Bias effects on the electronic spectrum of a molecular bridge

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/98651/1/JChemPhys_134_054708.pd

Benchmarking the performance of density functional theory based Green’s function formalism utilizing different self-energy models in calculating electronic transmission through molecular systems

Electronic transmission through a metal-molecule-metal system is calculated by employing a Green’s function formalism in the scattering based scheme. Self-energy models representing the bulk and the potential bias are used to describe electron transport through the molecular system. Different self-energies can be defined by varying the partition between device and bulk regions of the metal-molecule-metal model system. In addition, the self-energies are calculated with different representations of the bulk through its Green’s function. In this work, the dependence of the calculated transmission on varying the self-energy subspaces is benchmarked. The calculated transmission is monitored with respect to the different choices defining the self-energy model. In this report, we focus on one-dimensional model systems with electronic structures calculated at the density functional level of theory.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/87873/2/204717_1.pd

Modeling transient aspects of coherence-driven electron transport

Non-equilibrium Green's function formalism (NEGF) by employing time-dependent (TD) perturbation theory is used to solve the electronic equations of motion of model systems under potential biasing conditions. The time propagation is performed in the full frequency domain of the two time variables representation. We analyze transient aspects of the resulting conductance under effects of applied direct-current and alternating current potentials. The coherence induced response dependence on different aspects of the applied perturbation is resolved in time and analyzed using calculated TD distributions of the current operator.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85419/1/jpconf10_220_012008.pd

An early warning risk prediction tool (RECAP-V1) for patients diagnosed with COVID-19: the protocol for a statistical analysis plan

Background: Since the start of the Covid-19 pandemic efforts have been made to develop early warning risk scores to help clinicians decide which patient is likely to deteriorate and require hospitalisation. The RECAP (Remote COVID Assessment in Primary Care) study investigates the predictive risk of hospitalisation, deterioration, and death of patients with confirmed COVID-19, based on a set of parameters chosen through a Delphi process done by clinicians. The study aims to use rich data collected remotely through the use of electronic data templates integrated in the electronic health systems of a number of general practices across the UK to construct accurate predictive models that will use pre-existing conditions and monitoring data of a patient’s clinical parameters such as blood oxygen saturation to make reliable predictions as to the patient’s risk of hospital admission, deterioration, and death. Objective: We outline the statistical methods to build the prediction model to be used in the prioritisation of patients in the primary care setting. The statistical analysis plan for the RECAP study includes as primary outcome the development and validation of the RECAP-V1 prediction model. Such prediction model will be adapted as a three-category risk score split into red (high risk), amber (medium risk), and green (low risk) for any patient with suspected covid-19. The model will predict risk of deterioration, hospitalisation, and death. Methods: After the data has been collected, we will assess the degree of missingness and use a combination of traditional data imputation using multiple imputation by chained equations, as well as more novel machine learning approaches to impute the missing data for the final analysis. For predictive model development we will use multiple logistic regressions to construct the model on a training dataset, as well as validating the model on an independent dataset. The model will also be applied for multiple different datasets to assess both its performance in different patient groups, and applicability for different methods of data collection. Results: As of 5th of May 2021 we have recruited 2280 patients for the main dataset for model development, as well as a further 1741 patients for the validation dataset. Final analysis will commence as soon as data for 2880 are collected. Conclusions: We believe that the methodology for the development of the RECAP V1 prediction model as well as the risk score will provide clinicians with a statistically robust tool to help prioritise Covid-19 patients. Clinical Trial: Trial registration number: NCT0443504

Bone Marrow Transplantation for Feline Mucopolysaccharidosis I

Severe mucopolysaccharidosis type I (MPS I) is a fatal neuropathic lysosomal storage disorder with significant skeletal involvement. Treatment involves bone marrow transplantation (BMT), and although effective, is suboptimal, due to treatment sequelae and residual disease. Improved approaches will need to be tested in animal models and compared to BMT. Herein we report on bone marrow transplantation to treat feline mucopolysaccharidosis I (MPS I). Five MPS I stably engrafted kittens, transplanted with unfractionated bone marrow (6.3 × 107–1.1 × 109 nucleated bone marrow cells per kilogram) were monitored for 13–37 months post-engraftment. The tissue total glycosaminoglycan (GAG) content was reduced to normal levels in liver, spleen, kidney, heart muscle, lung, and thyroid. Aorta GAG content was between normal and affected levels. Treated cats had a significant decrease in the brain GAG levels relative to untreated MPS I cats and a paradoxical decrease relative to normal cats. The α-l-iduronidase (IDUA) activity in the livers and spleens of transplanted MPS I cats approached heterozygote levels. In kidney cortex, aorta, heart muscle, and cerebrum, there were decreases in GAG without significant increases in detectable IDUA activity. Treated animals had improved mobility and decreased radiographic signs of disease. However, significant pathology remained, especially in the cervical spine. Corneal clouding appeared improved in some animals. Immunohistochemical and biochemical analysis documented decreased central nervous system ganglioside storage. This large animal MPS I study will serve as a benchmark of future therapies designed to improve on BMT

Academic Performance and Behavioral Patterns

Identifying the factors that influence academic performance is an essential part of educational research. Previous studies have documented the importance of personality traits, class attendance, and social network structure. Because most of these analyses were based on a single behavioral aspect and/or small sample sizes, there is currently no quantification of the interplay of these factors. Here, we study the academic performance among a cohort of 538 undergraduate students forming a single, densely connected social network. Our work is based on data collected using smartphones, which the students used as their primary phones for two years. The availability of multi-channel data from a single population allows us to directly compare the explanatory power of individual and social characteristics. We find that the most informative indicators of performance are based on social ties and that network indicators result in better model performance than individual characteristics (including both personality and class attendance). We confirm earlier findings that class attendance is the most important predictor among individual characteristics. Finally, our results suggest the presence of strong homophily and/or peer effects among university students

Argumentation for explainable reasoning with conflicting medical recommendations

Designing a treatment path for a patient suffering from mul- tiple conditions involves merging and applying multiple clin- ical guidelines and is recognised as a difficult task. This is especially relevant in the treatment of patients with multiple chronic diseases, such as chronic obstructive pulmonary dis- ease, because of the high risk of any treatment change having potentially lethal exacerbations. Clinical guidelines are typi- cally designed to assist a clinician in treating a single condi- tion with no general method for integrating them. Addition- ally, guidelines for different conditions may contain mutually conflicting recommendations with certain actions potentially leading to adverse effects. Finally, individual patient prefer- ences need to be respected when making decisions. In this work we present a description of an integrated frame- work and a system to execute conflicting clinical guideline recommendations by taking into account patient specific in- formation and preferences of various parties. Overall, our framework combines a patient’s electronic health record data with clinical guideline representation to obtain personalised recommendations, uses computational argumentation tech- niques to resolve conflicts among recommendations while re- specting preferences of various parties involved, if any, and yields conflict-free recommendations that are inspectable and explainable. The system implementing our framework will allow for continuous learning by taking feedback from the decision makers and integrating it within its pipeline