Isolation of a Chiral Anthracene Cation Radical: X-Ray Crystallography and Computational Interrogation of its Racemization

Chiral cation-radical salts hold significant promise as charge-transfer materials, chiroptical switches, and electron-transfer catalysts for enantioselective synthesis. Herein we demonstrate that the readily-available chiral 9,10-diphenyleanthracene derivative (i.e.SANT) forms a robust cation radical, whose structure was elucidated by X-ray crystallography and DFT calculations. While SANT was observed to racemize on a timescale (t1/2) of 1.1 hours, a computational conformational search and kinetic analysis of the racemization pathway led us to identify a simple methyl substituted SANT derivative, which does not racemize (racemization t1/2 1013–1017 years)

A prospective observational study to determine the utility of placental laterality for prediction of preeclampsia in pregnancy

Background: Preeclampsia is a multisystem disorder of unknown aetiology and recently its link with placental laterality has been explored. The objective of this study was to find the association of placental laterality with maternal and fetal outcomes in pregnancy. Study also determined the predictive ability of placenta laterality for the development of hypertension in pregnancy.Methods: A prospective observational cohort study was conducted on 200 pregnant women. Routine investigations and doppler analysis were done. Placenta position was categorized into central and lateral. Maternal and fetal outcomes were recorded. The data was entered in MS excel spreadsheet and analysis was done using statistical package for social sciences (SPSS) version 21.0. A p value of 0.05). Even the maternal outcomes like mode of delivery, onset of labor, indication of labor induction and caesarean deliveries were comparable among women with central or lateral placenta (p > 0.05). On applying univariate logistic regression analysis, previous history of hypertension in pregnancy was a significant risk factor for development of preeclampsia with odds ratio of 168.43 (p < 0.05).Conclusions: It can be concluded that the maternal and fetal outcomes are independent of the placenta laterality. The doppler characteristics and placenta laterality did not show any increased risk for hypertension in pregnancy. However future studies are recommended with large sample size including more women with diagnosed hypertension in the pregnancy so that a better association can be derived with placenta laterality and doppler characteristics

Self-aware SGD: reliable incremental adaptation framework for clinical AI models

Healthcare is dynamic as demographics, diseases, and therapeutics constantly evolve. This dynamic nature induces inevitable distribution shifts in populations targeted by clinical AI models, often rendering them ineffective. Incremental learning provides an effective method of adapting deployed clinical models to accommodate these contemporary distribution shifts. However, since incremental learning involves modifying a deployed or in-use model, it can be considered unreliable as any adverse modification due to maliciously compromised or incorrectly labelled data can make the model unsuitable for the targeted application. This paper introduces self-aware stochastic gradient descent (SGD) , an incremental deep learning algorithm that utilises a contextual bandit-like sanity check to only allow reliable modifications to a model. The contextual bandit analyses incremental gradient updates to isolate and filter unreliable gradients. This behaviour allows self-aware SGD to balance incremental training and integrity of a deployed model. Experimental evaluations on the Oxford University Hospital datasets highlight that self-aware SGD can provide reliable incremental updates for overcoming distribution shifts in challenging conditions induced by label noise

Continuous patient state attention models

Irregular time-series (ITS) are prevalent in the electronic health records (EHR) as the data is recorded in EHR system as per the clinical guidelines/requirements but not for research and also depends on the patient health status. ITS present challenges in training of machine learning algorithms, which are mostly built on assumption of coherent fixed dimensional feature space. In this paper, we propose a computationally efficient variant of the transformer based on the idea of cross-attention, called Perceiver, for time-series in healthcare. We further develop continuous patient state attention models, using the Perceiver and the transformer to deal with ITS in EHR. The continuous patient state models utilise neural ordinary differential equations to learn the patient health dynamics, i.e., patient health trajectory from the observed irregular time-steps, which enables them to sample any number of time-steps at any time. The performance of the proposed models is evaluated on in-hospital-mortality prediction task on Physionet-2012 challenge and MIMIC-III datasets. The Perceiver model significantly outperforms the baselines and reduces the computational complexity, as compared with the transformer model, without significant loss of performance. The carefully designed experiments to study irregularity in healthcare also show that the continuous patient state models outperform the baselines. The code is publicly released and verified at https://codeocean.com/capsule/4587224

Adversarial De-confounding in Individualised Treatment Effects Estimation

Observational studies have recently received significant attention from the machine learning community due to the increasingly available non-experimental observational data and the limitations of the experimental studies, such as considerable cost, impracticality, small and less representative sample sizes, etc. In observational studies, de-confounding is a fundamental problem of individualised treatment effects (ITE) estimation. This paper proposes disentangled representations with adversarial training to selectively balance the confounders in the binary treatment setting for the ITE estimation. The adversarial training of treatment policy selectively encourages treatment-agnostic balanced representations for the confounders and helps to estimate the ITE in the observational studies via counterfactual inference. Empirical results on synthetic and real-world datasets, with varying degrees of confounding, prove that our proposed approach improves the state-of-the-art methods in achieving lower error in the ITE estimation.Comment: accepted to AISTATS 202

Optimasi Portofolio Resiko Menggunakan Model Markowitz MVO Dikaitkan dengan Keterbatasan Manusia dalam Memprediksi Masa Depan dalam Perspektif Al-Qur`an

Risk portfolio on modern finance has become increasingly technical, requiring the use of sophisticated mathematical tools in both research and practice. Since companies cannot insure themselves completely against risk, as human incompetence in predicting the future precisely that written in Al-Quran surah Luqman verse 34, they have to manage it to yield an optimal portfolio. The objective here is to minimize the variance among all portfolios, or alternatively, to maximize expected return among all portfolios that has at least a certain expected return. Furthermore, this study focuses on optimizing risk portfolio so called Markowitz MVO (Mean-Variance Optimization). Some theoretical frameworks for analysis are arithmetic mean, geometric mean, variance, covariance, linear programming, and quadratic programming. Moreover, finding a minimum variance portfolio produces a convex quadratic programming, that is minimizing the objective function ðð¥with constraintsð ð 𥠥 ðandð´ð¥ = ð. The outcome of this research is the solution of optimal risk portofolio in some investments that could be finished smoothly using MATLAB R2007b software together with its graphic analysis

Measuring progress and projecting attainment on the basis of past trends of the health-related Sustainable Development Goals in 188 countries: an analysis from the Global Burden of Disease Study 2016

The UN’s Sustainable Development Goals (SDGs) are grounded in the global ambition of “leaving no one behind”. Understanding today’s gains and gaps for the health-related SDGs is essential for decision makers as they aim to improve the health of populations. As part of the Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016), we measured 37 of the 50 health-related SDG indicators over the period 1990–2016 for 188 countries, and then on the basis of these past trends, we projected indicators to 2030