Miracle-dz/UniFAR-FTUs huggingface dataset:
Laryngeal precancer: A review of the literature, commentary, and comparison with oral leukoplakia

Laryngeal keratosis (LK) is a precancerous mucosal change with great similarity to oral leukoplakia. Its malignant transformation rate varies from 1% to 40%, with the highest rates being found in patients microscopically diagnosed as “keratosis with atypia” (KWA). Recent evidence indicates that even cases with only mild or moderate epithelial dysplasias are at increased risk for malignant transformation, with the highest rates occurring in patients with more severe dysplasia or carcinoma in situ. Approximately 81% of LK patients are men and the average age at diagnosis is 50 years, a decade younger than that for laryngeal carcinoma patients. A high proportion of LK patients are tobacco smokers (84%) and alcohol abusers (at least 35%). LK is almost always found on the true vocal cords and is usually bilateral (67%). Clinical signs of high risk include, in decreasing order of importance: erythroplakia, surface granularity, increased keratin thickness, increased size, recurrence after conservative removal, and long duration. The annual incidence of LK in the United States is 10.2 and 2.1 lesions per 100,000 males and females, respectively.
Ubiquitin ligase RNF123 Mediates Degradation of Heterochromatin Protein 1α and β in Lamin A/C Knock-Down Cells

Background The nuclear lamina is a key determinant of nuclear architecture, integrity and functionality in metazoan nuclei. Mutations in the human lamin A gene lead to highly debilitating genetic diseases termed as laminopathies. Expression of lamin A mutations or reduction in levels of endogenous A-type lamins leads to nuclear defects such as abnormal nuclear morphology and disorganization of heterochromatin. This is accompanied by increased proteasomal degradation of certain nuclear proteins such as emerin, nesprin-1α, retinoblastoma protein and heterochromatin protein 1 (HP1). However, the pathways of proteasomal degradation have not been well characterized. Methodology/Principal Findings To investigate the mechanisms underlying the degradation of HP1 proteins upon lamin misexpression, we analyzed the effects of shRNA-mediated knock-down of lamins A and C in HeLa cells. Cells with reduced levels of expression of lamins A and C exhibited proteasomal degradation of HP1α and HP1β but not HP1γ. Since specific ubiquitin ligases are upregulated in lamin A/C knock-down cells, further studies were carried out with one of these ligases, RNF123, which has a putative HP1-binding motif. Ectopic expression of GFP-tagged RNF123 directly resulted in degradation of HP1α and HP1β. Mutational analysis showed that the canonical HP1-binding pentapeptide motif PXVXL in the N-terminus of RNF123 was required for binding to HP1 proteins and targeting them for degradation. The role of endogenous RNF123 in the degradation of HP1 isoforms was confirmed by RNF123 RNAi experiments. Furthermore, FRAP analysis suggested that HP1β was displaced from chromatin in laminopathic cells. Conclusions/Significance Our data support a role for RNF123 ubiquitin ligase in the degradation of HP1α and HP1β upon lamin A/C knock-down. Hence lamin misexpression can cause degradation of mislocalized proteins involved in key nuclear processes by induction of specific components of the ubiquitin-proteasome system.
Scenario Integration via Higher-Order Graphs ?

Requirements engineering (RE) is an inherently piecemeal and dynamic process. Arrangement, correlation and integration of models presenting different views of the system are an important component of the requirements engineer’s work. If manually performed, these operations are error-prone and time consuming and, thus, an integrated computer-aided environment for them would be very useful. In the paper, we present an extendable framework that provides a formalization and a generic integration pattern for scenario management. The framework is based on mathematical category theory machinery of algebraic operations with higher-order graphs. We demonstrate how the framework works by a (not entirely trivial) example of scenario integration. A distinctive feature of our integration pattern is an essential use of derived elements: for setting correspondences between views we augment view scenarios with derived executions. We show that this augmentation is essential for a proper integration: information explicitly specified in one view can be implicit in another view, where it is derived from the information considered basic in that view.
Diagnostic microsatellite markers for the barley net blotch pathogens, Pyrenophora teres f. maculata and Pyrenophora teres f. teres

The barley net blotch pathogen Pyrenophora teres occurs in two forms, evident as spot (P. teres f. maculata) or net (P. teres f. teres) type symptoms. The two forms can be difficult to differentiate based on disease symptoms or spore morphology. Here we report nine simple sequence repeat markers that reveal differences between the two forms, providing diagnostic markers that are potentially useful.
Development and evaluation of a self-administered on-line test of memory and attention for middle-aged and older adults

There is a need for rapid and reliable Internet-based screening tools for cognitive assessment in middle-aged and older adults. We report the psychometric properties of an on-line tool designed to screen for cognitive deficits that require further investigation. The tool is composed of measures of memory and executive attention processes known to be sensitive to brain changes associated with aging and with cognitive disorders that become more prevalent with age. Measures included a Spatial Working Memory task, Stroop Interference task, Face-Name Association task, and Number-Letter Alternation task. Normative data were collected from 361 healthy adults age 50–79 who scored in the normal range on a standardized measure of general cognitive ability. Participants took the 20-minute on-line test on their home computers, and a subset of 288 participants repeated the test 1 week later. Analyses of the individual tasks indicated adequate internal consistency, construct validity, test-retest reliability, and alternate version reliability. As expected, scores were correlated with age. The four tasks loaded on the same principle component. Demographically-corrected z-scores from the individual tasks were combined to create an overall score, which showed good reliability and classification consistency. These results indicate the tool may be useful for identifying middle-aged and older adults with lower than expected scores who may benefit from clinical evaluation of their cognition by a health care professional.
Development of a robotic device for upper limb stroke rehabilitation: A user-centered design approach

Stroke is one of the major causes of permanent adult disability. Stroke frequently affects motor control of the arm, leading to diffculties in doing activities of daily living. This research focuses on developing an upper limb rehabilitation robotic prototype through user-centered design to aid stroke survivors in rehabilitating their arm. To gather requirements from end users, stroke therapy sessions were observed and a survey of stroke therapists was conducted. End user requirements were evaluated to determine technical targets for the mechanical design of the prototype. Evaluation of the prototype was done with stroke therapists in a focus group and a preliminary biomechanical study. As user-centered design would require more iterations of design, testing and evaluation, this project reports a first step in developing an affordable, portable device, which could increase access to stroke rehabilitation for the arm.
A Reinforcement Learning-Based Deflection Routing Scheme for Buffer-Less OBS Networks

Optical burst switching (OBS) is a promising switching paradigm for the next generation Internet. A buffer-less OBS network can be implemented simply and cost-effectively without the need for either wavelength converters or optical buffers which are, currently, neither cost-effective nor technologically mature. However, this type of OBS networks suffers from relatively high loss probability caused by wavelength contentions at core nodes. This issue could prevent or, at least, delay the adoption of OBS networks as a solution for the next generation optical Internet. Deflection routing is one of the contention resolution approaches that have been proposed to tackle this problem. In addition to be cost-effective, it is also efficient in reducing loss probability, especially with low and moderate traffic loads. In this paper, we propose an adaptive reinforcement learning-based deflection routing scheme (RLDRS) which focuses on the route selection issue by choosing the optimal alternative output port in terms of both loss probability and delay when deflection is performed. Moreover, RLDRS limits the number of authorized deflections of each burst in order to reduce the additional traffic caused by deflection routing and to prohibit excessive deflections. Simulation results show that RLDRS reduces effectively loss probability and outperforms shortest path deflection routing (SPDR).
A Novel ZVZCS Full-Bridge DC/DC Converter Used for Electric Vehicles

This paper presents a novel ZVZCS full-bridge DC/DC converter, which is able to process and deliver power efficiently over very wide load variations. The proposed DC/DC converter is part of a plug-in AC/DC converter used to charge the traction battery (high voltage battery) in an electric vehicle. The key challenge in this application is operation of the full-bridge converter from absolutely no-load to full-load conditions. In order to confirm reliable operation of the full-bridge converter under such wide load variations, the converter should not only operate with soft-switching from full load to no-load condition with satisfactory efficiency for the full range of operation, but also the voltage across the output diode bridge needs to be clamped to avoid any adverse voltage overshoots arising during turn-OFF of the output diodes as commonly found in regular full bridge converters. In order to achieve such stringent requirements and high reliability, the converter employs a symmetric passive near lossless auxiliary circuit to provide the reactive current for the full-bridge semiconductor switches, which guarantees zero voltage switching at turn-ON times for all load conditions. Moreover the proposed topology is based on a current driven rectifier in order to clamp the voltage of the output diode bridge and also satisfy ZVZCS operation of the converter resulting in superior efficiency for all load conditions. In this paper operation of the converter is presented in detail followed by analytical design procedure. Experimental results provided from a 3KW prototype validate the feasibility and superior performance of the proposed converter.
A particle filter for 2D indoor localization relying on magnetic disturbances and magnetic-inertial measurement units

Distortions of the Earth's magnetic field are critical when a magnetic sensor is used for aiding the orientation estimation, especially indoors. However, based on suitable maps of the magnetic field, the spatial patterns of indoor magnetic fields may aid the position estimation. In this paper, we propose a method based on a Particle Filter (PF) for two-dimensional (2D) localization of a human walker wearing a Magnetic/Inertial Measurement Unit (MIMU) at the waist. Magnetic disturbances in the global reference frame and pelvis rotation are estimated using a Kalman-based orientation estimator. The pelvis rotation and the output from an accelerometer-based step detector drive the propagation of the particles; the estimated magnetic disturbances are looked up in the magnetic map for weight update. The proposed PF-based 2D localization method was tested using simulated and real MIMU data collected from a healthy subject walking for three minutes along eight-shaped paths within a 3×2 m area. Using reference data from an optical motion capture system, the 2D positioning accuracy in terms of the Root Mean Square Error (RMSE) was about 0.40 m.
Weight Optimization for Bimodal Unit-Selection Talking Head Synthesis

This paper addresses talking head synthesis based on the concatenation of units comprising of both acoustic and visual information. Selection of appropriate diphone units to synthesize ag iven text string is based on the minimization of aw eighted linear combination of four costs that reflect linguistic, acoustic, and visual considerations. We present initial work toward a method to determine automatically the weights applied to each cost, using a series of metrics that assess quantitatively the performance of synthesis. Index Terms :t alking head, audiovisual speech synthesis, selection, optimization
Superiority of Classification Tree versus Cluster, Fuzzy and Discriminant Models in a Heartbeat Classification System

This study presents a 2-stage heartbeat classifier of supraventricular (SVB) and ventricular (VB) beats. Stage 1 makes computationally-efficient classification of SVB-beats, using simple correlation threshold criterion for finding close match with a predominant normal (reference) beat template. The non-matched beats are next subjected to measurement of 20 basic features, tracking the beat and reference template morphology and RR-variability for subsequent refined classification in SVB or VB-class by Stage 2. Four linear classifiers are compared: cluster, fuzzy, linear discriminant analysis (LDA) and classification tree (CT), all subjected to iterative training for selection of the optimal feature space among extended 210-sized set, embodying interactive second-order effects between 20 independent features. The optimization process minimizes at equal weight the false positives in SVB-class and false negatives in VB-class. The training with European ST-T, AHA, MIT-BIH Supraventricular Arrhythmia databases found the best performance settings of all classification models: Cluster (30 features), Fuzzy (72 features), LDA (142 coefficients), CT (221 decision nodes) with top-3 best scored features: normalized current RR-interval, higher/lower frequency content ratio, beat-to-template correlation. Unbiased test-validation with MIT-BIH Arrhythmia database rates the classifiers in descending order of their specificity for SVB-class: CT (99.9%), LDA (99.6%), Cluster (99.5%), Fuzzy (99.4%); sensitivity for ventricular ectopic beats as part from VB-class (commonly reported in published beat-classification studies): CT (96.7%), Fuzzy (94.4%), LDA (94.2%), Cluster (92.4%); positive predictivity: CT (99.2%), Cluster (93.6%), LDA (93.0%), Fuzzy (92.4%). CT has superior accuracy by 0.3–6.8% points, with the advantage for easy model complexity configuration by pruning the tree consisted of easy interpretable ‘if-then’ rules.
Antagonistic mono- and bi-articular pneumatic muscle actuator control for gait training system using contraction model

In recent years, the use of the pneumatic muscle actuator (PMA) to acquire greater power from the actuation system especially for the development of medical rehabilitation robotic for gait training system has increased. Usually, the biarticular actuators are treated as a redundancy in actuation since the number of actuators is greater than the number of joints. However, these actuators are able to generate a strong force due to wider range of motion compared to the mono-articular actuators and it is thought to generate instantaneous force. In the case of lower orthotic gait training system, the implementation of antagonistic bi-articular actuators along with mono-articular actuators plays a major role to achieve the required afferent input for the lower limb and hip joint as well as smooth and precise movements at the endpoint. One of the important characteristics of PMA is based on its muscle contraction. In this study, we modelled mathematical equations to determine the muscle contraction pattern for the antagonistic mono- and biarticular PMAs as a function of the hip and knee angles in which its magnitude is influenced by the anterior and posterior muscle activation levels. From this model, we are able to determine the input pressure for each of the antagonistic mono- and bi-articular PMAs and then control the system using a feedback controller.
Nitrogen removal by wetlands in a cold climate

Nitrogen (N), a fundamental component of living organisms, has become one of the main global concerns for human society due to the myriad of negative effects of excessive N on ecosystems. Anthropogenic activities such as agriculture, industrial production, urbanisation and mining are major sources of N to freshwaters. Semi-natural and constructed wetlands planted with macrophytes are now widely used in many parts of the world to remove N from water. However, the potential of constructed wetlands for N removal under cold climatic conditions is still not well studied. We also have limited understanding of how macrophyte species and growth form richness as well as functional trait diversity affect N-cycling in constructed wetlands by influencing plant N accumulation, plant associated denitrification and abundance of denitrifying bacterial communities. In mesocosm experiments and in situ studies, I investigated how species and growth form (emerging and submerged macrophytes and bryophytes) richness as well as plant functional trait diversity of macrophytes affect N-cycling in wetlands. Moreover, I tested the applicability of constructed floating wetlands for improved N removal at the local scale in a cold climate. My results highlight that macrophytes are important for both main N removal pathways, viz. uptake and denitrification. Moreover, bacterial denitrification gene abundance on roots and shoots of macrophytes were an important predictor of the denitrification potential of macrophytes. Species and growth form richness of macrophytes enhanced N removal in wetlands. Moreover, I identified complementarity and a selection effect as important diversity related mechanisms, explaining the total N removal from water including plant N accumulation. Functional traits of macrophytes affected N-cycling in wetlands through direct and indirect pathways. The application of constructed floating wetlands at the local scale is feasible in a cold climate with denitrification as the main N removal pathway in these wetland type. Further in situ studies with high numbers of species and growth forms are needed to generalize my findings. Future studies should also consider plant secondary metabolites to better understand the function of the macrophyte-denitrifier interplay.
Identification of G1 kinase activity for cdk6, a novel cyclin D partner

A family of vertebrate cdc2-related kinases has been identified, and these kinases are candidates for roles in cell cycle regulation. Here, we show that the human PLSTIRE gene product is a novel cyclin-dependent kinase, cdk6. The cdk6 kinase is associated with cyclins D1, D2, and D3 in lysates of human cells and is activated by coexpression with D-type cyclins in Sf9 insect cells. Furthermore, we demonstrate that endogenous cdk6 from human cell extracts is an active kinase which can phosphorylate pRB, the product of the retinoblastoma tumor suppressor gene. The activation of cdk6 kinase occurs during mid-G1 in phytohemagglutinin-stimulated T cells, well prior to the activation of cdk2 kinase. This timing suggests that cdk6, and by analogy its homolog cdk4, links growth factor stimulation with the onset of cell cycle progression.
Backwards State-space Reduction for Planning in Dynamic Knowledge Bases

In this paper we address the problem of planning in rich domains, where knowledge representation is a key aspect for managing the complexity and size of the planning domain. We follow the approach of Description Logic (DL) based Dynamic Knowledge Bases, where a state of the world is represented concisely by a (possibly changing) ABox and a (fixed) TBox containing the axioms, and actions that allow to change the content of the ABox. The plan goal is given in terms of satisfaction of a DL query. In this paper we start from a traditional forward planning algorithm and we propose a much more efficient variant by combining backward and forward search. In particular, we propose a Backward State-space Reduction technique that consists in two phases: first, an Abstract Planning Graph P is created by using the Abstract Backward Planning Algorithm (ABP), then the abstract planning graph P is instantiated into a corresponding planning graph P by using the Forward Plan Instantiation Algorithm (FPI). The advantage is that in the preliminary ABP phase we produce a symbolic plan that is a pattern to direct the search of the concrete plan. This can be seen as a kind of informed search where the preliminary backward phase is useful to discover properties of the state-space that can be used to direct the subsequent forward phase. We evaluate the effectiveness of our ABP+FPI algorithm in the reduction of the explored planning domain by comparing it to a standard forward planning algorithm and applying both of them to a concrete business case study.
Bayesian estimation of differential transcript usage from RNA-seq data

Abstract Next generation sequencing allows the identification of genes consisting of differentially expressed transcripts, a term which usually refers to changes in the overall expression level. A specific type of differential expression is differential transcript usage (DTU) and targets changes in the relative within gene expression of a transcript. The contribution of this paper is to: (a) extend the use of cjBitSeq to the DTU context, a previously introduced Bayesian model which is originally designed for identifying changes in overall expression levels and (b) propose a Bayesian version of DRIMSeq, a frequentist model for inferring DTU. cjBitSeq is a read based model and performs fully Bayesian inference by MCMC sampling on the space of latent state of each transcript per gene. BayesDRIMSeq is a count based model and estimates the Bayes Factor of a DTU model against a null model using Laplace’s approximation. The proposed models are benchmarked against the existing ones using a recent independent simulation study as well as a real RNA-seq dataset. Our results suggest that the Bayesian methods exhibit similar performance with DRIMSeq in terms of precision/recall but offer better calibration of False Discovery Rate.
Substrate Noise Reduction in CMOS Transistors : a methodology definition

Substrate coupling can corrupt low level analog signals and impair the performance of monolithic integrated circuits. Therefore, a characterization methodology able to evaluate the efficiency of the countermeasures against crosstalk, appears very important for designers. In this paper, after the verification that PMOS transistors are more sensitive to substrate voltage fluctuations than NMOS ones, a new method is indicated for the noise reduction Key-Words: substrate crosstalk, coupling, sensors, small signal model
Modeling the Impact of Device and Pipeline Scaling on the Soft Error Rate of Processor Elements

This paper examines the effect of technology scaling and microarchitectural trends on the rate of soft errors in CMOS memory and logic circuits. We describe and validate an end-to-end model that enables us to compute the soft error rates (SER) for existing and future microprocessor-style designs. The model captures the effects of two important masking phenomena, electrical masking and latching-window masking, which inhibit soft errors in combinational logic. We quantify the SER due to high-energy neutrons in SRAM cells, latches, and logic circuits for feature sizes from 600nm to 50nm and clock periods from 16 to 6 fan-out-of-4 inverter delays. Our model predicts that the SER per chip of logic circuits will increase nine orders of magnitude from 1992 to 2011 and at that point will be comparable to the SER per chip of unprotected memory elements. Our result emphasizes that computer system designers must address the risks of soft errors in logic circuits for future designs.