Miracle-dz/UniFAR-FTUs huggingface dataset:
Disruption of the Mammalian Ccr4–Not Complex Contributes to Transcription-Mediated Genome Instability

The mammalian Ccr4–Not complex, carbon catabolite repression 4 (Ccr4)-negative on TATA-less (Not), is a large, highly conserved, multifunctional assembly of proteins that acts at different cellular levels to regulate gene expression. It is involved in the control of the cell cycle, chromatin modification, activation and inhibition of transcription initiation, control of transcription elongation, RNA export, and nuclear RNA surveillance; the Ccr4–Not complex also plays a central role in the regulation of mRNA decay. Growing evidence suggests that gene transcription has a vital role in shaping the landscape of genome replication and is also a potent source of replication stress and genome instability. Here, we have examined the effects of the inactivation of the Ccr4–Not complex, via the depletion of the scaffold subunit CNOT1, on DNA replication and genome integrity in mammalian cells. In CNOT1-depleted cells, the elevated expression of the general transcription factor TATA-box binding protein (TBP) leads to increased RNA synthesis, which, together with R-loop accumulation, results in replication fork slowing, DNA damage, and senescence. Furthermore, we have shown that the stability of TBP mRNA increases in the absence of CNOT1, which may explain its elevated protein expression in CNOT1-depleted cells. Finally, we have shown the activation of mitogen-activated protein kinase signalling as evidenced by ERK1/2 phosphorylation in the absence of CNOT1, which may be responsible for the observed cell cycle arrest at the border of G1/S.
The Outlook of Healthcare Providers on the Involvement of Diabetic Patients as Health Promoters for Diabetes Prevention Among Their Family Members: A Qualitative Study

Background and objective Examples of patients becoming health promoters for diabetes prevention in their own families, although few, are on the rise. Nevertheless, despite this increase in patient involvement in diabetes prevention, there is scarce research regarding healthcare providers’ perspectives on the active engagement of patients as health promoters for their family members. In light of this, we aimed to explore the perspectives of healthcare providers working at primary health clinics regarding patient involvement in diabetes prevention among their own family members and close relatives. Methodology This study was conducted between July and December 2022 at the Primary Healthcare Clinics at King Abdulaziz Medical City, Ministry of National Guard Health Affairs, in Riyadh and Jeddah, Saudi Arabia. Semistructured interviews based on purposive sampling were conducted with 13 participants, and their data were thematically analyzed. Results Two main themes emerged from the interviews: the patients' readiness to be health promoters and the willingness of healthcare staff to support promoter patients. Healthcare providers perceived the involvement of diabetes patients in promoting the health of their family members and close relatives as beneficial; however, several barriers may prevent these patients from becoming effective health promoters. Conclusions Healthcare providers understand the significance of involving patients with diabetes as health promoters for their family members and close relatives. Patients can offer unique insights into the lived experience of diabetes management, as well as provide practical advice for lifestyle adjustments. Nevertheless, healthcare providers should also recognize the limits of patients’ knowledge and skills and ensure that patients receive proper training and support to serve as effective health educators.
Correlations between interacting Rydberg atoms

This paper is a short introduction to Rydberg physics and quantum nonlinear optics using Rydberg atoms. It has been prepared as a compliment to a series of lectures delivered during the Latin American School of Physics “Marcos Moshinsky” 2017. We provide a short introduction to the properties of individual Rydberg atoms and discuss in detail how the interaction potential between Rydberg atom pairs is calculated. We then discuss how this interaction gives rise to the Rydberg blockade mechanism. With the aid of hallmark experiments in the field applications of the blockade for creating correlated quantum systems are discussed. Our aim is to give an overview of this exciting and rapidly evolving field. The interested reader is referred to original work and more comprehensive reviews and tutorials for further details on these subjects.This paper is a short introduction to Rydberg physics and quantum nonlinear optics using Rydberg atoms. It has been prepared as a compliment to a series of lectures delivered during the Latin American School of Physics “Marcos Moshinsky” 2017. We provide a short introduction to the properties of individual Rydberg atoms and discuss in detail how the interaction potential between Rydberg atom pairs is calculated. We then discuss how this interaction gives rise to the Rydberg blockade mechanism. With the aid of hallmark experiments in the field applications of the blockade for creating correlated quantum systems are discussed. Our aim is to give an overview of this exciting and rapidly evolving field. The interested reader is referred to original work and more comprehensive reviews and tutorials for further details on these subjects.
The role of ICTs on the function of sleep and its effects on learning and brai

Numerous strategies have the potential to improve learning. Since memory is a fundamental cognitive skill that underpins learning, many approaches are employed in education to enhance it. If learning is a natural process, does this mean that there is a natural mechanism that supports learning? It is confirmed in this review that sleep is such a mechanism. According to the population's age, various effects of sleep on the brain and learning are supported by the research on this topic. For proper brain development and cognition, sleep is a crucial activity. In order to effectively exploit this knowledge and create fresh educational initiatives, additional studies need be conducted.
Tortoise or Hare? The Associations between Physical Activity Volume and Intensity Distribution and the Risk of All-Cause Mortality: A Large Prospective Analysis of the UK Biobank

Analysis methods to determine the optimal combination of volume and intensity of objectively measured physical activity (PA) with prospective outcomes are limited. Participants in UK Biobank were recruited in the UK between 2006 and 2010. We linked the questionnaire and accelerometer with all-cause mortality data from the NHS Information Centre and NHS Central Register up to April 2021. We developed a novel method, extending the penalized spline model of Augustin et al. to a smooth additive Cox model for survival data, and estimated the prospective relationship between intensity distribution and all-cause mortality, adjusting for the overall volume of PA. We followed 84,166 men and women (aged 40–69) for an average of 6.4 years (range 5.3–7.9), with an observed mortality rate of 22.2 deaths per 1000. Survival rates differed by PA volume quartile, with poorer outcomes for the lowest PA volumes. Participants with more sedentary to light intensity PA (<100 milligravities (mg)) and/or less vigorous intensity PA (>250 mg) than average for a given volume of PA, had higher mortality rates than vice versa. Approximate hazard ratios were 0.83 (95% credible interval [CI]: 0.79, 0.88) for an average-risk profile compared to a high-risk profile and 0.80 (95% CI: 0.74, 0.87) for a low-risk profile compared to an average-risk profile. A high- versus low-risk profile has the equivalent of 15 min more slow walking, but 10 min less moderate walking. At low PA volumes, increasing overall volume suggests the most benefit in reducing all-cause mortality risk. However, at higher overall volumes, substituting lighter with more vigorous intensity activity suggests greater benefit.
A single-column ocean biogeochemistry model (GOTM–TOPAZ) version 1.0

Abstract. Recently, Earth system models (ESMs) have begun to consider the marine ecosystem to reduce errors in climate simulations. However, many models are unable to fully represent the ocean-biology-induced climate feedback, which is due in part to significant bias in the simulated biogeochemical properties. Therefore, we developed the Generic Ocean Turbulence Model–Tracers of Phytoplankton with Allometric Zooplankton (GOTM–TOPAZ), a single-column ocean biogeochemistry model that can be used to improve ocean biogeochemical processes in ESMs. This model was developed by combining GOTM, a single-column model that can simulate the physical environment of the ocean, and TOPAZ, a biogeochemical module. Here, the original form of TOPAZ has been modified and modularized to allow easy coupling with other physical ocean models. To demonstrate interactions between ocean physics and biogeochemical processes, the model was designed to allow ocean temperature to change due to absorption of visible light by chlorophyll in phytoplankton. We also added a module to reproduce upwelling and the air–sea gas transfer process for oxygen and carbon dioxide, which are of particular importance for marine ecosystems. The simulated variables (e.g., chlorophyll, oxygen, nitrogen, phosphorus, silicon) of GOTM–TOPAZ were evaluated by comparison against observations. The temporal variability in the observed upper-ocean (0–20 m) chlorophyll is well captured by the GOTM–TOPAZ with a correlation coefficient of 0.53 at point 107 in the Sea of Japan. The surface correlation coefficients among GOTM–TOPAZ oxygen, nitrogen, phosphorus, and silicon are 0.47, 0.31, 0.16, and 0.19, respectively. We compared the GOTM–TOPAZ simulations with those from MOM–TOPAZ and found that GOTM–TOPAZ showed relatively lower correlations, which is most likely due to the limitations of the single-column model. Results also indicate that source–sink terms may contribute to the biases in the surface layer (<60 m), while initial values are important for realistic simulations in the deep sea (>250 m). Despite this limitation, we argue that our GOTM–TOPAZ model is a good starting point for further investigation of key biogeochemical processes and is also useful to couple complex biogeochemical processes with various oceanic global circulation models.
Predicting Parkinson's Disease Severity using Telemonitoring Data and Machine Learning Models: A Principal Component Analysis-based Approach for Remote Healthcare Services during COVID-19 Pandemic

Parkinson's disease (PD) is a progressive and chronic neurological condition that affects about 1% of the world's over-60 population. The COVID-19 pandemic has emphasized the significance of remote healthcare services, such as telemedicine, in managing chronic diseases such as PD. This research intends to construct machine learning (ML) models to predict PD severity utilizing vocal data derived from the UCI database for motor and total Unified Parkinson's disease rating scale (UPDRS) ratings. ML was used to study the association between voice vibration and PD, and PCA and ML models were utilized to minimize model complexity and compare the predictive performance of various statistical models for PD regression. The dataset included 5,875 medical voice records from 42 patients with early-stage PD who participated in a six-month clinical trial. The proposed PCA model simplified the model and achieved a root-mean-square error of 1.78 with an R-squared value of 0.95 for predicting the motor UPDRS and 1.78 with an R-squared value of 0.97 for predicting the total UPDRS. This work can give a framework for developing remote healthcare services for Parkinson's disease and other chronic conditions, which can be helpful during pandemics and other situations where access to in-person care is limited.
The Price of Differential Privacy for Low-Rank Factorization

In this paper, we study what price one has to pay to release {\em differentially private low-rank factorization} of a matrix. We consider various settings that are close to the real world applications of low-rank factorization: (i) the manner in which matrices are updated (row by row or in an arbitrary manner), (ii) whether matrices are distributed or not, and (iii) how the output is produced (once at the end of all updates, also known as {\em one-shot algorithms} or continually). Even though these settings are well studied without privacy, surprisingly, there are no private algorithm for these settings (except when a matrix is updated row by row). We present the first set of differentially private algorithms for all these settings. Our algorithms when private matrix is updated in an arbitrary manner promise differential privacy with respect to two stronger privacy guarantees than previously studied, use space and time {\em comparable} to the non-private algorithm, and achieve {\em optimal accuracy}. To complement our positive results, we also prove that the space required by our algorithms is optimal up to logarithmic factors. When data matrices are distributed over multiple servers, we give a non-interactive differentially private algorithm with communication cost independent of dimension. In concise, we give algorithms that incur optimal cost. We also perform experiments to verify that all our algorithms perform well in practice and outperform the best known algorithms until now for large range of parameters. We give experimental results for total approximation error and additive error for varying dimensions, $\alpha$ and $k$.
Change point detection by Filtered Derivative with p-Value : Choice of the extra-parameters

This paper deals with off-line change point detection using the FDpV method. The Filtered Derivative with p-Value method (FDpV) is a two-step procedure for change point analysis. In the first step, we use the Filtered Derivative (FD) to select a set of potential change points, using its extra-parameters-namely the threshold for detection, and the sliding window size. In the second one, we calculate the p-value for each change point in order to only retain the true positives (true change points) and discard the false positives (false alarms). We give a way to estimate the optimal extra-parameters of the function FD, in order to have the fewest possible false positives and non-detected change points (ND). Indeed, the estimated potential change points may differ slightly from the theoretically correct ones. After setting the extra-parameters, we need to know whether the absence of detection or the false alarm has more impact on the Mean Integrated Square Error (MISE), which prompts us to calculate the MISE in both cases. Finally, we simulate some examples with a Monte-Carlo method to better understand the positive and negative ways the parametrisation can affect the results.
Flexible Time-Delay Designs for Harmonic Sequence Filter/Extraction: A Further Generalization of Two-Sample Methods

Time-delay is an inevitable operator causing slow dynamic response of related systems employing harmonic sequence (HS) filters/extractors. This letter presents a new concept of flexible time-delay designs as a further generalization of two-sample methods (i.e., delayed signal cancellation) to improve the dynamic response and robustness of HS filters/extractors. The new concept offers flexible designs of filters/extractors to improve both the steady-state quality and dynamic response of filter/extraction stages. Comparative experimental results show significant improvements in filtering performance regarding fast dynamic response, small steady-state errors, improved robustness, and flexible designs to filter implicit harmonic sidebands. These feasibilities are especially beneficial for applications in power systems and power electronics with the available information on harmonic sidebands.
Changes in cortisol and cortisone in hair of pigs reared under heat stress conditions

Heat stress accounts for millions of dollars in losses for swine producers worldwide. The aim of the present study was to determine and evaluate cortisol and cortisone in hair as indicators of thermal stress in growing pigs reared under high environmental temperatures. The study was carried out in two independent batches of commercial crosses of Lean Duroc and Pietrain in trials 1 and 2, respectively, during the growing period (from 40 to 100 kg; 81 days in trial 1 and 77 days in trial 2) in the same commercial farm in Spain during the summers of 2020 and 2021. In both cases, four rooms were used. In Trial 1, Room 1 had cooling and 11 pigs per pen; Room 2 had no cooling and 13 pigs per pen; Room 3 had no cooling and 11 pigs per pen, and Room 4 had cooling and 13 pigs per pen. In Trial 2, Rooms 2 and 3 had cooling and rooms 1 and 4 had no cooling, and all of them had 13 pigs per pen. Mean THI value was higher (p < 0.0001) in rooms without cooling systems (75.0 trial 1; 74.9 trial 2) than with them (71.3 trial 1; 71.7 trial 2). A total of four pens per room (16 in total) was selected for analysis of hair corticoids and all pigs inside were sampled at the end of the study. Fifty percent of the pigs were males (castrated and intact in trial 1 and 2, respectively) and 50% females. In total, 44, 52, 44, and 52 pigs, respectively, were sampled in four rooms from the first trial and 52 for each of four rooms in Trial 2. Cortisol concentrations in hair did not show any significant change in relation to cooling-non-cooling in any trial. However, hair cortisone concentration was 172.3 pg. /mg and 105.8 pg. /mg less (p < 0.001) in pigs housed with cooling systems compared to those without them in Trial 1 and 2, respectively. In addition, the cortisone/cortisol ratio, which is an estimator of the activity of 11β-hydroxysteroid dehydrogenase (11β-HSD) type 2, was also greater in rooms without cooling than in rooms with cooling in both trials (p < 0.0001 and p = 0.0105 for Trials 1 and 2, respectively). In relation to the sex effect, the results showed greater levels in females than in castrated males both in cortisone and the cortisol/cortisone ratio while cortisol hair levels were greater in intact males than in females. Therefore, the use of cortisone and the estimation of 11β-HSD type 2 activity in hair is recommended to evaluate the chronic stress produced by high environmental conditions in pigs instead of using hair cortisol concentrations alone.
Characterization and Valorization of Maize Landraces from Aosta Valley

While there is a rich collection of maize germplasm from Italy, it lacks genetic resources from the Aosta Valley, an isolated mountain region where landraces have been preserved in the absence of modern germplasm introductions. These local materials, which are still cultivated mainly at household level, can have high importance from a genetic and historical point of view. In the present study, five landraces named, after the collecting sites, Arnad, Arnad-Crest, Châtillon, Entrebin and Perloz, were sampled in Aosta Valley and subjected to historic, morphologic and genetic characterization. This study provided evidence for the landraces’ long presence in Aosta Valley, a significant genetic variability and differentiation among the investigated landraces. Globally, 67 different alleles were detected ranging from 4 for markers phi127 and p-bnlg176 to 10 for phi031, with a mean of 6.7 alleles per locus. Observed heterozygosity levels were comprised from 0.16 to 0.51 and are generalkly lower than expected heterozigosity supporting fixation at some loci. STRUCTURE analysis revealed clear separation between accessions revealing the presence of four ancestral populations. This may be explained by the long reproductive isolation experienced by these materials. Finally, morphological observations confirm the high diversity between landraces revealing that they generally have flint kernels, variable color from yellow to dark red (Châtillon) while Perloz showed kernels with an apical beak. The present work confirms the importance of mountain areas in conserving biodiversity and increases the rich Italian maize germplasm with materials well adapted to marginal areas. Such new genetic variability may be used to breed new materials for more resilient agriculture.
Artificial intelligence and bioinformatics analyze markers of children's transcriptional genome to predict autism spectrum disorder

Introduction Autism spectrum disorder (ASD), characterized by difficulties in social interaction and communication as well as restricted interests and repetitive behaviors, is extremely challenging to diagnose in toddlers. Early diagnosis and intervention are crucial however. Methods In this study, we developed a machine learning classification model based on mRNA expression data from the peripheral blood of 128 toddlers with ASD and 126 controls. Differentially expressed genes (DEGs) between ASD and controls were identified. Results We identified genes such as UBE4B, SPATA2 and RBM3 as DEGs, mainly involved in immune-related pathways. 21 genes were screened as key biomarkers using LASSO regression, yielding an accuracy of 86%. A neural network model based on these 21 genes achieved an AUC of 0.88. Discussion Our findings suggest that the identified neurotransmitters and 21 immune-related biomarkers may facilitate the early diagnosis of ASD. The mRNA expression profile sheds light on the biological underpinnings of ASD in toddlers and potential biomarkers for early identification. Nevertheless, larger samples are needed to validate these biomarkers.
Pleistocene glaciations caused the latitudinal gradient of within-species genetic diversity

Abstract Intraspecific genetic diversity is a key aspect of biodiversity. Quaternary climatic change and glaciation influenced intraspecific genetic diversity by promoting range shifts and population size change. However, the extent to which glaciation affected genetic diversity on a global scale is not well established. Here we quantify nucleotide diversity, a common metric of intraspecific genetic diversity, in more than 38,000 plant and animal species using georeferenced DNA sequences from millions of samples. Results demonstrate that tropical species contain significantly more intraspecific genetic diversity than nontropical species. To explore potential evolutionary processes that may have contributed to this pattern, we calculated summary statistics that measure population demographic change and detected significant correlations between these statistics and latitude. We find that nontropical species are more likely to deviate from neutral expectations, indicating that they have historically experienced dramatic fluctuations in population size likely associated with Pleistocene glacial cycles. By analyzing the most comprehensive data set to date, our results imply that Quaternary climate perturbations may be more important as a process driving the latitudinal gradient in species richness than previously appreciated.
Data sharpening on unknown manifold

ABSTRACT This article is concerned with data sharpening (DS) technique in nonparametric regression under the setting where the multivariate predictor is embedded in an unknown low-dimensional manifold. Theoretical asymptotic bias is derived, which reveals that the proposed DS estimator has a reduced bias compared to the usual local linear estimator. The asymptotic normality of the DS estimator is also developed. It can be confirmed from simulation and applications to real data that the bias reduction for the DS estimator supported on unknown manifold is evident.