Miracle-dz/UniFAR-FTUs huggingface dataset:
Patulin Stimulates Progenitor Leydig Cell Proliferation but Delays Its Differentiation in Male Rats during Prepuberty

Patulin is a mycotoxin with potential reproductive toxicity. We explored the impact of patulin on Leydig cell (LC) development in male rats. Male Sprague Dawley rats (21 days postpartum) were gavaged patulin at doses of 0.5, 1, and 2 mg/kg/day for 7 days. Patulin markedly lowered serum testosterone at ≥0.5 mg/kg and progesterone at 1 and 2 mg/kg, while increasing LH levels at 2 mg/kg. Patulin increased the CYP11A1+ (cholesterol side-chain cleavage, a progenitor LC biomarker) cell number and their proliferation at 1 and 2 mg/kg. Additionally, patulin downregulated Lhcgr (luteinizing hormone receptor), Scarb1 (high-density lipoprotein receptor), and Cyp17a1 (17α-hydroxylase/17,20-lyase) at 1 and 2 mg/kg. It increased the activation of pAKT1 (protein kinase B), pERK1/2 (extracellular signal-related kinases 1 and 2), pCREB (cyclic AMP response binding protein), and CCND1 (cyclin D1), associated with cell cycle regulation, in vivo. Patulin increased EdU incorporation into R2C LC and stimulated cell cycle progression in vitro. Furthermore, patulin showed a direct inhibitory effect on 11β-HSD2 (11β-hydroxysteroid dehydrogenase 2) activity, which eliminates the adverse effects of glucocorticoids. This study provides insights into the potential mechanisms via which patulin affects progenitor LC development in young male rats.
Cell Cycle Reactivation, at the Start of Neurodegeneration, Induced by Forskolin and Aniline in Differentiated Neuroblastoma Cells

A characteristic hallmark of Alzheimer’s disease (AD) is the intracellular accumulation of hyperphosphorylated tau protein, a phenomenon that appears to have associations with oxidative stress, double-stranded DNA breakage, and the de-condensation of heterochromatin. Re-entry into the cell division cycle appears to be involved in the onset of this neurodegenerative process. Indeed, the cell cycle cannot proceed regularly in the differentiated neurons leading to cell death. Here, we induced cell cycle reactivation in neuronal-like cells, obtained by neuroblastoma cells treated with retinoic acid, by exposure to forskolin or aniline. These compounds determine tau hyperphosphorylation or oxidative stress, respectively, resulting in the appearance of features resembling the start of neuronal degeneration typical of AD, such as tau hyperphosphorylation and re-entry into the cell cycle. Indeed, we detected an increased transcriptional level of cyclins and the appearance of a high number of mitotic cells. We also observed a delay in the initiation of the cell cycle when forskolin was co-administered with pituitary adenylate cyclase-activating polypeptide (PACAP). This delay was not observed when PACAP was co-administered with aniline. Our data demonstrate the relevance of tau hyperphosphorylation in initiating an ectopic cell cycle in differentiated neuronal cells, a condition that can lead to neurodegeneration. Moreover, we highlight the utility of neuroblastoma cell lines as an in vitro cellular model to test the possible neuroprotective effects of natural molecules.
Probing Prior Knowledge Needed in Challenging Chinese Machine Reading Comprehension

With an ultimate goal of narrowing the gap between human and machine readers in text comprehension, we present the first collection of Challenging Chinese machine reading Comprehension datasets (C^3) collected from language and professional certification exams, which contains 13,924 documents and their associated 23,990 multiple-choice questions. Most of the questions in C^3 cannot be answered merely by surface-form matching against the given text. As a pilot study, we closely analyze the prior knowledge (i.e., linguistic, domain-specific, and general world knowledge) needed in these real-world reading comprehension tasks. We further explore how to leverage linguistic knowledge including a lexicon of idioms and proverbs, graphs of general world knowledge (e.g., ConceptNet), and domain-specific knowledge such as textbooks to aid machine readers, through fine-tuning a pre-trained language model. Experimental results demonstrate that linguistic and general world knowledge may help improve the performance of the baseline reader in both general and domain-specific tasks. C^3 will be available at this http URL.
Small Organic Compounds Mimicking the Effector Domain of Myristoylated Alanine-Rich C-Kinase Substrate Stimulate Female-Specific Neurite Outgrowth

Myristoylated alanine-rich C-kinase substrate (MARCKS) is a critical member of a signaling cascade that influences disease-relevant neural functions such as neural growth and plasticity. The effector domain (ED) of MARCKS interacts with the extracellular glycan polysialic acid (PSA) through the cell membrane to stimulate neurite outgrowth in cell culture. We have shown that a synthetic ED peptide improves functional recovery after spinal cord injury in female but not male mice. However, peptides themselves are unstable in therapeutic applications, so we investigated more pharmacologically relevant small organic compounds that mimic the ED peptide to maximize therapeutic potential. Using competition ELISAs, we screened small organic compound libraries to identify molecules that structurally and functionally mimic the ED peptide of MARCKS. Since we had shown sex-specific effects of MARCKS on spinal cord injury recovery, we assayed neuronal viability as well as neurite outgrowth from cultured cerebellar granule cells of female and male mice separately. We found that epigallocatechin, amiodarone, sertraline, tegaserod, and nonyloxytryptamine bind to a monoclonal antibody against the ED peptide, and compounds stimulate neurite outgrowth in cultured cerebellar granule cells of female mice only. Therefore, a search for compounds that act in males appears warranted.
Integrating and evaluating planning primitives for robot manipulation tasks in warehouse logistics

OF THE THESIS Integrating and Evaluating Planning Primitives for Robot Manipulation Tasks in Warehouse Logistics by Zacharias Psarakis Thesis Director: Kostas Bekris The Amazon Robotics Challenge was an event created by Amazon to bring robotics teams together and try to push forward the research on robotics automation related to warehouse logistics. The challenge consists of two tasks. The picking task requires a fully autonomous robotic system to remove target objects from a shelving unit into a tote. The stowing task requires placing objects from a tote into the shelving unit. The purpose of this thesis is to present state-of-the-art approaches related to motion, grasp and task planning, pose estimation solutions, and how different end-effector modalities can affect the performance of the autonomous system. Two grasp planning and three task planning approaches are evaluated and combined with a PRM* motion planner in the context of the Amazon Robotics Challenge tasks.
The Global Observing System in the Assimilation Context

Abstract Weather and climate analyses and predictions all rely on the global observing system. However, the observing system, whether atmosphere, ocean, or land surface, yields a diverse set of incomplete observations of the different components of Earth’s environment. Data assimilation systems are essential to synthesize the wide diversity of in situ and remotely sensed observations into four-dimensional state estimates by combining the various observations with model-based estimates. Assimilation, or associated tools and products, are also useful in providing guidance for the evolution of the observing system of the future. This paper provides a brief overview of the global observing system and information gleaned through assimilation tools, and presents some evaluations of observing system gaps and issues. 1. Introduction Global networks that observe the Earth provide a huge variety of observations of the state of the atmosphere, ocean and land surface from in situ
Performance, effectiveness and computational efficiency of powerline extraction methods for quantifying ecosystem structure from light detection and ranging

ABSTRACT National and regional data products of the ecosystem structure derived from airborne laser scanning (ALS) surveys with Light Detection And Ranging (LiDAR) technology are essential for ecology, biodiversity, and ecosystem monitoring. However, noises like powerlines often remain, hindering the accurate measurement of 3D ecosystem structures from LiDAR. Currently, there is a lack of studies assessing powerline noise removal in the context of generating data products of ecosystem structures from ALS point clouds. Here, we assessed the (1) performance and accuracy, (2) effectiveness, and (3) time efficiency and execution time of three powerline extraction methods (i.e. two point-based methods based on deep learning and eigenvalue decomposition, respectively, and one hybrid method) for removing powerline noise when quantifying 3D ecosystem structures in landscapes with varying canopy heights and vegetation openness. Twenty-five LiDAR metrics representing three key dimensions of the ecosystem structure (i.e. vegetation height, cover, and vertical variability) across 10 study areas in the Netherlands were used for our assessment. The deep learning method had the best performance and showed the highest accuracy of powerline removal across various landscape types (average $F1$F1 score = 96%), closely followed by the hybrid method (average $F1$F1 score = 95%). In contrast, the accuracy of the eigenvalue decomposition method was lower (average $F1$F1 score = 82%) and depended on landscape context and vegetation composition (e.g. the $F1$F1 score decreased from 96% to 63% when the average canopy height increased across landscapes). Powerline noise removal had the highest effectiveness (i.e. generating LiDAR metrics closest to those derived from manually labeled ground truth data) for LiDAR metrics capturing height and cover of low- and high-vegetation layers. Time efficiency (processed points per second) was highest for the eigenvalue decomposition method, yet the hybrid method reduced the execution time by > 50% compared to the deep learning method (ranging from 20% to 89% in study areas with different landscape composition). Based on our findings, we recommend the hybrid method for upscaling powerline removal on multi-terabyte ALS datasets to a regional or national extent because of its high accuracy and computational efficiency. Remaining misclassifications in LiDAR metrics could be further minimized by improving the training dataset for deep learning models (e.g. including various shapes of transmission towers from different datasets). Our findings provide novel insights into the performance of different powerline extraction methods, how their effectiveness varies for improving vegetation metrics and mapping the 3D ecosystem structure from LiDAR, and their computational efficiency for upscaling powerline removal in multi-terabyte ALS datasets to a national extent.
Effect of thymol and Enterocin M administration on biochemical, antioxidant and immunological parameters, small intestinal morphology and microbiota in rabbits.

Abstract To find natural feed additives with a beneficial effect on rabbit health, thymol alone and in combination with Enterocin M were administered in drinking water for 42 days (35 -77 days of age). A total of 48 rabbits based on their weight were randomly divided into four experimental groups: C – control (basal diet), T – thymol (250 mg/L), E – Enterocin M (Ent M) (50 μL/animal/day), T + E (thymol with Ent M). Ent M (p < .05) and thymol (p < .01) separately decreased malondialdehyde in the liver. Thymol separately and in combination significantly increased phagocytic activity in the blood (p = .0051) and lactic acid in the caecum (p = .0142) and decreased coagulase-positive staphylococci in the caecum (p = .0329). Ent M separately and in combination increased immunoglobulin A content in the jejunal wall (p = .002) and decreased coliform bacteria in faeces (p = .0002). Thymol and Ent M application separately or in combination improved the antioxidant and immune response of rabbits and demonstrated an antibacterial effect. HIGHLIGHTS Thymol demonstrated antibacterial effect against coagulase-positive staphylococci (CoPS) the in caecum, the formation of malondialdehyde (MDA) as a product of lipid peroxidation in liver, and a stimulation effect on fermentation the processes in the caecum and on phagocytic activity in blood. Ent M decreased coliform bacteria in faeces and stimulated the production of IgA in jejunum. Ent M together with thymol improved the structure of the jejunal wall and liver function.
Exploration of Pyrolysis Behaviors of Waste Plastics (Polypropylene Plastic/Polyethylene Plastic/Polystyrene Plastic): Macro-Thermal Kinetics and Micro-Pyrolysis Mechanism

Pyrolysis is a promising technology used to recycle both the energy and chemicals in plastics. Three types of plastics, polyethylene plastic (PE), polypropylene plastic (PP) and polystyrene plastic (PS) were investigated using thermogravimetry–mass spectrometry (TG–MS) and reactive force field molecular dynamics (ReaxFF-MD) simulation. The thermogravimetric analysis showed that all three plastics lost weight during the pyrolysis in one step. The thermal decomposition stability is PS < PP < PE. The activation energies and reaction mechanism function of the three plastics were determined by the Kissinger and CR methods. Meanwhile, the ReaxFF-MD combined with density functional theory (DFT) was used to calculate the kinetics, as well as explore the pyrolysis mechanism. The calculated kinetic results agree well with the experimental methods. The common pyrolysis reaction process follows the dissociation sequence of the polymer to polymeric monomer and, then, to the gas molecules. Based on the bond length between the monomers and the bond dissociation energy for different plastics, the required energy for polymer dissociation is PS < PP < PE, which microscopically explains the macro-activation energy sequence and thermal stability. Moreover, due to the retention of aromatic rings in its monomers, PS almost completely converts into oil.
It’s not just about how long you play. Indirect gaming involvement and genre preferences in predicting gaming disorder risk: evidence from preregistered studies

Introduction The strength of the association between gaming involvement and gaming disorder is weak to moderate. Gamers cannot be directly involved in gaming all the time, but how much they are involved in activities indirectly related to gaming during gaming-free time may play an important role. Also, specific game genres may matter. The present investigation focuses on the role of indirect gaming involvement and genres in gaming disorder risk prediction. Methods Two pre-registered studies were conducted. Study 1 (N = 205) was conducted online, whereas Study 2 (N = 250) was conducted in a lab. In both cases, participants reported their direct and indirect involvement in gaming (DGI and IGI, respectively) and completed a screening tool that estimates the risk of gaming disorder (Gaming Disorder Test). Results Both IGI and DGI were weakly to moderately correlated with gaming disorder (GD) and moderately with each other. The correlations between DGI and GD were similar to those obtained in related studies; the correlation between IGI and GD has not been previously reported. Hierarchical regression that took IGI together with DGI into account showed an increase in the percentage of explained variance, but only in Study 1. Contrary to expectations, IGI did not interact with DGI. As is consistent with previous research, some game genres were found to be more closely related to GD than others: in both studies, this was an RPG; in Study 1, this was also an MMORPG; in Study 2, driving and shooting games also predicted GD risk. Discussion Overall, the results clearly indicate that not only gaming time plays a role in GD risk assessment: IGI can also predict it and in some cases may allow for more accurate predictions. Gaming genres once again proved to play a role, but these and similar results should be treated with caution due to the partial lack of repeatability.
Geochemical and mineralogical analysis of Mars analogue materials and the creation of the International Space Analogue Rock Store (ISAR)

In order to prepare for the next in situ missions to Mars (MSL-2011 and ExoMars-2018), the objective of mythesis is to create a collection of relevant analogue rocks and minerals for calibrating and testing future (and existing) space flight instruments, in accordance with the geology of Mars. They were characterized using standard laboratory instrumentation (optical microscopy, Raman, IR, XRD, SEM, electron microprobe and ICP-MS), as well as by flight instrumentation in development (Mossbauer MIMOSII, ExoMars Raman and IR (MicrOmega) spectrometers). All the samples are described in an online database in the following web site: www.isar. cnrs-orleans.fr. A part of this thesis is dedicated to the development of a cathodoluminescence (CL) instrument that could potentially be adapted for space flight. Study of alteration processes of basalts on Earth that show some similarities to surface and subsurface processes occurring on Mars may help understand and interpret martian features. Therefore, to complete the collection, samples of hydrothermal and acidic weathered basalts were collected from the Skouriotissa mine in Cyprus. The mineralogical evolution of the basalt through different alteration facies was studied. Because terrestrial basalts are poorer in Fe and Mg than martian basalts, I synthesized two artificial martian basalts. The two artificial basalts are different in terms of cooling rate (~110°C/h and drop-quenched, >1200°C/h). Interestingly, the more slowly-cooled sample exhibits a spinifex texture, similar to that of komatiites. If similar basalts occur on Mars, such rocks when altered by aqueous processes may have astrobiological implications.
User experience of mixed reality applications for healthy ageing: A systematic review

Mixed reality (MR) technologies are being used increasingly to support healthy ageing, but past reviews have concentrated on the efficacy of the technology. This systematic review provides a synthesis of recent experimental studies on the instrumental, emotional and non-instrumental aspects of user experience of healthy older adults in relation to MR-related applications. The review was listed on PROSPERO, utilised a modified PICOS framework, and canvassed all published work between January 2010 to July 2021 that appeared in major databases (Scopus, PubMed, CINAHL, Web of Science, and the Cochrane Library). The literature search revealed 15 eligible studies. Results indicated that all included studies measured the instrumental quality of their applications, all but two studies measured the emotional reactions triggered by gameplay, and only six studies examined participants’ perception of non-instrumental quality of the applications. All included studies focused on improving a health domain such as cognitive or physical training. This suggests that the instrumental quality of the MR applications remains the focus of user experience studies, with far fewer studies examining the non-instrumental quality of the applications. Implications for game design and future research are discussed.
Silt Geochronology and Mineralogy, Mount Daisen, Japan

The mineralogy and geochronology of silt fractions were determined for a representative Andisol (volcanic soil) from the Mt. Daisen area as well as for collected bulk reference fallout materials from Japan, and Kyoto dust samples. X-ray diffraction techniques and K-Ar methods were used to determine the age values and mineralogy of test materials. The reference fallout material was the youngest (21 Ma). The Kyoto dust was oldest (210 Ma) of all materials. The silt from the Andisol yielded older age values closer to the top and younger values closer to the parent tephra. The silt mineralogy revealed small amounts of 10 Å and 14 Å phases. The presence of these phases in the silt fractions of soils having parent dactic-basaltic tephra (<1 Ma) combined with the geochronology ages of the silt fraction (21 – 210 Ma) support the hypothesis that there is an aeolian influence present in the Andisol. INDEX WORDS: Japan, Loess, Andisol, K-Ar, Geochronology, Dust, Soil
Orbit Determination Using Batch Sequential Filter

Data filtering is an important technique used for modeling in many areas of disciplines. The paper discusses the square root information estimation, starting from its beginning in least-squares parameter estimation. Special attention is devoted to discussions of the sensitivity and perturbation matrices, computed solutions and their formal statistics, consider parameters and consider-covariance, and the effects of a prior statistics. The constant-parameter model is extended to include time-varying parameters and process noise, and also the error analysis capabilities are generalized. Efficient and elegant smoothing results are obtained as easy consequences of the filter formulation. This paper characterizes the improved computational efficiency of Batch-Sequential orbit determination. Filter tests consider improvements for several simulated satellite scenarios with several combinations of measurements provide for estimation. One way to improve orbit determination for the satellite is to make use of new estimation techniques. This has been accomplished by applying the Batch-Sequential Filter to the problem of orbit determination. The BSF out performs other nonlinear filters because it treats non-linearity in both the measurement and dynamic functions. The performance of this filter is evaluated in comparison to an existing Kalman Filter.
Cross-Sensor Correlative Feature Learning and Fusion for Intelligent Fault Diagnosis

With the maturity of big data and computing power, deep learning has provided an end-to-end efficient solution for fault diagnosis of rotating machinery. However, the diagnosis performance is commonly affected by complex working environment and limited labeled samples. While considering these undesirable effects and borrowing from multisource fusion techniques, we propose a novel fault diagnosis method based on cross-sensor correlative feature learning and fusion. First, global–local temporal encoder is utilized to learn the time-domain features of multiple sensor data. Meanwhile, time–frequency encoder is performed to obtain the corresponding time–frequency domain features. Then, features of the two modes are fused to get the initial results. Finally, they are put through cross-sensor correlative channel-aware fusion to achieve a final result. Furthermore, two datasets are selected to verify the effectiveness of the proposed method. The results demonstrate that our method is effective, robust, and suitable for diagnosis under limited data and complex conditions.
Challenges and Prospects in Vision and Language Research

Language grounded image understanding tasks have often been proposed as a method for evaluating progress in artificial intelligence. Ideally, these tasks should test a plethora of capabilities that integrate computer vision, reasoning, and natural language understanding. However, the datasets and evaluation procedures used in these tasks are replete with flaws which allows the vision and language (V&L) algorithms to achieve a good performance without a robust understanding of vision and language. We argue for this position based on several recent studies in V&L literature and our own observations of dataset bias, robustness, and spurious correlations. Finally, we propose that several of these challenges can be mitigated by creation of carefully designed benchmarks.