Miracle-dz/UniFAR-FTUs huggingface dataset:
Regulators of Lysosome Function and Dynamics in Caenorhabditis elegans

Lysosomes, the major membrane-bound degradative organelles, have a multitude of functions in eukaryotic cells. Lysosomes are the terminal compartments in the endocytic pathway, though they display highly dynamic behaviors, fusing with each other and with late endosomes in the endocytic pathway, and with the plasma membrane during regulated exocytosis and for wound repair. After fusing with late endosomes, lysosomes are reformed from the resulting hybrid organelles through a process that involves budding of a nascent lysosome, extension of the nascent lysosome from the hybrid organelle, while remaining connected by a membrane bridge, and scission of the membrane bridge to release the newly formed lysosome. The newly formed lysosomes undergo cycles of homotypic fusion and fission reactions to form mature lysosomes. In this study, we used a forward genetic screen in Caenorhabditis elegans to identify six regulators of lysosome biology. We show that these proteins function in different steps of lysosome biology, regulating lysosome formation, lysosome fusion, and lysosome degradation.
Risk Taking in Hierarchical Markets: A Theory and Test on Acquisition Behavior

In order to prevent corrosion of economizers and the like structures carried in the output section of steam generators burning sulphur-containing fuel, the feed water circuit for the steam generator includes the usual heat exchangers followed by a deaerator feeding the economizers and the temperature of the water supplied by the deaerator to the economizers is raised on an adjustable extent above corrosion temperature by steam extracted from different points of the steam turbine. Gates controlled by the pressure in the deaerator control the flow of steam out of these different points to ensure the desired pressure and temperature in the deaerator.
Two-way saliency for airport detection in remote sensing images

State-of-the-art methods for airport detection in panchromatic remote sensing images utilize very limit geometrical features of airport line segments. This paper proposes a novel method based on both bottom-up and top-down saliency. Noticed that airport runways have features of vicinity and parallelity, and their lengths are among certain range, the concept of near parallelity is introduced after using a line segments detector (LSD) and a connection process. It is treated as a priori knowledge which can fully exploit geometrical relationship of airport runways to get top-down saliency. Meanwhile, an improved graph-based visual saliency (GBVS) model is used to extract bottom-up saliency. Candidate regions can be gotten by combining those two-way results. After that, scale-invariant features transform (SIFT) and support vector machine (SVM) are used to finally determine whether the regions contain airports or not. The proposed method is tested on an image dataset composed of different kinds of airports. The experimental results show that the method has advantages in terms of speed, recognition rate and false alarm rate. Also, the method is more robust to complex background.
Energy Taxes and Natural Gas Demand in Eu-Countries

Producers or consumers faced with an increase in taxes are usually able to shift parts of it to other levels in the value chain. We examine who is actually bearing the burden of increased energy taxes in the EU-area - consumers or exporters. Traditional tax incidence theory presumes spot markets. Natural gas in the EU-area, however, is to a large extent regulated by incomplete long-term contracts. Still, spot market forces could be indicative for tax shifting, by determining the ex post bargaining power in contract renegotiations. By examining tax shifting in actual gas sales contracts we test whether this is the case. To calculate tax incidence we derive demand elasticities, income elasticities and cross price elasticities for natural gas, oil and electricity, for different market segments (households, industry, power generators) in EU countries. Particular focus is on tax incidence in gas markets regulated by incomplete long-term contracts. Based on our findings we discuss normative energy tax issues related to revenue, environmental obligations and security of supply.
Paired Spectral Content Measure for Gene and Exon Prediction in Eukaryotes

In this paper, we report on a new discrete Fourier transform (DFT) based method for gene and exon prediction in eukaryotes. The proposed paired spectral content (PSC) measure is compared with the well-known DFT based spectral content and spectral rotation measures, using the GENSCAN test set. False positive vs. sensitivity, receiver operating characteristic (ROC) curve and exonic nucleotides detected as false positive results all show that the newly proposed PSC outperforms existing methods, with 50% less DFT processing.
E CONOMIC L OAD D ISPATCH USING S IMPLE AND R EFINED G ENETIC A LGORITHM

In present era it is important to economize generation cost by satisfying operational constraints. Economic load dispatch is important tool to solve this problem. This paper presents the simple genetic algorithm (SGA) and refined genetic algorithm (RGA) method applied to economic dispatch problem which accounts for minimization of cost along with operational constraints. As lambda iteration method requires exact adjustment of lambda and it does not give global optimum solution. Results proved that GA based technique give global optimum solution and by varying probabilities of crossover and mutation computer usage time can be drastically reduced in RGA. Elitism is a technique which save early solution by ensuring the survival of highest fittest string. So it improves performance capability of Genetic Algorithm
Multimodality imaging in heart valve disease

In patients with heart valve disease, echocardiography is the mainstay for diagnosis, assessment and serial surveillance. However, other modalities, notably cardiac MRI and CT, are used if echocardiographic imaging is suboptimal but can also give complementary information to improve assessment of the valve lesion and cardiac compensation to aid the timing of surgery and determine risk. This statement discusses the way these imaging techniques are currently integrated to improve care beyond what is possible with echocardiography alone.
Human-in-the-Loop Model Predictive Control of an Irrigation Canal [Applications of Control]

Until now, advanced model-based control techniques have been predominantly employed to control problems that are relatively straightforward to model. Many systems with complex dynamics or containing sophisticated sensing and actuation elements can be controlled if the corresponding mathematical models are available, even if there is uncertainty in this information. Consequently, the application of model-based control strategies has flourished in numerous areas, including industrial applications [1]-[3].
MULTI MODAL BIOMETRICS USING PALMPRINT AND PALMVEIN

Personal identification technology is applicable to various systems including area-access control, PC login and e-commerce. Biometrics is a statistical measurement of human physiological/behavioral traits. Biometric techniques for personal identification attracted attention as conventional means like keys, passwords or PIN numbers face problems regarding theft, loss, and reliance on user’s memory. A multimodal biometric system using palmvein and palmprint is proposed by this work. Wavelet based texture features extract features from palmprint while autoregressive model based texture features are extracted for palmvein. Obtained features are normalized using z score normalization and are fused using concatenation. Feature selection is achieved by Correlation based Feature Selection (CFS) and classification by using K NN and Naive Bayes for 50, 75 and 100 features.
Crossmodal Modulation of Spatial Localization by Mimetic Words

The present study investigated whether aurally presented mimetic words affect the judgment of the final position of a moving object. In Experiment 1, horizontal apparent motion of a visual target was presented, and an auditory mimetic word of “byun” (representing rapid forward motion), “pitari” (representing stop of motion), or “nisahi” (nonsense syllable) was presented via headphones. Observers were asked to judge which of two test stimuli was horizontally aligned with the target. The results showed that forward displacement in the “pitari” condition was significantly smaller than in the “byun” and “nisahi” conditions. However, when non-mimetic but meaningful words were presented (Experiment 2), this effect did not occur. Our findings suggest that the mimetic words, especially that meaning stop of motion, affect spatial localization by means of mental imagery regarding “stop” established by the phonological information of the word.
Multivariate mathematical morphology applied to colour image analysis

Mathematical morphology (MM) theory, founded by G. Matheron [MAT 75] and J. Serra [SER 82], is a powerful image analysis framework, currently fully developed for both binary and gray-level images. Its popularity in the image processing community is mainly due to its rigorous mathematical foundation as well as its inherent ability to exploit the spatial relationships of pixels. The morphological framework provides a rich set of tools able to perform from the simplest to the most demanding tasks: noise reduction, edge detection, segmentation, texture and shape analysis, etc. As a methodology, it has been applied to almost all application areas dealing with digital image processing [SOI 03]. Consequently, it was only a matter of time before attempting to extend the same concepts to color and more generally to multivalued images, i.e. images with more than one channel. This extension is not straightforward, however. Specifically, the morphological framework is based on complete lattices [HEI 94]. In order to accommodate multi-valued images, a way of calculating the extrema of vector data is essential. However, unlike scalars, there is no unambiguous way of ordering vectors. Even if most of the known vector ordering schemes have already been employed for defining multivariate morphological operators, none of them has yet been widely accepted.
A new procedure for the optimized design of CMFBs based on the frequency-response masking technique

The frequency-response masking (FRM) method allows the design of selective prototype filters for cosine-modulated filter banks (CMFB) with a reduced number of distinct coefficients. Such a methodology may result in filter banks with a large number of bands (e.g. 1024 or more) and a simplified optimization procedure, as there are less parameters to adjust. This work introduces a numerically efficient optimization procedure, based on a quasi-Newton algorithm, for designing selective FRM-based CMFB. The proposed method uses a perfect-reconstruction FRM prototype filter as a starting point and updates the number of bands of the filter bank during the optimization procedure. Examples provided indicate that figures-of-merit, such as intersymbol and intercarrier interference, for the optimized FRM-CMFB structure are significantly improved without increasing the complexity of the resulting structure.
Effects of daily hemodialysis on heart rate variability: results from the Frequent Hemodialysis Network (FHN) Daily Trial.

BACKGROUND End-stage renal disease is associated with reduced heart rate variability (HRV), components of which generally are associated with advanced age, diabetes mellitus and left ventricular hypertrophy. We hypothesized that daily in-center hemodialysis (HD) would increase HRV. METHODS The Frequent Hemodialysis Network (FHN) Daily Trial randomized 245 patients to receive 12 months of six versus three times per week in-center HD. Two hundred and seven patients had baseline Holter recordings. HRV measures were calculated from 24-h Holter electrocardiograms at both baseline and 12 months in 131 patients and included low-frequency power (LF, a measure of sympathetic modulation), high-frequency power (HF, a measure of parasympathetic modulation) and standard deviation (SD) of the R-R interval (SDNN, a measure of beat-to-beat variation). RESULTS Baseline to Month 12 change in LF was augmented by 50% [95% confidence interval (95% CI) 6.1-112%, P =0.022] and LF + HF was augmented by 40% (95% CI 3.3-88.4%, P = 0.03) in patients assigned to daily hemodialysis (DHD) compared with conventional HD. Changes in HF and SDNN were similar between the randomized groups. The effects of DHD on LF were attenuated by advanced age and diabetes mellitus (predefined subgroups). Changes in HF (r = -0.20, P = 0.02) and SDNN (r = -0.18, P = 0.04) were inversely associated with changes in left ventricular mass (LVM). CONCLUSIONS DHD increased the LF component of HRV. Reduction of LVM by DHD was associated with increased vagal modulation of heart rate (HF) and with increased beat-to-beat heart rate variation (SDNN), suggesting an important functional correlate to the structural effects of DHD on the heart in uremia.
["Epidemiology of mental disorders and access to mental health care. EZOP - Poland" - research methodology].

AIM Since the second half of the twentieth century, with the development of structured diagnostic tools, population based studies on epidemiology of mental disorders are carried out. A special role is played by World Mental Health Survey Initiative which brings together a group of countries from different continents in order to carry out research projects according to standard methodology using the Composite International Diagnostic Interview. Polish EZOP study, which was conducted in accordance with the guidelines of WMH joined that group. The project was implemented under the Norwegian Financial Mechanism and the Financial Mechanism of the European Economic Area. Its aim was to estimate the prevalence of mental disorders in the Polish population of adults, assess the distance to mental disorders and to obtain knowledge about the perception of psychiatric disorders and treatment. METHODS The research tool was the Polish version of CIDI-WHO ver.3. 0. , which was developed for the project. The study was performed in accordance with the guidelines of WMH (cognitive interviews, interviewers training, standardization of field procedures) using electronic version of CIDI questionnaire (CAPI) within the 2-stages procedure in randomly selected representative sample of the Polish population aged 18-64. The quality of the study was systematically controlled and reported by MB SMG/KRC, and completed data (10,081 interviews) were sent to the Department-Centre of Monitoring and Analyses of Population Health NIPH-NIH. After the initial analysis data were sent to the analytical center of WMH, which applied additional cleaning tools and added new variables representing psychiatric diagnoses in DSM-IV and ICD-10.
A Scalable and Modular Architecture for High-Performance Packet Classification

Packet classification is widely used as a core function for various applications in network infrastructure. With increasing demands in throughput, performing wire-speed packet classification has become challenging. Also the performance of today's packet classification solutions depends on the characteristics of rulesets. In this work, we propose a novel modular Bit-Vector (BV) based architecture to perform high-speed packet classification on Field Programmable Gate Array (FPGA). We introduce an algorithm named StrideBV and modularize the BV architecture to achieve better scalability than traditional BV methods. Further, we incorporate range search in our architecture to eliminate ruleset expansion caused by range-to-prefix conversion. The post place-and-route results of our implementation on a state-of-the-art FPGA show that the proposed architecture is able to operate at 100+ Gbps for minimum size packets while supporting large rulesets up to 28 K rules using only the on-chip memory resources. Our solution is ruleset-feature independent , i.e. the above performance can be guaranteed for any ruleset regardless the composition of the ruleset.
A Simplified Broadband Design Methodology for Linearized High-Efficiency Continuous Class-F Power Amplifiers

This paper describes the design approach employed for achieving approximated continuous Class-F power amplifier (PA) modes over wide bandwidths. The importance of the nonlinear device capacitance for wave-shaping the continuous Class-F voltage and current waveforms is highlighted, thus reducing the device sensitivity to second and third harmonic impedance terminations. By identifying the high-efficiency regions on the reactance plane for harmonic band placement, the design can be reduced to a fundamental matching problem. The distributed simplified real frequency technique synthesis algorithm can then be utilized to achieve wideband operation. Using a 10-W Cree GaN HEMT device, greater than 70% efficiency has been measured over a 51% bandwidth from 1.45 to 2.45 GHz, with output powers of 11-16.8 W. The nonlinear PA was then linearized using digital predistortion with 20-MHz long-term evolution and 40-MHz eight-carrier W-CDMA excitation signals, to attain adjacent channel power ratios below -53 and -49 dBc, respectively. To the best of the authors' knowledge, the measured results represent the best performance obtained from a broadband switch-mode PA, and the best linearized switch-mode performance using 20- and 40-MHz modulated signals.
Estimating Detection Probabilities from Sign of Collared Peccary

Abstract Determining presence or absence of collared peccaries (Pecari tajacu) from surveys of sign (tracks and feces) requires information on whether animals in sample units are detected. We estimated detection probabilities of collared peccary from sign surveys using occupancy models. Because it was unlikely that residence status of collared peccary in sampling units remained the same over a survey season, which is a primary assumption of occupancy models, we first determined the time interval for which to pool data. We then examined the influence of rainfall and peccary abundance on detection probabilities. We placed 90 sign stations (25-m-diam circular plots) throughout Chaparral Wildlife Management Area, south Texas, USA. We surveyed plots weekly for the presence or non-presence of collared peccary during 2 11-week sampling seasons in spring and fall 2003. We examined sign data weekly and we pooled the data in intervals from 2 weeks to 5 weeks. Estimates of detection probabilities increased from 1 week to 3 weeks of pooled data and leveled off thereafter. We needed a 3-week time interval to meet the assumption of unchanging residence status. Using sign data pooled in 3-week increments, detection probabilities were influenced by areas that differed in peccary abundance, but they were not influenced by rainfall. Estimates of detection probabilities ranged from 0.43 to 0.77 for 3-week time intervals. Sign surveys and occupancy modeling of data can be used to measure spatial patterns of collared peccary in south Texas as long as multiple 3-week time intervals are sampled.
Molecular Diversity and Population Structure of a Worldwide Collection of Cultivated Tetraploid Alfalfa (Medicago sativa subsp. sativa L.) Germplasm as Revealed by Microsatellite Markers

Information on genetic diversity and population structure of a tetraploid alfalfa collection might be valuable in effective use of the genetic resources. A set of 336 worldwide genotypes of tetraploid alfalfa (Medicago sativa subsp. sativa L.) was genotyped using 85 genome-wide distributed SSR markers to reveal the genetic diversity and population structure in the alfalfa. Genetic diversity analysis identified a total of 1056 alleles across 85 marker loci. The average expected heterozygosity and polymorphism information content values were 0.677 and 0.638, respectively, showing high levels of genetic diversity in the cultivated tetraploid alfalfa germplasm. Comparison of genetic characteristics across chromosomes indicated regions of chromosomes 2 and 3 had the highest genetic diversity. A higher genetic diversity was detected in alfalfa landraces than that of wild materials and cultivars. Two populations were identified by the model-based population structure, principal coordinate and neighbor-joining analyses, corresponding to China and other parts of the world. However, lack of strictly correlation between clustering and geographic origins suggested extensive germplasm exchanges of alfalfa germplasm across diverse geographic regions. The quantitative analysis of the genetic diversity and population structure in this study could be useful for genetic and genomic analysis and utilization of the genetic variation in alfalfa breeding.
Spatiotemporal Pattern Recognition and Nonlinear PCA for Global Horizontal Irradiance Forecasting

This letter presents a novel technique for the forecast of the ground horizontal irradiance (GHI) from satellite-based images. To enhance the forecast accuracy, spatial information in addition to temporal information has been considered. This produced an increase in the computational load of the forecast process. Dimensionality reduction techniques based on nonlinear principal component analysis (PCA) are used to project the original data set into low-dimension feature space. A multilayer feedforward neural network classifier is used to model the signal through a training operation involving past history of the considered spatiotemporal signal. Experiments have been carried out on two different data sets. Comparisons with classical forecasting techniques demonstrate that the introduction of the spatial information permits to obtain better short-term forecast measurements for all types of sky conditions. Moreover, further analysis demonstrates that, compared with linear PCA, the nonlinear PCA is more appropriate for dimensionality reduction of spatiotemporal GHI data set.
Modélisation explicite des objets et de l'environnement en combinant les approches topologique et métrique pour la localisation

Nous presentons dans cet article une nouvelle formulation de la modelisation et de la reconnaissance de lieu. Les milieux interieurs sont composes d'une quantite d'objets susceptibles d'etre deplaces. Nous souhaitons exploiter les multiples passages d'une camera dans un meme environnement pour modeliser d'une part la structure de la scene et d'autre part les objets le constituant. Nous proposons une association de methodes de SLAM metrique et topologique pour detecter et representer les objets, ainsi qu'un formalisme probabiliste pour la reconnaissance de lieu prenant en compte l'evolution des objets.
A Reinforcement Learning Approach to Emotion-based Automatic Playlist Generation

A novel trend emerged in music exploration is to organize and search songs according to their emotions. However, research on automatic playlist generation (APG) primarily focuses on metadata and audio similarity. Mainstream solutions view APG as a static problem. This paper argues that the APG problem is better modeled as a continuous optimization problem, and proposes an adaptive preference model for personalized APG based on emotions. The main idea is to collect a user’s behavior in music playing, e.g., rating, skipping and replaying, as immediate feedback in learning the user’s preferences for music emotion within a playlist. Reinforcement learning is adopted to learn the user’s current preferences, which are used to generate personalized playlists. Learning parameters are tuned by simulation of two hypothetical users. A two-month user study is conducted to evaluate the APG solutions. The results show that the proposed approach reduces the Miss Ratio by 10% in comparison with the baseline approach.