Miracle-dz/UniFAR-FTUs huggingface dataset:
Sensory modulation of muscle synergies for motor adaptation during natural behaviors

To achieve any motor behavior, the central nervous system (CNS) must coordinate the many degrees of freedom in the musculoskeletal apparatus. It has been suggested that the CNS simplifies this formidable task of coordination by grouping multiple muscles together into units of activation, or muscle synergies. Previous studies have shown that electromyogram (EMG) signals collected from many muscles during natural behaviors can be reconstructed by linearly combining a few synergies, identified by the non-negative matrix factorization algorithm. But to what extent synergies are neural constraints, or merely structures reflecting experimental constraints, has remained an open question. I address this question with the hypothesis that, muscle synergies are robust neural patterns constraining motor outputs. The strategy adopted was that of analyzing EMGs collected before and after delivery of a perturbation to the motor system. In my first experiment, EMGs from bullfrog muscles were recorded during locomotor behaviors before and after deafferentation. Systematic comparison of intact and deafferented synergies suggests that most of the synergies remained unchanged after afferent removal. In my second experiment, the frog hindlimb was perturbed by either an inertial load or an elastic load. Using a novel algorithm capable of simultaneously extracting shared and specific synergies, I demonstrate that, most synergies were shared between the different conditions, but their activation patterns were reversibly altered by loading. Overall, my results suggest that muscle synergies are robust, centrally organized structures, and descending and afferent signals cooperate in modulating their activations so that the resulting motor commands can be efficiently adapted to the external environment. Thesis Supervisor: Emilio Bizzi, M.D. Title: Institute Professor, MIT
Effect of a structured, active, home-based cancer-treatment program for the management of patients on oral chemotherapy

Introduction The advent of oral chemotherapy agents has had a strong impact on several aspects of the management of cancer patients, including survival rates, health-care expenditure, and health-related quality of life. However, access to care and adherence to oral chemotherapy are central to optimal outcomes. Patients and methods In this multicenter observational study, we assessed the effect of the “Active Home Care” initiative – a structured, active, home-based cancer-treatment program – on quality of life, health-care utilization, and patient adherence and satisfaction using self-administered questionnaires. Sixty-two patients treated with oral chemotherapy (capecitabine, vinorelbine, imatinib, sunitinib, sorafenib, temozolomide, ibandronate) were enrolled in the program. Weekly home visits were scheduled, each one with a trained nurse who delivered the home-based chemotherapy and reviewed patients’ compliance and treatment toxicity. An oncologist evaluated patients and modified the dosage of oral chemotherapy based on toxicity reported during the previous cycle at bi-weekly visits. Results A total of 460 home visits were performed between April 2012 and February 2013. The Active Home Care initiative was associated with significant improvements in physical functioning and symptoms, and reductions in the access to cancer facilities. Satisfaction with oral chemotherapy and care received was high. All patients reported having taken their medications according to their prescription, and no patient reported difficulties in managing the oral chemotherapy regimen. Conclusion The Active Home Care program was associated with improvements in the quality of life of patients and caregivers, better adherence to treatment, and the effective management of therapy and cancer-related symptoms. Home-based cancer treatment may also optimize the utilization of health-care resources.
Breeding High-Impact Mutations

Mutation testing was developed to measure the adequacy of a test suite by seeding artificial bugs (mutations) into a program, and checking whether the test suite detects them. An undetected mutation either indicates a insufficiency in the test suite and provides means for improvement, or it is an equivalent mutation that cannot be detected because it does not change the program's semantics. Impact metrics-that quantify the difference between a run of the original and the mutated version of a program-are one way to detectnon-equivalent mutants. In this paper we present a genetic algorithm that aims to produce a set of mutations that have a high impact, are not detected by the test suite, and at the same time are well spread all over the code. We believe that such a set is useful for improving a test suite, as a high impact of a mutation implies it caused a grave damage, which is not detected by the test suite, and that the mutation is likely to be non-equivalent. First results are promising: The number of undetected mutants in a set of evolved mutants increases from 20 to over 70 percent, the average impact of these undetected mutants grows at the same time by a factor of 5.
Serotype distribution and antibiotics susceptibility pattern of Streptococcus pneumonia in Iran

Background The development of antibiotic resistance among Streptococcus pneumoniae strains has caused significant health problems worldwide. Objectives The aim of this study was to determine antibiotic resistance pattern and serotypes distribution of Streptococcus pneumoniae strains isolated from clinical specimens. Material and Methods A total of fifty Streptococcus pneumoniae strains were isolated from Tehran Hospital’s laboratory from 2008 to 2012. Antimicrobial susceptibility testing was performed using broth microdilution method and minimum inhibitory concentration (MIC) of each strain was determined. to verify the resistant strains and demonstrate the presence of antibiotic resistant genes, the PCR was performed. Results The study showed that three strains (6%) and six strains (12%) indicated intermediate resistance and complete resistance to penicillin, respectively, 58% strains were susceptible to ceftazidime, two ones (4%) indicated resistance to ciprofloxacin, one (2%) indicated intermediate resistance to ceftriaxone , two strains (4%) indicated complete resistance and four (8%) strains indicated resistance to vancomycin. Conclusions The emergence of Streptococcus pneumoniae strains with multiple resistance needs permanent monitoring of antibiotic susceptibility patterns of clinical isolates. We have found that ceftazidime is not a suitable drug for choosing the treatment of pneumococcal infections.
A deep-submicron CMOS flow for general-purpose timing-detection insertion

This paper introduces a design independent extension to RTL-to-GDS design-flows for seamless insertion of timing-detection flip-flops at critical paths of a digital CMOS standard-cell circuit. It is possible to detect timing-errors for general purposes at any critical path including enable-inputs of clock-gating cells with typically 15% area overhead for 20% endpoint coverage while maintaining DFT capability. To ensure matching of critical-path endpoints and detector cells 3 incremental place &route iterations are needed on average.
Experience on applying software architecture recovery to automotive embedded systems

The importance and potential advantages with a comprehensive product architecture description are well described in the literature. However, developing such a description takes additional resources, and it is difficult to maintain consistency with evolving implementations. This paper presents an approach and industrial experience which is based on architecture recovery from source code at truck manufacturer Scania CV AB. The extracted representation of the architecture is presented in several views and verified on CAN signal level. Lessons learned are discussed.
The Roles of Explicit Information and Grammatical Sensitivity in the Processing of Clitic Direct Object Pronouns and Word Order in Spanish L2

In the present study, we examine the roles of 1) explicit information about language provided to learners prior to treatment and 2) aptitude (specifically grammatical sensitivity) within Processing Instruction. Forty-two learners of Spanish in their third-semester of study were divided into two groups: those who received explicit information (EI) prior to treatment (+EI) and those who did not (−EI). All participants also took the grammatical sensitivity portion of the Modern Language Aptitude Test. Treatment consisted of 50 structured input activities based on VanPatten and Cadierno (1993), in which learners heard a sentence and then indicated what they thought they heard by selecting one of two drawings. The processing problem for the treatment was the First Noun Principle and the target structure was clitic direct object pronouns with object-verb-subject (OVS) and subject-object-verb (SOV) word orders. Treatment was delivered via computer using SuperLab 4.0. Participants’ responses were tracked and the measurement taken was trials-to-criterion: how long it took participants to begin processing sentences correctly. Results using ANOVA procedure revealed that EI did not make a difference: the +EI and −EI groups began processing sentences correctly at about the same point during the treatment. Results using standard r correlations also yielded no correlation between grammatical sensitivity and trials-to-criterion.
A Paraconsistent Logic Programming Approach for Querying Inconsistent Knowledge Bases

When integrating data coming from multiple different sources we are faced with the possibility of inconsistency in databases. A paraconsistent approach for knowledge base integration allows keeping inconsistent information and reasoning in its presence. In this paper, we use a paraconsistent logic (LFI1) as the underlying logic for the specification of P-Datalog, a deductive query language for databases containing inconsistent information. We present a declarative semantics which captures the desired meaning of a recursive query executed over a database containing inconsistent facts and whose rules allow infering information from inconsistent premises. We also present a bottom-up evaluation method for P-Datalog programs based on an alternating fixpoint operator.
Cerebellum Abnormalities in Idiopathic Generalized Epilepsy with Generalized Tonic-Clonic Seizures Revealed by Diffusion Tensor Imaging

Although there is increasing evidence suggesting that there may be subtle abnormalities in idiopathic generalized epilepsy (IGE) patients using modern neuroimaging techniques, most of these previous studies focused on the brain grey matter, leaving the underlying white matter abnormalities in IGE largely unknown, which baffles the treatment as well as the understanding of IGE. In this work, we adopted multiple methods from different levels based on diffusion tensor imaging (DTI) to analyze the white matter abnormalities in 14 young male IGE patients with generalized tonic-clonic seizures (GTCS) only, comparing with 29 age-matched male healthy controls. First, we performed a voxel-based analysis (VBA) of the fractional anisotropy (FA) images derived from DTI. Second, we used a tract-based spatial statistics (TBSS) method to explore the alterations within the white matter skeleton of the patients. Third, we adopted region-of-interest (ROI) analyses based on the findings of VBA and TBSS to further confirm abnormal brain regions in the patients. At last, considering the convergent evidences we found by VBA, TBSS and ROI analyses, a subsequent probabilistic fiber tractography study was performed to investigate the abnormal white matter connectivity in the patients. Significantly decreased FA values were consistently observed in the cerebellum of patients, providing fresh evidence and new clues for the important role of cerebellum in IGE with GTCS.
Practical interference management strategies in Gaussian networks

Increasing demand for bandwidth intensive activities on high-penetration wireless hand-held personal devices, combined with their processing power and advanced radio features, has necessitated a new look at the problems of resource provisioning and distributed management of coexistence in wireless networks. Information theory, as the science of studying the ultimate limits of communication efficiency, plays an important role in outlining guiding principles in the design and analysis of such communication schemes. Network information theory, the branch of information theory that investigates problems of multiuser and distributed nature in information transmission is ideally poised to answer questions about the design and analysis of multiuser communication systems. In the past few years, there have been major advances in network information theory, in particular in the generalized degrees of freedom framework for asymptotic analysis and interference alignment which have led to constant gap to capacity results for Gaussian interference channels. Unfortunately, practical adoption of these results has been slowed by their reliance on unrealistic assumptions like perfect channel state information at the transmitter and intricate constructions based on alignment over transcendental dimensions of real numbers. It is therefore necessary to devise transmission methods and coexistence schemes that fall under the umbrella of existing interference management and cognitive radio toolbox and deliver close to optimal performance. In this thesis we work on the theme of designing and characterizing the performance of conceptually simple transmission schemes that are robust and achieve performance that is close to optimal. In particular, our work is broadly divided into two parts. In the first part, looking at cognitive radio networks, we seek to relax the assumption of non-causal knowledge of primary user’s message at the secondary user’s transmitter. We study a cognitive channel
Developing a Framework for the Assessment of eGovernment Initiatives

The area of egovernment has received increased attention over the last few years. Much of this interest is derived from growth and development of electronic commerce. In spite of the current developments many avenues in the area of egovernment remain unexplored. One such area encompasses the determination of the value derived from the implementation of egovernment projects through comprehensive assessment. Assessment of Information Technology initiatives is conducted either as an ex-ante or ex-post procedure. In this study, we present an ex-post framework for the assessment of egovernment projects. Assessment is defined in terms of identifying the value of the egovernment project post implementation. We use the socio-technical model to create a three dimensional framework for the assessment of value of an egovernment initiative. The three dimensions presented are egovernment maturity level; stakeholders and assessment levels. Ultimately, this framework can be used as a tool locally, regionally and internationally to understand the true value that an egovernment initiative presents to its constituents.
Proficient Replication of the Yeast Genome by a Viral DNA Polymerase*

DNA replication in eukaryotic cells requires minimally three B-family DNA polymerases: Pol α, Pol δ, and Pol ϵ. Pol δ replicates and matures Okazaki fragments on the lagging strand of the replication fork. Saccharomyces cerevisiae Pol δ is a three-subunit enzyme (Pol3-Pol31-Pol32). A small C-terminal domain of the catalytic subunit Pol3 carries both iron-sulfur cluster and zinc-binding motifs, which mediate interactions with Pol31, and processive replication with the replication clamp proliferating cell nuclear antigen (PCNA), respectively. We show that the entire N-terminal domain of Pol3, containing polymerase and proofreading activities, could be effectively replaced by those from bacteriophage RB69, and could carry out chromosomal DNA replication in yeast with remarkable high fidelity, provided that adaptive mutations in the replication clamp PCNA were introduced. This result is consistent with the model that all essential interactions for DNA replication in yeast are mediated through the small C-terminal domain of Pol3. The chimeric polymerase carries out processive replication with PCNA in vitro; however, in yeast, it requires an increased involvement of the mutagenic translesion DNA polymerase ζ during DNA replication.
A density-based approach for text extraction in images

In this paper we describe a new approach to distinguish and extract text from images with various objects and complex backgrounds. The goal of our approach is to present characters in images with clear background and without other objects. The proposed approach mainly includes two steps. Firstly, a density-based clustering method is employed to segment candidate characters by integrating spatial connectivity and color feature of characterspsila pixels. In most images, colors of pixels in one character are commonly non-uniform due to the noise. So a new histogram segmentation method is proposed in this step to obtain the color thresholds of characters. Secondly, priori knowledge and texture-based method are performed on the candidate characters to filter the non-characters. Experimental results show that the proposed approach has a good performance in character extraction rate.
Severe episodic neurological deficits and permanent mental retardation in a child with a novel FHM2 ATP1A2 mutation

Attacks of familial hemiplegic migraine (FHM) are usually associated with transient, completely reversible symptoms. Here, we studied the ATP1A2 FHM2 gene in a young girl with episodes of both very severe and transient neurological symptoms that were triggered by mild head trauma as well as permanent mental retardation. Her family members suffered from hemiplegic and confusional migraine attacks.
Effects of an employee exercise programme on mental health.

Background Prior research indicates that workplace wellness programmes (WWPs) are generally associated with lowered healthcare costs and improved employee health. Despite the importance of mental well-being in workplace productivity and attendance, few WWP studies have focused on improvements in psychological well-being. Aims To examine the effects of the Bruin Health Improvement Program (BHIP), a 3-month exercise and nutrition WWP, on seven domains of health: physical and mental health, stress, energy level, social satisfaction, self-efficacy and quality of life. Methods Using data from BHIP completers, we conducted multiple one-way multivariate analyses of variance and follow-up univariate t-tests to examine changes in physical and mental health, stress, energy level, social satisfaction, self-efficacy and quality of life. Effect sizes were also calculated post hoc to determine the magnitude of each effect. Results Results for the 281 participants reveal significant improvements across all seven domains (P < 0.001). Effect sizes ranged from 0.19 to 0.67. Conclusions This study is unique in revealing the effects of a WWP on multiple domains of psychological well-being. Given rising healthcare costs associated with mental health, targeting mental health through WWP may be an effective strategy for reducing indirect healthcare costs associated with absenteeism and presenteeism.
An Algebra for Semantic Construction in Constraint-based Grammars

We develop a framework for formalizing semantic construction within grammars expressed in typed feature structure logics, including HPSG. The approach provides an alternative to the lambda calculus; it maintains much of the desirable flexibility of unification-based approaches to composition, while constraining the allowable operations in order to capture basic generalizations and improve maintainability.
Mobile Photo Album Management with Multiscale Timeline

Traditional photo browsing systems developed for PCs are inefficient for browsing and searching of large photo albums on mobile devices due to the small screen size and limited mobile processing power. We propose a new concept in this paper, the multiscale timeline, where photos are grouped into clusters and displayed sequentially on a scaled timeline with user controllable time scales, enabling multiscale overview of the photo album for efficient browsing and searching. To address the slow speed of re-clustering when new photos are added, a new incremental spectral clustering algorithm is further developed, which is an order of magnitude faster than the traditional spectral clustering algorithm and its conventional incremental version. Our implementation of the system on mobile devices shows a better user experience and browsing efficiency based on the experiments over large real-world photo collections.
Time Series Seasonal Analysis Based on Fuzzy Transforms

We define a new seasonal forecasting method based on fuzzy transforms. We use the best interpolating polynomial for extracting the trend of the time series and generate the inverse fuzzy transform on each seasonal subset of the universe of discourse for predicting the value of an assigned output. In the first example, we use the daily weather dataset of the municipality of Naples (Italy) starting from data collected from 2003 to 2015 making predictions on mean temperature, max temperature and min temperature, all considered daily. In the second example, we use the daily mean temperature measured at the weather station “Chiavari Caperana” in the Liguria Italian Region. We compare the results with our method, the average seasonal variation, Auto Regressive Integrated Moving Average (ARIMA) and the usual fuzzy transforms concluding that the best results are obtained under our approach in both examples. In addition, the comparison results show that, for seasonal time series that have no consistent irregular variations, the performance obtained with our method is comparable with the ones obtained using Support Vector Machine- and Artificial Neural Networks-based models.
A Survey on Novel Dictionary Learning Method for Multi-label Image Annotation

The automatic image annotation is relatively new research topic or area for researcher. The task of automatic image annotation is uses to associates the subset of word from given dictionary to images or assign the human provided keyword to the images. Previous methods of weakly supervised multi-label image annotation are relying on the unsupervised feature representation. The components of unsupervised feature representation are not directly correlated with specific labels. In practical cases, there is a big gap between training data (offline part) and the testing data (online part), say the label combination of the testing data and training data is not always consistent. When the features and labels are extracted from the training images, we first construct the label matrix to explore the exclusive label group. After that, we follow the dictionary learning framework to learn the discriminative dictionary representation. Then we measure the relevance between the visual words and their corresponding labels. After that, a multi-task learning framework is used to expand the labels of visual words. Finally the label propagation is obtained, by using the Rank SVM algorithm to computes the score for image annotation. The main aim of this paper is to study the various techniques used for automatic image annotation and dictionary learning for multi-label image annotation.