Miracle-dz/UniFAR-FTUs huggingface dataset:
An Online Inference-Aided Incentive Framework for Information Elicitation Without Verification

We study the design of incentive mechanisms for the problem of information elicitation without verification (IEWV). In IEWV, a data requester seeks to design proper incentives to optimize the tradeoff between the quality of information (collected from distributed crowd workers) and the total cost of incentives (provided to crowd workers) without verifiable ground truth. While prior work often relies on sufficient knowledge of worker information, we study a scenario where the data requester cannot access workers’ heterogeneous information quality and costs ex-ante. We propose a continuum-armed bandit-based incentive mechanism that dynamically learns the optimal reward level from workers’ reported information. A key challenge is that the data requester cannot evaluate the workers’ information quality without verification, which motivates the design of an inference algorithm. The inference problem is non-convex, yet we reformulate it as a bi-convex problem and derive an approximate solution with a performance guarantee, which ensures the effectiveness of our online reward design. We further enhance the inference algorithm using part of the workers’ historical reports. We also propose a novel rule for the data requester to aggregate workers’ solutions more effectively. We show that our mechanism achieves a sub-linear regret $\tilde {O}(T^{1/2})$ and outperforms several celebrated benchmarks.
Greenhouse gas radiative forcing: Effects of averaging and inhomogeneities in trace gas distribution

Radiative forcing is a useful diagnostic quantity for indicating the size of climate change mechanisms, and for interpreting the results of General Circulation Model (GCM) experiments. It is also ideal for exploring the potential sensitivity of climate change simulations to assumptions regarding, for example, the horizontal and vertical distribution of changes in greenhouse gases, as a guide to whether these effects need to be incorporated into GCMs. This paper examines this issue for a range of greenhouse gases, and also examines the dependence of the forcing on horizontal averaging and the definition of tropopause position.
Disturbance Rejection for Stewart Platform Based on Integration of Equivalent-Input- Disturbance and Sliding-Mode Control Methods

This study integrated an improved equivalent-input-disturbance (IEID) and a sliding-mode control (SMC) methods to ensure reference tracking and enhance disturbance-rejection performance for a Stewart platform. The internal principle ensures steady-state tracking of the system. A sliding-mode controller enhances the disturbance-rejection performance and tracking accuracy. The effects of the external disturbances are regarded as an overall disturbance. Then, a state observer and an IEID estimator estimate and compensate for the overall disturbance. The chattering phenomenon when implementing the SMC method is reduced because a small sliding-mode gain ensures the tracking precision in this control method. A stability condition of the closed-loop system is analyzed based on the Lyapunov stability theory. Gains of the IEID estimator and the state observer are designed by a linear matrix inequality that ensures the stability of the system. This method has been verified on an actual Stewart platform. Experimental results show that our method has better disturbance-rejection performance than an SMC method and an IEID method under external disturbances.
Sequence-Structure Embeddings via Protein Language Models Improve on Prediction Tasks

Building on the transformer architecture and its revolutionizing of language models for natural language processing, protein language models (PLMs) are now emerging as a powerful tool for learning over large numbers of sequences in protein sequence databases and linking protein sequence to function. PLMs are shown to learn useful, task-agnostic sequence representations that allow predicting protein secondary structure, protein subcellular localization, and evolutionary relationships within protein families. However, existing models are strictly trained over protein sequences and miss an opportunity to leverage and integrate the information present in heterogeneous data sources. In this paper, inspired by the intrinsic role of three-dimensional/tertiary protein structure in determining a broad range of protein properties, we propose a PLM that integrates and attends to both protein sequence and tertiary structure. In particular, this paper posits that learning joint sequence-structure representations yields better representations for function-related prediction tasks. A detailed experimental evaluation shows that such joint sequence-structure representations are more powerful than sequence-based representations, yield better performance on superfamily membership across various metrics, and capture interesting relationships in the PLM-learned embedding space.
Modulatory Effect of Lifestyle-Related, Environmental and Genetic Factors on Paraoxonase-1 Activity: A Review

Paraoxonase-1 (PON1) is a calcium-dependent, HDL-bound serum hydrolase active toward a wide variety of substrates. PON1 displays three types of activities, among which lactonase, paraoxonase, arylesterase and phosphotriesterase can be distinguished. Not only is this enzyme a major organophosphate compound detoxifier, but it is also an important constituent of the cellular antioxidant system and has anti-inflammatory and antiatherogenic functions. The concentration and activity of PON1 is highly variable among individuals, and these differences can be both of genetic origin and be a subject of epigenetic regulation. Owing to the fact that, in recent decades, the exposure of humans to an increasing number of different xenobiotics has been continuously rising, the issues concerning the role and activity of PON1 shall be reconsidered with particular attention to growing pharmaceuticals intake, dietary habits and environmental awareness. In the following manuscript, the current state of knowledge concerning the influence of certain modifiable and unmodifiable factors, including smoking, alcohol intake, gender, age and genotype variation on PON1 activity, along with pathways through which these could interfere with the enzyme’s protective functions, is presented and discussed. Since exposure to certain xenobiotics plays a key role in PON1 activity, the influence of organophosphates, heavy metals and several pharmaceutical agents is also specified.
How technostressors influence job and family satisfaction: Exploring the role of work–family conflict

Recent developments in information and communication technology have blurred the line between the workplace and the home. This can have a negative influence on employees' well‐being and thus has gained increasing attention from academics and practitioners. In this study, we developed a research model based on the transactional perspective of stress and the challenge–hindrance stressor framework. We defined the two dimensions of work–family conflict as the perceptual stress resulting from a chronic challenge and hindrance technostressors, which ultimately affect employees' satisfaction in both the work and family domains. We tested our model using a three‐wave time‐lagged survey study with data collected from 268 employees. Challenge and hindrance technostressors had different effects on these two main forms of work–family conflict (time‐based and strain‐based) but further induced negative effects on both job and family satisfaction. Overall, we make both scientific and practical contributions to the fields of work‐related technology use and work–family conflict.
The Prevalence of HPV in Oral Cavity Squamous Cell Carcinoma

Human papillomavirus (HPV) is an important risk factor in a subset of head and neck squamous cell carcinomas (HNSCC), but the association with oral cavity squamous cell carcinomas (OCSCC) remains controversial. This study aimed to identify the prevalence of HPV infection in OCSCC. A systematic search on PubMed and EMBASE was performed, including articles assessing the prevalence of HPV-positive (HPV+) OCSCC published from January 2017 to December 2022. OCSCC was considered HPV+ by the detection of HPV DNA, HPV RNA, and/or p16 overexpression in the tumor mass. A meta-analysis was made determining the overall HPV+ OCSCC prevalence. We included 31 studies comprising 5007 patients from 24 countries. The study size ranged from 17 to 940 patients. The HPV+ OCSCC proportion variated widely and ranged from 0% to 37%. Tumors in the tongue were the predominant sublocation for HPV in the oral cavity. The meta-analysis revealed that the overall HPV+ OCSCC prevalence is 6% (95% CI; 3–10%), and only one study found HPV and OCSCC significantly associated. Thus, HPV may not be a necessary or a strong risk factor in OCSCC oncogenesis, and the possibility of a site misclassification of a mobile tongue with the root of the tongue cannot be excluded.
Biodegradation of Aflatoxin B1 in Maize Grains and Suppression of Its Biosynthesis-Related Genes Using Endophytic Trichoderma harzianum AYM3

Aflatoxin B1 is one of the most deleterious types of mycotoxins. The application of an endophytic fungus for biodegradation or biosuppression of AFB1 production by Aspergillus flavus was investigated. About 10 endophytic fungal species, isolated from healthy maize plants, were screened for their in vitro AFs-degrading activity using coumarin medium. The highest degradation potential was recorded for Trichoderma sp. (76.8%). This endophyte was identified using the rDNA-ITS sequence as Trichoderma harzianum AYM3 and assigned an accession no. of ON203053. It caused a 65% inhibition in the growth of A. flavus AYM2 in vitro. HPLC analysis revealed that T. harzianum AYM3 had a biodegradation potential against AFB1. Co-culturing of T. harazianum AYM3 and A. flavus AYM2 on maize grains led to a significant suppression (67%) in AFB1 production. GC-MS analysis identified two AFB1-suppressing compounds, acetic acid and n-propyl acetate. Investigating effect on the transcriptional expression of five AFB1 biosynthesis-related genes in A. flavus AYM2 revealed the downregulating effects of T. harzianum AYM3 metabolites on expression of aflP and aflS genes. Using HepaRG cell line, the cytotoxicity assay indicated that T. harazianum AYM3 metabolites were safe. Based on these results, it can be concluded that T. harzianum AYM3 may be used to suppress AFB1 production in maize grains.
Depolymerization of Heparin Benzyl Ester in a Flow System

: The preparation of enoxaparin via the base-promoted depolymerization of heparin is well known. In this paper, we present our efforts to convert this preparation into a ﬂow method. Streams of base and ester solutions are combined and heated in a ﬂow reactor, and the variations in ﬂow rate, temperature, and residence time were examined. NMR analysis demonstrated that careful control of the reaction parameters affords enoxaparin-like materials. An analysis was carried out by NMR spectroscopy and the optimum conditions were determined
A Compact Low-Profile Vertically Polarized Endfire Leaky-Wave Antenna Based on Double-Sided Parallel Strip Line

This article presents a vertically polarized (VP) endfire leaky-wave antenna (LWA) on a low-profile substrate by loading a series of shorting pins and slots along the double-sided parallel strip line (DSPSL). The periodically loaded shorting pins act as the VP radiator elements of the antenna, and also have the function to make the normalized phase constant of the original DSPSL equal to one so as to meet the endfire radiation condition. A series of slots etched on strip conductor around shorting pins are utilized to improve the radiation efficiency of the antenna in addition to its vertical polarization. In this way, the VP endfire LWA can be eventually realized with efficient radiation on a low-profile substrate. In our design, the mechanism of the proposed antenna is first analyzed based on the equivalent circuit model to investigate the effects of pins and slots on the antenna operation. Then, the attenuation properties of the antenna are detailly discussed under various structural dimensions to explore the relation between its physical sizes and radiation capability. Finally, a prototype is designed and fabricated to verify the predicted properties. The measured results match well with the simulated ones, thus demonstrating and confirming that the proposed endfire LWA can indeed realize efficient radiation with vertical polarization based on a single low-profile substrate.
Carbon in the Envelopes of White Dwarfs

Current theory of stellar evolution predicts that stars of initial masses up to 4-6 M evolve into Carbon-Oxygen White Dwarfs surrounded by a Helium envelope and, possibly, by a Hydrogen envelope. It also predicts that the mass of the Helium envelope which remains on the star at the end of its double shell burning evolution is a function of the Carbon-Oxygen core mass (Paczynski 1975). It can be shown that this mass can be reduced but only slightly during the following evolution of the star towards the White Dwarf region,either by nuclear burning or by mass loss (D'Antona and Mazzitelli 1979). During the White Dwarf stage, Helium convection grows into White Dwarfs having Helium atmospheres. The maximum extension of Helium convective mass is a function of the mass of the star (Fontaine and Van Horn 1976; D'Antona and Mazzitelli 1975,1979). It turns out that the Helium envelope remnant mass is always at least three orders of magnitude larger than the maximum Helium convective mass, whatever the mass of the star may be. This statement is unlikely to be changed by refinements either in the theory of double shell burning or in the theory of White Dwarf envelope convection.
Tracking Population-Level Anxiety Using Search Engine Data: Ecological Study

Background Anxiety disorders are the most prevalent mental disorders globally, with a substantial impact on quality of life. The prevalence of anxiety disorders has increased substantially following the COVID-19 pandemic, and it is likely to be further affected by a global economic recession. Understanding anxiety themes and how they change over time and across countries is crucial for preventive and treatment strategies. Objective The aim of this study was to track the trends in anxiety themes between 2004 and 2020 in the 50 most populous countries with high volumes of internet search data. This study extends previous research by using a novel search-based methodology and including a longer time span and more countries at different income levels. Methods We used a crowdsourced questionnaire, alongside Bing search query data and Google Trends search volume data, to identify themes associated with anxiety disorders across 50 countries from 2004 to 2020. We analyzed themes and their mutual interactions and investigated the associations between countries’ socioeconomic attributes and anxiety themes using time-series linear models. This study was approved by the Microsoft Research Institutional Review Board. Results Query volume for anxiety themes was highly stable in countries from 2004 to 2019 (Spearman r=0.89) and moderately correlated with geography (r=0.49 in 2019). Anxiety themes were predominantly long-term and personal, with “having kids,” “pregnancy,” and “job” the most voluminous themes in most countries and years. In 2020, “COVID-19” became a dominant theme in 27 countries. Countries with a constant volume of anxiety themes over time had lower fragile state indexes (P=.007) and higher individualism (P=.003). An increase in the volume of the most searched anxiety themes was associated with a reduction in the volume of the remaining themes in 13 countries and an increase in 17 countries, and these 30 countries had a lower prevalence of mental disorders (P<.001) than the countries where no correlations were found. Conclusions Internet search data could be a potential source for predicting the country-level prevalence of anxiety disorders, especially in understudied populations or when an in-person survey is not viable.
Dynamic properties of three-dimensional speckles

Dynamic properties of three-dimensional speckles formed in free space on a diffuse object by an illuminating Gaussian beam have been investigated by evaluation of the space–time cross-correlation function. It has been found that the major axis of three-dimensional static speckles is always directed to the center of the beam spot on the object but that dynamic speckles are directed to a position other than the center according to the velocity of the object. Moreover, the motion of dynamic speckles is composed of two types: shifting and tilting.
FabNet: A Features Agglomeration-Based Convolutional Neural Network for Multiscale Breast Cancer Histopathology Images Classification

Simple Summary Histology sample images are usually diagnosed definitively based on the radiologist’s extensive knowledge, yet, owing to the highly gritty visual appearance of such images, specialists sometimes differ on their evaluations. Automating the image diagnostic process and decreasing the analysis time may be achieved via the use of advanced deep learning algorithms. Diagnostic objectivity may be improved with the use of more effective and accurate automated technologies by lessening the differences between the humans. In this research, we propose a CNN model architecture for cancer image classification by accumulating layers closer together to further merge the semantic and spatial features. Regarding precision, our suggested cutting-edge model improves upon the current state-of-the-art approaches. Abstract The definitive diagnosis of histology specimen images is largely based on the radiologist’s comprehensive experience; however, due to the fine to the coarse visual appearance of such images, experts often disagree with their assessments. Sophisticated deep learning approaches can help to automate the diagnosis process of the images and reduce the analysis duration. More efficient and accurate automated systems can also increase the diagnostic impartiality by reducing the difference between the operators. We propose a FabNet model that can learn the fine-to-coarse structural and textural features of multi-scale histopathological images by using accretive network architecture that agglomerate hierarchical feature maps to acquire significant classification accuracy. We expand on a contemporary design by incorporating deep and close integration to finely combine features across layers. Our deep layer accretive model structure combines the feature hierarchy in an iterative and hierarchically manner that infers higher accuracy and fewer parameters. The FabNet can identify malignant tumors from images and patches from histopathology images. We assessed the efficiency of our suggested model standard cancer datasets, which included breast cancer as well as colon cancer histopathology images. Our proposed avant garde model significantly outperforms existing state-of-the-art models in respect of the accuracy, F1 score, precision, and sensitivity, with fewer parameters.
Paper characterization by measurement of thickness and plate resonances using air-coupled ultrasound

Measurements of different types of paper (surface density from 80 gr/m/sup 2/ to 300 gr/m/sup 2/) using air-coupled, wide frequency band ultrasound and analysis in the frequency domain is presented. The working frequency range is 0.6-2.2 MHz. At normal incidence, thickness resonances in the paper samples are excited and sensed. From the analysis in the frequency domain, of such resonances, density, longitudinal velocity and attenuation of sound waves in the paper samples are obtained. At oblique incidence, Lamb waves are observed by use of Cremer's coincidence rule. The velocity of different Lamb modes is measured.
Elbow Joint Stiffness Functional Scales Based on Hill’s Muscle Model and Genetic Optimization

The ultimate goal of rehabilitation engineering is to provide objective assessment tools for the level of injury and/or the degree of neurorehabilitation recovery based on a combination of different sensing technologies that enable the monitoring of relevant measurable variables, as well as the assessment of non-measurable variables (such as muscle effort/force and joint mechanical stiffness). This paper aims to present a feasibility study for a general assessment methodology for subject-specific non-measurable elbow model parameter prediction and elbow joint stiffness estimation. Ten participants without sensorimotor disorders performed a modified “Reach and retrieve” task of the Wolf Motor Function Test while electromyography (EMG) data of an antagonistic muscle pair (the triceps brachii long head and biceps brachii long head muscle) and elbow angle were simultaneously acquired. A complete list of the Hill’s muscle model and passive joint structure model parameters was generated using a genetic algorithm (GA) on the acquired training dataset with a maximum deviation of 6.1% of the full elbow angle range values during the modified task 8 of the Wolf Motor Function Test, and it was also verified using two experimental test scenarios (a task tempo variation scenario and a load variation scenario with a maximum deviation of 8.1%). The recursive least square (RLS) algorithm was used to estimate elbow joint stiffness (Stiffness) based on the estimated joint torque and the estimated elbow angle. Finally, novel Stiffness scales (general patterns) for upper limb functional assessment in the two performed test scenarios were proposed. The stiffness scales showed an exponentially increasing trend with increasing movement tempo, as well as with increasing weights. The obtained general Stiffness patterns from the group of participants without sensorimotor disorders could significantly contribute to the further monitoring of motor recovery in patients with sensorimotor disorders.
Distributed Caregiving for Cognitively Impaired Individuals: A Case Report

Many caregivers of people with cognitive impairment spend a significant amount of their time helping patients with instrumental daily functions. Distributed caregiving is an innovative model designed to reduce an individual caregiver’s time burden and increase the likelihood of continued independent living for the patient. Echo Show and Google Home platforms were used to enable the participation of remote family members in caregiving, specifically the socialization and entertainment of a person with cognitive impairment. Caregiver interviews, review of medical records, and case study analysis were used to measure caregiver burden, after distributing some components of caregiving to distant family members with human-in-the-loop artificial intelligence. This case explores the use of Alexa, Echo Show, and other commercial technologies in the management of a patient with cognitive impairment. The human-in-the-loop system introduced in this case study is a creative, accessible, low-cost, and sustainable way to potentially reduce caregiver burden and improve patient outcomes with targeted intervention. Targeted distributed caregiving reduced time spent in caregiving, reduced caregiver guilt and frustration, improved patient’s compliance with requests for behavior changes (e.g., voiding before leaving the house), and improved the relationship between the caregiver and the person with cognitive impairment. This case study demonstrates how distributed caregiving, including human-in-the-loop artificial intelligence, can lead to better use of technology in reducing the social isolation of persons with cognitive impairment and in reducing caregiver burden.