Enhancing cross-domain robustness in phonocardiogram signal classification using domain-invariant preprocessing and transfer learning.

Background And Objective: Phonocardiogram (PCG) signal analysis is a non-invasive and cost-efficient approach for diagnosing cardiovascular diseases. Existing PCG-based approaches employ signal processing and machine learning (ML) for automatic disease detection. However, machine learning techniques are known to underperform in cross-corpora arrangements.

Fano resonances in tilted Weyl semimetals in an oscillating quantum well.

Considering the low-energy model of tilted Weyl semimetal, we study the electronic transmission through a periodically driven quantum well, oriented in the transverse direction with respect to the tilt. We adopt the formalism of Floquet scattering theory and investigate the emergence of Fano resonances as an outcome of matching between the Floquet sidebands and quasi-bound states. The Fano resonance energy changes linearly with the tilt strength suggesting the fact that tilt-mediated part of quasi-bound states energies depends on the above factor.

Optimization of EWOC principle in BLRM design for phase 1 oncology trials.

Bayesian logistic regression model (BLRM) is widely used to guide dose escalation decisions in phase 1 oncology trials. An important feature of BLRM design is the appealing safety performance due to its escalation with overdose control (EWOC). However, some recent literature indicates that BLRM with EWOC may have a relatively low probability to find the maximum tolerated dose (MTD) compared to some other dose escalation designs.

Association between F2-isoprostanes and self-reported stressors in pregnant americans of African and European ancestry.

Background: Poor birth outcomes such as preterm birth/delivery disproportionately affect African Americans compared to White individuals. Reasons for this disparity are likely multifactorial, and include prenatal psychosocial stressors, and attendant increased lipid peroxidation; however, empirical data linking psychosocial stressors during pregnancy to oxidative status are limited.

Methods: We used established scales to measure five psychosocial stressors.

Immortalised chronic myeloid leukemia (CML) derived mesenchymal stromal cells (MSCs) line retains the immunomodulatory and chemoprotective properties of CML patient-derived MSCs.

Despite the success of Tyrosine kinase inhibitors (TKIs) in treating chronic myeloid leukemia (CML), leukemic stem cells (LSCs) persist, contributing to relapse and resistance. CML Mesenchymal Stromal Cells (MSCs) help in LSC maintenance and protection from TKIs. However, the limited passage and self-differentiation abilities of primary CML MSCs hinder extensive research.

Time-Frequency Fragment Selection for Disease Detection from Imbalanced Phonocardiogram Data.

Cardiovascular disease (CVD) has become the most concerning disease worldwide. A Phonocardiogram (PCG), the graphical representation of heart sound, is a non-invasive method that helps to detect CVD by analyzing its characteristics. Several machine learning (ML) approaches have been proposed in the last decade to assist practitioners in interpreting this disease accurately.

Scalable graphene sensor array for real-time toxins monitoring in flowing water.

Risk management for drinking water often requires continuous monitoring of various toxins in flowing water. While they can be readily integrated with existing water infrastructure, two-dimensional (2D) electronic sensors often suffer from device-to-device variations due to the lack of an effective strategy for identifying faulty devices from preselected uniform devices based on electronic properties alone, resulting in sensor inaccuracy and thus slowing down their real-world applications. Here, we report the combination of wet transfer, impedance and noise measurements, and machine learning to facilitate the scalable nanofabrication of graphene-based field-effect transistor (GFET) sensor arrays and the efficient identification of faulty devices.

Critical review on uranium and arsenic content and their chemical mobilization in groundwater: A case study of the Malwa region Punjab, India.

The presence of pollutants like uranium and arsenic in the groundwater can have a terrible impact on people's health (both radiologically and toxicologically) and their economic conditions. Their infiltration into groundwater can occur through geochemical reactions, natural mineral deposits, mining and ore processing. Governments and scientists are working to address these issues, and significant progress has been achieved, but it's challenging to deal with and mitigate without adequately understanding the different chemical processes and the mobilization mechanism of these hazardous chemicals.

Spin-Controlled Helical Quantum Sieve Chiral Spectrometer.

This article reports on a molecular-spin-sensitive-antenna (MSSA) that is based on stacked layers of organically functionalized graphene on a fibrous helical cellulose network for carrying out spatiotemporal identification of chiral enantiomers. The MSSA structures combine three complementary features: (i) chiral separation via a helical quantum sieve for chiral trapping, (ii) chiral recognition by a synthetically implanted spin-sensitive center in a graphitic lattice; and (iii) chiral selectivity by a chirality-induced-spin mechanism that polarizes the local electronic band-structure in graphene through chiral-activated Rashba spin-orbit interaction field. Combining the MSSA structures with decision-making principles based on neuromorphic artificial intelligence shows fast, portable, and wearable spectrometry for the detection and classification of pure and a mixture of chiral molecules, such as butanol (S and R), limonene (S and R), and xylene isomers, with 95-98% accuracy.

Gate-Controlled Chiral Recognition and Spin Assessment with All-Electric Hybrid Quantum Wire-Based Transistors.

The search for inexpensive and all-electric tunable methods for portable and fast recognition and discrimination between various chiral enantiomers, mainly those found in the gas phase, has been one of the most challenging tasks in the field of analytical chemistry. The current article reports on a chiral sensitive electric architecture (CSEA) of a helical polyaniline (PANI)@carbon nanotube (CNT) hybrid quantum-wire based field effect transistor (FET) platform. The CSEA architecture exhibits gate-controlled-channel-chirality modulation for the selective distinction of Limonene (S(+)/R(-)) at ≈12 V intervals.

Maternal Mediterranean Diet Adherence and Its Associations with Maternal Prenatal Stressors and Child Growth.

Background: Psychosocial and physiologic stressors, such as depression and obesity, during pregnancy can have negative consequences, such as increased systemic inflammation, contributing to chronic disease for both mothers and their unborn children. These conditions disproportionately affect racial/ethnic minorities. The effects of recommended dietary patterns in mitigating the effects of these stressors remain understudied.

Adaptive Bayesian variable clustering via structural learning of breast cancer data.

The clustering of proteins is of interest in cancer cell biology. This article proposes a hierarchical Bayesian model for protein (variable) clustering hinging on correlation structure. Starting from a multivariate normal likelihood, we enforce the clustering through prior modeling using angle-based unconstrained reparameterization of correlations and assume a truncated Poisson distribution (to penalize a large number of clusters) as prior on the number of clusters.

Ultra-Fast Portable and Wearable Sensing Design for Continuous and Wide-Spectrum Molecular Analysis and Diagnostics.

The design and characterization of spatiotemporal nano-/micro-structural arrangement that enable real-time and wide-spectrum molecular analysis is reported and demonestrated in new horizons of biomedical applications, such as wearable-spectrometry, ultra-fast and onsite biopsy-decision-making for intraoperative surgical oncology, chiral-drug identification, etc. The spatiotemporal sesning arrangement is achieved by scalable, binder-free, functionalized hybrid spin-sensitive (<↑| or <↓|) graphene-ink printed sensing layers on free-standing films made of porous, fibrous, and naturally helical cellulose networks in hierarchically stacked geometrical configuration (HSGC). The HSGC operates according to a time-space-resolved architecture that modulate the mass-transfer rate for separation, eluation and detection of each individual compound within a mixture of the like, hereby providing a mass spectrogram.

Inference in Functional Linear Quantile Regression.

In this paper, we study statistical inference in functional quantile regression for scalar response and a functional covariate. Specifically, we consider a functional linear quantile regression model where the effect of the covariate on the quantile of the response is modeled through the inner product between the functional covariate and an unknown smooth regression parameter function that varies with the level of quantile. The objective is to test that the regression parameter is constant across several quantile levels of interest.

Circadian Gene Selection for Time-to-event Phenotype by Integrating CNV and RNAseq Data.

Background: The endogenous circadian clock, which controls daily rhythms in the expression of at least half of the mammalian genome, has a major influence on cell physiology. Consequently, disruption of the circadian system is associated with wide range of diseases including cancer. While several circadian clock genes have been associated with cancer progression, little is known about the survival when two or more platforms are considered together.

Variable selection in nonlinear function-on-scalar regression.

We develop a new method for variable selection in a nonlinear additive function-on-scalar regression (FOSR) model. Existing methods for variable selection in FOSR have focused on the linear effects of scalar predictors, which can be a restrictive assumption in the presence of multiple continuously measured covariates. We propose a computationally efficient approach for variable selection in existing linear FOSR using functional principal component scores of the functional response and extend this framework to a nonlinear additive function-on-scalar model.

Stretchable and Highly Permeable Nanofibrous Sensors for Detecting Complex Human Body Motion.

Wearable strain sensors have been attracting special attention in the detection of human posture and activity, as well as for the assessment of physical rehabilitation and kinematics. However, it is a challenge to fabricate stretchable and comfortable-to-wear permeable strain sensors that can provide highly accurate and continuous motion recording while exerting minimal constraints and maintaining low interference with the body. Herein, covalently grafting nanofibrous polyaniline (PANI) onto stretchable elastomer nanomeshes is reported to obtain a freestanding ultrathin (varying from 300 to 10 000 nm) strain sensor that has high gas permeability (10-33 mg h ).

Bayesian Criterion Based Variable Selection.

Bayesian approaches for criterion based selection include the marginal likelihood based highest posterior model (HPM) and the deviance information criterion (DIC). The DIC is popular in practice as it can often be estimated from sampling based methods with relative ease and DIC is readily available in various Bayesian software. We find that sensitivity of DIC based selection can be high, in the range of 90 - 100%.

Integration of Survival and Binary Data for Variable Selection and Prediction: A Bayesian Approach.

We consider the problem where the data consist of a survival time and a binary outcome measurement for each individual, as well as corresponding predictors. The goal is to select the common set of predictors which affect both the responses, and not just only one of them. In addition, we develop a survival prediction model based on data integration.

Joint modeling of longitudinal continuous, longitudinal ordinal, and time-to-event outcomes.

In this paper, we propose an innovative method for jointly analyzing survival data and longitudinally measured continuous and ordinal data. We use a random effects accelerated failure time model for survival outcomes, a linear mixed model for continuous longitudinal outcomes and a proportional odds mixed model for ordinal longitudinal outcomes, where these outcome processes are linked through a set of association parameters. A primary objective of this study is to examine the effects of association parameters on the estimators of joint models.

A Dinickel Catalyzed Cyclopropanation without the Formation of a Metal Carbene Intermediate.

(NDI)Ni catalysts (NDI=naphthyridine-diimine) promote cyclopropanation reactions of 1,3-dienes using (Me Si)CHN . Mechanistic studies reveal that a metal carbene intermediate is not part of the catalytic cycle. The (NDI)Ni (CHSiMe ) complex was independently synthesized and found to be unreactive toward dienes.

Bayesian structural equation modeling in multiple omics data with application to circadian genes.

Motivation: It is well known that the integration among different data-sources is reliable because of its potential of unveiling new functionalities of the genomic expressions, which might be dormant in a single-source analysis. Moreover, different studies have justified the more powerful analyses of multi-platform data. Toward this, in this study, we consider the circadian genes' omics profile, such as copy number changes and RNA-sequence data along with their survival response.

Robust kernel association testing (RobKAT).

Testing the association between single-nucleotide polymorphism (SNP) effects and a response is often carried out through kernel machine methods based on least squares, such as the sequence kernel association test (SKAT). However, these least-squares procedures are designed for a normally distributed conditional response, which may not apply. Other robust procedures such as the quantile regression kernel machine (QRKM) restrict the choice of the loss function and only allow inference on conditional quantiles.

Sensitive field-effect transistor sensors with atomically thin black phosphorus nanosheets.

Atomically thin black phosphorus (BP) field-effect transistors have excellent potential for sensing applications. However, commercial scaling of PFET sensors is still in the early stage due to various technical challenges, such as tedious fabrication, low response% caused by rapid oxidation, non-ideal response output (spike/bidirectional), and large device variation due to poor control over layer thickness among devices. Attempts have been made to address these issues.