Hubbard NEGF Analysis of Photocurrent in Nitroazobenzene Molecular Junction

We present combined experimental and theoretical study of photo-induced current in molecular junctions consisting of monolayers of nitroazobenzene oligomers chemisorbed on carbon surfaces and illuminated by UV-Vis light through a transparent electrode. Experimentally observed dependence of the photocurrent on light frequency, temperature and monolayer thickness is analyzed within first principles simulations employing the Hubbard NEGF diagrammatic technique. We reproduce qualitatively correct behavior and discuss mechanisms leading to characteristic behavior of dark and photo-induced currents in response to changes in bias, frequency of radiation, temperature and thickness of molecular layer.Comment: 22 pages, 4 figure

Antilymphoid antibody preconditioning and tacrolimus monotherapy for pediatric kidney transplantation

Objective: Heavy post-transplant immunosuppression may contribute to long-term immunosuppression dependence by subverting tolerogenic mechanisms; thus, we sought to determine if this undesirable consequence could be mitigated by pretransplant lymphoid depletion and minimalistic post-transplant monotherapy. Study design: Lymphoid depletion in 17 unselected pediatric recipients of live (n = 14) or deceased donor kidneys (n = 3) was accomplished with antithymocyte globulin (ATG) (n = 8) or alemtuzumab (n = 9). Tacrolimus was begun post-transplantation with subsequent lengthening of intervals between doses (spaced weaning). Maintenance immunosuppression, morbidity, graft function, and patient/graft survival were collated. Results: Steroids were added temporarily to treat rejection in two patients (both ATG subgroup) or to treat hemolytic anemia in two others. After 16 to 31 months (mean 22), patient and graft survival was 100% and 94%, respectively. The only graft loss was in a nonweaned noncompliant recipient. In the other 16, serum creatinine was 0.85 ± 0.35 mg/dL and creatinine clearance was 90.8 ± 22.1 mL/1.73 m2. All 16 patients are on monotherapy (15 tacrolimus, one sirolimus), and 14 receive every other day or 3 times per week doses. There were no wound or other infections. Two patients developed insulin-dependent diabetes. Conclusion: The strategy of lymphoid depletion and minimum post-transplant immunosuppression appears safe and effective for pediatric kidney recipients. © 2006 Elsevier Inc. All rights reserved

Osteoarthritis of the Temporomandibular Joint can be diagnosed earlier using biomarkers and machine learning

After chronic low back pain, Temporomandibular Joint (TMJ) disorders are the second most common musculoskeletal condition affecting 5 to 12% of the population, with an annual health cost estimated at $4 billion. Chronic disability in TMJ osteoarthritis (OA) increases with aging, and the main goal is to diagnosis before morphological degeneration occurs. Here, we address this challenge using advanced data science to capture, process and analyze 52 clinical, biological and high-resolution CBCT (radiomics) markers from TMJ OA patients and controls. We tested the diagnostic performance of four machine learning models: Logistic Regression, Random Forest, LightGBM, XGBoost. Headaches, Range of mouth opening without pain, Energy, Haralick Correlation, Entropy and interactions of TGF-β1 in Saliva and Headaches, VE-cadherin in Serum and Angiogenin in Saliva, VE-cadherin in Saliva and Headaches, PA1 in Saliva and Headaches, PA1 in Saliva and Range of mouth opening without pain; Gender and Muscle Soreness; Short Run Low Grey Level Emphasis and Headaches, Inverse Difference Moment and Trabecular Separation accurately diagnose early stages of this clinical condition. Our results show the XGBoost + LightGBM model with these features and interactions achieves the accuracy of 0.823, AUC 0.870, and F1-score 0.823 to diagnose the TMJ OA status. Thus, we expect to boost future studies into osteoarthritis patient-specific therapeutic interventions, and thereby improve the health of articular joints

A comparative analysis of multi-level computer-assisted decision making systems for traumatic injuries

<p>Abstract</p> <p>Background</p> <p>This paper focuses on the creation of a predictive computer-assisted decision making system for traumatic injury using machine learning algorithms. Trauma experts must make several difficult decisions based on a large number of patient attributes, usually in a short period of time. The aim is to compare the existing machine learning methods available for medical informatics, and develop reliable, rule-based computer-assisted decision-making systems that provide recommendations for the course of treatment for new patients, based on previously seen cases in trauma databases. Datasets of traumatic brain injury (TBI) patients are used to train and test the decision making algorithm. The work is also applicable to patients with traumatic pelvic injuries.</p> <p>Methods</p> <p>Decision-making rules are created by processing patterns discovered in the datasets, using machine learning techniques. More specifically, CART and C4.5 are used, as they provide grammatical expressions of knowledge extracted by applying logical operations to the available features. The resulting rule sets are tested against other machine learning methods, including AdaBoost and SVM. The rule creation algorithm is applied to multiple datasets, both with and without prior filtering to discover significant variables. This filtering is performed via logistic regression prior to the rule discovery process.</p> <p>Results</p> <p>For survival prediction using all variables, CART outperformed the other machine learning methods. When using only significant variables, neural networks performed best. A reliable rule-base was generated using combined C4.5/CART. The average predictive rule performance was 82% when using all variables, and approximately 84% when using significant variables only. The average performance of the combined C4.5 and CART system using significant variables was 89.7% in predicting the exact outcome (home or rehabilitation), and 93.1% in predicting the ICU length of stay for airlifted TBI patients.</p> <p>Conclusion</p> <p>This study creates an efficient computer-aided rule-based system that can be employed in decision making in TBI cases. The rule-bases apply methods that combine CART and C4.5 with logistic regression to improve rule performance and quality. For final outcome prediction for TBI cases, the resulting rule-bases outperform systems that utilize all available variables.</p

Whole genome sequencing and progress toward full inbreeding of the mouse collaborative cross population

Two key features of recombinant inbred panels are well-characterized genomes and reproducibility. Here we report on the sequenced genomes of six additional Collaborative Cross (CC) strains and on inbreeding progress of 72 CC strains. We have previously reported on the sequences of 69 CC strains that were publicly available, bringing the total of CC strains with whole genome sequence up to 75. The sequencing of these six CC strains updates the efforts toward inbreeding undertaken by the UNC Systems Genetics Core. The timing reflects our competing mandates to release to the public as many CC strains as possible while achieving an acceptable level of inbreeding. The new six strains have a higher than average founder contribution from non-domesticus strains than the previously released CC strains. Five of the six strains also have high residual heterozygosity (.14%), which may be related to non-domesticus founder contributions. Finally, we report on updated estimates on residual heterozygosity across the entire CC population using a novel, simple and cost effective genotyping platform on three mice from each strain. We observe a reduction in residual heterozygosity across all previously released CC strains. We discuss the optimal use of different genetic resources available for the CC population

Pathological Margin Clearance and Survival After Pancreaticoduodenectomy in a US and European Pancreatic Center

Background: The optimal definition of a margin-negative resection and its exact prognostic significance on survival in resected pancreatic adenocarcinoma remains unknown. This study was designed to assess the relationship between pathological margin clearance, margin type, and survival. Methods: Patients who underwent pancreaticoduodenectomy with curative intent at two academic institutions, in Amsterdam, the Netherlands, and Boston, Massachusetts, between 2000 and 2014 were retrospectively evaluated. Overall survival, recurrence rates, and progression-free survival (PFS) were assessed by Kaplan–Meier estimates and multivariate Cox proportional hazards analysis, according to pathological margin clearance and type of margin involved. Results: Of 531 patients identified, the median PFS was 12.9, 15.4, and 24.1 months, and the median overall survival was 17.4, 22.9, and 27.7 months for margin clearances of 0, < 1, and ≥1 mm, respectively (all log-rank p < 0.001). On multivariate analysis, patients with a margin clearance of ≥1 mm demonstrated a survival advantage relative to those with 0 mm clearance [hazard ratio (HR) 0.71, p < 0.01], whereas survival was comparable for patients with a margin clearance of < 1 mm versus 0 mm (HR: 0.93, p = 0.60). Patients with involvement (0 or < 1 mm margin clearance) of the SMV/PV margin demonstrated prolonged median overall survival (25.7 months) relative to those with SMA involvement (17.5 months). Conclusions: In patients undergoing pancreaticoduodenectomy for pancreatic adenocarcinoma, a margin clearance of ≥1 mm correlates with improved survival relative to < 1 mm clearance and may be a more accurate predictor of a complete margin-negative resection in pancreatic cancer. The type of margin involved also appears to impact survival. Electronic supplementary material The online version of this article (10.1245/s10434-018-6467-9) contains supplementary material, which is available to authorized users