Prevalence of PI*Z and PI*S alleles of alpha-1-antitrypsin deficiency: A Study on Hardy-Weinberg Equilibrium

The α1-Antitrypsin (AT) functions as the primary serum inhibitor of proteolytic enzymes, including neutrophil elastase. The presence of these abnormal alleles disrupts the normal functioning of α1-Antitrypsin (AT), leading to Alpha-1 Antitrypsin Deficiency (AATD). Homozygotes for type Z experience a substantial reduction in serum AT concentration, increasing the risk of pulmonary emphysema or hepatic cirrhosis. Homozygous S alleles and heterozygotes of type SZ exhibit a less severe reduction in serum AT concentration. Clinicians suggest that even though the reduction is less severe, it may still lead to Mild AAT Deficiency. The primary objective of this study was to enhance our understanding of the frequency of the Z and S alleles, enabling accurate estimates of the prevalence and count of PiZZ and PiSS genotypes within the state of Tennessee, USA. We present the analysis of data collected from the Tennessee population and screened following ATS guidelines. We study the prevalence of homozygote (ZZ) AAT, heterozygote AAT deficiency, and the relationship to lung function, and potential risk of Chronic Obstructive pulmonary Disease (COPD) in hospital admission, readmission and all-cause mortality. The findings presented here may serve as a valuable resource for healthcare professionals, researchers, and policymakers involved in respiratory health and genetic disorders

Performance testing of a cross-flow membrane-based liquid desiccant dehumidification system

A membrane-based liquid desiccant dehumidification system is one of high energy efficient dehumidification approaches, which allows heat and moisture transfers between air stream and desiccant solution without carryover problem. The system performance is investigated experimentally with calcium chloride, and the impacts of main operating parameters on dehumidification effectiveness (i.e. sensible, latent and total effectiveness) are evaluated, which include dimensionless parameters (i.e. solution to air mass flow rate ratio m∗ and number of heat transfer units NTU) and solution properties (i.e. concentration Csol and inlet temperature Tsol,in). The sensible, latent and total effectiveness reach the maximum values of 0.49, 0.55, and 0.53 respectively at m∗= 3.5 and NTU = 12, and these effectiveness are not limited by m∗ and NTU when m∗ > 2 and NTU > 10. Both the latent and total effectiveness increase with Csol , while almost no variation is observed in the sensible effectiveness. All effectiveness can be improved by decreasing Tsol,in. The experimental data provide a full map of main design parameters for the membrane-based liquid desiccant air conditioning technology

DiffMimic: Efficient Motion Mimicking with Differentiable Physics

Motion mimicking is a foundational task in physics-based character animation. However, most existing motion mimicking methods are built upon reinforcement learning (RL) and suffer from heavy reward engineering, high variance, and slow convergence with hard explorations. Specifically, they usually take tens of hours or even days of training to mimic a simple motion sequence, resulting in poor scalability. In this work, we leverage differentiable physics simulators (DPS) and propose an efficient motion mimicking method dubbed DiffMimic. Our key insight is that DPS casts a complex policy learning task to a much simpler state matching problem. In particular, DPS learns a stable policy by analytical gradients with ground-truth physical priors hence leading to significantly faster and stabler convergence than RL-based methods. Moreover, to escape from local optima, we utilize a Demonstration Replay mechanism to enable stable gradient backpropagation in a long horizon. Extensive experiments on standard benchmarks show that DiffMimic has a better sample efficiency and time efficiency than existing methods (e.g., DeepMimic). Notably, DiffMimic allows a physically simulated character to learn Backflip after 10 minutes of training and be able to cycle it after 3 hours of training, while the existing approach may require about a day of training to cycle Backflip. More importantly, we hope DiffMimic can benefit more differentiable animation systems with techniques like differentiable clothes simulation in future research.Comment: ICLR 2023 Code is at https://github.com/jiawei-ren/diffmimic Project page is at https://diffmimic.github.io

Cloth2Body: Generating 3D Human Body Mesh from 2D Clothing

In this paper, we define and study a new Cloth2Body problem which has a goal of generating 3D human body meshes from a 2D clothing image. Unlike the existing human mesh recovery problem, Cloth2Body needs to address new and emerging challenges raised by the partial observation of the input and the high diversity of the output. Indeed, there are three specific challenges. First, how to locate and pose human bodies into the clothes. Second, how to effectively estimate body shapes out of various clothing types. Finally, how to generate diverse and plausible results from a 2D clothing image. To this end, we propose an end-to-end framework that can accurately estimate 3D body mesh parameterized by pose and shape from a 2D clothing image. Along this line, we first utilize Kinematics-aware Pose Estimation to estimate body pose parameters. 3D skeleton is employed as a proxy followed by an inverse kinematics module to boost the estimation accuracy. We additionally design an adaptive depth trick to align the re-projected 3D mesh better with 2D clothing image by disentangling the effects of object size and camera extrinsic. Next, we propose Physics-informed Shape Estimation to estimate body shape parameters. 3D shape parameters are predicted based on partial body measurements estimated from RGB image, which not only improves pixel-wise human-cloth alignment, but also enables flexible user editing. Finally, we design Evolution-based pose generation method, a skeleton transplanting method inspired by genetic algorithms to generate diverse reasonable poses during inference. As shown by experimental results on both synthetic and real-world data, the proposed framework achieves state-of-the-art performance and can effectively recover natural and diverse 3D body meshes from 2D images that align well with clothing.Comment: ICCV 2023 Poste

Performance of a new Candida anti-mannan IgM and IgG assays in the diagnosis of candidemia

Candida is one of the most frequent pathogens of bloodstream infections, which is associated with high morbidity and mortality rates. Rapid immunological detection methods are essential in the early diagnosis of candidemia. Anti-mannan is one of host-derived biomarkers against cell wall components of Candida. We conducted this study to evaluate the diagnostic performance of two anti-mannan assays (IgM, IgG) for candidemia through the analysis of 40 candidemia patients, 48 participants with Candida colonization and 213 participants with neither Candida colonization nor Candida infections (13 patients with other bloodstream infections, 145 hospitalized patients and 55 healthy controls). The performance of the two assays were evaluated by calculating their sensitivity and specificity. The sensitivity ranged from 0.78 to 0.80 for the IgM assay and 0.68 to 0.75 for the IgG assay. The specificity ranged from 0.97 to 0.98 for the IgM assay and 0.91 to 0.94 for the IgG assay. The diagnostic performance of the anti-mannan IgM assay was better than that of IgG, with higher sensitivity and specificity. Combining the two assays (positive results of single or both assays are both considered as positive) could improve the sensitivity up to 0.93 (0.79-0.98) and only slightly reduce the specificity (0.93(0.89-0.95)). The anti-mannan IgM, IgG assays are rapid and cost-effective assays that may be probably useful in the diagnosis of candidemia

Application of Low Voltage Treatment Device Based on Electrochemical Energy Storage

With the rapid development of social economy, the power supply capacity of the existing power grid has been far from the increasing load demand, especially the summer (winter) peak period. The problems such as heavy overload of power grid equipment and low voltage of line are extremely prominent and seriously affects the safe and stable operation of the power grid. This problem is more serious in the rural power grid. In order to increase the progress, shorten the construction cost of upgrading and transformation of the traditional power grid, a technical scheme is proposed in this paper by using electrochemical energy storage technology to solve the overload and low voltage problems of local power grid equipment, and obvious results have been achieved through pilot project

Class-Specific Attention (CSA) for Time-Series Classification

Most neural network-based classifiers extract features using several hidden layers and make predictions at the output layer by utilizing these extracted features. We observe that not all features are equally pronounced in all classes; we call such features class-specific features. Existing models do not fully utilize the class-specific differences in features as they feed all extracted features from the hidden layers equally to the output layers. Recent attention mechanisms allow giving different emphasis (or attention) to different features, but these attention models are themselves class-agnostic. In this paper, we propose a novel class-specific attention (CSA) module to capture significant class-specific features and improve the overall classification performance of time series. The CSA module is designed in a way such that it can be adopted in existing neural network (NN) based models to conduct time series classification. In the experiments, this module is plugged into five start-of-the-art neural network models for time series classification to test its effectiveness by using 40 different real datasets. Extensive experiments show that an NN model embedded with the CSA module can improve the base model in most cases and the accuracy improvement can be up to 42%. Our statistical analysis show that the performance of an NN model embedding the CSA module is better than the base NN model on 67% of MTS and 80% of UTS test cases and is significantly better on 11% of MTS and 13% of UTS test cases.Comment: 12 page

Logic-Scaffolding: Personalized Aspect-Instructed Recommendation Explanation Generation using LLMs

The unique capabilities of Large Language Models (LLMs), such as the natural language text generation ability, position them as strong candidates for providing explanation for recommendations. However, despite the size of the LLM, most existing models struggle to produce zero-shot explanations reliably. To address this issue, we propose a framework called Logic-Scaffolding, that combines the ideas of aspect-based explanation and chain-of-thought prompting to generate explanations through intermediate reasoning steps. In this paper, we share our experience in building the framework and present an interactive demonstration for exploring our results.Comment: The 17th ACM International Conference on Web Search and Data Mining (WSDM 2024