Miracle-dz/UniFAR-FTUs huggingface dataset:
The Associations Between Workplace Noise, Ototoxic Chemicals, and Tinnitus

Objective: To investigate the occupational risk factors associated with tinnitus in the Australian working population. Design: The research was conducted using data collected from the Australian Workplace Exposure Survey-Hearing, a national cross-sectional study of 4970 workers conducted in 2016 to 2017. Workers were asked if they experienced tinnitus and if they answered affirmatively, they were asked about the frequency and length of the presentations. Based on their answers, each worker was categorized as having no tinnitus or any tinnitus, with an additional group of workers with any tinnitus subcategorized as having constant tinnitus. Exposure assessment was conducted using an automated expert assessment method. Exposures included daily noise (LAeq,8h), hand-arm vibration (A(8)), impulse noise, smoking status, styrene, trichloroethylene, toluene, n-hexane, p-xylene, ethylbenzene, lead, and carbon monoxide. Univariate and multivariate logistic regression models were used to examine the associations between workplace exposures and tinnitus. Results: Workers with an estimated noise exposure above the workplace limit (LAeq,8h > 85 dBA) had 1.73 (95% confidence interval [CI]: 1.42 to 2.11) increased odds of any tinnitus and 2.15 (95% CI: 1.60 to 2.89) odds of constant tinnitus. The odds of workers having any tinnitus increased with increasing noise exposure levels in a dose–response relationship that strengthened when considering only those with constant tinnitus. In the fully adjusted model, statistically significant associations were seen for lead exposure with both any and constant tinnitus, toluene exposure with constant tinnitus, and carbon monoxide exposure with any tinnitus. Conclusions: These results suggest that, in addition to workplace noise, occupational exposures to lead, toluene, and carbon monoxide are associated with tinnitus.
Urolithin A: A promising selective estrogen receptor modulator and 27‐hydroxycholesterol attenuator in breast cancer

27‐hydroxycholesterol (27‐HC) is an oxysterol that acts as an endogenous selective estrogen receptor modulator (SERM), and its adverse effects on breast cancer via the estrogen receptor (ER) have provided new insights into the pathology of cholesterol–linked breast cancer. Our earlier in vitro experiments showed that the methanolic extract of pomegranate could exhibit SERM properties and compete with 27‐HC. The major constituents of pomegranate are ellagitannins and ellagic acid, which are converted into urolithins by the colonic microbiota. In recent years, urolithins, especially urolithin A (UA) and urolithin B (UB), have been reported to have a plethora of advantageous effects, including antiproliferative and estrogenic activities. In this study, we attempted to determine the potential of urolithins in antagonizing and counteracting the adverse effects of 27‐HC in breast cancer cells. Our findings suggested that UA had an antiproliferative capacity and attenuated the proliferative effects of 27‐HC, resulting in subsequent loss of membrane potential and apoptosis in breast cancer cells. Further, UA induced estrogen response element (ERE) transcriptional activity and modulated estrogen‐responsive genes, exhibiting a SERM‐like response concerning receptor binding. Our in vivo hollow fiber assay results showed a loss of cell viability in breast cancer cells upon UA consumption, as well as a reduction in 27‐HC‐induced proliferative activity. Additionally, it was shown that UA did not induce uterine proliferation or alter blood biochemical parameters. Based on these findings, we can conclude that UA has the potential to act as a potent estrogen receptor alpha (ERα) modulator and 27‐HC antagonist. UA is safe to consume and is very well tolerated. This study further opens up the potential of UA as ER modulator and its benefits in estrogen‐dependent tissues.
Variable selection in single index varying coefficient models with LASSO

VARIABLE SELECTION IN SINGLE IDNEX VARYING COEFFICIENT MDOELS WITH LASSO September 2015 PENG WANG, B.S., UNIVERSITY OF SCIENCE AND TECHNOLOGY OF CHINA Ph.D., UNIVERSITY OF MASSACHUSETTS AMHERST Directed by: Professor Anna Liu Single index varying coefficient model is a very attractive statistical model due to its ability to reduce dimensions and easy-of-interpretation. There are many theoretical studies and practical applications with it, but typically without features of variable selection, and no public software is available for solving it. Here we propose a new algorithm to fit the single index varying coefficient model, and to carry variable selection in the index part with LASSO. The core idea is a two-step scheme which alternates between estimating coefficient functions and selecting-and-estimating the single index. Both in simulation and in application to a Geoscience dataset, we showed that it works very well. We also presented our R package sivcm with the algorithm implemented and with ideas that can be extended beyond.
NAC controls cotranslational N-terminal methionine excision in eukaryotes

N-terminal methionine excision from newly synthesized proteins, catalyzed cotranslationally by methionine aminopeptidases (METAPs), is an essential and universally conserved process that plays a key role in cell homeostasis and protein biogenesis. However, how METAPs interact with ribosomes and how their cleavage specificity is ensured is unknown. We discovered that in eukaryotes the nascent polypeptide–associated complex (NAC) controls ribosome binding of METAP1. NAC recruits METAP1 using a long, flexible tail and provides a platform for the formation of an active methionine excision complex at the ribosomal tunnel exit. This mode of interaction ensures the efficient excision of methionine from cytosolic proteins, whereas proteins targeted to the endoplasmic reticulum are spared. Our results suggest a broader mechanism for how access of protein biogenesis factors to translating ribosomes is controlled. Description Editor’s summary All proteins are initially synthesized with methionine as their first amino acid. Membrane proteins and proteins translocated across the endoplasmic reticulum retain this methionine, but most cytosolic proteins have it removed by methionine aminopeptidase 1. The mechanism through which METAP1 discerns its target proteins has remained elusive. Gamerdinger et al. revealed the crucial role of the nascent polypeptide–associated complex (NAC) in this process. NAC binds to the ribosome to guide the aminopeptidase to the correct proteins, ensuring the removal of methionine residues from cytosolic proteins while sparing those targeted to the endoplasmic reticulum. —Stella M. Hurtley Elucidating the mechanism underlying how newly synthesized proteins are sorted into the correct protein biogenesis pathway at the ribosome.
An empirical Comparison of Arabic Named Entity Recognition Methods: Application to the ALP Corpus

This study compares the performance of some existing approaches to the problem of Arabic Named Entity Recognition. The approaches under consideration are based on Sequence La-belling and Multi-Label Classiﬁcation meth-ods. We will use the ALP corpus, a newly produced corpus with more than 58 tags, as our single corpus for comparison in order to ensure a fair comparison. In other words, we’ll use a 58-way categorization procedure to ﬁgure out what each token’s tags are. Despite just employing a portion of the ALP corpus—ALP2 (50%) and ALP3 (25%)—an average accuracy of more than 88% was achieved, which make the results highly encouraging.
Pathogenesis and Diagnosis of Proximal Hamstring Tendinopathies

Abstract The proximal hamstring complex is a highly vulnerable area that is especially prone to injury. Proximal hamstring tendinopathies (PHTs) remain challenging in diagnosis, treatment, rehabilitation, and prevention due to a large variety of different injuries, slow healing response, persistent symptoms, and functional impairments. PHTs are often misdiagnosed or underdiagnosed, leading to delayed treatment and therapy failure. In addition, many athletes are at a high risk of PHT recurrence, a leading cause of prolonged rehabilitation and impaired individual performance. Until now, there have been no clear criteria for the diagnosis and classification of PHT. Tendinopathies can be graded based on their symptoms and onset. Additionally, radiological characteristics exist that describe the severity of tendinopathies. The diagnosis usually includes a battery of pain provocation tests, functional tests, and imaging to ensure a proper classification. Understanding the specific tasks in the pathogenesis and diagnostic process of PHT requires knowledge of functional anatomy, injury pattern and pathophysiological mechanisms as well as examination and imaging techniques. This work provides a structured overview of the pathogenesis and diagnostic work-up of PHT, emphasizing structured examination and imaging to enable a reliable diagnosis and rapid treatment decisions.
On the Effect of ulti-parents ecombination in Genetic Algorithms

Recombination operator plays a very im- portant role in genetic algorithms. In this paper, we present binary coded genetic algorithms in which more than two parents are involved in recombina- tion operation. We propose two types of multi-par- ent recombination operators, the multi-cut (MX) and seed crossover (SX). Each of these operators is a natu- ral generalization of two parents recombination op- erator. These operators are evaluated on the De Jong standard test functions. The results showed clearly that the multi-parent recombinations lead to better performance, although the performance improvement for different techniques were found to be dependent on problems.
Vortex-Shedding Induced Trailing-Edge Acoustics

The operational requirements for naval vessels have seen an increasing demand for quieter ships, and hence the management of the ship’s hydrodynamic signature requires improved predictions of flow induced noise and particularly propeller noise. Studies involving model-scale and full-scale ships are complicated and expensive, and in order to better understand the detailed physics and to develop better prediction models, simpler problems can be useful. In this work we consider the flow and flow induced noise from the trailing edge of a flat airfoil with a 45° asymmetrically beveled trailing edge and an elliptical leading edge. For the airfoil under consideration simultaneous flow and noise measurements from the University of Notre Dame are available for comparison with computational flow and acoustic models, making this a rather unique validation test case. Here we use Detached Eddy Simulation (DES), Large Eddy Simulation (LES) and Implicit LES (ILES) methods coupled to an acoustic model for the density (or pressure) perturbations similar to the standard Ffowcs-Williams & Hawkings or Lighthill-Curle's models. The comparison with the experimental data shows that both the flow and the acoustics are reasonably well predicted by all three models, but with the LES model resulting in somewhat more accurate flow and acoustic predictions.
An enhanced YOLOv5 model for fish species recognition from underwater environments

Species recognition is an important aspect of video based surveys, which support stock assessments, inspecting the ecosystem, handling production management, and protecting endangered species. It is a challenging task to implement fish species detection algorithms in underwater environments. In this work, we introduce the YOLOv5 model for the recognition of fish species that can be implemented as an object detection model for analyzing multiple fishes in a single image. Moreover, we have modified the depth scale of different layers in the backbone of the YOLOv5 model to obtain improved results on fish species recognition. In addition, we have implemented a transformer block in the backbone network and introduced a class balance loss function to obtain enhanced performance. It can perform fish species recognition as an object detection approach by classifying each of the fish species in addition to localizing for the estimation of the position and size of the fish in an image. Experiments are conducted on the fine-grained and large-scale reef fish dataset that we have obtained from the Gulf of Mexico – the Southeast Area Monitoring and Assessment Program Dataset 2021 (SEAMAPD21). The experimental results demonstrate that an enhanced YOLOv5 model can yield better detection results in comparison to YOLOv5 for underwater fish species recognition.
Calibration and preliminary performance of an in-water, integrated total scattering and backscattering sensor

The design and the calibration of the Beam-attenuation, b and bb Laser Underwater Environment Sensor (4BLUES), which borrows from advanced designs developed by the authors’ individual groups in the course of the last two decades. The sensor features unparalleled accuracy in determining the optical scattering and backscattering coefficients, which are critical parameters for the remote sensing of water constituents and in-water bio-optical applications. Calibration procedures performed in the laboratory with spheres of known properties are presented together with an assessment of the performance in two field experiments in coastal waters. The more complex calibration of the scattering channel includes a novel inversion approach, which accounts for scattering and absorption losses along the laser-beam path. Strong agreement was observed with current advanced sensors for extinction and the backscattering measurement for one of the field experiments. Total scattering comparison with the current state of the art showed reasonable agreement (within 25%) for all stations except one, where the interpretation for the higher discrepancies is unresolved. These preliminary results suggest further assessment is warranted.
Decision level fusion experiments on MWIR, VNIR, and SAR imagery

Decision level fusion algorithms combine separate classification scores of a test sample to make a unified class declaration. The aim of decision level fusion algorithms is to achieve better classification performance by combining decisions rather than picking the single best performing algorithm. Multi-modal fusion can be achieved by fusing scores of deep learning models trained on different sensing modalities and tested on a target imaged from co-located sensors. Given differences in phenomenology, fusing EO sensors with SAR may boost performance when extended operating conditions are detrimental to the performance of one modality over the other. The EO modalities discussed in this work (VNIR and MWIR) are susceptible to the time of day while SAR is robust to time of day. Conversely, SAR returns of a target can vary greatly when aspect angle changes while EO modalities are relatively robust. This work analyzes the effectiveness of decision level fusion algorithms on MWIR, VNIR, and SAR modalities given disparate times of day and collection aspects.
Pressure Wave Propagation in a Bubbly Medium: A Multiscale Modelling Approach

This work uses a compressible Eulerian multi-material solver with three modeling approaches to examine shock and pressure wave propagation in a bubbly medium. These approaches represent different levels of complexity from fully resolving the dispersed bubbles to treating the bubbly medium as a homogeneous mixture. An intermediate approach is based on treating bubbles as discrete singularities. Propagation of the pressure wave through the bubbly medium is compared between the simplified approaches and the fully resolved bubble simulation. Different scenarios demonstrating the effect of pressure amplitude, void fraction, and bubble size distribution are presented to further understand wave propagation in bubbly media.Copyright © 2014 by ASME
Elastic RAID: Implementing RAID over SSDs with Built-in Transparent Compression

This paper studies how RAID (redundant array of independent disks) could take full advantage of modern SSDs (solid-state drives) with built-in transparent compression. In current practice, RAID users are forced to choose a specific RAID level (e.g., RAID 10 or RAID 5) with a fixed storage cost vs. speed performance trade-off. The commercial market is witnessing the emergence of a new family of SSDs that can internally perform hardware-based lossless compression on each 4KB LBA (logical block address) block, transparent to host OS and user applications. Beyond straightforwardly reducing the RAID storage cost, such modern SSDs make it possible to relieve RAID users from being locked into a fixed storage cost vs. speed performance trade-off. In particular, RAID systems could opportunistically leverage higher-than-expected runtime user data compressibility to enable dynamic RAID level conversion to improve the speed performance without compromising the effective storage capacity. This paper presents techniques to enable and optimize the practical implementation of such elastic RAID systems. We implemented a Linux software-based elastic RAID prototype that supports dynamic conversion between RAID 5 and RAID 10. Compared with a baseline software-based RAID 5, under sufficient runtime data compressibility that enables the conversion from RAID 5 to RAID 10 over 60% of user data, the elastic RAID could improve the 4KB random write IOPS (I/O per second) by 42% and 4KB random read IOPS in degraded mode by 46%, while maintaining the same effective storage capacity.
Human-AI Cooperation to Tackle Misinformation and Polarization

I M A G E B Y H U R C A A DOMINANT NARRATIVE of the past decade is that algorithms contribute to a misinformed and segregated society. Perhaps paradoxically, algorithms are often sought as solutions to such problems. We describe a significant emerging trend away from this techno-solutionist approach that seeks to create and understand a new paradigm: a productive interplay between algorithms and people. Two relevant test cases are being explored in our region: The first addresses a new framework to tackle misinformation by assisting fact-checkers with computational methods, and the second seeks new models to understand how search engines deliver personalized search results when little or no algorithmic personalization exists. In late 2020 and early 2021, the Australian Communication and Media Authority conducted a study to analyze the state of misinformation in Australia. The findings, reported to the Australian Government in June 2021, showed that four out of five Australian adults had been exposed to misinformation about COVID-19. They also found that online misinformation, such as the propagation of anti-vaccine narratives within the Australian community, had a direct negative impact on the trust that people place in democratic institutions and public health agencies. These narratives often originate overseas but quickly spread through local communities. The fact-checking organizations that have traditionally verified statements made by public figures or politicians in public and mainstream media now must also monitor and debunk dramatically faster-spreading claims on social media platforms. Narratives containing misinformation are having a direct and negative impact on how people consume information: They may influence the content we engage with and the search terms we enter.10 Given that an informed citizenry is a cornerstone of democracy, public decision making is at risk. The significance of the problem was also recognized in the International Cyber and Critical Technology Engagement Strategy released by the Australian Government, which identifies digital misinformation as a clear risk to the security and safety of Australia, the Indo-Pacific region, and beyond. Countries across East Asia and Oceania introduced legislation that specifically targets so-called ‘fake news’ and they created voluntary codes of practice developed in partnership with the technology industry. Despite such efforts, as of December 2022, out of the 122 currently verified signatories of the Poynter’s International FactChecking Network (IFCN), only eight Human-AI Cooperation to Tackle Misinformation and Polarization DOI:10.1145/3588431
Development of hybrid storage system based on Open CAS technology, optimized for HPC workload

HPC runs in a distributed structure with a single shared pool of data. In our case, the distributed structure is Lustre file system [4], and the single shared pool of data is our declustered HDD RAID (denoted as DCR). To increase performance, it is suggested to use Open CAS technology [3] as a cache on RAM/NVDIMM with special parameters, optimized for heavy data-intensive sequential HPC workload and an online-algorithm which reduces the number of RMW operations, by merging sequential requests into one full-stripe one.
Heuristics For The Design Of Thick CompositeCylinders

A heuristic is proposed for a problem that occurs in the design of thick composite cylinders: the selection of the angles of wrap of all the layers forming a cylinder for given material properties and layer thicknesses. The heuristic consists of a local search algorithm that improves on a given initial design by modifying the angle of a single layer in each iteration. Each design is evaluated by computing a safety factor. Computational experience with the heuristic is presented and discussed.
Brain asymmetry: a novel perspective on hemispheric network

Brain asymmetry, involving structural and functional differences between the two hemispheres, is a major organizational principle of the human brain. The structural and functional connectivity within each hemisphere defines the hemispheric network or connectome. Elucidating left-right differences of the hemispheric network provides opportunities for brain asymmetry exploration. This review examines the asymmetry in the hemispheric white matter and functional network to assess health and brain disorders. In this article, the brain asymmetry in structural and functional connectivity including network topologies of healthy individuals, involving brain cognitive systems and the development trend, is highlighted. Moreover, the abnormal asymmetry of the hemispheric network related to cognition changes in brain disorders, such as Alzheimer’s disease, schizophrenia, autism spectrum disorder, attention deficit hyperactivity disorder, and bipolar disorder, is presented. This review suggests that the hemispheric network is highly conserved for measuring human brain asymmetries and has potential in the study of the cognitive system and brain disorders.
Neural mechanisms of top-down divided and selective spatial attention in visual and auditory perception

Top-down attention mechanisms require the selection of specific objects or locations; however, the brain mechanism involved when attention is allocated across different modalities is not well understood. The aim of this study was to use functional magnetic resonance imaging to define the neural mechanisms underlying divided and selective spatial attention. A concurrent audiovisual stimulus was used, and subjects were prompted to focus on a visual, auditory and audiovisual stimulus in a Posner paradigm. Our behavioral results confirmed the better performance of selective attention compared to devided attention. We found differences in the activation level of the frontoparietal network, visual/auditory cortex, the putamen and the salience network under different attention conditions. We further used Granger causality (GC) to explore effective connectivity differences between tasks. Differences in GC connectivity between visual and auditory selective tasks reflected the visual dominance effect under spatial attention. In addition, our results supported the role of the putamen in redistributing attention and the functional separation of the salience network. In summary, we explored the audiovisual top-down allocation of attention and observed the differences in neural mechanisms under endogenous attention modes, which revealed the differences in cross-modal expression in visual and auditory attention under attentional modulation.