Miracle-dz/UniFAR-FTUs huggingface dataset:
Association between psychosocial factors and musculoskeletal symptoms among Iranian nurses.

BACKGROUND While psychosocial factors have been associated with musculoskeletal symptoms among nurses in some countries, previous studies of Iranian nurses show little association using a demand and control questionnaire. The aim of this study is to assess and evaluate the prevalence of musculoskeletal symptoms and to assess their relationships with psychosocial factors among nurses in Iran. METHODS In a cross-sectional study, 347 hospital nurses completed a self-reported questionnaire containing the Standardized Nordic questionnaire for musculoskeletal symptoms and the General Nordic questionnaire for Psychological and Social factors at work (QPS Nordic 34+ Questionnaire). RESULTS Prevalence of low back pain, knee pain, shoulder pain, and neck pain were 73.2%, 68.7%, 48.6%, and 46.3%, respectively. Middle and high stress groups had higher crude and adjusted odds than the low stress group for all body sites. The association for neck, wrist/hand, and upper back and ankle/foot reports (adjusted odds ratio for high stress ranging from 2.4 to 3.0) were statistically significant. CONCLUSIONS We observed a high prevalence of self-reported musculoskeletal symptoms at a number of body sites, which were associated with psychosocial factors and specifically stress as defined by the QPS Nordic 34+ Questionnaire.
Distinct Sensory Pathways in Vibrio cholerae El Tor and Classical Biotypes Modulate Cyclic Dimeric GMP Levels To Control Biofilm Formation

ABSTRACT Quorum sensing (QS), or cell-cell communication in bacteria, is achieved through the production and subsequent response to the accumulation of extracellular signal molecules called autoinducers (AIs). To identify AI-regulated target genes in Vibrio cholerae El Tor (V. cholerae El), the strain responsible for the current cholera pandemic, luciferase expression was assayed in an AI− strain carrying a random lux transcriptional reporter library in the presence and absence of exogenously added AIs. Twenty-three genes were identified and shown to require the QS transcription factor, HapR, for their regulation. Several of the QS-dependent target genes, annotated as encoding hypothetical proteins, in fact encode HD-GYP proteins, phosphodiesterases that degrade the intracellular second messenger cyclic dimeric GMP (c-di-GMP), which is important for controlling biofilm formation. Indeed, overexpression of a representative QS-activated HD-GYP protein in V. cholerae El reduced the intracellular concentration of c-di-GMP, which in turn decreased exopolysaccharide production and biofilm formation. The V. cholerae classical biotype (V. cholerae Cl), which caused previous cholera pandemics and is HapR−, controls c-di-GMP levels and biofilm formation by the VieA signaling pathway. We show that the VieA pathway is dispensable for biofilm formation in V. cholerae El but that restoring HapR in V. cholerae Cl reestablishes QS-dependent repression of exopolysaccharide production. Thus, different pandemic strains of V. cholerae modulate c-di-GMP levels and control biofilm formation in response to distinct sensory pathways.
A role for chromatin remodeling in regulation of CUC gene expression in the Arabidopsis cotyledon boundary

The CUP-SHAPED COTYLEDON (CUC) genes CUC1, CUC2 and CUC3 act redundantly to control cotyledon separation in Arabidopsis. In order to identify novel regulators of this process, we have performed a phenotypical enhancer screen using a null allele of cuc2, cuc2-1. We identified three nonsense alleles of AtBRM, an Arabidopsis SWI/SNF chromatin remodeling ATPase, that result in strong cotyledon fusion in cuc2-1. atbrm also enhances cotyledon fusion in loss-of-function cuc1 and cuc3 mutants, suggesting a general requirement for this ATPase in cotyledon separation. By contrast, a null allele of SPLAYED (SYD), the closest homolog of AtBRM in Arabidopsis, enhances only the loss-of-function cuc1 mutant. By investigating the activities of the CUC promoters in the cotyledon boundary during embryogenesis in sensitized backgrounds, we demonstrate that AtBRM upregulates the transcription of all three CUC genes, whereas SYD upregulates the expression of CUC2. Our results uncover a specific role for both chromatin remodeling ATPases in the formation and/or maintenance of boundary cells during embryogenesis.
Vision-Related MLS Image Deformation Using Saliency Map

We provide a vision-related image deformation method based on Moving Least Squares (MLS) and visual saliency map. As the improvement of image deformation using MLS, we propose a new weight function to improve the MLS image deformation. A visual saliency-related deformation weight definition is proposed to ensure deformations are more easily to kept local shape on high visual saliency region and changed the non-vital part of image. Being different from the original MLS method, our weight function not only based on the distance between current pixel and a certain control points but also has benefited from the visual saliency map of undeformed image. In order to adjust the saliency of salient region during deformation, we have revised original visual saliency map by 2 parameters: segmentation threshold and gray level increment. All experiments and analysis have shown that our deformation can achieve a more realistic effect than original MLS image deformation.
MPC for Tracking applied to rendezvous with non-cooperative tumbling targets ensuring stability and feasibility

A Model Predictive Controller for Tracking is introduced for rendezvous with non-cooperative tumbling targets in active debris removal applications. The target's three-dimensional non-periodic rotational dynamics as well as other state and control constraints are considered. The approach is based on applying an intermediate coordinate transformation that eliminates the time-dependency due to rotations in the constraints. The control law is then found as the solution to a QP problem with linear constraints and dynamics, as derived from the HCW equations, that provides feasibility and stability guarantees by means of a terminal LQR and dead-beat region. The proposed control algorithm performs well in a realistic simulation scenario, namely a near rendezvous with the Envisat spacecraft.
A Two-Dimensional Fluid-Structure Coupling Algorithm for the Interaction of High-Speed Flows with Open Shells

The design of future light aerospace structures will require numerical tools to accurately describe the strongly coupled dynamics of the interactions between a light structure and a flow surrounding it. Specific examples include inflatable structures and parachutes used as deceleration devices during planet entry. In this work, an algorithm for simulating the solid-fluid interactions of a highly-deformable open shell structure and a compressible fluid flow is presented. The main objective is to extend the algorithm previously presented by the authors to the case of open shell structures immersed in a fluid. For simplicity, we restrict our attention to the two-dimensional case. The computational strategy is based on combining an Eulerian finite volume formulation for the fluid and a Lagrangian formulation for the structural response. The coupling between the fluid and the solid response is achieved via a novel approach based on extrapolation and velocity reconstruction aided by level sets. The accuracy of the proposed approach is verified against exact solutions of supersonic flow past a rigid flat plate. The numerical results reproduce all the details of the flow field, including the-very important-forces on the structure. The versatility of the proposed coupling algorithm is demonstrated in simulations of supersonic flow past a highly-deformable infinite plate. Thesis Supervisor: Radll Radovitzky Title: Assistant Professor
Dynamic Disulfide Scanning of the Membrane-inserting Pf3 Coat Protein Reveals Multiple YidC Substrate Contacts*

Background: YidC is required for the insertion of membrane proteins. Results: YidC meets its substrate Pf3 coat at multiple contact sites in four of six transmembrane helices, each on one helical face. Conclusion: The contact sites are important for substrate binding and transmembrane insertion. Significance: YidC provides a hydrophobic surface and a surrounding for the inserting substrate proteins. The membrane insertase YidC inserts newly synthesized proteins into the plasma membrane. While defects in YidC homologs in animals and plants cause diseases, YidC in bacteria is essential for life. Membrane insertion and assembly of ATP synthase and respiratory complexes is catalyzed by YidC. To investigate how YidC interacts with membrane-inserting proteins, we generated single cysteine mutants in YidC and in the model substrate Pf3 coat protein. The single cysteine mutants were expressed and analyzed for disulfide formation during 30 s of synthesis. The results show that the substrate contacts different YidC residues in four of the six transmembrane regions. The residues are located either in the region of the inner leaflet, in the center, as well as in the periplasmic leaflet, consistent with the hypothesis that YidC presents a hydrophobic platform for inserting membrane proteins. In a YidC mutant where most of the contacting residues were mutated to serines, YidC function was severely disturbed and no longer active in a complementation test, suggesting that the residues are important for function. In addition, a Pf3 mutant with a defect in membrane insertion was deficient to contact the periplasmic residues of YidC.
Unsupervised Collaborative Metric Learning with Mixed-Scale Groups for General Object Retrieval

The task of searching for visual objects in a large image dataset is difficult because it requires efficient matching and accurate localization of objects that can vary in size. Although the segment anything model (SAM) offers a potential solution for extracting object spatial context, learning embeddings for local objects remains a challenging problem. This paper presents a novel unsupervised deep metric learning approach, termed unsupervised collaborative metric learning with mixed-scale groups (MS-UGCML), devised to learn embeddings for objects of varying scales. Following this, a benchmark of challenges is assembled by utilizing COCO 2017 and VOC 2007 datasets to facilitate the training and evaluation of general object retrieval models. Finally, we conduct comprehensive ablation studies and discuss the complexities faced within the domain of general object retrieval. Our object retrieval evaluations span a range of datasets, including BelgaLogos, Visual Genome, LVIS, in addition to a challenging evaluation set that we have individually assembled for open-vocabulary evaluation. These comprehensive evaluations effectively highlight the robustness of our unsupervised MS-UGCML approach, with an object level and image level mAPs improvement of up to 6.69% and 10.03%, respectively. The code is publicly available at https://github.com/dengyuhai/MS-UGCML.
A general artificial neural network extension for HTK

Copyright © 2015 ISCA. This paper describes the recently developed artificial neural network (ANN) modules in HTK hidden Markov model toolkit, which enables ANN models with very general feed-forward architectures to be used for either acoustic modelling or feature extraction. The HTK ANN extension includes many recent ANN-based speech processing techniques, such as sequence training, model stacking, speaker adaptation, and parameterised activation functions. The implementation allows efficient training by supporting GPUs and various types of data cache. The ANN modules are fully integrated into the rest of the HTK toolkit, which allows existing GMM-HMM methods to be easily used in the ANN-HMM framework. Speech recognition results on a 300 hours DARPA BOLT conversational Mandarin task show that HTK can produce tandem and hybrid systems with state-of-the-art performance on this very challenging task. Furthermore, the flexibility of the implementation is illustrated using demo systems for a Wall Street Journal (WSJ) task. The HTK ANN extension is planned for release in HTK version 3.5.
The composition of pancreatic juice as compared to sweat, parotid saliva and tears.

Summary. The concentrations of sodium, potassium, chloride, bicarbonate. glucose, urea and the pH of pancreatic juice, obtained in response to secretion, were examined in two dogs over a 100-fold range of variation in secretory rate. The concentrations of sodium and of potassium were 161 (± 4) and 5.3 (± 1.1) mEq per liter respectively and independent of secietory rate. The concentration of bicarbonate was independent of secretory rate at flows exceeding 0.5 ml per minute and averaged 120 mEq per liter in one of the dogs and 85 mEq per liter in the other. At secretory rates lower than 0.5 ml per minute the concentration of bicarbonate gradually dropped to values equal to or lower than plasma concentration. The concentration of chloride varied inversely with that of bicarbonate, the sum of the concentrations of the two anions amounting to 87 (± 5) per cent of the sum of the concentrations of the two cations at all secretory rates. The pH averaged 8.05 at secretory ratos exceeding 0.5 ml per minute. The secretion/plasma concentration ratio for urea was 0.9 and 0.8 in the two dogs and in dependent of secretory rate. The secretion/plasma concentration ratio for glucose was approximately 0.2 and independent of the. concentration of glucose in the plasma. The findings are compared to previous similar studies on human sweat. parotid saliva and tears. The hypothesis is forwarded that glandular secretion involves the formation of precursor solutions which are modified by the reabsorption of sodium and of water in the ducts of the sweat and parotid glands. whereas similar reabsorptive processes cannot be demonstrated in the case of the lacrymal and pancreatic glands. Moreover. glucose appears to he conserved by the sweat and parotid glands. but not by the two other glands.
Vespertilionid bats control the width of their biosonar sound beam dynamically during prey pursuit

Animals using sound for communication emit directional signals, focusing most acoustic energy in one direction. Echolocating bats are listening for soft echoes from insects. Therefore, a directional biosonar sound beam greatly increases detection probability in the forward direction and decreases off-axis echoes. However, high directionality has context-specific disadvantages: at close range the detection space will be vastly reduced, making a broad beam favorable. Hence, a flexible system would be very advantageous. We investigated whether bats can dynamically change directionality of their biosonar during aerial pursuit of insects. We trained five Myotis daubentonii and one Eptesicus serotinus to capture tethered mealworms and recorded their echolocation signals with a multimicrophone array. The results show that the bats broaden the echolocation beam drastically in the terminal phase of prey pursuit. M. daubentonii increased the half-amplitude angle from approximately 40° to approximately 90° horizontally and from approximately 45° to more than 90° vertically. The increase in beam width is achieved by lowering the frequency by roughly one octave from approximately 55 kHz to approximately 27.5 kHz. The E. serotinus showed beam broadening remarkably similar to that of M. daubentonii. Our results demonstrate dynamic control of beam width in both species. Hence, we propose directionality as an explanation for the frequency decrease observed in the buzz of aerial hawking vespertilionid bats. We predict that future studies will reveal dynamic control of beam width in a broad range of acoustically communicating animals.
Paragraph-, Word-, and Coherence-based Approaches to Sentence Ranking: A Comparison of Algorithm and Human Performance

Sentence ranking is a crucial part of generating text summaries. We compared human sentence rankings obtained in a psycholinguistic experiment to three different approaches to sentence ranking: A simple paragraph-based approach intended as a baseline, two word-based approaches, and two coherence-based approaches. In the paragraph-based approach, sentences in the beginning of paragraphs received higher importance ratings than other sentences. The word-based approaches determined sentence rankings based on relative word frequencies (Luhn (1958); Salton & Buckley (1988)). Coherence-based approaches determined sentence rankings based on some property of the coherence structure of a text (Marcu (2000); Page et al. (1998)). Our results suggest poor performance for the simple paragraph-based approach, whereas word-based approaches perform remarkably well. The best performance was achieved by a coherence-based approach where coherence structures are represented in a non-tree structure. Most approaches also outperformed the commercially available MSWord summarizer.
Fusion of vision, 3D gyro and GPS for camera dynamic registration

This paper presents a novel framework of hybrid camera pose tracking system for outdoor navigation system. Traditional vision based or inertial sensor based solutions are mostly designed for well-structured environment, which is however unavailable for most outdoor uncontrolled applications. Our system combines vision, GPS and 3D inertial gyroscope sensors to obtain accurate and robust camera pose estimation result. The fusion approach is based on our PMM (parameterized model matching) algorithm, in which the road shape model is derived from the digital map referring to GPS absolute road position, and matches with road features extracted from the real image. Inertial data estimates the initial state of searching parameters, and also serves as relative tolerance to stable the pose output. The algorithms proposed in this paper are validated with the experimental results of real road tests under different road conditions.
LVCSR rescoring with modified loss functions: a decision theoretic perspective

The problem of speech decoding is considered in a decision theoretic framework and a modified speech decoding procedure to minimize the expected risk under a general loss function is formulated. A specific word error rate loss function is considered and an implementation in an N-best list rescoring procedure is presented. Methods for estimation of the parameters of the resulting decision rules are provided for both supervised and unsupervised training. Preliminary experiments on an LVCSR task show small but statistically significant error rate improvements.
X-LLaVA: Optimizing Bilingual Large Vision-Language Alignment

The impressive development of large language models (LLMs) is expanding into the realm of large multimodal models (LMMs), which incorporate multiple types of data beyond text. However, the nature of multimodal models leads to significant expenses in the creation of training data. Furthermore, constructing multilingual data for LMMs presents its own set of challenges due to language diversity and complexity. Therefore, in this study, we propose two cost-effective methods to solve this problem: (1) vocabulary expansion and pretraining of multilingual LLM for specific languages, and (2) automatic and elaborate construction of multimodal datasets using GPT4-V. Based on015 these methods, we constructed a 91K English-Korean-Chinese multilingual, multimodal training dataset. Additionally, we developed a bilingual multimodal model that exhibits excellent performance in both Korean and English, surpassing existing approaches.
Development of durable superhydrophobic materials for ice- and snow-free airport concrete pavements

Superhydrophobic surfaces that mimic surfaces found in nature, such as the lotus leaf, are an attractive research topic in various fields of study because of their numerous applications. More recent studies have focused on superhydrophobic surfaces that reduce or completely stop the accretion of ice and snow on power lines and aircraft that operate in cold regions. The superhydrophobic phenomena is usually achieved by creating a dual-scale roughness that is composed of microand nano-scale structures that trap air in-between themselves and reduce the surface energy of the textured surface. The objective of this study was to assess the tribological behavior of micro/nano particle based superhydrophobic coating mixtures composed of PTFE, composite PTFE/PEEK, diatomaceous earth (DE), and composite PTFE/ZnO that can be potential candidates for anti-wetting and anti-icing applications for transportation systems. *Corresponding author: srirams@iastate.edu
CantonMT: Cantonese to English NMT Platform with Fine-Tuned Models Using Synthetic Back-Translation Data

Neural Machine Translation (NMT) for low-resource languages is still a challenging task in front of NLP researchers. In this work, we deploy a standard data augmentation methodology by back-translation to a new language translation direction Cantonese-to-English. We present the models we fine-tuned using the limited amount of real data and the synthetic data we generated using back-translation including OpusMT, NLLB, and mBART. We carried out automatic evaluation using a range of different metrics including lexical-based and embedding-based. Furthermore. we create a user-friendly interface for the models we included in this\textsc{ CantonMT} research project and make it available to facilitate Cantonese-to-English MT research. Researchers can add more models into this platform via our open-source\textsc{ CantonMT} toolkit \url{https://github.com/kenrickkung/CantoneseTranslation}.
Hemispheric Specialization Varies with EEG Brain Resting States and Phase of Menstrual Cycle

A growing body of behavioral studies has demonstrated that women’s hemispheric specialization varies as a function of their menstrual cycle, with hemispheric specialization enhanced during their menstruation period. Our recent high-density electroencephalogram (EEG) study with lateralized emotional versus neutral words extended these behavioral results by showing that hemispheric specialization in men, but not in women under birth-control, depends upon specific EEG resting brain states at stimulus arrival, suggesting that hemispheric specialization may be pre-determined at the moment of the stimulus onset. To investigate whether EEG brain resting state for hemispheric specialization could vary as a function of the menstrual phase, we tested 12 right-handed healthy women over different phases of their menstrual cycle combining high-density EEG recordings and the same lateralized lexical decision paradigm with emotional versus neutral words. Results showed the presence of specific EEG resting brain states, associated with hemispheric specialization for emotional words, at the moment of the stimulus onset during the menstruation period only. These results suggest that the pre-stimulus EEG pattern influencing hemispheric specialization is modulated by the hormonal state.
Applying Many-to-Many Alignments and Hidden Markov Models to Letter-to-Phoneme Conversion

Letter-to-phoneme conversion generally requires aligned training data of letters and phonemes. Typically, the alignments are limited to one-to-one alignments. We present a novel technique of training with many-to-many alignments. A letter chunking bigram prediction manages double letters and double phonemes automatically as opposed to preprocessing with fixed lists. We also apply an HMM method in conjunction with a local classification model to predict a global phoneme sequence given a word. The many-to-many alignments result in significant improvements over the traditional one-to-one approach. Our system achieves state-of-the-art performance on several languages and data sets.