Miracle-dz/UniFAR-FTUs huggingface dataset:
Neurological soft signs and brain morphology in first-episode schizophrenia

Background Although minor motor and sensory deficits, or neurological soft signs (NSS), are a well-established finding in schizophrenia, the cerebral changes underlying these signs are only partly understood. We therefore investigated the cerebral correlates of NSS by using magnetic resonance imaging (MRI) in patients with schizophrenia and healthy controls. Method Forty-two patients, all receiving atypical neuroleptics, with first-episode schizophrenia or schizophreniform disorder and 22 healthy controls matched for age and gender were included. NSS were examined on the Heidelberg Scale after remission of the acute symptoms before discharge and correlated to density values by using optimized voxel-based morphometry (VBM). Results NSS scores were significantly higher in patients than healthy controls. Within the patient group NSS were significantly associated with reduced grey or white-matter densities in the pre- and post-central gyrus, pre-motor area, middle and inferior frontal gyri, cerebellum, caudate nucleus and thalamus. These associations did not apply for the control group, in whom only the associations between NSS and reduced frontal gyri densities could be confirmed. Conclusions The pattern of cerebral changes associated with NSS clearly supports the model of ‘cognitive dysmetria’ with a disrupted cortico-cerebellar-thalamic-cortical circuit in schizophrenia. The variety of sites may correspond with the clinical diversity of NSS, which comprises both motor and sensory signs, and with the putative heterogeneity of the pathogenetic changes involved. That the respective associations did not apply for the healthy control group indicates that NSS in patients and controls refer to different pathogenetic factors.
Partial Fingerprint Image Enhancement using Region Division Technique and Morphological Transform

Fingerprints are the most renowned biometric trait for identification and verification. The quality of fingerprint image plays a vital role in feature extraction and matching. Existing algorithms work well for good quality fingerprint images and fail for partial fingerprint images as they are obtained from excessively dry fingers or affected by disease resulting in broken ridges. We propose an algorithm to enhance partial fingerprint images using morphological operations with region division technique. The proposed method divides low quality image into six regions from top to bottom. Morphological operations choose an appropriate Structuring Element (SE) that joins broken ridges and thus enhance the image for further processing. The proposed method uses SE “line” with suitable angle 𝜃 and radius 𝑟 in each region based on the orientation of the ridges. The algorithm is applied to 14 low quality fingerprint images from FVC-2002 database. Experimental results show that percentage accuracy has been improved using the proposed algorithm. The manual markup has been reduced and accuracy of 76.16% with Equal Error Rate (EER) of 3.16% is achieved.
Being an "Other": The Significance of Teachers' Lived Experiences in Working with Culturally and Linguistically Diverse Students

An abstract of the dissertation of Yumiko Otsuki for the Doctor of Education in Educational Leadership: Curriculum and Instruction presented June 11, 2009. Title: Being an "Other": The Significance of Teachers' Lived Experiences in Working with Culturally and Linguistically Diverse Students The study's goal was to understand what contributes to the formation of teachers' perspectives regarding the education of culturally and linguistically diverse students. Its focus was to portray through teachers' stories the experiences in their lives that may have contributed to constructing who they are as people and as educators. Four teachers from one rural district participated. Using qualitative methods, the study explores and describes incidents in teachers' lives, looking at the way their life experiences are reflected in their relationships with students. In considering ways in which those experiences may have helped shape teaching philosophies and practices, the study also considers the teachers' reported statements about their classroom teaching methods to see how what they may have learned from early life experiences are incorporated. 2 Despite each teacher's individual uniqueness, similarities emerged during data analysis. Examining these similarities contributed to an understanding of these teachers and what processes and attitudes made them good teachers of ELLs. The study reports three broad findings: (a) that the teachers possess a riskand challenge-taking nature; (b) they are life-long learners; and (c) that the teachers had two different kinds of cultural experience during their lives, one that the researcher labeled "Regular" cultural experiences and a second kind which she labeled "Otherness" cultural experiences. The teachers were judged to share a willingness to make choices involving challenges, many of which proved to be learning opportunities, and they tried to instill in their students the same resiliency and willingness to take risks. All the teachers personally had both "regular" and "otherness" cultural experiences. "Regular" cultural experiences helped them understand new or unknown cultural values and practices by observation and participation. Through "Otherness" cultural experiences, each experienced the emotion of "being different" and "being marginalized" via the personal, lived experience of being an "Other. " This was judged to have contributed to shaping each teacher's ability to better understand the experiences of culturally and linguistically diverse students, enabling greater compassion for them and teaching them more effectively. BEING AN "OTHER": THE SIGNIFICANCE OF TEACHERS' LIVED EXPERIENCES IN WORKING WITH CULTURALLY AND LINGUISTICALLY DIVERSE STUDENTS
Two-dimensional crystal structure of aquaporin-4 bound to the inhibitor acetazolamide

Acetazolamide (AZA) reduces the water permeability of aquaporin-4, the predominant water channel in the brain. We determined the structure of aquaporin-4 in the presence of AZA using electron crystallography. Most of the features of the 5-Å density map were consistent with those of the previously determined atomic model. The map showed a protruding density from near the extracellular pore entrance, which most likely represents the bound AZA. Molecular docking simulations supported the location of the protrusion as the likely AZA-binding site. These findings suggest that AZA reduces water conduction by obstructing the pathway at the extracellular entrance without inducing a large conformational change in the protein.
Who's the Alien? Xenophobia in Post-Apartheid South Africa

In May 2008, South Africa became the theatre of widespread violent attacks against undesirable `outsiders´. Over 60 were killed, hundreds wounded, and tens of thousands displaced. This analysis aims at identifying the characteristics of the victims in an attempt to portray the `alien´ using household data collected in February 2009 in the Johannesburg inner city and in Alexandra township. Results con? rm that foreigners face a higher probability of being victimized on the ground of xenophobia. Relative poverty appears to have a positive impact on the probability of being attacked, especially in Alexandra township. Zimbabweans do also face a lower chance of victimization than other foreign nationals. Finally, the characteristics of the location, in particular high unemployment rate, influence the probability of attacks.
Genotype-Specific Steroid Profiles Associated With Aldosterone-Producing Adenomas

Primary aldosteronism comprises 2 main subtypes: unilateral aldosterone-producing adenoma (APA) and bilateral adrenal hyperplasia. Somatic KCNJ5 mutations are found in APA at a prevalence of around 40% that drive and sustain aldosterone excess. Somatic APA mutations have been described in other genes (CACNA1D, ATP1A1, and ATP2B3) albeit at a lower frequency. Our objective was to identify genotype-specific steroid profiles in adrenal venous (AV) and peripheral venous (PV) plasma in patients with APAs. We measured the concentrations of 15 steroids in AV and PV plasma samples by liquid chromatography–tandem mass spectrometry from 79 patients with confirmed unilateral primary aldosteronism. AV sampling lateralization ratios of steroids normalized either to cortisol or to DHEA+androstenedione were also calculated. The hybrid steroid 18-oxocortisol exhibited 18- and 16-fold higher concentrations in lateralized AV and PV plasma, respectively, from APA with KCNJ5 mutations compared with all other APA combined together (P<0.001). Lateralization ratios for the KCNJ5 group were also generally higher. Strikingly, we demonstrate that a distinct steroid signature can differentiate APA genotype in AV and PV plasma. Notably, a 7-steroid fingerprint in PV plasma correctly classified 92% of the APA according to genotype. Prospective studies are necessary to translate these findings into clinical practice and determine if steroid fingerprinting could be of value to select patients with primary aldosteronism who are particularly suitable candidates for adrenal venous sampling because of a high probability of having an APA.
A Reset State Estimator Using an Accelerometer for Enhanced Motion Control With Sensor Quantization

Sensor quantization is a key factor that deteriorates the tracking performance of positioning systems with low-resolution optical encoders. This paper presents a method to improve the performance of such systems by merging an accelerometer of low cost. First, to reject the external disturbance, friction force and system perturbations, we design a disturbance observer (DOB) based on acceleration signals. Second, a reset kinematic state estimator (RKSE) is designed using acceleration signals to make the state estimate immune to both system perturbations and input disturbances. Third, a state feedback controller is designed based on the internal model principle (IMP) for accurate sinusoidal reference tracking. Simulations and experimental results are used to demonstrate the effectiveness of the proposed control method for tracking position reference commands and its robustness to system uncertainties.
Fluctuating volume-current formulation of electromagnetic fluctuations in inhomogeneous media: Incandescence and luminescence in arbitrary geometries

We describe a fluctuating volume-current formulation of electromagnetic fluctuations that extends our recent work on heat exchange and Casimir interactions between arbitrarily shaped homogeneous bodies [A. W. Rodriguez, M. T. H. Reid, and S. G. Johnson, Phys. Rev. B 88, 054305 (2013)] to situations involving incandescence and luminescence problems, including thermal radiation, heat transfer, Casimir forces, spontaneous emission, fluorescence, and Raman scattering, in inhomogeneous media. Unlike previous scattering formulations based on field and/or surface unknowns, our work exploits powerful techniques from the volume-integral equation (VIE) method, in which electromagnetic scattering is described in terms of volumetric, current unknowns throughout the bodies. The resulting trace formulas (boxed equations) involve products of well-studied VIE matrices and describe power and momentum transfer between objects with spatially varying material properties and fluctuation characteristics. We demonstrate that thanks to the low-rank properties of the associated matrices, these formulas are susceptible to fast-trace computations based on iterative methods, making practical calculations tractable. We apply our techniques to study thermal radiation, heat transfer, and fluorescence in complicated geometries, checking our method against established techniques best suited for homogeneous bodies as well as applying it to obtain predictions of radiation from complex bodies with spatially varying permittivities and/or temperature profiles.
Open data framework for sustainable assessment in software forges

In a project-based learning experience, the detailed monitoring of the activities in which team members participate can be useful to evaluate their work. Using learning-oriented assessment procedures, supervisors can assess the teamwork abilities with a formative purpose. Evaluation strategies such as self-assessment, peer assessment and co-assessment are often used to make evaluation formative and sustainable. Conducting an assessment strategy is not easy for team members, since they need before to have a reasonable understanding of the evaluation process and criteria. This paper describes a learning-oriented evaluation methodology and a open data framework that can be applied to collaborative software development project settings. An evaluation rubric and a series of indicators that provide evidences about the developed skills have been elaborated and applied in a small-scale project-based course on Web Engineering. Projects were managed and developed with the help of an open source software forge that contains a ticketing tool for planning and tracking of tasks, a version control repository to save the software deliverables, and using a wiki to host text deliverables. The experience provides evidences in favor of using the assessment method and open data framework to make teamwork evaluation more sustainable.
Differentially Private Password Frequency Lists Or , How to release statistics from 70 million passwords ( on purpose )

Given a dataset of user-chosen passwords, the frequency list reveals the frequency of each unique password. We present a novel mechanism for releasing perturbed password frequency lists with rigorous security, efficiency, and distortion guarantees. Specifically, our mechanism is based on a novel algorithm for sampling that enables an efficient implementation of the exponential mechanism for differential privacy (naive sampling is exponential time). It provides the security guarantee that an adversary will not be able to use this perturbed frequency list to learn anything of significance about any individual user’s password even if the adversary already possesses a wealth of background knowledge about the users in the dataset. We prove that our mechanism introduces minimal distortion, thus ensuring that the released frequency list is close to the actual list. Further, we empirically demonstrate, using the now-canonical password dataset leaked from RockYou, that the mechanism works well in practice: as the differential privacy parameter varies from 8 to 0.002 (smaller implies higher security), the normalized distortion coefficient (representing the distance between the released and actual password frequency list divided by the number of users N ) varies from 8.8 × 10−7 to 1.9 × 10−3. Given this appealing combination of security and distortion guarantees, our mechanism enables organizations to publish perturbed password frequency lists. This can facilitate new research comparing password security between populations and evaluating password improvement approaches. To this end, we have collaborated with Yahoo! to use our differentially private mechanism to publicly release a corpus of 50 password frequency lists representing approximately 70 million Yahoo! users. This dataset is now the largest password frequency corpus available. Using our perturbed dataset we are able to closely replicate the original published analysis of this data.
Moving Object Detection Using Graph’s Axis Change Method

The detection and identify moving objects in a video sequence is very important concept. The currently available techniques for motion estimation can be broadly categorized into main classes: block matching methods and optical flow methods. But here detection of moving objects is detected by using Graph’s axis change method. Here we convert the image in to pixels and then calculate the values when the pixel change its position according to x axis and y axis. There is no problem of fixed and movable background. This paper investigates the different motion estimation algorithms used for video processing applications. Among the available motion estimation methods, the Lucas kanade Algorithm using changed axis’s method has been used in this paper for detection of moving objects in a video sequence. The algorithm produces the result of Horizontal and Vertical components velocity, u and v respectively. This velocity is measured in the form of vectors and has been used to segment the moving objects from the video sequence. The algorithm has been applied to different sets of synthetic and real video sequence
Sublinear Time Motif Discovery from Multiple Sequences

In this paper, a natural probabilistic model for motif discovery has been used to experimentally test the quality of motif discovery programs. In this model, there are k background sequences, and each character in a background sequence is a random character from an alphabet, Σ. A motif G = g1g2 . gm is a string of m characters. In each background sequence is implanted a probabilistically-generated approximate copy of G. For a probabilistically-generated approximate copy b1b2 . bm of G, every character, bi, is probabilistically generated, such that the probability for bi ≠ gi is at most α. We develop two new randomized algorithms and one new deterministic algorithm. They make advancements in the following aspects: (1) The algorithms are much faster than those before. Our algorithms can even run in sublinear time. (2) They can handle any motif pattern. (3) The restriction for the alphabet size is a lower bound of four. This gives them potential applications in practical problems, since gene sequences have an alphabet size of four. (4) All algorithms have rigorous proofs about their performances. The methods developed in this paper have been used in the software implementation. We observed some encouraging results that show improved performance for motif detection compared with other software.
Infrared hyperspectral imaging for enhanced tissue visualization and discrimination during surgical operation

A new hyperspectral imaging system has been designed in infrared 950 nm-1700 nm spectral range to be integrated in operating rooms in order to detect some vital and anatomical tissues, hardly noticeable by the surgeon's naked eye during surgical interventions. The main objective of this exploratory study is to find the best three relevant bands to create a RGB image that maximizes the contrast between the target tissue and its surroundings. One of the main challenges in HSI processing is the dimensionality reduction step which was carried out using band transformation and band selection methods in order to remove redundancy in the collected data without losing valuable information. Preclinical experiments were performed in vivo in a porcine ureter. Objective and comparative evaluations, based on the simulated perceived image, were performed between five methods. Experimental results show that the proposed method that combines two band selection methods has the best performance to enhance surgeon's visual skills.
SCOR: Constraint Programming-based Northbound Interface for SDN

In this paper, we introduce SCOR (Software-defined Constrained Optimal Routing), a new SDN Northbound Interface for QoS routing and traffic engineering. SCOR is based on constraint programming techniques and is implemented in the MiniZinc modelling language. It provides a powerful, high level abstraction, consisting of 9 basic constraint programming predicates. A key feature of SCOR is that it is declarative, where only the constraints and utility function of the routing problem need to be expressed, and the complexity of solving the problem is hidden from the user, and handled by a powerful generic solver. We show that the interface (set of predicates) of SCOR is sufficiently expressive to handle all the known and relevant QoS routing problems. We further demonstrate the practicality and scalability of the approach via a number of example scenarios, with varying network topologies, network sizes and number of flows.
Metaproteomics as a Complementary Approach to Gut Microbiota in Health and Disease

Classic studies on phylotype profiling are limited to the identification of microbial constituents, where information is lacking about the molecular interaction of these bacterial communities with the host genome and the possible outcomes in host biology. A range of OMICs approaches have provided great progress linking the microbiota to health and disease. However, the investigation of this context through proteomic mass spectrometry-based tools is still being improved. Therefore, metaproteomics or community proteogenomics has emerged as a complementary approach to metagenomic data, as a field in proteomics aiming to perform large-scale characterization of proteins from environmental microbiota, such as the human gut. The advances in molecular separation methods coupled with mass spectrometry (e.g., LC-MS/MS) and proteome bioinformatics have been fundamental in these novel large-scale metaproteomic studies, which have further been performed in a wide range of samples including soil, plant and human environments. Metaproteomic studies will make major progress if a comprehensive database covering the genes and expresses proteins from all gut microbial species is developed. To this end, we here present some of the main limitations of metaproteomic studies in complex microbiota environments, such as the gut, also addressing the up-to-date pipelines in sample preparation prior to fractionation/separation and mass spectrometry analysis. In addition, a novel approach to the limitations of metagenomic databases is also discussed. Finally, prospects are addressed regarding the application of metaproteomic analysis using a unified host-microbiome gene database and other meta-OMICs platforms.
The impact of cluster mergers on arc statistics

We study the impact of merger events on the strong lensing properties of galaxy clusters. Previous lensing simulations were not able to resolve dynamical time scales of cluster lenses, which arise on time scales which are of order a Gyr. In this case study, we first describe qualitatively with an analytic model how some of the lensing properties of clusters are expected to change during merging events. We then analyse a numerically simulated lens model for the variation in its efficiency for producing both tangentia l and radial arcs while a massive substructure falls onto the main cluster body. We find that: ( 1) during the merger, the shape of the critical lines and caustics changes substantially; (2) the lensing cross sections for long and thin arcs can grow by one order of magnitude and reach their maxima when the extent of the critical curves is largest; (3) the cross section for radial arcs also grows, but the cluster can efficiently produce this kind of arcs only while the merging substructure crosses the main cluster centre; (4) while the arc cross sections pass through their m axima as the merger proceeds, the cluster’s X-ray emission increases by a factor of ∼ 5. Thus, we conclude that accounting for these dynamical processes is very important for arc statist ics studies. In particular, they may provide a possible explanation for the arc statistics probl em.
The Genetic Convolutional Neural Network Model Based on Random Sample

Convolutional neural network (CNN) -- the result of the training is affected by of initial value of the weights. It is concluded that the model is not necessarily the best features of expression. The use of genetic algorithm can help choosing the better characteristics. But there almost was not literature study of the combining genetic algorithm with CNN. So this research has a lot of space and prospects. GACNN convolution genetic neural network model based on random sample has a better solution to obtain the unknown character expression. CNN individual training set uses a random data set. At the same time, the crossover and the mutation genetic algorithm bring random factors. There may are unknown feature expressions that may be appropriate. Experiments are based on accepted MNIST data sets, and the experimental results proved the advantages of the model.