Improvement in cardiac energetics by perhexiline in heart failure due to dilated cardiomyopathy

Objectives: The aim of this study was to determine whether short-term treatment with perhexiline improves cardiac energetics, left ventricular function, and symptoms of heart failure by altering cardiac substrate utilization. Background: Perhexiline improves exercise capacity and left ventricular ejection fraction (LVEF) in patients with heart failure (HF). P cardiac magnetic resonance spectroscopy can be used to quantify the myocardial phosphocreatine/adenosine triphosphate ratio. Because improvement of HF syndrome can improve cardiac energetics secondarily, we investigated the effects of short-term perhexiline therapy. Methods: Patients with systolic HF of nonischemic etiology (n= 50, 62 ± 1.8 years of age, New York Heart Association functional class II to IV, LVEF: 27.0 ± 1.44%) were randomized to receive perhexiline 200 mg or placebo for 1 month in a double-blind fashion. Clinical assessment, echocardiography, and P cardiac magnetic resonance spectroscopy were performed at baseline and after 1 month. A substudy of 22 patients also underwent cross-heart blood sampling at completion of the study to quantify metabolite utilization. Results: Perhexiline therapy was associated with a 30% increase in the phosphocreatine/adenosine triphosphate ratio (from 1.16 ± 0.39 to 1.51 ± 0.51; p< 0.001) versus a 3% decrease with placebo (from 1.36 ± 0.31 to 1.34 ± 0.31; p=0.37). Perhexiline therapy also led to an improvement in New York Heart Association functional class compared with placebo (p= 0.036). Short-term perhexiline therapy did not change LVEF. Cross-heart measures of cardiac substrate uptake and respiratory exchange ratio (which reflects the ratio of substrates used) did not differ between patients who received perhexiline versus placebo. Conclusions: Perhexiline improves cardiac energetics and symptom status with no evidence of altered cardiac substrate utilization. No change in LVEF is seen at this early stage. (Metabolic Manipulation in Chronic Heart Failure; NCT00841139)

CUDA: Contradistinguisher for Unsupervised Domain Adaptation

In this paper, we propose a simple model referred as Contradistinguisher (CTDR) for unsupervised domain adaptation whose objective is to jointly learn to contradistinguish on unlabeled target domain in a fully unsupervised manner along with prior knowledge acquired by supervised learning on an entirely different domain. Most recent works in domain adaptation rely on an indirect way of first aligning the source and target domain distributions and then learn a classifier on a labeled source domain to classify target domain. This approach of an indirect way of addressing the real task of unlabeled target domain classification has three main drawbacks. (i) The sub-task of obtaining a perfect alignment of the domain in itself might be impossible due to large domain shift (e.g., language domains). (ii) The use of multiple classifiers to align the distributions unnecessarily increases the complexity of the neural networks leading to over-fitting in many cases. (iii) Due to distribution alignment, the domain-specific information is lost as the domains get morphed. In this work, we propose a simple and direct approach that does not require domain alignment. We jointly learn CTDR on both source and target distribution for unsupervised domain adaptation task using contradistinguish loss for the unlabeled target domain in conjunction with a supervised loss for labeled source domain. Our experiments show that avoiding domain alignment by directly addressing the task of unlabeled target domain classification using CTDR achieves state-of-the-art results on eight visual and four language benchmark domain adaptation datasets.Comment: International Conference on Data Mining, ICDM 201

A new multicompartmental reaction-diffusion modeling method links transient membrane attachment of E. coli MinE to E-ring formation

Many important cellular processes are regulated by reaction-diffusion (RD) of molecules that takes place both in the cytoplasm and on the membrane. To model and analyze such multicompartmental processes, we developed a lattice-based Monte Carlo method, Spatiocyte that supports RD in volume and surface compartments at single molecule resolution. Stochasticity in RD and the excluded volume effect brought by intracellular molecular crowding, both of which can significantly affect RD and thus, cellular processes, are also supported. We verified the method by comparing simulation results of diffusion, irreversible and reversible reactions with the predicted analytical and best available numerical solutions. Moreover, to directly compare the localization patterns of molecules in fluorescence microscopy images with simulation, we devised a visualization method that mimics the microphotography process by showing the trajectory of simulated molecules averaged according to the camera exposure time. In the rod-shaped bacterium _Escherichia coli_, the division site is suppressed at the cell poles by periodic pole-to-pole oscillations of the Min proteins (MinC, MinD and MinE) arising from carefully orchestrated RD in both cytoplasm and membrane compartments. Using Spatiocyte we could model and reproduce the _in vivo_ MinDE localization dynamics by accounting for the established properties of MinE. Our results suggest that the MinE ring, which is essential in preventing polar septation, is largely composed of MinE that is transiently attached to the membrane independently after recruited by MinD. Overall, Spatiocyte allows simulation and visualization of complex spatial and reaction-diffusion mediated cellular processes in volumes and surfaces. As we showed, it can potentially provide mechanistic insights otherwise difficult to obtain experimentally

Towards Sensitivity Analysis: A Workflow

Establishing causal claims is one of the primary endeavors in sociological research. Statistical causal inference is a promising way to achieve this through the potential outcome framework or structural causal models, which are based on a set of identification assumptions. However, identification assumptions are often not fully discussed in practice, which harms the validity of causal claims. In this article, we focus on the unmeasurededness assumption that assumes no unmeasured confounders in models, which is often violated in practice. This article reviews a set of papers in two leading sociological journals to check the practice of causal inference and relevant identification assumptions, indicating the lack of discussion on sensitivity analysis methods on unconfoundedness in practice. And then, a blueprint of how to conduct sensitivity analysis methods on unconfoundedness is built, including six steps of proper choices on practices of sensitivity analysis to evaluate the impacts of unmeasured confounders

Self-health Monitoring and Reporting System for COVID-19 Patients Using CAN Data Logger

In the evolving situation of highly infectious coronavirus, the number of confirmed cases in India has largely increased, which has resulted in a shortage of health care resources. Thus, the Ministry of Health and Family Welfare- Government of India issued guidelines for the ‘Home isolation of COVID-19 positive patients’ methodology for asymptomatic patients or with mild symptoms. During home isolation, the patients are required to monitor and record the pulse rate, body temperature, and oxygen saturation three times a day. This paper proposes a system that can request data from the required sensor to measure the pulse rate, body temperature, or oxygen saturation. The requested data is sensed by the respective sensor placed near the patients’ body and sent to the CAN data logger over the CAN bus. The CAN data logger live streams the sensor values and stores the same to an excel sheet along with details like the patient’s name, patient’s age, and date. The physicians can then access this information

Deep Learning With DAGs

Social science theories often postulate causal relationships among a set of variables or events. Although directed acyclic graphs (DAGs) are increasingly used to represent these theories, their full potential has not yet been realized in practice. As non-parametric causal models, DAGs require no assumptions about the functional form of the hypothesized relationships. Nevertheless, to simplify the task of empirical evaluation, researchers tend to invoke such assumptions anyway, even though they are typically arbitrary and do not reflect any theoretical content or prior knowledge. Moreover, functional form assumptions can engender bias, whenever they fail to accurately capture the complexity of the causal system under investigation. In this article, we introduce causal-graphical normalizing flows (cGNFs), a novel approach to causal inference that leverages deep neural networks to empirically evaluate theories represented as DAGs. Unlike conventional approaches, cGNFs model the full joint distribution of the data according to a DAG supplied by the analyst, without relying on stringent assumptions about functional form. In this way, the method allows for flexible, semi-parametric estimation of any causal estimand that can be identified from the DAG, including total effects, conditional effects, direct and indirect effects, and path-specific effects. We illustrate the method with a reanalysis of Blau and Duncan's (1967) model of status attainment and Zhou's (2019) model of conditional versus controlled mobility. To facilitate adoption, we provide open-source software together with a series of online tutorials for implementing cGNFs. The article concludes with a discussion of current limitations and directions for future development

ρρ-GNF: A Copula-based Sensitivity Analysis to Unobserved Confounding Using Normalizing Flows

We propose a novel sensitivity analysis to unobserved confounding in observational studies using copulas and normalizing flows. Using the idea of interventional equivalence of structural causal models, we develop $ρ$-GNF ($ρ$-graphical normalizing flow), where $ρ{\in}[-1,+1]$ is a bounded sensitivity parameter. This parameter represents the back-door non-causal association due to unobserved confounding, and which is encoded with a Gaussian copula. In other words, the $ρ$-GNF enables scholars to estimate the average causal effect (ACE) as a function of $ρ$, while accounting for various assumed strengths of the unobserved confounding. The output of the $ρ$-GNF is what we denote as the $ρ_{curve}$ that provides the bounds for the ACE given an interval of assumed $ρ$ values. In particular, the $ρ_{curve}$ enables scholars to identify the confounding strength required to nullify the ACE, similar to other sensitivity analysis methods (e.g., the E-value). Leveraging on experiments from simulated and real-world data, we show the benefits of $ρ$-GNF. One benefit is that the $ρ$-GNF uses a Gaussian copula to encode the distribution of the unobserved causes, which is commonly used in many applied settings. This distributional assumption produces narrower ACE bounds compared to other popular sensitivity analysis methods.12 main pages (+8 reference pages), 4 Figures, Accepted at Probabilistic Graphical Models (PGM) 2024. Oral Presentatio

STRATEGI KESIAPAN PEMERINTAH KOTA BOGOR DALAM PENERAPAN PERENCANAAN PEMBANGUNAN BERBASIS E-PLANNING

ABSTRACTSystem Information of Planning, Monitoring and Evaluation of Development (SIMRAL) usually called e-planning, as the medium of digital data analysis, is used in data collection, technical guidance arrangement and evaluating of local government affair management, which refers to the regulation that are accordance to its function in this case is Regulatory Affairs Minister Number 54 Year 2010. According to the analysis of logistic regression equation, it is found that the variable of human resources and variable system information significantly affect the readiness of Bogor City Municipality in implementing e-planning development. The goals of this research in general is to formulate readiness strategy of Bogor Municipality in implementing e-planning development by using SWOT Analysis, it is able to identify internal and external factors that both result in 5 strength factors, 8 weakness factor, 4 opportunity factors and 3 threat factors. After weighting to each factors, alternatives of grand strategy is devided, in which the highest weight strategy is Weakness-Opportunity (WO) strategy, namely to improve human resources quality particularly the operators of e-planning system and to improve commitment of e-planning implementation. The conversion of alternative strategy to strategic action will be done conducted by making operational policies which will be the guidance in deriving programmes and implementating e-planning.Keywords: Readiness, e-planning, Implementation, SWOT. ABSTRAKSistem Informasi Perencanaan, Monitoring dan Evaluasi Pembangunan (SIMRAL) atau yang biasa disebut e-planning sebagai sarana pengolahan data elektronik, melaksanakan pengumpulan bahan dan penyusunan pedoman dan petunjuk teknis serta evaluasi penyelenggaraan urusan pemerintahan daerah yang mengacu pada peraturan perundang – undangan yang bersesuaian dengan fungsinya yang dalam hal ini adalah Peraturan Menteri Dalam Negeri Nomor 54 Tahun 2010. Berdasarkan hasil analisis persamaan Regresi Logistik menunjukkan bahwa variabel sumber daya manusia dan sistem informasi mempengaruhi secara signifikan terhadap kesiapan Pemerintah Kota Bogor dalam menerapkan perencanaan pembangunan berbasis e-planning ini. Tujuan kajian ini secara umum adalah merumuskan strategi kesiapan Pemerintah Kota Bogor dalam penerapan perencanaan pembangunan berbasis e-planning. Melalui analisis SWOT dapat diidentifikasi faktor internal dan faktor eksternal yang menghasilkan 5 (lima) faktor kekuatan, 8 (delapan) faktor kelemahan, 4 (empat) faktor peluang, dan 3 (tiga) faktor ancaman. Setelah dilakukan pembobotan pada masing – masing faktor dirumuskanlan alternatif grand strategy, adapun strategi yang menperoleh bobot paling tinggi adalah strategi Weakness-Opportunity (WO) yakni tingkatkan kualitas SDM khususnya para operator e-planning dan tingkatkan kesiapan berbagai faktor untuk menunjukkan komitmen terhadap penerapan e-planning. Penterjemahan alternatif strategi ke dalam tindakan strategik akan dilakukan melalui penyusunan kebijakan operasional yang merupakan acuan dalam bentuk penjabaran melalui program dan kegiatan.Kata Kunci: Kesiapan, e-planning, Implementasi, SWOT

A mechanistic target of rapamycin complex 1/2 (mTORC1)/V-Akt murine thymoma viral oncogene homolog 1 (AKT1)/cathepsin H axis controls filaggrin expression and processing in skin, a novel mechanism for skin barrier disruption in patients with atopic dermatitis

Background Filaggrin, which is encoded by the filaggrin gene (FLG), is an important component of the skin's barrier to the external environment, and genetic defects in FLG strongly associate with atopic dermatitis (AD). However, not all patients with AD have FLG mutations. Objective We hypothesized that these patients might possess other defects in filaggrin expression and processing contributing to barrier disruption and AD, and therefore we present novel therapeutic targets for this disease. Results We describe the relationship between the mechanistic target of rapamycin complex 1/2 protein subunit regulatory associated protein of the MTOR complex 1 (RAPTOR), the serine/threonine kinase V-Akt murine thymoma viral oncogene homolog 1 (AKT1), and the protease cathepsin H (CTSH), for which we establish a role in filaggrin expression and processing. Increased RAPTOR levels correlated with decreased filaggrin expression in patients with AD. In keratinocyte cell cultures RAPTOR upregulation or AKT1 short hairpin RNA knockdown reduced expression of the protease CTSH. Skin of CTSH-deficient mice and CTSH short hairpin RNA knockdown keratinocytes showed reduced filaggrin processing, and the mouse had both impaired skin barrier function and a mild proinflammatory phenotype. Conclusion Our findings highlight a novel and potentially treatable signaling axis controlling filaggrin expression and processing that is defective in patients with AD