Computer vision and deep transfer learning for automatic gauge reading detection.

This manuscript proposes an automatic reading detection system for an analogue gauge using a combination of deep learning, machine learning, and image processing. The study suggests image-processing techniques in manual analogue gauge reading that include generating readings for the image to provide supervised data to address difficulties in unsupervised data in gauges and to achieve better accuracy using DenseNet 169 compared to other approaches. The model uses artificial intelligence to automate reading detection using deep transfer learning models like DenseNet 169, InceptionNet V3, and VGG19. The models were trained using 1011 labeled pictures, 9 classes, and readings from 0 to 8. The VGG19 model exhibits a high training precision of 97.00% but a comparatively lower testing precision of 75.00%, indicating the possibility of overfitting. On the other hand, InceptionNet V3 demonstrates consistent precision across both datasets, but DenseNet 169 surpasses other models in terms of precision and generalization capabilities.

Accelerating the Reduction Kinetics of V4+ to V3+ on Atomically Fe─N4 Decorated Carbon Nanotubes for Vanadium Electrolyte Preparation.

The high manufacturing cost of vanadium electrolytes is caused by the sluggish kinetics of V4+ to V3+, which restricts the commercialization of all vanadium flow batteries (VFBs). Here, density functional theory calculations first reveal the detailed reaction pathway and point out the rate-determined step by the desorption of the end product [V(H2O)6]3+. Catalytic site engineering at the molecular level can optimize the adsorption energy of [V(H2O)6]3+ to boost the kinetics. Furthermore, iron single-atoms embedded nitrogen-doped carbon nanotubes (FeSA/NCNT) are designed to decrease the adsorption energy of [V(H2O)6]3+. The reaction rate constant of FeSA/NCNT toward V4+ to V3+ is 1.62 × 10-7 cm s-1, 37.5 times that of the commercial carbon catalyst. Therefore, the energy consumption is reduced by 22.5%. Meanwhile, the prepared vanadium electrolyte is of high quality with the ideal oxidation state of + 3.5 without impurities. This work reveals the catalytic mechanism of V4+ to V3+ and proposes a simple but practical strategy to reduce the preparation cost of V3.5+ electrolyte.

The Significance of Right-Sided Precordial ECG Leads (V3R and V4R) in Assessing Right Ventricular Dysfunction: A Single Center Cross-Sectional Study.

BACKGROUND: Right ventricular systolic dysfunction is associated with poor prognosis and increased mortality rates. Our objective was to investigate ECG changes in patients with this condition, focusing on the right-sided precordial leads. METHODS: In this cross-sectional study, 60 patients with right ventricular dysfunction were included from April 2020 to April 2021. Cardiac structure and function were assessed using 2D transthoracic echocardiography. Standard 12-lead electrocardiograms and right-sided precordial ECGs (V3R-V4R) were obtained and analyzed for QRS complex configuration, ST-segment elevation, and T-wave morphology. RESULTS: In our study, the majority were male (70.0%) with a mean age of 58.76 years. The most common initial diagnoses were pulmonary thromboembolism (43.3%), chronic obstructive pulmonary disease (26.7%), and pulmonary hypertension (25.0%). The predominant ECG finding in the right-sided precordial leads (V3R, V4R) was a deep negative T wave (90.0%). Patients with severe right ventricular systolic dysfunction often exhibited a qR pattern (41.2%), whereas those with nonsevere dysfunction showed rS and QS patterns (55.8%). Approximately 41.0% of severe RV dysfunction cases had ST segment depression in the right-sided precordial leads, and 28.0% of patients displayed signs of right atrial abnormality. CONCLUSION: The study found that qR, rS, and QS patterns were more prevalent in V3R and V4R leads among patients with severe and nonsevere right ventricular systolic dysfunction. The most common ECG feature observed was deep T-wave inversion in these leads. The study recommends using right-sided precordial leads in all patients with RV systolic dysfunction for early detection and risk stratification.

Improved MobileNet V3-Based Identification Method for Road Adhesion Coefficient.

To enable the timely adjustment of the control strategy of automobile active safety systems, enhance their capacity to adapt to complex working conditions, and improve driving safety, this paper introduces a new method for predicting road surface state information and recognizing road adhesion coefficients using an enhanced version of the MobileNet V3 model. On one hand, the Squeeze-and-Excitation (SE) is replaced by the Convolutional Block Attention Module (CBAM). It can enhance the extraction of features effectively by considering both spatial and channel dimensions. On the other hand, the cross-entropy loss function is replaced by the Bias Loss function. It can reduce the random prediction problem occurring in the optimization process to improve identification accuracy. Finally, the proposed method is evaluated in an experiment with a four-wheel-drive ROS robot platform. Results indicate that a classification precision of 95.53% is achieved, which is higher than existing road adhesion coefficient identification methods.

Discriminative and predictive validity of risk assessment measures for women incarcerated for serious violent offences in Australia.

Despite the growing population of women in Australian prisons, limited research has explored whether commonly used risk assessments - predominantly developed and tested on men - are valid for women. We investigated the discriminative and predictive validity of the Level of Service Inventory-Revised: Screening Version (LSI-R:SV), Level of Service/Risk, Need, Responsivity (LS/RNR), and the Historical, Clinical, Risk Management 20-Version 3 (HCR-20v3) for Victorian women imprisoned for serious violence (N = 79). The LS/RNR was related to any, violent, and non-violent recidivism, and both the LSI-R:SV and the H-Scale of the HCR-20v3 were related to violent recidivism, with the H-Scale demonstrating strong predictive validity for violence. Four LS/RNR needs domains demonstrated discriminative and predictive validity for any and/or violent recidivism (criminal history, family/marital, alcohol/drug problem, antisocial pattern). Findings are locally significant, showing that the LS/RNR and HCR-20v3 H-Scale are useful for the prediction and discrimination of recidivism for Australian women incarcerated for serious violence.

Secondary ACMG and non-ACMG genetic findings in a multiethnic cohort of 16,713 pediatric participants.

PURPOSE: Clinical next-generation sequencing is an effective approach for identifying pathogenic sequence variants that are medically actionable for participants and families but are not associated with the participant's primary diagnosis. These variants are called secondary findings (SFs). According to the literature, there is no report of the types and frequencies of SFs in a large pediatric cohort that includes substantial African-American participants. We sought to investigate the types (including American College of Medical Genetics and Genomics [ACMG] and non-ACMG-recommended gene lists), frequencies, and rates of SFs, as well as the effects of SF disclosure on the participants and families of a large pediatric cohort at the Center for Applied Genomics at The Children's Hospital of Philadelphia. METHODS: We systematically identified pathogenic (P) and likely pathogenic (LP) variants in established disease-causing genes, adhering to ACMG v3.2 secondary finding guidelines and beyond. For non-ACMG SFs, akin to incidental findings in clinical settings, we utilized a set of criteria focusing on pediatric onset, high penetrance, moderate to severe phenotypes, and the clinical actionability of the variants. This criteria-based approach was applied rather than using a fixed gene list to ensure that the variants identified are likely to affect participant health significantly. To identify and categorize these variants, we used a clinical-grade variant classification standard per ACMG/AMP recommendations; additionally, we conducted a detailed literature search to ensure a comprehensive exploration of potential SFs relevant to pediatric participants. RESULTS: We report a distinctive distribution of 1464 P/LP SF variants in 16,713 participants. There were 427 unique variants in ACMG genes and 265 in non-ACMG genes. The most frequently mutated genes among the ACMG and non-ACMG gene lists were TTR(41.6%) and CHEK2 (7.16%), respectively. Overall, variants of possible medical importance were found in 8.76% of participants in both ACMG (5.81%) and non-ACMG (2.95%) genes. CONCLUSION: Our study revealed that 8.76% of a large, multiethnic pediatric cohort carried actionable secondary genetic findings, with 5.81% in ACMG genes and 2.95% in non-ACMG genes. These findings emphasize the importance of including diverse populations in genetic research to ensure that all groups benefit from early identification of disease risks. Our results provide a foundation for expanding the ACMG gene list and improving clinical care through early interventions.

Research on terahertz image analysis of thin-shell seeds based on semantic segmentation.

Assessing crop seed phenotypic traits is essential for breeding innovations and germplasm enhancement. However, the tough outer layers of thin-shelled seeds present significant challenges for traditional methods aimed at the rapid assessment of their internal structures and quality attributes. This study explores the potential of combining terahertz (THz) time-domain spectroscopy and imaging with semantic segmentation models for the rapid and non-destructive examination of these traits. A total of 120 watermelon seed samples from three distinct varieties, were curated in this study, facilitating a comprehensive analysis of both their outer layers and inner kernels. Utilizing a transmission imaging modality, THz spectral images were acquired and subsequently reconstructed employing a correlation coefficient method. Deep learning-based SegNet and DeepLab V3+ models were employed for automatic tissue segmentation. Our research revealed that DeepLab V3+ significantly surpassed SegNet in both speed and accuracy. Specifically, DeepLab V3+ achieved a pixel accuracy of 96.69 % and an intersection over the union of 91.3 % for the outer layer, with the inner kernel results closely following. These results underscore the proficiency of DeepLab V3+ in distinguishing between the seed coat and kernel, thereby furnishing precise phenotypic trait analyses for seeds with thin shells. Moreover, this study accentuates the instrumental role of deep learning technologies in advancing agricultural research and practices.

Neuro-XAI: Explainable deep learning framework based on deeplabV3+ and bayesian optimization for segmentation and classification of brain tumor in MRI scans.

The prevalence of brain tumor disorders is currently a global issue. In general, radiography, which includes a large number of images, is an efficient method for diagnosing these life-threatening disorders. The biggest issue in this area is that it takes a radiologist a long time and is physically strenuous to look at all the images. As a result, research into developing systems based on machine learning to assist radiologists in diagnosis continues to rise daily. Convolutional neural networks (CNNs), one type of deep learning approach, have been pivotal in achieving state-of-the-art results in several medical imaging applications, including the identification of brain tumors. CNN hyperparameters are typically set manually for segmentation and classification, which might take a while and increase the chance of using suboptimal hyperparameters for both tasks. Bayesian optimization is a useful method for updating the deep CNN's optimal hyperparameters. The CNN network, however, can be considered a "black box" model because of how difficult it is to comprehend the information it stores because of its complexity. Therefore, this problem can be solved by using Explainable Artificial Intelligence (XAI) tools, which provide doctors with a realistic explanation of CNN's assessments. Implementation of deep learning-based systems in real-time diagnosis is still rare. One of the causes could be that these methods don't quantify the Uncertainty in the predictions, which could undermine trust in the AI-based diagnosis of diseases. To be used in real-time medical diagnosis, CNN-based models must be realistic and appealing, and uncertainty needs to be evaluated. So, a novel three-phase strategy is proposed for segmenting and classifying brain tumors. Segmentation of brain tumors using the DeeplabV3+ model is first performed with tuning of hyperparameters using Bayesian optimization. For classification, features from state-of-the-art deep learning models Darknet53 and mobilenetv2 are extracted and fed to SVM for classification, and hyperparameters of SVM are also optimized using a Bayesian approach. The second step is to understand whatever portion of the images CNN uses for feature extraction using XAI algorithms. Using confusion entropy, the Uncertainty of the Bayesian optimized classifier is finally quantified. Based on a Bayesian-optimized deep learning framework, the experimental findings demonstrate that the proposed method outperforms earlier techniques, achieving a 97 % classification accuracy and a 0.98 global accuracy.

Constructing Multi-Electron Reactions by Doping Mn2+ to Increase Capacity and Stability in K3.2V2.8Mn0.2(PO4)4/C of Phosphate Cathodes for Potassium-Ion Batteries.

Vanadium-based phosphate cathode materials (e.g., K3V2(PO4)3) have attracted widespread concentration in cathode materials in potassium-ion batteries owing to their stable structure but suffer from low capacity and poor conductivity. In this work, an element doping strategy is applied to promote its electrochemical performance so that K3.2V2.8Mn0.2(PO4)4/C is prepared via a simple sol-gel method. The heterovalent Mn2+ is introduced to stimulated multiple electron reactions to improve conductivity and capacity, as well as interlayer spacing. Galvanostatic intermittent titration technique (GITT) and in situ X-ray diffraction results further confirm that Mn-doping in the original electrode can obtain superior electrode process kinetics and structural stability. The prepared K3.2V2.8Mn0.2(PO4)4/C exhibits a high-capacity retention of 80.8% after 1 500 cycles at 2 C and an impressive rate capability, with discharge capacities of 87.6 at 0.2 C and 45.4 mA h g-1 at 5 C, which is superior to the majority of reported vanadium-based phosphate cathode materials. When coupled K3.2V2.8Mn0.2(PO4)4/C cathode with commercial porous carbon (PC) anode as the full cell, a prominent energy density of 175 Wh kg-1 is achieved based on the total active mass. Overall, this study provides an effective strategy for meliorating the cycling stability and capacity of the polyanion cathodes for KIB.

Automated assessment of mental workload from PPG sensor data using cross-wavelet coherence and transfer learning.

Highly complex cognitive works require more brain power. The productivity of a person suffers due to this strain, which is sometimes referred to as a mental burden or psychological load. A person's mental health and safety in high-stress working conditions can be improved with the help of mental workload assessment. A photoplethysmogram (PPG) signal is a non-invasive and easily acquired physiological signal that contains information related to blood volume changes in the micro-vascular bed of tissues and can indicate psychologically relevant information to assess a person's mental workload (MW). An individual under a high MW possesses an increase in sympathetic nervous system activity, which results in morphological changes in the PPG waveform. In this work, a time-frequency analysis framework is developed to capture these distinguishing PPG features for the automatic assessment of MW. In particular, a cross-wavelet coherence (WTC) approach is proposed to extract simultaneous time-frequency information of the PPG during MW relative to the resting PPG. The suggested technique is validated on a publicly available data set of 22 healthy individuals who took part in an N-back task with PPG recording. Under three different fixed window lengths, images are obtained using WTC between PPG records during N-back task activity and rest. The images are used further to obtain PPG classification in two broad classes of low and high MW using a customized pre-trained Inception-V3 model. The best validation and test accuracy of 93.86% and 93.07%, respectively obtained in the window setting of 1200 samples used for WTC image creation.

Toward the design of persuasive systems for a healthy workplace: a real-time posture detection.

Persuasive technologies, in connection with human factor engineering requirements for healthy workplaces, have played a significant role in ensuring a change in human behavior. Healthy workplaces suggest different best practices applicable to body posture, proximity to the computer system, movement, lighting conditions, computer system layout, and other significant psychological and cognitive aspects. Most importantly, body posture suggests how users should sit or stand in workplaces in line with best and healthy practices. In this study, we developed two study phases (pilot and main) using two deep learning models: convolutional neural networks (CNN) and Yolo-V3. To train the two models, we collected posture datasets from creative common license YouTube videos and Kaggle. We classified the dataset into comfortable and uncomfortable postures. Results show that our YOLO-V3 model outperformed CNN model with a mean average precision of 92%. Based on this finding, we recommend that YOLO-V3 model be integrated in the design of persuasive technologies for a healthy workplace. Additionally, we provide future implications for integrating proximity detection taking into consideration the ideal number of centimeters users should maintain in a healthy workplace.

Mask region-based convolutional neural network and VGG-16 inspired brain tumor segmentation.

The process of brain tumour segmentation entails locating the tumour precisely in images. Magnetic Resonance Imaging (MRI) is typically used by doctors to find any brain tumours or tissue abnormalities. With the use of region-based Convolutional Neural Network (R-CNN) masks, Grad-CAM and transfer learning, this work offers an effective method for the detection of brain tumours. Helping doctors make extremely accurate diagnoses is the goal. A transfer learning-based model has been suggested that offers high sensitivity and accuracy scores for brain tumour detection when segmentation is done using R-CNN masks. To train the model, the Inception V3, VGG-16, and ResNet-50 architectures were utilised. The Brain MRI Images for Brain Tumour Detection dataset was utilised to develop this method. This work's performance is evaluated and reported in terms of recall, specificity, sensitivity, accuracy, precision, and F1 score. A thorough analysis has been done comparing the proposed model operating with three distinct architectures: VGG-16, Inception V3, and Resnet-50. Comparing the proposed model, which was influenced by the VGG-16, to related works also revealed its performance. Achieving high sensitivity and accuracy percentages was the main goal. Using this approach, an accuracy and sensitivity of around 99% were obtained, which was much greater than current efforts.

Exploring oral bacterial compositional network in two oral disease groups using a convergent approach of NGS-molecular diagnostics.

BACKGROUND: Since most of the commonly known oral diseases are explained in link with balance of microbial community, an accurate bacterial taxonomy profiling for determining bacterial compositional network is essential. However, compared to intestinal microbiome, research data pool related to oral microbiome is small, and general 16S rRNA screening method has a taxonomy misclassification issue in confirming complex bacterial composition at the species level. OBJECTIVE: Present study aimed to explore bacterial compositional networks at the species level within saliva of 39 oral disease patients (Dental Caries group: n = 26 and Periodontitis group: n = 13) through comparison with public Korean-specific healthy oral microbiome data. METHODS: Here, we applied comprehensive molecular diagnostics based on qRT-PCR and Sanger sequencing methods to complement the technical limitations of NGS-based 16S V3-V4 amplicon sequencing technology. RESULTS: As a result of microbiome profiling at the genus level, relative frequencies of many nitrate-reducing bacteria within each oral disease group were found to be significantly low compared to the healthy group. In addition, the molecular diagnostics-based bacterial identification method allowed the determination of the correct taxonomy of screened primary colonizers (Streptococcus and Actinomyces unclassification clusters) for each oral disease. Finally, as with the results of microbiome profiling at the genus level, many core-species classified within the saliva of each oral disease group were also related to nitrate-reduction, and it was estimated that various pathogens associated with each disease formed a bacterial network with the core-species. CONCLUSION: Our study introduced a novel approach that can compensate for the difficulty of identifying an accurate bacterial compositional network at the species level due to unclear taxonomy classification by using the convergent approach of NGS-molecular diagnostics. Ultimately, we suggest that our experimental approach and results could be potential reference materials for researchers who intend to prevent oral disease by determining the correlation between oral health and bacterial compositional network according to the changes in the relative frequency for nitrate-reducing species.

Which is a more appropriate recipient for posterior circulation bypass surgery: V3 or V2? Differences in underlying disease and lesion sites between extracranial vertebral artery dissection and atherosclerotic changes. Illustrative cases.

BACKGROUND: Revascularization for extracranial vertebral artery dissection or vertebral artery atherosclerotic occlusive lesions caused by vertebrobasilar insufficiency or posterior circulation infarction is relatively rare. When bypassing the cervical external carotid artery (ECA) or common carotid artery (CCA) using a radial artery (RA) or saphenous vein (SV) graft, it is difficult to determine whether the recipient site should be the V2 or V3 portion. OBSERVATIONS: In case 1, cervical ECA-RA-V3 bypass was performed for bilateral extracranial vertebral artery dissection with the onset of ischemia, and cervical CCA-SV-V3 bypass was added 12 days later. Nine years after surgery, the bilateral vertebral artery dissection had improved, and the patient still had a patent bypass. In case 2, cervical ECA-RA-V2 bypass was performed for arteriosclerotic bilateral extracranial vertebral artery occlusion. The bypass was patent 5 years after surgery. The postoperative course was uneventful in both patients. LESSONS: The authors present cases of posterior fossa revascularization using the vertebral artery V3 and V2 portions via skull base surgery and note that it is important to consider each patient's individual characteristics when selecting the V3 or V2 portion.

Histopathology-based breast cancer prediction using deep learning methods for healthcare applications.

Breast cancer (BC) is the leading cause of female cancer mortality and is a type of cancer that is a major threat to women's health. Deep learning methods have been used extensively in many medical domains recently, especially in detection and classification applications. Studying histological images for the automatic diagnosis of BC is important for patients and their prognosis. Owing to the complication and variety of histology images, manual examination can be difficult and susceptible to errors and thus needs the services of experienced pathologists. Therefore, publicly accessible datasets called BreakHis and invasive ductal carcinoma (IDC) are used in this study to analyze histopathological images of BC. Next, using super-resolution generative adversarial networks (SRGANs), which create high-resolution images from low-quality images, the gathered images from BreakHis and IDC are pre-processed to provide useful results in the prediction stage. The components of conventional generative adversarial network (GAN) loss functions and effective sub-pixel nets were combined to create the concept of SRGAN. Next, the high-quality images are sent to the data augmentation stage, where new data points are created by making small adjustments to the dataset using rotation, random cropping, mirroring, and color-shifting. Next, patch-based feature extraction using Inception V3 and Resnet-50 (PFE-INC-RES) is employed to extract the features from the augmentation. After the features have been extracted, the next step involves processing them and applying transductive long short-term memory (TLSTM) to improve classification accuracy by decreasing the number of false positives. The results of suggested PFE-INC-RES is evaluated using existing methods on the BreakHis dataset, with respect to accuracy (99.84%), specificity (99.71%), sensitivity (99.78%), and F1-score (99.80%), while the suggested PFE-INC-RES performed better in the IDC dataset based on F1-score (99.08%), accuracy (99.79%), specificity (98.97%), and sensitivity (99.17%).

G protein ß subunits regulate Cav3.3 T-type channel activity and current kinetics via interaction with the Cav3.3 C-terminus.

Ca2+ influx through Cav3.3 T-type channel plays crucial roles in neuronal excitability and is subject to regulation by various signaling molecules. However, our understanding of the partners of Cav3.3 and the related regulatory pathways remains largely limited. To address this quest, we employed the rat Cav3.3 C-terminus as bait in yeast-two-hybrid screenings of a cDNA library, identifying rat Gß2 as an interaction partner. Subsequent assays revealed that the interaction of Gß2 subunit was specific to the Cav3.3 C-terminus. Through systematic dissection of the C-terminus, we pinpointed a 22 amino acid sequence (amino acids 1789-1810) as the Gß2 interaction site. Coexpression studies of rat Cav3.3 with various Gßγ compositions were conducted in HEK-293 cells. Patch clamp recordings revealed that coexpression of Gß2γ2 reduced Cav3.3 current density and accelerated inactivation kinetics. Interestingly, the effects were not unique to Gß2γ2, but were mimicked by Gß2 alone as well as other Gßγ dimers, with similar potencies. Deletion of the Gß2 interaction site abolished the effects of Gß2γ2. Importantly, these Gß2 effects were reproduced in human Cav3.3. Overall, our findings provide evidence that Gß(γ) complexes inhibit Cav3.3 channel activity and accelerate the inactivation kinetics through the Gß interaction with the Cav3.3 C-terminus.

A Prospective Study of Publicly Funded Molecular Testing of Indeterminate Thyroid Nodules: Canada's Experience.

CONTEXT: Indeterminate thyroid nodules (ITNs) lead to diagnostic surgeries in many countries. Use of molecular testing (MT) is endorsed by several guidelines, but costs are limitative, especially in public healthcare systems like in Canada. OBJECTIVES: Primary objective: evaluate the clinical value of Thyroseq® v3 (TSv3) using benign call rate (BCR) in a real-world practice. Secondary objective: assess cost-effectiveness of MT. DESIGN: This is a multicentric prospective study. SETTING: This study was conducted in 5 academic centers in Quebec, Canada. PATIENTS OR OTHER PARTICIPANTS: 500 consecutive patients with Bethesda III (on 2 consecutive cytopathologies) or IV and TIRADS 3 or 4 nodules measuring 1 to 4 cm were included. INTERVENTION: MT was performed between November 2021 and November 2022. Patients with a positive TSv3 were referred to surgery. Patients with a negative TSv3 were planned for follow-up by ultrasonography for a minimum of 2 years. MAIN OUTCOME MEASURE: The BCR, corresponding to the proportion of ITNs with negative TSv3 results, was assessed. RESULTS: 500 patients underwent TSv3 testing, with a BCR of 72.6% (95% CI: 68.5-76.5; p<0.001). 99.7% of patients with a negative result avoided surgery. The positive predictive value of TSv3 was 68.2% (95% CI: 58.5-76.9). The cost-benefit analysis identified that the implementation of MT would yield cost savings of $6.1 million over the next 10 years. CONCLUSIONS: Use of MT (TSv3) in a well-selected population with ITNs led to a BCR of 72.6%. It is cost-effective and prevents unnecessary surgeries in a public healthcare setting.

Characterizing the blood microbiota in healthy and febrile domestic cats via 16s rRNA sequencing.

This study aimed to evaluate the blood bacterial microbiota in healthy and febrile cats. High-quality sequencing reads from the 16S rRNA gene variable region V3-V4 were obtained from genomic blood DNA belonging to 145 healthy cats, and 140 febrile cats. Comparisons between the blood microbiota of healthy and febrile cats revealed dominant presence of Actinobacteria, followed by Firmicutes and Proteobacteria, and a lower relative abundance of Bacteroidetes. Upon lower taxonomic levels, the bacterial composition was significantly different between healthy and febrile cats. The families Faecalibacterium and Kineothrix (Firmicutes), and Phyllobacterium (Proteobacteria) experienced increased abundance in febrile samples. Whereas Thioprofundum (Proteobacteria) demonstrated a significant decrease in abundance in febrile. The bacterial composition and beta diversity within febrile cats was different according to the affected body system (Oral/GI, systemic, skin, and respiratory) at both family and genus levels. Sex and age were not significant factors affecting the blood microbiota of febrile cats nor healthy ones. Age was different between young adult and mature adult healthy cats. Alpha diversity was unaffected by any factors. Overall, the findings suggest that age, health status and nature of disease are significant factors affecting blood microbiota diversity and composition in cats, but sex is not.

A novel hierarchical framework for plant leaf disease detection using residual vision transformer.

Early detection of plant leaf diseases accurately and promptly is very crucial for safeguarding agricultural crop productivity and ensuring food security. During their life cycle, plant leaves get diseased because of multiple factors like bacteria, fungi, weather conditions, etc. In this work, the authors propose a model that aids in the early detection of leaf diseases using a novel hierarchical residual vision transformer using improved Vision Transformer and ResNet9 models. The proposed model can extract more meaningful and discriminating details by reducing the number of trainable parameters with a smaller number of computations. The proposed method is evaluated on the Local Crop dataset, Plant Village dataset, and Extended Plant Village Dataset with 13, 38, and 51 different leaf disease classes. The proposed model is trained using the best trail parameters of Improved Vision Transformer and classified the features using ResNet 9. Performance evaluation is carried out on a wide aspects over the aforementioned datasets and results revealed that the proposed model outperforms other models such as InceptionV3, MobileNetV2, and ResNet50.

Sulfide and polysulfide as pronociceptive mediators: Focus on Cav3.2 function enhancement and TRPA1 activation.

Reactive sulfur species including sulfides, polysulfides and cysteine hydropersulfide play extensive roles in health and disease, which involve modification of protein functions through the interaction with metals bound to the proteins, cleavage of cysteine disulfide (S-S) bonds and S-persulfidation of cysteine residues. Sulfides over a wide micromolar concentration range enhance the activity of Cav3.2 T-type Ca2+ channels by eliminating Zn2+ bound to the channels, thereby promoting somatic and visceral pain. Cav3.2 is under inhibition by Zn2+ in physiological conditions, so that sulfides function to reboot Cav3.2 from Zn2+ inhibition and increase the excitability of nociceptors. On the other hand, polysulfides generated from sulfides activate TRPA1 channels via cysteine S-persulfidation, thereby facilitating somatic, but not visceral, pain. Thus, Cav3.2 function enhancement by sulfides and TRPA1 activation by polysulfides, synergistically accelerate somatic pain signals. The increased activity of the sulfide/Cav3.2 system, in particular, appears to have a great impact on pathological pain, and may thus serve as a therapeutic target for treatment of neuropathic and inflammatory pain including visceral pain.