Discovering type I cis-AT polyketides through computational mass spectrometry and genome mining with Seq2PKS.

Type 1 polyketides are a major class of natural products used as antiviral, antibiotic, antifungal, antiparasitic, immunosuppressive, and antitumor drugs. Analysis of public microbial genomes leads to the discovery of over sixty thousand type 1 polyketide gene clusters. However, the molecular products of only about a hundred of these clusters are characterized, leaving most metabolites unknown. Characterizing polyketides relies on bioactivity-guided purification, which is expensive and time-consuming. To address this, we present Seq2PKS, a machine learning algorithm that predicts chemical structures derived from Type 1 polyketide synthases. Seq2PKS predicts numerous putative structures for each gene cluster to enhance accuracy. The correct structure is identified using a variable mass spectral database search. Benchmarks show that Seq2PKS outperforms existing methods. Applying Seq2PKS to Actinobacteria datasets, we discover biosynthetic gene clusters for monazomycin, oasomycin A, and 2-aminobenzamide-actiphenol.

Improving memory via automated targeted memory reactivation during sleep.

A widely accepted view in memory research is that previously acquired information can be reactivated during sleep, leading to persistent memory storage. Targeted memory reactivation (TMR) was developed as a technique whereby specific memories can be reactivated during sleep using a sensory stimulus linked to prior learning. As a research tool, TMR can improve memory, raising the possibility that it may be useful for cognitive enhancement and clinical therapy. A major challenge for the expanded use of TMR is that a skilled operator must manually control stimulation, which is impractical in many settings. To address this limitation, we developed the SleepStim system for automated TMR in the home. SleepStim includes a smartwatch to collect movement and heart-rate data, plus a smartphone to emit auditory cues. A machine-learning model identifies periods of deep sleep and triggers TMR sounds within these periods. We tested whether this system could replicate the spatial-memory benefit of in-laboratory TMR. Participants learned locations of objects on a grid, and then half of the object locations were reactivated during sleep over 3 nights. Recall was tested each morning. In an experiment with 61 participants, the TMR effect was not significant but varied systematically with stimulus intensity; low-intensity but not high-intensity stimuli produced memory benefits. In a second experiment with 24 participants, we limited stimulus intensity and found that TMR reliably improved spatial memory, consistent with effects observed in laboratory studies. We conclude that SleepStim can effectively accomplish automated TMR, and that avoiding sleep disruption is critical for TMR benefits.

Wearable myoelectric interface enables high-dose, home-based training in severely impaired chronic stroke survivors.

BACKGROUND: High-intensity occupational therapy can improve arm function after stroke, but many people lack access to such therapy. Home-based therapies could address this need, but they don't typically address abnormal muscle co-activation, an important aspect of arm impairment. An earlier study using lab-based, myoelectric computer interface game training enabled chronic stroke survivors to reduce abnormal co-activation and improve arm function. Here, we assess feasibility of doing this training at home using a novel, wearable, myoelectric interface for neurorehabilitation training (MINT) paradigm. OBJECTIVE: Assess tolerability and feasibility of home-based, high-dose MINT therapy in severely impaired chronic stroke survivors. METHODS: Twenty-three participants were instructed to train with the MINT and game for 90 min/day, 36 days over 6 weeks. We assessed feasibility using amount of time trained and game performance. We assessed tolerability (enjoyment and effort) using a customized version of the Intrinsic Motivation Inventory at the conclusion of training. RESULTS: Participants displayed high adherence to near-daily therapy at home (mean of 82 min/day of training; 96% trained at least 60 min/day) and enjoyed the therapy. Training performance improved and co-activation decreased with training. Although a substantial number of participants stopped training, most dropouts were due to reasons unrelated to the training paradigm itself. INTERPRETATION: Home-based therapy with MINT is feasible and tolerable in severely impaired stroke survivors. This affordable, enjoyable, and mobile health paradigm has potential to improve recovery from stroke in a variety of settings. Clinicaltrials.gov: NCT03401762.

Predicting Significant Iliac Vein Compression Using a Probability Scoring System Derived From Minimal Luminal Area on Computed Tomography Angiography in Patients 65 Years of Age or Younger.

BACKGROUND: The presence of 50% or more stenosis on intravascular ultrasound (IVUS) is considered diagnostic of iliac vein compression (ILVC) by most operators. We have previously developed a scoring system combining minimal luminal area (MLA) at the compression site and age to predict ILVC as seen on IVUS. We present a revised and improved scoring system following an additional number of patients and limited to patients 65 years of age and younger. METHODS: Patients were included from retrospective (n = 52) and prospective (n = 18) registries of consecutive patients who underwent computed tomography angiography (CTA) of the pelvis with venous filling and IVUS within a few weeks apart to evaluate for symptomatic ILVC at a single cardiovascular practice. Quantitative vascular analysis was performed on all images obtained. MLA and age were used to calculate a score derived from a previously published logistic regression formula. Patients >65 years in age were excluded. The predicted findings from the score were compared with the actual presence of compression on IVUS. The revised scoring system is based on a score of < or ≥ 0.533824 and MLA (mm²) of <140, ≥140 to <200, and ≥200. The negative predictive value (NPV) and positive predictive value (NPV and PPV) of each cut-off in predicting ILVC on IVUS were calculated. RESULTS: A total of 70 symptomatic patients were included (mean age, 52.6 ± 12.3 years). The model offered the following: accuracy = 54/70 (77.1%); sensitivity = 51/52 (98.1%); specificity = 3/18 (16.7%); PPV = 51/66 (77.3%); and NPV = 3/4 (75.0%). CONCLUSION: A scoring system using MLA on CTA and age provides a fairly accurate diagnostic test to predict the presence of significant ILVC as seen on IVUS.