Ariadne: Analysis for Machine Learning Program

Machine learning has transformed domains like vision and translation, and is now increasingly used in science, where the correctness of such code is vital. Python is popular for machine learning, in part because of its wealth of machine learning libraries, and is felt to make development faster; however, this dynamic language has less support for error detection at code creation time than tools like Eclipse. This is especially problematic for machine learning: given its statistical nature, code with subtle errors may run and produce results that look plausible but are meaningless. This can vitiate scientific results. We report on Ariadne: applying a static framework, WALA, to machine learning code that uses TensorFlow. We have created static analysis for Python, a type system for tracking tensors---Tensorflow's core data structures---and a data flow analysis to track their usage. We report on how it was built and present some early results

Brain mechanisms of affect and learning

Learning and affect are considered empirically separable, but these constructs bidirectionally interact. While it has been demonstrated that dopamine supports the informational component of reward learning, the term "reward" inherently infers that a subjective positive experience is necessary to drive appetitive behavior. In this dissertation, I will first review the ways in which dopamine operates on the levels of physiology and systems neuroscience to support learning from both positive and negative outcomes, as well as how this framework may be employed to study mechanism and disease. I will then review the ways in which learning may interact with or be supported by other brain systems, starting with affective networks and extending into systems that support memory and other types of broader decision making processes. Finally, my introduction will discuss a disease model, schizophrenia, and how applying questions pertaining to learning theory may contribute to understanding symptom-related mechanisms. The first study (Chapter 2) will address the way in which affective and sensory mechanisms may alter pain-related decisions. I will demonstrate that subjects will choose to experience a stimulus that incorporates a moment of pain relief over a shorter stimulus that encompasses less net pain, and will suggest that the positive prediction error associated with the pain relief may modulate explicit memory in such a way that impacts later decision making. In the second study (Chapter 3), I will examine reward learning in patients with schizophrenia, and demonstrate selective learning deficits from gains as opposed to losses, as well as relationships in performance to affective and motivational symptoms. The third study (Chapter 4) will extend this disease model to a novel cohort of subjects who perform the same reward learning task while undergoing functional MRI. The data from this chapter will reveal deficits in the patient group during choice in orbitofrontal cortex, as well as an abnormal pattern of learning signal responses during feedback versus outcome, particularly in orbitofrontal cortex, a finding that correlates with affective symptoms in medial PFC. Taken together, these data demonstrate that learning is comprised of both informational and affective processes that incorporate input from dopaminergic midbrain neurons and its targets, as well as integration from other affective, mnemonic, and sensory regions to support healthy learning, emotion, and adaptive behavior

PainPoints: A Framework for Language-based Detection of Chronic Pain and Expert-Collaborative Text-Summarization

Chronic pain is a pervasive disorder which is often very disabling and is associated with comorbidities such as depression and anxiety. Neuropathic Pain (NP) is a common sub-type which is often caused due to nerve damage and has a known pathophysiology. Another common sub-type is Fibromyalgia (FM) which is described as musculoskeletal, diffuse pain that is widespread through the body. The pathophysiology of FM is poorly understood, making it very hard to diagnose. Standard medications and treatments for FM and NP differ from one another and if misdiagnosed it can cause an increase in symptom severity. To overcome this difficulty, we propose a novel framework, PainPoints, which accurately detects the sub-type of pain and generates clinical notes via summarizing the patient interviews. Specifically, PainPoints makes use of large language models to perform sentence-level classification of the text obtained from interviews of FM and NP patients with a reliable AUC of 0.83. Using a sufficiency-based interpretability approach, we explain how the fine-tuned model accurately picks up on the nuances that patients use to describe their pain. Finally, we generate summaries of these interviews via expert interventions by introducing a novel facet-based approach. PainPoints thus enables practitioners to add/drop facets and generate a custom summary based on the notion of "facet-coverage" which is also introduced in this work

Health-related quality of life and survival in liver transplant candidates.

Health-related quality of life (HRQOL) is an important measure of the effects of chronic liver disease in affected patients that helps guide interventions to improve well-being. However, the relationship between HRQOL and survival in liver transplant candidates remains unclear. We examined whether the Physical Component Summary (PCS) and Mental Component Summary (MCS) scores from the Short Form 36 (SF-36) Health Survey were associated with survival in liver transplant candidates. We administered the SF-36 questionnaire (version 2.0) to patients in the Pulmonary Vascular Complications of Liver Disease study, a multicenter prospective cohort of patients evaluated for liver transplantation in 7 academic centers in the United States between 2003 and 2006. Cox proportional hazards models were used with death as the primary outcome and adjustment for liver transplantation as a time-varying covariate. The mean age of the 252 participants was 54 +/- 10 years, 64% were male, and 94% were white. During the 422 person years of follow-up, 147 patients (58%) were listed, 75 patients (30%) underwent transplantation, 49 patients (19%) died, and 3 patients were lost to follow-up. Lower baseline PCS scores were associated with an increased mortality rate despite adjustments for age, gender, Model for End-Stage Liver Disease score, and liver transplantation (P for the trend = 0.0001). The MCS score was not associated with mortality (P for the trend = 0.53). In conclusion, PCS significantly predicts survival in liver transplant candidates, and interventions directed toward improving the physical status may be helpful in improving outcomes in liver transplant candidates

Gene expression links functional networks across cortex and striatum

The human brain is comprised of a complex web of functional networks that link anatomically distinct regions. However, the biological mechanisms supporting network organization remain elusive, particularly across cortical and subcortical territories with vastly divergent cellular and molecular properties. Here, using human and primate brain transcriptional atlases, we demonstrate that spatial patterns of gene expression show strong correspondence with limbic and somato/motor cortico-striatal functional networks. Network-associated expression is consistent across independent human datasets and evolutionarily conserved in non-human primates. Genes preferentially expressed within the limbic network (encompassing nucleus accumbens, orbital/ventromedial prefrontal cortex, and temporal pole) relate to risk for psychiatric illness, chloride channel complexes, and markers of somatostatin neurons. Somato/motor associated genes are enriched for oligodendrocytes and markers of parvalbumin neurons. These analyses indicate that parallel cortico-striatal processing channels possess dissociable genetic signatures that recapitulate distributed functional networks, and nominate molecular mechanisms supporting cortico-striatal circuitry in health and disease

Risk factors and impact of chronic obstructive pulmonary disease in candidates for liver transplantation

Chronic obstructive pulmonary disease (COPD) may cause significant symptoms and have an impact on survival. Smoking is an important risk factor for COPD and is common in candidates for liver transplantation; however, the risk factors for and outcomes of COPD in this population are unknown. We performed a prospective cohort study of 373 patients being evaluated for liver transplantation at 7 academic centers in the United States. COPD was characterized by expiratory airflow obstruction and defined as follows: prebronchodilator forced expiratory volume in 1 second/forced vital capacity < 0.70. Patients completed the Liver Disease Quality of Life Questionnaire 1.0, which included the Short Form-36. The mean age of the study sample was 53 ± 9 years, and 234 (63%) were male. Sixty-seven patients (18%, 95% confidence interval 14%–22%) had COPD, and 224 (60%) had a history of smoking. Eighty percent of patients with airflow obstruction did not previously carry a diagnosis of COPD, and 27% were still actively smoking. Older age and any smoking (odds ratio = 3.74, 95% confidence interval 1.94–7.23, P < 0.001) were independent risk factors for COPD. Patients with COPD had worse New York Heart Association functional class and lower physical component summary scores on the 36-Item Short Form but had short-term survival similar to that of patients without COPD. In conclusion, COPD is common and often undiagnosed in candidates for liver transplantation. Older age and smoking are significant risk factors of COPD, which has adverse consequences on functional status and quality of life in these patients

The Efficacy of Wristband Activity Trackers during Vigorous Exercise

Patients and clinicians rely on activity trackers to monitor heart rate, calorie expenditure, and steps during training and interventions. However, the efficacy of activity trackers during vigorous exercise, is not widely studied. PURPOSE: The objective of this study was to compare the effectiveness of activity trackers during vigorous activity. METHODS: Nineteen participants completed twenty minutes of vigorous intensity exercise by running or incline walking on a treadmill. Measurement devices worn during the testing period included two wristband activity trackers (Garmin (G) Forerunner 735xt™ and Fitbit (F) Surge™) and industry standard devices: a pedometer(P), Polar™ HR Chest Strap and Cosmed (C) Quark CPET face mask. RESULTS: No significant difference was found among the devices or industry standard for step count (STPG = 3096.56+/-380.05; STPF = 3072.72+/-353.26; STPP = 3052.44+/-408.52). No significant difference was found between the two trackers and the industry standard for energy expenditure (KCALG = 249.19+/-61.06; KCALF = 211.88+/-34.43; KCALC = 234.07+/-64.24). However, there was a significant difference between the two trackers for this same variable. At multiple times throughout the testing period, a significant difference was noted between the activity trackers and industry standard for heart rate. All testing significance was set at pCONCLUSION: This study sought to examine the efficacy of personal activity trackers as compared to industry standards during vigorous exercise. Both devices proved accurate in measuring steps and energy expenditure but proved inconsistent when monitoring heart rate