Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterization of DNA Repair Foci in Root Cells of Arabidopsis in Response to DNA Damage

As a sessile organism, plants are constantly challenged by diverse environmental stresses that threaten genome integrity by way of induction of DNA damage. In plants, each tissue is composed of differentiated cell types, and the response to DNA damage differs among each cell type. However, limited information is available on the subnuclear dynamics of different cell types in response to DNA damage in plants. A chromatin remodeling factor RAD54, which plays an important role in the exchange reaction and alteration of chromatin structure during homologous recombination, specifically accumulates at damaged sites, forming DNA repair foci (termed RAD54 foci) in nuclei after γ-irradiation. In this study, we performed a time-course analysis of the appearance of RAD54 foci in root cells of Arabidopsis after γ-irradiation to characterize the subnuclear dynamics in each cell type. A short time after γ-irradiation, no significant difference in detection frequency of RAD54 foci was observed among epidermal, cortical, and endodermal cells in the meristematic zone of roots. Interestingly, cells showing RAD54 foci persisted in roots at long time after γ-irradiation, and RAD54 foci in these cells localized to nuclear periphery with high frequency. These observations suggest that the nuclear envelope plays a role in the maintenance of genome stability in response to DNA damage in Arabidopsis roots.
Vitamin D Regulates the Microbiota to Control the Numbers of RORγt/FoxP3+ Regulatory T Cells in the Colon

The active form of vitamin D (1,25(OH)2D) suppresses experimental models of inflammatory bowel disease in part by regulating the microbiota. In this study, the role of vitamin D in the regulation of microbe induced RORγt/FoxP3+ T regulatory (reg) cells in the colon was determined. Vitamin D sufficient (D+) mice had significantly higher frequencies of FoxP3+ and RORγt/FoxP3+ T reg cells in the colon compared to vitamin D deficient (D–) mice. The higher frequency of RORγt/FoxP3+ T reg cells in D+ colon correlated with higher numbers of bacteria from the Clostridium XIVa and Bacteroides in D+ compared to D– cecum. D– mice with fewer RORγt/FoxP3+ T reg cells were significantly more susceptible to colitis than D+ mice. Transfer of the cecal bacteria from D+ or D– mice to germfree recipients phenocopied the higher numbers of RORγt/FoxP3+ cells and reduced susceptibility to colitis in D+ vs. D– recipient mice. 1,25(OH)2D treatment of the D– mice beginning at 3 weeks of age did not completely recover RORγt/FoxP3+ T reg cells or the Bacteriodes, Bacteriodes thetaiotaomicron, and Clostridium XIVa numbers to D+ values. Early vitamin D status shapes the microbiota to optimize the population of colonic RORγt/FoxP3+ T reg cells important for resistance to colitis.
Threat to the French Swine Industry of African Swine Fever: Surveillance, Spread, and Control Perspectives

African swine fever (ASF) has one of the highest case-fatality rates among pig diseases. Europe was considered ASF-free for about two decades until 2007, when the virus was introduced into Georgia. Since then, it has been identified throughout Eastern Europe, and reached Belgium in late 2018, increasing the risk of ASF being introduced into neighboring countries—namely Germany, Luxembourg, the Netherlands, and France. French authorities have therefore reinforced surveillance measures to improve the probability of detecting ASF rapidly if it emerges in France. Predictive modeling may help to anticipate the extent of virus spread and evaluate the efficiency of these surveillance measures. A previously published and well-documented model that simulates ASF virus spread was therefore tailored to realistically represent the French situation in terms of the geographic distribution of swine production sites and the commercial trade between them on the one hand, and the implementation of surveillance protocols on the other. The outcomes confirmed the moderate spread of ASF through the swine trade network, a situation that had been previously highlighted for the case of Denmark. However, the diversity of the French pig production landscape has revealed a huge potential for the geographic dispersal of the virus, especially should the index case occur in a low-density area, with a median source-to-case distance reaching 300 km. Free-range herds, which are more likely to have interactions with wild boars, were also identified as potential entrance gate for the virus. Transmissions from conventional herds were quasi-exclusively due to swine movement on the commercial network, representing 99% of transmission events. In contrast, 81% of transmission events occurred in the neighborhood of the index herd when the virus was introduced in free-range herds. The current surveillance measures were found relatively efficient for detecting the virus in large herds, leading to detection rates of 94%. However, infections on smaller production sites—which often have free-range herds—were more difficult to detect and would require screening protocols specifically targeting these smaller herds.
Self-Deception Reduces Cognitive Load: The Role of Involuntary Conscious Memory Impairment

People often hear classic allusions such as plugging one’s ears while stealing a bell, drawing cakes to satisfy one’s hunger, and the emperor’s new clothes. These allusions reflect a principle that people believe in nonexistent phenomena to satisfy their desires, also called self-deception. The current research used three experiments to examine the impact of social status and cognitive load on self-deception, and further to explore the inner connection about cognitive load and self-deception. The results found that deceiving individuals of high social status can play a role through the intrinsic mechanism of involuntary conscious memory (ICM). The higher the cognitive load of the deceiver, the greater the possibility of deception. The study demonstrated that involuntary conscious memory is the internal mechanism of self-deception, further explore the origin of self-deception, and enrich the self-deception theory.
Genome-Wide Identification of Mango (Mangifera indica L.) Polygalacturonases: Expression Analysis of Family Members and Total Enzyme Activity During Fruit Ripening

Mango (Mangifera indica L.) is an important commercial fruit that shows a noticeable loss of firmness during ripening. Polygalacturonase (PG, E.C. 3.2.1.15) is a crucial enzyme for cell wall loosening during fruit ripening since it solubilizes pectin and its activity correlates with fruit softening. Mango PGs were mapped to a genome draft using seventeen PGs found in mango transcriptomes and 48 bonafide PGs were identified. The phylogenetic analysis suggests that they are related to Citrus sinensis, which may indicate a recent evolutive divergence and related functions with orthologs in the tree. Gene expression analysis for nine PGs showed differential expression for them during post-harvest fruit ripening, MiPG21-1, MiPG14, MiPG69-1, MiPG17, MiPG49, MiPG23-3, MiPG22-7, and MiPG16 were highly up-regulated. PG enzymatic activity also increased during maturation and these results correlate with the loss of firmness observed in mango during post-harvest ripening, between the ethylene production burst and the climacteric peak. The analysis of PGs promoter regions identified regulatory sequences associated to ripening such as MADS-box, ethylene regulation like ethylene insensitive 3 (EIN3) factors, APETALA2-like and ethylene response element factors. During mango fruit ripening the action of at least these nine PGs contribute to softening, and their expression is regulated at the transcriptional level. The prediction of the tridimensional structure of some PGs showed a conserved parallel beta-helical fold related to polysaccharide hydrolysis and a modular architecture, where exons correspond to structural elements. Further biotechnological approaches could target specific softening-related PGs to extend mango post-harvest shelf life.
The Genetic Architecture of Chronic Mountain Sickness in Peru

Chronic mountain sickness (CMS) is a pathological condition resulting from chronic exposure to high-altitude hypoxia. While its prevalence is high in native Andeans (>10%), little is known about the genetic architecture of this disease. Here, we performed the largest genome-wide association study (GWAS) of CMS (166 CMS patients and 146 controls living at 4,380 m in Peru) to detect genetic variants associated with CMS. We highlighted four new candidate loci, including the first CMS-associated variant reaching GWAS statistical significance (rs7304081; P = 4.58 × 10−9). By looking at differentially expressed genes between CMS patients and controls around these four loci, we suggested AEBP2, CAST, and MCTP2 as candidate CMS causal genes. None of the candidate loci were under strong natural selection, consistent with the observation that CMS affects fitness mainly after the reproductive years. Overall, our results reveal new insights on the genetic architecture of CMS and do not provide evidence that CMS-associated variants are linked to a strong ongoing adaptation to high altitude.
Age and Gender Effects in Sensitivity to Social Rewards in Adolescents and Young Adults

Adolescence is a sensitive period for socio-cultural processing and a vast literature has established that adolescents are exceptionally attuned to the social context. Theoretical accounts posit that the social reward of social interactions plays a large role in adolescent sensitivity to the social context. Yet, to date it is unclear how sensitivity to social reward develops across adolescence and young adulthood and whether there are gender differences. The present cross-sectional study (N = 271 participants, age 11–28 years) examined age and gender effects in self-reported sensitivity to different types of social rewards. In order to achieve this aim, the Dutch Social Reward Questionnaire for Adolescents was validated. Findings revealed that each type of social reward was characterized by distinct age and gender effects. Feeling rewarded by gaining positive attention from others showed a peak in late adolescence, while enjoying positive reciprocal relationships with others showed a linear increase with age. Enjoying cruel behavior toward others decreased with age for girls, while boys showed no changes with age and reported higher levels across ages. Reward from giving others control showed a mid-adolescent dip, while enjoying group interactions did not show any changes with age. Taken together, the results imply that the social reward of social interactions is a nuanced and complex construct, which encompasses multiple components that show unique effects with age and gender. These findings enable us to gain further traction on the ubiquitous effects of the social context on decision-making in adolescent’s lives.
Validation of the Measurement of Need Frustration

Until recently, need frustration was considered to be the absence of need satisfaction, rather than a separate dimension. Whilst the absence of need satisfaction can hamper growth, experiencing need frustration can lead to malfunctioning and subsequent psychopathology. Therefore, examining these constructs separately is vital, as they produce different outcomes, with the consequences of need frustration potentially more severe. This study sought to examine predictors of need frustration using undergraduate students and individuals from the wider community (N = 510, females N = 404, Mage = 24.15). Participants completed the new need satisfaction frustration scale and measures of anxiety, stress, depression, and negative and positive affect. Support for the position that need frustration is separate to Need Satisfaction and is related to psychological health problems (i.e., ill-being) was found. However, autonomy frustration was not found to be a significant predictor of ill-being. Extending previous research, this study found relationships of stress and somatic anxiety with need frustration. Further, a relationship between need frustration with anxiety and depression occurred, when these symptom dimensions were examined separately, through distinct questionnaires. Support for the construct of need frustration highlights the necessity of examining need frustration in addition to need satisfaction within future studies. Interventions specific to reducing need frustration, specifically competence and relatedness frustration within both the educational and workplace setting are outlined.
Deprivation of Muscleblind-Like Proteins Causes Deficits in Cortical Neuron Distribution and Morphological Changes in Dendritic Spines and Postsynaptic Densities

Myotonic dystrophy (Dystrophia Myotonica; DM) is the most common adult-onset muscular dystrophy and its brain symptoms seriously affect patients’ quality of life. It is caused by extended (CTG)n expansions at 3′-UTR of DMPK gene (DM type 1, DM1) or (CCTG)n repeats in the intron 1 of CNBP gene (DM type 2, DM2) and the sequestration of Muscleblind-like (MBNL) family proteins by transcribed (CUG)n RNA hairpin is the main pathogenic mechanism for DM. The MBNL proteins are splicing factors regulating posttranscriptional RNA during development. Previously, Mbnl knockout (KO) mouse lines showed molecular and phenotypic evidence that recapitulate DM brains, however, detailed morphological study has not yet been accomplished. In our studies, control (Mbnl1+/+; Mbnl2cond/cond; Nestin-Cre−/−), Mbnl2 conditional KO (2KO, Mbnl1+/+; Mbnl2cond/cond; Nestin-Cre+/−) and Mbnl1/2 double KO (DKO, Mbnl1ΔE3/ΔE3; Mbnl2cond/cond; Nestin-Cre+/−) mice were generated by crossing three individual lines. Immunohistochemistry for evaluating density and distribution of cortical neurons; Golgi staining for depicting the dendrites/dendritic spines; and electron microscopy for analyzing postsynaptic ultrastructure were performed. We found distributional defects in cortical neurons, reduction in dendritic complexity, immature dendritic spines and alterations of postsynaptic densities (PSDs) in the mutants. In conclusion, loss of function of Mbnl1/2 caused fundamental defects affecting neuronal distribution, dendritic morphology and postsynaptic architectures that are reminiscent of predominantly immature and fetal phenotypes in DM patients.
“Switch-Off” of Respiratory Sinus Arrhythmia May Be Associated With the Activation of an Oscillatory Source (Pacemaker) in the Brain Stem

Recently, we reported on the unusual “switch-off” of respiratory sinus arrhythmia (RSA) by analyzing heart rate (HR) beat-to-beat interval (RRI) signals and respiration in five subjects during a potentially anxiety-provoking first-time functional magnetic resonance imaging (fMRI) scanning with slow spontaneous breathing waves (Rassler et al., 2018). This deviation from a fundamental physiological phenomenon is of interest and merits further research. Therefore, in this study, the interplay between blood-oxygen level-dependent (BOLD) activity in the cerebellum/brain stem, RRI, and respiration was probed. Both the cardiovascular and the respiratory centers are located in the medulla oblongata and pons, indicating that dominant slow rhythmic activity is present in the brain stem. The recording of BOLD signals provides a way to investigate associated neural activity fluctuation in the brain stem. We found slow spontaneous breathing waves associated with two types of slow BOLD oscillations with dominant frequencies at 0.10 and 0.15 Hz in the brain stem. Both BOLD oscillations were recorded simultaneously. One is hypothesized as vessel motion-based phenomenon (BOLDv) associated with the start of expiration; the other one as pattern associated with neural activity (BOLDn) acting as a driving force for spontaneous inspiration and RRI increase (unusual cessation of RSA) about 2–3 s after BOLDv. This time delay of 2–3 s corresponds to the neurovascular coupling time.
Prediction of 1p/19q Codeletion in Diffuse Glioma Patients Using Pre-operative Multiparametric Magnetic Resonance Imaging

This study compared the predictive power and robustness of texture, topological, and convolutional neural network (CNN) based image features for measuring tumors in MRI. These features were used to predict 1p/19q codeletion in the MICCAI BRATS 2017 challenge dataset. Topological data analysis (TDA) based on persistent homology had predictive performance as good as or better than texture-based features and was also less susceptible to image-based perturbations. Features from a pre-trained convolutional neural network had similar predictive performances and robustness as TDA, but also performed better using an alternative classification algorithm, k-top scoring pairs. Feature robustness can be used as a filtering technique without greatly impacting model performance and can also be used to evaluate model stability.
LFOC: A Lightweight Fairness-Oriented Cache Clustering Policy for Commodity Multicores

Multicore processors constitute the main architecture choice for modern computing systems in different market segments. Despite their benefits, the contention that naturally appears when multiple applications compete for the use of shared resources among cores, such as the last-level cache (LLC), may lead to substantial performance degradation. This may have a negative impact on key system aspects such as throughput and fairness. Assigning the various applications in the workload to separate LLC partitions with possibly different sizes, has been proven effective to mitigate shared-resource contention effects. In this article we propose LFOC, a clustering-based cache partitioning scheme that strives to deliver fairness while providing acceptable system throughput. LFOC leverages the Intel Cache Allocation Technology (CAT), which enables the system software to divide the LLC into different partitions. To accomplish its goals, LFOC tries to mimic the behavior of the optimal cache-clustering solution, which we could approximate by means of a simulator in different scenarios. To this end, LFOC effectively identifies streaming aggressor programs and cache sensitive applications, which are then assigned to separate cache partitions. We implemented LFOC in the Linux kernel and evaluated it on a real system featuring an Intel Skylake processor, where we compare its effectiveness to that of two state-of-the-art policies that optimize fairness and throughput, respectively. Our experimental analysis reveals that LFOC is able to bring a higher reduction in unfairness by leveraging a lightweight algorithm suitable for adoption in a real OS.
Transfer Learning based Failure Prediction for Minority Disks in Large Data Centers of Heterogeneous Disk Systems

The storage system in large scale data centers is typically built upon thousands or even millions of disks, where disk failures constantly happen. A disk failure could lead to serious data loss and thus system unavailability or even catastrophic consequences if the lost data cannot be recovered. While replication and erasure coding techniques have been widely deployed to guarantee storage availability and reliability, disk failure prediction is gaining popularity as it has the potential to prevent disk failures from occurring in the first place. Recent trends have turned toward applying machine learning approaches based on disk SMART attributes for disk failure predictions. However, traditional machine learning (ML) approaches require a large set of training data in order to deliver good predictive performance. In large-scale storage systems, new disks enter gradually to augment the storage capacity or to replace failed disks, leading storage systems to consist of small amounts of new disks from different vendors and/or different models from the same vendor as time goes on. We refer to this relatively small amount of disks as minority disks. Due to the lack of sufficient training data, traditional ML approaches fail to deliver satisfactory predictive performance in evolving storage systems which consist of heterogeneous minority disks. To address this challenge and improve the predictive performance for minority disks in large data centers, we propose a minority disk failure prediction model named TLDFP based on a transfer learning approach. Our evaluation results on two realistic datasets have demonstrated that TLDFP can deliver much more precise results, compared to four popular prediction models based on traditional ML algorithms and two state-of-the-art transfer learning methods.
Application of Deep Learning in Integrated Pest Management: A Real-Time System for Detection and Diagnosis of Oilseed Rape Pests

In this paper, we proposed an approach to detect oilseed rape pests based on deep learning, which improves the mean average precision (mAP) to 77.14%; the result increased by 9.7% with the original model. We adopt this model to mobile platform to let every farmer able to use this program, which will diagnose pests in real time and provide suggestions on pest controlling. We designed an oilseed rape pest imaging database with 12 typical oilseed rape pests and compared the performance of five models, SSD w/Inception is chosen as the optimal model. Moreover, for the purpose of the high mAP, we have used data augmentation (DA) and added a dropout layer. The experiments are performed on the Android application we developed, and the result shows that our approach surpasses the original model obviously and is helpful for integrated pest management. This application has improved environmental adaptability, response speed, and accuracy by contrast with the past works and has the advantage of low cost and simple operation, which are suitable for the pest monitoring mission of drones and Internet of Things (IoT).
Sustainable Security & Safety: Challenges and Opportunities

A significant proportion of today's information and communication technology (ICT) systems are entrusted with high value assets, and our modern society has become increasingly dependent on these systems operating safely and securely over their anticipated lifetimes. However, we observe a mismatch between the lifetimes expected from ICT-supported systems (such as autonomous cars) and the duration for which these systems are able to remain safe and secure, given the spectrum of threats they face. Whereas most systems today are constructed within the constraints of foreseeable technology advancements, we argue that long term, i.e., sustainable security & safety, requires anticipating the unforeseeable and preparing systems for threats not known today. In this paper, we set out our vision for sustainable security & safety. We summarize the main challenges in realizing this desideratum in real-world systems, and we identify several design principles that could address these challenges and serve as building blocks for achieving this vision.