Miracle-dz/UniFAR-FTUs huggingface dataset:
Microcalcification Detection in Mamography Images Using 2D Wavelet Coefficients Histogram

Breast Cancer is one of the most common illnesses in recent years. Diagnosing cancer at early stages can have a considerable effect on the therapy, so that many several attempts have been made for diagnosing this illness at its first stage recently. Mammography imaging is the most commonly used technique to detect breast cancer before appearing the clinical symptoms. Extracting features which facilitate cancer symptoms detection without significant decrease in sensitivity, minimizes false positives and is of great importance. Microcalcification is an important indicator of cancer. In this research a new method for detecting microcalcifications in mammography is presented. Due to the ability of wavelet transform in image decomposition and detaching details, it can be used to expose this symptom in mammograms. In this work, a two dimensional wavelet transform is performed for feature extraction; and these features are used to diagnose cancer symptoms in mammography images. After the feature extraction step, classification is done using Support Vector Machine (SVM). In the performed evaluation, Regions of Interest (ROIs) with different dimensions have been used as input data and the results show that the proposed feature extraction method can have a significant impact in improving the performance of detection systems.
Blind adaptive channel estimation in ofdm systems

We consider the problem of blind channel estimation in zero padding OFDM systems, and propose blind adaptive algorithms in order to identify the impulse response of the multipath channel. In particular, we develop RLS and LMS schemes that exhibit rapid convergence combined with low computational complexity and numerical stability. Both versions are obtained by properly modifying the orthogonal iteration method used in numerical analysis for the computation of singular vectors. With a number of simulation experiments we demonstrate the satisfactory performance of our adaptive schemes under diverse signaling conditions
Survey and Analysis of Microsatellites in the Silkworm, Bombyx mori

We studied microsatellite frequency and distribution in 21.76-Mb random genomic sequences, 0.67-Mb BAC sequences from the Z chromosome, and 6.3-Mb EST sequences of Bombyx mori. We mined microsatellites of ≥15 bases of mononucleotide repeats and ≥5 repeat units of other classes of repeats. We estimated that microsatellites account for 0.31% of the genome of B. mori. Microsatellite tracts of A, AT, and ATT were the most abundant whereas their number drastically decreased as the length of the repeat motif increased. In general, tri- and hexanucleotide repeats were overrepresented in the transcribed sequences except TAA, GTA, and TGA, which were in excess in genomic sequences. The Z chromosome sequences contained shorter repeat types than the rest of the chromosomes in addition to a higher abundance of AT-rich repeats. Our results showed that base composition of the flanking sequence has an influence on the origin and evolution of microsatellites. Transitions/transversions were high in microsatellites of ESTs, whereas the genomic sequence had an equal number of substitutions and indels. The average heterozygosity value for 23 polymorphic microsatellite loci surveyed in 13 diverse silkmoth strains having 2–14 alleles was 0.54. Only 36 (18.2%) of 198 microsatellite loci were polymorphic between the two divergent silkworm populations and 10 (5%) loci revealed null alleles. The microsatellite map generated using these polymorphic markers resulted in 8 linkage groups. B. mori microsatellite loci were the most conserved in its immediate ancestor, B. mandarina, followed by the wild saturniid silkmoth, Antheraea assama.
An Integrative Model Linking IT-Business Strategic Alignment and Firm Performance: The Mediating Role of Pursuing Innovation and Knowledge Management Strategies

While researchers have encouraged more research on the causal chains between IT investments and firm performance, results of empirical studies have been non-conclusive. This is partly due to the exclusion of IT-business strategic alignment. Further, scholars have continuously called for research that addresses the antecedent factors that lead to the alignment. Moreover, the elusive link between strategic alignment and firm performance calls for further research into intermediate variables which might in turn affect firm performance. Therefore, the purpose of this paper is to provide a detailed roadmap in which practitioners can use to understand the resources required to realize the potential values of their IT investments. This could be achieved by presenting further insight to the factors that could lead to strategic alignment; and investigating the relationships among strategic alignment antecedents, strategic alignment and firm performance through vital intermediary variables namely innovation and knowledge management (KM) strategies.
Modelling environmental-economic phenomena at a regional level : methodological progress

The availability and nature of information are important issues in modeling environmental-economic phenomena. This article addresses three types of limitation that may characterize such issues, including 1) insufficient precision of information for drawing reliable quantitative conclusions regarding policy aspects, 2) the wide variety of means of data gathering with their corresponding varying levels of measurement, and 3) the multivariate nature of environmental-economic phenomena. Various appropriate methodological tools have been developed over the past years to deal with such limitations. The relevance of the approaches will be illustrated for a regional-economic and environmental analysis of recreation.
Does confirmatory tumor biopsy alter the management of breast cancer patients with distant metastases?

Background: Decisions about systemic treatment of women with metastatic breast cancer are often based on estrogen receptor (ER), progesterone receptor (PgR), and Her2 status of the primary tumor. This study prospectively investigated concordance in receptor status between primary tumor and distant metastases and assessed the impact of any discordance on patient management. Materials and methods: Biopsies of suspected metastatic lesions were obtained from patients and analyzed for ER/PgR and Her2. Receptor status was compared for metastases and primary tumors. Questionnaires were completed by the oncologist before and after biopsy to determine whether the biopsy results changed the treatment plan. Results: Forty women were enrolled; 35 of them underwent biopsy, yielding 29 samples sufficient for analysis; 3/29 biopsies (10%) showed benign disease. Changes in hormone receptor status were observed in 40% (P = 0.003) and in Her2 status in 8% of women. Biopsy results led to a change of management in 20% of patients (P = 0.002). Conclusions: This prospective study demonstrates the presence of substantial discordance in receptor status between primary tumor and metastases, which led to altered management in 20% of cases. Tissue confirmation should be considered in patients with clinical or radiological suspicion of metastatic recurrence.
RAB3GAP1 and RAB3GAP2 modulate basal and rapamycin-induced autophagy

Macroautophagy is a degradative pathway that sequesters and transports cytosolic cargo in autophagosomes to lysosomes, and its deterioration affects intracellular proteostasis. Membrane dynamics accompanying autophagy are mostly elusive and depend on trafficking processes. RAB GTPase activating proteins (RABGAPs) are important factors for the coordination of cellular vesicle transport systems, and several TBC (TRE2-BUB2-CDC16) domain-containing RABGAPs are associated with autophagy. Employing C. elegans and human primary fibroblasts, we show that RAB3GAP1 and RAB3GAP2, which are components of the TBC domain-free RAB3GAP complex, influence protein aggregation and affect autophagy at basal and rapamycin-induced conditions. Correlating the activity of RAB3GAP1/2 with ATG3 and ATG16L1 and analyzing ATG5 punctate structures, we illustrate that the RAB3GAPs modulate autophagosomal biogenesis. Significant levels of RAB3GAP1/2 colocalize with members of the Atg8 family at lipid droplets, and their autophagy modulatory activity depends on the GTPase-activating activity of RAB3GAP1 but is independent of the RAB GTPase RAB3. Moreover, we analyzed RAB3GAP1/2 in relation to the previously reported suppressive autophagy modulators FEZ1 and FEZ2 and demonstrate that both reciprocally regulate autophagy. In conclusion, we identify RAB3GAP1/2 as novel conserved factors of the autophagy and proteostasis network.
The Combined Expression Patterns of Ikaros Isoforms Characterize Different Hematological Tumor Subtypes

A variety of genetic alterations are considered hallmarks of cancer development and progression. The Ikaros gene family, encoding for key transcription factors in hematopoietic development, provides several examples as genetic defects in these genes are associated with the development of different types of leukemia. However, the complex patterns of expression of isoforms in Ikaros family genes has prevented their use as clinical markers. In this study, we propose the use of the expression profiles of the Ikaros isoforms to classify various hematological tumor diseases. We have standardized a quantitative PCR protocol to estimate the expression levels of the Ikaros gene exons. Our analysis reveals that these levels are associated with specific types of leukemia and we have found differences in the levels of expression relative to five interexonic Ikaros regions for all diseases studied. In conclusion, our method has allowed us to precisely discriminate between B-ALL, CLL and MM cases. Differences between the groups of lymphoid and myeloid pathologies were also identified in the same way.
Improving Situation Awareness In Traffic Management

Information overload is a severe problem for operators of large-scale control systems, as such systems typically provide a vast amount of information about a large number of real-world objects. Systems supporting situation awareness have recently gained attention as way to help operators to grasp the overall meaning of available information. To fulll this task, data quality has to be ensured by assessment and improvement strategies. In this paper, a vision towards a methodology for data quality assessment and improvement for situation awareness systems is presented.
Thermodynamic analysis of the system CaCO 3 ; MgCOs in the tetrahedron approximation of the Cluster Variation Method

A thermodynamic model of phase relations in the system CaCOTMgCO3 is presented. The model is based on the tetrahedron-approximation in the Cluster Variation Method, for a trigonally distorted face-centered-cubic (fcc) Ising lattice. We treat dolomite [CaMg(CO3)u] type ordering as the separation of species-l and-2 atoms onto alternate fcc-(lll)-+ hexagonal-(000l) planes and relate this ordering to an intersublattice pseudopair-wise energy parameter (e1". (0). we also consider intrasublattice interactions associated with the energy parameter (ei.) 0) and relate these interactions to exsolution. For stoichiometric dolomite, we compare the tetrahedron-approximation to a Bragg-Williams approximation for calculation of the stabilization energy, from experimental brackets for the critical temperature of cation disordering (? "). We demonstrate that the tetrahedron-approximation gives a better result for the stabilization energy precisely because it includes short-range order (SRO), and we note that it predicts considerable SRO above T". For the segment calcite-dolomite, our calculated diagram (for Re =-ei,^lei., = 6) is in qualitative to semiquantitative agreement with the experimental diagram of Goldsmith and Heard (1961). With just the two parameters €;". ood e;o the theoretical diagram is symmetric about the dolomite composition and with three parameters an asymmetric diagram can be obtained. For the segment dolomite-magnesite, the model is inadequate in some respects, and we note that another configurational degree of freedom is required to achieve full qualitative agreement between the calculated and experimental diagrams for this region.
Graph connectivity for unsupervised Word Sense Disambiguation for HINDI language

Word Sense Disambiguation (WSD) aims to identify the correct sense of a word in a given sentence. WSD is considered to be an open and AI-hard problem in Natural Language Processing (NLP). WSD is most important in many applications like Machine Translation (MT), Information Retrieval (IR), Information Extraction (IE), Text mining, and Lexicography etc. Supervised, Semi-supervised and Unsupervised Approaches to WSD are important and successful learning approaches. In this paper, we proposed the graph-based Unsupervised Word Sense Disambiguation Algorithm to resolve the ambiguity of a word in a given HINDI Language sentence. Finding the proper meaning of a word here implies identification of the most important node from the set of graph nodes which are representing the senses. We make use of HINDI wordnet developed at IIT Bombay as reference library of words to form the sense graph.
A collaborative BCI approach to autonomous control of a prosthetic limb system

Existing brain-computer interface (BCI) control of highly dexterous robotic manipulators and prosthetic devices typically rely solely on neural decode algorithms to determine the user's intended motion. Although these approaches have made significant progress in the ability to control high degree of freedom (DOF) manipulators, the ability to perform activities of daily living (ADL) is still an ongoing research endeavor. In this paper, we describe a hybrid system that combines elements of autonomous robotic manipulation with neural decode algorithms to maneuver a highly dexterous robotic manipulator for a reach and grasp task. This system was demonstrated using a human patient with cortical micro-electrode arrays allowing the user to manipulate an object on a table and place it at a desired location. The preliminary results for this system are promising in that it demonstrates the potential to blend robotic control to perform lower level manipulation tasks with neural control that allows the user to focus on higher level tasks thereby reducing the cognitive load and increasing the success rate of performing ADL type activities.
On-demand strong update analysis via value-flow refinement

We present a new Strong UPdate Analysis for C programs, called Supa, that enables computing points-to information on-demand via value-flow refinement, in environments with small time and memory budgets such as IDEs. We formulate Supa by solving a graph-reachability problem on a value- flow graph representation of the program, so that strong updates are performed where needed, as long as the total analysis budget is not exhausted. Supa facilitates efficiency and precision tradeoffs by allowing different pointer analyses to be applied in a hybrid multi-stage analysis framework. We have implemented Supa in LLVM with its artifact available at [1]. We evaluate Supa by choosing uninitialized pointer detection as a major client on 12 open-source C programs. As the analysis budget increases, Supa achieves improved precision, with its single-stage flow-sensitive analysis reaching 97% of that achieved by whole-program flow- sensitive analysis by consuming about 0.19 seconds and 36KB of memory per query, on average (with a budget of at most 10000 value-flow edges per query).
Nonparametric analysis of intergenerational income mobility with application to the United States

This paper concerns the problem of inferring the effects of covariates on intergenerational income mobility, i.e. on the relationship between the incomes of parents and future earnings of their children. We focus on two different measures of mobility- (i) traditional transition probability of movement across income quantiles over generations and (ii) a new direct measure of upward mobility, viz. the probability that an adult child's relative position exceeds that of the parents. We estimate the effect of possibly continuously distributed covariates from data using nonparametric regression and average derivatives and derive the distribution theory for these measures. The analytical novelty in the derivation is that the dependent variables involve nonsmooth functions of estimated components- marginal quantiles for transition probabilities and relative ranks for upward mobility- thus necessitating nontrivial modifications of standard nonparametric regression theory. We use these methods on US data from the National Longitudinal Survey of Youth to study black-white differences in intergenerational mobility, a topic which has received scant attention in the literature. We document that whites experience greater intergenerational mobility than blacks. Estimates of conditional mobility using nonparametric regression reveal that most of the interracial mobility gap can be accounted for by differences in cognitive skills during adolescence. The methods developed here have wider applicability to estimation of nonparametric regression and average derivatives where the dependent variable either involves a preliminary finite-dimensional estimate in a nonsmooth way or is a nonsmooth functional of ranks of one or more random variables.
The genetics of alcohol dependence: Twin and SNP‐based heritability, and genome‐wide association study based on AUDIT scores

Alcohol dependence (AD) is among the most common and costly public health problems contributing to morbidity and mortality throughout the world. In this study, we investigate the genetic basis of AD in a Dutch population using data from the Netherlands Twin Register (NTR) and the Netherlands Study of Depression and Anxiety (NESDA). The presence of AD was ascertained via the Alcohol Use Disorders Identification Test (AUDIT) applying cut‐offs with good specificity and sensitivity in identifying those at risk for AD. Twin‐based heritability of AD‐AUDIT was estimated using structural equation modeling of data in 7,694 MZ and DZ twin pairs. Variance in AD‐AUDIT explained by all SNPs was estimated with genome‐wide complex trait analysis (GCTA). A genome‐wide association study (GWAS) was performed in 7,842 subjects. GWAS SNP effect concordance analysis was performed between our GWAS and a recent AD GWAS using DSM‐IV diagnosis. The twin‐based heritability of AD‐AUDIT was estimated at 60% (55–69%). GCTA showed that common SNPs jointly capture 33% (SE = 0.12, P = 0.002) of this heritability. In the GWAS, the top hits were positioned within four regions (4q31.1, 2p16.1, 6q25.1, 7p14.1) with the strongest association detected for rs55768019 (P = 7.58 × 10−7). This first GWAS of AD using the AUDIT measure found results consistent with previous genetic studies using DSM diagnosis: concordance in heritability estimates and direction of SNPs effect and overlap with top hits from previous GWAS. Thus, the use of appropriate questionnaires may represent cost‐effective strategies to phenotype samples in large‐scale biobanks or other population‐based datasets. © 2015 Wiley Periodicals, Inc.
Daily Reservoir Runoff Forecasting Method Using Artificial Neural Network Based on Quantum-behaved Particle Swarm Optimization

Accurate daily runoff forecasting is of great significance for the operation control of hydropower station and power grid. Conventional methods including rainfall-runoff models and statistical techniques usually rely on a number of assumptions, leading to some deviation from the exact results. Artificial neural network (ANN) has the advantages of high fault-tolerance, strong nonlinear mapping and learning ability, which provides an effective method for the daily runoff forecasting. However, its training has certain drawbacks such as time-consuming, slow learning speed and easily falling into local optimum, which cannot be ignored in the real world application. In order to overcome the disadvantages of ANN model, the artificial neural network model based on quantum-behaved particle swarm optimization (QPSO), ANN-QPSO for short, is presented for the daily runoff forecasting in this paper, where QPSO was employed to select the synaptic weights and thresholds of ANN, while ANN was used for the prediction. The proposed model can combine the advantages of both QPSO and ANN to enhance the generalization performance of the forecasting model. The methodology is assessed by using the daily runoff data of Hongjiadu reservoir in southeast Guizhou province of China from 2006 to 2014. The results demonstrate that the proposed approach achieves much better forecast accuracy than the basic ANN model, and the QPSO algorithm is an alternative training technique for the ANN parameters selection.
An Analytical and Empirical Measure of the Degree of Conditional Conservatism

There is a profound gap between models of accounting conservatism and the proxies for conditional conservatism currently used by the empirical literature. Not one of the proxies employed by the empirical literature to date obtains from a rigorous definition of conditional conservatism. In contrast, this study defines conditional conservatism in terms of truncated distributions and derives analytically a nonlinear relation between revisions to returns and earnings news for the conservative firm. This nonlinear relation is shown to be mathematically equivalent to two linear relations conditioned on the firm’s degree of conservatism (DCON). From these relations, we derive a model-based proxy of the DCON at the firm-year level, which is a function of the determinants of conditional conservatism. To account for the endogeneity of the firm’s DCON and mitigate sample selection bias, the model is implemented empirically using a switching regression approach in which the switch point, namely, the DCON, is unobservable and endogenously determined. Consistent estimates of the parameters of the switching regression, including the endogenous determinants of conservatism posited by Watts (2003a, 2003b), are obtained by simultaneous maximum likelihood estimation. The results indicate that the DCON is a positive function of contractual information asymmetry and litigation risk but a negative function of taxes.
Genetic variation and causes of genotype-environment interaction in the body size of blue tit (Parus caeruleus).

In several studies of natural populations of birds, the heritability of body size estimated by parent-offspring regression has been lower when offspring have developed in poor feeding regimens than when they developed in good feeding regimens. This has led to the suggestion that adaptation under poor regimens may be constrained by lack of genetic variation. We examined the influence of environmental conditions on expression of genetic variation in body size of nestling blue tits (Parus caeruleus) by raising full sibs in artificially reduced and enlarged broods, corresponding to good and poor feeding regimens, respectively. Individuals grown in the poor regimen attained smaller body size than their sibs grown in the good regimen. However, there was among-family variation in response to the treatments--i.e., genotype-environment interactions (GEIs). Partitioning the GEI variance into contributions attributable to (1) differences in the among-family genetic variance between the treatments and (2) imperfect correlation of genotypic values across treatments identified the latter as the main cause of the GEI. Parent-offspring regressions were not significantly different when offspring were reared in the good environment (h2 = 0.75) vs. when they were reared in the poor environment (h2 = 0.63). Thus, there was little evidence that genetic variance in body size was lower under the poor conditions than under the good conditions. These results do not support the view that the genetic potential for adaptation to poor feeding conditions is less than that for adaptation to good conditions, but they do suggest that different genotypes may be favored under the different conditions.