A Mobile App-Based Gratitude Intervention's Effect on Mental Well-Being in University Students: Randomized Controlled Trial.

Background: Gratitude interventions are used to cultivate a sense of gratitude for life and others. There have been mixed results of the efficacy of gratitude interventions' effect on psychological well-being with a variety of populations and methodologies.

Objectives: The objective of our study was to test the effectiveness of a gratitude intervention smartphone app on university students' psychological well-being.

Synovial cysts at the cervicothoracic junction: Illustrative series of three cases.

Background: Spinal synovial cysts are an uncommon pathology, estimated to affect 0.65-2.6% of the population.

Associations of active and passive smartphone use with measures of youth mental health during the COVID-19 pandemic.

Smartphone use provides a significant amount of screen-time for youth, and there have been growing concerns regarding its impact on their mental health. While time spent in a passive manner on the device is frequently considered deleterious, more active engagement with the phone might be protective for mental health. Recent developments in mobile sensing technology provide a unique opportunity to examine behaviour in a naturalistic manner.

Individualized Seizure Cluster Prediction Using Machine Learning and Chronic Ambulatory Intracranial EEG.

Epilepsy patients often experience acute repetitive seizures, known as seizure clusters, which can progress to prolonged seizures or status epilepticus if left untreated. Predicting the onset of seizure clusters is crucial to enable patients to receive preventative treatments. Additionally, studying the patterns of seizure clusters can help predict the seizure type (isolated or cluster) after observing a just occurred seizure.

Predicting cholangiocarcinoma in primary sclerosing cholangitis: using artificial intelligence, clinical and laboratory data.

Background: Primary sclerosing cholangitis (PSC) patients have a risk of developing cholangiocarcinoma (CCA). Establishing predictive models for CCA in PSC is important.

Methods: In a large cohort of 1,459 PSC patients seen at Mayo Clinic (1993-2020), we quantified the impact of clinical/laboratory variables on CCA development using univariate and multivariate Cox models and predicted CCA using statistical and artificial intelligence (AI) approaches.

Dream Recall Frequency, Lucid Dream Frequency, and Personality During the Covid-19 Pandemic.

Dream recall frequency and lucid dream frequency showed large inter-individual differences that are partly related to personality dimensions. However, as dream research is a small field, independent studies are necessary to build a solid empirical foundation. The present online survey included 1,537 participants (1150 women, 387 men) with a mean age of 35.

Noniatrogenic spinal cord ischemia: A patient level meta-analysis of 125 case reports and series.

Background: Guidelines are needed to manage spinal cord infarctions. Here, we evaluated the incidence of noniatrogenic spinal ischemia, focusing on the spinal levels involved, and the relative efficacy of different management strategies.

Methods: We performed a meta-analysis of 147 patients who sustained noniatrogenic spinal cord ischemia within the past 10 years.

Personality-targeted persuasive gamified systems: exploring the impact of application domain on the effectiveness of behaviour change strategies.

Persuasive gamified systems for health are interventions that promote behaviour change using various persuasive strategies. While research has shown that these strategies are effective at motivating behaviour change, there is little knowledge on whether and how the effectiveness of these strategies vary across multiple domains for people of distinct personality traits. To bridge this gap, we conducted a quantitative study with 568 participants to investigate (a) whether the effectiveness of the persuasive strategies implemented vary within each domain (b) whether the effectiveness of various strategies vary across two distinct domains, (c) how people belonging to different personality traits respond to these strategies, and (d) if people high in a personality trait would be influenced by a persuasive strategy within one domain and not in the other.

Deep learning identifies brain structures that predict cognition and explain heterogeneity in cognitive aging.

Specific brain structures (gray matter regions and white matter tracts) play a dominant role in determining cognitive decline and explain the heterogeneity in cognitive aging. Identification of these structures is crucial for screening of older adults at risk of cognitive decline. Using deep learning models augmented with a model-interpretation technique on data from 1432 Mayo Clinic Study of Aging participants, we identified a subset of brain structures that were most predictive of individualized cognitive trajectories and indicative of cognitively resilient vs.

Telogator: a method for reporting chromosome-specific telomere lengths from long reads.

Motivation: Telomeres are the repetitive sequences found at the ends of eukaryotic chromosomes and are often thought of as a 'biological clock,' with their average length shortening during division in most cells. In addition to their association with senescence, abnormal telomere lengths are well known to be associated with multiple cancers, short telomere syndromes and as risk factors for a broad range of diseases. While a majority of methods for measuring telomere length will report average lengths across all chromosomes, it is known that aberrations in specific chromosome arms are biomarkers for certain diseases.

Genomic and Synthetic Biology Digital Biosecurity.

Trends toward automation of synthetic biology and the individualization of biology and medicine raise varied and critical security issues. Digital biosecurity brings together researchers working in secure algorithms, vulnerability assessments, and emerging threat models. The fundamental goal of this digital biosecurity workshop is to identify and present distinct areas of research around making the next generation of biology safer and more secure.

Stool microbiota are superior to saliva in distinguishing cirrhosis and hepatic encephalopathy using machine learning.

Background & Aims: Saliva and stool microbiota are altered in cirrhosis. Since stool is logistically difficult to collect compared to saliva, it is important to determine their relative diagnostic and prognostic capabilities. We aimed to determine the ability of stool vs.

Lumbar Spine Surgery and What We Lost in the Era of the Coronavirus Pandemic: A Survey of the Lumbar Spine Research Society.

Study Design: This was a survey of the surgeon members of the Lumbar Spine Research Society (LSRS).

Objective: The purpose of this study was to assess trends in surgical practice and patient management involving elective and emergency surgery in the early months of the coronavirus pandemic.

Summary Of Background Data: The novel coronavirus has radically disrupted medical care in the first half of 2020.

Exogene: A performant workflow for detecting viral integrations from paired-end next-generation sequencing data.

The integration of viruses into the human genome is known to be associated with tumorigenesis in many cancers, but the accurate detection of integration breakpoints from short read sequencing data is made difficult by human-viral homologies, viral genome heterogeneity, coverage limitations, and other factors. To address this, we present Exogene, a sensitive and efficient workflow for detecting viral integrations from paired-end next generation sequencing data. Exogene's read filtering and breakpoint detection strategies yield integration coordinates that are highly concordant with long read validation.

Leveraging electrophysiologic correlates of word encoding to map seizure onset zone in focal epilepsy: Task-dependent changes in epileptiform activity, spectral features, and functional connectivity.

Objective: Verbal memory dysfunction is common in focal, drug-resistant epilepsy (DRE). Unfortunately, surgical removal of seizure-generating brain tissue can be associated with further memory decline. Therefore, localization of both the circuits generating seizures and those underlying cognitive functions is critical in presurgical evaluations for patients who may be candidates for resective surgery.

PB-Motif-A Method for Identifying Gene/Pseudogene Rearrangements With Long Reads: An Application to CYP21A2 Genotyping.

Long read sequencing technologies have the potential to accurately detect and phase variation in genomic regions that are difficult to fully characterize with conventional short read methods. These difficult to sequence regions include several clinically relevant genes with highly homologous pseudogenes, many of which are prone to gene conversions or other types of complex structural rearrangements. We present PB-Motif, a new method for identifying rearrangements between two highly homologous genomic regions using PacBio long reads.

Characterizing the electrophysiological abnormalities in visually reviewed normal EEGs of drug-resistant focal epilepsy patients.

Routine scalp EEG is essential in the clinical diagnosis and management of epilepsy. However, a normal scalp EEG (based on expert visual review) recorded from a patient with epilepsy can cause delays in diagnosis and clinical care delivery. Here, we investigated whether normal EEGs might contain subtle electrophysiological clues of epilepsy.

Prediction of short-term antidepressant response using probabilistic graphical models with replication across multiple drugs and treatment settings.

Heterogeneity in the clinical presentation of major depressive disorder and response to antidepressants limits clinicians' ability to accurately predict a specific patient's eventual response to therapy. Validated depressive symptom profiles may be an important tool for identifying poor outcomes early in the course of treatment. To derive these symptom profiles, we first examined data from 947 depressed subjects treated with selective serotonin reuptake inhibitors (SSRIs) to delineate the heterogeneity of antidepressant response using probabilistic graphical models (PGMs).

Low Predictability of Readmissions and Death Using Machine Learning in Cirrhosis.

Introduction: Readmission and death in cirrhosis are common, expensive, and difficult to predict. Our aim was to evaluate the abilities of multiple artificial intelligence (AI) techniques to predict clinical outcomes based on variables collected at admission, during hospitalization, and at discharge.

Methods: We used the multicenter North American Consortium for the Study of End-Stage Liver Disease (NACSELD) cohort of cirrhotic inpatients who are followed up through 90-days postdischarge for readmission and death.

Electrophysiological Correlates of Brain Health Help Diagnose Epilepsy and Lateralize Seizure Focus.

The absence of epileptiform activity in a scalp electroencephalogram (EEG) recorded from a potential epilepsy patient can cause delays in clinical care delivery. Here we present a machine-learning-based approach to find evidence for epilepsy in scalp EEGs that do not contain any epileptiform activity, according to expert visual review (i.e.

Sex is associated with differences in gut microbial composition and function in hepatic encephalopathy.

Background & Aims: Altered microbiota can affect the gut-liver-brain axis in cirrhosis and hepatic encephalopathy (HE), but the impact of sex on these changes is unclear. We aimed to determine differences in fecal microbiota composition/functionality between men and women with cirrhosis and HE on differing treatments.

Methods: Cross-sectional stool microbiome composition (16s rRNA sequencing) and microbial functional analyses were performed in men and women with cirrhosis, and controls.

Correction to: Recommendations for performance optimizations when using GATK3.8 and GATK4.

Following publication of the original article [1], the author explained that Table 2 is displayed incorrectly. The correct Table 2 is given below. The original article has been corrected.

Unsupervised machine-learning classification of electrophysiologically active electrodes during human cognitive task performance.

Identification of active electrodes that record task-relevant neurophysiological activity is needed for clinical and industrial applications as well as for investigating brain functions. We developed an unsupervised, fully automated approach to classify active electrodes showing event-related intracranial EEG (iEEG) responses from 115 patients performing a free recall verbal memory task. Our approach employed new interpretable metrics that quantify spectral characteristics of the normalized iEEG signal based on power-in-band and synchrony measures.

Recommendations for performance optimizations when using GATK3.8 and GATK4.

Background: Use of the Genome Analysis Toolkit (GATK) continues to be the standard practice in genomic variant calling in both research and the clinic. Recently the toolkit has been rapidly evolving. Significant computational performance improvements have been introduced in GATK3.