Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessing Google Searches for Toothache during COVID-19 Lockdowns

Background Lockdowns due to the coronavirus disease 2019 (COVID-19) pandemic forced many dental offices to be closed. This study aims to investigate the association between COVID-19 imposed lockdowns and online searches for toothache using Google Trends (GT). Methods We investigated GT online searches for the term “toothache” within the past 5 years. The time frame for data gathering was considered as the initiation and end dates of national/regional lockdowns in each country. We used 1-way analysis of variance to identify statistical differences in relative search volumes (RSVs) between 2020 and 2016-2019 for each country. Results Overall, 16 countries were included in our analyses. Among all countries, Indonesia (n = 100), Jamaica (n = 56), Philippines (n = 56), Iran (n = 52), and Turkey (47) had the highest RSVs for toothache in the specified period. Compared with the previous 4 years, higher RSVs were seen in the world (as a whole) (2020 RSVs, 94.4; vs 2019 RSVs, 77.8 [ P < 0.001]) and 13 countries (81.3% of the included countries). Conclusion Generally, searching for the term “toothache” showed an increase during the COVID-19 lockdowns in 2020 compared with the past 4 years. This can imply the importance of dental care as urgent medical care during public health emergencies such as COVID-19.
A parasite DNA binding protein with potential to influence disease susceptibility acts as an analogue of mammalian HMGA transcription factors

Intracellular pathogens construct their environmental niche, and influence disease susceptibility, by deploying factors that manipulate infected host cell gene expression. Theileria annulata is an important tick-borne parasite of cattle that causes tropical theileriosis. Excellent candidates for modulating host cell gene expression are DNA binding proteins bearing AT-hook motifs encoded within the TashAT gene cluster of the parasite genome. In this study, TashAT2 was transfected into bovine BoMac cells to generate three expressing and three non-expressing (opposite orientation) cell lines. RNA-Seq was conducted and differentially expressed (DE) genes identified. The resulting dataset was compared with genes differentially expressed between infected cells and non-infected cells, and DE genes between infected cell lines from susceptible Holstein vs tolerant Sahiwal cattle. Over 800 bovine genes displayed differential expression associated with TashAT2, 209 of which were also modulated by parasite infection. Network analysis showed enrichment of DE genes in pathways associated with cellular adhesion, oncogenesis and developmental regulation by mammalian AT-hook bearing high mobility group A (HMGA) proteins. Overlap of TashAT2 DE genes with Sahiwal vs Holstein DE genes revealed that a significant number of shared genes were associated with disease susceptibility. Altered protein levels encoded by one of these genes (GULP1) was strongly linked to expression of TashAT2 in BoMac cells and was demonstrated to be higher in infected Holstein leucocytes compared to Sahiwal. We conclude that TashAT2 operates as an HMGA analogue to differentially mould the epigenome of the infected cell and influence disease susceptibility.
Singular Hochschild Cohomology of Radical Square Zero Algebras

Following the approach developed by Cibils and S\'{a}nchez for ordinary Hochschild cohomology, we compute the singular Hochschild cohomology of radical square zero algebras. We show that the singular Hochschild cohomology of radical square zero algebras has a very combinatorial structure. A PROP interpretation for the Gerstenhaber bracket is given in the case of radical square zero algebras. This interpretation allows us to completely determine the Gerstenhaber algebra structures for several examples.
Eosinophil: An innate immune cell with anti‐filarial vaccine and biomarker potential

Filarial infections continue to pose a great challenge in endemic countries. One of the central goals in the fight against human filarial infections is the development of strategies that will lead to the inhibition of microfilariae (mf) transmission. Keeping mf under a certain threshold within endemic populations will stop transmission and eliminate the infection.
Pulmonary vasodilator therapy in sarcoidosis‐associated pulmonary hypertension may decrease lung function decline and mortality

Abstract The efficacy of treating sarcoidosis‐associated pulmonary hypertension (SAPH) with pulmonary vasodilator therapy is unclear. The INCREASE trial showed improvement in 6‐minute walk distance (6MWD) and in decline in functional vital capacity (FVC) in patients with interstitial lung disease and pulmonary hypertension. We hypothesize that patients with SAPH treated with pulmonary vasodilators have reduced decline in FVC. We retrospectively analyzed patients with SAPH who underwent lung transplantation evaluation. The primary objective was to compare change in FVC between patients with SAPH who received pulmonary vasodilators (treated) and those who did not (untreated). Secondary objectives were to compare the change in 6MWD, change in oxygen requirement, transplant rates, and mortality between treated and untreated SAPH patients. We identified 58 patients with SAPH; 38 patients received pulmonary vasodilator therapy, and 20 patients did not. Treated SAPH patients had significantly less decline in FVC than untreated SAPH patients (+54 mL vs. −357 mL, p < 0.01). Treated SAPH patients had significantly higher survival than untreated SAPH patients. Receiving PH therapy was significantly associated with a change in FVC (estimate 0.36 ± 0.07, p < 0.01) and decreased mortality (hazard ratio 0.29, confidence interval 0.12–0.67, p < 0.01). Among patients with SAPH, those who received pulmonary vasodilator therapy had significantly less decline in FVC and increased survival. Receiving pulmonary vasodilator therapy was significantly associated with FVC change and decreased mortality. These study findings point towards potential benefit of pulmonary vasodilator therapy in SAPH patients. Further prospective studies are required to fully elucidate the benefits of pulmonary vasodilator therapy in SAPH.
Reduced cutout for reverse oblique intertrochanteric hip fractures treated with trochanteric fixation advanced (TFN-A) nail compared to the short gamma-3 nail

Background: Reverse oblique fractures (AO/OTA 31-A3) account for 5–23% of all intertrochanteric fractures and are challenging to manage. The Gamma 3-Proximal Femoral Nail (GPFN) and the Trochanteric Fixation Nail Advanced (TFNA) are two common cephalomedullary systems used to treat this fracture. No study has reported on outcomes with the TFN-A for reverse oblique fractures. This study aimed to compare outcomes and complication rates in patients with reverse oblique fractures, treated with either TFNA or GPFN. Patients and methods: A total of 203 patients with reverse oblique fractures (137 in the GPFN group and 66 in the TFNA group), were treated in our institution between June 2010 and May 2019. Data was collected on postoperative radiological variables including screw or blade location, and tip-apex distance (TAD). Data were also collected for non-orthopaedic complication rates and orthopaedic complications. A sub-group analysis was additionally performed for different nail lengths. Results: We found no significant difference in the overall rate of complications and revisions between the two groups. Patients treated with the 235 mm TFN-A nail sustained lower rates of cutout, compared to 180 mm GPFN (GPFN: 6% TFN-A: 0%, p = 0.043). The frequency of revision surgeries and malunions/non-unions did not differ significantly between the two groups and additionally showed no difference in the subgroup analysis. Conclusion: The 235 mm TFN-A was associated with lower rates of cut-out compared to the short GPFN for reverse oblique intertrochanteric fractures. Future well-designed prospective studies are warranted to investigate the role of the TFN-A in improving outcomes for such fractures.
Origin and genetic variability of populations of the invasive plant Rumex alpinus L. in the Giant (Krkonoše) Mountains

Abstract Monk's rhubarb, Rumex alpinus L. (R. alpinus), is a perennial plant native to the mountains of Central and Southern Europe. Currently, the distribution of R. alpinus has been partly affected by its utilization as a vegetable and a medicinal herb. In the mountains of the Czech Republic, it is considered an invasive plant, probably introduced into the Krkonoše Mountains by colonists from the Alps. This study's main aim was to verify whether R. alpinus was introduced into the Krkonoše Mountains by alpine colonists or whether it was anthropogenically introduced from the Carpathians. Furthermore, the genetic structure of native and introduced populations of R. alpinus was determined. For the evaluation of genetic structure, 417 samples of R. alpinus were collected from the Alps, Carpathians, Balkan, Pyrenees, and Czech Mountains. In total, 12 simple sequence repeat (SSR) markers were applied. The results of AMOVA showed a high 60% variation within populations, 27% variation among groups, and 13% among the population within groups. The overall unbiased gene diversity was high (^ĥ = 0.55). The higher level of genetic differentiation among populations (FST = 0.35; p < .01) indicated restricted gene flow between populations. Compared to native populations, limited genetic variability was observed in the nonnative populations. It was concluded that local adaptation, low gene exchange, and genetic drift affected the genetic diversity of nonnative R. alpinus. The results support a genetic link between Alpine and Czech genotypes of R. alpinus, while the Carpathians genotypes corresponded to the Balkan genotype.
A single-cell transcriptome atlas of pig skin characterizes anatomical positional heterogeneity

Different anatomical locations of the body skin show differences in their gene expression patterns depending on different origins, and the inherent heterogeneous information can be maintained in adults. However, highly resolvable cellular specialization is less well characterized in different anatomical regions of the skin. Pig is regarded as an excellent model animal for human skin research in view of its similar physiology to human. In this study, single-cell RNA sequencing was performed on pig skin tissues from six different anatomical regions of Chenghua (CH) pigs, with a superior skin thickness trait, and the back site of large white (LW) pigs. We obtained 233,715 cells, representing seven cell types, among which we primarily characterized the heterogeneity of the top three cell types, including smooth muscle cells (SMCs), endothelial cells (ECs), and fibroblasts (FBs). Then, we further identified several subtypes of SMCs, ECs, and FBs, and discovered the expression patterns of site-specific genes involved in some important pathways such as the immune response and extracellular matrix (ECM) synthesis in different anatomical regions. By comparing differentially expressed genes of skin FBs among different anatomical regions, we considered TNN, COL11A1, and INHBA as candidate genes for facilitating ECM accumulation. These findings of heterogeneity in the main skin cell types from different anatomical sites will contribute to a better understanding of inherent skin information and place the potential focus on skin generation, transmission, and transplantation, paving the foundation for human skin priming.
Analyzing Trends in Demographic, Laboratory, Imaging, and Clinical Outcomes of ICU-Hospitalized COVID-19 Patients

Background COVID-19 has led to significant hospitalization and intensive care unit admission rates. The demographic parameters of COVID-19 patients, such as age, underlying illnesses, and clinical symptoms, substantially influence the incidence and mortality of these individuals. The current study examined the clinical and demographic characteristics of COVID-19 intensive care unit (ICU) patients in Yazd, Iran. Methods The descriptive-analytical cross-sectional study was conducted on ICU patients with a positive RT-PCR test for coronavirus, admitted to the ICU in Yazd province, Iran, over 18 months. To this end, demographic, clinical, laboratory, and imaging data were collected. Moreover, patients were divided into good and worse clinical outcome groups based on their clinical outcomes. Subsequently, data analysis was performed at a 95% confidence interval (CI) using SPSS 26 software. Results 391 patients with positive PCR were analyzed. The average age of the patients in the study was 63.59 ± 17.76, where 57.3% were male. On the high-resolution computed tomography (HRCT) scan, the mean lung involvement score was 14.03 ± 6.04, where alveolar consolidation (34%) and ground-glass opacity (25.6%) were the most prevalent type of lung involvement. The most common underlying illnesses in the study participants were hypertension (HTN) (41.4%), diabetes mellitus (DM) (39.9%), ischemic heart disease (IHD) (21%), and chronic kidney disease (CKD) (20.7%). In hospitalized patients, the rates of endotracheal intubation and mortality were 38.9% and 38.1%, respectively. Age, DM, HTN, dyslipidemia, CKD, cerebral vascular accident (CVA), cerebral hemorrhage, and cancer were reported to be significantly different between these two groups of patients, indicating an increase in the rate of intubation and mortality among these patients. Furthermore, the multivariate logistic regression analysis revealed that DM, HTN, CKD, CVA, neutrophil-to-lymphocyte ratio (NLR), the percentage of lung involvement, and initial O2 saturation significantly increase the mortality of ICU patients. Conclusion Several features of COVID-19 patients influence the mortality in these individuals. According to the findings, early detection of this disease in people at high risk of death can prevent its progression and lower mortality rates.
Infusing Lattice Symmetry Priors in Attention Mechanisms for Sample-Efficient Abstract Geometric Reasoning

The Abstraction and Reasoning Corpus (ARC) (Chollet, 2019) and its most recent language-complete instantiation (LARC) has been postulated as an important step towards general AI. Yet, even state-of-the-art machine learning models struggle to achieve meaningful performance on these problems, falling behind non-learning based approaches. We argue that solving these tasks requires extreme generalization that can only be achieved by proper accounting for core knowledge priors. As a step towards this goal, we focus on geometry priors and introduce LatFormer, a model that incorporates lattice symmetry priors in attention masks. We show that, for any transformation of the hypercubic lattice, there exists a binary attention mask that implements that group action. Hence, our study motivates a modification to the standard attention mechanism, where attention weights are scaled using soft masks generated by a convolutional network. Experiments on synthetic geometric reasoning show that LatFormer requires 2 orders of magnitude fewer data than standard attention and transformers. Moreover, our results on ARC and LARC tasks that incorporate geometric priors provide preliminary evidence that these complex datasets do not lie out of the reach of deep learning models.
Matched Pair Calibration for Ranking Fairness

We propose a test of fairness in score-based ranking systems called matched pair calibration. Our approach constructs a set of matched item pairs with minimal confounding differences between subgroups before computing an appropriate measure of ranking error over the set. The matching step ensures that we compare subgroup outcomes between identically scored items so that measured performance differences directly imply unfairness in subgroup-level exposures. We show how our approach generalizes the fairness intuitions of calibration from a binary classification setting to ranking and connect our approach to other proposals for ranking fairness measures. Moreover, our strategy shows how the logic of marginal outcome tests extends to cases where the analyst has access to model scores. Lastly, we provide an example of applying matched pair calibration to a real-word ranking data set to demonstrate its efficacy in detecting ranking bias.
Generalized Pseudospectral Shattering and Inverse-Free Matrix Pencil Diagonalization

We present a randomized, inverse-free algorithm for producing an approximate diagonalization of any $n \times n$ matrix pencil $(A,B)$. The bulk of the algorithm rests on a randomized divide-and-conquer eigensolver for the generalized eigenvalue problem originally proposed by Ballard, Demmel, and Dumitriu [Technical Report 2010]. We demonstrate that this divide-and-conquer approach can be formulated to succeed with high probability provided the input pencil is sufficiently well-behaved, which is accomplished by generalizing the recent pseudospectral shattering work of Banks, Garza-Vargas, Kulkarni, and Srivastava [Foundations of Computational Mathematics 2022]. In particular, we show that perturbing and scaling $(A,B)$ regularizes its pseudospectra, allowing divide-and-conquer to run over a simple random grid and in turn producing an accurate diagonalization of $(A,B)$ in the backward error sense. The main result of the paper states the existence of a randomized algorithm that with high probability (and in exact arithmetic) produces invertible $S,T$ and diagonal $D$ such that $||A - SDT^{-1}||_2 \leq \varepsilon$ and $||B - ST^{-1}||_2 \leq \varepsilon$ in at most $O \left(\log^2 \left( \frac{n}{\varepsilon} \right) T_{\text{MM}}(n) \right)$ operations, where $T_{\text{MM}}(n)$ is the asymptotic complexity of matrix multiplication. This not only provides a new set of guarantees for highly parallel generalized eigenvalue solvers but also establishes nearly matrix multiplication time as an upper bound on the complexity of inverse-free, exact arithmetic matrix pencil diagonalization.
RLtools: A Fast, Portable Deep Reinforcement Learning Library for Continuous Control

Deep Reinforcement Learning (RL) can yield capable agents and control policies in several domains but is commonly plagued by prohibitively long training times. Additionally, in the case of continuous control problems, the applicability of learned policies on real-world embedded devices is limited due to the lack of real-time guarantees and portability of existing libraries. To address these challenges, we present RLtools, a dependency-free, header-only, pure C++ library for deep supervised and reinforcement learning. Its novel architecture allows RLtools to be used on a wide variety of platforms, from HPC clusters over workstations and laptops to smartphones, smartwatches, and microcontrollers. Specifically, due to the tight integration of the RL algorithms with simulation environments, RLtools can solve popular RL problems up to 76 times faster than other popular RL frameworks. We also benchmark the inference on a diverse set of microcontrollers and show that in most cases our optimized implementation is by far the fastest. Finally, RLtools enables the first-ever demonstration of training a deep RL algorithm directly on a microcontroller, giving rise to the field of Tiny Reinforcement Learning (TinyRL). The source code as well as documentation and live demos are available through our project page at https://rl.tools.
Evaluating the Effectiveness of Natural Language Inference for Hate Speech Detection in Languages with Limited Labeled Data

Most research on hate speech detection has focused on English where a sizeable amount of labeled training data is available. However, to expand hate speech detection into more languages, approaches that require minimal training data are needed. In this paper, we test whether natural language inference (NLI) models which perform well in zero- and few-shot settings can benefit hate speech detection performance in scenarios where only a limited amount of labeled data is available in the target language. Our evaluation on five languages demonstrates large performance improvements of NLI fine-tuning over direct fine-tuning in the target language. However, the effectiveness of previous work that proposed intermediate fine-tuning on English data is hard to match. Only in settings where the English training data does not match the test domain, can our customised NLI-formulation outperform intermediate fine-tuning on English. Based on our extensive experiments, we propose a set of recommendations for hate speech detection in languages where minimal labeled training data is available.
Synthesizing Affective Neurophysiological Signals Using Generative Models: A Review Paper

The integration of emotional intelligence in machines is an important step in advancing human-computer interaction. This demands the development of reliable end-to-end emotion recognition systems. However, the scarcity of public affective datasets presents a challenge. In this literature review, we emphasize the use of generative models to address this issue in neurophysiological signals, particularly Electroencephalogram (EEG) and Functional Near-Infrared Spectroscopy (fNIRS). We provide a comprehensive analysis of different generative models used in the field, examining their input formulation, deployment strategies, and methodologies for evaluating the quality of synthesized data. This review serves as a comprehensive overview, offering insights into the advantages, challenges, and promising future directions in the application of generative models in emotion recognition systems. Through this review, we aim to facilitate the progression of neurophysiological data augmentation, thereby supporting the development of more efficient and reliable emotion recognition systems.
Non-parametric Probabilistic Time Series Forecasting via Innovations Representation

Probabilistic time series forecasting predicts the conditional probability distributions of the time series at a future time given past realizations. Such techniques are critical in risk-based decision-making and planning under uncertainties. Existing approaches are primarily based on parametric or semi-parametric time-series models that are restrictive, difficult to validate, and challenging to adapt to varying conditions. This paper proposes a nonparametric method based on the classic notion of {\em innovations} pioneered by Norbert Wiener and Gopinath Kallianpur that causally transforms a nonparametric random process to an independent and identical uniformly distributed {\em innovations process}. We present a machine-learning architecture and a learning algorithm that circumvent two limitations of the original Wiener-Kallianpur innovations representation: (i) the need for known probability distributions of the time series and (ii) the existence of a causal decoder that reproduces the original time series from the innovations representation. We develop a deep-learning approach and a Monte Carlo sampling technique to obtain a generative model for the predicted conditional probability distribution of the time series based on a weak notion of Wiener-Kallianpur innovations representation. The efficacy of the proposed probabilistic forecasting technique is demonstrated on a variety of electricity price datasets, showing marked improvement over leading benchmarks of probabilistic forecasting techniques.