience high body temperature, cough, weariness, poor appetite, muscle discomfort, myalgia, nausea and vomiting. It is likely that the virus will spread from animals to humans. Currently, the health authorities of Taiwan and other health organizations are tracking the progress of the ailment to ensure it does not reach humans. Researchers have examined 25 species of small wild animals for presence of the virus, and so far, shrews are the only ones that have tested positive for the virus's RNA. Based on these results, shrews are a possible candidate for the virus's natural reservoir. Too far, no therapies or vaccines have been developed and licensed for henipaviruses like the LayV. When other therapies fail to alleviate viral infections, ribavirin may be the next best thing. The need for novel vaccinations against the LayV infection and the timely delivery of these vaccines to areas at high epidemiological risk is real. To lessen the likelihood of a health calamity being caused by this newly developing virus, it is crucial to conduct active surveillance in a transparent and globally collaborative manner. The questions that have not been answered yet require additional study.

A recent paradigm shift in diagnostics of medulloblastoma allows the distinction of four major groups defined by genetic data rather than histology. This new molecular classification correlates better with prognosis and will allow better clinical management for therapies targeting druggable mutations, but also offers a new combination of monitoring tumor development in real-time and treatment response by sequential liquid biopsy. This review highlights recent developments after a century of milestones in neurosurgery, radio- and chemotherapy, but also controversial theories on the cell of origin, animal models and the use of liquid biopsy.

Determining genetic and non-genetic sources of variation in a breed is vital for the formulation of strategies for its conservation and improvement. The present study was aimed at estimating the (co)variance components and genetic parameters of Mecheri sheep by fitting six different animal models in the restricted maximum likelihood method, with a preliminary investigation on the performance of animals for non-genetic sources of variation. A total of 2616 lambs were studied, and varying levels of significance were found for the effect of period, season, parity of dam and birth type on different body weight traits. Direct heritability estimates derived from the best animal model for body weight at birth, 3 months, 6 months, 9 months and 12 months were 0.21, 0.24, 0.10, 0.15 and 0.09, respectively, and maternal heritabilities of the corresponding traits were 0.12, 0.05, 0.04, 0.04 and 0.04, respectively. The genetic correlations between body weight traits were all positive and moderate to strong except for birth weight with the other body weight traits. The significance of non-genetic factors studied in this work demanded a correction to improve the accuracy of the direct selection of lambs for body weight traits. The estimated genetic parameters identified the weaning weight as a selection criterion for the improvement in body weight of Mecheri lambs at different ages. Inbred individuals accounted for approximately 13% of the total population in the Mecheri sheep population studied. There were 877 founders in the population, and the actual effective size of the population was 128.48. The population's mean generation interval was 3.26. The mean inbreeding values ranged from 0.005 to 0.010 across generations. The population's average relatedness ranged from 0.001 to 0.014 across generations. Individual inbreeding was found to be 0.45 per cent for the entire population and 3.4 per cent for the inbred population.

Scientific, technical and bioinformatics advances have made it possible to establish analytics-based molecular biosimilarity for the approval of biosimilars. If the molecular structure and other product- and process-related attributes are comparable within the limits of testing then a biosimilar candidate would have safe safety and efficacy as its reference products. The current model of animal and human testing becomes redundant since all of these studies have much lower sensitivity and reproducibility in confirming biosimilarity. The recent AI-based protein structure prediction model has confirmed that the 3D structure can be predicted from the amino acid sequence, reducing the need for structural analysis; however, the new test methods based on MS are millions of times more sensitive and accurate. While the regulatory agencies have begun waiving animal testing and, in some cases, clinical efficacy testing, removing clinical pharmacology profiling brings a dramatic paradigm shift, reducing development costs without compromising safety and efficacy. Also shared is a list of 160+ products ready to enter as biosimilars. Major actions from regulatory agencies and developers are required to make this paradigm shift.

In an attempt to improve free-range beef cattle herds and explore the economic viability of utilizing Opuntia ficus-indica (spineless cactus) cladodes as supplementary feed, we investigated the impact of cactus diets on animal growth performance and carcass characteristics of Nguni cattle heifers. Four dietary treatments were randomly assigned to 32 heifers aged 24-months, weighing on average 172.2±27.1 kg, with each dietary treatment replicated to 8 individually penned heifers for 90 days. The dietary treatments were control diet (pasture-based energy + protein sources), 10% cactus diet, 20% cactus diet and commercial diet (crop-based energy and commercial protein source). The heifers fed commercial and control diets attained significantly (P < 0.05) higher dry matter intake, average daily gains, fat thickness, carcass conformation scores and lower feed conversion ratio than those fed cactus diets. However, the final body weight gains, slaughter and carcass weights, rib-eye muscle area and meat pH45 min and 24h were comparable (P > 0.05) between heifers fed cactus diets and those fed commercial and control diets. The 10 and 20% cactus diets had greater gross margins (P < 0.05) of R278.6 and R296.9, respectively than the other diets, due largely to reduced total variable costs. The comparability of carcass traits of heifers fed cactus diets and those fed non-cactus diets as well as higher economic returns from cactus inclusion warrants the use of cactus diets, particularly during drought when commercial feed prices rise.

This study evaluated the use of filtration and UV-A photocatalysis for the reduction of particulate matter (PM) and airborne bacterial pathogens in swine barns. Two MERV filters (8 and 15) were used to mitigate PM concentrations measured at the PM 1, PM 2.5, respirable PM, and PM 10 ranges. Filtration was also used to generate different levels of airborne pathogens to be treated by UV-A. Results show that MERV 8 and 15 filters effectively reduced PM concentrations (96-98%) in air exhausted from a swine barn (p ranged from < 0.01 to 0.04). UV-A photocatalysis did not mitigate PM concentrations. UV-A photocatalysis treatment reduced measured colony-forming units (CFUs) by 15-95%. The CFU percent reduction was higher when airborne PM concentration was low. The numeric results suggested a real mitigation effect despite p-values that did not meet the usual statistical cut-off of <0.05 for significance due to the large variability of the CFU control samples. Normalization of measured airborne pathogen concentrations by smaller PM size range concentrations led to emerging significant treatment differences for CFUs. A significant decrease (~60% reduction; p < 0.03) in the concentration of viable airborne bacteria was shown for all PM below the 10-micron range.

Posterior capsule opacification (PCO) is the most common complication of cataract surgery. It causes a gradual deterioration of visual acuity, which would otherwise remain improved after a successful procedure. Despite recent advances in ophthalmology, this complication has not been eradicated and the incidence of PCO can be as high as 10%. This article reviews the literature concerning the pathomechanism of PCO and examines the biochemical pathways involved in its formation and methods to prevent this complication. We also review the reported tests performed in cell cultures under laboratory conditions, in experimental animal models, and in ex vivo human lens capsules. Finally, we describe research involving human eyes in the clinical setting and pharmacological methods that may reduce the frequency of PCO. Due to the multifactorial eti-ology of PCO, in vitro studies make it possible to assess the factors contributing to its complica-tions and search for new therapeutic targets. Not all pathways involved in cell proliferation, mi-gration, and contraction of the lens capsule are reproducible in laboratory conditions; moreover, PCO in humans and laboratory animals may be additionally stimulated by various degrees of postoperative reactions depending on the course of surgery. Therefore, further studies are necessary.

The classifier system proposed in this work combines the dissimilarity spaces produced by a set of Siamese neural networks (SNNs) designed using 4 different backbones, with different clustering techniques for training SVMs for automated animal audio classification. The system is evaluated on two animal audio datasets: one for cat and another for bird vocalizations. Different clustering methods reduce the spectrograms in the dataset to a set of centroids that generate (in both a supervised and unsupervised fashion) the dissimilarity space through the Siamese networks. In addition to feeding the SNNs with spectrograms, additional experiments process the spectrograms using the Heterogeneous Auto-Similarities of Characteristics. Once the similarity spaces are computed, a vector space representation of each pattern is generated that is then trained on a Support Vector Machine (SVM) to classify a spectrogram by its dissimilarity vector. Results demonstrate that the proposed approach performs competitively (without ad-hoc optimization of the clustering methods) on both animal vocalization datasets. To further demonstrate the power of the proposed system, the best stand-alone approach is also evaluated on the challenging Dataset for Environmental Sound Classification (ESC50) dataset. The MATLAB code used in this study is available at https://github.com/LorisNanni.

Malassezia is a lipid-dependent genus of yeasts known for being an important part of the skin mycobiota. These yeasts have been associated in the development of skin disorders and cataloged as a causal agent of systemic infections under specific conditions, making them opportunistic pathogens. Little is known about the host-microbe interaction of Malassezia spp., and unraveling this implies the implementation of infection models. In this mini review we present different models that have been implemented in the fungal infections study with greater attention in Malassezia spp. infections. These models range from in vitro (cell cultures and ex vivo tissue), to in vivo (murine models, rabbits, guinea pigs, insects, nematodes, and amoebas). We additionally highlight the alternative models that reduce the use of mammals as model organisms, which have been gaining importance in the study of fungal host-microbe interactions. This is due to the fact that these systems have shown to have reliable results, which correlate with those obtained from mammalian models. Example of alternative models are Caenorhabditis elegans, Drosophila melanogaster, Tenebrio molitor, and Galleria mellonella. These are invertebrates that have been implemented in the study of Malassezia spp. infections in order to identify differences in virulence between Malassezia species.

Olive trees have a unique reproductive pattern marked by biennial fruiting. This study delves into the repercussions of alternate fruit bearing on the water relations of olive trees and the associated ecophysiological mechanisms. The experiment spanned two consecutive years: the "ON" year, characterized by a high crop load, and the "OFF" year, marked by minimal fruit production. Key ecophysiological parameters, including sap flow, stomatal conductance, and photosynthetic rate, were monitored in both years. Pre-dawn water potential was measured using continuous stem psychrometers and the pressure chamber technique. Biochemically, non-structural carbohydrate concentrations (starch, sucrose, and mannitol) and the carbon-stable isotope ratio (δ13C) in olive leaves were examined. Results unveiled heightened leaf gas exchange during the "ON" year, leading to increased water consumption and photosynthetic rates. This escalated water usage during the "ON" year significantly reduced leaf water potential. Sucrose and starch concentrations were notably elevated in the "ON" year, with no significant impact on mannitol concentration. Regarding the carbon stable isotope ratio, leaves from the "OFF" year exhibited significantly higher δ13C abundance, indicative of heightened resistance to the CO2 pathway from the atmosphere to carboxylation sites compared to the "ON" year plants.

Fine-tuning is an important technique in transfer learning that has achieved significant success in tasks that lack training data. However, as it is difficult to extract effective features for single-source domain fine-tuning when the data distribution difference between the source and the target domain is large, we propose a transfer learning framework based on multi-source domain called adaptive multi-source domain collaborative fine-tuning to address this issue. This approach utilizes multiple source domain models for collaborative fine-tuning, thereby improving the model's feature extraction capability in the target task. Specifically, this approach employs an adaptive multi-source domain layer selection strategy to customize appropriate layer fine-tuning schemes for the target task among multiple source domain models. The objective is to extract more efficient features. Furthermore, a novel multi-source domain collaborative loss function is designed to facilitate the precise extraction of target data features by each source domain model. Simultaneously, it works towards minimizing the output difference among various source domain models, thereby enhancing the adaptability of the source domain model to the target data. In order to validate the effectiveness of the proposed the adaptive multi-source domain collaborative fine-tuning framework, it is analyzed and compared with the most widely used fine-tuning methods by applying them to seven publicly available visual categorization datasets commonly employed in transfer learning. Experimental results demonstrate that, in comparison with the existing fine-tuning approaches, our method not only enhances the accuracy of feature extraction in the model but also provides precise layer fine-tuning schemes for the target task, resulting in state-of-the-art performance.

To obtain a better estimation frequency band for source bearing estimation, source bearing estimation experiments using an ice-mounted geophone were performed at three sites on the Arctic ice cap. By transforming to the frequency domain, errors of the bearing estimation in a number of frequency bands within 1000 Hz and the horizontal peak displacements received by the geophone in the corresponding frequency bands were obtained. After comparing the variation of errors to the peak displacements in the same frequency band, it is found that the errors are related to the peak displacements, and higher peak displacement values mean smaller errors. When the incident angle of the acoustic source and the ice thickness meet certain conditions, some modes of the Lamb waves of the ice plate are excited, the peak displacements at the same frequency band are increased, the errors of bearing estimation are reduced, and the effective frequency band for bearing estimation is broadened. These results provide some new ideas for the bearing estimation of acoustic sources beneath Arctic ice.

The unsupervised domain-adaptive vehicle reidentification approach aims to transfer knowledge from a labeled source domain to an unlabeled target domain, however there are knowledge differences between the target domain and the source domain. To reduce domain differences, existing unsupervised domain-adaptive rerecognition methods generally require access to the source domain data to assist in re-training the target domain model. But for security reasons, in many cases, data between different domains cannot communicate. To this end, this paper proposes an unsupervised domain-adaptive vehicle re-identification method based on source-free knowledge transfer. First, by constructing a passive domain knowledge migration module, the target domain is consistent with the source domain model output to train a generator to generate the "source-like samples". Then, it can effectively reduce the model knowledge difference and improve the model generalization performance. According to the experiment and testing of VeRi776 and VehicleID, two mainstream public data sets in this field, the rank-k and mAP indexes obtained are both improved, and are suitable for object re-recognition tasks without source data.

Due to resource constraints, most Diamond Open Access journals publish less than 25 articles per year, and 75% of journals are not able to provide their content in XML and HTML, primarily providing only PDFs (Bosman et al., 2021, p. 7-8). In order to keep up with larger commercial publishers, a high degree of automation and streamlining of processes is necessary. The Open Source Academic Publishing Suite (OS-APS) project funded by the German Federal Ministry of Education and Research aims to achieve this. OS-APS automatically extracts the underlying XML from Word manuscripts and offers optimization and export options in various formats (PDF, HTML, EPUB). The professional corporate design, e.g., of the PDFs, is managed automatically by using templates or creating one's own using a Template Development Kit. OS-APS will also connect to scholarly-led and community-driven publishing platforms such as Open Journal Systems (OJS), Open Monograph Press (OMP), and DSpace: the software will be able to be integrated into a wide range of publication processes, whether at small, low-resource commercial Open Access Publishers, or institutional and Diamond Open Access Publishers. References: Bosman, J., Frantsvåg, J. E., Kramer, B., Langlais, P.‑C., &amp; Proudman, V. (2021). Oa Diamond Journals Study. Part 1: Findings. https://doi.org/10.5281/zenodo.4558703

In this study, divalent Yb2+-doped silica fiber was fabricated using rod-in-tube technology. The fiber core of Yb2+-doped silica glass was prepared by high-temperature melting technology under vacuum conditions. The spectroscopic properties of the Yb2+-doped glass and fiber were studied. The experiments indicate that divalent Yb2+-doped glass has a high quantum efficiency and superbroadband fluorescence in the visible region with an excitation wavelength of 405 nm. In additionally, the results suggest that Yb2+-doped fiber has a potential for application in visible fiber laser and fiber amplification.

With the current technological transformation in the automotive industry, autonomous vehicles are getting closer to the Society of Automative Engineers (SAE) automation level 5. This level corresponds to the full vehicle automation, where the driving system autonomously monitors and navigates the environment. With SAE-level 5, the concept of a Shared Autonomous Vehicle (SAV) will soon become a reality and mainstream. The main purpose of an SAV is to allow unrelated passengers to share an autonomous vehicle without a driver/moderator inside the shared space. However, to ensure their safety and well-being until they reach their final destination, it is required an active monitoring of all passengers. In this context, this article presents a microphone-based sensor system that is able to localize sound events inside an SAV. The solution is composed of a Micro-Electro-Mechanical System (MEMS) microphone array with a circular geometry connected to an embedded processing platform that resorts to Field-Programmable Gate Array (FPGA) technology to successfully process in hardware the sound localization algorithms.

The testing requirements of the active phased array antennas are very different from those of traditional passive antennas, due to its beam steering capability. Usually, each beam profile of the active phased array needs a separate radiation pattern test, which makes the overall testing time extremely long. Thus the traditional antenna test method can no longer meet the efficiency and cost requirements of new active phased array antennas test. In this paper, a fast test method tailored for phased array antennas is proposed that offers significantly reduced testing time at the expense of slight sacrifice of the accuracy. Using the simulated element pattern in array and ideal port excitation, the beam profile in any direction can be predicted by testing only a certain beam profile. Through theoretical derivation and experiments, the effectiveness of the method is verified, and the testing efficiency of the phased array antenna is demonstrated to be improved by ten times or even more.

Background: Developing patient-centric baseline standards that enable the detection of clinically significant outlier gene products on a genome-scale remains an unaddressed challenge required for advancing personalized medicine beyond the small pools of subjects implied by “precision medicine”. This manuscript proposes a novel approach for reference standard development to evaluate the accuracy of single-subject analyses of metabolomes, proteomes, or transcriptomes. Since distributional assumptions of statistical testing may inadequately model genome dynamics of gene products, the so-called significant results of previous studies may artefactually conflate with real signals. Model confirmation biases escalate when studies use the same analytical methods in the discovery sets and reference standards, as corroboration of results leads to an evaluation of reproducibility confounded with replicated biases rather than a measure of accuracy. We hypothesized that developing method-agnostic reference standards using effect-size and expression-level filtering of results, obtained from multiple discovery methods that are distinct from the one evaluated, would maximize the evaluation of clinical-transcriptomic signals and minimize statistical artefactual biases. We developed and released an R package “referenceNof1” to facilitate the construction of robust reference standards. Results: Since RNA-Seq data analysis methods often rely on binomial and negative binomial assumptions to non-parametric analyses, the differences create statistical noise and make the reference standards method dependent. In our experimental design, the accuracy of 30 distinct combinations of fold changes (FC) and expression levels (EL) were determined for five types of RNA analyses in two different datasets. This design was applied to two distinct datasets: breast cancer cell lines and a yeast study with isogenic biological replicates in two experimental conditions. In addition, the reference standard (RS) comprised all RNA analytical methods with the exception of the method testing accuracy. To mitigate for biased optimization of the RS parameters towards a specific analytical method, similarity between observed results of distinct analytical methods were calculated across all methods (Jaccard Concordance Index). The greatest differences were observed across diametric extremes. For example, filtering out differentially expressed genes (DEGs) using a fold change < 1.2 leads to a 50% increase in concordance between techniques when compared to results with FC > 1.2. Combining this FC cutoff with genes with mean expressions > 30 counts leads to a 65% increase in concordance in comparison to genes with expression levels < 30 counts and with FC < 1.2. Conclusions: We have demonstrated that comparing accuracies of different single-subject analysis methods for clinical optimization requires a new evaluation framework. Reliable and robust reference standards, independent of the evaluated method, can be obtained under a limited number of parameter combinations: fold change (FC) ranges thresholds, expression level cutoffs, and exclusion of the tested method from the RS development process. When applying anticonservative reference standard frameworks (e.g., using the same method for RS development and for prediction), a majority of the concordant signal between prediction and Gold Standard (GS) cannot be confirmed by other methods, which we conclude as biased results. Statistical tests to determine DEGs from a single-subject study generate many biased results that require subsequent filtering for increasing their reliability. Conventional single-subject studies pertain to one or a few measures in one patient over time [1]and need a substantial conceptual framework extension in order to address the tens of thousands of measures in genome-wide analyses of gene products. The proposed referenceNof1 framework addresses some of the inherent challenges in improving transcriptome scale single-subject analyses by providing a robust approach to constructing reference standards. Github: https://github.com/SamirRachidZaim/referenceNof1

Learning disorders (LD) are diagnosed in children whose academic skills of reading, writing or mathematics are impaired and lagged according to their age, schooling and intelligence. Children with LD experience substantial working memory (WM) deficits, even more pronounced if more than one of the academic skills is affected. We compared the task-related EEG power spectral density of children with LD (n= 23), with a control group of children with good academic achievement (n= 22), during the performance of a WM task. sLoreta was used to estimate the current distribution at the sources, and 18 brain regions of interests (ROIs) were chosen with an extended version of the eigenvector centrality mapping technique. In this way, we lessen some drawbacks of the traditional EEG at the sensor space by an analysis at the brain sources level over data-driven selected ROIs. Results: The LD group showed fewer correct responses at the WM task, an overall slower EEG with more theta activity in all ROIs, less upper-alpha power at posterior areas, and less high-frequency beta activity in frontal areas. We explain these EEG patterns in LD children as indices of an inefficient neural resource management related with a delay in the neural development.

During the period January 2014 – October 2018, four strong earthquakes occurred in the Ionian Sea, Greece. A rich aftershock sequence followed each event of them. More analytically, according to the manual solutions of National Observatory of Athens, the first event (K1), occurred on 26 January 2014 in Kefallinia Island with magnitude M_L = 5.8, which was followed by another in the same region (K2) on 3 February 2014 with magnitude M_L = 5.7. The third event occurred on 17 November 2015, M_L = 6.0 in Lefkas Island (L1) and the last on 25 October 2018, M_L = 6.6 in Zande Island (Z1). The first three of these earthquakes caused moderate structural damages mainly in houses and produced particular unrest to the local population. This work presents first the calculation of the source parameters of the strong events as well as for all earthquakes with magnitude M_L > 4.0, using the methodology of the Moment tensor inversion and secondary data from permanent GPS stations were analyzed to confirm the findings from seismological data and to investigate the displacement due to the earthquakes.

Hydrogen gas is seeping from the sedimentary basin of São Franciso, Brazil. The seepages of H₂ are accompanied by helium whose isotopes reveal a strong crustal signature. Geophysical data indicates that this intra-cratonic basin is characterized by i) a relatively high geothermal gradient, ii) deep faults delineating a horst and graben structure and affecting the entire sedimentary sequence, iii) an archean to paleoproterozoïc basements enriched in radiogenic elements and displaying mafic and ultramafic units, and iv) a possible karstic reservoir located 400 m below the surface. The high geothermal gradient could be due to a thin lithosphere enriched in radiogenic elements, which can also contribute to a massive radiolysis process of water at depth, releasing an important amount of H₂. Alternatively, ultramafic rocks that may have generated H₂ during their serpentinization are also documented in the basement. The seismic profiles show that the faults seen at the surface are deeply rooted in the basement, and can drain deep fluids to shallow depths in a short time scale. The carbonate reservoirs within the Bambuí group which forms the main part of the sedimentary layers are crossed by the fault system and represent good candidates for temporary H₂ accumulation zones. The formation by chemical dissolution of sinkholes located at 400 m depth might explain the presence of sub-circular depressions seen at the surface. These sinkholes might control the migration of gas from temporary storage reservoirs in the upper layer of the Bambuí formation to the surface. The very high fluxes of H₂ escaping out of these structures which have been recently documented are, however, in disagreement with the newly developed H₂ production model in the Precambrian continental crust. They either question the validity of these models or the measurement methodology.

NMR spectral datasets, especially in systems with limited samples, can be difficult to interpret if they contain multiple chemical components (phases, polymorphs, molecules, crystals, glasses, etc…) and the possibility of overlapping resonances. In this paper, we benchmark several blind source separation techniques for analysis of NMR spectral datasets containing negative intensity. For benchmarking purposes, we generated a large synthetic datasbase of quadrupolar solid-state NMR-like spectra that model spin-lattice T1 relaxation or nutation tip/flip angle experiments. Our benchmarking approach focused exclusively on the ability of blind source separation techniques to reproduce the spectra of the underlying pure components. In general, we find that FastICA (Fast Independent Component Analysis), SIMPLISMA (SIMPLe-to-use-Interactive Self-modeling Mixture Analysis), and NNMF (Non-Negative Matrix Factorization) are top-performing techniques. We demonstrate that dataset normalization approaches prior to blind source separation do not considerably improve outcomes. Within the range of noise levels studied, we did not find drastic changes to the ranking of techniques. The accuracy of FastICA and SIMPLISMA degrades quickly if excess (unreal) pure components are predicted. Our results indicate poor performance of SVD (Singular Value Decomposition) methods, and we propose alternative techniques for matrix initialization. The benchmarked techniques are also applied to real solid state NMR datasets. In general, the recommendations from the synthetic datasets agree with the recommendations and results from the real data analysis. The discussion provides some additional recommendations for spectroscopists applying blind source separation to NMR datasets, and for future benchmark studies. Applications of blind source separation to NMR datasets containing negative intensity may be especially useful for understanding complex and disordered systems with limited samples and mixtures of chemical components.

We introduce PyNTA, a modular instrumentation software for live particle tracking. By using the multiprocessing library of Python and the distributed messaging library pyZMQ, PyNTA allows users to acquire images from a camera at close to maximum readout bandwidth while simultaneously performing computations on each image on a separate processing unit. This publisher/subscriber pattern generates a small overhead and leverages the multi-core capabilities of modern computers. We demonstrate capabilities of the PyNTA package on the featured application of nanoparticle tracking analysis. Real-time particle tracking on megapixel images at a rate of 50 Hz is presented. Reliable live tracking reduces the required storage capacity for particle tracking measurements by a factor of approximately 10³, as compared with raw data storage, allowing for a virtually unlimited duration of measurements

Best management practices (BMPs) are a way to control pollution in river basins. Prioritization of BMPs helps improve efficiency and effectiveness of pollution reduction, especially in critical source areas (CSAs) that produce the highest pollution loads. Recently, the Dez River, Khuzestan, Iran, has become highly eutrophic from overuse of fertilizers and pesticides. Dry and irrigated farming produce 77.34% and 6.3% of the total nitrogen (TN) load, and 83.56% and 4.3% of the total phosphorus (TP) load in this basin, respectively. Residential, pasture, and forest land uses account for 16.36% of the TN and 12.14% of the TP load cumulatively. In this study, the Soil and Water Assessment Tool (SWAT) was implemented to model the Dez River basin, and evaluate the applicability of several BMPs including point source elimination, filter strips, livestock grazing, and river channel management, in reducing the entry of pollution loads to the river. Sensitivity analysis and calibration/validation of the model was performed using the SUFI-2 algorithm in the SWAT Calibration Uncertainties Program (SWAT-CUP). CSAs were identified using individual (sediment, TN, TP) and combined indices, based on the amount of pollution produced. Among the BMPs implemented, filter strips were most effective in reducing TN loads (59%), and, increasing the D₅₀ of particles for river channel management was most effective in reducing TP loads (49%).

Background: New open-source electric-grid planning models have the potential to improve power system planning and bring a wider range of stakeholders into the planning process for next-generation, high-renewable power systems. However, it has not yet been established whether open-source models perform similarly to the more established commercial models for power system analysis. This reduces their credibility and attractiveness to stakeholders, postponing the benefits they could offer. In this paper, we report the first model intercomparison between an open-source power system model and an established commercial production cost model. Results: We compare the open-source Switch 2.0 to GE Energy Consulting’s Multi Area Production Simulation (MAPS) for production-cost modeling, considering hourly operation under 17 scenarios of renewable energy adoption in Hawaii. We find that after configuring Switch with similar inputs to MAPS, the two models agree closely on hourly and annual production from all power sources. Comparing production gave a coefficient of determination of 0.996 across all energy sources and scenarios, indicating that the two models agree on 99.6% of the variation. For individual energy sources, the coefficient of determination was 69–100. Conclusions: Although some disagreement remains between the two models, this work indicates that Switch is a viable choice for renewable integration modeling, at least for the small power systems considered here. Although some disagreement remains between the two models, this work indicates that Switch is a viable choice for renewable integration modeling, at least for the small power systems considered here.

Flow and transport processes in soil and rock play a critical role in agricultural non-point source pollution (ANSP) loads. In this study, we investigated the ANPS load discharged into rivers from an irrigation district in the Tibetan Plateau, and simulated ANPS load using a distributed model involving detailed descriptions of flow and ANPS transport and transformation processes in the soil and rock. Experiments were conducted for two years to measure soil water content and nitrogen concentrations and the quality and quantity of lateral flow in the rock and at the drainage canal outlet during the highland barley growing period. A distributed model, in which the subsurface lateral flow was described using a step-wise method, was developed to simulate flow and ammonium nitrogen and nitrate nitrogen transport. Sobol’s method was used to evaluate the sensitivity of simulated flow and transport processes to model inputs. The results showed that, with a 21.2% increase of rainfall and irrigation in the highland barley growing period, the average NH4+-N and NO3--N concentrations in the soil layer decreased by 10.8% and 14.3%, respectively, due to increased deep seepage. Deep seepage of rainfall water accounted for 0–52.4% of total rainfall, whereas deep seepage of irrigation water accounted for 36.6–45.3% of total irrigation. NH4+-N and NO3--N discharged into the drainage channel represented 19.9–30.4% and 19.4–26.7% of the deep seepage, respectively. The mean Nash-Sutcliffe coefficients, root mean square errors, and cumulative deviations between the measured and simulated flow rates and NH4+-N and NO3--N concentrations at the surface drainage canal outlet were 0.694, 0.081, and 0.242, respectively, indicating that the proposed method can effectively describe the hydrological and ANPS pollution migration in the plateau irrigation zone. The Sobol’ sensitivity analysis results demonstrated that subsurface lateral flow had the most important first order and total effect on the simulated flow and NH4+-N and NO3--N concentrations at the surface drainage outlet.

Software defect prediction is an important means to guarantee software quality. Because there are no sufficient historical data within a project to train the classifier, cross-project defect prediction (CPDP) has been recognized as a fundamental approach. However, traditional defect prediction methods using feature attributes to represent samples, which can not avoid negative transferring, may result in poor performance model in CPDP. This paper proposes a multi-source cross-project defect prediction method based on dissimilarity space ( DM-CPDP). This method first uses the density-based clustering method to construct the prototype set with the cluster center of samples in the target set. Then, the arc-cosine kernel is used to form the dissimilarity space, and in this space the training set is obtained with the earth mover’s distance (EMD) method. For the unlabeled samples converted from the target set, the KNN algorithm is used to label those samples. Finally, we use TrAdaBoost method to establish the prediction model. The experimental results show that our approach has better performance than other traditional CPDP methods.

Polycyclic aromatic hydrocarbons (PAHs) in street dust pose a serious problem threatening both environment and human health. Street dust were collected from five different land use patterns (traffic areas TRA, urban area URA, residential areas REA, mixed residential commercial areas MCRA and suburban areas SUA) in a Saudi coastal city, Jeddah, and one in rural area (RUA) in Hada Al Sham. This study aimed to investigate the status, profile, sources of PAHs and estimate their human health risk. The results revealed an average concentration of total PAHs of 3320 ng/g in street dust of Jeddah and 223 ng/g in RUA dust. PAHs with high molecular weight represented 83.38% of total PAHs in street dust of Jeddah, while the carcinogenic PAH compounds accounted 57.84%. The highest average concentration of total PAHs in street dust of Jeddah was found in TRA (4980 ng/g) and the lowest in REA (1660 ng/g). PAHs ratios indicated that the principal source of PAHs in street dust of Jeddah is pyrogenic, mainly traffic emission. Benzo(a)anthracene/ chrysene (BaA/CHR) ratio suggests that PAHs in street dusts of Jeddah come mainly from emission of local sources, while PAHs in RUA might be transported from the surrounding urban areas. The estimated Incremental Lifetime Cancer Risk (ILCR) associated with exposure to PAHs in street dusts indicated that both dermal contact and ingestion pathways are major contributed to cancer risk for both children and adults. Based on BaP_equivalence concentrations of total PAHs, ILCR_Ingestion, ILCR_dermal and cancer risk values for children and adults exposed to PAHs in street dust of different areas in Jeddah were found between 10⁻⁶ and 10⁻⁴, indicating potential risk. The sequence of cancer risk was TRA > URA > MCRA > SUA > REA. Only exposure to BaP and DBA compounds had potential risk for both children and adults.

Over the years, sustainability, impact on the environment, as well as the operation expenditure have been a major concern to the deployment of mobile cellular base stations worldwide. This is because the mobile cellular base stations are known to consume a high percentage of power within the mobile cellular network. Such energy consumption contributes to the emission of Greenhouse Gases (GHG) through the use of conventional diesel generating a set. As a result, the mobile cellular operators are faced with the dilemma of minimising the power consumption, GHG emission, and the operation cost, while improving the Quality of Service of the networks. In attempting to find a solution, this study presents the feasibility and simulation of a solar photovoltaic (PV) with battery hybrid power system (HPS) as a predominant source of power for a specific mobile cellular base station site situated in Soshanguve area of the city of Pretoria, South Africa. It also presents the technical development, showed the environmental advantage and cost benefits of using a solar PV-battery HPS to power a base station site of a 24 hrs daily load of 241.10 kWh/d and the peak load of 20.31 kW as compared to using the HPS of solar PV-diesel generating set-battery. The solar resource pattern for the city of Pretoria was collected from The National Aeronautics and Space Administration and modelled statistically. Thus, the statistical modelling done using solar radiation resource exposure characteristic patterns of Pretoria, South Africa, revealed an average annual daily solar radiation of 5.4645 Wh/m2/d and 0.605 clearness index. The simulation and the design were done using the Hybrid Optimization Model for Electric Renewables and Matlab/Simulink software. The simulation finding shows that the HPS of solar PV-battery combination has about 59.62 % saving on Net Present Cost, Levelized Cost of Energy, and 80.87% saving on Operating cost as against conventional BS powered with Gen Set-Battery.

In this paper we study the initial boundary value problem for logarithmic Higher Order Wave equation. Introducing the Logarithmic Sobolev inequality and using the combination of Galerkin method, we consider the theorem of existence of a global weak solution to problem for the initial boundary value problem of the logarithmic wave equation. By constructing an appropriate Lyapunov function, we obtain the decay estimates of energy for logarithmic Higher Order Wave equation. The proof of the main theorem is given.

The in vitro cultivation of M. leprae has not been possible since it was described as causing leprosy, and the limitation of animal models for clinical aspects makes studies on leprosy and bacteria-human host interaction a challenge. Our aim was to standardize the ex vivo skin model (hOSEC) to maintenance and study of M. leprae as an alternative animal model. Bacillary suspensions were inoculated into human skin explants and sustained in DMEM medium until 60 days. Explants were evaluated by RT-PCR-16SrRNA, and cytokines gene expression. Viability and infectivity of bacilli recovered from explant (D28 and D60) were evaluated using the Shepard’s model. All explants were RT-PCR-16srRNA positive. Viability and infectivity of recovered bacilli from explants, analyzed after 5 months of inoculation in mice showed an average positivity of 31%, being the highest positivity in the D28 groups (80%). Furthermore, our work showed different pattern in cytokines gene expression (TGF-β, IL-10, IL-8 and TNF-α) in the presence of alive or dead bacilli. Although changes can be made for improve future experiments, our results have demonstrated that it is possible to use the hOSEC to maintain M. leprae for 60 days, interacting with the host system, an important step in the development of experimental models for studies on the biology of the bacillus, its interactions, and drug susceptibility.

The social- and self-directed behaviours of two golden lion tamarins (Leontopithecus rosalia) was investigated while having residence in the tropical house of Aalborg Zoo, Denmark. The behaviour of the female had been distinct from the male since 2018. Despite several diagnostic examinations and medicinal treatments, the cause of the aberrant and apparently stereotypical behavioural patterns remained undetermined, which initiated this study. The pair were recorded throughout a predetermined observation period and these recordings were analysed to illuminate the behaviours of the individuals. The results showed that there were differences in the type, frequency, and durations of both social- and self-directed behaviours between the sick female and the healthy male. The female expressed both social- and self-directed behaviours more frequently and for longer durations, while the male were more socially reserved and did not engage in social behaviours as frequently. Furthermore, subsequent necropsy of the female revealed uro-genital disease.

Few studies have examined the dietary protein intake and sources in combination with the longitudinal changes in brain structure markers. Our study aimed to examine the association between dietary protein intake and different sources of dietary protein with the longitudinal rate of change in brain structural markers. A total of 2723 and 2679 participants from the UK Biobank were included in the analysis. The relative and absolute amounts of dietary protein intake were calculated through a 24-hour dietary recall questionnaire. The longitudinal change rates of brain structural biomarkers were computed using two waves of brain imaging data. We utilized multiple linear regression to examine the association between dietary protein and different sources and the longitudinal changes in brain structural biomarkers. Restrictive cubic splines were used to explore nonlinear relationships, and stratified and sensitivity analyses were conducted. Increasing the proportion of animal protein in dietary protein intake is associated with a slower reduction in the total hippocampus volume (THV, β: -0.02524, P&lt;0.05), left hippocampus volume (LHV, β: -0.02435, P&lt;0.01) and right hippocampus volume (RHV, β: -0.02544, P&lt;0.05).Higher intake of animal protein relative to plant protein is linked to a lower atrophy rate in THV (β: -0.01249, P&lt;0.05) and LHV (β: -0.01173, P&lt;0.05) and RHV (β: -0.01193, P&lt;0.05). Individuals with higher intake of seafood exhibit lower longitudinal rate of change in HV compared to those with no consume (THV, β: -0.004514; P&lt;0.05; RHV, β: -0.005527, P&lt;0.05). In subgroup and sensitivity analyses, there was not a significantly alteration. A moderate increase in the intake and proportion of animal protein in the diet, especially from seafood, is associated with a lower longitudinal change rate in hippocampus volume.

Chlamydia trachomatis (Ct) infections are the most common sexually-transmitted infection (STI). Despite effective antibiotics for Ct, undetected infections or delayed treatment can lead to infertility, ectopic pregnancies, and chronic pelvic pain. Besides humans, chlamydia poses similar health challenges in animals such as C. suis (Cs) in pigs. Based on the similarities between humans and pigs as well as their chlamydia species, we use pigs as a large biomedical animal model for chlamydia research. In this study, we use the pig model to develop a vaccine candidate against Ct. The vaccine candidate consists of TriAdj-adjuvanted chlamydial protease-like activity factor (CPAF) protein. We tested two weekly administration options – twice intranasal (IN) followed by twice intramuscular (IM), or twice IM followed by twice IN. We assessed the humoral immune response not only in serum by CPAF-specific IgG including antibody avidity determination but also in cervical and rectal swabs by CPAF-specific IgG and IgA ELISAs. The systemic T-cell response was analyzed upon in vitro CPAF-restimulation via IFN-γ and IL-17 ELISpots as well as intracellular cytokine staining flow cytometry. Our data demonstrate that while the IN/IM vaccination mainly led to non-significant systemic immune responses, the vaccine candidate is highly immunogenic if administered IM/IN. This vaccination strategy induced high serum anti-CPAF IgG levels with strong avidity as well as high IgA and IgG levels in vaginal and rectal swabs and in uterine horn flushes. In addition, this vaccination strategy prompted a pronounced cellular immune response: Besides inducing IL-17 production, the vaccine candidate induced a strong IFN-γ response by CD4 T cells. In the IM/IN vaccinated pigs, these cells also significantly downregulated their CCR7 expression – a sign for differentiation into peripheral tissue homing effector/ memory cells. Conclusively, this study demonstrates strong immunogenicity of the IM/IN administered TriAdj-adjuvanted Ct CPAF vaccine candidate. Future studies will test vaccine efficacy of this promising Ct vaccine candidate. In addition, this project demonstrates suitability of the Cs pre-exposed outbred pig model for Ct vaccine development. Thereby, we aim to open the bottleneck of large animal models to facilitate the progression of Ct vaccine candidates into clinical trials.

As brachycephalic dog breed ownership increases, there is a growing concern for the welfare of these breeds due to extreme brachycephalism. Understanding the motivations and behaviours of those choosing to own these breeds is important if we wish to address these concerns. The aim of this study was to investigate how owners of brachycephalic and non-brachycephalic dog breeds use social media dog breed groups to discuss common health issues. The purpose of Facebook posts in relation to common health issues, owner awareness of health issues, and the role of Facebook facilitated social support were explored between brachycephalic and non-brachycephalic dog owners. Twelve Facebook breed groups were selected (brachycephalic breed groups, n=6, non-brachycephalic breed groups, n=6). Using key word searches we extracted the first twenty posts from each group. Thematic analysis revealed three overarching themes: advice seeking, advice giving and community bonding mechanisms. Whilst the purpose of posting did not differ between groups, non-brachycephalic owners appeared to display greater awareness of breed-specific health issues, whilst social support played a more prominent role in brachycephalic groups. This research highlights that social media groups can act as platforms for knowledge exchange and emotional support. These could be utilised by owners, veterinarians and welfare organisations to more effectively enhance dog health and wellbeing.

Marine lipids are recognized for their health promoting features, mainly for being the primary sources of omega-3 fatty acids, and therefore critical for human nutrition in an age when the global supply for these nutrients is experiencing an unprecedent pressure due to an ever-increasing demand. The seafood industry originates a considerable yield of co-products worldwide that, while already explored for other purposes, remain mostly undervalued as sustainable sources of healthy lipids, often being explored for low value oil production. These co-products are especially appealing as lipid sources since, besides the well-known nutritional upside of marine animal fat, particularly rich in omega-3 polyunsaturated fatty acids (PUFAs), they have also interesting bioactive properties, which may garner them further interest, not only as food, but also for other high-end applications. Besides the added value that these co-products may represent as valuable lipid sources, there is also the obvious ecological upside of reducing seafood industry waste. In this sense, repurposing these bioresources will contribute to a more sustainable use of marine animal food, reducing the strain on already heavily depleted seafood stocks. Therefore, untapping the potential of marine animal co-products as valuable lipid sources aligns with both health and environmental goals by guaranteeing additional sources of healthy lipids and promoting more eco-conscious practices.

In the context of globalization One Health enables and concurs to the achievement of Global Health. One Health approach involves wide knowledge and understanding of its critical factors, next to an adequate implementation, also in farm animal sector. Moreover, animal farming and breeding shows particularities depending on country. Therefore, well-defined and specific strategies are still needed to be developed and adapted to the current conditions found in various countries for ensuring a well implementation, thus driving to better and healthier global outcomes and security. This paper will explore the One Health status in Romania, highlighting the current progress registered until now and the future perspectives based on its potential, in the context of the livestock farming framework and challenges. Our findings may help to disclose the challenges of putting in practice One Health in Romania, alongside to the pathways showing potential to enhance sustainability in livestock farming.

Nuclear bodies (NBs) are dynamic structures present in eukaryotic cell nuclei. They are not bounded by membranes and are often considered biomolecular condensates, defined structurally and functionally by the localisation of core components. Nuclear architecture can be reorganised during normal cellular processes such as the cell cycle as well as in response to cellular stress. Many plant and animal viruses target their proteins to NBs, in some cases triggering their structural disruption and redistribution. Although not all such interactions have been well characterised, subversion of NBs and their functions may form a key part of the life cycle of eukaryotic viruses that require the nucleus for their replication. This review will focus on Cajal bodies (CBs) and the viruses that target them. Since CBs are dynamic structures, other NBs (principally nucleoli and promyelocytic leukaemia, PML, bodies), whose components interact with CBs, will also be considered. As well as providing important insights into key virus: host cell interactions, studies on Cajal and associated NBs may identify novel cellular targets for development of anti-viral compounds.

This study evaluated five annual and eleven perennial Indian pasture legumes species for their nutritive value, dry matter, mineral contents and in vitro fermentation parameters, aiming to boost their potential as animal feed. Legume species significantly differed (p<0.05) in organic matter, crud protein (CP), ether extracts, fibres, and protein fractions. Clitoria ternatea (CT) a perennial had higher (p<0.05) buffer soluble protein (477), while neutral detergent soluble protein was highest in annually grown Lablab purpureus (420 g/kg CP). Atylosia scarabaeoides (AS) had higher NSC (392 g/kg DM) than SC (367 g/kg DM). Rapidly degradable fraction (51.7 g/kg tCHO) was lower (p<0.05) than other fractions of carbohydrate. Total digestible nutrients, digestible energy and metabolisable energy varied and values were higher for Desmenthus virgatus and lowest for Stylosanthas seabrana (SSe). The NE values for lactation (NEL), maintenance (NEM) and gain (NEG) were also higher for DV (6.6, 7.95 and 4.28) and lowest for SSe (3.54, 4.24 and 0.58 kJ/g DM), respectively. dry matter intake, digestible DM (DDM) and relative feed value of legumes differed (p<0.05) with mean values of 2.22% body weight, 592 g/kg DM and 102%, respectively. Annual grasses like Dolichos biflorus, Macroptilium atropurpureum, Rhynchosia minima (RM) were found better with micro minerals compared to other perennials. In vitro dry matter degradability, partition factor, short chain fatty acids and microbial protein production of legumes varied (P<0.05) and mean values were 578 g/kg DM, 5.61 mg DDM/mL, 2.42 mm/g and 352 mg/g, respectively. Gas and CH4 production (mL/g and mL/g DDM) varied (p<0.05) being lowest gas from AS (55.5) and highest from CT-blue (141), while CH4 production was lower (p<0.05) from CT-white and AS (8.24 and 9.14) and higher from Arachis glabrata (AG) and Arachis hagenbackii (AH) (15.2 and 15.1). Methane in total gas was low for DV, RM and CT-w (8.99, 9.72 and 9.51%) and loss of DE and ME as CH4 varied (P<0.05) amongst the legumes with mean values of 4.62 and 7.86%, respectively. Based on these findings, each legume offers unique benefits, allowing for tailored combinations of annual and perennial legumes to optimize rumen feed efficiency.

Dog Assisted Therapy is hypothesized to lower stress in children with Autism Spectrum Disorder (ASD) and children with Down Syndrome (DS), which may be visible on a physiological level. In this study, we measured Heart Rate Variability (HRV) and salivary cortisol of 20 children with DS or ASD at the beginning and end of 6 weekly sessions of Dog-Assisted Therapy. We found a decrease of cortisol levels during single sessions, but no overall effect after 6 sessions (6 weeks). The effect of Dog Assisted Therapy on the increase of HRV could not be confirmed. This study is one of the first to use physiological measurements to test the effects of DAT.

Carrageenan (car-), used in the pharmaceutical as well as food industry, is marine sulfated polysaccharides. The carrageenan blends (CB) of kappa (κ-), iota (ι-), and lambda (λ-) of 50/50 weight by weight are multipurpose and biocompatible hydrophilic polymers. They were subjected to adsorption of copper (Cu2+) ions. The aim of the study was to conduct extensive rheological and mechanical studies rendering them suitable for biological purposes. The rheology was conducted at various temperatures using a frequency curve, frequency sweep, loss modulus, and storage modulus analysis. Models of rheology like Ostwald, Bingham, and Modified Bingham models were utilized. The rheological results demonstrated non-Newtonian/pseudo-plastic behavior at various temperatures. The models of rheology demonstrated good flow properties and rheological performance of these materials. These materials could be exploited for their application in pharmaceuticals, food, and cosmetics. Thus, our study reflects that these CB can be safely exploited in the healthcare, pharmaceutical, and food industry. However, further studies should be conducted to evaluate their effectiveness in clinical studies.

Conservation of wildlife depends on precise and unbiased knowledge on the abundance and distribution of species. It is challenging to choose appropriate methods to obtain a sufficiently high detectability and spatial coverage matching the species characteristics and spatiotemporal use of the landscape. In remote regions, such as in the Arctic, monitoring efforts are often resource-intensive and there is a need for cheap and precise alternative methods. Here, we compare an uncrewed aerial vehicle (UAV; quadcopter) pilot-survey of the non-gregarious Svalbard reindeer to traditional population abundance surveys from ground and helicopter to investigate whether UAVs can be an efficient alternative technology. We found that the UAV survey underestimated reindeer abundance compared to the traditional abundance surveys when used at management relevant spatial scales. Observer variation in reindeer detection on UAV imagery was influenced by the RGB greenness index and mean blue channel. In future studies, we suggest to test long-range fixed-wing UAVs to increase the sample size of reindeer and area coverage and incorporate detection probability in animal density models from UAV imagery. In addition, we encourage focus on more efficient post-processing techniques, including automatic animal object identification with machine learning and analytical methods that account for uncertainties.

Biological drugs are inaccessible to more than 80% of the world population due mainly to their high costs; this is a significant concern of the World Health Organization. Biosimilars are supposed to reduce the cost burden, but their approval process is complex, including expensive and irrelevant studies. While the Stringent Regulatory Authorities (SRAs) have adopted the guidance of the FDA or EMA, such adoptions are neither necessary nor practical for the rest of the world (ROW). We present a science-driven, rational approach to formulate regulatory guidelines that will enable faster biosimilars' entry into the ROW without compromising their safety and efficacy. The key recommendations include removing animal and safety efficacy testing, making analytical assessment more robust, and cGMP compliance assured through third-party audits. The ROW countries are also recommended to initiate a rapporteur system available in the EU, to overcome the biases and assure state-of-the-science evaluation as the common understanding of the critical quality attributes evolves. It is anticipated that stronger regional agencies like those in the MENA region, with the leadership of the Kingdom of Saudi Arabia, will help propel the idea faster across the globe.

Pig behavior is an integral part of health and welfare management, as pigs usually reflect their inner emotions through behavior change. The livestock environment plays a key role in pigs' health and wellbeing. A poor farm environment increases the toxic GHGs, which might deteriorate pigs' health and welfare. In this study a computer-vision-based automatic monitoring and tracking model was proposed to detect short-term pigs' physical activities in a compromised environment. The ventilators of the livestock barn were closed for an hour, three times in a day (07:00-08:00, 13:00-14:00, and 20:00-21:00) to create a compromised environment, which increases the GHGs level significantly. The corresponding pig activities were observed before, during, and after an hour of the treatment. Two widely used object detection models (YOLOv4 and Fast-er R-CNN) were trained and compared their performances in terms of pig localization and posture detection. The YOLOv4, which outperformed the Faster R-CNN model, coupled with a Deep-SORT tracking algorithm to detect and track the pig activities. The results showed that the pigs became more inactive with the increase in GHG concentration, reducing their standing and walking activities. Moreover, the pigs also shortened their sternal-lying posture increasing the lateral lying posture duration at higher GHG concentration. The high detection accuracy (mAP: 98.67%) and tracking accuracy (MOTA: 93.86% and MOTP: 82.41%) signify the models’ efficacy in monitoring and tracking pigs' physical activities non-invasively.

Molecular methods have revolutionised virtually every area of biology, and metazoan phylogenetics is no exception: molecular phylogenies, molecular clocks, comparative phylogenomics, and developmental genetics have collectively transformed our understanding of the evolutionary history of animals. Moreover, the diversity of methods and models within molecular phylogenetics has resulted in significant disagreement among molecular phylogenies as well as between these and traditional phylogenies. Here, I argue that tackling this multifaceted problem lies in integrating evidence to infer the best evolutionary scenario. I begin with an overview of recent developments in early metazoan phylogenetics, followed by a discussion of key conceptual issues in phylogenetics revolving around phylogenetic evidence and theory. I then argue that integration of different kinds of evidence is necessary for arriving at the best evolutionary scenario rather than the best-fitting cladogram. Finally, I discuss the prospects of this view in stimulating interdisciplinary cross-talk in early metazoan research and beyond.

In the last decades farm animals kept in confined and mechanically ventilated livestock buildings are increasingly confronted with heat stress (HS) due to global warming. These adverse conditions cause a depression of animal health and welfare and a reduction of the performance up to an increase of the mortality. To facilitate sound management decisions, livestock farmers need relevant arguments, which quantify the expected economic risk and the corresponding uncertainty. The economic risk was determined for the pig fattening sector based on the probability of HS and the calculated decrease in the gross margin. The model calculation for confined livestock buildings showed, that HS indices calculated by easily available meteorological parameters can be used for assessment quantification of indoor HS, which is so far difficult to determine. These weather-related HS indices can be applied not only for an economic risk assessment but also for a weather-index based insurance for livestock farms. Based on the temporal trend between 1981 and 2017, a simple model was derived to assess the likelihood of HS for 2020 and 2030. Due to global warming, the return period for a 90-percentile HS index is reduced from 10 years in 2020 to 3-4 years in 2030. The economic impact of HS on livestock farms was calculated by the relationship between an HS index based on the temperature-humidity index (THI) and the reduction of the gross margin. From the likelihood of the HS and this economic impact function, the probability of the economic risk could be determined. The reduction of the gross margin for a 10 year return period was determined for 1980 with 0.27 € per year and animal place and increased by the 20-fold to 5.13 € per year and animal place in 2030.

Bovine tuberculosis (bTB) outbreaks, caused by Mycobacterium bovis infection, are a costly animal health challenge. Understanding factors associated with the duration of outbreaks, known as breakdowns, could lead to better disease management policy development. We undertook a retrospective observational study (2012-2018) and employed Finite Mixture Models (FMM) to model the outcome parameter, and to investigate how factors were associated with duration for differing subpopulations identified. In addition to traditional risk factors (e.g. herd size, bTB history), we also explored farm geographic area, parcels/farm fragmentation, metrics of intensity via nitrogen loading, and whether herds were designated controlled beef finishing units (CBFU) as potential risk factors for increased duration. The final model fitted log-normal distributions, with two latent classes (k) which partitioned the population into a subpopulation around the central tendency of the distribution, and a second around the tails of the distribution. The latter subpopulation included longer breakdowns of policy interest. Increasing duration was positively associated with recent (<3 yrs) TB history and the number of reactors disclosed, (log) herd size, beef herd-type relative to other herd types, number of land parcels, area, and being designated a controlled finishing unit (“feedlot”), and having high annual inward cattle movements within the “tails” subpopulation. Breakdown length was negatively associated with year of commencement of breakdown (i.e. a decreasing trend) and non-significantly with the organic nitrogen produced on the farm (N kg/hectare), a measure of stocking density. The latter finding may be due to confounding effects with herd size and area. Most variables contributed only moderately to explaining variation in breakdown duration, that is, they had moderate size effects on duration. Herd-size and CBFU had greater effect sizes on the outcome. The findings contribute to evidence-based policy formation in Ireland.

Since December 2019, a rapid increase in the number of SARS-CoV-2 (COVID-19) cases was reported worldwide, despite strict infection control and lock down measures. Current paper investigated the actual facts behind this rapid increase in the number of cases. Study of genomic sequence reveals that domestic and wild animals were likely ancestors and zoonotic source for SARS-CoVs, MERS-CoVs, and SARS-CoV-2. Strong evidence suggest that these viruses already existed and replicated in animals and humans during past several decades, exhibiting diverse mutations, evolutions and self-limiting diseases, except during outbreaks. Serious zoonotic reservoir investigations are required to investigate animal transmission of SARS-CoVs and SARS-CoV-2 to limit current pandemic. This might be the reason of increasing number of cases via animals. SARS-CoV-2 has been retrospectively isolated in different studies in August 2019, several months before Wuhan announced. Hence, there is a possibility that viruses existed, went undetected, infecting subclinically, in past several years, and SARS-CoV-2 antigens and neutralizing antibodies may have been present in humans since long time. This might be another reason of increasing number of cases by screening as mass screening and antigen or antibody testing was not carried out in the past years. Randomized controlled trials are required to investigate human to human transmission by touch, as the current evidence is limited with conflicting results. As all SARS-CoVs are basically respiratory viruses, droplet precautions and infection control measures are essential, especially for hospital staff. Increased number of SARS-CoV-2 asymptomatic, or subclinical cases are detected worldwide. This silent phase of transmission can be beneficial for humans. Lack of symptoms eventually lessen virus transmission and reduce the pathogen's long-term survival and provide humoral herd immunity up to several years. Hence, seropositivity with diverse antibodies develops against mutating SARS-CoVs which will confer strong immunity during epidemics. Strategies such as identification, contact tracing and quarantine are costly and practically difficult. Hence, asymptomatic persons can continue their work with droplet precautions and standard infection control procedures, while symptomatic or sick persons can isolate themselves in their homes without the need for strict quarantine until clinical recovery, with reduced hospital visits and minimizing chances of hospital acquired infections. RT-PCR has low sensitivity and specificity, carries a high risk of handling live virus antigens, and requires difficult protocols. As viral load also sharply declines after few days of onset of infection, this technique might overlook infection. Furthermore, SARS-CoV-2 infection may be present in blood when oropharyngeal swabs are negative by RT-PCR. Additionally, RT-PCR usually gives false negative and false positive results and must be interpreted cautiously. This might be again a reason of increasing number of cases by false positive RT-PCR reporting. Moreover, antibodies against SARS-CoVs develop robustly in serum even by reduced amount of antigens. In contrast to RT-PCR, ELISA for diagnosing antibodies against SARS-CoV-2 demonstrates 100% specificity and 100% sensitivity, even in clinically asymptomatic individuals. These antibodies can be used for serologic surveys, monitoring and screening. However, screening tests for SARS-COV-2 should be avoided in unhygienic public places by nasopharyngeal swabs, which carry a high risk of further transmission, co-infection or superinfection. Such highly infectious virus must be isolated and tested in highly sterilized laboratory. Further strict international laws and policies are required to stop the possible spread of experimental viruses, biological warfare and bioterrorism.

SARS-CoV-2 causes severe pneumonia epidemics and probably originated in horseshoe bats, but the intermediate host is unknown. The interaction of SARS-CoV-2 spike protein and its acceptor protein ACE2 is an important issue in determining viral host range and cross-species infection, while the binding capacity of Spike protein to ACE2 of different species is unknown. Here, we used the atomic structure model of SARS-CoV-2 and human ACE2 to assess the receptor utilization capacity of ACE2s from different species including cats, chimpanzees, dogs, cattles. Our results show, domestic cats (Felis catusc) and dogs (Canis lupus familiaris) are more susceptible to infection by SARS-CoV-2 and that they can efficiently transmit the virus to previously uninfected animals that are housed with them. Especially, cats could be a choice of animal model for screening antiviral drugs or vaccine candidates against SARS-CoV-2.

We present a laser-plasma electron accelerator module designed to be driven by high repetition rate lasers for industrial applications of laser-driven electron beams. It consists of a single vacuum chamber containing all the necessary components for producing, optimizing, and monitoring electron beams generated via laser wakefield acceleration in a gas jet. The core methods involve comprehensive metrological assessments, including rigorous temporal laser pulse characterization and contrast measurements, supplemented by detailed spatio-temporal distribution analyses of the laser system. Results demonstrate the good stability and reproducibility of the laser system, confirming its suitability for advanced scientific and industrial applications. We demonstrate the full functionality of the laser-plasma accelerator module and perform target density and laser-plasma characterizations. Our work is a first step towards the wide dissemination of fully integrated laser-plasma accelerator technology.

Abstract In order to solve the problem that it is difficult to obtain the accurate trigger signal moment when using the time difference of UAV flight acoustic signals collected by acoustic sensors arranged in advance to locate the position of UAVs when they cannot be tracked by radar and are difficult to be observed by the human eye, an acoustic source localisation method with improved empirical modal decomposition (REMD) under an adaptive frequency window is proposed. Firstly, the collected UAV flight signals are smoothed and filtered, then the robust empirical modal decomposition (REMD) is used for IMF modal decomposition and spectral analysis of the collected signals, and a panning frequency window with variable bandwidth is introduced for corresponding frequency locking and extracting the IMFs; the GWO optimisation is used and the sliding metrics are set to determine the position of the panning frequency window automatically; finally, the extracted Finally, the extracted IMF components are reconstructed according to the criterion, and the trigger signal moments in the reconstructed IMF components are resolved and recorded, and the algorithm is used to calculate the difference between the reception moments of different sensors; for the traditional sound source localisation algorithms with a small detection range, which are prone to fall into the no-solution or false-solution, i.e., unable to achieve the localisation, a weighted least-squares-based Chan-Taylor algorithm is proposed for the localisation computation, and a weighted least-squares algorithm is proposed for the localisation computation, through the Inputting the sensor delay parameter and solving the linear equation system to get the target position information; finally, this paper verifies the robustness and performance of the design method with simulated and actual signals. The results show that the maximum positioning error is no more than 5% in an area with a side length of 15m in the measurement area. With the improved accuracy of time delay estimation, the positioning simulation results meet the positioning requirements even though the positioning error is further expanded when the measurement area is further expanded.