Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification of Genes with Altered Roles in Transcription Network in Alzheimer's Disease

Abstract: Differentially expressed genes (DEGs) are commonly used to study the pathological mechanism of various brain diseases. However, these analyses are not sufficient to identify genes that alter their roles in the transcription networks but without significant changes in the expression level. By analyzing an RNA microarray dataset (GSE36980) from Gene Expression Omnibus (GEO), we find that many genes exhibit changes in how their transcriptions are correlated with that of the entire genome in the samples of Alzheimer's disease (AD) patients, when compared with the normal controls. We name such genes as role-alteration genes (RAGs). Only a minority of RAGs also show significant changes in the expression level. Within the pools of RAGs, we further identified clustered biological functions through gene ontology (GO) analyses, suggesting the functional significance of these RAGs. Our work provides novel insights into the genes that may be involved in AD but cannot be captured by common DEG analyses, suggesting more profound changes in the transcription network during diseases than previously appreciated.
An Abstract Drawing Method for Same Shaped but Densely Arranged Many Objects

We propose a hierarchical abstract drawing method for many 3D objects which are densely arranged and have almost the same shape. First, an abstract drawing of all the 3D objects is drawn based on a couple of the primal colors, which is one of the global properties of all the 3D objects. Then, an abstract painting is added focusing on a part of each object as local properties. Several results of abstractly rendered scenes are shown, and are confirmed the effectiveness of our method.
Category Map Guided Ordinal Depth Prediction for 3D Human Pose Estimation

In this paper, we propose a two-stage method to estimate 3D human pose, which focuses on the uncertainty of lifting 2D detected pose to 3D pose. Firstly, a novel category map is introduced to predict the ordinal depth category which depicts three kinds of depth ordering relationship for linked joints. Compared with the common probability of vector, our category map can provide better association between prediction with image appearance, and lead to a higher classification accuracy. Secondly, taking predicted 2D pose and ordinal depth category as input, we put forward a temporal convolution network to regress 3D pose, which exploits the temporal context to alleviate the 2D-to-3D uncertainty and reduce prediction errors rate from single image further. Experimental results show that our method can outperform promising results on several benchmarks.
The Present and Future Perspective of Deep Learning in Genomics

As high-throughput sequencing technologies and human genomics have developed rapidly, there are vast amounts of genomic data to be generated and analyzed. It is significant to discover valuable information from vast amounts of genomic data. Deep learning, which can identify highly complex patterns in large data sets, created an unprecedented momentum in bioinformatics. In this paper, several popular deep learning models used in genomics are described, and the applications of deep learning in genomics are introduced. Finally, the conclusion is drawn, and future perspectives of deep learning in genomics are discussed. This paper will lay the foundation for selection of appropriate deep learning models applied for genomics study.
Identifying Hub Genes and Key Pathways Associated with Thyroid Cancer by Integrated Bioinformatics Analysis

Objective: To identify thyroid cancer hub genes and key pathways using survival and bioinformatics analysis. Methods: Two data sets GSE138198 and GSE53072 from the Gene Expression Omnibus (GEO) database together with GEO2R online software and Venn Diagrams are used to screen out differentially expressed genes (DEGs). Some genes significantly associated with overall survival time are further screened out from these DEGs by the Kaplan-Meier plotter. The DAVID (Database for Annotation, Visualization and Integrated Discovery) online tools are used to perform Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Using STRING and Cytoscape to make PPI network diagrams to obtain the hub genes. Results: 127 genes are found to be significantly correlated with overall survival time of thyroid cancer patients and 10 hub genes are identified. Conclusion: Through the survival analysis-based bioinformatics method, the key genes and pathways involved in the occurrence and development of thyroid cancer have been screened out. It is shown that these genes and pathways are mainly related to Nucleosome function and cell differentiation.
Android Security: An Overview of Risks and Security Measures

New technologies always create new areas of concern for information security teams. Usually early adoption of such products is slow and it allows some maturity to develop in the play store(previously Market), providing time for the development of effective security controls. Indeed often these new devices will be slowly introduced to the corporate/government environment as part of an internal strategy. The rapid growth of the Smartphone play store(previously Market) and the use of these devices for email, online banking, and accessing other forms of sensitive content has lead to the emergence of a new and ever-changing threat landscape. This combined with the fact that anyone can be user has led to smart phones appearing as part of the corporate/government estate before the appropriate controls are in place. Even without these technologies connecting to the corporate/government network, there are security implications brought about by their mere presence in an office environment. A malicious user or malware on the device can create a number of risks for an organisation, and so the fact that these devices are not necessarily connected does not translate to a lack of security risks. This paper will discuss why it is important to secure an Android device, what some of the potential vulnerabilities are, and security measures that can be introduced to provide a baseline of security on Google"s mobile OS.
Construction of Dysregulated lncRNA-Associated ceRNA Network in Atrial Fibrillation

Enough evidence has revealed that competing endogenous RNAs (ceRNAs) activity of long non-coding RNAs (lncRNAs) plays an important role in pathological processes of human diseases including cardiovascular diseases. However, the role of lncRNA in atrial fibrillation (AF) and their implications still remain unclear to date. In this study, lncRNA-mRNA network related to AF was constructed based on the ceRNA theory. We first obtained lncRNAs with topological characteristics in the lncRNA-mRNA network. We then construct a co-expression network to identify the lncRNA-associated functions, and the mRNAs in the network were analyzed for the enriched pathways which showed that the pathways were related to AF. We then analyzed the dysregulation of lncRNA-mRNA in ceRNA network and obtained three lncRNAs as hub lncRNAs. The obtained results indicate that lncRNAs might exert the pathogenesis of AF. This might help in the understanding of molecular mechanisms of lncRNA in AF, providing novel lncRNAs as potential biomarkers and/or therapeutic targets.
Deep Color-Normal Residual Networks for Geometry Refinement Extracting Color Consistency and Fine Geometry

In recent years, texture mapping in 3D modeling has been remarkably improved for realistic rendering. However, small error in reconstructed 3D geometry makes serious error in texture mapping. To address the problem, most of prior methods are devoted to refinement of 3D geometry without visual clues. In this work, we refine 3D geometry based on color consistency and surface normal using a deep neural network. Our method optimizes the location of each vertex maximizing the quality of related textures.
Visualizing Biclustering Results on Gene Expression Data: A Survey

Biclustering is a non-supervised data mining technique used to analyze gene expression data, consists to classify subgroups of genes that behave similarly under subgroups of conditions. These same classified genes can behave independently under other subgroups of conditions. Discovering such co-expressed genes (called biclusters) can be helpful to find specific biological aims such as gene interactions under different circumstances. Biclustering has two characteristics: bi-dimensionality which means grouping both genes and conditions together and overlapping which means allowing genes to be in more than one bicluster at the same time. Biclustering algorithms which continue to be developed at a constant pace, give as output a large number of overlapped biclusters. Visualizing biclustering results still a non-trivial task due to biclustering features. In this paper, we make a survey on different techniques to visualize groups of biclusters in the same view.
Learning English to Chinese Character: Calligraphic Art Production based on Transformer

We propose a transformer-based model to learn Square Word Calligraphy to write English words in the format of a square that resembles Chinese characters. To achieve this task, we compose a dataset by collecting the calligraphic characters created by artist Xu Bing, and labeling the position of each alphabet in the characters. Taking the input of English alphabets, we introduce a modified transformer-based model to learn the position relationship between each alphabet and predict the transformation parameters for each part to reassemble them as a Chinese character. We show the comparison results between our predicted characters and corresponding characters created by the artist to indicate our proposed model has a good performance on this task, and we also created new characters to show the “creativity” of our model.
WORLD-SPACE SPATIOTEMPORAL RESERVOIR REUSE FOR RAY-TRACED GLOBAL ILLUMINATION

Path-traced global illumination of scenes with complex lighting remains particularly challenging at real-time framerates. Reservoir-based resampling methods for light sampling allow for significant noise reduction at the cost of very few shadow rays per pixel. However, current image-space approaches to reservoir reuse do not scale to sample lighting at further bounces, as is required for efficiently evaluating indirect illumination. We present a novel approach to performing reservoir-based spatiotemporal importance resampling in world space, allowing for efficient light sampling at arbitrary vertices along the eye path. Our approach caches the reservoirs of the path vertices into the cells of a hash grid built entirely on the GPU. Such a structure allows for stochastic reuse of neighboring reservoirs across space and time for efficient spatiotemporal reservoir resampling at any point in space.
Careless Participants Are Essential for Our Phishing Study: Understanding the Impact of Screening Methods

Online surveys using crowdsourcing services have been widely adopted in academic research projects aimed at understanding human perception and behavior. Because there is a concern that online surveys may include dishonest or careless responses by crowdworkers who perform a large number of tasks, or responses by bots, several screening methods have been proposed to discard such low-quality responses. However, in security research, especially in phishing research where the attention of participants is considered to influence the results, the elimination of careless responses may lead to the removal of participants who should be included in the research. In this study, we address the following research question: “Does the adoption of existing screening methods bias the results of security surveys? ” Using Amazon Mechanical Turk and Prolific Academic, two popular crowdsourcing platforms used in online surveys, we conducted online user studies (N = 600) on security knowledge, security behavior, and phishing email detection performance to elucidate the influence of screening methods on the results. The obtained results indicate that the adoption of the instructional manipulation check (IMC) screening method triggers bias in the demographics of the participants, as well as differences in the results of phishing email detection performance. In addition, the degree of these differences depends on the crowdsourcing platform. We also demonstrated that it is non-trivial to determine the correlation between screening methods and factors that can influence the results of a survey on security behavior. These findings suggest that caution should be exercised when applying screening methods such as attention checks and IMC in studies where the extent of user attention could have a significant impact on the results.
Production of novel Spike truncations in Chinese hamster ovary cells

SARS-CoV-2 Spike is a key protein that mediates viral entry into cells and elicits antibody responses. Its importance in infection, diagnostics, and vaccinations has created a large demand for purified Spike for clinical and research applications. Spike is difficult to express, prompting modifications to the protein and expression platforms to improve yields. Alternatively, Spike receptor binding domain (RBD) is commonly expressed with higher titers, though it has lower sensitivity in serological assays. Here, we improve transient Spike expression in Chinese hamster ovary (CHO) cells. We demonstrate that Spike titers increase significantly over the expression period, maximizing at 14 mg/L at day 7. In comparison, RBD titers peak at 54 mg/L at day 3. Next, we develop 8 Spike truncations (T1-T8) in pursuit of a truncation with high expression and antibody binding. The truncations T1 and T4 express at 130 mg/L and 73 mg/L, respectively, which are higher than our RBD titers. Purified proteins were evaluated for binding to antibodies raised against full-length Spike. T1 has similar sensitivity as Spike against a monoclonal antibody and even outperforms Spike for a polyclonal antibody. These results suggest T1 is a promising Spike alternative for use in various applications.
Computational models reveal how chloride dynamics determine the optimal distribution of inhibitory synapses to minimise dendritic excitability

Many neurons in the mammalian central nervous system have complex dendritic arborisations and active dendritic conductances that enable these cells to perform sophisticated computations. How dendritically targeted inhibition affects local dendritic excitability is not fully understood. Here we use computational models of branched dendrites to investigate where GABAergic synapses should be placed to minimise dendritic excitability over time. To do so, we formulate a metric we term the “Inhibitory Level” (IL), which quantifies the effectiveness of synaptic inhibition for reducing the depolarising effect of nearby excitatory input. GABAergic synaptic inhibition is dependent on the reversal potential for GABAA receptors (EGABA), which is primarily set by the transmembrane chloride ion (Cl-) concentration gradient. We, therefore, investigated how variable EGABA and dynamic chloride affects dendritic inhibition. We found that the inhibitory effectiveness of dendritic GABAergic synapses combines at an encircled branch junction, “inhibitory accumulation”. The extent of inhibitory accumulation is dependent on the number of branches and location of synapses but is independent of EGABA. This inhibitory accumulation occurs even for very distally placed inhibitory synapses when they are hyperpolarising – but not when they are shunting. When accounting for Cl- fluxes and dynamics in Cl- concentration, we observed that Cl- loading is detrimental to inhibitory effectiveness. This enabled us to determine the most inhibitory distribution of GABAergic synapses which is close to – but not at – a shared branch junction. This distribution balances a trade-off between a stronger combined inhibitory influence when synapses closely encircle a branch junction with the deleterious effects of increased Cl- loading that occurs when inhibitory synapses are co-located.
Cerebellar contributions to a brainwide network for reversal learning

The cerebellum regulates nonmotor behavior, but the routes by which it exerts its influence are not well characterized. Here we report a necessary role for the posterior cerebellum in guiding a reversal learning task, acting through a network of diencephalic and neocortical structures. After chemogenetic inhibition of Purkinje cells in lobule VI or crus I, mice could learn a water Y-maze task but were impaired in their ability to reverse their initial choice. To map targets of perturbation, we imaged c-Fos activation in cleared whole brains using light-sheet microscopy. Reversal learning activated diencephalic regions and associative neocortical regions. Distinctive subsets of structures were altered by perturbation of lobule VI (including thalamus and habenula) and crus I (including hypothalamus and prelimbic/orbital cortex), and both perturbations influenced anterior cingulate and infralimbic cortex. To identify functional networks, we used correlated variation in c-Fos activation within each test group. Lobule VI inactivation weakened within-thalamus correlations, while crus I inactivation divided neocortical activity into well-separated sensorimotor and associative subnetworks. In both groups, high-throughput automated analysis of complex whole-body movement revealed deficiencies in across-day adaptation to an open field environment. Neither perturbation affected gait, within-day open-field adaptation, or location preference. Taken together, these experiments reveal brainwide systems for cerebellar influence that can affect multiple flexible responses. Significance statement The cerebellum, a part of all vertebrate brains, provides feedback to the rest of the brain on a split-second basis in response to unexpected events. The consequent changes can range from minute adjustments in movement to broad changes in behavior. In mice, we investigated cerebellar regions that help guide flexible behavior. Inactivation of these regions prevented mice from responding flexibly to a changing Y-shaped maze. We used light-sheet microscopy of c-fos protein expression to map brainwide effects of different stages of learning and cerebellar inactivation. Inactivation caused changes in thalamocortical activity that were similar in pattern, but opposite in sign, from normal learning. Lobule VI inactivation weakened thalamic functional networks, while crus I inactivation divided sensorimotor from associative neocortical networks. The same inactivation also impaired multiday adaptation to an open arena, as tracked using machine vision. Our work shows how the cerebellum’s influence over flexible response can be mediated by a widespread brain network.
Enabling Complex Fibre Geometries Using 3D Printed Axon-Mimetic Phantoms

Purpose To introduce a method to create 3D-printed axon-mimetic phantoms with complex fibre orientations to characterize the performance of diffusion MRI models and representations in the presence of orientation dispersion. Methods An extension to an open source 3D printing package was created to 3D print a set of five 3D-printed axon-mimetic (3AM) phantoms with various combinations of bending and crossing fibre orientations. A two-shell diffusion MRI scan of the five phantoms in water was performed at 9.4T. Diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), the ball and stick model, neurite orientation density and dispersion imaging (NODDI), and Bingham-NODDI were all fit to the resulting diffusion MRI data. A fiducial in each phantom was used to register a ground truth map of that phantom’s crossing angles and/or arc radius to the diffusion-weighted images. Metrics from each model and representation were compared to the ground-truth maps, and a quadratic regression model was fit to each combination of output metric and ground-truth metric. Results The mean diffusivity (MD) metric defined by DTI was insensitive to crossing angle, but increased with fibre curvature. Axial diffusivity (AD) decreased sharply with increasing crossing angle. DKI’s diffusivity metrics replicated the trends seen in DTI, and its mean kurtosis (MK) metric, decreased with fibre curvature, except in regions with high crossing angles. The estimated stick volume fraction in the ball and stick model decreased with increasing fibre curvature and crossing angle. NODDI’s intra-neurite volume fraction was insensitive to crossing angle, and its orientation dispersion index (ODI) was strongly correlated to crossing angle. Bingham-NODDI’s intra-neurite volume fraction was also insensitive to crossing angle, while its primary ODI (ODIP) was also strongly correlated to crossing angle and its secondary ODI (ODIS) was insensitive to crossing angle. For both NODDI models, the volume fractions of the extra-neurite and CSF compartments had low reliability with no clear relationship to crossing angle. Conclusions This study demonstrates that inexpensive 3D-printed axon-mimetic phantoms can be used to investigate the effect of fibre curvature and crossings on diffusion MRI representations and models of diffusion signal. As a proof of concept, the dependence of several representations and models on fibre dispersion/crossing were investigated. As expected, Bingham-NODDI was best able to characterize planar fibre dispersion in the phantoms.
mCherry contains a fluorescent protein isoform that interferes with its reporter function

Fluorescent proteins are essential reporters in cell biology and molecular biology. Here, we reveal that red-fluorescent proteins possess an alternative translation initiation site that produces a short functional protein isoform. The short isoform creates significant background fluorescence that biases the outcome of expression studies. Our investigation identifies the short protein isoform, traces its origin, and determines the extent of the issue within the family of red fluorescent protein. Our analysis shows that the short isoform defect of the red fluorescent protein family may affect the interpretation of many published studies. Finally, we provide a re-engineered mCherry variant that lacks background expression as an improved tool for imaging and protein expression studies.
A dynamic model of the ABA Signaling pathway with its core components: translation rate of PP2C determines the kinetics of ABA-induced gene expression

The abscisic acid (ABA) signaling pathway is the key defense mechanism against drought stress in plants, yet the connectivity of cellular molecules related to gene expression in response to ABA is little understood. A dynamic model of the core components of the ABA signaling pathway was built using ordinary differential equations to understand the connectivity. Parameter values of protein-protein interactions and enzymatic reactions in the model were implemented from the data obtained by previously conducted experiments. On the other hand, parameter values of gene expression and translation were determined by comparing the kinetics of gene expression in the model to those of ABA-induced RD29A (response to desiccation 29A) in actual plants. Based on the analyses of the optimized model, we hypothesized that the translation rate of PP2C (protein phosphatase type 2C) is downregulated by ABA to increase the ABRE (ABA-responsive element) promoter activity. The hypotheses were preliminarily supported by newly conducted experiments using transgenic Arabidopsis plants that carry a luciferase expression cassette driven by the RD29A promoter (RD29A::LUC). The model suggests that identifying a mechanism that alters PP2C translation rate would be one of the next research frontiers in the ABA signaling pathway.