Miracle-dz/UniFAR-FTUs huggingface dataset:
High Fall Risk Associated With Memory Deficit and Brain Lobes Atrophy Among Elderly With Amnestic Mild Cognitive Impairment and Mild Alzheimer’s Disease

Objectives This study aimed to primarily examine the association between memory deficit and increased fall risk, second, explore the underlying neuroanatomical linkage of this association in the elderly with aMCI and mild AD. Methods In this cross-sectional study, a total of 103 older adults were included (55 cognitively normal, CN; 48 cognitive impairment, CI, elderly with aMCI, and mild AD). Memory was assessed by the Auditory Verbal Learning Test (AVLT). Fall risk was evaluated by the Timed Up and Go (TUG) Test, heel strike angles, and stride speed, which were collected by an inertial-sensor-based wearable instrument (the JiBuEn™ gait analysis system). Brain volumes were full-automatic segmented and quantified using AccuBrain® v1.2 from three-dimensional T1-weighted (3D T1W) MR images. Multivariable regression analysis was used to examine the extent of the association between memory deficit and fall risk, the association of brain volumes with memory, and fall risk. Age, sex, education, BMI, and HAMD scores were adjusted. Sensitivity analysis was conducted. Results Compared to CN, participants with aMCI and mild AD had poorer cognitive performance (p < 0.001), longer TUG time (p = 0.018), and smaller hippocampus and medial temporal volumes (p = 0.037 and 0.029). In the CI group, compared to good short delayed memory (SDM) performance (AVLT > 5), the elderly with bad SDM performance (AVLT ≤ 3) had longer TUG time, smaller heel strike angles, and slower stride speed. Multivariable regression analysis showed that elderly with poor memory had higher fall risk than relative good memory performance among cognitive impairment elderly. The TUG time increased by 2.1 s, 95% CI, 0.54∼3.67; left heel strike angle reduced by 3.22°, 95% CI, −6.05 to −0.39; and stride speed reduced by 0.09 m/s, 95% CI, −0.19 to −0.00 for the poor memory elderly among the CI group, but not found the association in CN group. In addition, serious medial temporal atrophy (MTA), small volumes of the frontal lobe and occipital lobe were associated with long TUG time and small heel strike angles; small volumes of the temporal lobe, frontal lobe, and parietal lobe were associated with slow stride speed. Conclusion Our findings suggested that memory deficit was associated with increased fall risk in the elderly with aMCI and mild AD. The association might be mediated by the atrophy of medial temporal, frontal, and parietal lobes. Additionally, increased fall risk, tested by TUG time, heel stride angles, and stride speed, might be objective and convenient kinematics markers for dynamic monitoring of both memory function and fall risk.
Shifting Trade-offs: Finding the Sustainable Nexus of Hydropower and Environmental Flows in the San Joaquin River Watershed, California

Environmental flow management in watersheds with multi-objective reservoirs is often presented as an additional constraint to an already strained and over-allocated stream system. Nevertheless, environmental flow legislation and regulatory policies are increasingly being developed and implemented globally. In California, USA, recent legislative and regulatory policies place environmental flows at the forefront of the state’s water management objectives; however, the increased reliance on hydropower to support climate change mitigation goals may complicate efforts on both issues. This study modelled alternative environmental flow strategies in the major tributaries to the San Joaquin River in California. Strategies included detailed water management rules for hydropower production, flood control, and water deliveries, and three methodological approaches to environmental flow releases: minimum instream flows (“baseline”) year-round, 40% of full natural flow (FNF) during the spring runoff season and minimum releases the remainder of the year, and functional flows year-round. Results show that environmental flow strategies affect downstream flow releases in each of the San Joaquin’s four sub-basins differently depending on infrastructure capacity, water management objectives, and hydrologic year types. While hydropower production was comparable or declined in the Stanislaus, Tuolumne, and Merced basins, functional flow and 40% FNF strategies increased hydropower production in the Upper San Joaquin basin by 11%. Uncontrolled spill of high flow events decreased when high flow releases were based on hydrologic cues rather than exclusively on flood storage capacity. Water deliveries were reduced in all years regardless of environmental flow strategy. The 40% FNF and functional flow strategies both increased water released to the river relative to baseline, but in different ways. The functional flow strategy allocated water in a holistic approach that enhanced ecological functions in all years, but particularly in moderate and wet years. In contrast, the 40% FNF strategy provided increased flows relative to baseline and some ecological benefit in dry years, but less ecological benefit in other years. This study shows that alternative environmental flow strategies will have different and important trade-offs for integrated water management, and may mutually benefit seemingly conflicting objectives.
Entrepreneurs Can Know More Than They Can Tell: Conceptualizing and Measuring Tacit Entrepreneurial Knowledge

Organizational knowledge components dominate research on tacit knowledge. In order to overcome this dominance, we introduce Tacit Entrepreneurial Knowledge (TEK). TEK is conceptualized as one’s experiential learning from past experiences and insights that result in tacit knowledge regarding entrepreneurship that is implicit, personal, and uncodified. For this study the situational judgment test (SJT) approach is adopted to overcome the common limitations in quantifying an individual’s tacit knowledge. The SJT is a scenario-based measurement instrument that allows us to quantify an individual’s TEK. The SJT is developed using three steps: first, scenarios were collected through interviews, followed by formulating responses to the scenarios, and finally, the effectiveness of the responses for each scenario was evaluated. The outcome of this research article is threefold; first, a comprehensive conceptualization of TEK, including delineation of its nomological network. Second, the development of a measurement instrument for TEK and subsequent scoring method. Finally, an antecedent-consequence model which includes potential contingencies associated with these relationships. In the debate on tacit knowledge, our measurement is innovative and relevant, as previous research failed to uncover an individual’s tacit knowledge in the context of entrepreneurship, despite its importance in various entrepreneurial processes. This study aspires to ignite research into TEK by demonstrating important research opportunities unlocked by our conceptualization and subsequent measurement, offering future researchers a wide range of avenues to uncover the black box of tacit knowledge in entrepreneurship.
Genome-Wide Characterization of Superoxide Dismutase (SOD) Genes in Daucus carota: Novel Insights Into Structure, Expression, and Binding Interaction With Hydrogen Peroxide (H2O2) Under Abiotic Stress Condition

Superoxide dismutase (SOD) proteins are important antioxidant enzymes that help plants to grow, develop, and respond to a variety of abiotic stressors. SOD gene family has been identified in a number of plant species but not yet in Daucus carota. A total of 9 DcSOD genes, comprising 2 FeSODs, 2 MnSODs, and 5 Cu/ZnSODs, are identified in the complete genome of D. carota, which are dispersed in five out of nine chromosomes. Based on phylogenetic analysis, SOD proteins from D. carota were categorized into two main classes (Cu/ZnSODs and MnFeSODs). It was predicted that members of the same subgroups have the same subcellular location. The phylogenetic analysis was further validated by sequence motifs, exon–intron structure, and 3D protein structures, with each subgroup having a similar gene and protein structure. Cis-regulatory elements responsive to abiotic stresses were identified in the promoter region, which may contribute to their differential expression. Based on RNA-seq data, tissue-specific expression revealed that DcCSD2 had higher expression in both xylem and phloem. Moreover, DcCSD2 was differentially expressed in dark stress. All SOD genes were subjected to qPCR analysis after cold, heat, salt, or drought stress imposition. SODs are antioxidants and play a critical role in removing reactive oxygen species (ROS), including hydrogen peroxide (H2O2). DcSODs were docked with H2O2 to evaluate their binding. The findings of this study will serve as a basis for further functional insights into the DcSOD gene family.
A Novel Rabies Vaccine Based on a Recombinant Bovine Herpes Virus Type 1 Expressing Rabies Virus Glycoprotein

Rabies is a highly prevalent zoonotic disease and a public health threat worldwide. Currently licensed rabies vaccines are effective but less is known which would protect cattle. This study describes the construction of a novel recombinant bovine herpes virus type I (BHV-1) expressing rabies virus glycoprotein (RABV G) instead of its gE glycoprotein (gE) by CRISPR-Cas9 and homologous recombination technology (BHV-1-ΔgE-G). Insertion of the RABV G gene is stable after 20 rounds of in vitro passaging and the recombinant virus replicates to high titers in MDBK cells. The RABV G expresses in the recombinant virus-infected cells and on the virion surface of BHV-1-ΔgE-G. One single immunization with BHV-1-ΔgE-G-activated dendritic cells (DCs) and B cells furthermore induced a protective immune response in mice against severe lethal challenge infection. A protective level of RABV-specific virus-neutralizing antibody (VNA) was detected in intramuscular immunized mice and cattle without any clinical symptoms. This research demonstrated that the BHV-1 vector-based RABV vaccine is a potential candidate for cattle.
VAP Proteins – From Organelle Tethers to Pathogenic Host Interactors and Their Role in Neuronal Disease

Vesicle-associated membrane protein (VAMP)-associated proteins (VAPs) are ubiquitous ER-resident tail-anchored membrane proteins in eukaryotic cells. Their N-terminal major sperm protein (MSP) domain faces the cytosol and allows them to interact with a wide variety of cellular proteins. Therefore, VAP proteins are vital to many cellular processes, including organelle membrane tethering, lipid transfer, autophagy, ion homeostasis and viral defence. Here, we provide a timely overview of the increasing number of VAPA/B binding partners and discuss the role of VAPA/B in maintaining organelle-ER interactions and cooperation. Furthermore, we address how viruses and intracellular bacteria hijack VAPs and their binding partners to induce interactions between the host ER and pathogen-containing compartments and support pathogen replication. Finally, we focus on the role of VAP in human disease and discuss how mutated VAPB leads to the disruption of cellular homeostasis and causes amyotrophic lateral sclerosis.
T-SVD Based Broadband Non-Synchronous Measurements

It is a challenge for the state-of-the-art non-synchronous measurements beamforming method to localize multiple broa-dband sources due to the difficulty in selecting an appropriate operating frequency without any prior information about the target signals. In this paper, we propose a tensor singular value decomposition based non-synchronous measurements method for broadband multiple sound source localization. By adopting the proposed method, the working frequency range of the microphone array is no longer limited by the array geometry. While the proposed tensor completion approach via alternating direction method of multipliers algorithm could provide a sound map with distinct global view of three different speech signal sources with high accuracy in both simulation and experimental validations.
AuxFormer: Robust Approach to Audiovisual Emotion Recognition

A challenging task in audiovisual emotion recognition is to implement neural network architectures that can leverage and fuse multimodal information while temporally aligning modalities, handling missing modalities, and capturing information from all modalities without losing information during training. These requirements are important to achieve model robustness and to increase accuracy on the emotion recognition task. A recent approach to perform multimodal fusion is to use the transformer architecture to properly fuse and align the modalities. This study proposes the AuxFormer framework, which addresses in a principled way the aforementioned challenges. AuxFormer combines the transformer framework with auxiliary networks. It uses shared losses to infuse information from single-modality networks that are separately embedded. The extra layer of audiovisual information added to our main network retains information that would otherwise be lost during training. The results show that the AuxFormer architecture achieves macro and micro F1Scores of 71.3% and 71.7%, respectively, on the CREMA-D corpus. For the MSP-IMPROV corpus, AuxFormer achieves a macro and micro F1-Scores of 70.4% and 76.5%, respectively. The results for both corpora are significantly better than strong baselines, indicating that our framework benefits from auxiliary networks. We also show that under non-ideal conditions (e.g., missing modalities) our architecture is able to sustain strong performance under audio-only and video-only scenarios, benefiting from a optimized training strategy.
Multi-Task fMRI Data Fusion Using IVA and PARAFAC2

Data fusion—the joint analysis of multiple datasets—through coupled factorizations has the promise to enable enhanced knowledge discovery, and hence is an active area. Various formulations of coupled matrix factorizations have been proposed, each with its own modeling assumptions. In this paper, we study two such methods, namely Independent Vector Analysis (IVA), i.e., extension of Independent Component Analysis (ICA) to multiple datasets, and PARAFAC2, a tensor factorization approach. We demonstrate the modeling assumptions of IVA and PARAFAC2 using simulations, revealing that both methods can accurately capture the latent components, albeit with certain differences in capturing the corresponding subject scores. By making use of a rich multi-task functional Magnetic Resonance Imaging (fMRI) dataset, we show how the two methods can be used for achieving two important goals at once, namely capturing group differences between patients with schizophrenia and healthy controls with interpretable components, as well as understanding the relationship across multiple tasks. This is achieved through the definition of source component vectors across datasets.
Data-Driven Spatially Dependent PDE Identification

We propose a data-driven partial differential equation (PDE) identification scheme based on ℓ1-norm minimization which can identify spatially-dependent PDEs from measurements. Spatially-dependent PDEs refers to that the terms in the PDEs vary across space. In reality a physical system is often governed by spatially-dependent PDEs because the properties of the medium can be various across space, and the proposed method is the first data-driven spatially-dependent PDEs identification scheme. In addition, our method is efficient owing to its non-iterative nature and efficient implementation by coordinate descent.1
Multimodal Transformer with Learnable Frontend and Self Attention for Emotion Recognition

In this work, we propose a novel approach for multi-modal emotion recognition from conversations using speech and text. The audio representations are learned jointly with a learnable audio front-end (LEAF) model feeding to a CNN based classifier. The text representations are derived from pre-trained bidirectional encoder representations from transformer (BERT) along with a gated recurrent network (GRU). Both the textual and audio representations are separately processed using a bidirectional GRU network with self-attention. Further, the multi-modal information extraction is achieved using a transformer that is input with the textual and audio embeddings at the utterance level. The experiments are performed on the IEMOCAP database, where we show that the proposed framework improves over the current state-of-the-art results under all the common test settings. This is primarily due to the improved emotion recognition performance achieved in the audio domain. Further, we also show that the model is more robust to textual errors caused by an automatic speech recognition (ASR) system.
Bounded Simplex-Structured Matrix Factorization

In this paper, we propose a new low-rank matrix factorization model, dubbed bounded simplex-structured matrix factorization (BSSMF). Given an input matrix X and a factorization rank r, BSSMF looks for a matrix W with r columns and a matrix H with r rows such that X ≈ W H where the entries in each column of W are bounded, that is, they belong to given intervals, and the columns of H belong to the unit simplex, that is, H is column stochastic. BSSMF generalizes nonnegative matrix factorization (NMF), and simplex-structured matrix factorization (SSMF). BSSMF is particularly well suited when the entries of the input matrix X themselves belong to a given interval; for example when the columns of X represent images. In this paper, we first provide identifiability conditions for BSSMF, that is, we provide conditions under which BSSMF admits a unique decomposition, up to trivial ambiguities. Then we propose a fast inertial algorithm for BSSMF. Finally, we illustrate the effectiveness of BSSMF to obtain interpretable features in the MNIST dataset.
Iterative Channel Estimation and Data Detection Algorithm For OTFS Modulation

In this paper, we design an iterative channel estimation and data detection algorithm in delay-Doppler domain for orthogonal time frequency space (OTFS) system by taking advantage of the sparse nature of the channel in this domain. The proposed algorithm iterates between message-passing-aided data detection and data-aided channel estimation. This sparse channel estimation is reformulated as a specific marginalization of maximum a posteriori (MAP) problem. To deal with the intractability of this problem, we provide a Bayesian approach based on the variational mean-field approximation via the variational Bayesian expectation maximization (VB-EM) algorithm. Finally, we compare the complexity and performance in term of Bit Error Rate (BER) and Normalized Mean Square Error (NMSE) of the proposed solution to a reference solution in the literature (SP-I).
of effect of supplementation with EPA or DHA on platelet-monocyte aggregates and vascular function in healthy men. 1-3

Data were tested for normality and analysed per protocol using SPSS version 17.0. Data were 186 analysed by analysis of covariance (ANCOVA) of endpoint values, adjusted for ethnicity, age, BMI and baseline value. Normality of the residuals was checked for analysis and log transformation of the data was attempted when required. palmitic stearic linoleic alpha-linolenic P value is from univariate analysis of variance of the follow-up value by treatment with baseline value, ethnicity, BMI and age as covariates. Values in the same row with different superscripts are significantly different from each other P <0.05 using Bonferroni’s test for 3 comparisons.
What Do Users Care About? Detecting Actionable Insights from User Feedback

Users often leave feedback on a myriad of aspects of a product which, if leveraged successfully, can help yield useful insights that can lead to further improvements down the line. Detecting actionable insights can be challenging owing to large amounts of data as well as the absence of labels in real-world scenarios. In this work, we present an aggregation and graph-based ranking strategy for unsupervised detection of these insights from real-world, noisy, user-generated feedback. Our proposed approach significantly outperforms strong baselines on two real-world user feedback datasets and one academic dataset.
What Happens When We Get Rid of Levels? Instructors’ Perceptions of Mixed-Ability EFL Courses

The present study aims to investigate how general education EFL instructors who teach in a higher education institution in Korea perceive mixed-ability classes and the teaching strategies that they utilized to teach learners who have varying English proficiency levels and may have different instructional needs. Twenty instructors at a higher education institution in Korea who teach general education EFL courses participated in a questionnaire and three of the twenty instructors participated in follow-up interviews. The findings from the questionnaire and interviews showed that instructors' experiences teaching mixed-ability classes were positive. They offered clear guidelines for participation to help motivate learners and found that higher level students helped lower level students through group work. The instructors also felt that ESP could be a possibility in mixed-ability classes to meet the learners educational needs.
Fluid phase biomarkers in multiple sclerosis

Purpose of review Multiple sclerosis (MS) is highly heterogenic disorder with respect to clinical course, diagnosis, and treatment response. There is an urgent need to search for simply and reliable fluid body biomarker which would assist the diagnosis and prediction of clinical and treatment prognosis. Recent findings ‘Traditional’ MS biomarkers, with exception of cerebrospinal fluid oligoclonal bands, still are having limited clinical value. Therefore, there is growing interest in novel molecules and ingredients. The most robust results have been generated with regard to cerebrospinal fluid and serum levels of neurofilament light chains (NfL). However, there are still some limitations related to specificity of NfL which delays its use in everyday practice. We present a new approach to search for biomarkers involving extracellular RNA, particularly microRNA (miRNA), and small extracellular vesicles. MiRNA represents an important molecular mechanism influencing gene expression, including those involved in MS pathogenesis and extracellular vesicles transfer multiple cargo, including myelin molecules from parental cells of central nervous system to the long-distance targets. Summary MiRNAs which control gene expression in cells involved in autoimmune processes in MS as well as extracellular vesicles transferring myelin content might generate a new promising categories of biomarkers of MS.