Functional Form and Spatial Dependence in Dynamic Panels

Wharton-SMU Research Centr

CCDWT-GAN: Generative Adversarial Networks Based on Color Channel Using Discrete Wavelet Transform for Document Image Binarization

To efficiently extract the textual information from color degraded document images is an important research topic. Long-term imperfect preservation of ancient documents has led to various types of degradation such as page staining, paper yellowing, and ink bleeding; these degradations badly impact the image processing for information extraction. In this paper, we present CCDWT-GAN, a generative adversarial network (GAN) that utilizes the discrete wavelet transform (DWT) on RGB (red, green, blue) channel splited images. The proposed method comprises three stages: image preprocessing, image enhancement, and image binarization. This work conducts comparative experiments in the image preprocessing stage to determine the optimal selection of DWT with normalization. Additionally, we perform an ablation study on the results of the image enhancement stage and the image binarization stage to validate their positive effect on the model performance. This work compares the performance of the proposed method with other state-of-the-art (SOTA) methods on DIBCO and H-DIBCO ((Handwritten) Document Image Binarization Competition) datasets. The experimental results demonstrate that CCDWT-GAN achieves a top two performance on multiple benchmark datasets, and outperforms other SOTA methods

Case Report: The Clinical Toxicity of Dimethylamine Borane

Context: Dimethylamine borane (DMAB) is a reducing agent used in nonelectric plating of semiconductors. Exposures are usually through occupational contact. We report here four cases of people who suffered from work-related exposure to DMAB. Case presentation: Three patients exposed to DMAB decontaminated immediately by drinking a lot of water; they reported dizziness, nausea, diarrhea 6–8 hr later. The other patient did not decontaminate at once, and he suffered from more severe symptoms, including dizziness, nausea, limb numbness, slurred speech, irritable mood, and ataxia 13 hr later. Magnetic resonance imaging showed symmetric lesions with hyperintensity on T2WI and FLAIR in bilateral cerebellar dantate nuclei. This patient was readmitted to the hospital due to difficulty in walking and climbing 18 days after exposure. Lower leg weakness and drop foot were found bilaterally. A nerve conduction study revealed polyneuropathy with motor-predominant axonal degeneration. This patient receives regular outpatient followups and still walks with a clumsy gait and has difficulty with hand-grasping activity. Discussion: This case study demonstrates that DMAB is highly toxic to humans through any route of exposure, and dermal absorption is the major route of neurotoxicity. DMAB induces acute cortical and cerebellar injuries and delayed peripheral neuropathy. Relevance: Further investigation of the toxic mechanism of DMAB is warranted. Early decontamination with copious water is the best current treatment for exposure to DMAB

Towards General-Purpose Text-Instruction-Guided Voice Conversion

This paper introduces a novel voice conversion (VC) model, guided by text instructions such as "articulate slowly with a deep tone" or "speak in a cheerful boyish voice". Unlike traditional methods that rely on reference utterances to determine the attributes of the converted speech, our model adds versatility and specificity to voice conversion. The proposed VC model is a neural codec language model which processes a sequence of discrete codes, resulting in the code sequence of converted speech. It utilizes text instructions as style prompts to modify the prosody and emotional information of the given speech. In contrast to previous approaches, which often rely on employing separate encoders like prosody and content encoders to handle different aspects of the source speech, our model handles various information of speech in an end-to-end manner. Experiments have demonstrated the impressive capabilities of our model in comprehending instructions and delivering reasonable results.Comment: Accepted to ASRU 202

A model for predicting physical function upon discharge of hospitalized older adults in Taiwan—a machine learning approach based on both electronic health records and comprehensive geriatric assessment

BackgroundPredicting physical function upon discharge among hospitalized older adults is important. This study has aimed to develop a prediction model of physical function upon discharge through use of a machine learning algorithm using electronic health records (EHRs) and comprehensive geriatrics assessments (CGAs) among hospitalized older adults in Taiwan.MethodsData was retrieved from the clinical database of a tertiary medical center in central Taiwan. Older adults admitted to the acute geriatric unit during the period from January 2012 to December 2018 were included for analysis, while those with missing data were excluded. From data of the EHRs and CGAs, a total of 52 clinical features were input for model building. We used 3 different machine learning algorithms, XGBoost, random forest and logistic regression.ResultsIn total, 1,755 older adults were included in final analysis, with a mean age of 80.68 years. For linear models on physical function upon discharge, the accuracy of prediction was 87% for XGBoost, 85% for random forest, and 32% for logistic regression. For classification models on physical function upon discharge, the accuracy for random forest, logistic regression and XGBoost were 94, 92 and 92%, respectively. The auROC reached 98% for XGBoost and random forest, while logistic regression had an auROC of 97%. The top 3 features of importance were activity of daily living (ADL) at baseline, ADL during admission, and mini nutritional status (MNA) during admission.ConclusionThe results showed that physical function upon discharge among hospitalized older adults can be predicted accurately during admission through use of a machine learning model with data taken from EHRs and CGAs

Intrinsic Correlation between Hardness and Elasticity in Polycrystalline Materials and Bulk Metallic Glasses

Though extensively studied, hardness, defined as the resistance of a material to deformation, still remains a challenging issue for a formal theoretical description due to its inherent mechanical complexity. The widely applied Teter's empirical correlation between hardness and shear modulus has been considered to be not always valid for a large variety of materials. Here, inspired by the classical work on Pugh's modulus ratio, we develop a theoretical model which establishes a robust correlation between hardness and elasticity for a wide class of materials, including bulk metallic glasses, with results in very good agreement with experiment. The simplified form of our model also provides an unambiguous theoretical evidence for Teter's empirical correlation.Comment: 10 pages, 4 figures and 3 table

Phenanthrene-Based Tylophorine-1 (PBT-1) Inhibits Lung Cancer Cell Growth through the Akt and NF-κB Pathways

Tylophorine and related natural compounds exhibit potent antitumor activities. We previously showed that PBT-1, a synthetic C9-substituted phenanthrene-based tylophorine (PBT) derivative, significantly inhibits growth of various cancer cells. In this study, we further explored the mechanisms and potential of PBT-1 as an anticancer agent. PBT-1 dose-dependently suppressed colony formation, induced cell cycle G2/M arrest and apoptosis. DNA microarray and pathway analysis showed that PBT-1 activated the apoptosis pathway and mitogen-activated protein kinase signaling. In contrast, PBT-1 suppressed the nuclear factor kappaB (NF-κB) pathway and focal adhesion. We further confirmed that PBT-1 suppressed Akt activation accelerated RelA degradation via IκB kinase-α, and downregulated NF-κB target gene expression. The reciprocal recruitment of RelA and RelB on COX-2 promoter region led to downregulation of transcriptional activity. We conclude that PBT-1 induces cell cycle G2/M arrest and apoptosis by inactivating Akt and by inhibiting the NF-κB signaling pathway. PBT-1 may be a good drug candidate for anticancer chemotherapy

Faraday rotation in graphene

We study magneto--optical properties of monolayer graphene by means of quantum field theory methods in the framework of the Dirac model. We reveal a good agreement between the Dirac model and a recent experiment on giant Faraday rotation in cyclotron resonance. We also predict other regimes when the effects are well pronounced. The general dependence of the Faraday rotation and absorption on various parameters of samples is revealed both for suspended and epitaxial graphene.Comment: 10 pp; v2: typos corrected and references added, v3, v4: small changes and more reference

Discovery of an orally active benzoxaborole prodrug effective in the treatment of Chagas disease in non-human primates

Trypanosoma cruzi, the agent of Chagas disease, probably infects tens of millions of people, primarily in Latin America, causing morbidity and mortality. The options for treatment and prevention of Chagas disease are limited and underutilized. Here we describe the discovery of a series of benzoxaborole compounds with nanomolar activity against extra- and intracellular stages of T. cruzi. Leveraging both ongoing drug discovery efforts in related kinetoplastids, and the exceptional models for rapid drug screening and optimization in T. cruzi, we have identified the prodrug AN15368 that is activated by parasite carboxypeptidases to yield a compound that targets the messenger RNA processing pathway in T. cruzi. AN15368 was found to be active in vitro and in vivo against a range of genetically distinct T. cruzi lineages and was uniformly curative in non-human primates (NHPs) with long-term naturally acquired infections. Treatment in NHPs also revealed no detectable acute toxicity or long-term health or reproductive impact. Thus, AN15368 is an extensively validated and apparently safe, clinically ready candidate with promising potential for prevention and treatment of Chagas disease

One pot electrochemical synthesis of poly(melamine) entrapped gold nanoparticles composite for sensitive and low level detection of catechol

A simple and cost effective synthesis of nanomaterials with advanced physical and chemical properties have received much attention to the researchers, and is of interest to the researchers from different disciplines. In the present work, we report a simple and one pot electrochemical synthesis of poly(melamine) entrapped gold nanoparticles (PM-AuNPs) composite. The PM-AuNPs composite was prepared by a single step electrochemical method, wherein the AuNPs and PM were simultaneously fabricated on the electrode surface. The as-prepared materials were characterized by various physicochemical methods. The PM-AuNPs composite modified electrode was used as an electrocatalyst for oxidation of catechol (CC) due to its well-defined redox behavior and enhanced electro-oxidation ability towards CC than other modified electrodes. Under optimized conditions, the differential pulse voltammetry (DPV) was used for the determination of CC. The DPV response of CC was linear over the concentration ranging from 0.5 to 175.5 μM with a detection limit of 0.011 μM. The PM-AuNPs composite modified electrode exhibits the high selectivity in the presence of range of potentially interfering compounds including dihydroxybenzene isomers. The sensor shows excellent practicality in CC containing water samples, which reveals the potential ability of PM-AuNPs composite modified electrode towards the determination of CC in real samples