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Patients with obstructive sleep apnea (OSA) show brain injury in sites responsible for autonomic, cognitive, and respiratory functions. Brain changes in OSA may vary with disease severity as assessed by the apnea-hypopnea index (AHI), which does not provide information about the apnea depth and length in contrast to oxygen desaturation. Although significant associations with brain injury and AHI are known in OSA, it is unclear whether AHI or the extent of oxygen desaturations better correlate with brain damage. We evaluated associations between brain changes, AHI, and oxygen desaturation using diffusion tensor imaging-based measures.We acquired diffusion tensor imaging data from 19 patients with OSA using a 3.0-Tesla MRI scanner and calculated, normalized, and smoothed mean, axial, and radial diffusivity maps that were used for correlations between brain changes, oxygen desaturation, and AHI values.Positive correlations with extent of injury (mean, axial, and radial diffusivity values) and AHI appeared in the frontal areas, cingulate and insula, amygdala, hippocampus, and basal pons, and negative associations emerged in the putamen, internal-capsule, globus-pallidus, and cerebellar cortices. Regional diffusivity values and oxygen desaturation showed positive correlations in the cingulate, frontal, putamen, and cerebellar sites, and negative relationships in several areas, including the occipital cortex.Patients with OSA show negative and positive correlations, indicated by increased and decreased diffusivity values, resulting from chronic and acute changes in those areas. The extent of injury in OSA partially depends on the extent of AHI and oxygen desaturation, with the effects representing continued development from acute to chronic processes.Sahib A, Roy B, Kang D, Aysola RS, Wen E, Kumar R. Relationships between brain tissue damage, oxygen desaturation, and disease severity in obstructive sleep apnea evaluated by diffusion tensor imaging.

The availability of vast video collections and the accuracy of ML models has generated significant interest in video analytics systems. Since naively processing all frames using expensive models is impractical, researchers have proposed optimizations such as selectively using faster but less accurate models to replace or filter frames for expensive models. However, these optimizations are difficult to apply on queries with multiple predicates and models, as users must manually explore a large optimization space. Without significant systems expertise or time investment, an analyst may manually create an execution plan that is unnecessarily expensive and/or terribly inaccurate. We propose Relational Hints , a declarative interface that allows users to suggest ML model relationships based on domain knowledge. Users can express two key relationships: when a model can replace another (CAN REPLACE) and when a model can be used to filter frames for another (CAN FILTER). We aim to design an interface to express model relationships informed by domain specific knowledge and define the constraints by which these relationships hold. We then present the VIVA video analytics system that uses relational hints to optimize SQL queries on video datasets. VIVA automatically selects and validates the hints applicable to the query, generates possible query plans using a formal set of transformations, and finds the best performance plan that meets a user's accuracy requirements. VIVA relieves users from rewriting and manually optimizing video queries as new models become available and execution environments evolve. We evaluate VIVA implemented on top of Spark and show that hints improve performance up to 16.6X without sacrificing accuracy.

While deep neural networks (DNNs) are an increasingly popular way to query large corpora of data, their significant runtime remains an active area of research. As a result, researchers have proposed systems and optimizations to reduce these costs by allowing users to trade off accuracy and speed. In this work, we examine end-to-end DNN execution in visual analytics systems on modern accelerators. Through a novel measurement study, we show that the preprocessing of data (e.g., decoding, resizing) can be the bottleneck in many visual analytics systems on modern hardware. To address the bottleneck of preprocessing, we introduce two optimizations for end-to-end visual analytics systems. First, we introduce novel methods of achieving accuracy and throughput trade-offs by using natively present, low-resolution visual data. Second, we develop a runtime engine for efficient visual DNN inference. This runtime engine a) efficiently pipelines preprocessing and DNN execution for inference, b) places preprocessing operations on the CPU or GPU in a hardware- and input-aware manner, and c) efficiently manages memory and threading for high throughput execution. We implement these optimizations in a novel system, Smol, and evaluate Smol on eight visual datasets. We show that its optimizations can achieve up to 5.9X end-to-end throughput improvements at a fixed accuracy over recent work in visual analytics.

Systems for ML inference are widely deployed today, but they typically optimize ML inference workloads using techniques designed for conventional data serving workloads and miss critical opportunities to leverage the statistical nature of ML. In this demo, we present Willump, an optimizer for ML inference that introduces statistically-motivated optimizations targeting ML applications whose performance bottleneck is feature computation. Willump automatically cascades feature computation for classification queries: Willump classifies most data inputs using only high-value, low-cost features selected by a cost model, improving query performance by up to 5 x without statistically significant accuracy loss. In this demo, we use interactive and easily-downloadable Jupyter notebooks to show VLDB attendees which applications Willump can speed up, how to use Willump, and how Willump produces such large performance gains.

PURPOSE To validate healthcare claim-based algorithms for neurodevelopmental disorders (NDD) in children using medical records as the reference. METHODS Using a clinical data warehouse of patients receiving outpatient or inpatient care at two hospitals in Boston, we identified children (≤14 years between 2010 and 2014) with at least one of the following NDDs according to claims-based algorithms: autism spectrum disorder/pervasive developmental disorder (ASD), attention deficit disorder/other hyperkinetic syndromes of childhood (ADHD), learning disability, speech/language disorder, developmental coordination disorder (DCD), intellectual disability, and behavioral disorder. Fifty cases per outcome were randomly sampled and their medical records were independently reviewed by two physicians to adjudicate the outcome presence. Positive predictive values (PPVs) and 95% confidence intervals (CIs) were calculated. RESULTS PPVs were 94% (95% CI, 83%-99%) for ASD, 88% (76%-95%) for ADHD, 98% (89%-100%) for learning disability, 98% (89%-100%) for speech/language disorder, 82% (69%-91%) for intellectual disability, and 92% (81%-98%) for behavioral disorder. A total of 19 of the 50 algorithm-based cases of DCD were confirmed as severe coordination disorders with functional impairment, with a PPV of 38% (25%-53%). Among the 31 false-positive cases of DCD were 7 children with coordination deficits that did not persist throughout childhood, 7 with visual-motor integration deficits, 12 with coordination issues due to an underlying medical condition and 5 with ADHD and at least one other severe NDD. CONCLUSIONS PPVs were generally high (range: 82%-98%), suggesting that claims-based algorithms can be used to study NDDs. For DCD, additional criteria are needed to improve the classification of true cases.

Pharmaceuticals 14(8), 818 (2021). doi:10.3390/ph14080818 special issue: "Special Issue "Targets, Tracers and Translation, Part 2 - New Horizons in Radiopharmaceutical Development" / Special Issue Editor: Prof. Dr. Gerald Reischl, Guest Editor", Published by MDPI, Basel

Researchers and industry analysts are increasingly interested in computing aggregation queries over large, unstructured datasets with selective predicates that are computed using expensive deep neural networks (DNNs). As these DNNs are expensive and because many applications can tolerate approximate answers, analysts are interested in accelerating these queries via approximations. Unfortunately, standard approximate query processing techniques to accelerate such queries are not applicable because they assume the result of the predicates are available ahead of time. Furthermore, recent work using cheap approximations (i.e., proxies) do not support aggregation queries with predicates. To accelerate aggregation queries with expensive predicates, we develop and analyze a query processing algorithm that leverages proxies (ABAE). ABAE must account for the key challenge that it may sample records that do not satisfy the predicate. To address this challenge, we first use the proxy to group records into strata so that records satisfying the predicate are ideally grouped into few strata. Given these strata, ABAE uses pilot sampling and plugin estimates to sample according to the optimal allocation. We show that ABAE converges at an optimal rate in a novel analysis of stratified sampling with draws that may not satisfy the predicate. We further show that ABAE outperforms on baselines on six real-world datasets, reducing labeling costs by up to 2.3X.

Over the past 20 years, 68Ga-labelled radiopharmaceuticals have become an important part in clinical routine. However, the worldwide supply with 68Ge/68Ga generators is limited as well as the number of patient doses per batch of 68Ga radiopharmaceutical. In the recent years, a new technique appeared, making use of the ease of aqueous labelling via chelators as with 68Ga but using 18F instead. This technique takes advantage of the strong coordinative bond between aluminium and fluoride, realized in the aqueous cation [Al18F]2+. Most applications to date make use of one-pot syntheses with free Al(III) ions in the system. In contrast, we investigated the labelling approach split into two steps: generating the Al-bearing precursor in pure form and using this Al compound as a precursor in the labelling step with aqueous [18F]fluoride. Hence, no free Al3+ ions are present in the labelling step. We investigated the impact of parameters: temperature, pH, addition of organic solvent, and reaction time using the model chelator NH2-MPAA-NODA. With optimized parameters we could stably achieve a 80% radiochemical yield exerting a 30-min reaction time at 100 °C. This technique has the potential to become an important approach in radiopharmaceutical syntheses.

A liquid film with two distinct contact lines underneath bubbles during boiling on structured surfaces has been discovered., Enhancing critical heat flux (CHF) during boiling with structured surfaces has received much attention because of its important implications for two-phase flow. The role of surface structures on bubble evolution and CHF enhancement remains unclear because of the lack of direct visualization of the liquid- and solid-vapor interfaces. Here, we use high-magnification in-liquid endoscopy to directly probe bubble behavior during boiling. We report the previously unidentified coexistence of two distinct three-phase contact lines underneath growing bubbles on structured surfaces, resulting in retention of a thin liquid film within the structures between the two contact lines due to their disparate advancing velocities. This finding sheds light on a previously unidentified mechanism governing bubble evolution on structured surfaces, which has notable implications for a variety of real systems using bubble formation, such as thermal management, microfluidics, and electrochemical reactors.

Sub-micron pixels have been widely adopted in recent CMOS image sensors to implement high resolution cameras in small form factors, i.e. slim mobile-phones. Even with shrinking pixels, customers demand higher image quality, and the pixel performance must remain comparable to that of the previous generations. Conventionally, to suppress the optical crosstalk between pixels, a metal grid has been used as an isolation structure between adjacent color filters. However, as the pixel size continues to shrink to the sub-micron regime, an optical loss increases because the focal spot size of the pixel’s microlens does not downscale accordingly with the decreasing pixel size due to the diffraction limit: the light absorption inevitably occurs in the metal grid. For the first time, we have demonstrated a new lossless, dielectric-only grid scheme. The result shows 29 % increase in sensitivity and +1.2-dB enhancement in Y-SNR when compared to the previous hybrid metal-and-dielectric grid.

Study objective To provide a contemporary medicolegal analysis of claims brought against anesthesia providers in the United States related to neuraxial blocks for surgery and obstetrics. Design In this retrospective analysis, we analyzed closed claims data from the Controlled Risk Insurance Company (CRICO) Comparative Benchmarking System (CBS) database between 2007 and 2016. Setting Closed claims from inpatient and outpatient settings related to neuraxial anesthesia for surgical procedures and obstetrics. Patients Forty-five claims were identified for analysis. These patients underwent a variety of surgical procedures, included both children and adults, and with ages ranging from 6 to 82. Interventions Patients receiving neuraxial anesthesia (spinals, epidurals) for surgery or obstetrics. Measurements Data collected includes patient demographics, alleged injury type/severity, surgical specialty, likely contributors to the alleged damaging event, and case outcome. Some of the data were drawn directly from coded variables in the CRICO database, and some were gathered from narrative case summaries. Main results Settlement payments were made in 20% of claims. Reported adverse outcomes ranged from temporary minor to permanent major injuries. Most closed claims were classified as permanent minor injuries. The greatest number of claims involved residual weakness and radiculopathy resulting from epidurals. The largest contributing factor to these injuries was noted to be “Technical Knowledge/Performance” of the anesthesia provider followed by “Missing or Documentation Error.” Over half of the claims arose from obstetric patients (31%) and patients undergoing orthopedic surgery (27%). Conclusions Patients with pre-existing radiculopathy or comorbidities may warrant more thorough informed consent about the increased risk of injury. Additionally, prompt follow-up, monitoring, and documentation of post-operative symptoms, such as weakness or radiculopathy, are crucial for improving patient safety and satisfaction. More timely communication with the patient and the surgical team regarding residual neurologic symptoms is important for earlier diagnosis of injury.

Researchers have proposed hardware, software, and algorithmic optimizations to improve the computational performance of deep learning. While some of these optimizations perform the same operations faster (e.g., increasing GPU clock speed), many others modify the semantics of the training procedure (e.g., reduced precision), and can impact the final model's accuracy on unseen data. Due to a lack of standard evaluation criteria that considers these trade-offs, it is difficult to directly compare these optimizations. To address this problem, we recently introduced DAWNBENCH, a benchmark competition focused on end-to-end training time to achieve near-state-of-the-art accuracy on an unseen dataset-a combined metric called time-to-accuracy (TTA). In this work, we analyze the entries from DAWNBENCH, which received optimized submissions from multiple industrial groups, to investigate the behavior of TTA as a metric as well as trends in the best-performing entries. We show that TTA has a low coefficient of variation and that models optimized for TTA generalize nearly as well as those trained using standard methods. Additionally, even though DAWNBENCH entries were able to train ImageNet models in under 3 minutes, we find they still underutilize hardware capabilities such as Tensor Cores. Furthermore, we find that distributed entries can spend more than half of their time on communication. We show similar findings with entries to the MLPERF v0.5 benchmark.

Background: Closed malpractice claims can provide insight into low-frequency adverse events in many areas of perioperative and chronic pain care. Over the last decade, there have been changes in surgical and regional anesthetic practice, likely impacting adverse event patterns. Given the wide variability and low frequency of complications associated with peripheral nerve blocks, the study of closed malpractice claims offers an opportunity to examine adverse events, and the patient, technical, and provider factors that led to the claim. Knowledge gained from examination of closed claims has already resulted in multiple improvements in processes of care and patient safety. Objectives: An investigation of the factors that contributed to medicolegal claims against anesthesia providers related to peripheral nerve blocks. Study Design: Retrospective analysis. Setting: Inpatient and outpatient surgery facilities. Methods: The Comparative Benchmarking System database is a medical liability database that contains more than 400,000 malpractice claims from more than 400 academic and communitybased institutions accounting for over 30% of malpractice claims in the United States. The present investigation reviewed all (n = 113) available closed malpractice claims related to regional anesthesia (RA) in surgical patients closed between 2006 and 2016, and investigated factors that may have contributed to patient injury, including type of nerve block, type of surgery, nerves injured, resulting neurologic deficits, and potential factors contributing to the injury. Results: Our data analyzed 62 claims related to RA and showed that most closed claims were classified as permanent minor injuries. The greatest number of claims were for brachial plexus injuries associated with interscalene blocks performed for shoulder or rotator cuff repairs. Femoral and sciatic nerve blocks with resulting lower extremity injuries were the most common nerve blocks resulting in payment. The largest contributing factor to these injuries was noted to be “Technical Knowledge/Performance” of the regionalist followed by “Pre-existing Injury/Radiculopathy.” Symptom onset from these claims was most likely to be delayed with the leading initial presenting symptom being paresthesia. Limitations: It is difficult to establish cause-effect relationship, and the small sample size limits the ability to detect clinical differences and associations with specific comorbidities or techniques. There was also limited information related to regional anesthetic techniques and medications used that would have helped explore further relationships between the procedure and cause for litigation. Conclusions: There remains significant room for risk reduction in regional anesthetic practice. Patterns based on the analysis of closed claims show that interscalene blocks are the most common peripheral nerve block resulting in litigation, even when compared with other blocks involving the brachial plexus. Furthermore, patients with existing nerve injury/radiculopathy may also warrant alternative techniques or greater emphasis during informed consent on the increased risk of injury. As most of the presenting symptoms associated with claims are delayed, an opportunity for improvement in postregional care may be better communication with patients following discharge to discuss their postoperative recovery. Key words: Regional, pain, anesthesia, complications, closed claims, liability, nerve, block, injury

Objectives To determine if the Patient-Reported Outcome Measurement Information System (PROMIS) physical function, pain interference, self-efficacy, and global rating of normal function (GRNF) scales are able to accurately characterize a patient’s acceptable symptom state (PASS). Design A cross-sectional analysis, using receiver operator curves and chi-square analysis to explore criteria to determine thresholds (80% and 95% sensitivity/specificity) for PASS that are applicable to PROMIS and GRNF scales. Setting Phone survey after primary care. Participants Patients (N=94) attending primary care for musculoskeletal problems. Interventions Not applicable. Main Outcomes Measures Accuracy and proportion of patients classified as PASS Yes or No. Results Receiver operator curve analysis showed significant area under the curve (AUC) values for each PROMIS scale (AUC>.72) and the GRNF rating (AUC=.74). Identified PROMIS thresholds suggested PASS was achieved when scores were at or slightly worse than the US population average. A score of ≥7 and ≤4 characterized patients that were PASS Yes and No, respectively, on the GRNF rating. A moderate (80%) specificity/sensitivity criteria yielded 72.3%-73.5% accuracy for a majority of participants (>69.9%). Conclusion This analysis suggests the PROMIS and GRNF scales are able to characterize PASS status with moderate accuracy (∼70%) for a large portion of patients (∼70%). New to this study is the association of self-efficacy with PASS status. PROMIS scales at or slightly worse than the US population average characterized PASS status.

A proof of concept study was conducted to implement an automated preoperative triage and preparation service with 2 components: (1) triage between preoperative visits and phone screens, and (2) delivery of preoperative instructions to patients. The study enrolled 2604 patients, of whom 75.7% completed the preoperative triage questionnaire and 47.6% were triaged to preoperative phone screens. For the preoperative preparation component, 90.8% of patients viewed surgery preparation instructions, and average patient satisfaction score was 9.3/10. An automated preoperative triage and preparation service can facilitate preoperative triage using patient self-reported information and guide patient preparation with instructions delivered at relevant times.

We measure the yellow P-exciton absorption spectra of Cu2O via temperature-dependent spectroscopy (4-100 K). We study exciton resonance spectral properties in terms of peak energy, spectral linewidth, and oscillator strength according to well-known scaling laws.

Given the Food and Drug Administration's (FDA's) acceptance of master protocol designs in recent guidance documents, the oncology field is rapidly moving to address the paradigm shift to molecular subtype focused studies. Identifying new "marker-based" treatments requires new methodologies to address the growing demand to conduct clinical trials in smaller molecular subpopulations, identify effective treatment and marker interactions, and control for false positives. We introduce our methodology, Hierarchical Bayesian Clustering Design of Multiple Biomarker Subgroups (HCOMBS), a two-stage umbrella Phase II design with effect size clustering and information borrowing across multiple biomarker-treatment pairs. HCOMBS was designed to reduce required sample size, differentiate between varying effect sizes, and control for operating characteristics in the multi-arm setting. When compared to independently applied Simon's Optimal two-stage design, we showed through simulations that HCOMBS required less participants per treatment arm with a well-controlled family-wise error rate and desirable marginal power. Additionally, HCOMBS features a statistical approach that simultaneously conducts clustering and hypothesis testing in one step. We also applied the proposed design on the alliance brain metastases umbrella trial.

Background Value-based healthcare models will require prioritization of the patient’s voice in their own care toward better outcomes. The Patient-Reported Outcomes Measurement Information System® (PROMIS) gives patients a voice and leads providers to actionable treatments across a broad range of diagnoses. However, better interpretation of PROMIS measures is needed. The purpose of this study was to evaluate the accuracy of PROMIS Physical Function (PF), Self-Efficacy for Managing Symptoms (SE), Pain Interference (PI), Fatigue, and Depression measures to discriminate patient acceptable symptom state (PASS) in primary care, determining if that accuracy is stable over time and/or retained when PROMIS score thresholds are set at either ½ or 1 SD worse than the reference population mean. Methods Primary care patients completed the five PROMIS measures and answered the PASS yes/no question at intake (n = 360), 3–14 days follow-up (n = 230), and 45–60 days follow-up (n = 227). Thresholds (optimal, ½ SD, and 1 SD worse than reference values) for PROMIS T-scores associated with PASS were determined through receiver-operator curve analysis. Accuracy was calculated at the three time points for each threshold value. Logistic regression analyses were used to determine combinations of PROMIS measures that best predicted PASS. Results PROMIS PF, SE, PI, and Fatigue optimal score thresholds (maximizing sensitivity and specificity) yielded area under the curve values of 0.77–0.85, with accuracies ranging from 71.7% to 79.1%. Accuracy increased minimally (1.9% to 5.5%) from intake to follow-ups. Thresholds of 1 SD worse than the mean for PROMIS PF and PI measures and ½ SD worse for SE and Fatigue overall retained accuracy versus optimal (+ 1.3% to − 3.6%). Regression models retained SE, PI, and Fatigue as independent predictors of PASS, and minimally increased accuracy to 83.1?%. Conclusions This study establishes actionable PROMIS score thresholds that are stable over time and anchored to patient self-reported health status, increasing interpretability of PF, SE, PI, and Fatigue scores. The findings support the use of these PROMIS measures in primary care toward improving provider-patient communication, prioritizing patient concerns, and optimizing clinical decision making.

Neural language models are usually trained to match the distributional properties of a large-scale corpus by minimizing the log loss. While straightforward to optimize, this approach forces the model to reproduce all variations in the dataset, including noisy and invalid references (e.g., misannotation and hallucinated facts). Worse, the commonly used log loss is overly sensitive to such phenomena and even a small fraction of noisy data can degrade performance. In this work, we show that the distinguishability of the models and reference serves as a principled and robust alternative for handling invalid references. To optimize distinguishability, we propose loss truncation, which adaptively removes high loss examples during training. We show this is as easy to optimize as log loss and tightly bounds distinguishability under noise. Empirically, we demonstrate that loss truncation outperforms existing baselines on distinguishability on a summarization task, and show that samples generated by the loss truncation model have factual accuracy ratings that exceed those of baselines and match human references., ACL 2020 Camera Ready Submission

Due to the falling costs of data acquisition and storage, researchers and industry analysts often want to find all instances of rare events in large datasets. For instance, scientists can cheaply capture thousands of hours of video, but are limited by the need to manually inspect long videos to identify relevant objects and events. To reduce this cost, recent work proposes to use cheap proxy models, such as image classifiers, to identify an approximate set of data points satisfying a data selection filter. Unfortunately, this recent work does not provide the statistical accuracy guarantees necessary in scientific and production settings. In this work, we introduce novel algorithms for approximate selection queries with statistical accuracy guarantees. Namely, given a limited number of exact identifications from an oracle, often a human or an expensive machine learning model, our algorithms meet a minimum precision or recall target with high probability. In contrast, existing approaches can catastrophically fail in satisfying these recall and precision targets. We show that our algorithms can improve query result quality by up to 30 x for both the precision and recall targets in both real and synthetic datasets.

Background: Value-based care models call for better interpretation of patient-reported outcomes. Patients may reference health status differently when appraising if an intervention was successful versus if their current state is acceptable. The purpose of this study was to determine the association between success of treatment (SOT), patient acceptable symptom state (PASS), and PROMIS measure T-scores, following a single primary care physical therapy encounter. Methods: Cross-sectional study. Ninety-two patients with musculoskeletal complaints were administered standard SOT and PASS questions, and PROMIS Physical Function, Pain Interference, and Self-Efficacy for Managing Symptoms measures. Association of PASS and SOT was determined using rank biserial correlation and chi-squared analysis. Accuracy of PROMIS T-scores to discriminate PASS and SOT was determined through receiver-operator curve analysis and likelihood ratios. Results: There was significant association between PASS and SOT (r=.393, p 0.70 (accuracy 76.1%). Conclusion: PASS was more strongly associated with health status than SOT. Patients make a meaningful distinction between these two questions. Accurate clinician interpretation of PASS, SOT, and PROMIS T-scores can allow more targeted goal setting and treatment decision making.

Background: Obstructive sleep apnea (OSA) is accompanied by both gray and white matter differences in brain areas that regulate autonomic, cognitive, and mood functions, which are deficient in the condition. Such tissue changes have been examined through diffusion tensor and diffusion kurtosis imaging-based procedures. However, poor in-plane spatial resolution of these techniques precludes precise determination of the extent of tissue injury. Tissue texture maps derived from the ratio of T1-weighted and T2-weighted images can provide more adequate in-plane assessment of brain tissue differences. Objectives: To examine brain tissue integrity in recently diagnosed, treatment-naïve OSA subjects, relative to age- and sex-comparable control subjects using T1-weighted and T2-weighted images. Design: A cross-sectional study. Methods: We examined the extent of tissue changes in 106 OSA over 115 control subjects using high-resolution T1- and T2-weighted images collected from a 3.0-Tesla scanner (analysis of covariance; covariates: age, sex, body-mass-index, Pittsburgh sleep quality index, Epworth sleepiness scale, Beck Anxiety Inventory, and Beck Depression Inventory II; false discovery rate corrected; p Results: OSA subjects showed significantly lowered tissue integrity in several brain regions, including the frontal, cingulate and insular cortices, cingulum bundle, thalamus, corpus callosum, caudate and putamen, pons, temporal, occipital, and parietal sites, cerebellar peduncles, and medial medullary sites, compared with controls. Conclusion: OSA subjects show widespread lowered tissue integrity in autonomic, mood, and cognitive control sites over healthy controls. The pathological processes contributing to the alterations may include repetitive hypoxic and hypercarbic processes and excitotoxic injury, leading to altered brain tissue integrity in OSA.

BACKGROUND: Understanding how symptoms influence patient judgements of their health informs providers where to direct care. Patient reported physical outcomes (physical function, pain interference) and self-efficacy of symptom management (SEsm)) predict a patient's health state (i.e. patient acceptable symptom state (PASS)). However, it's unclear if therapist should consider a psychological outcome like SEsm separately or combine this outcome with other physical outcomes for clinical decisions. OBJECTIVE: To determine if patient reported outcome information system (PROMIS) SEsm scale when combined with PROMIS physical function or pain interference is able to accurately predict a patient's health state defined by PASS. METHODS: One hundred ninety-six patients (initial sample (n = 94) and separate sample (n = 102)) were surveyed by phone after care for a musculoskeletal problem. Patients completed PASS, PROMIS physical function, pain interference and SEsm outcomes. Logistic regression was used to estimate odds ratios (OR) for determining PASS in the initial sample. Criteria for determining PASS developed from the regression analysis were applied to a separate sample to assess accuracy. Accuracy for PASS status were also assessed at 1-7 days and 45-60 days. RESULTS: Three combinations including SEsm/pain interference and SEsm/physical function showed significant OR's (

Recent advances in computer vision-in the form of deep neural networks-have made it possible to query increasing volumes of video data with high accuracy. However, neural network inference is computationally expensive at scale: applying a state-of-the-art object detector in real time (i.e., 30+ frames per second) to a single video requires a $4000 GPU. In response, we present NoScope, a system for querying videos that can reduce the cost of neural network video analysis by up to three orders of magnitude via inference-optimized model search. Given a target video, object to detect, and reference neural network, NoScope automatically searches for and trains a sequence, or cascade, of models that preserves the accuracy of the reference network but is specialized to the target video and are therefore far less computationally expensive. NoScope cascades two types of models: specialized models that forego the full generality of the reference model but faithfully mimic its behavior for the target video and object; and difference detectors that highlight temporal differences across frames. We show that the optimal cascade architecture differs across videos and objects, so NoScope uses an efficient cost-based optimizer to search across models and cascades. With this approach, NoScope achieves two to three order of magnitude speed-ups (265-15,500x real-time) on binary classification tasks over fixed-angle webcam and surveillance video while maintaining accuracy within 1-5% of state-of-the-art neural networks., PVLDB 2017

Background: Racial inequities for patients with heart failure (HF) have been widely documented. HF patients who receive cardiology care during a hospital admission have better outcomes. It is unknown whether there are differences in admission to a cardiology or general medicine service by race. This study examined the relationship between race and admission service, and its effect on 30-day readmission and mortality Methods: We performed a retrospective cohort study from September 2008 to November 2017 at a single large urban academic referral center of all patients self-referred to the emergency department and admitted to either the cardiology or general medicine service with a principal diagnosis of HF, who self-identified as white, black, or Latinx. We used multivariable generalized estimating equation models to assess the relationship between race and admission to the cardiology service. We used Cox regression to assess the association between race, admission service, and 30-day readmission and mortality. Results: Among 1967 unique patients (66.7% white, 23.6% black, and 9.7% Latinx), black and Latinx patients had lower rates of admission to the cardiology service than white patients (adjusted rate ratio, 0.91; 95% CI, 0.84–0.98, for black; adjusted rate ratio, 0.83; 95% CI, 0.72–0.97 for Latinx). Female sex and age >75 years were also independently associated with lower rates of admission to the cardiology service. Admission to the cardiology service was independently associated with decreased readmission within 30 days, independent of race. Conclusions: Black and Latinx patients were less likely to be admitted to cardiology for HF care. This inequity may, in part, drive racial inequities in HF outcomes.

Context: Blood flow restriction (BFR) training utilizes a tourniquet, applied to the proximal portion of one or more extremities, to occlude blood flow during exercise. Significant gains in strength and cross-sectional area can be achieved in muscles, both distal and proximal to BFR cuff application. Purpose: To compare strength gains of the rotator cuff and changes in tendon size in subjects who performed side-lying external-rotation exercise with or without BFR. Methods: Forty-six subjects (mean age 25.0 [2.2] y) were randomized to either a BFR + exercise group or to the exercise-only group. Subjects performed 4 sets of the exercise (30/15/15/15 repetitions) at 30% 1-repetition maximum 2 days per week for 8 weeks. Results: Subjects in both groups experienced strength gains in the supraspinatus and the external rotators (P = .000, P = .000). However, there was no difference in strength gains between groups for the supraspinatus (P = .750) or the external rotators (P = .708). Subjects in both groups experienced increases in supraspinatus tendon thickness (BFR P = .041, exercise only P = .011). However, there was no difference between groups (P = .610). Conclusions: Exercise with BFR applied to the proximal upper extremity did not augment rotator cuff strength gains or tendon thickness when compared with subjects who only exercised. This study did demonstrate that performing multiple sets of high repetitions at a low load led to significant increases in rotator cuff strength and tendon size in the dominant upper extremity.

Closed malpractice claims can provide insight into low-frequency adverse events in many areas of perioperative and chronic pain care. Over the last decade, there have been changes in surgical and regional anesthetic practice, likely impacting adverse event patterns. Given the wide variability and low frequency of complications associated with peripheral nerve blocks, the study of closed malpractice claims offers an opportunity to examine adverse events, and the patient, technical, and provider factors that led to the claim. Knowledge gained from examination of closed claims has already resulted in multiple improvements in processes of care and patient safety.An investigation of the factors that contributed to medicolegal claims against anesthesia providers related to peripheral nerve blocks.Retrospective analysis.Inpatient and outpatient surgery facilities.The Comparative Benchmarking System database is a medical liability database that contains more than 400,000 malpractice claims from more than 400 academic and community-based institutions accounting for over 30% of malpractice claims in the United States. The present investigation reviewed all (n = 113) available closed malpractice claims related to regional anesthesia (RA) in surgical patients closed between 2006 and 2016, and investigated factors that may have contributed to patient injury, including type of nerve block, type of surgery, nerves injured, resulting neurologic deficits, and potential factors contributing to the injury.Our data analyzed 62 claims related to RA and showed that most closed claims were classified as permanent minor injuries. The greatest number of claims were for brachial plexus injuries associated with interscalene blocks performed for shoulder or rotator cuff repairs. Femoral and sciatic nerve blocks with resulting lower extremity injuries were the most common nerve blocks resulting in payment. The largest contributing factor to these injuries was noted to be "Technical Knowledge/Performance" of the regionalist followed by "Pre-existing Injury/Radiculopathy." Symptom onset from these claims was most likely to be delayed with the leading initial presenting symptom being paresthesia.It is difficult to establish cause-effect relationship, and the small sample size limits the ability to detect clinical differences and associations with specific comorbidities or techniques. There was also limited information related to regional anesthetic techniques and medications used that would have helped explore further relationships between the procedure and cause for litigation.There remains significant room for risk reduction in regional anesthetic practice. Patterns based on the analysis of closed claims show that interscalene blocks are the most common peripheral nerve block resulting in litigation, even when compared with other blocks involving the brachial plexus. Furthermore, patients with existing nerve injury/radiculopathy may also warrant alternative techniques or greater emphasis during informed consent on the increased risk of injury. As most of the presenting symptoms associated with claims are delayed, an opportunity for improvement in postregional care may be better communication with patients following discharge to discuss their postoperative recovery.Regional, pain, anesthesia, complications, closed claims, liability, nerve, block, injury.

Thermal radiation transfer between two objects separated by a subwavelength gap (near-field thermal radiation transfer) can be orders of magnitude larger than that in free space, which is attracting increasing attention with respect to both fundamental nanoscience and its potential for high-power-density and high-efficiency conversion of heat to electricity in thermophotovoltaic (TPV) systems. However, the realization of near-field thermal radiation transfer in TPV systems involves significant challenges because it requires a subwavelength gap and large temperature difference between the emitter and the PV cell while minimizing the heat transfer that does not contribute to the photocurrent generation. To overcome these challenges, here we demonstrate a one-chip near-field TPV device consisting of a thin-film Si emitter and InGaAs PV cell with an intermediate Si substrate, which enables the suppression of the heat transfer due to sub-bandgap radiation by free carriers and surface modes. Through the one-chip integration and thermal isolation using Si process technologies, we realize a deep subwavelength gap (150 nm) between the emitter and the intermediate substrate without using any external positioners while maintaining a large temperature difference (700 K). Compared to the equivalent device operating in the far-field regime, we achieve 10-fold enhancement of the photocurrent in the PV cell without degrading the open-circuit voltage and fill factor, demonstrating the potential of our one-chip device for the future applications of near-field thermal radiation transfer.

Study objective To provide an analysis of closed malpractice claims brought against anesthesiologists for positioning-related perioperative nerve injury (PRPNI). Design In this retrospective study, we analyzed closed claims data from the Controlled Risk Insurance Company (CRICO) Comparative Benchmarking System (CBS) database. Setting Closed claims involving nerve injuries that occurred between January 1, 1996 and December 31, 2015 in all surgical settings, provided the alleged damaging event occurred under general anesthesia. Patients Patient ages ranged from 23 to 94. Patients underwent a variety of surgical procedures. Severity of nerve injury ranged from “Insignificant” to “Grave” according to the NAIC Severity of Injury Code. Interventions None. Measurements Patient age and gender, alleged nerve injury type and severity, operating surgical specialty, contributing factors to the alleged nerve injury, and case outcome. Some of these data were drawn directly from coded variables in the CBS database, and some were gathered by the authors from CRICO-encoded narrative case summaries. Main results Seventy-five claims were determined to represent PRPNI. Ninety-two percent of all PRPNI claims involved practitioner technical knowledge/performance. Of all the recorded nerve injuries in this series, 56% involved brachial plexus injuries, and supine patient positioning represented 55% of brachial plexus claims. Settlement payments were made in 33% of claims, and the average payment for all cases was $46,269. Twenty-four percent of PRPNI claims were found to be temporary, while 76% were permanent. Conclusions PRPNI is multifactorial, and stems both from practitioner errors as well as from patient comorbidities and pre-existing neuropathies. Supine positioning can increase PRPNI risk. There are likely still causes of PRPNI of which we are not yet aware, given that despite concerted efforts towards positioning and padding interventions, injuries such as those described in this study still occur.

Today’s robotic systems are increasingly turning to computationally expensive models such as deep neural networks (DNNs) for tasks like localization, perception, planning, and object detection. However, resource-constrained robots, like low-power drones, often have insufficient on-board compute resources or power reserves to scalably run the most accurate, state-of-the art neural network compute models. Cloud robotics allows mobile robots the benefit of offloading compute to centralized servers if they are uncertain locally or want to run more accurate, compute-intensive models. However, cloud robotics comes with a key, often understated cost: communicating with the cloud over congested wireless networks may result in latency or loss of data. In fact, sending high data-rate video or LIDAR from multiple robots over congested networks can lead to prohibitive delay for real-time applications, which we measure experimentally. In this paper, we formulate a novel Robot Offloading Problem—how and when should robots offload sensing tasks, especially if they are uncertain, to improve accuracy while minimizing the cost of cloud communication? We formulate offloading as a sequential decision making problem for robots, and propose a solution using deep reinforcement learning. In both simulations and hardware experiments using state-of-the art vision DNNs, our offloading strategy improves vision task performance by between 1.3 and 2.3 $$\times $$ of benchmark offloading strategies, allowing robots the potential to significantly transcend their on-board sensing accuracy but with limited cost of cloud communication.

We experimentally demonstrate narrowband (Q∼40) thermal emission in the middle-wavelength infrared region and its electrical control at a temperature of 500°C using GaN/AlGaN multiple quantum wells and photonic crystals.

Recent advances in neural networks (NNs) have enabled automatic querying of large volumes of video data with high accuracy. While these deep NNs can produce accurate annotations of an object's position and type in video, they are computationally expensive and require complex, imperative deployment code to answer queries. Prior work uses approximate filtering to reduce the cost of video analytics, but does not handle two important classes of queries, aggregation and limit queries; moreover, these approaches still require complex code to deploy. To address the computational and usability challenges of querying video at scale, we introduce BlazeIt, a system that optimizes queries of spatiotemporal information of objects in video. BlazeIt accepts queries via FrameQL, a declarative extension of SQL for video analytics that enables video-specific query optimization. We introduce two new query optimization techniques in BlazeIt that are not supported by prior work. First, we develop methods of using NNs as control variates to quickly answer approximate aggregation queries with error bounds. Second, we present a novel search algorithm for cardinality-limited video queries. Through these these optimizations, BlazeIt can deliver up to 83X speedups over the recent literature on video processing.

We study resonances of surfaces of revolution obtained by removing a disk from a cone and attaching a hyperbolic cusp in its place. These surfaces include ones with non-trapping geodesic flow (every maximally extended non-reflected geodesic is unbounded) and yet infinitely many long-living resonances (resonances with uniformly bounded imaginary part, i.e., decay rate).

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. We describe DNase-capture, an assay that increases the analytical resolution of DNase-seq by focusing its sequencing phase on selected genomic regions. We introduce a new method to compensate for capture bias called BaseNormal that allows for accurate recovery of transcription factor protection profiles from DNase-capture data. We show that these normalized data allow for nuanced detection of transcription factor binding heterogeneity with as few as dozens of sites., National Institute of General Medical Sciences (U.S.) (Grant 1U01HG007037)

Photonic nanocavities with high quality (Q) factors are essential components for integrated optical circuits. The use of crystalline silicon carbide (SiC) for such nanocavities enables the realization of devices with superior properties. We fabricate ultrahigh-Q SiC photonic crystal nanocavities by etching air holes into a 4H-SiC slab that is prepared without using hydrogen ion implantation, which usually causes higher absorption losses. In addition, compared to usual designs, a relatively thin slab is utilized to avoid losses through cross-polarized mode coupling induced by the tapered air holes. We obtain a heterostructure nanocavity with a high experimental Q factor of 6.3×105, which is 16 times larger than the highest Q among the previously reported values for nanocavities based on crystalline SiC. We also show that our nanocavity exhibits a high normalized second-harmonic conversion efficiency of 1900%/W.

In order to enhance second-harmonic generation (SHG) efficiency in a waveguide-coupled photonic nanocavity, we introduce a reflector at the edge of the waveguide and investigate its influence on input power and SHG efficiency. SHG efficiency and critical input power of the reflective waveguide-coupled cavity are controlled by the reflection amplitude and phase delay of the reflector. At input powers considerably lower than the critical power, SHG efficiency increases by up to three orders of magnitude higher than the case without reflector. Moreover, SHG efficiency of 100% at critical input power, which is twice that of the previous result, can be achieved over a wide phase range. These results prove the feasibility and controllability of highly efficient nonlinear optical devices.

Silicon carbide (SiC) is a promising optical material for stable and broadband nanophotonics. To date, thin crystalline SiC layers for nanophotonic platforms have been created by ion implantation or growth on other materials, which may cause optical absorption in the SiC layer. We fabricated SiC nanobeam photonic crystal cavities directly from a crystalline (4H) SiC bulk wafer using oblique plasma etching to avoid material-based optical absorptions. The measured quality (Q) factor of the nanobeam photonic crystal cavity reaches 4 × 104, which is the highest recorded Q factor in crystalline SiC cavities. Furthermore, we investigated theoretical Q factors by taking into account structural imperfections unique to this fabrication process and compared them with the experimental results.

We experimentally demonstrate a narrowband (Q>90) thermal emitter with a peak wavelength of ∼ 4 μm operating at 600°C by employing a photonic crystal structure containing n-doped GaN/AlGaN quantum wells.

We demonstrate a GaN/AlGaN photonic crystal thermal emitter supported by a semi-transparent low-refractive-index substrate for mid-wavelength infrared operation. The employment of the substrate increases the mechanical strength of the photonic crystal, and enables the fabrication of a large-area thermal emitter for high power operation. First, we numerically investigate the control of emission spectra of the GaN/AlGaN photonic crystal thermal emitters and found that nearly single-peak emission in the normal direction can be realized in spite of the lower and asymmetric refractive index contrast due to the existence of the substrate. Then we fabricate a GaN/AlGaN thermal emitter with an area of 3.4 mm × 3.4 mm on a sapphire substrate, and experimentally demonstrate narrowband thermal emission with a Q factor of 101 and an emission power of 25 mW/μm/sr at 800°C.

Because of its simplicity and intuitive approach, point-based rendering has been a very popular research area. Recent approaches have focused on hardware-accelerated techniques. By applying a deferred shading scheme, both high-quality images and high-performance rendering have been achieved. However, previous methods showed problems related to depth-based visibility computation. We propose an extended point-based rendering method using a visibility map. In our method we employ a distance-based visibility technique (replacing depth-based visibility), an averaged position map and an adaptive fragment processing scheme, resulting in more accurate and improved image quality, as well as improved rendering performance.

Enhancers and promoters commonly occur in accessible chromatin characterized by depleted nucleosome contact; however, it is unclear how chromatin accessibility is governed. We show that log-additive cis-acting DNA sequence features can predict chromatin accessibility at high spatial resolution. We develop a new type of high-dimensional machine learning model, the Synergistic Chromatin Model (SCM), which when trained with DNase-seq data for a cell type is capable of predicting expected read counts of genome-wide chromatin accessibility at every base from DNA sequence alone, with the highest accuracy at hypersensitive sites shared across cell types. We confirm that a SCM accurately predicts chromatin accessibility for thousands of synthetic DNA sequences using a novel CRISPR-based method of highly efficient site-specific DNA library integration. SCMs are directly interpretable and reveal that a logic based on local, nonspecific synergistic effects, largely among pioneer TFs, is sufficient to predict a large fraction of cellular chromatin accessibility in a wide variety of cell types., National Institutes of Health (U.S.) (Grants 5UL1DE019581, RL1DE019021, 1K01DK101684-01, 1U01HG007037, and 5P01NS055923)

Motivation: The majority of disease-associated variants identified in genome-wide association studies reside in noncoding regions of the genome with regulatory roles. Thus being able to interpret the functional consequence of a variant is essential for identifying causal variants in the analysis of genome-wide association studies. Results: We present GERV (generative evaluation of regulatory variants), a novel computational method for predicting regulatory variants that affect transcription factor binding. GERV learns a k-mer-based generative model of transcription factor binding from ChIP-seq and DNase-seq data, and scores variants by computing the change of predicted ChIP-seq reads between the reference and alternate allele. The k-mers learned by GERV capture more sequence determinants of transcription factor binding than a motif-based approach alone, including both a transcription factor’s canonical motif and associated co-factor motifs. We show that GERV outperforms existing methods in predicting single-nucleotide polymorphisms associated with allele-specific binding. GERV correctly predicts a validated causal variant among linked single-nucleotide polymorphisms and prioritizes the variants previously reported to modulate the binding of FOXA1 in breast cancer cell lines. Thus, GERV provides a powerful approach for functionally annotating and prioritizing causal variants for experimental follow-up analysis. Availability and implementation: The implementation of GERV and related data are available at http://gerv.csail.mit.edu/. Contact: gifford@mit.edu Supplementary information: Supplementary data are available at Bioinformatics online.