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The timely monitoring of crop disease development is very important for precision agriculture applications. Remote sensing-based vegetation indices (VIs) can be good indicators of crop disease severity, but current methods are mainly dependent on manual ground survey results. Based on VI normalization, an automated crop disease severity grading method without the use of ground surveys was proposed in this study. This technique was applied to two cotton fields infested with different levels of cotton root rot in south Texas in the United States, where airborne hyperspectral imagery was collected. Six typical VIs were calculated from the hyperspectral imagery and their histograms indicated that VI normalization could eliminate the influences of variable field conditions and the VI value range variations, allowing a potentially broader scope of application. According to the analysis of the obtained results from the spectral dimension, spatial dimension and descriptive statistics, the disease grading results were in general agreement with previous ground survey results, proving the validity of the disease severity grading method. Although satisfactory results could be achieved from different types of VI, there is still room for further improvement through the exploration of more VIs. With the advantages of independence of ground surveys and potential universal applicability, the newly proposed crop disease grading method will be of great significance for crop disease monitoring over large geographical areas.

Fusarium head blight (FHB) is one of the most detrimental wheat diseases. The accurate identification of FHB severity is significant to the sustainable management of FHB and the guarantee of food production and security. A total of 2752 images with five infection levels were collected to establish an FHB severity grading dataset (FHBSGD), and a novel lightweight GSEYOLOX-s was proposed to automatically recognize the severity of FHB. The simple, parameter-free attention module (SimAM) was fused into the CSPDarknet feature extraction network to obtain more representative disease features while avoiding additional parameters. Meanwhile, the ghost convolution of the model head (G-head) was designed to achieve lightweight and speed improvements. Furthermore, the efficient intersection over union (EIoU) loss was employed to accelerate the convergence speed and improve positioning precision. The results indicate that the GSEYOLOX-s model with only 8.06 MB parameters achieved a mean average precision (mAP) of 99.23% and a detection speed of 47 frames per second (FPS), which is the best performance compared with other lightweight models, such as EfficientDet, Mobilenet-YOLOV4, YOLOV7, YOLOX series. The proposed GSEYOLOX-s was deployed on mobile terminals to assist farmers in the real-time identification of the severity of FHB and facilitate the precise management of crop diseases.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants with a 382-nucleotide deletion (∆382) in the open reading frame 8 (ORF8) region of the genome have been detected in Singapore and other countries. We investigated the effect of this deletion on the clinical features of infection. We retrospectively identified patients who had been screened for the ∆382 variant and recruited to the PROTECT study—a prospective observational cohort study conducted at seven public hospitals in Singapore. We collected clinical, laboratory, and radiological data from patients' electronic medical records and serial blood and respiratory samples taken during hospitalisation and after discharge. Individuals infected with the ∆382 variant were compared with those infected with wild-type SARS-CoV-2. Exact logistic regression was used to examine the association between the infection groups and the development of hypoxia requiring supplemental oxygen (an indicator of severe COVID-19, the primary endpoint). Follow-up for the study's primary endpoint is completed. Between Jan 22 and March 21, 2020, 278 patients with PCR-confirmed SARS-CoV-2 infection were screened for the ∆382 deletion and 131 were enrolled onto the study, of whom 92 (70%) were infected with the wild-type virus, ten (8%) had a mix of wild-type and ∆382-variant viruses, and 29 (22%) had only the ∆382 variant. Development of hypoxia requiring supplemental oxygen was less frequent in the ∆382 variant group (0 [0%] of 29 patients) than in the wild-type only group (26 [28%] of 92; absolute difference 28% [95% CI 14–28]). After adjusting for age and presence of comorbidities, infection with the ∆382 variant only was associated with lower odds of developing hypoxia requiring supplemental oxygen (adjusted odds ratio 0·07 [95% CI 0·00–0·48]) compared with infection with wild-type virus only. The ∆382 variant of SARS-CoV-2 seems to be associated with a milder infection. The observed clinical effects of deletions in ORF8 could have implications for the development of treatments and vaccines. National Medical Research Council Singapore.

Adult-onset Still’s disease (AoSD) is a rare but clinically well-known, polygenic, systemic autoinflammatory disease. Owing to its sporadic appearance in all adult age groups with potentially severe inflammatory onset accompanied by a broad spectrum of disease manifestation and complications, AoSD is an unsolved challenge for clinicians with limited therapeutic options. This Review provides a comprehensive insight into the complex and heterogeneous nature of AoSD, describing biomarkers of the disease and its progression and the cytokine signalling pathways that contribute to disease. The efficacy and safety of biologic therapeutic options are also discussed, and guidance for treatment decisions is provided. Improving the approach to AoSD in the future will require much closer cooperation between paediatric and adult rheumatologists to establish common diagnostic strategies, treatment targets and goals.

Patients with systemic lupus erythematosus (SLE) display a complex blood transcriptome whose cellular origin is poorly resolved. Using single-cell RNA sequencing, we profiled ~276,000 peripheral blood mononuclear cells from 33 children with SLE with different degrees of disease activity and 11 matched controls. Increased expression of interferon-stimulated genes (ISGs) distinguished cells from children with SLE from healthy control cells. The high ISG expression signature (ISG hi ) derived from a small number of transcriptionally defined subpopulations within major cell types, including monocytes, CD4 + and CD8 + T cells, natural killer cells, conventional and plasmacytoid dendritic cells, B cells and especially plasma cells. Expansion of unique subpopulations enriched in ISGs and/or in monogenic lupus-associated genes classified patients with the highest disease activity. Profiling of ~82,000 single peripheral blood mononuclear cells from adults with SLE confirmed the expansion of similar subpopulations in patients with the highest disease activity. This study lays the groundwork for resolving the origin of the SLE transcriptional signatures and the disease heterogeneity towards precision medicine applications. Banchereau and colleagues provide a resource dataset that examines disease-related transcriptional profiles of peripheral whole-blood cells from adolescent patients with SLE by single-cell RNA-seq analysis.

Background Classifying and predicting Parkinson's disease (PD) is challenging because of its diverse subtypes based on severity levels. Currently, identifying objective biomarkers associated with disease severity that can distinguish PD subtypes in clinical trials is necessary. This study aims to address the clinical applicability and heterogeneity of PD using PD severity subtypes classification and digital biomarker development by combining objective multimodal data with machine learning (ML) approaches. Methods We analyzed datasets that combine clinical characteristics, physical function and lifestyle data, gait parameters in motion analysis systems, and wearable sensors collected from persons with PD (n = 102) to perform clustering for subtype classification. Results We identified three PD severity subtypes, each exhibiting different patterns of clinical severity, with the severity increasing as it progressed from clusters 1 to 3. We found significant mutual information between all/single modalities and the unified PD rating scale scores, identifying potential modalities with high feature importance using ML. Among all modalities, the principal components of gait parameters derived from wearable sensors were identified as the most associated indicators of PD severity. A model utilizing the first principal component of the left and right ankle achieved perfect classification with an area under the curve of 1.0, accurately distinguishing clinically severe subtypes from mild subtypes of PD. These findings suggest that gait features in both ankles can reflect asymmetry factors associated with PD severity subtypes, which contributes to high classification performance. Conclusions Digital biomarkers obtained from wearable sensors attached bilaterally to body segments demonstrate potential for classifying PD severity subtypes and tracking disease progression. Our findings emphasized the clinical value of sensor-based gait analysis in PD management, which suggested its integration into personalized monitoring systems and therapeutic interventions for persons with PD.

This review explores how imaging techniques are being developed with a focus on deployment for crop monitoring methods. Imaging applications are discussed in relation to both field and glasshouse-based plants, and techniques are sectioned into ‘healthy and diseased plant classification’ with an emphasis on classification accuracy, early detection of stress, and disease severity. A central focus of the review is the use of hyperspectral imaging and how this is being utilised to find additional information about plant health, and the ability to predict onset of disease. A summary of techniques used to detect biotic and abiotic stress in plants is presented, including the level of accuracy associated with each method.

A higher level of education was correlated with less severe motor impairment in Parkinson’s Disease (PD). Nevertheless, there is limited evidence on the relationship between cognitive reserve and motor performance in complex situations in PD. To investigate the association between cognitive reserve and the dual-task gait effect in PD. Additionally, we examined the relationship between executive function, clinical and sociodemographic variables and, dual-task gait effects. We conducted a cross-sectional study with 44 PD participants. We evaluated dual-task effect on cadence, stride length, and gait velocity. Dual-task effects were correlated with neurophysiological factors, including cognitive reserve (Cognitive Reserve Index Questionnaire), overall cognitive performance of executive functions, a specific executive function domain (Trail Making Test), and the global cognitive status (Montreal Cognitive Assessment and Mini-Mental State Examination). Age, gender, and disease severity were considered as variables to be examined for correlation. We found that cognitive reserve did not influence gait performance under dual-task conditions in this sample. However, executive functions, age, and disease severity were associated with the dual-task effect on gait. The overall cognitive performance with respect to the Trail Making Test showed an inverse relationship in the dual-task gait effect on cadence. Our study’s findings have important implications for understanding the association between executive functions, age, and disease severity with the dual-task effect on gait in PD. Pre-life factors, such as education, occupation, and leisure activity, did not contribute to coping with complex gait situations in PD.

Pediatric inflammatory bowel disease (IBD) is challenging to diagnose because of the non-specificity of symptoms; an unequivocal diagnosis can only be made using colonoscopy, which clinicians are reluctant to recommend for children. Diagnosis of pediatric IBD is therefore frequently delayed, leading to inappropriate treatment plans and poor outcomes. We investigated the use of 16S rRNA sequencing of fecal samples and new analytical methods to assess differences in the microbiota of children with IBD and other gastrointestinal disorders. We applied synthetic learning in microbial ecology (SLiME) analysis to 16S sequencing data obtained from i) published surveys of microbiota diversity in IBD and ii) fecal samples from 91 children and young adults who were treated in the gastroenterology program of Children's Hospital (Boston, USA). The developed method accurately distinguished control samples from those of patients with IBD; the area under the receiver-operating-characteristic curve (AUC) value was 0.83 (corresponding to 80.3% sensitivity and 69.7% specificity at a set threshold). The accuracy was maintained among data sets collected by different sampling and sequencing methods. The method identified taxa associated with disease states and distinguished patients with Crohn's disease from those with ulcerative colitis with reasonable accuracy. The findings were validated using samples from an additional group of 68 patients; the validation test identified patients with IBD with an AUC value of 0.84 (e.g. 92% sensitivity, 58.5% specificity). Microbiome-based diagnostics can distinguish pediatric patients with IBD from patients with similar symptoms. Although this test can not replace endoscopy and histological examination as diagnostic tools, classification based on microbial diversity is an effective complementary technique for IBD detection in pediatric patients.

The objectives of this study were to identify risk factors for severe coronavirus disease 2019 (COVID-19) and to lay the basis for risk stratification based on demographic data and health records. The design was a matched case-control study. Severe COVID-19 was defined as either a positive nucleic acid test for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the national database followed by entry to a critical care unit or death within 28 days or a death certificate with COVID-19 as underlying cause. Up to 10 controls per case matched for sex, age, and primary care practice were selected from the national population register. For this analysis-based on ascertainment of positive test results up to 6 June 2020, entry to critical care up to 14 June 2020, and deaths registered up to 14 June 2020-there were 36,948 controls and 4,272 cases, of which 1,894 (44%) were care home residents. All diagnostic codes from the past 5 years of hospitalisation records and all drug codes from prescriptions dispensed during the past 240 days were extracted. Rate ratios for severe COVID-19 were estimated by conditional logistic regression. In a logistic regression using the age-sex distribution of the national population, the odds ratios for severe disease were 2.87 for a 10-year increase in age and 1.63 for male sex. In the case-control analysis, the strongest risk factor was residence in a care home, with rate ratio 21.4 (95% CI 19.1-23.9, p = 8 × 10-644). Univariate rate ratios for conditions listed by public health agencies as conferring high risk were 2.75 (95% CI 1.96-3.88, p = 6 × 10-9) for type 1 diabetes, 1.60 (95% CI 1.48-1.74, p = 8 × 10-30) for type 2 diabetes, 1.49 (95% CI 1.37-1.61, p = 3 × 10-21) for ischemic heart disease, 2.23 (95% CI 2.08-2.39, p = 4 × 10-109) for other heart disease, 1.96 (95% CI 1.83-2.10, p = 2 × 10-78) for chronic lower respiratory tract disease, 4.06 (95% CI 3.15-5.23, p = 3 × 10-27) for chronic kidney disease, 5.4 (95% CI 4.9-5.8, p = 1 × 10-354) for neurological disease, 3.61 (95% CI 2.60-5.00, p = 2 × 10-14) for chronic liver disease, and 2.66 (95% CI 1.86-3.79, p = 7 × 10-8) for immune deficiency or suppression. Seventy-eight percent of cases and 52% of controls had at least one listed condition (51% of cases and 11% of controls under age 40). Severe disease was associated with encashment of at least one prescription in the past 9 months and with at least one hospital admission in the past 5 years (rate ratios 3.10 [95% CI 2.59-3.71] and 2.75 [95% CI 2.53-2.99], respectively) even after adjusting for the listed conditions. In those without listed conditions, significant associations with severe disease were seen across many hospital diagnoses and drug categories. Age and sex provided 2.58 bits of information for discrimination. A model based on demographic variables, listed conditions, hospital diagnoses, and prescriptions provided an additional 1.07 bits (C-statistic 0.804). A limitation of this study is that records from primary care were not available. We have shown that, along with older age and male sex, severe COVID-19 is strongly associated with past medical history across all age groups. Many comorbidities beyond the risk conditions designated by public health agencies contribute to this. A risk classifier that uses all the information available in health records, rather than only a limited set of conditions, will more accurately discriminate between low-risk and high-risk individuals who may require shielding until the epidemic is over.