3-hour genome sequencing and targeted analysis to rapidly assess genetic risk.

Purpose: Rapid genetic testing in the critical care setting may guide diagnostic evaluation, direct therapies, and help families and care providers make informed decisions about goals of care. We tested whether a simplified DNA extraction and library preparation process would enable us to perform ultra-rapid assessment of genetic risk for a Mendelian condition, based on information from an affected sibling, using long-read genome sequencing and targeted analysis.

Methods: Following extraction of DNA from cord blood and rapid library preparation, genome sequencing was performed on an Oxford Nanopore PromethION.

Profiling complex repeat expansions in RFC1 in Parkinson's disease.

A biallelic (AAGGG) expansion in the poly(A) tail of an AluSx3 transposable element within the gene RFC1 is a frequent cause of cerebellar ataxia, neuropathy, vestibular areflexia syndrome (CANVAS), and more recently, has been reported as a rare cause of Parkinson's disease (PD) in the Finnish population. Here, we investigate the prevalence of RFC1 (AAGGG) expansions in PD patients of non-Finnish European ancestry in 1609 individuals from the Parkinson's Progression Markers Initiative study. We identified four PD patients carrying the biallelic RFC1 (AAGGG) expansion and did not identify any carriers in controls.

Thyroid hormone concentrations in female baboons: Metabolic consequences of living in a highly seasonal environment.

How female mammals adapt metabolically in response to environmental variation remains understudied in the wild, because direct measures of metabolic activity are difficult to obtain in wild populations. However, recent advances in the non-invasive measurement of fecal thyroid hormones, triiodothyronine (T3), an important regulator of metabolism, provide an opportunity to understand how female baboons living in the harsh Amboseli ecosystem in southern Kenya adapt to environmental variability and escape strict reproductive seasonality. Specifically, we assessed how a female's activity budget, diet, and concentrations of fecal T3 metabolites (mT3) changed over the course of the year and between years.

Semantic segmentation of methane plumes with hyperspectral machine learning models.

Methane is the second most important greenhouse gas contributor to climate change; at the same time its reduction has been denoted as one of the fastest pathways to preventing temperature growth due to its short atmospheric lifetime. In particular, the mitigation of active point-sources associated with the fossil fuel industry has a strong and cost-effective mitigation potential. Detection of methane plumes in remote sensing data is possible, but the existing approaches exhibit high false positive rates and need manual intervention.

Deep Learning for Visual Localization and Mapping: A Survey.

Deep-learning-based localization and mapping approaches have recently emerged as a new research direction and receive significant attention from both industry and academia. Instead of creating hand-designed algorithms based on physical models or geometric theories, deep learning solutions provide an alternative to solve the problem in a data-driven way. Benefiting from the ever-increasing volumes of data and computational power on devices, these learning methods are fast evolving into a new area that shows potential to track self-motion and estimate environmental models accurately and robustly for mobile agents.

Reducing intrusive memories after trauma via an imagery-competing task intervention in COVID-19 intensive care staff: a randomised controlled trial.

Intrusive memories (IMs) after traumatic events can be distressing and disrupt mental health and functioning. We evaluated the impact of a brief remotely-delivered digital imagery-competing task intervention on the number of IMs for intensive care unit (ICU) staff who faced repeated trauma exposure during the COVID-19 pandemic using a two-arm, parallel-group, single-blind randomised controlled trial, with the comparator arm receiving delayed access to active treatment (crossover). Eligible participants worked clinically in a UK NHS ICU during the pandemic and had at least 3 IMs of work-related traumatic events in the week before recruitment.

A multiplexed, paired-pooled droplet digital PCR assay for detection of SARS-CoV-2 in saliva.

In response to the SARS-CoV-2 pandemic, we developed a multiplexed, paired-pool droplet digital PCR (MP4) screening assay. Key features of our assay are the use of minimally processed saliva, 8-sample paired pools, and reverse-transcription droplet digital PCR (RT-ddPCR) targeting the SARS-CoV-2 nucleocapsid gene. The limit of detection was determined to be 2 and 12 copies per µl for individual and pooled samples, respectively.

Evaluating expert-based habitat suitability information of terrestrial mammals with GPS-tracking data.

Aim: Macroecological studies that require habitat suitability data for many species often derive this information from expert opinion. However, expert-based information is inherently subjective and thus prone to errors. The increasing availability of GPS tracking data offers opportunities to evaluate and supplement expert-based information with detailed empirical evidence.

Concept: A randomised multicentre trial of first line chemotherapy comparing three weekly cabazitaxel versus weekly paclitaxel in HER2 negative metastatic breast cancer.

Background: Paclitaxel is commonly used as first-line chemotherapy for HER2-negative metastatic breast cancer (MBC) patients. However, with response rates of 21.5-53.

Evaluation of a pediatric dermatology electronic consult program in a rural state with subanalysis of infantile hemangioma cases.

An electronic consultation (e-Consult) store-and-forward teledermatology program was implemented to improve access to dermatologic care in Maine. While initially designed to triage potential skin cancers, we found this program to be heavily used for pediatric patients, especially infants. Our findings suggest e-Consult expedites care for pediatric dermatology patients, particularly those with infantile hemangiomas.

Representations of machine vision technologies in artworks, games and narratives: A dataset.

This data paper documents a dataset that captures cultural attitudes towards machine vision technologies as they are expressed in art, games and narratives. The dataset includes records of 500 creative works (including 77 digital games, 190 digital artworks and 233 movies, novels and other narratives) that use or represent machine vision technologies like facial recognition, deepfakes, and augmented reality. The dataset is divided into three main tables, relating to the works, to specific situations in each work involving machine vision technologies, and to the characters that interact with the technologies.

You Only Train Once: Learning General and Distinctive 3D Local Descriptors.

Extracting distinctive, robust, and general 3D local features is essential to downstream tasks such as point cloud registration. However, existing methods either rely on noise-sensitive handcrafted features, or depend on rotation-variant neural architectures. It remains challenging to learn robust and general local feature descriptors for surface matching.

Learning Selective Sensor Fusion for State Estimation.

Autonomous vehicles and mobile robotic systems are typically equipped with multiple sensors to provide redundancy. By integrating the observations from different sensors, these mobile agents are able to perceive the environment and estimate system states, e.g.

Multiscale Human Activity Recognition and Anticipation Network.

Deep convolutional neural networks have been leveraged to achieve huge improvements in video understanding and human activity recognition performance in the past decade. However, most existing methods focus on activities that have similar time scales, leaving the task of action recognition on multiscale human behaviors less explored. In this study, a two-stream multiscale human activity recognition and anticipation (MS-HARA) network is proposed, which is jointly optimized using a multitask learning method.

Gefapixant: First Approval.

Gefapixant (Lyfnua; Merck & Co.) is a first-in-class, non-narcotic, selective P2X3 receptor antagonist being developed for the treatment of refractory chronic cough or unexplained chronic cough. Marketing approval for gefapixant is being sought in the EU and USA, and the drug was recently approved for marketing in Japan as treatment for refractory or unexplained chronic cough.

A Single Short 'Tone Burst' Results in Optimal Drug Delivery to Tumours Using Ultrasound-Triggered Therapeutic Microbubbles.

Advanced drug delivery systems, such as ultrasound-mediated drug delivery, show great promise for increasing the therapeutic index. Improvements in delivery by altering the ultrasound parameters have been studied heavily in vitro but relatively little in vivo. Here, the same therapeutic microbubble and tumour type are used to determine whether altering ultrasound parameters can improve drug delivery.

SelfVIO: Self-supervised deep monocular Visual-Inertial Odometry and depth estimation.

In the last decade, numerous supervised deep learning approaches have been proposed for visual-inertial odometry (VIO) and depth map estimation, which require large amounts of labelled data. To overcome the data limitation, self-supervised learning has emerged as a promising alternative that exploits constraints such as geometric and photometric consistency in the scene. In this study, we present a novel self-supervised deep learning-based VIO and depth map recovery approach (SelfVIO) using adversarial training and self-adaptive visual-inertial sensor fusion.

Daridorexant: First Approval.

Daridorexant (Quviviq™; Idorsia Pharmaceuticals Ltd.) is an orally administered dual orexin type 1 and type 2 (OX and OX) receptor antagonist (DORA) being developed for the treatment of insomnia. It was selected from a pool of drug candidates on the basis of an expected effect duration of ≈ 8 h at a dose of 25 mg, with a half-life intended to minimize residual effects that might impair daytime functioning.