Miracle-dz/UniFAR-FTUs huggingface dataset:
A hybrid neural system for the automatic segmentation of the interventricular septum in echocardiographic images

Echocardiographic exams allow the observation and extraction of measures related to cardiac structures. In the longitudinal parasternal view, these measures include the left ventricle end-diastolic and end-systolic diameters, end-diastolic interventricular septum thickness (IVSd), and end-diastolic left ventricle posterior wall thickness (LVPWd). Among these measures, the IVSd is important for diagnosing pathologies like hypertrophic cardiomyopathy, aneurysms, abnormal movement and structural faults. This work presents a hybrid neural network system to segment interventricular septum in echocardiographic images of parasternal longitudinal view. The hybrid system developed here consist of a Self-Organizing Map and a Multilayer Perceptron (MLP) neural network. The approach has two phases: clustering and classification. First, the Self-Organizing Map clusters image patches that are previously labeled as Septum and Non-septum. Later, an MLP is trained with information generated by the map. The MLP is then employed to classify patches of a new image resulting in a mask that indicates the probable septum regions. To validate the results, we did a semi-automatic extraction of septum thickness. The average error between the septum thicknesses obtained by the algorithm and the one manually traced was 0.5477mm ± 0.5277mm. Future recommendations are presented to improve the hybrid system performance to get more accurate results.
A Study on Clustering High Dimensional Data Using Hubness Phenomenon

Data mining is the non-trivial process of extracting information from the very large database. In recent years, data repository has a high dimensional data, which makes a complete search in most of the data mining problems leads computationally infeasible. To eradicate this problem clustering plays a vital role in handling low dimensional data and high dimensional data. Low dimensional data makes a task very simple and easy to cluster. High dimensional data is a crucial fact to cluster and it has to resolve using hubness phenomenon. Here hubness refers a data point which may frequently occurr among the groups. The existing system has to determine and manage the hyperspheric clusters. To overcome this problem, the proposed system is to use hub based clustering technique to improve the quality of cluster in terms of effectiveness and accuracy, and to avoid only detecting hyper- spherical cluster. Index terms: Clustering, Nearest neighbour, Hubs, High dimensional data, biological data.
Image Based Interactive Rendering with View Dependent Geometry

In this paper we present a novel approach for interactive rendering of virtual views from real image sequences.Combining the concepts of light fields, depth‐compensated image warping and view dependent texture mapping,this plenoptic modeling approach can handle large and complex scenes. A portable, handheld multi‐camera systemhas been developed that allows to record multiple image streams by simply walking around the scene. Theseimage streams are automatically calibrated and depth maps for all views are generated as input to the renderingstage. For rendering a view dependent warping surface is constructed on the fly and depth‐compensated imageinterpolation is applied with view‐dependent texture mapping. Rendering quality is scalable to allow fast previewand to achieve high‐end quality with the same approach. The system can handle large and geometrically complexscenes with hundreds of real images at interactive rates.
Comprehensive Identification of Long Non-coding RNAs in Purified Cell Types from the Brain Reveals Functional LncRNA in OPC Fate Determination

Long non-coding RNAs (lncRNAs) (> 200 bp) play crucial roles in transcriptional regulation during numerous biological processes. However, it is challenging to comprehensively identify lncRNAs, because they are often expressed at low levels and with more cell-type specificity than are protein-coding genes. In the present study, we performed ab initio transcriptome reconstruction using eight purified cell populations from mouse cortex and detected more than 5000 lncRNAs. Predicting the functions of lncRNAs using cell-type specific data revealed their potential functional roles in Central Nervous System (CNS) development. We performed motif searches in ENCODE DNase I digital footprint data and Mouse ENCODE promoters to infer transcription factor (TF) occupancy. By integrating TF binding and cell-type specific transcriptomic data, we constructed a novel framework that is useful for systematically identifying lncRNAs that are potentially essential for brain cell fate determination. Based on this integrative analysis, we identified lncRNAs that are regulated during Oligodendrocyte Precursor Cell (OPC) differentiation from Neural Stem Cells (NSCs) and that are likely to be involved in oligodendrogenesis. The top candidate, lnc-OPC, shows highly specific expression in OPCs and remarkable sequence conservation among placental mammals. Interestingly, lnc-OPC is significantly up-regulated in glial progenitors from experimental autoimmune encephalomyelitis (EAE) mouse models compared to wild-type mice. OLIG2-binding sites in the upstream regulatory region of lnc-OPC were identified by ChIP (chromatin immunoprecipitation)-Sequencing and validated by luciferase assays. Loss-of-function experiments confirmed that lnc-OPC plays a functional role in OPC genesis. Overall, our results substantiated the role of lncRNA in OPC fate determination and provided an unprecedented data source for future functional investigations in CNS cell types. We present our datasets and analysis results via the interactive genome browser at our laboratory website that is freely accessible to the research community. This is the first lncRNA expression database of collective populations of glia, vascular cells, and neurons. We anticipate that these studies will advance the knowledge of this major class of non-coding genes and their potential roles in neurological development and diseases.
Brief Announcement: Space-Time Tradeoffs for Distributed Verification

Verifying that a network configuration satisfies a given boolean predicate is a fundamental problem in distributed computing. Many variations of this problem have been studied, for example, in the context of proof labeling schemes (PLS), locally checkable proofs (LCP), and non-deterministic local decision (NLD). In all of these contexts, verification time is assumed to be constant. Korman, Kutten and Masuzawa presented a proof-labeling scheme for MST, with poly-logarithmic verification time, and logarithmic memory at each vertex. In this paper we introduce the notion of a t-PLS, which allows the verification procedure to run for super-constant time. Our work analyzes the tradeoffs of t-PLS between time, label size, message length, and computation space. We construct a universal t-PLS and prove that it uses the same amount of total communication as a known one-round universal PLS, and t factor smaller labels. In addition, we provide a general technique to prove lower bounds for space- time tradeoffs of t-PLS. We use this technique to show an optimal tradeoff for testing that a network is acyclic (cycle free). Our optimal t-PLS for acyclicity uses label size and computation space O((log n)/t). We further describe a recursive O(log* n) space verifier for acyclicity which does not assume previous knowledge of the run-time t.
Statics modeling of planar continuum robots using virtual energy method

Continuum robot structures are bio inspired mechanisms made of flexible backbones. These robots have been developed in the last decade in a variety of applications, such as dexterous surgical applications and working in industrial hazardous areas. Precise modeling of continuum robots is necessary for accurate control. In addition to precision, the computation time of such models need to be reasonable, so that they can be used in real-time control applications. Many works has been done on modeling of continuum robots. In this paper, we develop our previous works on modeling of continuum robots, in order to achieve faster solutions. The backbone kinematics is approximated by a series of constant-curvature elements. Then, the robot is modeled using the virtual energy method. The new model is compared with the previous methods, and its two noticeable advantages are discussed. Finally, the accuracy of the results are compared with the results from previous methods, and validated using our experimental setup. The experimental results show the accuracy of the proposed model.
Proportional-Integral-Derivative (PID) controller design for robust stability of arbitrary order plants with time-delay and additive uncertainty

In the process control industry, majority of control loops are based on ProportionalIntegral-Derivative (PID) controllers. The basic structure of the PID controllers makes it easy to regulate the process output. Design methods leading to an optimal and effective operation of the PID controllers are economically vital for process industries. Robust control has been a recent addition to the field of control engineering that primarily deals with obtaining system robustness in presences of uncertainties. In this thesis, a graphical design method for obtaining the entire range of PID controller gains that robustly stabilize a system in the presence of time delays and additive uncertainty is introduced. This design method primarily depends on the frequency response of the system, which can serve to reduce the complexities involved in plant modeling. The fact that time-delays and parametric uncertainties are almost always present in real time processes makes our controller design method very vital for process control. We have applied our design method to a DC motor model with a communication delay and a single area nonreheat steam generation unit. The results were satisfactory and robust stability was achieved for the perturbed plants.
Serum Zn/Cu Ratio Is Associated with Renal Function, Glycemic Control, and Metabolic Parameters in Japanese Patients with and without Type 2 Diabetes: A Cross-sectional Study

Background Zinc (Zn) and copper (Cu) may play a pivotal role in the pathogenesis of diabetes and diabetic complications by mediating oxidative stress. Both Zn deficiency and excess of Cu are associated with an increased risk of type 2 diabetes and cardiovascular disease. We aimed to investigate the relationships between serum Zn/Cu ratio and glycemic status, renal function, and metabolic parameters in patients with and without type 2 diabetes. Methods We conducted a cross-sectional study on 355 subjects (149 type 2 diabetic and 206 non-diabetic) in whom serum Zn and Cu levels were measured at the same time. Associations between serum Zn/Cu ratio and clinical data were evaluated using multiple regression analysis. We also evaluated associations between serum Zn/Cu ratio and the prevalence of type 2 diabetes and glycemic control by multivariate logistic regression analysis. Results Serum Zn/Cu ratio was positively associated with estimated glomerular filtration rate after adjustment for body mass index (BMI) (β = 0.137, p = 0.014). Plasma B-type natriuretic peptide levels were negatively associated with serum Zn/Cu ratio after adjustment for age, sex, and BMI (β = −0.258, p = 0.032). In patients with type 2 diabetes, serum Zn/Cu ratio was negatively associated with plasma HbA1c levels after adjustment for age, sex, and BMI (β = −0.239, p = 0.003). In addition, multivariate logistic regression analysis revealed that the highest quartile of serum Zn/Cu ratio was associated with a reduced risk of poor (HbA1c ≥ 7%) glycemic control (odds ratio = 0.382; 95% confidence interval, 0.165–0.884; p = 0.025) in patients with type 2 diabetes. Conclusion Serum Zn/Cu ratio was favorably associated with renal function in all subjects and glycemic control in patients with type 2 diabetes. The Zn/Cu ratio, in addition to the individual serum levels of trace elements, is important for metabolism in humans.
Ranking the pages of the World Wide Web

Querysearchenginesarefundamentaltoolsinlocatingdocuments satisfyingtoWebsurfers’interests. A Web search engine enumerates no more than few hundreds of documents for any key word search query. The quality of a search engine largely depends on its ranking algorithm, the heuristics applied forselectingthe hitlistfromthepages containing thekey word. Thisextended abstract discusses general issues about ranking algorithms, then focuses on PageRank, the ranking algorithm implemented in Google. We provide an alternative formulation of PageRank and discuss some further extensions derived from our formulation. Finally, the performance of the modified algorithms is evaluated and compared with other well-known ranking schemes in novel graph based experiments computed on a collection of approximately one million documents downloaded from the Irish domain.
Evaluation of Passive Samplers as a Monitoring Tool for Early Warning of Dinophysis Toxins in Shellfish

From June 2006 to January 2007 passive samplers (solid phase adsorbing toxin tracking, SPATT) were tested as a monitoring tool with weekly monitoring of phytoplankton and toxin content (liquid chromatography–mass spectrometry, LC-MS) in picked cells of Dinophysis and plankton concentrates. Successive blooms of Dinophysis acuminata, D. acuta and D. caudata in 2006 caused a long mussel harvesting closure (4.5 months) in the Galician Rías (NW Spain) and a record (up to 9246 ng·g resin-week−1) accumulation of toxins in SPATT discs. Best fit of a toxin accumulation model was between toxin accumulation in SPATT and the product of cell densities by a constant value, for each species of Dinophysis, of toxin content (average) in picked cells. Detection of Dinophysis populations provided earlier warning of oncoming diarrhetic shellfish poisoning (DSP) outbreaks than the SPATT, which at times overestimated the expected toxin levels in shellfish because: (i) SPATT accumulated toxins did not include biotransformation and depuration loss terms and (ii) accumulation of toxins not available to mussels continued for weeks after Dinophysis cells were undetectable and mussels were toxin-free. SPATT may be a valuable environmental monitoring and research tool for toxin dynamics, in particular in areas with no aquaculture, but does not provide a practical gain for early warning of DSP outbreaks.
Prevention of radiocontrast-media-induced nephropathy in patients with pre-existing renal insufficiency by hydration in combination with the adenosine antagonist theophylline.

BACKGROUND Radiographic contrast media (CM) application causes a decline in renal function, especially in patients with pre-existing renal dysfunction. In addition to hydration, several vasodilating substances have been evaluated for their ability to prevent renal damage after CM application. In a prospective, double-blind, placebo-controlled study we investigated the effect of the oral administration of theophylline, an adenosine receptor antagonist, on changes in renal haemodynamics and tubular injury induced by CM in well-hydrated patients with mild-to-moderate renal insufficiency. METHODS We studied 80 patients with pre-existing chronic renal insufficiency (creatinine > 1.5 mg/dl) who received more than 100 ml iopromide. Hydration (either oral or intravenous) started at least 24 h before and lasted until 24 h after CM application. In addition, patients were randomly assigned to receive either theophylline (810 mg daily) or placebo. Serum creatinine and creatinine clearance were measured before and for 3 days after CM application. Urine was collected to measure N-acetyl-beta-glucosaminidase (NAG) enzymuria for the same period. Sixty-four patients completed the entire study protocol (theophylline, n = 35 and placebo, n = 29). RESULTS During the study period serum creatinine concentration and creatinine clearance did not change significantly in either group. Acute renal failure (increase of serum creatinine of at least 0.5 mg/dl) could be observed in two patients from the theophylline group (5.7%) and one from the placebo group (3.4%). The increase in NAG excretion reached statistical significance (P < 0.05) in the placebo group on days 2 and 3 after CM application. CONCLUSIONS Our results indicate a role for adenosine in CM-induced tubulotoxicity. However, the glomerular filtration rate is preserved by hydration alone in these patients. The application of theophylline did not bring an additional benefit. The use of adenosine antagonists may be beneficial in patients where sufficient hydration may be impossible or in patients with a concomitant decrease in renal blood flow (e.g. congestive heart failure).
Positive Affective Interactions: The Role of Repeated Exposure and Copresence

We describe and evaluate a new interface to induce positive emotions in users: a digital, interactive adaptive mirror. We study whether the induced affect is repeatable after a fixed interval (Study 1) and how copresence influences the emotion induction (Study 2). Results show that participants systematically feel more positive after an affective mirror session, that this effect is repeatable, and stronger when a friend is copresent.
The computation of averages from equilibrium and nonequilibrium Langevin molecular dynamics

a la Talay-Tubaro on the invariant distribution for small stepsize, and compare the sampling bias obtained for various choices of splitting method. We further investigate the overdamped limit and apply the methods in the context of driven systems where the goal is sampling with respect to a non-equilibrium steady state. Our analyses are illustrated by numerical experiments.
Value Chains for nutrition

Value-chain approaches are already used as development strategies to enhance the livelihoods of food producers, but they have, to date, rarely been used explicitly as a tool to achieve nutritional goals, and they have not been sensitive to nutritional concerns. This brief seeks to identify if, why, and how value-chain concepts could and should be applied to enhance the ability of agriculture to improve nutrition.
The IDEA Eligibility Mess

The Individuals with Disabilities Education Act (IDEA) guarantees students with disabilities a free public education appropriate to their needs. To be eligible for that entitlement, a student must meet the definition of "child with a disability. " The law governing special education eligibility, however, is charitably characterized as a mess. There are several sources of the current eligibility confusion. First, recent court cases have reached conflicting conclusions about how much adverse educational impact the child's disabling condition must have, what constitutes a sufficient need for special education, and when children with emotional disabilities are eligible. Second, long-established methods for assessing learning disabilities have withered under criticism from educational experts, and a new method of approaching learning disabilities, response-to-intervention, is being touted by the United States Department of Education. Nevertheless, that innovation remains largely unproven and may be impossible to implement at scale. Third, Congress and others have focused long-overdue attention on the disproportionate percentage of African-Americans who are found eligible for special education under the disability categories of mental retardation and emotional disturbance, but neither Congress nor anyone else appears to have a promising idea about how to address the situation. This Article analyzes and critiques the recent cases, describes and comments on the new learning disability assessment methodology, and evaluates competing ideas about how to respond to ethnic disproportion. It concludes that the solution to the entire set of problems is not a redefinition of special education eligibility under IDEA, but rather a renewed attention to the actual terms of the statute and the goal of full educational opportunity. This step will promote what might be called "not-quite-so-special education," that is, an entitlement for a broad class of children to high quality special education supports provided in the regular educational environment.
Detecting forensically relevant information from PE executables

Cyber forensics analysis is the procedure to find crucial evidence with respect to a crime from a digital media. Malware forensics and Network security plays a crucial role in the current scenario where malware attacks are a common problem. A malicious software which can be commonly termed as a malware would cause interruption to a computer operation and may collect necessary information or illegally access private systems. A malware may either take the form of a script, code, spyware and many other kinds of malicious programs. Reverse engineering principles are applied in this domain to analyze malware. It is the comprehensive process of breaking software to figure out how it works. This paper proposes an advanced and resource friendly malware forensics analysis procedure which uses the principles of static analysis to figure out the exact purpose of an executable file. Portable executable format can be explored with higher accuracy using the proposed method.
A Generic Sensor Interface in Robot Simulation and Control

Focusing on creating a module which will cooperate with Computer Aided Robotics (CAR) applications used both off-line and on-line, this paper outlines a generic interface, i.e. an architecture that can handle virtual as well as physical sensors in a transparent way to the user. The design which allows autonomous behavior at sensor level, is exemplified with an implementation for arc welding using a seam tracking device and a control unit connected to a CAR application. In this context, a virtual sensor is a software model of a physical sensor with similar characteristics, using geometrical and/or process specific data from a computerized model of a real work cell. Generic refers to the ability of the interface to be able to handle different sensors, where each physical sensor may be exchanged to a virtual. Key-words:robotics, sensors, simulation, CAR, autonomous, arc welding
Analysis of eight out genes in a cluster required for pectic enzyme secretion by Erwinia chrysanthemi: sequence comparison with secretion genes from other gram-negative bacteria

Many extracellular proteins produced by Erwinia chrysanthemi require the out gene products for transport across the outer membrane. In a previous report (S. Y. He, M. Lindeberg, A. K. Chatterjee, and A. Collmer, Proc. Natl. Acad. Sci. USA 88:1079-1083, 1991) cosmid pCPP2006, sufficient for secretion of Erwinia chrysanthemi extracellular proteins by Escherichia coli, was partially sequenced, revealing four out genes sharing high homology with pulH through pulK from Klebsiella oxytoca. The nucleotide sequence of eight additional out genes reveals homology with pulC through pulG, pulL, pulM, pulO, and other genes involved in secretion by various gram-negative bacteria. Although signal sequences and hydrophobic regions are generally conserved between Pul and Out proteins, four out genes contain unique inserts, a pulN homolog is not present, and outO appears to be transcribed separately from outC through outM. The sequenced region was subcloned, and an additional 7.6-kb region upstream was identified as being required for secretion in E. coli. out gene homologs were found on Erwinia carotovora cosmid clone pAKC651 but were not detected in E. coli. The outC-through-outM operon is weakly induced by polygalacturonic acid and strongly expressed in the early stationary phase. The out and pul genes are highly similar in sequence, hydropathic properties, and overall arrangement but differ in both transcriptional organization and the nature of their induction.