Miracle-dz/UniFAR-FTUs huggingface dataset:
Expression levels of long noncoding natural antisense transcripts overlapping the UGT73C6 gene affect rosette size of Arabidopsis thaliana

Natural antisense long noncoding RNAs (lncNATs) are involved in the regulation of gene expression in plants, modulating different relevant developmental processes and responses to various stimuli. We identified and characterized two lncNATs (NAT1UGT73C6 and NAT2UGT73C6, collectively NATsUGT73C6) in Arabidopsis thaliana that are transcribed from a gene overlapping UGT73C6, a member of the UGT73C subfamily of genes encoding UDP-glycosyltransferases (UGTs). Expression of both NATsUGT73C6 is developmentally controlled and occurs independently of the transcription of UGT73C6 in cis. Downregulation of NATsUGT73C6 levels through artificial microRNAs results in a reduction of the rosette area, while constitutive overexpression of NAT1UGT73C6 or NAT2UGT73C6 leads to the opposite phenotype, an increase in rosette size. This activity of NATsUGT73C6 relies on its RNA sequence, and, although modulation of UGT73C6 in cis cannot be excluded, the observed phenotypes are not a consequence of the regulation of UGT73C6 in trans. The NATsUGT73C6 levels were shown to affect cell proliferation and thus individual leaf size. Consistent with this concept, our data suggest that the NATsUGT73C6 modulate the expression levels of key transcription factors involved in regulating leaf growth by modulating cell proliferation. These findings thus reveal an additional regulatory layer on the process of leaf growth.
Daily changes in neuronal activities of the dorsal motor nucleus of the vagus under standard and short-term high fat diet – implications for circadian modulation of parasympathetic outflow

The suprachiasmatic nuclei (SCN) of the hypothalamus functions as the brain’s primary circadian clock, but circadian clock genes are also rhythmically expressed in several extra-SCN brain sites where they can exert local temporal control over physiology and behaviour. Recently, we found that the hindbrain dorsal vagal complex possesses strong daily timekeeping capabilities, with the area postrema and nucleus of the solitary tract exhibiting the most robust clock properties. The possibility that the executory part of this complex – the dorsal motor nucleus of the vagus (DMV), also exhibits daily changes has not been extensively studied. The DMV is the source of vagal efferent motoneurons largely responsible for the regulation of gastric motility and emptying and consequently influence meal size and energy homeostasis. We used a combination of multi-channel electrophysiology and patch clamp recordings to gain insight into possible daily variation in these DMV cells and how this is influenced by diet. We found that DMV neurons increase their spontaneous activity, excitability and responsiveness to metabolic neuromodulators at late day which was paralleled with an enhanced synaptic input to these neurons. A high-fat diet typically damps circadian rhythms, but we found that short-term exposure to a high-fat diet paradoxically amplified daily variation of DMV neuronal activity, while blunting their responsiveness to metabolic neuromodulators. In summary, we show for the first time that neural activity at a source of vagal efferents varies with time of day and that this temporal variation is modulated by diet. These findings have clear implications for our understanding of the daily control of parasympathetic outflow.
High-content screening of coronavirus genes for innate immune suppression reveals enhanced potency of SARS-CoV-2 proteins

Suppression of the host intracellular innate immune system is an essential aspect of viral replication. Here, we developed a suite of medium-throughput high-content cell-based assays to reveal the effect of individual coronavirus proteins on antiviral innate immune pathways. Using these assays, we screened the 196 protein products of seven coronaviruses (SARS-CoV-2, SARS-CoV-1, 229E, NL63, OC43, HKU1 and MERS). This includes a previously unidentified gene in SARS-CoV-2 encoded within the Spike gene. We observe immune-suppressing activity in both known host-suppressing genes (e.g., NSP1, Orf6, NSP3, and NSP5) as well as other coronavirus genes, including the newly identified SARS-CoV-2 protein. Moreover, the genes encoded by SARS-CoV-2 are generally more potent immune suppressors than their homologues from the other coronaviruses. This suite of pathway-based and mechanism-agnostic assays could serve as the basis for rapid in vitro prediction of the pathogenicity of novel viruses based on provision of sequence information alone.
Multi-scale hierarchical neural network models that bridge from single neurons in the primate primary visual cortex to object recognition behavior

Primate visual object recognition relies on the representations in cortical areas at the top of the ventral stream that are computed by a complex, hierarchical network of neural populations. While recent work has created reasonably accurate image-computable hierarchical neural network models of those neural stages, those models do not yet bridge between the properties of individual neurons and the overall emergent behavior of the ventral stream. One reason we cannot yet do this is that individual artificial neurons in multi-stage models have not been shown to be functionally similar to individual biological neurons. Here, we took an important first step by building and evaluating hundreds of hierarchical neural network models in how well their artificial single neurons approximate macaque primary visual cortical (V1) neurons. We found that single neurons in certain models are surprisingly similar to their biological counterparts and that the distributions of single neuron properties, such as those related to orientation and spatial frequency tuning, approximately match those in macaque V1. Critically, we observed that hierarchical models with V1 stages that better match macaque V1 at the single neuron level are also more aligned with human object recognition behavior. Finally, we show that an optimized classical neuroscientific model of V1 is more functionally similar to primate V1 than all of the tested multi-stage models, suggesting room for further model improvements with tangible payoffs in closer alignment to human behavior. These results provide the first multi-stage, multi-scale models that allow our field to ask precisely how the specific properties of individual V1 neurons relate to recognition behavior. Highlights Image-computable hierarchical neural network models can be naturally extended to create hierarchical “brain models” that allow direct comparison with biological neural networks at multiple scales – from single neurons, to population of neurons, to behavior. Single neurons in some of these hierarchical brain models are functionally similar to single neurons in macaque primate visual cortex (V1) Some hierarchical brain models have processing stages in which the entire distribution of artificial neuron properties closely matches the biological distributions of those same properties in macaque V1 Hierarchical brain models whose V1 processing stages better match the macaque V1 stage also tend to be more aligned with human object recognition behavior at their output stage
Modelling the impact of decidual senescence on embryo implantation in human endometrial assembloids

Decidual remodelling of midluteal endometrium leads to a short implantation window after which the uterine mucosa either breaks down or is transformed into a robust matrix that accommodates the placenta throughout pregnancy. To gain insights into the underlying mechanisms, we established and characterised endometrial assembloids, consisting of gland organoids and primary stromal cells. Single-cell transcriptomics revealed that decidualized assembloids closely resemble midluteal endometrium, harbouring differentiated and senescent subpopulations in both glands and stroma. We show that acute senescence in glandular epithelium drives secretion of multiple canonical implantation factors, whereas in the stroma it calibrates the emergence of anti-inflammatory decidual cells and pro-inflammatory senescent decidual cells. Pharmacological inhibition of stress responses in pre-decidual cells accelerated decidualization by inhibiting senescence and mesenchymal-epithelial transition, processes involved in endometrial breakdown and regeneration, respectively. Accelerated decidualization resulted in entrapment of co-cultured human blastocysts in a largely static decidual matrix. By contrast, the presence of senescent decidual cells created a dynamic implantation environment, enabling embryo expansion and attachment, although their persistence led to gradual disintegration of assembloids. Our findings demonstrate that senescence controls endometrial fate decisions at implantation and highlight how endometrial assembloids may accelerate the discovery of new treatments to prevent reproductive failure.
Human stem cell-based retina-on-chip as new translational model for validation of AAV retinal gene therapy vectors

Gene therapies using adeno-associated viruses (AAVs) are amongst the most promising strategies to treat or even cure hereditary and acquired retinal diseases. However, the development of new efficient AAV vectors is slow and costly, largely because of the lack of suitable non-clinical models. By faithfully recreating structure and function of human tissues, human induced pluripotent stem cell (iPSC)-derived retinal organoids could become an essential part of the test cascade addressing translational aspects. Organ-on-Chip (OoC) technology further provides the capability to recapitulate microphysiological tissue environments as well as a precise control over structural and temporal parameters. By employing our recently developed Retina-on-chip that merges organoid and OoC-technology, we analyzed the efficacy, kinetics and cell tropism of seven first and second generation AAV vectors. The presented data demonstrate the potential of iPSC-based OoC models as the next generation of screening platforms for future gene therapeutic studies.
Taxon-specific phytoplankton growth, nutrient utilization, and light limitation in the oligotrophic Gulf of Mexico

The highly stratified, oligotrophic regions of the oceans are predominantly nitrogen limited in the surface ocean and light limited at the deep chlorophyll maximum (DCM). Hence, determining light and nitrogen co-limitation patterns for diverse phytoplankton taxa is crucial to understanding marine primary production throughout the euphotic zone. During two cruises in the deep-water Gulf of Mexico, we measured primary productivity (H13CO3−), nitrate uptake (15NO3−), and ammonium uptake (15NH4+) throughout the water column. Primary productivity declined with depth from the mixed-layer to the DCM, averaging 27.1 mmol C m−2 d−1. The fraction of growth supported by NO3− was consistently low, with upper euphotic zone values ranging from 0.01 to 0.14 and lower euphotic zone values ranging from 0.03 to 0.44. Nitrate uptake showed strong diel patterns (maximum during the day), while ammonium uptake exhibited no diel variability. To parameterize taxon-specific phytoplankton nutrient and light utilization, we used a data assimilation approach (Bayesian Markov Chain Monte Carlo) including primary productivity, nutrient uptake, and taxon-specific growth rate measurements. Parameters derived from this analysis define distinct niches for five phytoplankton taxa (Prochlorococcus, Synechococcus, diatoms, dinoflagellates, and prymnesiophytes) and may be useful for constraining biogeochemical models of oligotrophic open-ocean systems.
Pericentromeric heterochromatin impacts genome compartmentalization and sex chromosome evolution in a fish

Compartmentalization is one of the principles of chromosome 3D organization and has been suggested to be driven by the attraction of heterochromatin. The extent to which the pericentromeric heterochromatin (PCH) impacts chromosome compartmentalization is yet unclear. Here we produced a chromosome-level and fully phased diploid genome of an aquaculture fish, zig-zag eel (Mastacembelus armatus), and identified the centromeric and pericentromeric regions in the majority of chromosomes of both haploid genomes. The PCH is on average 4.2 Mb long, covering 17.7% of the chromosomes, and is the major target of histone 3 lysine 9 trimethylation (H3K9me3). In nearly half of the chromosomes, the PCH drives the chromosomes into two or three megascale chromatin domains with the PCH being a single one. We further demonstrate that PCH has a major impact in submetacentric, metacentric and small telocentric chromosomes in which the PCH drives the distribution of active and inactive compartments along the chromosomes. Additionally, we identified the young and homomorphic XY sex chromosomes that are submetacentric with the entire short-arm heterochromatinized. Interestingly, the sex-determining region seems to arise within the PCH that has been in place prior to the X-Y divergence and recombination suppression. Together, we demonstrate that the PCH can cover a considerably large portion of the chromosomes, and when it does so, it drives chromosome compartmentalization; and we propose a new model for the origin and evolution of homomorphic sex chromosomes in fish.
A Novel Framework Design of Network Intrusion Detection Based on Machine Learning Techniques

Traditional machine learning-based intrusion detection often only considers a single algorithm to identify intrusion data, lack of the flexibility method, low detection rate, no handing high-dimensional data, and cannot solve these problems well. In order to improve the performance of intrusion detection system, a novel general intrusion detection framework was proposed in this paper, which consists of five parts: preprocessing module, autoencoder module, database module, classification module, and feedback module. The data processed by the preprocessing module are compressed by the autoencoder module to obtain a lower-dimensional reconstruction feature, and the classification result is obtained through the classification module. Compressed features of each traffic are stored in the database module which can both provide retraining and testing for the classification module and restore these features to the original traffic for postevent analysis and forensics. For evaluation of the framework performance proposed, simulation was conducted with the CICIDS2017 dataset to the real traffic of the network. As the experimental results, the accuracy of binary classification and multiclass classification is better than previous work, and high-level accuracy was reached for the restored traffic. At the last, the possibility was discussed on applying the proposed framework to edge/fog networks.
Empirical Evaluation of Noise Influence on Supervised Machine Learning Algorithms Using Intrusion Detection Datasets

Optimizing the detection of intrusions is becoming more crucial due to the continuously rising rates and ferocity of cyber threats and attacks. One of the popular methods to optimize the accuracy of intrusion detection systems (IDSs) is by employing machine learning (ML) techniques. However, there are many factors that affect the accuracy of the ML-based IDSs. One of these factors is noise, which can be in the form of mislabelled instances, outliers, or extreme values. Determining the extent effect of noise helps to design and build more robust ML-based IDSs. )is paper empirically examines the extent effect of noise on the accuracy of the ML-based IDSs by conducting a wide set of different experiments. )e used ML algorithms are decision tree (DT), random forest (RF), support vector machine (SVM), artificial neural networks (ANNs), and Naı̈ve Bayes (NB). In addition, the experiments are conducted on two widely used intrusion datasets, which are NSL-KDD and UNSW-NB15. Moreover, the paper also investigates the use of these ML algorithms as base classifiers with two ensembles of classifiers learning methods, which are bagging and boosting. )e detailed results and findings are illustrated and discussed in this paper.
Conformational Studies of Vibrational Properties and Electronic States of Leucoemeraldine Base and Its Oligomers

Ab initio geometry optimizations and normal mode analysis have been performed on a series of oligomers of leucoemeraldine base (LB, the fully reduced amine form of polyaniline) over conformational variations with the HF/6-31G* basis set. Aniline was used to obtain 12 force constant scaling factors with the best vibrational frequency root mean square error of 5.2 cm-1. These scaling factors are used in the analysis of the vibrational spectra of diphenylamine, N,N ‘-diphenyl-1,4-phenylenediamine, aniline end-capped trimer, and LB. A polymeric normal mode analysis has been performed using the scaled quantum mechanical oligomer force field (SQMOFF) method where polymer force fields are constructed from the extrapolation of scaled ab initio oligomer force fields. CN stretching, NH rocking, and CH in-plane bending modes are strongly affected by the molecular conformation. The peaks around 1220 and 1180 cm-1 in the Raman spectrum turn out to be indicators of molecular and polymeric planarity. The differences bet.
The Effects of Providing Starter Projects in Open-Ended Scratch Activities

Given the importance of broadening participation in the field of computing, goals of supporting personal expression and developing a sense of belonging must live alongside the goals of conceptual knowledge and developing disciplinary expertise. Integrating opportunities for students to be creative in how they enact computing ideas plays an important role when designing curricula. We examine how student creativity, as expressed through theme and the use of costumes, backdrops, and narrative in Scratch projects, is affected by using a themed starter project. Starter projects are Scratch projects that include a set of sprites and backdrops aligned to a theme (e.g. baseball), but no code. Using within-group and between-group comparisons, we establish a baseline of what students do when they are given a starter project and explore how their projects differ in the absence of a starter project. This work contributes to our understanding of the impacts of structured elements within open-ended learning tasks and how we can design computer science learning experiences for students that promote opportunities for self-expression while engaging them in computing.
Novice Reflections on Debugging

Despite decades of literature calling for research on teaching debugging, we still lack clear guidelines on how to teach debugging processes effectively. This is a significant problem as debugging is an activity that is a key component of software development and a frustrating and time-consuming barrier to students who are learning to program. In this paper we reflect on teaching and learning debugging through the lens of student beliefs about their own debugging practice. We find that few students use systematic approaches such as the scientific method of hypothesis generation and testing that is informally adopted by many software developers. Some students appeared to be satisfied with their ad hoc approaches while others expressed a desire to develop more thorough approaches.
GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks

Node classification is an important research topic in graph learning. Graph neural networks (GNNs) have achieved state-of-the-art performance of node classification. However, existing GNNs address the problem where node samples for different classes are balanced; while for many real-world scenarios, some classes may have much fewer instances than others. Directly training a GNN classifier in this case would under-represent samples from those minority classes and result in sub-optimal performance. Therefore, it is very important to develop GNNs for imbalanced node classification. However, the work on this is rather limited. Hence, we seek to extend previous imbalanced learning techniques for i.i. d data to the imbalanced node classification task to facilitate GNN classifiers. In particular, we choose to adopt synthetic minority over-sampling algorithms, as they are found to be the most effective and stable. This task is non-trivial, as previous synthetic minority over-sampling algorithms fail to provide relation information for newly synthesized samples, which is vital for learning on graphs. Moreover, node attributes are high-dimensional. Directly over-sampling in the original input domain could generates out-of-domain samples, which may impair the accuracy of the classifier. We propose a novel framework, \method, in which an embedding space is constructed to encode the similarity among the nodes. New samples are synthesize in this space to assure genuineness. In addition, an edge generator is trained simultaneously to model the relation information, and provide it for those new samples. This framework is general and can be easily extended into different variations. The proposed framework is evaluated using three different datasets, and it outperforms all baselines with a large margin.
Collaborative Learning, Self-Efficacy, and Student Performance in CS1 POGIL

Collaborative learning actively engages students in the learning process while developing professional skills that employers seek. Approaches such as Pair Programming and Peer Instruction have been shown to improve student outcomes in computer science. Process Oriented Guided Inquiry Learning (POGIL) is another col- laborative learning approach that aims to develop students' mastery of discipline-specific concepts as well as 'process skills' like com- munication, teamwork, and critical thinking. This paper examines students' perceptions of their content learning and process skills in courses that recently switched to POGIL. We explore the re- lationship between those perceptions, self-efficacy, and learning performance. Our results show that students have positive views about their teamwork, developing skills to work with others, and thinking through a problem. However, self-efficacy is the only sig- nificant predictor of their learning. We discuss the implications of these results for using POGIL and other collaborative learning in CS courses, and identify directions for future work.
An Overview of the Impact of COVID-19 and "Emergency Remote Teaching" on International CS Education Practitioners

The COVID-19 pandemic has imposed "emergency remote teaching" across education globally, leading to the closure of institutions across all settings, from schools through to universities. We draw on the quantitative and qualitative findings from a large-scale international survey (N=2,483) conducted in the immediate aftermath of closures, implementation of lockdown measures, and the shift to online delivery in March 2020. We report how those teaching the discipline of computer science (CS) across all educational levels (n=327) show significantly more positive attitudes towards the move to online learning, teaching and assessment (LT&A) than those working in other disciplines. However, while CS practitioners across all sectors/settings consistently noted the potential opportunities of these changes, they also raised a number of wider concerns on the impact of this shift to online, especially on workload and job precarity. More specifically for the discipline of CS, there were concerns raised regarding the ability to effectively deliver technical topics online, as well as the impact on various types of formal examinations and assessment.