Miracle-dz/UniFAR-FTUs huggingface dataset:
Prioritization and comprehensive analysis of genes associated with melanoma

Melanoma is a malignant tumor derived from melanocytes, which occurs mostly in the skin. A major challenge in cancer research is the biological interpretation of the complexity of cancer somatic mutation profiles. The aim of the present study was to obtain a comprehensive understanding of the formation and development of melanoma and to identify its associated genes. In the present study, a pipeline was proposed for investigating key genes associated with melanoma based on the Online Mendelian Inheritance in Man and Search Tool for the Retrieval of Interacting Genes/Proteins databases through a random walk model. Additionally, functional enrichment analysis was performed for key genes associated with melanoma. This identified a total of 17 biological processes and 30 pathways which may be associated with melanoma. In addition, melanoma-specific network analysis followed by Kaplan-Meier analysis along with log-rank tests identified tyrosinase, hedgehog acyltransferase, BRCA1-associated protein 1 and melanocyte inducing transcription factor as potential therapeutic targets for melanoma. In conclusion, the present study increased the knowledge of melanoma progression and may be helpful for improving its prognosis.
Partitioning CiteSeer ’ s Citation Graph Revised Version

CiteSeer is a freely available, online citation database. In this paper, we explore the citation graph of CiteSeer. First, we characterize that graph as given by CiteSeer. Second, considering a set of anchor papers, we give heuristics to find a partition of the graph whose cut is minimal. Finally, we evaluate our solution using different metrics.
Effect of tumor necrosis factor-α induced protein 8 like-2 on immune function of dendritic cells in mice following acute insults

Tumor necrosis factor-α induced protein 8 like-2 (TNFAIP8L2, TIPE2) is a lately discovered negative regulator of innate immunity and cellular immunity. The present study was designed to investigate whether naturally occurring dendritic cells (DCs) could express TIPE2 mRNA/protein and its potential significance. Expressions of co-stimulatory molecules on DC surface and cytokines were analyzed to assess the functional role of TIPE2 in controlling DC maturation as well as activation. The activated DCs were assessed for their capacity to stimulate the proliferation and differentiation of T cells. It was found that TIPE2 was a cytoplasmic protein expressed in DCs, and the percentage of DCs which expressed co-stimulatory molecules and cytokines were obviously up-regulated when TIPE2 gene silenced by siRNA in vitro and in vivo. DCs undergone TIPE2 knockdown were found to promote the maturation of DCs, T-cell proliferation as well as differentiation, and they were significantly elevated IL-2 level and intranuclear NF-AT activation. Conversely, in over-expressing TIPE2 DC cells, it could inhibit T-cell proliferation and differentiation, and markedly down-regulate IL-2 expression and intranuclear NF-AT activation after scald injury. The results suggested that TIPE2 appeared to be a critical immunoregulatory molecule which affected DC maturation and subsequent T-cell mediated immunity.
A computational model for nonrational bisector surfaces: curve-surface and surface-surface bisectors

The bisector of two rational surfaces in R/sup 3/ is, in general, nonrational; and so is the bisector of a rational curve and a rational surface. Thus, bisector surfaces in these two cases must be approximated numerically. Unfortunately, they are algebraic surfaces of very high degree and numerical approximation is non-trivial. This paper suggests a new computational model for constructing curve-surface and surface-surface bisectors in R/sup 3/. The curve-surface bisector problem is reformulated as the search for a trivariate zero-set; and the surface-surface bisector problem is reduced to that of finding the common zero-set of two four-variate functions.
Robust distributed framework of interpolation-based control for polytopic uncertain systems

Distributed control is an important framework to handle large-scale systems, however, robust distributed control for polytopic uncertain systems still being a challenge. In this work, we propose a robust distributed framework for interpolation-based control of polytopic uncertain systems. The algorithms proposed consist of both off-line and online computations. The entire system is decomposed into a number of subsystems with smaller number of control inputs. A sequence of nested invariant sets for the entire system, and sequences of state feedback gains corresponding to each subsystem are constructed off-line by minimizing the upper bound of worst-case performance cost in a centralized scheme. The invariant sets constructed are polyhedral sets. At each control iteration, when the state measured lies between any two adjacent invariant sets constructed, a state feedback gain for each subsystem is determined by an interpolation of associated state feedback gains pre-computed. The interpolation problems are based on minimization of a distant from the current state to the adjacent smaller invariant set, solved in a centralized fashion or iteratively solved in a cooperative scheme. Simulation example of quadruple tank system is used to illustrate the algorithms proposed. The cooperative algorithm is capable of inheriting the properties of centralized control scheme but requires lower computational burdens.
Extraction of relevant speech features using the information bottleneck method

We propose a novel approach to the design of efficient representations of speech for various recognition tasks. Using a principled information theoretic framework – the Information Bottleneck method – which enables quantization that preserves relevant information, we demonstrate that significantly smaller representations of the signal can be obtained that still capture most of the relevant information about phonemes or speakers. The significant implications for building more efficient speech and speaker recognition systems are discussed .
A note on rewriting theory for uniqueness of iteration.

Uniqueness for higher type term constructors in lambda calculi (e.g. surjective pairing for product types, or uniqueness of iterators on the natural numbers) is easily expressed using universally quanti ed conditional equations. We use a technique of Lambek[18] involving Mal'cev operators to equationally express uniqueness of iteration (more generally, higher-order primitive recursion) in a simply typed lambda calculus, essentially Godel's T [29, 13]. We prove the following facts about typed lambda calculus with uniqueness for primitive recursors: (i) It is undecidable, (ii) Church-Rosser fails, although ground Church-Rosser holds, (iii) strong normalization (termination) is still valid. This entails the undecidability of the coherence problem for cartesian closed categories with strong natural numbers objects, as well as providing a natural example of the following computational paradigm: a non-CR, ground CR, undecidable, terminating rewriting system.
Finite Element Model Updating Using Fish School Search Optimization Method

A recent nature inspired optimization algorithm, Fish School Search (FSS) is applied to the finite element model (FEM) updating problem. This method is tested on a GARTEUR SM-AG19 aeroplane structure. The results of this algorithm are compared with two other metaheuristic algorithms, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). It is observed that on average, the FSS and PSO algorithms give more accurate results than the GA. A minor modification to the FSS is proposed. This modification improves the performance of FSS on the FEM updating problem which has a constrained search space.
Pathways to social work supervision

ABSTRACT Social work research and literature in the area of supervision tends to focus on the supervision of field education students. Less attention is given to the supervision of social work practitioners and there is almost no information that examines how social workers become supervisors. This exploratory study interviewed 27 practicing social work supervisors across different fields of social work practice in Western Canada. The participants included 15 supervisors with a graduate degree and 12 supervisors with an undergraduate degree. The supervisors completed a brief questionnaire that was followed by a structured interview. Supervisors were asked how they came to be social work supervisors. Thematic analysis of the supervisors’ responses revealed three pathways to supervision: task exposure, supervision by happen chance, and deliberate decision. The responses suggested that additional focus on supervision might be included in undergraduate education and that universities, professional associations, and employers pay more attention to succession planning.
Dynamics of action potential firing in electrically connected striatal fast-spiking interneurons

Fast-spiking interneurons (FSIs) play a central role in organizing the output of striatal neural circuits, yet functional interactions between these cells are still largely unknown. Here we investigated the interplay of action potential (AP) firing between electrically connected pairs of identified FSIs in mouse striatal slices. In addition to a loose coordination of firing activity mediated by membrane potential coupling, gap junctions (GJ) induced a frequency-dependent inhibition of spike discharge in coupled cells. At relatively low firing rates (2–20 Hz), some APs were tightly synchronized whereas others were inhibited. However, burst firing at intermediate frequencies (25–60 Hz) mostly induced spike inhibition, while at frequencies >50–60 Hz FSI pairs tended to synchronize. Spike silencing occurred even in the absence of GABAergic synapses or persisted after a complete block of GABAA receptors. Pharmacological suppression of presynaptic spike afterhyperpolarization (AHP) caused postsynaptic spikelets to become more prone to trigger spikes at near-threshold potentials, leading to a mostly synchronous firing activity. The complex pattern of functional coordination mediated by GJ endows FSIs with peculiar dynamic properties that may be critical in controlling striatal-dependent behavior.
On multi-modal people tracking from mobile platforms in very crowded and dynamic environments

Tracking people is a key technology for robots and intelligent systems in human environments. Many person detectors, filtering methods and data association algorithms for people tracking have been proposed in the past 15+ years in both the robotics and computer vision communities, achieving decent tracking performances from static and mobile platforms in real-world scenarios. However, little effort has been made to compare these methods, analyze their performance using different sensory modalities and study their impact on different performance metrics. In this paper, we propose a fully integrated real-time multi-modal laser/RGB-D people tracking framework for moving platforms in environments like a busy airport terminal. We conduct experiments on two challenging new datasets collected from a first-person perspective, one of them containing very dense crowds of people with up to 30 individuals within close range at the same time. We consider four different, recently proposed tracking methods and study their impact on seven different performance metrics, in both single and multi-modal settings. We extensively discuss our findings, which indicate that more complex data association methods may not always be the better choice, and derive possible future research directions.
An explicit rate bound for over-relaxed ADMM

The framework of Integral Quadratic Constraints of Lessard et al. (2014) reduces the computation of upper bounds on the convergence rate of several optimization algorithms to semi-definite programming (SDP). Follow up work by Nishihara et al. (2015) applies this technique to the entire family of over-relaxed Alternating Direction Method of Multipliers (ADMM). Unfortunately, they only provide an explicit error bound for sufficiently large values of some of the parameters of the problem, leaving the computation for the general case as a numerical optimization problem. In this paper we provide an exact analytical solution to this SDP and obtain a general and explicit upper bound on the convergence rate of the entire family of over-relaxed ADMM. Furthermore, we demonstrate that it is not possible to extract from this SDP a general bound better than ours. We end with a few numerical illustrations of our result and a comparison between the convergence rate we obtain for ADMM with known convergence rates for Gradient Descent (GD).
Black-box testing of practical movie recommendation systems: A comparative study

Many practical recommendation systems have been studied, and also the services based on such recommendation systems have been opened in real world. The main research questions of this work are i) how these recommendation services provide users with useful information, and ii) how different the results from the systems are from each other. In this paper, we propose a black-box evaluation framework of the practical recommendation services. Thus, we have designed user modeling process for generating synthesized user models as the inputs for the recommendation services. User models (i.e., a set of user ratings) have been synthesized to discriminate the recommendation results. Given a set of practical recommendation systems, the proposed black-box testing scheme has been applied by comparing recommendation results. Particularly, we focus on investigating whether the services consider attribute selection.
Searching for Features using a Genetic Algorithm

Automatic classification of words use abstract representat io s of lexical items. The representations are usually not easily derived f rom the data available (strings of letters). This is a core problem in nearest neighbor metho ds. This article describes research towards a genetic algorithm for inventingfeatures of relevance for automatic word classification. The GA attempts to optimize a represent ation created by a random process. Factors in genetic algorithms are identified for th e task, and a novel solution is presented for creating features that can be used with larg e data sets of millions of words, with minimal demands on feedback. The result is an uns upervised categorization of a large corpus.
Quality Control of Widely Used Therapeutic Recombinant Proteins by a Novel Real-Time PCR Approach

Background: Existence of bacterial host-cell DNA contamination in biopharmaceutical products is a potential risk factor for patients receiving these drugs. Hence, the quantity of contamination must be controlled under the regulatory standards. Although different methods such as hybridization assays have been employed to determine DNA impurities, these methods are labor intensive and rather expensive. In this study, a rapid real-time PCR test was served as a method of choice to quantify the E. coli host- cell DNA contamination in widely used recombinant streptokinase (rSK), and alpha interferon (IFN-α) preparations. Methods: A specific primer pair was designed to amplify a sequence inside the E. coli 16S rRNA gene. Serial dilutions of DNA extracted from E. coli host cells, along with DNA extracted from Active Pharmaceutical Ingredients of rSK, and IFN-α samples were subjected to an optimized real-time PCR assay based on SYBR Green chemistry. Results: The test enabled us to detect a small quantity of genomic DNA contamination as low as 0.0002 pg in recombinant protein-based drugs. For the first time, this study showed that DNA contamination in rSK and IFN-α preparation manufactured in Pasteur Institute of Iran is much lower than the safety limit suggested by the US FDA. Conclusion: Real-time PCR is a reliable test for rapid detection of host-cell DNA contamination, which is a major impurity of therapeutic recombinant proteins to keep manufacturers’ minds on refining drugs, and provides consumers with safer biopharmaceuticals.
Relaxed $K$ -Best MIMO Signal Detector Design and VLSI Implementation

Signal detector is a key element in a multiple-input multiple-output (MIMO) wireless communication receiver. It has been well demonstrated that nonlinear tree search MIMO detectors can achieve near-optimum detection performance, nevertheless their efficient high-speed VLSI implementations are not trivial. For example, the hardware design of hard- or soft- output detectors for a 4 times 4 MIMO system with 64 quadrature amplitude modulation (QAM) still remains missing in the open literature. As an attempt to tackle this challenge, this paper presents an implementation-oriented breadth-first tree search MIMO detector design solution. The key is to appropriately modify the conventional breadth-first tree search detection algorithm in order to largely improve the suitability for efficient hardware implementation, while maintaining good detection performance. To demonstrate the effectiveness of the proposed design solution, using 0.13-mum CMOS standard cell and memory libraries, we designed a soft-output signal detector for 4 times 4 MIMO with 64-QAM. With the silicon area of about 31 mm2, the detector can achieve above 100 Mb/s and realize the performance very close to that of the sphere decoding algorithm
Semantics-enabled Policies for Super-Peer Data Integration and Protection

Extending from the previous semantic privacy-preserving model, we propose a widescale super-peer data integration and protection architecture. Any user from a superpeer domain can contribute new data, schema, or even mappings for other super-peer domains to integrate the information. Each super-peer domain is essentially a mediatorbased data integration system, where an agent at the super-peer performs semantic local mappings to manage a set of its local peers endowed with shareable relational data sources. Semantic global mappings are also possible from the current super-peer to interlink with other super-peers located in their super-peer domains. A super-peer is the only place, at the virtual platform (VP), where an agent can empower the data integration and access control services for a super-peer domain. Through the semanticsenabled privacy protection policies, authorized view-based queries posed to a super-peer can enable the data integration without losing a user’s privacy. The ontology mapping and merging algorithm with a local-as-view (LAV) source description that creates a global ontology schema in a super-peer by integrating multiple local ontology schemas for data integration. The perfect rules integration of datalog rules enforces the data query and protection services. Finally, using a global-local-as-view (GLAV) for global semantic mappings among super-peers, we have a greater flexibility of data integration and protection in the super-peer architecture.
Inclusive Development: Two Papers on Conceptualization, Application, and the ADB Perspective

This compendium brings together two companion papers on inclusive development. The first paper uses the global literature to formulate a conceptualisation of inclusive development and inclusive growth, and to put the conceptualisation through its paces by applying it to the specific case of donor assistance to rural infrastructure. The second paper conducts a detailed review and a synthesis of Asian Development Bank literature on inclusive growth and inclusive development, to see how one particular international organization has addressed, and attempted to resolve, the analytical and operational issues associated with inclusive development.
Digital biomarkers from geolocation data in bipolar disorder and schizophrenia: a systematic review

Abstract Objective The study sought to explore to what extent geolocation data has been used to study serious mental illness (SMI). SMIs such as bipolar disorder and schizophrenia are characterized by fluctuating symptoms and sudden relapse. Currently, monitoring of people with an SMI is largely done through face-to-face visits. Smartphone-based geolocation sensors create opportunities for continuous monitoring and early intervention. Materials and Methods We searched MEDLINE, PsycINFO, and Scopus by combining terms related to geolocation and smartphones with SMI concepts. Study selection and data extraction were done in duplicate. Results Eighteen publications describing 16 studies were included in our review. Eleven studies focused on bipolar disorder. Common geolocation-derived digital biomarkers were number of locations visited (n = 8), distance traveled (n = 8), time spent at prespecified locations (n = 7), and number of changes in GSM (Global System for Mobile communications) cell (n = 4). Twelve of 14 publications evaluating clinical aspects found an association between geolocation-derived digital biomarker and SMI concepts, especially mood. Geolocation-derived digital biomarkers were more strongly associated with SMI concepts than other information (eg, accelerometer data, smartphone activity, self-reported symptoms). However, small sample sizes and short follow-up warrant cautious interpretation of these findings: of all included studies, 7 had a sample of fewer than 10 patients and 11 had a duration shorter than 12 weeks. Conclusions The growing body of evidence for the association between SMI concepts and geolocation-derived digital biomarkers shows potential for this instrument to be used for continuous monitoring of patients in their everyday lives, but there is a need for larger studies with longer follow-up times.
A Fast-Multiplying PSO Algorithm for Real-Time Multiple Object Tracking

The problem of real-time object tracking in live video sequences is of increasing importance today mainly due to higher security requirements for surveillance applications. In this study we present a novel particle swarm optimization (PSO) algorithm with additional new features. The basic idea of PSO is to use one swarm or one hierarchical swarm of particles to find the best estimate or the global optimum for the object location in a given search space. Particles fly around, share information with each other, and optimize their behavior to find the global optimum. Until today, PSO was used to track one pre-classified pattern of objects. The existing algorithms apply only one swarm of particles to track predefined patterns. The algorithm we present in this paper extended the PSO algorithm to track different objects having non-predefined patterns: n swarms are used to track n objects, i.e. to find n local maxima in different parts of the search space. The proposed algorithm introduces two new components to PSO. A self-adapting component, which is robust against drastic brightness changes of the image sequence, and a self-splitting component, which decides to track the scene as one connected object, or as more stand-alone objects.