Miracle-dz/UniFAR-FTUs huggingface dataset:
The feasibility of using poroelastographic techniques for distinguishing between normal and lymphedematous tissues in vivo

Lymphedema is a common condition involving an abnormal accumulation of lymphatic fluid in the interstitial space that causes swelling, most often in the arm(s) and leg(s). Lymphedema is a significant lifelong concern that can be congenital or develop following cancer treatment or cancer metastasis. Common methods of evaluation of lymphedema are mostly qualitative making it difficult to reliably assess the severity of the disease, a key factor in choosing the appropriate treatment. In this paper, we investigate the feasibility of using novel elastographic techniques to differentiate between lymphedematous and normal tissues. This study represents the first step of a larger study aimed at investigating the combined use of elastographic and sonographic techniques for the detection and staging of lymphedema. In this preliminary study, poroelastographic images were generated from the leg (8) and arm (4) subcutis of five normal volunteers and seven volunteers having lymphedema, and the results were compared using statistical analyses. The preliminary results reported in this paper suggest that it may be feasible to perform poroelastography in different lymphedematous tissues in vivo and that poroelastography techniques may be of help in differentiating between normal and lymphedematous tissues.
MENGA: A New Comprehensive Tool for the Integration of Neuroimaging Data and the Allen Human Brain Transcriptome Atlas

Introduction Brain-wide mRNA mappings offer a great potential for neuroscience research as they can provide information about system proteomics. In a previous work we have correlated mRNA maps with the binding patterns of radioligands targeting specific molecular systems and imaged with positron emission tomography (PET) in unrelated control groups. This approach is potentially applicable to any imaging modality as long as an efficient procedure of imaging-genomic matching is provided. In the original work we considered mRNA brain maps of the whole human genome derived from the Allen human brain database (ABA) and we performed the analysis with a specific region-based segmentation with a resolution that was limited by the PET data parcellation. There we identified the need for a platform for imaging-genomic integration that should be usable with any imaging modalities and fully exploit the high resolution mapping of ABA dataset. Aim In this work we present MENGA (Multimodal Environment for Neuroimaging and Genomic Analysis), a software platform that allows the investigation of the correlation patterns between neuroimaging data of any sort (both functional and structural) with mRNA gene expression profiles derived from the ABA database at high resolution. Results We applied MENGA to six different imaging datasets from three modalities (PET, single photon emission tomography and magnetic resonance imaging) targeting the dopamine and serotonin receptor systems and the myelin molecular structure. We further investigated imaging-genomic correlations in the case of mismatch between selected proteins and imaging targets.
Crop Type Classification Using Vegetation Indices of RapidEye Imagery

Abstract. Cutting-edge remote sensing technology has a significant role for managing the natural resources as well as the any other applications about the earth observation. Crop monitoring is the one of these applications since remote sensing provides us accurate, up-to-date and cost-effective information about the crop types at the different temporal and spatial resolution. In this study, the potential use of three different vegetation indices of RapidEye imagery on crop type classification as well as the effect of each indices on classification accuracy were investigated. The Normalized Difference Vegetation Index (NDVI), the Green Normalized Difference Vegetation Index (GNDVI), and the Normalized Difference Red Edge Index (NDRE) are the three vegetation indices used in this study since all of these incorporated the near-infrared (NIR) band. RapidEye imagery is highly demanded and preferred for agricultural and forestry applications since it has red-edge and NIR bands. The study area is located in Aegean region of Turkey. Radial Basis Function (RBF) kernel was used here for the Support Vector Machines (SVMs) classification. Original bands of RapidEye imagery were excluded and classification was performed with only three vegetation indices. The contribution of each indices on image classification accuracy was also tested with single band classification. Highest classification accuracy of 87, 46 % was obtained using three vegetation indices. This obtained classification accuracy is higher than the classification accuracy of any dual-combination of these vegetation indices. Results demonstrate that NDRE has the highest contribution on classification accuracy compared to the other vegetation indices and the RapidEye imagery can get satisfactory results of classification accuracy without original bands.
On optimal beamforming for noise reduction and interference rejection

In this paper, we study the performance of the minimum variance distortionless response (MVDR) and linearly constrained minimum variance (LCMV) noise reduction filters when a source of interference and ambient noise coexist with the target signal. We demonstrate that both filters are related as we decompose the MVDR filter into the LCMV and a matched filter (MVDR solution in the absence of interference). Both components are properly weighted to achieve maximum interference-plus-noise reduction at each frequency bin. Furthermore, we elaborate new closed-form expressions for the signal-to-interference ratio (SIR) and signal-to-noise ratio (SNR) at the output of the LCMV, MVDR, and matched filters. These expressions theoretically prove that a tradeoff between noise reduction and interference rejection has to be made. In fact, the total removal of the interference may severely amplify the output ambient noise. Conversely, totally focussing on noise reduction leads to increased level of residual interference. The proposed study is finally supported by numerical examples.
Fast localization and tracking using event sensors

The success of many robotics applications hinges on the speed at which the underlying sensing and inference tasks are carried out. Many high-speed applications such as autonomous driving and evasive maneuvering of quadrotors require high run time performance, which traditional cameras can seldom provide. In this paper we develop a fast localization and tracking algorithm using an event sensor, which produces on the order of million asynchronous events per second at pixels where luminance changes. The events are usually fired at the high gradient pixels (edges), where luminance changes occur as the sensor moves. We develop a fast spatio-temporal binning scheme to detect lines from these events at the edges. We represent the 3D model of the world using vertical lines, and the sensor pose can be estimated using the correspondences from 2D event lines to 3D world lines. The inherent simplicity of our method enables us to achieve a run time performance of 1000 Hertz.
Sheep preference for leafy spurge from Idaho and North Dakota.

Three trials were conducted to determine if low ingestion of some leafy spurge (Euphorbia esula L.) by sheep is primarily due to differences in sheep or in leafy spurge. In the first trial, pastures in Idaho and North Dakota were grazed by sheep originating from both states. Generally, sheep from both states grazed the leafy spurge growing in the Idaho pastures reluctantly but grazed the leafy spurge growing in the North Dakota pastures in proportion to its availability (P = 0.003). In the second trial, ingestion of air-dried leafy spurge by penned sheep was compared by offering samples from the 2 locations simultaneously. Sheep consumed more (P = 0.0001) leafy spurge from North Dakota than from Idaho. In the third trial, penned sheep were simultaneously offered Idaho leafy spurge harvested from fertilized and non-fertilized sites. Initially, equal amounts of fertilized and unfertilized leafy spurge were consumed (P greater than or equal to .68), but by the fourth day sheep had an obvious preference for leafy spurge from the fertilized site (P = 0.01). These trials indicate that preference for leafy spurge by sheep differs depending on site, and that using sheep to manage leafy spurge may be more successful on soils with relatively high fertility.
Parrot: a practical runtime for deterministic, stable, and reliable threads

Multithreaded programs are hard to get right. A key reason is that the contract between developers and runtimes grants exponentially many schedules to the runtimes. We present Parrot, a simple, practical runtime with a new contract to developers. By default, it orders thread synchronizations in the well-defined round-robin order, vastly reducing schedules to provide determinism (more precisely, deterministic synchronizations) and stability (i.e., robustness against input or code perturbations, a more useful property than determinism). When default schedules are slow, it allows developers to write intuitive performance hints in their code to switch or add schedules for speed. We believe this "meet in the middle" contract eases writing correct, efficient programs. We further present an ecosystem formed by integrating Parrot with a model checker called dbug. This ecosystem is more effective than either system alone: dbug checks the schedules that matter to Parrot, and Parrot greatly increases the coverage of dbug. Results on a diverse set of 108 programs, roughly 10× more than any prior evaluation, show that Parrot is easy to use (averaging 1.2 lines of hints per program); achieves low overhead (6.9% for 55 real-world programs and 12.7% for all 108 programs), 10× better than two prior systems; scales well to the maximum allowed cores on a 24-core server and to different scales/types of workloads; and increases Dbug's coverage by 106--1019734 for 56 programs. Parrot's source code, entire benchmark suite, and raw results are available at github.com/columbia/smt-mc.
Investigation of the electronic properties of cadmium zinc telluride surfaces using pulsed laser microwave cavity perturbation

The spectroscopic performance of cadmium zinc telluride (CZT) room temperature radiation detectors is currently limited by both bulk and surface imperfections introduced during the growth, harvesting and fabrication of these devices. Bulk imperfections including impurities, vacancies, interstitials, grain boundaries and dislocations have been relatively well studied and are known to trap charge and reduce detector performance. However, the effect of specific traps on the electronic decay process has been difficult to quantify. Surface imperfections including mechanical damage or adsorbed chemical species are also known to trap charge or increase leakage current, but it has proven difficult to characterize the electronic properties of CZT surfaces prior to electrode deposition. Here it is shown that the pulsed laser microwave cavity perturbation method can provide important information on the electronic decay process both in the bulk and at the surface of high pressure Bridgman CZT crystals. Electronic decay process both in the bulk and at the surface of high pressure Bridgman CZT crystals. Electronic decay time was measured as a function of temperature and surface conditions. It is shown that the electronic decay process in bulk CZT is consistent witha single deep hole trap at an energy between 600meV and 700meV. The effect of surface quality was resolved by analyzing distinct features in the photoconductivity decay curves. Atomic force microscopy was used to characterize the surface roughness. Rough or damaged surfaces exhibited persistent photoconductivity, which could be eliminated by etching with a bromine methanol solution.
Riemannian Pursuit for Big Matrix Recovery

Low rank matrix recovery is a fundamental task in many real-world applications. The performance of existing methods, however, deteriorates significantly when applied to ill-conditioned or large-scale matrices. In this paper, we therefore propose an efficient method, called Riemannian Pursuit (RP), that aims to address these two problems simultaneously. Our method consists of a sequence of fixed-rank optimization problems. Each subproblem, solved by a nonlinear Riemannian conjugate gradient method, aims to correct the solution in the most important subspace of increasing size. Theoretically, RP converges linearly under mild conditions and experimental results show that it substantially outperforms existing methods when applied to large-scale and ill-conditioned matrices.
Modeling of the orientational and structural dynamics of single-stranded DNA probe molecules immobilized on fused silica and glass surfaces

Immobilization of single-stranded DNA onto fused silica and glass surfaces has been widely used for preparation of fiber optic sensors and DNA microarrays. Fundamental investigations of the orientation, motion and hybridization behavior of immobilized ssDNA is important to understand the design sensing devices. Using computational methods to evaluate molecular dynamics w have simulated a solid SiO2-linker-ssDNA system and studied the conformations assumed by such immobilized material under different physical constraints. We have also evaluated the molecular dynamics of fluorescent intercalating dyes that are attached to the ssDNA by tethers of various lengths, with the goal of preparing a label on ssDNA that will transducer hybridization. Periodic boundary conditions were applied to examine packing properties and nearest neighbor interactions of adjacent ssDNA molecules. Molecular dynamics have been performed at room temperature as well as on heated systems. Solvent effects were taken into account and the modeling assumed the presence of an aqueous environment. The results suggest the various conformations and orientations that immobilized ssDNA can assume, and indicate that the orientation is generally far more that is perpendicular to the interface. Exploration of tether lengths indicates the minimum lengths that are required for a fluorescent label to operate efficiently as an intercalating agent.
Automating a severity score guideline for community-acquired pneumonia employing medical language processing of discharge summaries

Obtaining encoded variables is often a key obstacle to automating clinical guidelines. Frequently the pertinent information occurs as text in patient reports, but text is inadequate for the task. This paper describes a retrospective study that automates determination of severity classes for patients with community-acquired pneumonia (i.e. classifies patients into risk classes 1-5), a common and costly clinical problem. Most of the variables for the automated application were obtained by writing queries based on output generated by MedLEE1, a natural language processor that encodes clinical information in text. Comorbidities, vital signs, and symptoms from discharge summaries as well as information from chest x-ray reports were used. The results were very good because when compared with a reference standard obtained manually by an independent expert, the automated application demonstrated an accuracy, sensitivity, and specificity of 93%, 92%, and 93% respectively for processing discharge summaries, and 96%, 87%, and 98% respectively for chest x-rays. The accuracy for vital sign values was 85%, and the accuracy for determining the exact risk class was 80%. The remaining 20% that did not match exactly differed by only one class.
An empirical study of software developers' management of dependencies and changes

Different approaches and tools have been proposed to support change impact analysis, i.e., the identification of the potential consequences of a change, or the estimation of what needs to be modified to accomplish a change. However, just a few empirical studies of software developers' actual change impact analysis approaches have been reported in the literature. To minimize this gap, this paper describes an empirical study of two software development teams. It describes, through the presentation of ethnographic data, the strategies used by software developers to handle the effect of software dependencies and changes in their work. The concept of impact management is proposed as an analytical framework to present these practices and is used to suggest avenues for future research in change impact analysis techniques.
A novel output transformer based highly linear RF-DAC architecture

A major limitation of the linearity of Current Steering (CS) RF-DACs is the large output voltage swing (typically 1Vpp), which couples to sensitive internal nodes and thereby causes non-linear distortion. This paper proposes a novel approach for the linearization of the CS RF-DAC. An output transformer decouples the output from the circuit core and attenuates the voltage swing seen by the RF-DAC current cells. A lumped element model of a transformer is used in calculations and simulations to analyze the performance of the transformer in the Mixing-DAC application, and to select optimal design parameters for high linearity. Verification with a simulation model of an RF-DAC shows that the output related non-linearity (IMD3) of the CS RF-DAC improves with about 14dB when the proposed transformer parameters are used.
A Novel Methodology for Enhanced and Consistent Heterologous Expression of Unmodified Human Cytochrome P450 1B1 (CYP1B1)

Cytochrome P450 1B1 (CYP1B1) is a universal cancer marker and is implicated in many other disorders. Mutations in CYP1B1 are also associated with childhood blindness due to primary congenital glaucoma (PCG). To understand the CYP1B1 mediated etiopathology of PCG and pathomechanism of various cancers, it is important to carry out its functional studies. Heterologous expression of CYP1B1 in prokaryotes is imperative because bacteria yield a higher amount of heterologous proteins in lesser time and so the expressed protein is ideal for functional studies. In such expression system there is no interference by other eukaryotic proteins. But the story is not that simple as expression of heterologous CYP1B1 poses many technical difficulties. Investigators have employed various modifications/deletions of CYP N-terminus to improve CYP1B1 expression. However, the drawback of these studies is that it changes the original protein and, as a result, invalidates functional studies. The present study examines the role of various conditions and reagents in successful and consistent expression of sufficient quantities of unmodified/native human CYP1B1 in E. coli. We aimed at expressing CYP1B1 in various strains of E. coli and in the course developed a protocol that results in high expression of unmodified protein sufficient for functional/biophysical studies. We examined CYP1B1 expression with respect to different expression vectors, bacterial strains, types of culture media, time, Isopropyl β-D-1-thiogalactopyranoside concentrations, temperatures, rotations per minute, conditioning reagents and the efficacy of a newly described technique called double colony selection. We report a protocol that is simple, easy and can be carried out in any laboratory without the requirement of a fermentor. Though employed for CYP1B1 expression, this protocol can ideally be used to express any eukaryotic membrane protein.
Automatic Detection of Events and Tracking of Moving Objects in Video Sequences

Digital video is being used widely in a variety of applications such as surveillance and security. Big amount of video in surveillance and security requires systems capable to process video automatically to detect events and track moving objects to alleviate the load on humans and enable preventive actions when events are detected [4]. our paper focuses to develop an intelligent visual surveillance system to replace the traditional passive video surveillance that is proving ineffective as the number of cameras exceeds the capability of human operators to monitor them, and it is able to track objects within a maximum solid angle speed which is measured at about 0.3 to 0.2 radian per second, further it also depends on the complexity of the system and the processor speed as well.
Compositional high level Petri nets with timing constraints - a comparison

We aim at comparing various timed extensions of a fully compositional class of high-level Petri nets, called M-nets. On one hand, as a classical approach, a class of timed M-nets is considered, where intervals of duration are attached to each transition. More precisely, we study this class enriched by the recently introduced hierarchical operation of timed refinement. On the other hand, M-nets enriched by the causal time concept (H. Klaudel et al., 1999); Franck Pommereau, 2002) are considered, they are called causal time M-nets and allow us to introduce timing constraints without extending the original untimed model of M-nets. Causal time M-nets are shown to form the more powerful class. It covers timed and even hierarchical timed M-nets. Moreover, as shown by some significant examples, it permits the modelling of systems involving overlapping timing constraints. Some special cases are quoted where hierarchical timed M-nets may become favorite.
Visual Algorithm Simulation Exercise System with Automatic Assessment: TRAKLA2

Interaction and feedback are key factors supporting the learning process. Therefore many automatic assessment and feedback systems have been developed for computer science courses dur- ing the past decade. In this paper we present a new framework, TRAKLA2, for building interactive algorithm simulation exercises. Exercises constructed in TRAKLA2 are viewed as learning objects in which students manipulate conceptual visualizations of data structures in order to simulate the working of given algorithms. The framework supports randomized input values for the assignments, as well as automatic feedback and grading of students' simulation sequences. Moreover, it supports automatic generation of model solutions as algorithm animations and the logging of statistical data about the interaction process resulting as students s olve exercises. The system has been used in two universities in Finland for several courses invol ving over 1000 students. Student response has been very positive.
EFFECT OF CORTICOSTEROIDS ON RIBONUCLEASE AND NUCLEIC ACID CONTENT IN LYMPHOSARCOMA P1798.

Administration of 9α-fluoroprednisolone to mice bearing the corticoid-sensitive strain of Lymphosarcoma P1798 resulted in a prompt regression of tumor size. This regression was accompanied by a selective decrease in tumor RNA. Tumor DNA concentration remained at control levels. These changes were preceded by an increased tumor ribonuclease activity in homogenates of tumor after the in vivo administration of corticosteroids. Regression of tumor size and activation of tumor ribonuclease were not observed in the resistant strain of tumor after steroid administration.