Miracle-dz/UniFAR-FTUs huggingface dataset:
A Haptoglobin Exon Copy Number Variant Associates With HIV-Associated Neurocognitive Impairment in European and African-Descent Populations

A common two-exon deletion distinguishes the gene encoding the free hemoglobin capturing protein—haptoglobin (HP)–into two alleles: HP1 and HP2. To evaluate the impact of this copy number variant (CNV) on neurocognitive impairment (NCI) in people living with HIV, we imputed this variant in 432 European-descent (EUR) and 491 African-descent (AFR) participants from the CNS HIV Antiretroviral Therapy Effects Research Study using an optimized imputation pipeline and evaluated its associations with NCI. At baseline, in AFR, the HP2 allele decreased the odds of NCI (defined by a global deficit score, GDS, ⩾ 0.5 ; Odds Ratio, OR = 0.584, p = 0.022). However, in EUR, HP2 increased the odds (OR = 2.081, p = 0.040) of NCI suggesting a detrimental effect. These effects were extended to longitudinal analyses using repeated measurements where the protective effect of the HP2 allele in AFR became marginally significant (p = 0.054) and in EUR the detrimental effect increased in significance (p = 0.037). In EUR, the HP2 allele slightly reduced the risk of NCI over time (OR = 0.028 per allele per year, p = 0.024). Further analyses of cognitive domain-specific impairment revealed that the HP-NCI effect was based on changes in learning, speed of information processing, and verbal domains over time differing by ancestry groups. Overall, these findings suggest that these functional HP CNV alleles influence the likelihood of NCI and contribute to changes in neurocognitive function over time in people living with HIV.
Audience-Dependent Explanations for AI-Based Risk Management Tools: A Survey

Artificial Intelligence (AI) is one of the most sought-after innovations in the financial industry. However, with its growing popularity, there also is the call for AI-based models to be understandable and transparent. However, understandably explaining the inner mechanism of the algorithms and their interpretation is entirely audience-dependent. The established literature fails to match the increasing number of explainable AI (XAI) methods with the different stakeholders’ explainability needs. This study addresses this gap by exploring how various stakeholders within the Swiss financial industry view explainability in their respective contexts. Based on a series of interviews with practitioners within the financial industry, we provide an in-depth review and discussion of their view on the potential and limitation of current XAI techniques needed to address the different requirements for explanations.
Automated Audio Captioning by Fine-Tuning BART with AudioSet Tags

Automated audio captioning is the multimodal task of describing environmental audio recordings with ﬂuent natural language. Most current methods utilize pre-trained analysis models to extract relevant semantic content from the audio input. However, prior information on language modeling is rarely introduced, and corresponding architectures are limited in capacity due to data scarcity. In this paper, we present a method leveraging the linguistic information contained in BART, a large-scale conditional language model with general purpose pre-training. The caption generation is conditioned on sequences of textual AudioSet tags. This input is enriched with temporally aligned audio embeddings that allows the model to improve the sound event recognition. The full BART architecture is ﬁne-tuned with few additional parameters. Experimental results demonstrate that, beyond the scaling properties of the architecture, language-only pre-training improves the text quality in the multi-modal setting of audio captioning. The best model achieves state-of-the-art performance on AudioCaps with 46.5 SPIDEr.
Modeling Risky Choices in Unknown Environments

Decision-theoretic models explain human behavior in choice problems involving uncertainty, in terms of individual tendencies such as risk aversion. However, many classical models of risk require knowing the distribution of possible outcomes (rewards) for all options, limiting their applicability outside of controlled experiments. We study the task of learning such models in contexts where the modeler does not know the distributions but instead can only observe the choices and their outcomes for a user familiar with the decision problems, for example a skilled player playing a digital game. We propose a framework combining two separate components, one for modeling the unknown decision-making environment and another for the risk behavior. By using environment models capable of learning distributions we are able to infer classical models of decision-making under risk from observations of the user’s choices and outcomes alone, and we also demonstrate alternative models for predictive purposes. We validate the approach on arti(cid:12)cial data and demonstrate a practical use case in modeling risk attitudes of professional esports teams.
Temporal Relation based Attentive Prototype Network for Few-shot Action Recognition

Few-shot action recognition aims at recognizing novel action classes with only a small number of labeled video samples. We propose a temporal relation based attentive prototype network (TRAPN) for few-shot action recognition. Concretely, we tackle this challenging task from three aspects. Firstly, we propose a spatio-temporal motion enhancement (STME) module to highlight object motions in videos. The STME module utilizes cues from content displacements in videos to enhance the features in the motion-related regions. Secondly, we learn the core common action transformations by our temporal relation (TR) module, which captures the temporal relations at short-term and long-term time scales. The learned temporal relations are encoded into descriptors to constitute sample-level features. The abstract action transformations are described by multiple groups of temporal relation descriptors. Thirdly, a vanilla prototype for the support class (e.g., the mean of the support class) cannot ﬁt well for diﬀerent query samples. We generate an attentive prototype constructed from temporal relation descriptors of support samples, which gives more weight to discriminative samples. We evaluate our TRAPN on Kinetics, UCF101 and HMDB51 real-world few-shot datasets. Results show that our network achieves the state-of-the-art performance.
Leveraging State-of-the-art ASR Techniques to Audio Captioning

This paper details our work towards leveraging state-of-the-art ASR techniques for the task of automated audio captioning. Our model architecture comprises of a convolution-augmented Transformer (Conformer) encoder and a Transformer decoder to generate nat-ural language descriptions of acoustic signals in an end-to-end manner. To overcome the limited availability of captioned audio samples for model training, we incorporate the Audioset-tags and audio-embeddings obtained from pretrained audio neural networks (PANNs) as an auxiliary input to our model. We train our model over audio samples from Clotho & AudioCaps datasets, and test over Clotho dataset’s validation and evaluation splits. Experimental results indicate that our trained models signiﬁcantly outperform the baseline system from DCASE 2021 challenge task 6.
Influence of Sandy Coast Vegetation on the Reproductive Success of Green Turtles at Cuban Nesting Beaches

Abstract. Vegetation has been shown to influence nest environment (temperature, humidity), affecting hatchling's development as well as emergence success, and has also been used as a cue during the selection of a nesting site. For green turtles, Chelonia mydas, nest density is typically higher in the vegetated zones. This study evaluated the influence of vegetation presence and percent cover on the nesting success and embryonic development of green turtles in Guanahacabibes Peninsula, Cuba. The vegetation cover had a positive and significant relationship with the number of nests (r = 0.56, p = 0.01, n = 17). Two vegetation patches are identified with different vegetation cover values: 29% for the first (closer to the high tide line) and 37% for the second. We found the highest nesting success (70%) and nest relative frequency (15% of total nesting) around the first patch of vegetation. Median hatchling emergence success was 88% (40%–96%, n = 54) in the first patch and 93% (78%–100%, n = 40) in the second. The incubation period was lower in the first patch (mean = 51 d, range = 48–60 d, n = 44) and higher in the second (mean = 54 d, range = 45–67 d, n = 39). This is the first study that reveals the positive impact of sandy coast vegetation on Guanahacabibes' green turtle nesting and will be useful in conservation actions such as native vegetation restoration plans and temperature control using shade management with vegetation.
Distribution and Relative Abundance of the Suwannee Alligator Snapping Turtle (Macrochelys suwanniensis)

Abstract. The Suwannee alligator snapping turtle (Macrochelys suwanniensis) was first described in 2014. The species is thought to occur in blackwater and spring-fed streams of sufficient size throughout the Suwannee River drainage, but we have limited detailed information regarding its range. To clarify the distribution of M. suwanniensis, we compiled 111 museum, 16 literature, and 40 other credible records and trapped streams throughout the Suwannee drainage in Georgia and Florida, plus 8 streams in the Big Bend region of Florida (total of 1893 trap nights). We documented the first records from the Willacoochee River and Okapilco, Piscola, Warrior, Jones, and Toms creeks in Georgia and from Rocky and Olustee creeks in Florida. Relative abundance based on catch per unit effort (CPUE) varied among streams (0.00–0.50) and sections of the same stream. Macrochelys suwanniensis is apparently scarce in the Okefenokee Swamp and in the Suwannee River upstream of White Springs, Florida (none trapped), but it occurs in small blackwater tributaries in this section of the river in both states. In the Suwannee River between White Springs and the estuary, we had a mean CPUE of 0.25, and the highest trapping success in Florida was in downstream reaches of the Suwannee River and in its major tributaries, the Santa Fe and New rivers. The species is widely distributed in Georgia, but relative abundance in most streams appears lower than in Florida. In Georgia, we had the highest trapping success in the Alapaha drainage and in sections of the Little River and Okapilco Creek in the Withlacoochee drainage. In Florida, we failed to trap Macrochelys in the purported distribution gap between the Suwannee and Ochlockonee drainages and in the Wacasassa River to the south, indicating that this species is restricted to the Suwannee drainage.
Short-Term Effects of Two Hurricanes on Bird Populations in Southwestern Louisiana

Abstract - Hurricanes can have strong effects on wildlife populations and their habitats, with complex positive and negative outcomes. I demonstrate the utility of Audubon Christmas Bird Count (CBC) data by comparing results of the first Creole CBC after Hurricanes Laura (Category 4 on 27 August) and Delta (Category 2 on 9 October) in 2020 against 7 years of pre-hurricane Creole CBC baseline data in coastal Cameron Parish, LA. Bird community structure following the hurricanes was significantly different compared to the previous 7 years. Species richness significantly decreased in core (i.e., absent from 2 or fewer baseline CBCs) and irregularly (absent from 3 or more baseline CBCs) occurring bird species. Particularly hard hit were landbirds, whereas waterfowl, waterbirds, and shorebirds were more likely to show positive responses. Marshbird and raptor counts had the most neutral post-hurricane responses. Migratory species across all 6 guilds were also less likely than resident species to show negative responses and more likely to show positive responses to the hurricanes. These results demonstrate differing vulnerabilities of birds to hurricanes, an understanding of which is critical for developing conservation strategies to minimize negative population effects in the face of future increased hurricane intensity.
Signal-to-noise ratio of spectrum-sliced incoherent light sources including optical modulation effects

We have derived the beat-noise-limited signal-to-noise ratio (SNR) of spectrum-sliced incoherent light sources, including optical modulation effects. SNR changes significantly in time depending on the received optical pulse shape and the receiver electrical filter. Thus, the optimum decision point at the optical receiver may be much different from the peak signal point and the sampled SNR may be different from the continuous-wave SNR. To obtain high SNR, small rise and fall times are required and the non-return-to-zero modulation format is recommended.
Enforcement FSMs – Specification and Verification of Non-Functional Properties of Program Executions on MPSoCs

Many embedded system applications impose hard real-time, energy or safety requirements on corresponding programs typically concurrently executed on a given MPSoC target platform. Even when mutually isolating applications in space or time, the enforcement of such properties, e.g., by adjusting the number of processors allocated to a program or by scaling the voltage/frequency mode of involved processors, is a difficult problem to solve, particularly in view of typically largely varying environmental input (workload) per execution. In this paper, we formalize the related control problem using finite state machine models for the uncertain environment determining the workload, the system response (feedback), as well as the enforcer strategy. The contributions of this paper are as follows: a) Rather than trace-based simulation, the uncertain environment is modeled by a discrete-time Markov chain (DTMC) as a random process to characterize possible input sequences an application may experience. b) A number of important verification goals to analyze different enforcer FSMs are formulated in PCTL for the resulting stochastic verification problem, i.e., the likelihood of violating a timing or energy constraint, or the expected number of steps for a system to return to a given execution time corridor. c) Applying stochastic model checking, i.e., PRISM to analyze and compare enforcer FSMs in these properties, and finally d) proposing an approach for reducing the environment DTMC by partitioning equivalent environmental states (i.e., input states leading to an equal system response in each MPSoC mode) such that verification times can be reduced by orders of magnitude to just a few ms for real-world examples.
The Role of the Microbiota in Graves’ Disease and Graves’ Orbitopathy

Graves‘ disease (GD) is a clinical syndrome with an enlarged and overactive thyroid gland, an accelerated heart rate, Graves’ orbitopathy (GO), and pretibial myxedema (PTM). GO is the most common extrathyroidal complication of GD. GD/GO has a significant negative impact on the quality of life. GD is the most common systemic autoimmune disorder, mediated by autoantibodies to the thyroid-stimulating hormone receptor (TSHR). It is generally accepted that GD/GO results from complex interactions between genetic and environmental factors that lead to the loss of immune tolerance to thyroid antigens. However, the exact mechanism is still elusive. Systematic investigations into GD/GO animal models and clinical patients have provided important new insight into these disorders during the past 4 years. These studies suggested that gut microbiota may play an essential role in the pathogenesis of GD/GO. Antibiotic vancomycin can reduce disease severity, but fecal material transfer (FMT) from GD/GO patients exaggerates the disease in GD/GO mouse models. There are significant differences in microbiota composition between GD/GO patients and healthy controls. Lactobacillus, Prevotella, and Veillonella often increase in GD patients. The commonly used therapeutic agents for GD/GO can also affect the gut microbiota. Antigenic mimicry and the imbalance of T helper 17 cells (Th17)/regulatory T cells (Tregs) are the primary mechanisms proposed for dysbiosis in GD/GO. Interventions including antibiotics, probiotics, and diet modification that modulate the gut microbiota have been actively investigated in preclinical models and, to some extent, in clinical settings, such as probiotics (Bifidobacterium longum) and selenium supplements. Future studies will reveal molecular pathways linking gut and thyroid functions and how they impact orbital autoimmunity. Microbiota-targeting therapeutics will likely be an essential strategy in managing GD/GO in the coming years.
COVID-19 Transcriptomic Atlas: A Comprehensive Analysis of COVID-19 Related Transcriptomics Datasets

Background: To develop anti-viral drugs and vaccines, it is crucial to understand the molecular basis and pathology of COVID-19. An increase in research output is required to generate data and results at a faster rate, therefore bioinformatics plays a crucial role in COVID-19 research. There is an abundance of transcriptomic data from studies carried out on COVID-19, however, their use is limited by the confounding factors pertaining to each study. The reanalysis of all these datasets in a unified approach should help in understanding the molecular basis of COVID-19. This should allow for the identification of COVID-19 biomarkers expressed in patients and the presence of markers specific to disease severity and condition. Aim: In this study, we aim to use the multiple publicly available transcriptomic datasets retrieved from the Gene Expression Omnibus (GEO) database to identify consistently differential expressed genes in different tissues and clinical settings. Materials and Methods: A list of datasets was generated from NCBI’s GEO using the GEOmetadb package through R software. Search keywords included SARS-COV-2 and COVID-19. Datasets in human tissues containing more than ten samples were selected for this study. Differentially expressed genes (DEGs) in each dataset were identified. Then the common DEGs between different datasets, conditions, tissues and clinical settings were shortlisted. Results: Using a unified approach, we were able to identify common DEGs based on the disease conditions, samples source and clinical settings. For each indication, a different set of genes have been identified, revealing that a multitude of factors play a role in the level of gene expression. Conclusion: Unified reanalysis of publically available transcriptomic data showed promising potential in identifying core targets that can explain the molecular pathology and be used as biomarkers for COVID-19.
The P3 O-Tert-Butyl-Threonine is Key to High Cellular and Antiviral Potency for Aldehyde-Based SARS-CoV-2 Main Protease Inhibitors

As an essential enzyme to SARS-CoV-2, main protease (MPro) is a viable target to develop antivirals for the treatment of COVID-19. By varying chemical compositions at both P2 and P3 sites and the N-terminal protection group, we synthesized a series of MPro inhibitors that contain β-(S-2-oxopyrrolidin-3-yl)-alaninal at the P1 site. These inhibitors have a large variation of determined IC50 values that range from 4.8 to 650 nM. The determined IC50 values reveal that relatively small side chains at both P2 and P3 sites are favorable for achieving high in vitro MPro inhibition potency, the P3 site is tolerable toward unnatural amino acids with two alkyl substituents on the α-carbon, and the inhibition potency is sensitive toward the N-terminal protection group. X-ray crystal structures of MPro bound with 16 inhibitors were determined. All structures show similar binding patterns of inhibitors at the MPro active site. A covalent interaction between the active site cysteine and a bound inhibitor was observed in all structures. In MPro, large structural variations were observed on residues N142 and Q189. All inhibitors were also characterized on their inhibition of MPro in 293T cells, which revealed their in cellulo potency that is drastically different from their in vitro enzyme inhibition potency. Inhibitors that showed high in cellulo potency all contain O-tert-butyl-threonine at the P3 site. Based on the current and a previous study, we conclude that O-tert-butyl-threonine at the P3 site is a key component to achieve high cellular and antiviral potency for peptidyl aldehyde inhibitors of MPro. This finding will be critical to the development of novel antivirals to address the current global emergency of concerning the COVID-19 pandemic.
Tackling social media data analysis: Comparing and contrasting QSR NVivo and Leximancer

PURPOSE: This paper offers insights into the ways two computer-aided qualitative data analysis software (CAQDAS) applications (QSR NVivo and Leximancer) can be used to analyse big, text-based data taken from consumer-to-consumer (C2C) social media communication. DESIGN/ METHODOLOGY/ APPROACH: This study used QSR NVivo and Leximancer, to explore 200 discussion threads containing 1,796 posts from forums on an Online Open Community and an Online Brand Community that involved Online Brand Advocacy. The functionality, in particular, the strengths and weaknesses of both programs are discussed. Examples of the types of analyses each program can undertake and the visual output available are also presented. FINDINGS: This research found that, while both programs had strengths and weaknesses when working with big, text-based, online data, they complemented each other. Each contributed a different visual and evidence-based perspective; providing a more comprehensive and insightful view of the characteristics unique to online consumer brand advocacy. RESEARCH IMPLICATIONS: Qualitative market researchers are offered insights into the advantages and disadvantages of using two different software packages for research projects involving big social media data. The ‘visual-first’ analysis, obtained from both programs can help researchers make sense of such data, particularly in exploratory research.
Naming the Most Anomalous Cluster in Hilbert Space for Structures with Attribute Information

We consider datasets consisting of arbitrarily structured entities (e.g., molecules, sequences, graphs, etc) whose similarity can be assessed with a reproducing ker- nel (or a family thereof). These entities are assumed to additionally have a set of named attributes (e.g.: number_of_atoms, stock_price, etc). These attributes can be used to classify the structured entities in discrete sets (e.g., ‘number_of_atoms < 3’, ‘stock_price ≤ 100’, etc) and can effectively serve as Boolean predicates. Our goal is to use this side-information to provide explain- able kernel-based clustering. To this end, we propose a method which is able to find among all possible entity subsets that can be described as a conjunction of the available predicates either a) the optimal cluster within the Reproducing Kernel Hilbert Space, or b) the most anomalous subset within the same space. Our method works employs combinatorial optimisation via an adaptation of the Maximum-Mean-Discrepancy measure that captures the above intuition. Finally, we propose a criterion to select the optimal one out of a family of kernels in a way that preserves the available side-information. We provide several real world datasets that demonstrate the usefulness of our proposed method.