Miracle-dz/UniFAR-FTUs huggingface dataset:
Methylation-Based Classification of Cervical Squamous Cell Carcinoma into Two New Subclasses Differing in Immune-Related Gene Expression

Cervical cancer is traditionally classified into two major histological subtypes, cervical squamous cell carcinoma (CSCC) and cervical adenocarcinoma (CA). However, heterogeneity exists among patients, comprising possible subpopulations with distinct molecular profiles. We applied consensus clustering to 307 methylation samples with cervical cancer from The Cancer Genome Atlas (TCGA). Fisher’s exact test was used to perform transcription factors (TFs) and genomic region enrichment. Gene expression profiles were downloaded from TCGA to assess expression differences. Immune cell fraction was calculated to quantify the immune cells infiltration. Putative neo-epitopes were predicted from somatic mutations. Three subclasses were identified: Class 1 correlating with the CA subtype and Classes 2 and 3 dividing the CSCC subtype into two subclasses. We found the hypomethylated probes in Class 3 exhibited strong enrichment in promoter region as compared with Class 2. Five TFs significantly enriched in the hypomethylated promoters and their highly expressed target genes in Class 3 functionally involved in the immune pathway. Gene function analysis revealed that immune-related genes were significantly increased in Class 3, and a higher level of immune cell infiltration was estimated. High expression of 24 immune genes exhibited a better overall survival and correlated with neo-epitope burden. Additionally, we found only two immune-related driver genes, CARD11 and JAK3, to be significantly increased in Class 3. Our analyses provide a classification of the largest CSCC subtype into two new subclasses, revealing they harbored differences in immune-related gene expression.
An Energy-Efficient Clustering Routing Protocol for Wireless Sensor Networks Based on AGNES with Balanced Energy Consumption Optimization

To further prolong the lifetime of wireless sensor network (WSN), researchers from various countries have proposed many clustering routing protocols. However, the total network energy consumption of most protocols is not well minimized and balanced. To alleviate this problem, this paper proposes an energy-efficient clustering routing protocol in WSNs. To begin with, this paper introduces a new network structure model and combines the original energy consumption model to construct a new method to determine the optimal number of clusters for the total energy consumption minimization. Based on the balanced energy consumption, then we optimize the AGglomerative NESting (AGNES) algorithm, including: (1) introduction of distance variance, (2) the dual-cluster heads (D-CHs) division of the energy balance strategy, and (3) the node dormancy mechanism. In addition, the CHs priority function is constructed based on the residual energy and position of the node. Finally, we simulated this protocol in homogeneous networks (the initial energy = 0.4 J, 0.6 J and 0.8 J) and heterogeneous networks (the initial energy = 0.4–0.8 J). Simulation results show that our proposed protocol can reduce the network energy consumption decay rate, prolong the network lifetime, and improve the network throughput in the above two networks.
CAFA: A Checksum-Aware Fuzzing Assistant Tool for Coverage Improvement

Fuzzing is an effective technique to discover vulnerabilities that involves testing applications by constructing invalid input data. However, for applications with checksum mechanism, fuzzing can only achieve low coverage because samples generated by the fuzzer are possibly incapable of passing the checksum verification. To solve this problem, most current fuzzers advise the user to comment out the checksum verification code manually, but it requires considerable time to audit the source code to identify the checksum point corresponding to checksum verification. In this paper, we present a novel approach based on taint analysis to identify the checksum point automatically. To implement this approach, the checksum-aware fuzzing assistant tool (CAFA) is designed. After the checksum point is identified, the application is statically patched in an antilogical manner at the checksum point. The fuzzing tool then tests the patched program to bypass the checksum verification. To evaluate CAFA, we use it to assist the American Fuzzy Lop (AFL) tool in fuzzing eight real-world applications with known input specification. The experimental results show that CAFA can accurately and quickly identify the checksum points and greatly improve the coverage of AFL. With the help of CAFA, multiple buffer overflow vulnerabilities have been discovered in the newest ImageMagick and RAR applications.
Diabetic retinopathy detection and classification using hybrid feature set

Complicated stages of diabetes are the major cause of Diabetic Retinopathy (DR) and no symptoms appear at the initial stage of DR. At the early stage diagnosis of DR, screening and treatment may reduce vision harm. In this work, an automated technique is applied for detection and classification of DR. A local contrast enhancement method is used on grayscale images to enhance the region of interest. An adaptive threshold method with mathematical morphology is used for the accurate lesions region segmentation. After that, the geometrical and statistical features are fused for better classification. The proposed method is validated on DIARETDB1, E‐ophtha, Messidor, and local data sets with different metrics such as area under the curve (AUC) and accuracy (ACC).
The association between renal function and neurological diseases in type 2 diabetes: a multicenter nationwide cross-sectional study

ABSTRACT Background: The evidence for an association between renal function and neurological diseases among type 2 diabetes mellitus (T2DM) patients, particularly in the Asian population, is limited. This study aimed to assess the association between glomerular filtration rate (GFR) and various neurological diseases among T2DM patients in Thailand using a nationwide patient sample. Methods: We conducted a nationwide cross-sectional study based on the DM/HT study of the Medical Research Network of the Consortium of Thai Medical Schools. This study evaluated adult T2DM patients receiving care at public Thailand hospitals in the year 2014. GFR was categorized into ≥60, 30–59, and < 30 mL/min/1.73 m2. Neurological diseases studied included ischemic stroke/transient ischemic attack (TIA), hemorrhagic stroke, dementia, all cerebrovascular disease, and peripheral neuropathy. Multivariate logistic regression was performed to assess the association between GFR and neurological diseases. Results: A total of 30,423 T2DM patients with available GFR data were included in the analysis. The mean GFR was 68.18 ± 26.45 mL/min/1.73 m2. The prevalence of ischemic stroke/TIA, hemorrhagic stroke, dementia, any cerebrovascular diseases and peripheral neuropathy were 2.9%, 0.3%, 0.1%, 3.2%, and 3.1%, respectively. Patients with GFR of 30–59 and <30 mL/min/1.73 m2 were significantly associated with increased rates of ischemic stroke/TIA, any cerebrovascular diseases, and peripheral neuropathy when compared with patients with GFR of ≥60 mL/min/1.73 m2. This association remained significant after adjustment for potential confounders. Conclusion: Decreased GFR was associated with increased ischemic stroke/TIA, all cerebrovascular diseases, and peripheral neuropathy. GFR should be monitored in diabetic patients for neurological disease awareness and prevention.
TUBB1 mutations cause thyroid dysgenesis associated with abnormal platelet physiology

The genetic causes of congenital hypothyroidism due to thyroid dysgenesis (TD) remain largely unknown. We identified three novel TUBB1 gene mutations that co‐segregated with TD in three distinct families leading to 1.1% of TUBB1 mutations in TD study cohort. TUBB1 (Tubulin, Beta 1 Class VI) encodes for a member of the β‐tubulin protein family. TUBB1 gene is expressed in the developing and adult thyroid in humans and mice. All three TUBB1 mutations lead to non‐functional α/β‐tubulin dimers that cannot be incorporated into microtubules. In mice, Tubb1 knock‐out disrupted microtubule integrity by preventing β1‐tubulin incorporation and impaired thyroid migration and thyroid hormone secretion. In addition, TUBB1 mutations caused the formation of macroplatelets and hyperaggregation of human platelets after stimulation by low doses of agonists. Our data highlight unexpected roles for β1‐tubulin in thyroid development and in platelet physiology. Finally, these findings expand the spectrum of the rare paediatric diseases related to mutations in tubulin‐coding genes and provide new insights into the genetic background and mechanisms involved in congenital hypothyroidism and thyroid dysgenesis.
The Flexible Socio Spatial Group Queries

A socio spatial group query finds a group of users who possess strong social connections with each other and have the minimum aggregate spatial distance to a meeting point. Existing studies limit to either finding the best group of a fixed size for a single meeting location, or a single group of a fixed size w.r. t. multiple locations. However, it is highly desirable to consider multiple locations in a real-life scenario in order to organize impromptu activities of groups of various sizes. In this paper, we propose Top k Flexible Socio Spatial Group Query (Top k-FSSGQ) to find the top k groups w.r. t. multiple POIs where each group follows the minimum social connectivity constraints. We devise a ranking function to measure the group score by combining social closeness, spatial distance, and group size, which provides the flexibility of choosing groups of different sizes under different constraints. To effectively process the Top k-FSSGQ, we first develop an Exact approach that ensures early termination of the search based on the derived upper bounds. We prove that the problem is NP-hard, hence we first present a heuristic based approximation algorithm to effectively select members in intermediate solution groups based on the social connectivity of the users. Later we design a Fast Approximate approach based on the relaxed social and spatial bounds, and connectivity constraint heuristic. Experimental studies have verified the effectiveness and efficiency of our proposed approaches on real datasets.
Development and evaluation of realistic optical cell models for rapid and label‐free cell assay by diffraction imaging

Methods for rapid and label‐free cell assay are highly desired in life science. Single‐shot diffraction imaging presents strong potentials to achieve this goal as evidenced by past experimental results using methods such as polarization diffraction imaging flow cytometry. We present here a platform of methods toward solving these problems and results of optical cell model (OCM) evaluations by calculations and analysis of cross‐polarized diffraction image (p‐DI) pairs. Four types of realistic OCMs have been developed with two prostate cell structures and adjustable refractive index (RI) parameters to investigate the effects of cell morphology and index distribution on calculated p‐DI pairs. Image patterns have been characterized by a gray‐level co‐occurrence matrix (GLCM) algorithm and four GLCM parameters and linear depolarization ratio δL have been selected to compare calculated against measured data of prostate cells. Our results show that the irregular shapes of and heterogeneity in RI distributions for organelles play significant roles in the spatial distribution of scattered light by cells in comparison to the average RI values and their differences among the organelles. Discrepancies in GLCM and δL parameters between calculated and measured p‐DI data provide useful insight for understanding light scattering by single cells and improving OCM.
Multilinear EigenECGs and FisherECGs for Individual Identification from Information Obtained by an Electrocardiogram Sensor

In this study, we present a third-order tensor-based multilinear eigenECG (MEECG) and multilinear Fisher ECG (MFECG) for individual identification based on the information obtained by an electrocardiogram (ECG) sensor. MEECG and MFECG are based on multilinear principal component analysis (MPCA) and multilinear linear discriminant analysis (MLDA) in the field of multilinear subspace learning (MSL), respectively. MSL directly extracts features without the vectorization of input data, while MSL extracts features without vectorizing the input data while maintaining most of the correlations shown in the original structure. In contrast with unsupervised linear subspace learning (LSL) techniques such as PCA (Principal Component Analysis) and LDA (Linear Discriminant Analysis), it is less susceptible to small-data problems because it learns more compact and potentially useful representations, and it can efficiently handle large tensors. Here, the third-order tensor is formed by reordering the one-dimensional ECG signal into a two-dimensional matrix, considering the time frame. The MSL consists of four steps. The first step is preprocessing, in which input samples are centered. The second step is initialization, in which eigen decomposition is performed and the most significant eigenvectors are selected. The third step is local optimization, in which input data is applied by eigenvectors from the second step, and new eigenvectors are calculated using the applied input data. The final step is projection, in which the resultant feature tensors after projection are obtained. The experiments are performed on two databases for performance evaluation. The Physikalisch-Technische Bundesanstalt (PTB)-ECG is a well-known database, and Chosun University (CU)-ECG is directly built for this study using the developed ECG sensor. The experimental results revealed that the tensor-based MEECG and MFECG showed good identification performance in comparison to PCA and LDA of LSL.
Security and Communication Networks Behaviors of High-Frequency Subscribers in Cellular Data Networks

Cellular networks operate under restrictive constraints of resources including radio channel capacity and network processing capability. The tremendous growth in the cellular data network usage brings operators with unprecedented signaling overloads and threatens the stability of the network. High-frequency subscribers, who produce low data volume but cause high signaling overhead, are extremely resource-inefficient. For subscribers who activate more than 5 sessions per hour, they only account for 1.19% of the total subscribers and generate about 3.81% data traffic but consume roughly 19.46% of the signaling resources, resulting in the inconsistent signaling-data bandwidth consumptions. Understanding the characteristics of those users has an important significance of capacity design and optimal allocation of resources. A lack of understanding of this active group potentially leads to low network performance and security threats. In this paper, we perform the first city-wide, large-scale investigation of high-frequency subscribers. By applying a set of novel approaches, such as pattern extraction and user behavior rebuilding, we observed that high-frequency subscribers correspond to a lower percentage of none-pattern traffic, showing positive correlation between access regularity and session activation frequency. Besides, we found that amount of high-frequency subscribers has abnormal behaviors, resulting in unwanted signaling loads. We demonstrate that our findings have significant implications on network optimization.
CCR2 Chemokine Receptors Enhance Growth and Cell-Cycle Progression of Breast Cancer Cells through SRC and PKC Activation

Basal-like breast cancers are an aggressive breast cancer subtype, which often lack estrogen receptor, progesterone receptor, and Her2 expression, and are resistant to antihormonal and targeted therapy, resulting in few treatment options. Understanding the underlying mechanisms that regulate progression of basal-like breast cancers would lead to new therapeutic targets and improved treatment strategies. Breast cancer progression is characterized by inflammatory responses, regulated in part by chemokines. The CCL2/CCR2 chemokine pathway is best known for regulating breast cancer progression through macrophage-dependent mechanisms. Here, we demonstrated important biological roles for CCL2/CCR2 signaling in breast cancer cells. Using the MCF10CA1d xenograft model of basal-like breast cancer, primary tumor growth was significantly increased with cotransplantation of patient-derived fibroblasts expressing high levels of CCL2, and was inhibited with CRISP/R gene ablation of stromal CCL2. CRISP/R gene ablation of CCR2 in MCF10CA1d breast cancer cells inhibited breast tumor growth and M2 macrophage recruitment and validated through CCR2 shRNA knockdown in the 4T1 model. Reverse phase protein array analysis revealed that cell-cycle protein expression was associated with CCR2 expression in basal-like breast cancer cells. CCL2 treatment of basal-like breast cancer cell lines increased proliferation and cell-cycle progression associated with SRC and PKC activation. Through pharmacologic approaches, we demonstrated that SRC and PKC negatively regulated expression of the cell-cycle inhibitor protein p27KIP1, and are necessary for CCL2-induced breast cancer cell proliferation. Implications: This report sheds novel light on CCL2/CCR2 chemokine signaling as a mitogenic pathway and cell-cycle regulator in breast cancer cells.
Identification of molecular determinants that govern distinct STIM2 activation dynamics

The endoplasmic reticulum (ER) Ca2+ sensors stromal interaction molecule 1 (STIM1) and STIM2, which connect ER Ca2+ depletion with extracellular Ca2+ influx, are crucial for the maintenance of Ca2+ homeostasis in mammalian cells. Despite the recent progress in unraveling the role of STIM2 in Ca2+ signaling, the mechanistic underpinnings of its activation remain underexplored. We use an engineering approach to direct ER-resident STIMs to the plasma membrane (PM) while maintaining their correct membrane topology, as well as Förster resonance energy transfer (FRET) sensors that enabled in cellulo real-time monitoring of STIM activities. This allowed us to determine the calcium affinities of STIM1 and STIM2 both in cellulo and in situ, explaining the current discrepancies in the literature. We also identified the key structural determinants, especially the corresponding G residue in STIM1, which define the distinct activation dynamics of STIM2. The chimeric E470G mutation could switch STIM2 from a slow and weak Orai channel activator into a fast and potent one like STIM1 and vice versa. The systemic dissection of STIM2 activation by protein engineering sets the stage for the elucidation of the regulation and function of STIM2-mediated signaling in mammals.
Dynamics of PAR proteins explain the oscillation and ratcheting mechanisms in dorsal closure

We present a vertex-based model for Drosophila dorsal closure that predicts the mechanics of cell oscillation and contraction from the dynamics of the PAR proteins. Based on experimental observations of how aPKC, Par-6 and Bazooka migrate from the circumference of the apical surface to the medial domain, and how they interact with each other and ultimately regulate the apicomedial actomyosin, we formulate a system of differential equations that capture the key features of the process. The oscillation in cell area in the early phase of dorsal closure results from an intracellular negative feedback loop that involves myosin, an actomyosin regulator, aPKC and Bazooka. In the slow phase, gradual sequestration of apicomedial aPKC into Bazooka clusters causes incomplete disassembly of the myosin network over each cycle of oscillation, thus producing the so-called ratchet. The fast phase of rapid cell and tissue contraction arises when medial myosin, no longer hindered by aPKC, builds up in time and produces sustained contraction. Thus, a minimal set of rules governing the dynamics of the PAR proteins, extracted from experimental observations, can account for all major mechanical outcomes of dorsal closure, including the transitions between its three distinct phases. Insert Received for publication Date and in final form Date
SIFTS: updated Structure Integration with Function, Taxonomy and Sequences resource allows 40-fold increase in coverage of structure-based annotations for proteins

Abstract The Structure Integration with Function, Taxonomy and Sequences resource (SIFTS; http://pdbe.org/sifts/) was established in 2002 and continues to operate as a collaboration between the Protein Data Bank in Europe (PDBe; http://pdbe.org) and the UniProt Knowledgebase (UniProtKB; http://uniprot.org). The resource is instrumental in the transfer of annotations between protein structure and protein sequence resources through provision of up-to-date residue-level mappings between entries from the PDB and from UniProtKB. SIFTS also incorporates residue-level annotations from other biological resources, currently comprising the NCBI taxonomy database, IntEnz, GO, Pfam, InterPro, SCOP, CATH, PubMed, Ensembl, Homologene and automatic Pfam domain assignments based on HMM profiles. The recently released implementation of SIFTS includes support for multiple cross-references for proteins in the PDB, allowing mappings to UniProtKB isoforms and UniRef90 cluster members. This development makes structure data in the PDB readily available to over 1.8 million UniProtKB accessions.
Information-Centric Network-Based Vehicular Communications: Overview and Research Opportunities

Information Centric Network (ICN) is expected to be the favorable deployable future Internet paradigm. ICN intends to replace the current IP-based model with the name-based content-centric model, as it aims at providing better security, scalability, and content distribution. However, it is a challenging task to conceive how ICN can be linked with the other most emerging paradigm, i.e., Vehicular Ad hoc Network (VANET). In this article, we present an overview of the ICN-based VANET approach in line with its contributions and research challenges.In addition, the connectivity issues of vehicular ICN model is presented with some other emerging paradigms, such as Software Defined Network (SDN), Cloud, and Edge computing. Moreover, some ICN-based VANET research opportunities, in terms of security, mobility, routing, naming, caching, and fifth generation (5G) communications, are also covered at the end of the paper.
Causal Transcription Regulatory Network Inference Using Enhancer Activity as a Causal Anchor

Transcription control plays a crucial role in establishing a unique gene expression signature for each of the hundreds of mammalian cell types. Though gene expression data have been widely used to infer cellular regulatory networks, existing methods mainly infer correlations rather than causality. We developed statistical models and likelihood-ratio tests to infer causal gene regulatory networks using enhancer RNA (eRNA) expression information as a causal anchor and applied the framework to eRNA and transcript expression data from the FANTOM Consortium. Predicted causal targets of transcription factors (TFs) in mouse embryonic stem cells, macrophages and erythroblastic leukaemia overlapped significantly with experimentally-validated targets from ChIP-seq and perturbation data. We further improved the model by taking into account that some TFs might act in a quantitative, dosage-dependent manner, whereas others might act predominantly in a binary on/off fashion. We predicted TF targets from concerted variation of eRNA and TF and target promoter expression levels within a single cell type, as well as across multiple cell types. Importantly, TFs with high-confidence predictions were largely different between these two analyses, demonstrating that variability within a cell type is highly relevant for target prediction of cell type-specific factors. Finally, we generated a compendium of high-confidence TF targets across diverse human cell and tissue types.