Miracle-dz/UniFAR-FTUs huggingface dataset:
Prognostic impact of MutT homolog‐1 expression on esophageal squamous cell carcinoma

MutT homolog‐1 (MTH1) is a pyrophosphatase that acts on oxidized nucleotides and hydrolyzes 8‐oxo‐2’‐deoxyguanosine triphosphate in deoxynucleoside triphosphate pool to prevent its incorporation into nuclear and mitochondrial DNA, result in reduce cytotoxicity in tumor cells. MTH1 is overexpressed in various cancers and is considered as a therapeutic target. Environmental factors such as cigarette smoking and alcohol consumption are critical risk factors for the development and progression of esophageal squamous cell carcinoma (ESCC), suggesting that oxidative stress contributes to the pathogenesis of ESCC. We examined the expression of MTH1 and the accumulation of 8‐oxo‐2’‐deoxyguanosine (8‐oxo‐dG) in 84 patients with ESCC who underwent curative resection without neoadjuvant therapy. MTH1 mRNA level was quantified by performing quantitative reverse transcription‐PCR. Immunohistochemical analysis of paraffin‐embedded cancer tissues was performed to determine MTH1 protein expression and 8‐oxo‐dG accumulation. MTH1 mRNA expression was higher in cancerous tissues than in the corresponding normal epithelium (P < 0.0001). Immunohistochemical analysis showed that high MTH1 expression was significantly associated with deeper tumor invasion and venous invasion, advanced cancer stage, and poor overall survival (P = 0.0021) and disease‐specific survival (P = 0.0013) compared with low MTH1 expression. Furthermore, high MTH1 expression was an independent predictor of poor disease‐specific survival (P = 0.0121). In contrast, 8‐oxo‐dG accumulation was not associated with any clinicopathological factor and poor prognosis. These results suggest that MTH1 overexpression is a predictor of ESCC progression and poor prognosis and that MTH1 can serve as a therapeutic target for treating patients with ESCC.
Statistical decision theory for mobile robotics: theory and application

In this paper we pioneer a method which, given an input of mobile robot pose measurements by a sensor-based localization algorithm, produces a minimax risk fixed-size confidence set estimate for the pose of the agent. This work constitutes the first application to the mobile robotics domain of optimal fixed-size confidence-interval decision theory. The approach is evaluated in terms of theoretical capture probability and empirical capture frequency during actual experiments with the mobile agent. The method is compared to several other procedures including the Kalman filter (minimum mean squared error estimate) and the maximum likelihood estimator (MLE). The minimax approach is found to dominate all the other methods in performance. In particular, for the minimax approach, a very close agreement is achieved between theoretical capture probability and empirical capture frequency. This allows performance to be accurately predicted, greatly facilitating the design of mobile robotic systems, and delineating the tasks that may be performed with a given system.
Delays and weakly coupled neuronal oscillators

We use weakly coupled oscillator theory to study the effects of delays on coupled systems of neuronal oscillators. We explore, first, simple pairs with constant delays and then examine the role of distributed delays as would occur in systems with dendritic branches or in networks where there is a distance-dependent conductance delay. In the latter, we use mean field theory to show the emergence of travelling waves and the loss of synchronization. Next, we consider phase models with stronger coupling and delays in the state variables. We show that they have a richer dynamics but one that is still similar to the weakly coupled case.
A New Algorithm Based on Generalized Target Maneuver Detection

In the airborne vehicles, complex target tracking with more accurately is highly desirable. Conventional techniques based on augmented or batch algorithms are computationally expensive, which is the major drawback for the real time target parameter estimation. In contrast, other conventional input estimation techniques which overcome this problem, assume constant acceleration and therefore this is not a generalized modeling (T.C. Wang and P.K. Varshney, 1993). The proposed algorithm is developed to overcome these drawbacks by using a new generalized dynamic modeling of acceleration and acceleration rate (jerk) with reduction on the vector state dimension. The proposed modified Kalman filter algorithm is based on a generalized formulation including our earlier works. Results show the significant improvement for high maneuver target tracking problem.
Contributions to the history of Telemedicine of the TICs

The first use of tests of communications technologies applied to medicine is almost as old as the invention of the telephone. Within a very short time, the telephone was shown as an essential element to ask or exchange information of medical content between health professionals. The spectacular development of broadcasting in the first quarter of the 20th century brought about the interest to apply the radio communications in medicine. The development of telemedicine on a world level has not been uniform, with different introduction and implementation depending on various health services. The clinical specialty most benefited by Telemedicine is radiology, followed by cardiology, dermatology, psychiatry, house attention, pathology and oncology. Nowadays, a large increase of the communications systems for house attention, especially in the care of elderly and chronic patients has taken place.
Evaluation and Simplification of rules created by 1-v-r Rough SVM multiclassification

Complexity of rules created by support vector machine (SVM) based multiclassifiers is an important issue in adopting these classifiers. Recently, we have shown how traditional SVMs can be represented using interval or rough sets. We have also extended the rough SVMs to multiclassification using both the 1-v-r and 1-v-1 approaches. In this paper, we describe an algorithmic implementation of the previously proposed mathematical formulation for 1-v-r approach. Analysis of the time requirements shows that the proposed classifier has a competitive linear time requirement. The approach presented here also will also help practitioners simplify the rules used in the classification process
Robust and Scalable Navmesh Generation with multiple levels and stairs support

Automatically planning motion for robots or humans in a virtual environment is a complex task. The navigation cannot be done directly on the geometric scene. An internal representation of the environment is necessary. However, virtual environments complexity is growing at an important rate, and it is not unusual to work with millions of triangles and area as large as some square kilometers. In these cases, grid based methods require too much memory, and are too slow for A* algorithms. Polygons based methods are more memory and computation efficient, but they are difficult to generate and require complex preprocessing of the input mesh to ensure nice topological properties. In this paper, we propose an hybrid method, which uses 3D voxelization to generate a clean polygon mesh simple enough to perform fast A* search. Using this approach, we robustly generate Navigation Meshes for large environments with fine details, described by millions of triangles, without any assumption on the quality of the input mesh. The input mesh can contains holes, degenerated or intersecting triangles.
Barriers and Drivers of Planning for Climate Change Adaptation across Three Levels of Government in Canada

ABSTRACT This study investigates the factors that constrain and enable adaptation planning for increasing flood risk in Canada. It uses a multiple-methods, multi-scalar approach to identify interconnected barriers and drivers that operate across municipal, provincial, and federal levels of government in Vancouver and Surrey, British Columbia. Through a policy content analysis (n = 54) and in-depth interviews with planners and other practitioners (n = 31), the study finds five major barriers to the mainstreaming of climate change adaptation: inadequate collaboration, absence of senior level political leadership, lack of public awareness, insufficient financial and staff capacity, and misalignment of policies within and between levels of government.
Experiments with Discourse Structure

An algorithm was implemented for automatically generating tree-like discourse structures. This was used to generate a variety of discourse trees by using diierent attachment rules. The information used by these rules was essentially referential, and included continuity of theme, temporal reference, and pronominalisation in general. The trees in turn formed the basis for reference resolution, including long-distance pronominalisation. The motivation for this approach to discourse structure was twofold. Firstly, there is the implausibility of assuming that reference resolution has available to it a discourse seg-mentation based on higher-level reasoning processes. Secondly, there is the ready availability of referential information and the associations of it, and theme in particular, with what the discourse is about.
The endocannabinoid system and the treatment of obesity

The endocannabinoids are endogenous lipids capable of binding to both cannabinoid receptors (CB) CB1 and CB2. These receptors belong to the G protein–coupled family receptors and they were discovered while investigating the mode of action of Δ9‐tetrahydrocannabinol, a component of Cannabis sativa, to which they bind with high affinity. Among many other brain sites, CB1 is present in the hypothalamic nuclei involved in the control of energy balance and body weight, as well as in neurons of the mesolimbic system which is believed to mediate the incentive value of food. At central nervous system level, CB1 activation is necessary to induce food intake after a short period of food deprivation, and when CB1 is activated by endocannabinoids produced in situ, a stimulation of the ingestion of palatable food has been described. CB1 stimulation leads to modulation of the release of some hypothalamic anorexigenic and orexigenic mediators, as well as of dopamine in the nucleus accumbens shell. Recent evidence has proved that CB1 is also present in the peripheral organs, such as the adipose tissue and gastrointestinal system, key organs in the regulation of energy metabolism. Animal models have provided solid evidence that genetically induced obesity leads to long‐lasting overstimulation of endocannabinoid system synthesis resulting in permanent overactivation of CB1, which may then contribute to the maintenance of this disease. Importantly, at peripheral level, CB1 activation has been shown to stimulate lipogenesis in adipocytes. CB1 blockers increase adiponectin production in adipocytes, which leads to increased fatty acid oxidation and free fatty acid clearance. Moreover, CB1 has been shown to be up‐regulated in adipocytes derived from obese rodents. These results support the role of endocannabinoids in the development and maintenance of obesity, paving the way for the development of a new class of drugs such as the CB1 blockers as a therapy for tackling obesity and the associated major cardiovascular risk factors.
Mesh editing with an embedded network of curves

We propose a new topological data structure for representing a set of polygonal curves embedded in a meshed surface. In this embedding, the vertices of the curve do not necessarily correspond to the vertices of the surface. The partition of the surface yielded by the intersecting curves is efficiently represented as a "cut-graph". The cut-graph stores combinatorial information of the network of curves. In our approach, the combinatorial form of information is systematically preferred to geometrical information since it improves both robustness and efficiency. Thanks to the topological data structure and algorithms, the cut-graph can be sketched through iterations of designing and erasing curves on the mesh surface in a "nondestructive" way, i.e. without modifying the mesh until the cutting operation is committed. We also demonstrate several prototype curve design tools inspired by 2D vector and bitmap graphics paradigms. We show how to sketch the cut-graph and how these tools can be mixedly used.
Transcriptomic Analysis of the Mouse Mammary Gland Reveals New Insights for the Role of Serotonin in Lactation

Serotonin regulates numerous processes in the mammary gland. Our objective was to discover novel genes, pathways and functions which serotonin modulates during lactation. The rate limiting enzyme in the synthesis of non-neuronal serotonin is tryptophan-hydroxylase (TPH1). Therefore, we used TPH1 deficient dams (KO; serotonin deficient, n = 4) and compared them to wild-type (WT; n = 4) and rescue (RC; KO + 100 mg/kg 5-hydroxytryptophan injected daily, n = 4) dams. Mammary tissues were collected on day 10 of lactation. Total RNA extraction, amplification, library preparation and sequencing were performed following the Illumina mRNA-Seq. Overall, 97 and 204 genes (false discovery rate, FDR ≤ 0.01) exhibited a minimum of a 2-fold expression difference between WT vs. KO and WT vs. RC dams, respectively. Most differentially expressed genes were related to calcium homeostasis, apoptosis regulation, cell cycle, cell differentiation and proliferation, and the immune response. Additionally, gene set enrichment analysis using Gene Ontology and Medical Subject Headings databases revealed the alteration of several biological processes (FDR ≤ 0.01) including fat cell differentiation and lipid metabolism, regulation of extracellular signal-related kinase and mitogen-activated kinase cascades, insulin resistance, nuclear transport, membrane potential regulation, and calcium release from the endoplasmic reticulum into the cytosol. The majority of the biological processes and pathways altered in the KO dams are central for mammary gland homeostasis. Increasing peripheral serotonin in the RC dams affects specific pathways that favor lactation. Our data confirms the importance of serotonin during lactation in the mammary gland.
3 D Shaded Graphics in Radiotherapy and Diagnostic Imaging

Shaded graphics display has been demonstrated to have the potential of providing comprehension of the 3D structure of and interrelationships between organs, diseased anatomy, and surgically implanted objects appearing in medical images such as computed tomograms (CT scans) and magnetic resonance images, as well as of and between calculated objects such as radiation beams and radiation dose distributions. In this paper we present some approaches taken at UNC toward shaded graphics for this purpose, some issues that have arisen, and some results. In particular, we discuss means for object definition, contour association when objects are defined slice by slice, tiling including bifurcation, previewing to set orientation and transparency parameters, high-quality rendering, and presentation.
An Improved Density-based Spatial Clustering Algorithm Based on Key Factors of Object's Distribution

Density-based spatial clustering algorithms can be used to filter out noise and outliers, and discover clusters of arbitrary shape, which are all relatively good algorithms. But when the problem of variable density distribution of spatial objects was taken in to consideration, the accuracy of clustering result can be largely affected by the distribution of spatial objects. Therefore, the strategy of choosing the neighborhood radius threshold between objects become the key of the algorithm. An algorithm with a neighborhood radius threshold choosing strategy that based on factors that influence the distribution of spatial objects is proposed in this paper. At the same time, it adopts quadtree indexing technology to improve the efficiency of this algorithm. Experiment shows that this algorithm can efficiently deal with the problem of clustering in spatial objects’ variable density distribution.
Transformation of water-wave energy in two-layer ocean in the presence of surface tension

In the present paper, transformation of water-wave energy in a two-layer fluid having irregular bottom is studied including the effect of the surface tension. In such fluid system, time harmonic waves of two distinct modes (wave numbers) exist. When an incident wave with a particular wave number encounters the irregular bottom, it is partially reflected as well as partially transmitted into waves of both wave numbers. As a result, some of the energy is transferred from one to another wave number. Perturbation analysis followed by Fourier transformation is used to study the reflection and transmission phenomena. Reflection and transmission coefficients of both modes are estimated up to the first-order. These coefficients are obtained in integral forms and depend on the bottom profile. One special type of shape function is considered to evaluate the related coefficients. These coefficients are shown in graphical forms to demonstrate the effects of some physical parameters like number of ripples, surface tension on the transformation of wave energy between the waves of two different modes. Moreover, a comparison has been made between the present results and the experimental results available in the literature.
Lung tissue detection and classification using novel intensity features and PNN

Content based image classification address the problem of retrieving images relevant to the user needs from image databases on the basis of low-level visual features that can be derived from the images. Grouping images into meaningful categories to reveal useful information is a challenging and important problem. Clustering is a data mining technique to group a set of unsupervised data based on the conceptual clustering principal: maximizing the intra class similarity and minimizing the interclass similarity. Proposed framework focuses on color as feature. Color Moment and Block Truncation Coding (BTC) are used to extract features for image dataset. Experimental study using K-Means clustering algorithm is conducted to group the image dataset into various clusters.
Gender and risk of depression in Saudi Arabia, a systematic review and meta-analysis

Depression is one of the leading causes of mortality and morbidity worldwide. In the year 2000 depression accounted for 4.4% of the global disability adjusted life years (DALYs). The Kingdom of Saudi Arabia (KSA) has a population of 28 million people and is one of the countries experiencing demographic transition in its population structure. Improvements in socioeconomic status have been shown to be associated with increased chronic diseases including chronic mental diseases like depression, but still there is no comprehensive review summarizing the various reports currently existing in the literature. Although individual studies within Saudi Arabia have reported prevalence rates and risks, the quality of such studies need to be subjected to rigorous assessment and their findings pooled to give combined weighted evidence that will provide basis for targeted intervention. Pooled risks have the advantage of adjusting inherent variations within sampled populations and therefore providing more reliable estimates even though there are concerns about possible magnification of smaller individual risks.
An Improved Speaker Diarization System for Multiple Distance Microphone Meetings

This paper describes an improved speaker diarization system for multiple distance microphone (MDM) meeting conversations. First, the new system includes a modified speech activity detector (SAD). Second, it adopts the new spectral features based on equivalent rectangular bandwidth (ERB) or bark scale, which are compared with the traditional Mel Frequency Cepstral Coefficients (MFCC) features. Third, the system adapts the segment model from a universal background model (UBM). Finally, it is evaluated in the NIST RT-04s MDM conditions. Experimental results show that: (1) the new speech/non-speech detector out-performs the one in the baseline system, (2) the proposed spectral features are more effective than MFCC features for speaker diarization, (3) The adaptation of segment models from UBM helps improving the system performance. Together, these improvements lead to the diarization error rate of 15.38% on RT-04s evaluation data excluding overlapping speech.
Asymmetry, Zero Morphology and Tractability

We present a theory of grammar based on asymmetrical relations, the Strict Asymmetry Theory, and we provide evidence form Indo-European and non Indo-European languages to show that argument structure restrictions on morphological composition follow in a principled way from the theory. We describe and implement a bottom-up parser for morphological selection in the Strict Asymmetry framework. Core lexical properties including argument structure and derivational affix selection are encoded in a uniform mechanism. We consider the computational implications of three different implementations. In particular, we examine the effect on bottom-up parsing from varying the Specifier-Head-Complement order. We provide computational motivation for the logical separation of overt and covert affixation.
Lower bounds for approximation schemes for Closest String

In the Closest String problem one is given a family $\mathcal S$ of equal-length strings over some fixed alphabet, and the task is to find a string $y$ that minimizes the maximum Hamming distance between $y$ and a string from $\mathcal S$. While polynomial-time approximation schemes (PTASes) for this problem are known for a long time [Li et al., J. ACM'02], no efficient polynomial-time approximation scheme (EPTAS) has been proposed so far. In this paper, we prove that the existence of an EPTAS for Closest String is in fact unlikely, as it would imply that $\mathrm{FPT}=\mathrm{W}[1]$, a highly unexpected collapse in the hierarchy of parameterized complexity classes. Our proof also shows that the existence of a PTAS for Closest String with running time $f(\varepsilon)\cdot n^{o(1/\varepsilon)}$, for any computable function $f$, would contradict the Exponential Time Hypothesis.
Weight Cycling Enhances Adipose Tissue Inflammatory Responses in Male Mice

Background Obesity is associated with low-grade chronic inflammation attributed to dysregulated production, release of cytokines and adipokines and to dysregulated glucose-insulin homeostasis and dyslipidemia. Nutritional interventions such as dieting are often accompanied by repeated bouts of weight loss and regain, a phenomenon known as weight cycling (WC). Methods In this work we studied the effects of WC on the feed efficiency, blood lipids, carbohydrate metabolism, adiposity and inflammatory markers in C57BL/6 male mice that WC two or three consecutive times by alternation of a high-fat (HF) diet with standard chow (SC). Results The body mass (BM) grew up in each cycle of HF feeding, and decreased after each cycle of SC feeding. The alterations observed in the animals feeding HF diet in the oral glucose tolerance test, in blood lipids, and in serum and adipose tissue expression of adipokines were not recuperated after WC. Moreover, the longer the HF feeding was (two, four and six months), more severe the adiposity was. After three consecutive WC, less marked was the BM reduction during SC feeding, while more severe was the BM increase during HF feeding. Conclusion In conclusion, the results of the present study showed that both the HF diet and WC are relevant to BM evolution and fat pad remodeling in mice, with repercussion in blood lipids, homeostasis of glucose-insulin and adipokine levels. The simple reduction of the BM during a WC is not able to recover the high levels of adipokines in the serum and adipose tissue as well as the pro-inflammatory cytokines enhanced during a cycle of HF diet. These findings are significant because a milieu with altered adipokines in association with WC potentially aggravates the chronic inflammation attributed to dysregulated production and release of adipokines in mice.