α,ω-Alkanedibromides Form Low Conductance Chemisorbed Junctions with Silver Electrodes.

Chemical groups capable of connecting molecules physically and electrically between electrodes are of critical importance in molecular-scale electronics, influencing junction conductance, variability, and function. While the development of such linkage chemistries has focused on interactions at gold, the distinct reactivity and electronic structure of other electrode metals provides underexplored opportunities to characterize and exploit new binding motifs. In this work we show that α,ω-alkanedibromides spontaneously form well-defined junctions using silver, but not gold, electrodes through application of the glovebox-based scanning tunneling microscope-based break junction method. We systematically evaluate, through a series of additional studies, whether these molecular components form physisorbed or chemisorbed contact geometries, and if they undergo secondary chemical reactions at the silver surface. Critically, we find that the same junctions form when using different halide, or trimethylstannyl, terminal groups, suggestive of an electronically transparent silver-carbon(sp3) contact chemistry. However, the experimental conductance of the junctions we measure with silver electrodes is ∼30× lower than that observed for such junctions comprising gold-carbon(sp3) contacts, which does not align with predictions based on first-principles calculations. We further exclude the possibility that the proposed silver alkyl species undergo α- or ß-hydride elimination reactions that result in a distinct contact chemistry through conductance measurements of control molecules that cannot undergo such processes. Applying insights provided from prior temperature-programmed desorption studies and a robust series of atomistic simulations, we ultimately propose that in these experiments we measure alkoxide-terminated junctions formed from the reaction of the chemisorbed alkyl with oxygen that is coadsorbed on the silver surface. This work, in demonstrating that high conductance contact chemistries established using model gold electrodes may not be readily transferred to other metals, underscores the need to directly characterize the interfacial electronic properties and reactivity of electrode metals of wider technological relevance.

Machine learning in the diagnosis, management, and care of patients with low back pain: a scoping review of the literature and future directions.

BACKGROUND CONTEXT: Low back pain (LBP) remains the leading cause of disability globally. In recent years, machine learning (ML) has emerged as a potentially useful tool to aid the diagnosis, management, and prognostication of LBP. PURPOSE: In this review, we assess the scope of ML applications in the LBP literature and outline gaps and opportunities. STUDY DESIGN/SETTING: A scoping review was performed in accordance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines. METHODS: Articles were extracted from the Web of Science, Scopus, PubMed, and IEEE Xplore databases. Title/abstract and full-text screening was performed by two reviewers. Data on model type, model inputs, predicted outcomes, and ML methods were collected. RESULTS: In total, 223 unique studies published between 1988 and 2023 were identified, with just over 50% focused on low-back-pain detection. Neural networks were used in 106 of these articles. Common inputs included patient history, demographics, and lab values (67% total). Articles published after 2010 were also likely to incorporate imaging data into their models (41.7% of articles). Of the 212 supervised learning articles identified, 168 (79.4%) mentioned use of a training or testing dataset, 116 (54.7%) utilized cross-validation, and 46 (21.7%) implemented hyperparameter optimization. Of all articles, only 8 included external validation and 9 had publicly available code. CONCLUSIONS: Despite the rapid application of ML in LBP research, a majority of articles do not follow standard ML best practices. Furthermore, over 95% of articles cannot be reproduced or authenticated due to lack of code availability. Increased collaboration and code sharing are needed to support future growth and implementation of ML in the care of patients with LBP.

Low Vapor Pressure Solvents for Single-Molecule Junction Measurements.

Nonpolar solvents commonly used in scanning tunneling microscope-based break junction measurements exhibit hazards and relatively low boiling points (bp) that limit the scope of solution experiments at elevated temperatures. Here we show that low toxicity, ultrahigh bp solvents such as bis(2-ethylhexyl) adipate (bp = 417 °C) and squalane (457 °C) can be used to probe molecular junctions at ≥100 °C. With these, we extend solvent- and temperature-dependent conductance trends for junction components such as 4,4'-bipyridine and thiomethyl-terminated oligophenylenes and reveal the gold snapback distance is larger at 100 °C due to increased surface atom mobility. We further show the rate of surface transmetalation and homocoupling reactions using phenylboronic acids increases at 100 °C, while junctions comprising anticipated boroxine condensation products form only at room temperature in an anhydrous glovebox atmosphere. Overall, this work demonstrates the utility of low vapor pressure solvents for the comprehensive characterization of junction properties and chemical reactivity at the single-molecule limit.

The ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2023: Intracranial Meningioma.

Meningiomas are the most common primary intracranial tumor in adults and can be associated with significant morbidity and mortality. Radiologists, neurosurgeons, neuro-oncologists, and radiation oncologists rely on multiparametric MRI (mpMRI) for diagnosis, treatment planning, and longitudinal treatment monitoring; yet automated, objective, and quantitative tools for non-invasive assessment of meningiomas on mpMRI are lacking. The BraTS meningioma 2023 challenge will provide a community standard and benchmark for state-of-the-art automated intracranial meningioma segmentation models based on the largest expert annotated multilabel meningioma mpMRI dataset to date. Challenge competitors will develop automated segmentation models to predict three distinct meningioma sub-regions on MRI including enhancing tumor, non-enhancing tumor core, and surrounding nonenhancing T2/FLAIR hyperintensity. Models will be evaluated on separate validation and held-out test datasets using standardized metrics utilized across the BraTS 2023 series of challenges including the Dice similarity coefficient and Hausdorff distance. The models developed during the course of this challenge will aid in incorporation of automated meningioma MRI segmentation into clinical practice, which will ultimately improve care of patients with meningioma.

Satellite monitoring of terrestrial plastic waste.

Plastic waste is a significant environmental pollutant that is difficult to monitor. We created a system of neural networks to analyze spectral, spatial, and temporal components of Sentinel-2 satellite data to identify terrestrial aggregations of waste. The system works at wide geographic scale, finding waste sites in twelve countries across Southeast Asia. We evaluated performance in Indonesia and detected 374 waste aggregations, more than double the number of sites found in public databases. The same system deployed in Southeast Asia identifies 996 subsequently confirmed waste sites. For each detected site, we algorithmically monitor waste site footprints through time and cross-reference other datasets to generate physical and social metadata. 19% of detected waste sites are located within 200 m of a waterway. Numerous sites sit directly on riverbanks, with high risk of ocean leakage.