Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring the ovine sperm transcriptome by RNAseq techniques. I Effect of seasonal conditions on transcripts abundance

Understanding the cell molecular changes occurring as a results of climatic circumstances is crucial in the current days in which climate change and global warming are one of the most serious challenges that living organisms have to face. Sperm are one of the mammals’ cells most sensitive to heat, therefore evaluating the impact of seasonal changes in terms of its transcriptional activity can contribute to elucidate how these cells cope with heat stress events. We sequenced the total sperm RNA from 64 ejaculates, 28 collected in summer and 36 collected in autumn, from 40 Manchega rams. A highly rich transcriptome (11,896 different transcripts) with 90 protein coding genes that exceed an average number of 5000 counts were found. Comparing transcriptome in the summer and autumn ejaculates, 236 significant differential abundance genes were assessed, most of them (228) downregulated. The main functions that these genes are related to sexual reproduction and negative regulation of protein metabolic processes and kinase activity. Sperm response to heat stress supposes a drastic decrease of the transcriptional activity, and the upregulation of only a few genes related with the basic functions to maintain the organisms’ homeostasis and surviving. Rams’ spermatozoids carry remnant mRNAs which are retrospectively indicators of events occurring along the spermatogenesis process, including abiotic factors such as environmental temperature.
Communication Robot for Science Museum with RFID Tags

This paper reports about an interactive humanoid robots, Robovie, that work in a science museum where visitors are supposed to study and grow interests toward science. Each visitor wore an RFID tag, and looked around exhibits in the museum. Robovie has an implemented function for an autonomous interaction as free-play [1] . Moreover, it obtains its exact position, visitors' positions, and their moving history in the museum from ubiquitous sensor networks, including RFID tag readers, recording cameras and infrared cameras. It performs exhibits-guiding by moving around to several exhibits and explaining these exhibits based on the sensor information, in addition to the freeplay interaction. We compared the effect of the exhibits-guiding and the free-play interaction under three operating conditions. As a result, the free-play interaction and exhibits-guiding contributed to promote visitors' interests for science, while Robovie mostly received higher subjective impression, which is probably due to its novelty.
Quantitative susceptibility mapping reveals alterations of dentate nuclei in common types of degenerative cerebellar ataxias

Abstract The cerebellar nuclei are a brain region with high iron content. Surprisingly, little is known about iron content in the cerebellar nuclei and its possible contribution to pathology in cerebellar ataxias, with the only exception of Friedreich’s ataxia. In the present exploratory cross-sectional study, quantitative susceptibility mapping was used to investigate volume, iron concentration and total iron content of the dentate nuclei in common types of hereditary and non-hereditary degenerative ataxias. Seventy-nine patients with spinocerebellar ataxias of types 1, 2, 3 and 6; 15 patients with Friedreich’s ataxia; 18 patients with multiple system atrophy, cerebellar type and 111 healthy controls were also included. All underwent 3 T MRI and clinical assessments. For each specific ataxia subtype, voxel-based and volumes-of-interest-based group analyses were performed in comparison with a corresponding age- and sex-matched control group, both for volume, magnetic susceptiblity (indicating iron concentration) and susceptibility mass (indicating total iron content) of the dentate nuclei. Spinocerebellar ataxia of type 1 and multiple system atrophy, cerebellar type patients showed higher susceptibilities in large parts of the dentate nucleus but unaltered susceptibility masses compared with controls. Friedreich’s ataxia patients and, only on a trend level, spinocerebellar ataxia of type 2 patients showed higher susceptibilities in more circumscribed parts of the dentate. In contrast, spinocerebellar ataxia of type 6 patients revealed lower susceptibilities and susceptibility masses compared with controls throughout the dentate nucleus. Spinocerebellar ataxia of type 3 patients showed no significant changes in susceptibility and susceptibility mass. Lower volume of the dentate nuclei was found to varying degrees in all ataxia types. It was most pronounced in spinocerebellar ataxia of type 6 patients and least prominent in spinocerebellar ataxia of type 3 patients. The findings show that alterations in susceptibility revealed by quantitative susceptibility mapping are common in the dentate nuclei in different types of cerebellar ataxias. The most striking changes in susceptibility were found in spinocerebellar ataxia of type 1, multiple system atrophy, cerebellar type and spinocerebellar ataxia of type 6. Because iron content is known to be high in glial cells but not in neurons of the cerebellar nuclei, the higher susceptibility in spinocerebellar ataxia of type 1 and multiple system atrophy, cerebellar type may be explained by a reduction of neurons (increase in iron concentration) and/or an increase in iron-rich glial cells, e.g. microgliosis. Hypomyelination also leads to higher susceptibility and could also contribute. The lower susceptibility in SCA6 suggests a loss of iron-rich glial cells. Quantitative susceptibility maps warrant future studies of iron content and iron-rich cells in ataxias to gain a more comprehensive understanding of the pathogenesis of these diseases.
Development of a dose-response model for porcine cysticercosis

Taenia solium is an important cause of acquired epilepsy worldwide and remains endemic in Asia, Africa, and Latin America. Transmission of this parasite is still poorly understood despite the design of infection experiments to improve our knowledge of the disease, with estimates for critical epidemiological parameters, such as the probability of human-to-pig infection after exposure to eggs, still lacking. In this paper, a systematic review was carried out and eight pig infection experiments were analyzed to describe the probability of developing cysts. These experiments included different pathways of inoculation: with ingestion of proglottids, eggs, and beetles that ingested eggs, and direct injection of activated oncospheres into the carotid artery. In these experiments, different infective doses were used, and the numbers of viable and degenerated cysts in the body and brain of each pig were registered. Five alternative dose-response models (exponential, logistic, log-logistic, and exact and approximate beta-Poisson) were assessed for their accuracy in describing the observed probabilities of cyst development as a function of the inoculation dose. Dose-response models were developed separately for the presence of three types of cysts (any, viable only, and cysts in the brain) and considered for each of the four inoculation methods (“Proglottids”, “Eggs”, “Beetles” and “Carotid”). The exact beta-Poisson model best fit the data for the three types of cysts and all relevant exposure pathways. However, observations for some exposure pathways were too scarce to reliably define a dose-response curve with any model. A wide enough range of doses and sufficient sample sizes was only found for the “Eggs” pathway and a merged “Oral” pathway combining the “Proglottids”, “Eggs” and “Beetles” pathways. Estimated parameter values from this model suggest that a low infective dose is sufficient to result in a 50% probability for the development of any cyst or for viable cyst infections. Although this is a preliminary model reliant on a limited dataset, the parameters described in this manuscript should contribute to the design of future experimental infections related to T. solium transmission, as well as the parameterization of simulation models of transmission aimed at informing control.
Analysis of cognitive dysfunction and its risk factors in patients with hypertension

Abstract To observe whether obstructive sleep apnea syndrome (OSAS) can aggravate the cognitive dysfunction of patients with hypertension (HTN), and to explore other risk factors. One hundred one hypertensive patients were selected for information collection. After the polysomnography test, they were divided into HTN-obstructive sleep apnea (OSA) and HTN groups. The Montreal cognitive assessment and the mini-mental state examination scales were used to appraise the patients’ cognitive function. Logistic regressive analysis was used to determine the risk factors of cognitive dysfunction in patients with HTN. Compared with the HTN patients, HTN-OSA patients performed worse in mini-mental state examination (25.5 ± 2.9 vs 23.5 ± 3.2; P = .01) and Montreal cognitive assessment (28 ± 1.58 vs 21.2 ± 3.96; P = .003), and patients in the HTN-OSA group seemed more likely to suffer from dementia (31% vs 66%; P < .01). The apnea-hypopnea index (AHI) in the HTN group was lower than HTN-OSA group. Through multivariate logistic regression analysis, we can found that alcohol drinking, body mass index, long-term medication, diabetes, hypercholesterolemia, coronary heart disease, and OSAS were the independent risk factors of cognitive dysfunction in patients with HTN. OSAS can aggravate the cognitive dysfunction of hypertensive patients, besides, drinking, high-body mass index, long-term medication, diabetes, hypercholesterolemia, and coronary heart disease were also the risk factors of cognitive dysfunction in patients with hypertension. The cognitive dysfunction of patients with HTN can benefit from sleep apnea treatment.
A comparison of high-flow nasal cannula and standard facemask as pre-oxygenation technique for general anesthesia

Abstract Background: Current practice guidelines recommend the use of nasal cannula as an alternative pre-oxygenation method for tracheal intubation. However, the efficacy of high-flow nasal oxygenation versus standard facemask oxygenation has not been fully evaluated. Methods: We searched PubMed, Cochrane Library, and ClinicalTrials.gov for English-language studies published from January 1, 2000 to November 30, 2021. We included randomized controlled trials which compared high-flow nasal oxygenation and facemask oxygenation as the pre-oxygenation maneuver. Primary outcome was arterial partial pressure of oxygen (PaO2) after pre-oxygenation. Secondary outcomes were safe apnea time, arterial desaturation during intubation, lowest peripheral capillary oxygen saturation during intubation, and patient comfort score. Random-effects models and Mantel–Haenszel method were used for data synthesis. Results: A total of 16 randomized controlled trials and 1148 patients were included. High-flow nasal oxygenation achieved a higher PaO2 compared with facemask, mean difference: 64.86 mm Hg (95% confidence interval [CI]: 32.33–97.40, P < .0001). Safe apnea time was longer in high-flow nasal oxygenation, mean difference: 131.03 seconds (95% CI: 59.39–202.66, P < .0001). There was no difference in the risk of peri-intubation desaturation or lowest peripheral capillary oxygen saturation between groups. Patient comfort score was higher in high-flow nasal oxygenation, mean difference: 1.00 (95% CI: 0.46–1.54, P = .0003). Conclusion: High-flow nasal oxygenation better enhanced PaO2 and extended safe apnea time and is not inferior to facemask oxygenation in preventing desaturation during tracheal intubation. High-flow nasal oxygenation may be considered as an alternative method, especially for patients with a potential difficult airway.
Influence of habitat complexity on the prey mortality in IGP system involving insect predators (Heteroptera) and prey (Diptera): Implications in biological control

Intraguild predation (IGP) is common in the freshwater insect communities, involving a top predator, intraguild prey (IG prey) and a shared prey. Influence of the habitat complexity on the prey-predator interactions is well established through several studies. In the present instance, the IGP involving the heteropteran predators and the dipteran prey were assessed in the background of the habitat complexity. The three predators Diplonychus rusticus, Ranatra filiformis, and Laccotrephes griseus, one intraguild prey Anisops bouvieri and two dipteran prey Culex quinquefasciatus and Chironomus sp. were used in different relative density against the complex habitat conditions to deduce the impact on the mortality on the prey. In comparison to the open conditions, the presence of the macrophytes and pebbles reduced the mortality of the shared prey under intraguild system as well as single predator system. The mortality of the shared prey was however dependent on the density of the predator and prey. Considering the shared prey mortality, predation on mosquito larvae was always higher in single predator system than chironomid larvae irrespective of identity and density of predators. However, for both the shared prey, complexity of habitat reduced the prey vulnerability in comparison to the simple habitat condition. Higher observed prey consumption depicts the higher risk to predation of shared prey, though the values varied with habitat conditions. Mortality of IG prey (A. bouvieri) in IGP system followed the opposite trend of the shared prey. The lower mortality in simple habitat and higher mortality in complex habitat conditions was observed for the IG prey, irrespective of shared prey and predator density. In IGP system, the shared prey mortality was influenced by the habitat conditions, with more complex habitat reducing the vulnerability of the shared prey and increased mortality of the IG prey. This implies that the regulation of the mosquitoes, in the IGP system will be impeded by the habitat conditions, with the heteropteran predators as the top predator.
Investigating the Effect of Expiratory Time Constant on Outcome in Intubated Patients with Acute Respiratory Failure Caused by COVID-19 in Critical Care Unit: A Research Study

Background The coronavirus disease 2019 (COVID-19) has a high prevalence and mortality worldwide. Thousands of patients with acute respiratory failure caused by COVID-19 are daily hospitalized in intensive care units (ICUs) around the world. Many of these patients require full mechanical respiratory support and long-term ventilator use. Using different ventilators and calculating important variables can be helpful in meeting therapeutic needs of patients. Objectives The aim of present study was to investigate the effect of expiratory time constant (RCEXP) on the course of treatment and duration of mechanical ventilation in patients with acute respiratory failure hospitalized in ICU. Methods The present cross-sectional study was conducted on 60 patients with acute respiratory failure who were hospitalized in the ICU and underwent mechanical ventilation due to COVID-19 in the first six months of 2020. The variables of RCEXP, lung compliance and lung resistance in all patients were recorded daily and analyzed. Then, based on clinical outcome, the patients were divided into two groups: the patients with wean outcome (N = 40) and those with death outcome (N = 20). Results The mean ± SD of lung compliance in patients who were separated from ventilator and patients with death outcome were 74.73 (18.58) mL/cm H2O and 36.92 (10.56) mL/cm H2O, respectively, which was statistically significant (P = 0.001). The mean ± SD of lung resistance in patients who were separated from ventilator and patients with death outcome were calculated at 9.25 (4.62) and 14 (6.5), respectively, which was statistically significant (P = 0.015). Also, there was a statistically significant difference between the two groups in terms of mean ± SD of RCEXP (0.67 (0.23) vs. 0.49 (0.19), P = 0.010). Conclusions According to the results of this study, there was a significant difference between high resistance, low compliance, RCEXP, and weaning success of intubation in patients hospitalized in the ICU.
A Novel Approach for Clutter Reduction in Through-the-wall UWB Radar Imaging

Abstract Through-the-wall imaging (TWI) is an emerging area of research and development which is very much useful in urban warfare situations. Ultra-wide band range radar is the best suitable for this application where this range of signals has the capability of penetrating through the materials but still can provide better resolution. The incorporation of signal processing techniques on the raw data will give better representation of the scanned scene. The paper uses impulse radar for TWI, and back projection algorithm is used to retrieve the B-scan signal. A constant gain of factor 100 is used to increase the echo strength. Singular value decomposition is used to reduce the clutter from the B-scan signal and the peak signal to noise ratio is observed to be −3.79 dB.
Computer Vision and Deep Learning for Fish Classification in Underwater Habitats: A Survey

Marine scientists use remote underwater video recording to survey fish species in their natural habitats. This helps them understand and predict how fish respond to climate change, habitat degradation, and fishing pressure. This information is essential for developing sustainable fisheries for human consumption, and for preserving the environment. However, the enormous volume of collected videos makes extracting useful information a daunting and time-consuming task for a human. A promising method to address this problem is the cutting-edge Deep Learning (DL) technology.DL can help marine scientists parse large volumes of video promptly and efficiently, unlocking niche information that cannot be obtained using conventional manual monitoring methods. In this paper, we provide an overview of the key concepts of DL, while presenting a survey of literature on fish habitat monitoring with a focus on underwater fish classification. We also discuss the main challenges faced when developing DL for underwater image processing and propose approaches to address them. Finally, we provide insights into the marine habitat monitoring research domain and shed light on what the future of DL for underwater image processing may hold. This paper aims to inform a wide range of readers from marine scientists who would like to apply DL in their research to computer scientists who would like to survey state-of-the-art DL-based underwater fish habitat monitoring literature.
Transverse Electron-Beam Shaping with Light

Interfacing electrons and light enables ultrafast electron microscopy, quantum control of electrons, as well as new optical elements for high sensitivity imaging. Here we demonstrate for the first time programmable transverse electron beam shaping in free space based on ponderomotive potentials from short intense laser pulses. We can realize both convex and concave electron lenses with a focal length of a few millimeters, comparable to those in state-of-the-art electron microscopes. We further show that we can realize almost arbitrary deflection patterns by shaping the ponderomotive potentials using a spatial light modulator. Our modulator is lossless, programmable, has unity fill factor, and could pave the way to electron wavefront shaping with hundreds of individually addressable pixels.
A Neural Pairwise Ranking Model for Readability Assessment

Automatic Readability Assessment (ARA), the task of assigning a reading level to a text, is traditionally treated as a classification problem in NLP research. In this paper, we propose the first neural, pairwise ranking approach to ARA and compare it with existing classification, regression, and (non-neural) ranking methods. We establish the performance of our approach by conducting experiments with three English, one French and one Spanish datasets. We demonstrate that our approach performs well in monolingual single/cross corpus testing scenarios and achieves a zero-shot cross-lingual ranking accuracy of over 80% for both French and Spanish when trained on English data. Additionally, we also release a new parallel bilingual readability dataset, that could be useful for future research. To our knowledge, this paper proposes the first neural pairwise ranking model for ARA, and shows the first results of cross-lingual, zero-shot evaluation of ARA with neural models.
Unsupervised Extractive Opinion Summarization Using Sparse Coding

Opinion summarization is the task of automatically generating summaries that encapsulate information expressed in multiple user reviews. We present Semantic Autoencoder (SemAE) to perform extractive opinion summarization in an unsupervised manner. SemAE uses dictionary learning to implicitly capture semantic information from the review text and learns a latent representation of each sentence over semantic units. Our extractive summarization algorithm leverages the representations to identify representative opinions among hundreds of reviews. SemAE is also able to perform controllable summarization to generate aspect-specific summaries using only a few samples. We report strong performance on SPACE and AMAZON datasets and perform experiments to investigate the functioning of our model.
TSM: Measuring the Enticement of Honeyfiles with Natural Language Processing

Honeyfile deployment is a useful breach detection method in cyber deception that can also inform defenders about the intent and interests of intruders and malicious insiders. A key property of a honeyfile, enticement, is the extent to which the file can attract an intruder to interact with it. We introduce a novel metric, Topic Semantic Matching (TSM), which uses topic modelling to represent files in the repository and semantic matching in an embedding vector space to compare honeyfile text and topic words robustly. We also present a honeyfile corpus created with different Natural Language Processing (NLP) methods. Experiments show that TSM is effective in inter-corpus comparisons and is a promising tool to measure the enticement of honeyfiles. TSM is the first measure to use NLP techniques to quantify the enticement of honeyfile content that compares the essential topical content of local contexts to honeyfiles and is robust to paraphrasing.
Smoothing Matters: Momentum Transformer for Domain Adaptive Semantic Segmentation

After the great success of Vision Transformer variants (ViTs) in computer vision, it has also demonstrated great potential in domain adaptive semantic segmentation. Unfortunately, straightforwardly applying local ViTs in domain adaptive semantic segmentation does not bring in expected improvement. We find that the pitfall of local ViTs is due to the severe high-frequency components generated during both the pseudo-label construction and features alignment for target domains. These high-frequency components make the training of local ViTs very unsmooth and hurt their transferability. In this paper, we introduce a low-pass filtering mechanism, momentum network, to smooth the learning dynamics of target domain features and pseudo labels. Furthermore, we propose a dynamic of discrepancy measurement to align the distributions in the source and target domains via dynamic weights to evaluate the importance of the samples. After tackling the above issues, extensive experiments on sim2real benchmarks show that the proposed method outperforms the state-of-the-art methods. Our codes are available at https://github.com/alpc91/TransDA
UofA-Truth at Factify 2022 : Transformer And Transfer Learning Based Multi-Modal Fact-Checking

Identifying fake news is a very difficult task, especially when considering the multiple modes of conveying information through text, image, video and/or audio. We attempted to tackle the problem of automated misinformation/disinformation detection in multi-modal news sources (including text and images) through our simple, yet effective, approach in the FACTIFY shared task at De-Factify@AAAI2022. Our model produced an F1-weighted score of 74.807%, which was the fourth best out of all the submissions. In this paper we will explain our approach to undertake the shared task.