Miracle-dz/UniFAR-FTUs huggingface dataset:
SUMOylation of GTF2IRD1 Regulates Protein Partner Interactions and Ubiquitin-Mediated Degradation

GTF2IRD1 is one of the genes implicated in Williams-Beuren syndrome, a disease caused by haploinsufficiency of certain dosage-sensitive genes within a hemizygous microdeletion of chromosome 7. GTF2IRD1 is a prime candidate for some of the major features of the disease, presumably caused by abnormally reduced abundance of this putative transcriptional repressor protein. GTF2IRD1 has been shown to interact with the E3 SUMO ligase PIASxβ, but the significance of this relationship is largely unexplored. Here, we demonstrate that GTF2IRD1 can be SUMOylated by the SUMO E2 ligase UBC9 and the level of SUMOylation is enhanced by PIASxβ. A major SUMOylation site was mapped to lysine 495 within a conserved SUMO consensus motif. SUMOylation of GTF2IRD1 alters the affinity of the protein for binding partners that contain SUMO-interacting motifs, including a novel family member of the HDAC repressor complex, ZMYM5, and PIASxβ itself. In addition, we show that GTF2IRD1 is targeted for ubiquitination and proteasomal degradation. Cross regulation by SUMOylation modulates this process, thus potentially regulating the level of GTF2IRD1 protein in the cell. These findings, concerning post-translational control over the activity and stability of GTF2IRD1, together with previous work showing how GTF2IRD1 directly regulates its own transcription levels suggest an evolutionary requirement for fine control over GTF2IRD1 activity in the cell.
Towards a Crowd Analytic Framework For Crowd Management in Majid-al-Haram

The scared cities of Makkah Al Mukarramah and Madina Al Munawarah host millions of pilgrims every year. During Hajj, the movement of large number of people has a unique spatial and temporal constraints, which makes Hajj one of toughest challenges for crowd management. In this paper, we propose a computer vision based framework that automatically analyses video sequence and computes important measurements which include estimation of crowd density, identification of dominant patterns, detection and localization of congestion. In addition, we analyze helpful statistics of the crowd like speed, and direction, that could provide support to crowd management personnel. The framework presented in this paper indicate that new advances in computer vision and machine learning can be leveraged effectively for challenging and high density crowd management applications. However, significant customization of existing approaches is required to apply them to the challenging crowd management situations in Masjid Al Haram. Our results paint a promising picture for deployment of computer vision technologies to assist in quantitative measurement of crowd size, density and congestion.
Risk Assessment in Social Networks Based on User Anomalous Behaviors

Although the dramatic increase in Online Social Network (OSN) usage, there are still a lot of security and privacy concerns. In such a scenario, it would be very beneficial to have a mechanism able to assign a risk score to each OSN user. For this reason, in this paper, we propose a risk assessment based on the idea that the more a user behavior diverges from what it can be considered as a ‘normal behavior’, the more it should be considered risky. In doing this, we have taken into account that OSN population is really heterogeneous in observed behaviors. As such, it is not possible to define a unique standard behavioral model that fits all OSN users’ behaviors. However, we expect that similar people tend to follow similar rules with the results of similar behavioral models. For this reason, we propose a risk assessment approach organized into two phases: similar users are first grouped together, then, for each identified group, we build one or more models for normal behavior. The carried out experiments on a real Facebook dataset show that the proposed model outperforms a simplified behavioral-based risk assessment where behavioral models are built over the whole OSN population, without a group identification phase.
Somatic Point Mutations in mtDNA Control Region Are Influenced by Genetic Background and Associated with Healthy Aging: A GEHA Study

Tissue specific somatic mutations occurring in the mtDNA control region have been proposed to provide a survival advantage. Data on twins and on relatives of long-lived subjects suggested that the occurrence/accumulation of these mutations may be genetically influenced. To further investigate control region somatic heteroplasmy in the elderly, we analyzed the segment surrounding the nt 150 position (previously reported as specific of Leukocytes) in various types of leukocytes obtained from 195 ultra-nonagenarians sib-pairs of Italian or Finnish origin collected in the frame of the GEHA Project. We found a significant correlation of the mtDNA control region heteroplasmy between sibs, confirming a genetic influence on this phenomenon. Furthermore, many subjects showed heteroplasmy due to mutations different from the C150T transition. In these cases heteroplasmy was correlated within sibpairs in Finnish and northern Italian samples, but not in southern Italians. This suggested that the genetic contribution to control region mutations may be population specific. Finally, we observed a possible correlation between heteroplasmy and Hand Grip strength, one of the best markers of physical performance and of mortality risk in the elderly. Our study provides new evidence on the relevance of mtDNA somatic mutations in aging and longevity and confirms that the occurrence of specific point mutations in the mtDNA control region may represent a strategy for the age-related remodelling of organismal functions.
On approximating the number of k-cliques in sublinear time

We study the problem of approximating the number of k-cliques in a graph when given query access to the graph. We consider the standard query model for general graphs via (1) degree queries, (2) neighbor queries and (3) pair queries. Let n denote the number of vertices in the graph, m the number of edges, and Ck the number of k-cliques. We design an algorithm that outputs a (1+ε)-approximation (with high probability) for Ck, whose expected query complexity and running time are O(n/Ck1/k+mk/2/Ck )(logn, 1/ε,k). Hence, the complexity of the algorithm is sublinear in the size of the graph for Ck = ω(mk/2−1). Furthermore, we prove a lower bound showing that the query complexity of our algorithm is essentially optimal (up to the dependence on logn, 1/ε and k). The previous results in this vein are by Feige (SICOMP 06) and by Goldreich and Ron (RSA 08) for edge counting (k=2) and by Eden et al. (FOCS 2015) for triangle counting (k=3). Our result matches the complexities of these results. The previous result by Eden et al. hinges on a certain amortization technique that works only for triangle counting, and does not generalize for larger cliques. We obtain a general algorithm that works for any k≥ 3 by designing a procedure that samples each k-clique incident to a given set S of vertices with approximately equal probability. The primary difficulty is in finding cliques incident to purely high-degree vertices, since random sampling within neighbors has a low success probability. This is achieved by an algorithm that samples uniform random high degree vertices and a careful tradeoff between estimating cliques incident purely to high-degree vertices and those that include a low-degree vertex.
Research on Temperature Measurement Based on Proton Resonance Frequency at Low Field MR

Objective To research the MR thermometry with high precision and high time resolution at low field MR.Methods The temperature of the cooling process of agar gel phantom was monitored using the method based on proton resonance frequency with 0.4 T home-built permanent MR equipment and Flash sequence,and then the results were compared with those of the thermometer.Results Compared with the thermometer readings,there was a temperature uncertainty as ±1.5 ℃ for head coil and ±2 ℃ for body coil.Scanning duration was 6 seconds.Conclusion The method of MR thermometry at low field based on chemical shifting of PRF with a high accuracy appears to be a viable technique for clinical application.But temporal resolution still needs to improve.
From the Cover: Lung-Specific Overexpression of Constitutively Active IKK2 Induces Pulmonary and Systemic Inflammations but Not Hypothalamic Inflammation and Glucose Intolerance

The lung is constantly exposed to ambient pollutants such as ambient fine particulate matter (PM2.5), making it one of the most frequent locations of inflammation in the body. Given the establishment of crucial role of inflammation in the pathogenesis of cardiometabolic diseases, pulmonary inflammation is thus widely believed to be an important risk factor for cardiometabolic diseases. However, the causality between them has not yet been well established. To determine if pulmonary inflammation is sufficient to cause adverse cardiometabolic effects, SFTPC-rtTA+/-tetO-cre+/-pROSA-inhibitor κB kinase 2(IKK2)ca+/- (LungIKK2ca) and littermate SFTPC-rtTA+/-tetO-cre-/-pROSA-IKK2ca+/- wildtype (WT) mice were fed with doxycycline diet to induce constitutively active Ikk2 (Ikk2ca) overexpression in the lung and their pulmonary, systemic, adipose, and hypothalamic inflammations, vascular function, and glucose homeostasis were assessed. Feeding with doxycycline diet resulted in IKK2ca overexpression in the lungs of LungIKK2ca but not WT mice. This induction of IKK2ca was accompanied by marked pulmonary inflammation as evidenced by significant increases in bronchoalveolar lavage fluid leukocytes, pulmonary macrophage infiltration, and pulmonary mRNA expression of tumor necrosis factor α (Tnfα) and interleukin-6 (Il-6). This pulmonary inflammation due to lung-specific overexpression of IKK2ca was sufficient to increase circulating TNFα and IL-6 levels, adipose expression of Tnfα and Il-6 mRNA, aortic endothelial dysfunction, and systemic insulin resistance. Unexpectedly, no significant alteration in hypothalamic expression of Tnfα and Il-6 mRNA and glucose intolerance were observed in these mice. Pulmonary inflammation is sufficient to induce systemic inflammation, endothelial dysfunction, and insulin resistance, but not hypothalamic inflammation and glucose intolerance.
Kernel Embedding Multiorientation Local Pattern for Image Representation

Local feature descriptor plays a key role in different image classification applications. Some of these methods such as local binary pattern and image gradient orientations have been proven effective to some extent. However, such traditional descriptors which only utilize single-type features, are deficient to capture the edges and orientations information and intrinsic structure information of images. In this paper, we propose a kernel embedding multiorientation local pattern (MOLP) to address this problem. For a given image, it is first transformed by gradient operators in local regions, which generate multiorientation gradient images containing edges and orientations information of different directions. Then the histogram feature which takes into account the sign component and magnitude component, is extracted to form the refined feature from each orientation gradient image. The refined feature captures more information of the intrinsic structure, and is effective for image representation and classification. Finally, the multiorientation refined features are automatically fused in the kernel embedding discriminant subspace learning model. The extensive experiments on various image classification tasks, such as face recognition, texture classification, object categorization, and palmprint recognition show that MOLP could achieve competitive performance with those state-of-the art methods.
The Identification of Markers of Macrophage Differentiation in PMA-Stimulated THP-1 Cells and Monocyte-Derived Macrophages

Differentiated macrophages are the resident tissue phagocytes and sentinel cells of the innate immune response. The phenotype of mature tissue macrophages represents the composite of environmental and differentiation-dependent imprinting. Phorbol-12-myristate-13-acetate (PMA) and 1,25-dihydroxyvitamin D3 (VD3) are stimuli commonly used to induce macrophage differentiation in monocytic cell lines but the extent of differentiation in comparison to primary tissue macrophages is unclear. We have compared the phenotype of the promonocytic THP-1 cell line after various protocols of differentiation utilising VD3 and PMA in comparison to primary human monocytes or monocyte-derived macrophages (MDM). Both stimuli induced changes in cell morphology indicative of differentiation but neither showed differentiation comparable to MDM. In contrast, PMA treatment followed by 5 days resting in culture without PMA (PMAr) increased cytoplasmic to nuclear ratio, increased mitochondrial and lysosomal numbers and altered differentiation-dependent cell surface markers in a pattern similar to MDM. Moreover, PMAr cells showed relative resistance to apoptotic stimuli and maintained levels of the differentiation-dependent anti-apoptotic protein Mcl-1 similar to MDM. PMAr cells retained a high phagocytic capacity for latex beads, and expressed a cytokine profile that resembled MDM in response to TLR ligands, in particular with marked TLR2 responses. Moreover, both MDM and PMAr retained marked plasticity to stimulus-directed polarization. These findings suggest a modified PMA differentiation protocol can enhance macrophage differentiation of THP-1 cells and identify increased numbers of mitochondria and lysosomes, resistance to apoptosis and the potency of TLR2 responses as important discriminators of the level of macrophage differentiation for transformed cells.
iNOS-derived nitric oxide promotes glycolysis by inducing pyruvate kinase M2 nuclear translocation in ovarian cancer

Aerobic glycolysis is essential for tumor growth and survival. Activation of multiple carcinogenic signals contributes to metabolism reprogramming during malignant transformation of cancer. Recently nitric oxide has been noted to promote glycolysis but the mechanism remains elusive. We report here the dual role of nitric oxide in glycolysis: low/physiological nitric oxide (≤ 100 nM) promotes glycolysis for ATP production, oxidative defense and cell proliferation of ovary cancer cells, whereas excess nitric oxide (≥ 500 nM) inhibits it. Nitric oxide has a positive effect on glycolysis by inducing PKM2 nuclear translocation in an EGFR/ERK2 signaling-dependent manner. Moreover, iNOS induced by mild inflammatory stimulation increased glycolysis and cell proliferation by producing low doses of nitric oxide, while hyper inflammation induced iNOS inhibited it by producing excess nitric oxide. Finally, iNOS expression is abnormally increased in ovarian cancer tissues and is correlated with PKM2 expression. Overexpression of iNOS is associated with aggressive phenotype and poor survival outcome in ovarian cancer patients. Our study indicated that iNOS/NO play a dual role of in tumor glycolysis and progression, and established a bridge between iNOS/NO signaling pathway and EGFR/ERK2/PKM2 signaling pathway, suggesting that interfering glycolysis by targeting the iNOS/NO/PKM2 axis may be a valuable new therapeutic approach of treating ovarian cancer.
Determining Physical Constraints in Transcriptional Initiation Complexes Using DNA Sequence Analysis

Eukaryotic gene expression is often under the control of cooperatively acting transcription factors whose binding is limited by structural constraints. By determining these structural constraints, we can understand the “rules” that define functional cooperativity. Conversely, by understanding the rules of binding, we can infer structural characteristics. We have developed an information theory based method for approximating the physical limitations of cooperative interactions by comparing sequence analysis to microarray expression data. When applied to the coordinated binding of the sulfur amino acid regulatory protein Met4 by Cbf1 and Met31, we were able to create a combinatorial model that can correctly identify Met4 regulated genes. Interestingly, we found that the major determinant of Met4 regulation was the sum of the strength of the Cbf1 and Met31 binding sites and that the energetic costs associated with spacing appeared to be minimal.
Parallel trends in cortical gray and white matter architecture and connections in primates allow fine study of pathways in humans and reveal network disruptions in autism

Noninvasive imaging and tractography methods have yielded information on broad communication networks but lack resolution to delineate intralaminar cortical and subcortical pathways in humans. An important unanswered question is whether we can use the wealth of precise information on pathways from monkeys to understand connections in humans. We addressed this question within a theoretical framework of systematic cortical variation and used identical high-resolution methods to compare the architecture of cortical gray matter and the white matter beneath, which gives rise to short- and long-distance pathways in humans and rhesus monkeys. We used the prefrontal cortex as a model system because of its key role in attention, emotions, and executive function, which are processes often affected in brain diseases. We found striking parallels and consistent trends in the gray and white matter architecture in humans and monkeys and between the architecture and actual connections mapped with neural tracers in rhesus monkeys and, by extension, in humans. Using the novel architectonic portrait as a base, we found significant changes in pathways between nearby prefrontal and distant areas in autism. Our findings reveal that a theoretical framework allows study of normal neural communication in humans at high resolution and specific disruptions in diverse psychiatric and neurodegenerative diseases.
A Double Auction Mechanism to Bridge Users’ Task Requirements and Providers’ Resources in Two-Sided Cloud Markets

Double auction-based pricing model is an efficient pricing model to balance users’ and providers’ benefits. Existing double auction mechanisms usually require both users and providers to bid with the unit price and the number of VMs. However, in practice users seldom know the exact number of VMs that meets their task requirements, which leads to users’ task requirements inconsistent with providers’ resource. In this paper, we propose a truthful double auction mechanism, including a matching process as well as a pricing and VM allocation scheme, to bridge users’ task requirements and providers’ resources in two-sided cloud markets. In the matching process, we design a cost-aware resource algorithm based on Lyapunov optimization techniques to precisely obtain the number of VMs that meets users’ task requirements. In the pricing and VM allocation scheme, we apply the idea of second-price auction to determine the final price and the number of provisioned VMs in the double auction. We theoretically prove our proposed mechanism is individual-rational, truthful and budget-balanced, and analyze the optimality of proposed algorithm. Through simulation experiments, the results show that the individual profits achieved by our algorithm are 12.35 and 11.02 percent larger than that of scale-out and greedy scale-up algorithms respectively for 90 percent of users, and the social welfare of our mechanism is only 7.01 percent smaller than that of the optimum mechanism in the worst case.
JU NITM at IJCNLP-2017 Task 5: A Classification Approach for Answer Selection in Multi-choice Question Answering System

This paper describes the participation of the JU NITM team in IJCNLP-2017 Task 5: “Multi-choice Question Answering in Examinations”. The main aim of this shared task is to choose the correct option for each multi-choice question. Our proposed model includes vector representations as feature and machine learning for classification. At first we represent question and answer in vector space and after that find the cosine similarity between those two vectors. Finally we apply classification approach to find the correct answer. Our system was only developed for the English language, and it obtained an accuracy of 40.07% for test dataset and 40.06% for valid dataset.
Importance of trophic information, simplification and aggregation error in ecosystem models

Ecosystem models are becoming increasingly important as pressure from fisheries intensifies and ecosystem-based fisheries management becomes more widely used. Trophic webs often form the basis of ecosystem models and ecosystem-specific dietary information is crucial for optimal model performance. This is particularly the case if model predictions are used in management decisions. The Tasmanian live fish fishery for banded morwong was used as a case study to investigate the importance of trophic information, model simplification and aggregation error on ecosystem model results. Dietary analysis of 6 commonly captured reef fish was undertaken. Significant trophic overlap was found between blue throat wrasse Notolabrus tetricus and purple wrasse N. fucicola, and banded morwong Cheilodactylus spectabilis and bastard trumpeter Latridopsis forsteri. Marblefish Aplodactylus arctidens and long-snouted boarfish Pentaceropsis recurvirostris had significantly different diets from other species studied. Using this information, a detailed qualitative model was produced and then simplified through the aggregation of variables. Variables were aggregated using 3 methods: Euclidean distance, Bray-Curtis similarity, and regular equivalence for inclusion in 3 simplified models. Variable aggregation is undertaken in many studies and may create aggregation error. Each aggregation method produced a different proportion of incorrect model predictions as a result of aggregation error. The model simplified using regular equivalence produced the least aggregation error and a web structure aligned with the dietary analysis. More widespread use of these methods in fisheries management should be considered.
Using machine learning techniques for subjectivity analysis based on lexical and non-lexical features

Machine learning techniques have been used to address various problems and classification of documents is one of the main applications of such techniques. Opinion mining has emerged as an active research domain due to its wide range of applications such as multi-document summarization, opinion mining of documents and users’ reviews analysis improving answers of opinion questions in forums. Existing works classify the documents using lexicon-based features only. In this work, four state of the art machine learning techniques have been applied to classify the content into subjective and objective. The subjective content contains opinionative information while objective content contains factual information. The main contribution lies in the introduction of non-lexical features and content based features in addition to the use of a conventional lexicon based feature set. We compare results of four machine learning techniques and discuss performance in diverse categories of lexical and non-lexical features. The comparative analysis has been accomplished using standard performance evaluation measures and experiments have been performed on a real-world dataset of the online forum related to diverse topics. It has been proven that proposed content and non-lexical thread specific features play their role in the classification of subjective and non-subjective content.