Bosubabu Sambana, Hillary Sunday Nnadi, Mohd Anas Wajid
et al.
Plant diseases pose significant challenges to farmers and the agricultural sector at large. However, early detection of plant diseases is crucial to mitigating their effects and preventing widespread damage, as outbreaks can severely impact the productivity and quality of crops. With advancements in technology, there are increasing opportunities for automating the monitoring and detection of disease outbreaks in plants. This study proposed a system designed to identify and monitor plant diseases using a transfer learning approach. Specifically, the study utilizes YOLOv7 and YOLOv8, two state-ofthe-art models in the field of object detection. By fine-tuning these models on a dataset of plant leaf images, the system is able to accurately detect the presence of Bacteria, Fungi and Viral diseases such as Powdery Mildew, Angular Leaf Spot, Early blight and Tomato mosaic virus. The model's performance was evaluated using several metrics, including mean Average Precision (mAP), F1-score, Precision, and Recall, yielding values of 91.05, 89.40, 91.22, and 87.66, respectively. The result demonstrates the superior effectiveness and efficiency of YOLOv8 compared to other object detection methods, highlighting its potential for use in modern agricultural practices. The approach provides a scalable, automated solution for early any plant disease detection, contributing to enhanced crop yield, reduced reliance on manual monitoring, and supporting sustainable agricultural practices.
Samuel Charles, Scott Wiseman, Molly D. Savadelis
et al.
Abstract Background Gastrointestinal nematodes such as hookworms (Ancylostoma caninum) and roundworms (Toxocara canis) commonly infect dogs and are zoonotic parasites capable of producing clinical disease in humans. This field study was conducted to confirm the clinical effectiveness and field safety of a novel, chewable tablet (Credelio Quattro™) containing lotilaner, moxidectin, praziquantel, and pyrantel (as pamoate salt) as compared with a control product containing sarolaner, moxidectin, and pyrantel (as pamoate salt) for the treatment of naturally occurring gastrointestinal nematode infections in dogs. Methods In this multicenter field study, 158 dogs were enrolled with an evaluable safety population of 155 dogs and an evaluable effectiveness population of 109 dogs. Dogs were randomized in a 2:1 ratio to receive either the investigational veterinary product (IVP) Credelio Quattro containing lotilaner, moxidectin, praziquantel, and pyrantel or the control product (CP) containing sarolaner, moxidectin, and pyrantel. On Day 0, after confirmation of infection, enrolled dogs were randomized, dispensed, and administered IVP or CP. Enrolled dogs returned to the study site on Day 10 (± 2 d) to quantify the concentration of nematode eggs post-treatment. Results Post-treatment fecal egg counts performed on Day 10 demonstrated a 99.9% reduction in A. caninum eggs per gram in both IVP-treated dogs and CP-treated dogs. In addition, a 98.7% and 96.6% reduction in T. canis fecal egg counts were observed in IVP- and CP-treated dogs, respectively. Adverse events were reported in both treatment groups. The most common adverse events reported in the IVP group included gastrointestinal signs, such as diarrhea and emesis, as well as lethargy. Conclusions This field study demonstrated that Credelio Quattro, a novel oral combination chewable tablet administered at the labeled dose ranges of 20–40 mg/kg lotilaner, 0.02–0.04 mg/kg moxidectin, 5–10 mg/kg praziquantel, and 5–10 mg/kg pyrantel (as pamoate salt), is safe and effective for the treatment and control of naturally occurring A. caninum and T. canis infections in dogs. Graphical Abstract
Giuseppina Albano, Virginia Giorno, Francisco Torres-Ruiz
We consider a time-inhomogeneous diffusion process able to describe the dynamics of infected people in a susceptible-infectious epidemic model in which the transmission intensity function is time-dependent. Such a model is well suited to describe some classes of micro-parasitic infections in which individuals never acquire lasting immunity and over the course of the epidemic everyone eventually becomes infected. The stochastic process related to the deterministic model is transformable into a non homogeneous Wiener process so the probability distribution can be obtained. Here we focus on the inference for such process, by providing an estimation procedure for the involved parameters. We point out that the time dependence in the infinitesimal moments of the diffusion process makes classical inference methods inapplicable. The proposed procedure is based on Generalized Method of Moments in order to find suitable estimate for the infinitesimal drift and variance of the transformed process. Several simulation studies are conduced to test the procedure, these include the time homogeneous case, for which a comparison with the results obtained by applying the MLE is made, and cases in which the intensity function are time dependent with particular attention to periodic cases. Finally, we apply the estimation procedure to a real dataset.
Md Aziz Hosen Foysal, Foyez Ahmed, Md Zahurul Haque
Plant diseases significantly impact agricultural productivity, resulting in economic losses and food insecurity. Prompt and accurate detection is crucial for the efficient management and mitigation of plant diseases. This study investigates advanced techniques in plant disease detection, emphasizing the integration of image processing, machine learning, deep learning methods, and mobile technologies. High-resolution images of plant leaves were captured and analyzed using convolutional neural networks (CNNs) to detect symptoms of various diseases, such as blight, mildew, and rust. This study explores 14 classes of plants and diagnoses 26 unique plant diseases. We focus on common diseases affecting various crops. The model was trained on a diverse dataset encompassing multiple crops and disease types, achieving 98.14% accuracy in disease diagnosis. Finally integrated this model into mobile apps for real-time disease diagnosis.
Pumpkin is a vital crop cultivated globally, and its productivity is crucial for food security, especially in developing regions. Accurate and timely detection of pumpkin leaf diseases is essential to mitigate significant losses in yield and quality. Traditional methods of disease identification rely heavily on subjective judgment by farmers or experts, which can lead to inefficiencies and missed opportunities for intervention. Recent advancements in machine learning and deep learning offer promising solutions for automating and improving the accuracy of plant disease detection. This paper presents a comprehensive analysis of state-of-the-art Convolutional Neural Network (CNN) models for classifying diseases in pumpkin plant leaves. Using a publicly available dataset of 2000 highresolution images, we evaluate the performance of several CNN architectures, including ResNet, DenseNet, and EfficientNet, in recognizing five classes: healthy leaves and four common diseases downy mildew, powdery mildew, mosaic disease, and bacterial leaf spot. We fine-tuned these pretrained models and conducted hyperparameter optimization experiments. ResNet-34, DenseNet-121, and EfficientNet-B7 were identified as top-performing models, each excelling in different classes of leaf diseases. Our analysis revealed DenseNet-121 as the optimal model when considering both accuracy and computational complexity achieving an overall accuracy of 86%. This study underscores the potential of CNNs in automating disease diagnosis for pumpkin plants, offering valuable insights that can contribute to enhancing agricultural productivity and minimizing economic losses.
Plant diseases pose significant threats to agriculture. It necessitates proper diagnosis and effective treatment to safeguard crop yields. To automate the diagnosis process, image segmentation is usually adopted for precisely identifying diseased regions, thereby advancing precision agriculture. Developing robust image segmentation models for plant diseases demands high-quality annotations across numerous images. However, existing plant disease datasets typically lack segmentation labels and are often confined to controlled laboratory settings, which do not adequately reflect the complexity of natural environments. Motivated by this fact, we established PlantSeg, a large-scale segmentation dataset for plant diseases. PlantSeg distinguishes itself from existing datasets in three key aspects. (1) Annotation type: Unlike the majority of existing datasets that only contain class labels or bounding boxes, each image in PlantSeg includes detailed and high-quality segmentation masks, associated with plant types and disease names. (2) Image source: Unlike typical datasets that contain images from laboratory settings, PlantSeg primarily comprises in-the-wild plant disease images. This choice enhances the practical applicability, as the trained models can be applied for integrated disease management. (3) Scale: PlantSeg is extensive, featuring 11,400 images with disease segmentation masks and an additional 8,000 healthy plant images categorized by plant type. Extensive technical experiments validate the high quality of PlantSeg's annotations. This dataset not only allows researchers to evaluate their image classification methods but also provides a critical foundation for developing and benchmarking advanced plant disease segmentation algorithms.
The emerging research shows that lncRNAs are associated with a series of complex human diseases. However, most of the existing methods have limitations in identifying nonlinear lncRNA-disease associations (LDAs), and it remains a huge challenge to predict new LDAs. Therefore, the accurate identification of LDAs is very important for the warning and treatment of diseases. In this work, multiple sources of biomedical data are fully utilized to construct characteristics of lncRNAs and diseases, and linear and nonlinear characteristics are effectively integrated. Furthermore, a novel deep learning model based on graph attention automatic encoder is proposed, called HGATELDA. To begin with, the linear characteristics of lncRNAs and diseases are created by the miRNA-lncRNA interaction matrix and miRNA-disease interaction matrix. Following this, the nonlinear features of diseases and lncRNAs are extracted using a graph attention auto-encoder, which largely retains the critical information and effectively aggregates the neighborhood information of nodes. In the end, LDAs can be predicted by fusing the linear and nonlinear characteristics of diseases and lncRNA. The HGATELDA model achieves an impressive AUC value of 0.9692 when evaluated using a 5-fold cross-validation indicating its superior performance in comparison to several recent prediction models. Meanwhile, the effectiveness of HGATELDA in identifying novel LDAs is further demonstrated by case studies. the HGATELDA model appears to be a viable computational model for predicting LDAs.
Natália Maria V. Pereira Caldeira, Nayara Gonçalves Barbosa, Flavia Azevedo Gomes-Sponholz
et al.
Introdução: A sífilis congênita é um problema de saúde pública significativo, podendo levar a diversos desfechos perinatais desfavoráveis e sequelas graves para a criança. No Brasil, os casos de sífilis congênita e gestacional continuam aumentando, destacando a necessidade de diagnóstico, tratamento e prevenção oportunos. A falta de acompanhamento adequado durante o pré-natal pode resultar em desfechos indesejados para o neonato, como hospitalização prolongada e impactos no neurodesenvolvimento. Apesar de muitos estudos se concentrarem nos aspectos clínicos da sífilis congênita, há uma lacuna na compreensão das experiências maternas, especialmente em relação aos aspectos afetivos e psicológicos. A importância das redes de apoio também é subestimada neste contexto, apesar de seu potencial para mitigar os efeitos da sífilis congênita. Objetivo: Conhecer as vivências de mães de crianças com sífilis congênita frente ao diagnóstico e hospitalização da criança. Método: Estudo descritivo, de abordagem qualitativa, fundamentado no conceito das redes de apoio social. Após aprovação ética, foram realizadas entrevistas individuais e semiestruturadas, submetidas à análise de conteúdo. Foram incluídas 14 mães de crianças com sífilis congênita em acompanhamento ambulatorial em um serviço de referência. Resultados: Identificou-se a culpa da mulher e sua responsabilização pela transmissão da sífilis congênita. As mulheres vivenciaram sentimentos de tristeza, dúvidas em relação ao filho, a concepção equivocada de tratar-se de uma doença incurável e o medo da morte da criança. A vivência da sífilis congênita foi permeada por estigma e preconceitos. A internação da criança foi um momento de choque, sobretudo diante da separação da criança, e da necessidade de realização de procedimentos invasivos. A perspectiva de melhora da criança, bem como o reconhecimento dos benefícios do tratamento, atrelado a fé e conformação de uma rede de apoio foram fundamentais no processo de superação. Conclusão: A presença da rede de apoio na jornada de enfrentamento da sífilis congênita é capaz de modular a experiência materna da doença, apontando para a necessidade de educação em saúde e ações mais inclusivas no contexto de saúde materno-infantil, desde o pré-natal.
Kimberly L. Evasco, Craig Brockway, Tamara Falkingham
et al.
Abstract Background Culex tritaeniorhynchus has long been considered the primary vector of Japanese encephalitis virus (JEV), but until recently, it was considered exotic to Australia. When the species was detected in the country’s Northern Territory (NT) for the first time, the Western Australia (WA) Department of Health was cognisant of the risk it posed to the State because of the shared border and continuous mosquito habitat adjoining the two jurisdictions. The aim of this study was to undertake intensive mosquito surveillance in the Kimberley region to ascertain whether Cx. tritaeniorhynchus was present in WA, define the extent of its distribution and undertake phylogenetic analysis of select specimens to support hypothesized routes of entry into the state. Methods Carbon dioxide (CO2)-baited encephalitis virus surveillance (EVS) mosquito traps were deployed at various sites throughout the Kimberley region by surveillance officers within the Medical Entomology unit of the Western Australia (WA) Department of Health. Mosquitoes were then morphologically identified, and a subset of four specimens were confirmed as Cx. tritaeniorhynchus by molecular identification using Cytochrome Oxidase I (COI) DNA data and phylogenetic analysis. Results From 31 March 2021 to 30 May 2024, a total of 211 female Cx. tritaeniorhynchus specimens were collected from 21 unique trap sites in the Kimberley’s Shire of Wyndham-East Kimberley (SWEK). Four COI DNA barcode regions were amplified and successfully sequenced for analysis. These sequences fell within a clade recognised as Cx. tritaeniorhynchus and specifically all sequences were in a clade with other specimens from the NT and Timor-Leste. Conclusions This study represents the first detection of Cx. tritaeniorhynchus in WA. Given the widespread nature of trap sites that yielded the species and consecutive seasons over which it was observed, the authors surmise that Cx. tritaeniorhynchus is now established within the northeast Kimberley region. The findings are significant given the detection of the species coincides with the first significant outbreak of JEV activity on mainland Australia involving an estimated 45 human cases of Japanese encephalitis, 80 impacted commercial piggeries and widespread feral pig activity. Although the role that Cx. tritaeniorhynchus may play in JEV transmission into the future is not yet understood, it presents a potential risk to public health in the region. Graphical abstract
Detecting dental diseases through panoramic X-rays images is a standard procedure for dentists. Normally, a dentist need to identify diseases and find the infected teeth. While numerous machine learning models adopting this two-step procedure have been developed, there has not been an end-to-end model that can identify teeth and their associated diseases at the same time. To fill the gap, we develop YOLOrtho, a unified framework for teeth enumeration and dental disease detection. We develop our model on Dentex Challenge 2023 data, which consists of three distinct types of annotated data. The first part is labeled with quadrant, and the second part is labeled with quadrant and enumeration and the third part is labeled with quadrant, enumeration and disease. To further improve detection, we make use of Tufts Dental public dataset. To fully utilize the data and learn both teeth detection and disease identification simultaneously, we formulate diseases as attributes attached to their corresponding teeth. Due to the nature of position relation in teeth enumeration, We replace convolution layer with CoordConv in our model to provide more position information for the model. We also adjust the model architecture and insert one more upsampling layer in FPN in favor of large object detection. Finally, we propose a post-process strategy for teeth layout that corrects teeth enumeration based on linear sum assignment. Results from experiments show that our model exceeds large Diffusion-based model.
J. P. Dubey, Aditya Gupta, Larissa S. de Araujo
et al.
Currently, 7 named Sarcocystis species infect cattle: Sarcocystis hirsuta, S. cruzi, S. hominis, S. bovifelis, S. heydorni, S. bovini and S. rommeli; other, unnamed species also infect cattle. Of these parasites of cattle, a complete life cycle description is known only for S. cruzi, the most pathogenic species in cattle. The life cycle of S. cruzi was completed experimentally in 1982, before related parasite species were structurally characterized, and before the advent of molecular diagnostics; to our knowledge, no archived frozen tissues from the cattle employed in the original descriptions remain for DNA characterization. Here, we isolated DNA from a paraffin-embedded kidney of a calf experimentally infected with S. cruzi in 1980; we then sequenced portions of 18S rRNA, 28S rRNA, COX1 and Acetyl CoA genes and verified that each shares 99–100% similarity to other available isolates attributed to S. cruzi from naturally infected cattle. We also reevaluated histological sections of tissues of calves experimentally infected with S. cruzi in the original description, exploiting improvements in photographic technology to render clearer morphological detail. Finally, we reviewed all available studies of the life cycle of S. cruzi, noting that S. cruzi was transmitted between bison (Bison bison) and cattle (Bos taurus) and that the strain of parasite derived from bison appeared more pathogenic than the cattle strain. Based on these newfound molecular, morphological and physiological data, we thereby redescribed S. cruzi and deposited reference material in the Smithsonian Museum for posterity.
To help facilitate a variety of simulations related to healthcare facilities in North Carolina, we have developed an agent-based model (ABM) to accurately simulate patient (i.e., agent) movement to and from these facilities. This is an Overview, Design Concepts, and Details (ODD) Protocol, a standardized method for describing ABMs. This ODD provides detailed information on healthcare facilities in North Carolina, the agent movement to and between them, and any decisions that were made during the creation of this model. This ABM is intended to be used alongside disease-specific submodels. It can be used for purposes such as simulating the success of interventions on reducing disease transmission, simulating strain on facility resources (including staff and materials), and forecasting hospital capacity. Disease-specific ODDs should accompany this document. No details related to any submodels that use this ABM as a base model are included.
In a highly simplified view, a disease can be seen as the phenotype emerging from the interplay of genetic predisposition and fluctuating environmental stimuli. We formalize this situation in a minimal model, where a network (representing cellular regulation) serves as an interface between an input layer (representing environment) and an output layer (representing functional phenotype). Genetic predisposition for a disease is represented as a loss of function of some network nodes. Reduced, but non-zero, output indicates disease. The simplicity of this genetic disease model and its deep relationship to percolation theory allows us to understand the interplay between disease, network topology and the location and clusters of affected network nodes. We find that our model generates two different characteristics of diseases, which can be interpreted as chronic and acute diseases. In its stylized form, our model provides a new view on the relationship between genetic mutations and the type and severity of a disease.
Gomon Yu.M., Kolbin A.S., Strizheletsky V.V.
et al.
Objective.
To evaluate safety of anti-interleukin drugs used as a pathogenetic therapy of COVID-19 as assessed by risks of infectious complications.
Materials and Methods.
A systematic review of publications related to safety assessment of anti-interleukin drugs recommended as pathogenetic therapy in COVID-19 patients in terms of incidence of serious adverse events and adverse events of “Infections and Invasions” class and a meta-analysis of the data were performed.
Results.
The meta-analysis included 16 randomized and 3 non-randomized studies. The hazard ratio of serious adverse events between the comparison groups was 0.93 [95% CI 0.85; 1.01] (p = 0.1), the hazard ratio of adverse event of “Infections and Invasions” class was 0.9 [95% CI 0.8; 1.02] (p = 0.09), showing no differences in the incidence of those events.
Conclusions.
This meta-analysis did not demonstrate statistically significant differences in the relative risks of serious adverse events and adverse events of “Infections and Invasions” class for the use of antiinterleukin drugs in COVID-19 patients.