Deep Reinforcement Learning for Autonomous Inspection System

The objective of the group is to investigate the application of Reinforced Deep Learning to autonomously complete damage inspection on buildings after natural disasters. By using Reinforced Deep Learning with Convolutional Neural Networks performing image processing, the drone will be trained to navigate itself towards buildings and perform building inspection without scanning the whole structure. The algorithms for Reinforced Deep Learning can be used to drive drones autonomously and perform inspections currently done by humans. The stability of the structure of buildings are unknown when these inspections are performed. This application would greatly reduce the risk of individuals involved in inspecting the building structures and provide greater insight into the stability of these structures

Exploring a prolonged enterovirus C104 infection in a severely ill patient using nanopore sequencing

Chronic enterovirus infections can cause significant morbidity, particularly in immunocompromised patients. This study describes a fatal case associated with a chronic untypeable enterovirus infection in an immunocompromised patient admitted to a Dutch university hospital over nine months. We aimed to identify the enterovirus genotype responsible for the infection and to determine potential evolutionary changes. Long-read sequencing was performed using viral targeted sequence capture on four respiratory and one faecal sample. Phylogenetic analysis was performed using a maximum likelihood method, along with a root-to-tip regression and time-scaled phylogenetic analysis to estimate evolutionary changes between sample dates. Intra-host variant detection, using a Fixed Ploidy algorithm, and selection pressure, using a Fixed Effect Likelihood and a Mixed Effects Model of Evolution, were also used to explore the patient samples. Near-complete genomes of enterovirus C104 (EV-C104) were recovered in all respiratory samples but not in the faecal sample. The recovered genomes clustered with a recently reported EV-C104 from Belgium in August 2018. Phylodynamic analysis including ten available EV-C104 genomes, along with the patient sequences, estimated the most recent common ancestor to occur in the middle of 2005 with an overall estimated evolution rate of 2.97 × 10(−3) substitutions per year. Although positive selection pressure was identified in the EV-C104 reference sequences, the genomes recovered from the patient samples alone showed an overall negative selection pressure in multiple codon sites along the genome. A chronic infection resulting in respiratory failure from a relatively rare enterovirus was observed in a transplant recipient. We observed an increase in single-nucleotide variations between sample dates from a rapidly declining patient, suggesting mutations are weakly deleterious and have not been purged during selection. This is further supported by the persistence of EV-C104 in the patient, despite the clearance of other viral infections. Next-generation sequencing with viral enrichment could be used to detect and characterise challenging samples when conventional workflows are insufficient

First detection of porcine respirovirus 1 in Germany and the Netherlands

Porcine respirovirus 1, also referred to as porcine parainfluenza virus 1 (PPIV-1), was first detected in deceased pigs from Hong Kong in 2013. It has since then been found in the USA, Chile and most recently in Hungary. Information on the pathogenicity and global spread is sparse. However, it has been speculated to play a role in the porcine respiratory disease complex. To investigate the porcine virome, we screened 53 pig samples from 26 farms within the Dutch-German border region using shotgun metagenomics sequencing (SMg). After detecting PPIV-1 in five farms through SMg, a real-time reverse transcriptase PCR (RT-qPCR) assay was designed, which not only confirmed the presence of the virus in 1 of the 5 farms but found an additional 6 positive farms. Phylogenetic analysis found the closest match to be the first detected PPIV-1 strain in Hong Kong. The Dutch-German region represents a significant area of pig farming within Europe and could provide important information on the characterization and circulation of porcine viruses, such as PPIV-1. With its recent detection in Hungary, these findings suggest widespread circulation of PPIV-1 in Central Europe, highlighting the need for further research on persistence, pathogenicity and transmission in Europe

Assessment of Viral Targeted Sequence Capture Using Nanopore Sequencing Directly from Clinical Samples

Shotgun metagenomic sequencing (SMg) enables the simultaneous detection and characterization of viruses in human, animal and environmental samples. However, lack of sensitivity still poses a challenge and may lead to poor detection and data acquisition for detailed analysis. To improve sensitivity, we assessed a broad scope targeted sequence capture (TSC) panel (ViroCap) in both human and animal samples. Moreover, we adjusted TSC for the Oxford Nanopore MinION and compared the performance to an SMg approach. TSC on the Illumina NextSeq served as the gold standard. Overall, TSC increased the viral read count significantly in challenging human samples, with the highest genome coverage achieved using the TSC on the MinION. TSC also improved the genome coverage and sequencing depth in clinically relevant viruses in the animal samples, such as influenza A virus. However, SMg was shown to be adequate for characterizing a highly diverse animal virome. TSC on the MinION was comparable to the NextSeq and can provide a valuable alternative, offering longer reads, portability and lower initial cost. Developing new viral enrichment approaches to detect and characterize significant human and animal viruses is essential for the One Health Initiative

Diabetic foot ulcers segmentation challenge report: benchmark and analysis

Monitoring the healing progress of diabetic foot ulcers is a challenging process. Accurate segmentation of foot ulcers can help podiatrists to quantitatively measure the size of wound regions to assist prediction of healing status. The main challenge in this field is the lack of publicly available manual delineation, which can be time consuming and laborious. Recently, methods based on deep learning have shown excellent results in automatic segmentation of medical images, however, they require large-scale datasets for training, and there is limited consensus on which methods perform the best. The 2022 Diabetic Foot Ulcers segmentation challenge was held in conjunction with the 2022 International Conference on Medical Image Computing and Computer Assisted Intervention, which sought to address these issues and stimulate progress in this research domain. A training set of 2000 images exhibiting diabetic foot ulcers was released with corresponding segmentation ground truth masks. Of the 72 (approved) requests from 47 countries, 26 teams used this data to develop fully automated systems to predict the true segmentation masks on a test set of 2000 images, with the corresponding ground truth segmentation masks kept private. Predictions from participating teams were scored and ranked according to their average Dice similarity coefficient of the ground truth masks and prediction masks. The winning team achieved a Dice of 0.7287 for diabetic foot ulcer segmentation. This challenge has now entered a live leaderboard stage where it serves as a challenging benchmark for diabetic foot ulcer segmentation

Deep learning in diabetic foot ulcers detection: A comprehensive evaluation

There has been a substantial amount of research involving computer methods and technology for the detection and recognition of diabetic foot ulcers (DFUs), but there is a lack of systematic comparisons of state-of-the-art deep learning object detection frameworks applied to this problem. DFUC2020 provided participants with a comprehensive dataset consisting of 2,000 images for training and 2,000 images for testing. This paper summarizes the results of DFUC2020 by comparing the deep learning-based algorithms proposed by the winning teams: Faster R–CNN, three variants of Faster R–CNN and an ensemble method; YOLOv3; YOLOv5; EfficientDet; and a new Cascade Attention Network. For each deep learning method, we provide a detailed description of model architecture, parameter settings for training and additional stages including pre-processing, data augmentation and post-processing. We provide a comprehensive evaluation for each method. All the methods required a data augmentation stage to increase the number of images available for training and a post-processing stage to remove false positives. The best performance was obtained from Deformable Convolution, a variant of Faster R–CNN, with a mean average precision (mAP) of 0.6940 and an F1-Score of 0.7434. Finally, we demonstrate that the ensemble method based on different deep learning methods can enhance the F1-Score but not the mAP

Does Sleep Improve Your Grammar? : Preferential Consolidation of Arbitrary Components of New Linguistic Knowledge

We examined the role of sleep-related memory consolidation processes in learning new form-meaning mappings. Specifically, we examined a Complementary Learning Systems account, which implies that sleep-related consolidation should be more beneficial for new hippocampally dependent arbitrary mappings (e.g. new vocabulary items) relative to new systematic mappings (e.g. grammatical regularities), which can be better encoded neocortically. The hypothesis was tested using a novel language with an artificial grammatical gender system. Stem-referent mappings implemented arbitrary aspects of the new language, and determiner/suffix+natural gender mappings implemented systematic aspects (e.g. tib scoiffesh + ballerina, tib mofeem + bride; ked jorool + cowboy, ked heefaff + priest). Importantly, the determiner-gender and the suffix-gender mappings varied in complexity and salience, thus providing a range of opportunities to detect beneficial effects of sleep for this type of mapping. Participants were trained on the new language using a word-picture matching task, and were tested after a 2-hour delay which included sleep or wakefulness. Participants in the sleep group outperformed participants in the wake group on tests assessing memory for the arbitrary aspects of the new mappings (individual vocabulary items), whereas we saw no evidence of a sleep benefit in any of the tests assessing memory for the systematic aspects of the new mappings: Participants in both groups extracted the salient determiner-natural gender mapping, but not the more complex suffix-natural gender mapping. The data support the predictions of the complementary systems account and highlight the importance of the arbitrariness/systematicity dimension in the consolidation process for declarative memories

Genome-Wide Association Study of Relative Telomere Length

Telomere function is essential to maintaining the physical integrity of linear chromosomes and healthy human aging. The probability of forming proper telomere structures depends on the length of the telomeric DNA tract. We attempted to identify common genetic variants associated with log relative telomere length using genome-wide genotyping data on 3,554 individuals from the Nurses' Health Study and the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial that took part in the National Cancer Institute Cancer Genetic Markers of Susceptibility initiative for breast and prostate cancer. After genotyping 64 independent SNPs selected for replication in additional Nurses' Health Study and Women's Genome Health Study participants, we did not identify genome-wide significant loci; however, we replicated the inverse association of log relative telomere length with the minor allele variant [C] of rs16847897 at the TERC locus (per allele β = −0.03, P = 0.003) identified by a previous genome-wide association study. We did not find evidence for an association with variants at the OBFC1 locus or other loci reported to be associated with telomere length. With this sample size we had >80% power to detect β estimates as small as ±0.10 for SNPs with minor allele frequencies of ≥0.15 at genome-wide significance. However, power is greatly reduced for β estimates smaller than ±0.10, such as those for variants at the TERC locus. In general, common genetic variants associated with telomere length homeostasis have been difficult to detect. Potential biological and technical issues are discussed

COVID-19 symptoms at hospital admission vary with age and sex: results from the ISARIC prospective multinational observational study

Background: The ISARIC prospective multinational observational study is the largest cohort of hospitalized patients with COVID-19. We present relationships of age, sex, and nationality to presenting symptoms. Methods: International, prospective observational study of 60 109 hospitalized symptomatic patients with laboratory-confirmed COVID-19 recruited from 43 countries between 30 January and 3 August 2020. Logistic regression was performed to evaluate relationships of age and sex to published COVID-19 case definitions and the most commonly reported symptoms. Results: ‘Typical’ symptoms of fever (69%), cough (68%) and shortness of breath (66%) were the most commonly reported. 92% of patients experienced at least one of these. Prevalence of typical symptoms was greatest in 30- to 60-year-olds (respectively 80, 79, 69%; at least one 95%). They were reported less frequently in children (≤ 18 years: 69, 48, 23; 85%), older adults (≥ 70 years: 61, 62, 65; 90%), and women (66, 66, 64; 90%; vs. men 71, 70, 67; 93%, each P < 0.001). The most common atypical presentations under 60 years of age were nausea and vomiting and abdominal pain, and over 60 years was confusion. Regression models showed significant differences in symptoms with sex, age and country. Interpretation: This international collaboration has allowed us to report reliable symptom data from the largest cohort of patients admitted to hospital with COVID-19. Adults over 60 and children admitted to hospital with COVID-19 are less likely to present with typical symptoms. Nausea and vomiting are common atypical presentations under 30 years. Confusion is a frequent atypical presentation of COVID-19 in adults over 60 years. Women are less likely to experience typical symptoms than men