Total RNA analysis of the active microbiome on moving bed biofilm reactor carriers under incrementally increasing micropollutant concentrations.

Micropollutants are increasingly prevalent in the aquatic environment. A major part of these originates from wastewater treatment plants since traditional treatment technologies do not remove micropollutants sufficiently. Moving bed biofilm reactors (MBBRs), however, have been shown to aid in micropollutant removal when applied to conventional wastewater treatment as a polishing step. Here, we used Total RNA sequencing to investigate both the active microbial community and functional dynamics of MBBR biofilms when these were exposed to increasing micropollutant concentrations over time. Concurrently, we conducted batch culture experiments using biofilm carriers from the MBBRs to assess micropollutant degradation potential. Our study showed that biofilm eukaryotes, in particular protozoa, were negatively influenced by micropollutant exposure, in contrast to prokaryotes that increased in relative abundance. Further, we found several functional genes that were differentially expressed between the MBBR with added micropollutants and the control. These include genes involved in aromatic and xenobiotic compound degradation. Moreover, the biofilm carrier batch experiment showed vastly different alterations in benzotriazole and diclofenac degradation following the increased micropollutant concentrations in the MBBR. Ultimately, this study provides essential insights into the microbial community and functional dynamics of MBBRs and how an increased load of micropollutants influences these dynamics.

Hybrid deep spatial and statistical feature fusion for accurate MRI brain tumor classification.

The classification of medical images is crucial in the biomedical field, and despite attempts to address the issue, significant challenges persist. To effectively categorize medical images, collecting and integrating statistical information that accurately describes the image is essential. This study proposes a unique method for feature extraction that combines deep spatial characteristics with handmade statistical features. The approach involves extracting statistical radiomics features using advanced techniques, followed by a novel handcrafted feature fusion method inspired by the ResNet deep learning model. A new feature fusion framework (FusionNet) is then used to reduce image dimensionality and simplify computation. The proposed approach is tested on MRI images of brain tumors from the BraTS dataset, and the results show that it outperforms existing methods regarding classification accuracy. The study presents three models, including a handcrafted-based model and two CNN models, which completed the binary classification task. The recommended hybrid approach achieved a high F1 score of 96.12 ± 0.41, precision of 97.77 ± 0.32, and accuracy of 97.53 ± 0.24, indicating that it has the potential to serve as a valuable tool for pathologists.

Association between acute peripancreatic fluid collections and early readmission in acute pancreatitis: A propensity-matched analysis.

BACKGROUND: Patients with acute pancreatitis (AP) frequently experience hospital readmissions, posing a significant burden to healthcare systems. Acute peripancreatic fluid collection (APFC) may negatively impact the clinical course of AP. It could worsen symptoms and potentially lead to additional complications. However, clinical evidence regarding the specific association between APFC and early readmission in AP remains scarce. Understanding the link between APFC and readmission may help improve clinical care for AP patients and reduce healthcare costs. AIM: To evaluate the association between APFC and 30-day readmission in patients with AP. METHODS: This retrospective cohort study is based on the Nationwide Readmission Database for 2016-2019. Patients with a primary diagnosis of AP were identified. Participants were categorized into those with and without APFC. A 1:1 propensity score matching for age, gender, and Elixhauser comorbidities was performed. The primary outcome was early readmission rates. Secondary outcomes included the incidence of inpatient complications and healthcare utilization. Unadjusted analyses used Mann-Whitney U and χ 2 tests, while Cox regression models assessed 30-day readmission risks and reported them as adjusted hazard ratios (aHR). Kaplan-Meier curves and log-rank tests verified readmission risks. RESULTS: A total of 673059 patients with the principal diagnosis of AP were included. Of these, 5.1% had APFC on initial admission. After propensity score matching, each cohort consisted of 33914 patients. Those with APFC showed a higher incidence of inpatient complications, including septic shock (3.1% vs 1.3%, P < 0.001), portal venous thrombosis (4.4% vs 0.8%, P < 0.001), and mechanical ventilation (1.8% vs 0.9%, P < 0.001). The length of stay (LOS) was longer for APFC patients [4 (3-7) vs 3 (2-5) days, P < 0.001], as were hospital charges ($29451 vs $24418, P < 0.001). For 30-day readmissions, APFC patients had a higher rate (15.7% vs 6.5%, P < 0.001) and a longer median readmission LOS (4 vs 3 days, P < 0.001). The APFC group also had higher readmission charges ($28282 vs $22865, P < 0.001). The presence of APFC increased the risk of readmission twofold (aHR 2.52, 95% confidence interval: 2.40-2.65, P < 0.001). The independent risk factors for 30-day readmission included female gender, Elixhauser Comorbidity Index ≥ 3, chronic pulmonary diseases, chronic renal disease, protein-calorie malnutrition, substance use disorder, depression, portal and splenic venous thrombosis, and certain endoscopic procedures. CONCLUSION: Developing APFC during index hospitalization for AP is linked to higher readmission rates, more inpatient complications, longer LOS, and increased healthcare costs. Knowing predictors of readmission can help target high-risk patients, reducing healthcare burdens.

Enhancing brain tumor classification in MRI scans with a multi-layer customized convolutional neural network approach.

Background: The necessity of prompt and accurate brain tumor diagnosis is unquestionable for optimizing treatment strategies and patient prognoses. Traditional reliance on Magnetic Resonance Imaging (MRI) analysis, contingent upon expert interpretation, grapples with challenges such as time-intensive processes and susceptibility to human error. Objective: This research presents a novel Convolutional Neural Network (CNN) architecture designed to enhance the accuracy and efficiency of brain tumor detection in MRI scans. Methods: The dataset used in the study comprises 7,023 brain MRI images from figshare, SARTAJ, and Br35H, categorized into glioma, meningioma, no tumor, and pituitary classes, with a CNN-based multi-task classification model employed for tumor detection, classification, and location identification. Our methodology focused on multi-task classification using a single CNN model for various brain MRI classification tasks, including tumor detection, classification based on grade and type, and tumor location identification. Results: The proposed CNN model incorporates advanced feature extraction capabilities and deep learning optimization techniques, culminating in a groundbreaking paradigm shift in automated brain MRI analysis. With an exceptional tumor classification accuracy of 99%, our method surpasses current methodologies, demonstrating the remarkable potential of deep learning in medical applications. Conclusion: This study represents a significant advancement in the early detection and treatment planning of brain tumors, offering a more efficient and accurate alternative to traditional MRI analysis methods.

Altering electronic structure of nickel foam supported CoNi-based oxide through Al ions modulation for efficient oxygen evolution reaction.

Developing highly active and durable non-precious metal-based electrocatalysts for the oxygen evolution reaction (OER) is crucial in achieving efficient energy conversion. Herein, we reported a CoNiAl0.5O/NF nanofilament that exhibits higher OER activity than previously reported IrO2-based catalysts in alkaline solution. The as-synthesized CoNiAl0.5O/NF catalyst demonstrates a low overpotential of 230 mV at a current density of 100 mA cm-2, indicating its high catalytic efficiency. Furthermore, the catalyst exhibits a Tafel slope of 26 mV dec-1, suggesting favorable reaction kinetics. The CoNiAl0.5O/NF catalyst exhibits impressive stability, ensuring its potential for practical applications. Detailed characterizations reveal that the enhanced activity of CoNiAl0.5O/NF can be attributed to the electronic modulation achieved through Al3+ incorporation, which promotes the emergence of higher-valence Ni metal, facilitating nanofilament formation and improving mass transport and charge transfer processes. The synergistic effect between nanofilaments and porous nickel foam (NF) substrate significantly enhances the electrical conductivity of this catalyst material. This study highlights the significance of electronic structures for improving the activity of cost-effective and non-precious metal-based electrocatalysts for the OER.

Normotensive metabolic syndrome in mice deleted of the gene, Type 1 transient receptor potential canonical channel.

Metabolic syndrome has become a global epidemic, affecting all developed countries/communities with growing economies. Worldwide, increasing efforts have been directed at curbing this growing problem. Mice deleted of the gene encoding Type 1 Transient Receptor Potential Canonical Channel (Trpc1) were found to weigh heavier than controls. They had fasting hyperglycemia and impaired glucose tolerance vs. wild type controls. Beyond 1 year of age, plasma triglyceride level in null mice was elevated. Plasma cholesterol tended to be higher than controls. Livers in null mice were heavier, richer in triglyceride, and more echogenic vs. controls on ultrasound evaluation. Hematocrit was lower in null mice of both genders beginning at 2nd/3rd month of age in the absence of bleeding/ hemolysis. Measured by indirect tail-cuff method or by the direct arterial cannulation, blood pressures in null mice were lower than controls. We conclude that Trpc1 gene regulates body metabolism and that except for hypertension, phenotypes of mice after deletion of the Trpc1 gene resemble the metabolic syndrome, suggesting that this could be a good experimental model for future investigation on the pathogenesis and management of this disorder.

Hamman Syndrome with a Rare Feature of Pneumorrhachis - An Unusual Complication of Polysubstance abuse and e-vaping.

Hamman syndrome is defined as dissection of air in mediastinum and skin fascia usually due to increased intrathoracic pressure. The air leak tends to make its way into pleural and pericardial layers; however, in rare instances air can also dissect into epidural spaces, regarded as pneumorrhachis. We present a case of a young male with a history of polysubstance abuse and e-vaping, who presented with symptoms of altered mental status. Given the concerning physical examination, a computed tomography of the chest was undertaken, which showed pneumothorax, pneumomediastinum and pneumorrhachis. The patient was closely monitored in the intensive care unit and improved after symptomatic management. The symptoms of pneumorrhachis depend on the volume and location of air in intracranial and intraspinal space. Although asymptomatic in our case, it is crucial for clinicians to be aware that pneumorrhachis with Hamman syndrome can potentially cause neurological deficits and cardiopulmonary arrest in severe cases due to increased intraspinal and intracranial hypertension, emphasising the need for close monitoring. LEARNING POINTS: Elevated intrathoracic pressure generated by deep inhalation of an aerosolised product is one of the triggers of air dissection in pleural, pericardial, and mediastinal regions. In rare instances, air can also translocate into intracranial and intraspinal spaces, which is referred to as pneumorrhachis.Mostly asymptomatic, pneumorrhachis has the potential to develop acute neurological deficits due to increased intracranial and intraspinal pressure, validating the need for acute monitoring.Most cases of pneumorrhachis are managed conservatively. However, severe cases warrant decompression or high concentrations of oxygen administration to facilitate air absorption.

Locally advanced nasopharyngeal carcinoma in adolescents treated with tomotherapy: Experience at King Faisal Specialist Hospital and Research Centre.

BACKGROUND: Nasopharyngeal carcinoma (NPC) is a rare disease worldwide; To the best of our knowledge, there is no established standard of care specifically tailored for the adolescent population. The majority of existing research relies on retrospective data analysis. OBJECTIVE: Evaluate clinical features, treatment results, prognostic factors and late toxicities of locally advanced NPC patients treated with tomotherapy. DESIGN: Retrospective. SETTINGS: Tertiary care hospital. PATIENTS AND METHODS: Between January 2007 and January 2020, we treated patients with NPC, aged between 14 and 21 years, with concomitant chemoradiotherapy using tomotherapy at our institution. We prospectively collected details of clinical characteristics, treatment modalities, outcomes and prognostic factors of these patients and then analysed them retrospectively. MAIN OUTCOME MEASURES: 3-5 years overall survival (OS), 3-5 years locoregional control rate, 3-5 years disease-free survival (DFS), prognostic factors. SAMPLE SIZE: 51 patients. RESULTS: There were 26 male and 25 female patients included in our study. The mean age was 16.5 years, 5 (9.8%) patients with stage III, and 46 (90.2%) with stage IVa according to the American Joint Committee on Cancer, 8th edition staging system. Most patients (98%) received two or more cycles of induction chemotherapy. All patients received concomitant chemoradiotherapy. The median total dose of radiotherapy delivered was 6600 cGy (range 4800-7000). With a median follow-up of 73 months (range 9-168 months), a 5-year locoregional control rate, 5-year OS and 5-year DFS rates were 100%, 86.8% and 71.7%, respectively. Five years later, disease control was 71.7%. Ten (19.6%) patients had disease recurrence in the form of distant metastases during the follow up. CONCLUSIONS: Helical tomotherapy has an excellent late toxicity profile without compromising clinical outcome for patients with NPC. Radiotherapy remains the mainstay of treatment of nasopharyngeal carcinoma to achieve remarkable local control rates. LIMITATIONS: Single institution experience, small number of patients, and retrospective design.

Recapitulation of Peste des Petits Ruminants (PPR) Prevalence in Small Ruminant Populations of Pakistan from 2004 to 2023: A Systematic Review and Meta-Analysis.

Peste des petits ruminants (PPR) is an extremely transmissible viral disease caused by the PPR virus that impacts domestic small ruminants, namely sheep and goats. This study aimed to employ a methodical approach to evaluate the regional occurrence of PPR in small ruminants in Pakistan and the contributing factors that influence its prevalence. A thorough search was performed in various databases to identify published research articles between January 2004 and August 2023 on PPR in small ruminants in Pakistan. Articles were chosen based on specific inclusion and exclusion criteria. A total of 25 articles were selected from 1275 studies gathered from different databases. The overall pooled prevalence in Pakistan was calculated to be 51% (95% CI: 42-60), with heterogeneity I2 = 100%, τ2 = 0.0495, and p = 0. The data were summarized based on the division into five regions: Punjab, Baluchistan, KPK, Sindh, and GB and AJK. Among these, the pooled prevalence of PPR in Sindh was 61% (95% CI: 46-75), I2 = 100%, τ2 = 0.0485, and p = 0, while in KPK, it was 44% (95% CI: 26-63), I2 = 99%, τ2 = 0.0506, and p < 0.01. However, the prevalence of PPR in Baluchistan and Punjab was almost the same. Raising awareness, proper surveillance, and application of appropriate quarantine measures interprovincially and across borders must be maintained to contain the disease.

Community-Acquired Meningitis Complicated With Pyogenic Ventriculitis and Hydrocephalus in a Patient With Haematological Malignancy: A Case Report and Literature Review.

Meningitis, an infection of the meninges of the central nervous system (CNS), can advance quickly and carries a mortality rate reaching 30% among affected patients. It may become complicated by conditions such as hydrocephalus, ventriculitis, and cerebral abscess. Here, we describe a case of meningitis that was complicated by pyogenic ventriculitis and hydrocephalus in a patient with diffuse large B-cell lymphoma (DLBCL) who underwent chemotherapy and radiotherapy. The patient presented with acute change in mental status and high-grade fever, with few episodes of non-bloody vomiting. Blood culture and cerebrospinal fluid (CSF) culture grew Streptococcus pneumoniae, which was sensitive to ceftriaxone. CT scan of the head showed ventriculomegaly, pansinusitis, and a large left mastoid effusion. MRI of the brain showed layering in ventricles, hydrocephalus, and dural enhancement consistent with pachymeningitis. She was treated with ceftriaxone for 21 days with a meaningful outcome. She was discharged home with near-baseline mental capacity for further physical therapy.

The chloroplast genome of Chrozophora sabulosa Kar. & Kir. and its exploration in the evolutionary position uncertainty of genus Chrozophora.

Chrozophora sabulosa Kar. & Kir. is a biennial herbaceous plant that belongs to the Euphorbiaceae family and has medicinal properties. This research aimed to identify the genetic characteristics and phylogenetic position of the Chrozophora genus within the Euphorbiaceae family. The evolutionary position of the Chrozophora genus was previously unknown due to insufficient research. Therefore, to determine the evolutionary link between C. sabulosa and other related species, we conducted a study using the NGS Illumina platform to sequence the C. sabulosa chloroplast (cp.) genome. The study results showed that the genome was 156,488 bp in length. It had a quadripartite structure consisting of two inverted repeats (IRb and IRa) of 24,649-bp, separated by an 87,696-bp LSC region and a 19,494-bp SSC region. The CP genome contained 113 unique genes, including four rRNA genes, 30 tRNA genes, and 79 CDS genes. In the second copy of the inverted repeat, there were 18 duplicated genes. The C. sabulosa lacks the petD, petB, rpl2, and rps16 intron. The analysis of simple sequence repeats (SSRs) revealed 93 SSR loci of 22 types and 78 oligonucleotide repeats of four kinds. The phylogenetic investigation showed that the Chrozophora genus evolved paraphyletically from other members of the Euphorbiaceae family. To support the phylogenetic findings, we selected species from the Euphorbiaceae and Phyllanthaceae families to compare with C. sabulosa for Ks and Ka substitution rates, InDels investigation, IR contraction and expansion, and SNPs analysis. The results of these comparative studies align with the phylogenetic findings. We identified six highly polymorphic regions shared by both families, which could be used as molecular identifiers for the Chrozophora genus (rpl33-rps18, rps18-rpl20, rps15-ycf1, ndhG-ndhI, psaI-ycf4, petA-psbJ). The cp. genome sequence of C. sabulosa reveals the evolution of plastid sequences in Chrozophora species. This is the first time the cp. genome of a Chrozophora genus has been sequenced, serving as a foundation for future sequencing of other species within the Chrozophoreae tribe and facilitating in-depth taxonomic research. The results of this research will also aid in identifying new Chrozophora species.

Timing of development of osteoradionecrosis post head and neck radiotherapy: does a safe time interval exist for dental extraction?

INTRODUCTION: Risk factors for developing osteoradionecrosis (ORN) are well known, but less is known about factors influencing the interval between radiotherapy and the onset of ORN. Also, it is unknown whether there is any specific period post-radiotherapy with a reduced probability of ORN when irradiated teeth require extraction. PURPOSE: The primary aim of this study was to identify factors influencing the interval in developing ORN in the following subgroups of patients: (1) patients who spontaneously developed ORN, (2) surgical-intervention-related ORN with a particular focus on patients after mandibulectomy. The secondary aim was to attempt to identify a possible time for safer dental intervention after primary treatment. MATERIALS AND METHODS: The authors retrospectively analysed 1608 head and neck cancer (HNC) patients treated in a single centre. Time intervals were measured from the end of radiotherapy to the development of ORN and further analysed in the subgroups listed above. RESULTS: In all, 141 patients (8.8%) developed intra-oral ORN. Median time from radiotherapy to ORN development in the whole cohort was 9 months. Median interval for spontaneous ORN was 8 months, 6.5 months for intervention-related ORN, and 15 months for patients post-mandibulectomy. In patients who required dental extraction preradiotherapy, median interval of ORN onset was 5 months. CONCLUSION: In our study, a slightly higher proportion of patients with intervention developed ORN earlier in comparison with spontaneous ORN. The period from 12-18 months after radiotherapy was identified as having the highest probability of developing ORN in patients after mandibulectomy. A time for safer dental intervention after primary treatment was not identified.

The Canadian VirusSeq Data Portal & Duotang: open resources for SARS-CoV-2 viral sequences and genomic epidemiology.

The COVID-19 pandemic led to a large global effort to sequence SARS-CoV-2 genomes from patient samples to track viral evolution and inform public health response. Millions of SARS-CoV-2 genome sequences have been deposited in global public repositories. The Canadian COVID-19 Genomics Network (CanCOGeN - VirusSeq), a consortium tasked with coordinating expanded sequencing of SARS-CoV-2 genomes across Canada early in the pandemic, created the Canadian VirusSeq Data Portal, with associated data pipelines and procedures, to support these efforts. The goal of VirusSeq was to allow open access to Canadian SARS-CoV-2 genomic sequences and enhanced, standardized contextual data that were unavailable in other repositories and that meet FAIR standards (Findable, Accessible, Interoperable and Reusable). In addition, the Portal data submission pipeline contains data quality checking procedures and appropriate acknowledgement of data generators that encourages collaboration. From inception to execution, the portal was developed with a conscientious focus on strong data governance principles and practices. Extensive efforts ensured a commitment to Canadian privacy laws, data security standards, and organizational processes. This Portal has been coupled with other resources like Viral AI and was further leveraged by the Coronavirus Variants Rapid Response Network (CoVaRR-Net) to produce a suite of continually updated analytical tools and notebooks. Here we highlight this Portal, including its contextual data not available elsewhere, and the 'Duotang', a web platform that presents key genomic epidemiology and modeling analyses on circulating and emerging SARS-CoV-2 variants in Canada. Duotang presents dynamic changes in variant composition of SARS-CoV-2 in Canada and by province, estimates variant growth, and displays complementary interactive visualizations, with a text overview of the current situation. The VirusSeq Data Portal and Duotang resources, alongside additional analyses and resources computed from the Portal (COVID-MVP, CoVizu), are all open-source and freely available. Together, they provide an updated picture of SARS-CoV-2 evolution to spur scientific discussions, inform public discourse, and support communication with and within public health authorities. They also serve as a framework for other jurisdictions interested in open, collaborative sequence data sharing and analyses.

Predicting short-term thromboembolic risk following Roux-en-Y gastric bypass using supervised machine learning.

BACKGROUND: Roux-en-Y gastric bypass (RYGB) is a widely recognized bariatric procedure that is particularly beneficial for patients with class III obesity. It aids in significant weight loss and improves obesity-related medical conditions. Despite its effectiveness, postoperative care still has challenges. Clinical evidence shows that venous thromboembolism (VTE) is a leading cause of 30-d morbidity and mortality after RYGB. Therefore, a clear unmet need exists for a tailored risk assessment tool for VTE in RYGB candidates. AIM: To develop and internally validate a scoring system determining the individualized risk of 30-d VTE in patients undergoing RYGB. METHODS: Using the 2016-2021 Metabolic and Bariatric Surgery Accreditation Quality Improvement Program, data from 6526 patients (body mass index ≥ 40 kg/m2) who underwent RYGB were analyzed. A backward elimination multivariate analysis identified predictors of VTE characterized by pulmonary embolism and/or deep venous thrombosis within 30 d of RYGB. The resultant risk scores were derived from the coefficients of statistically significant variables. The performance of the model was evaluated using receiver operating curves through 5-fold cross-validation. RESULTS: Of the 26 initial variables, six predictors were identified. These included a history of chronic obstructive pulmonary disease with a regression coefficient (Coef) of 2.54 (P < 0.001), length of stay (Coef 0.08, P < 0.001), prior deep venous thrombosis (Coef 1.61, P < 0.001), hemoglobin A1c > 7% (Coef 1.19, P < 0.001), venous stasis history (Coef 1.43, P < 0.001), and preoperative anticoagulation use (Coef 1.24, P < 0.001). These variables were weighted according to their regression coefficients in an algorithm that was generated for the model predicting 30-d VTE risk post-RYGB. The risk model's area under the curve (AUC) was 0.79 [95% confidence interval (CI): 0.63-0.81], showing good discriminatory power, achieving a sensitivity of 0.60 and a specificity of 0.91. Without training, the same model performed satisfactorily in patients with laparoscopic sleeve gastrectomy with an AUC of 0.63 (95%CI: 0.62-0.64) and endoscopic sleeve gastroplasty with an AUC of 0.76 (95%CI: 0.75-0.78). CONCLUSION: This simple risk model uses only six variables to assist clinicians in the preoperative risk stratification of RYGB patients, offering insights into factors that heighten the risk of VTE events.

Synthesis and Investigation of Mechanical Properties of the Acrylonitrile Butadiene Styrene Fiber Composites Using Fused Deposition Modeling.

Additive manufacturing is becoming a global phenomenon due to its versatile properties and numerous benefits, which is not possible by conventional machining processes. Fused deposition modeling (FDM) shows a huge potential of shift from rapid prototyping toward the rapid manufacturing. Nowadays, the strength of the FDM-printed parts is very important to consider along with all the printing parameters, which affect the strength of these parts. This study includes the investigation of printing parameters (infill density, layer thickness, and shell count) on the strength of FDM-printed parts of acrylonitrile butadiene styrene (ABS) and carbon fiber-reinforced ABS (ABS-CF). These printing parameters directly affect the quality as well as the strength of the 3D-printed parts through FDM. Tensile tests were performed on the universal testing machine on both types of printed parts. The optimized parameters for the 3D-printed samples of the pristine ABS are found to be 0.1045 mm of layer thickness, 57.72% of infill density, and 7.63 numbers of shell count, while the optimum parameters obtained for ABS-CF are 0.2780 mm of layer thickness, 28.37% of infill density, and 9.88 numbers of shell count. The results show that the layer thickness and shell count have a significant effect on the ultimate tensile strength of the 3D-printed parts.

Seroepidemiology and associated risk factors of brucellosis in small ruminants of district Khanewal, Pakistan.

Objectives: Keeping in view the economic and veterinary public health importance of brucellosis, this research was conducted to determine its seroprevalence and associated risk determinants in small ruminants in district Khanewal, Southern Punjab, Pakistan. Materials and Methods: Two-stage cluster sampling technique was used for sampling, and the sample size was calculated using C-survey 2.0. Accordingly, sera samples (n = 392) were collected from small ruminants in the study area from October 2022 to July 2023. All the samples were tested for the presence of anti-Brucella antibodies by Rose Bengal Plate Test (RBPT), followed by confirmation of all the samples using an enzyme linked immunosorbent assay (ELISA) kit (ID.vet®, France; sensitivity and specificity=100%, each). Results: The seropositivity rate of brucellosis was 7.14% [n = 28/392; 95% confidence interval (CI) = 4.87%-10.12%] by RBPT, whereas the results of ELISA showed an overall seroprevalence rate of 7.40% (n = 29/392; 95% CI = 5.11%-10.37%) in the study population. Univariate analysis of risk factors revealed that abortion history (AH), retained fetal membranes (RFMs), repeat breeding, flock size (FS), educational status of farmers (ESFs), awareness about brucellosis (AB), and farm hygiene had a significant association with the seroprevalence of brucellosis (p < 0.05). The multivariate analysis using a binary logistic regression model revealed that variables including tehsil, FS, AH, RFM, ESF, AB, and farming system were significant factors (p < 0.05) associated with brucellosis in the target population. Conclusion: Brucellosis is prevalent in small ruminants in Khanewal, Pakistan. The disease burden can be reduced by improving the reproductive health of animals, farm hygiene, and farmers' awareness about the diseases. Further studies are needed on a larger scale to devise stringent disease control strategies to avoid losses associated with brucellosis at regional, national, and global levels.

Security risk models against attacks in smart grid using big data and artificial intelligence.

The need to update the electrical infrastructure led directly to the idea of smart grids (SG). Modern security technologies are almost perfect for detecting and preventing numerous attacks on the smart grid. They are unable to meet the challenging cyber security standards, nevertheless. We need many methods and techniques to effectively defend against cyber threats. Therefore, a more flexible approach is required to assess data sets and identify hidden risks. This is possible for vast amounts of data due to recent developments in artificial intelligence, machine learning, and deep learning. Due to adaptable base behavior models, machine learning can recognize new and unexpected attacks. Security will be significantly improved by combining new and previously released data sets with machine learning and predictive analytics. Artificial Intelligence (AI) and big data are used to learn more about the current situation and potential solutions for cybersecurity issues with smart grids. This article focuses on different types of attacks on the smart grid. Furthermore, it also focuses on the different challenges of AI in the smart grid. It also focuses on using big data in smart grids and other applications like healthcare. Finally, a solution to smart grid security issues using artificial intelligence and big data methods is discussed. In the end, some possible future directions are also discussed in this article. Researchers and graduate students are the audience of our article.

Emotion detection from handwriting and drawing samples using an attention-based transformer model.

Emotion detection (ED) involves the identification and understanding of an individual's emotional state through various cues such as facial expressions, voice tones, physiological changes, and behavioral patterns. In this context, behavioral analysis is employed to observe actions and behaviors for emotional interpretation. This work specifically employs behavioral metrics like drawing and handwriting to determine a person's emotional state, recognizing these actions as physical functions integrating motor and cognitive processes. The study proposes an attention-based transformer model as an innovative approach to identify emotions from handwriting and drawing samples, thereby advancing the capabilities of ED into the domains of fine motor skills and artistic expression. The initial data obtained provides a set of points that correspond to the handwriting or drawing strokes. Each stroke point is subsequently delivered to the attention-based transformer model, which embeds it into a high-dimensional vector space. The model builds a prediction about the emotional state of the person who generated the sample by integrating the most important components and patterns in the input sequence using self-attentional processes. The proposed approach possesses a distinct advantage in its enhanced capacity to capture long-range correlations compared to conventional recurrent neural networks (RNN). This characteristic makes it particularly well-suited for the precise identification of emotions from samples of handwriting and drawings, signifying a notable advancement in the field of emotion detection. The proposed method produced cutting-edge outcomes of 92.64% on the benchmark dataset known as EMOTHAW (Emotion Recognition via Handwriting and Drawing).

Corrigendum: A metagenomic analysis of mosquito virome collected from different animal farms at Yunnan-Myanmar border of China.

[This corrects the article DOI: 10.3389/fmicb.2020.591478.].

Predicting the risk of early intensive care unit admission for patients hospitalized with acute pancreatitis using supervised machine learning.

Background: Acute pancreatitis (AP) is a complex and life-threatening disease. Early recognition of factors predicting morbidity and mortality is crucial. We aimed to develop and validate a pragmatic model to predict the individualized risk of early intensive care unit (ICU) admission for patients with AP. Methods: The 2019 Nationwide Readmission Database was used to identify patients hospitalized with a primary diagnosis of AP without ICU admission. A matched comparison cohort of AP patients with ICU admission within 7 days of hospitalization was identified from the National Inpatient Sample after 1:N propensity score matching. The least absolute shrinkage and selection operator (LASSO) regression was used to select predictors and develop an ICU acute pancreatitis risk (IAPR) score validated by 10-fold cross-validation. Results: A total of 1513 patients hospitalized for AP were included. The median age was 50.0 years (interquartile range: 39.0-63.0). The three predictors that were selected included hypoxia (area under the curve [AUC] 0.78), acute kidney injury (AUC 0.72), and cardiac arrhythmia (AUC 0.61). These variables were used to develop a nomogram that displayed excellent discrimination (AUC 0.874) (bootstrap bias-corrected 95% confidence interval 0.824-0.876). There was no evidence of miscalibration (test statistic = 2.88; P = 0.09). For high-risk patients (total score >6 points), the sensitivity was 68.94% and the specificity was 92.66%. Conclusions: This supervised machine learning-based model can help recognize high-risk AP hospitalizations. Clinicians may use the IAPR score to identify patients with AP at high risk of ICU admission within the first week of hospitalization.