Miracle-dz/UniFAR-FTUs huggingface dataset:
Psychometric Evaluation of the Life Problems Inventory, a Measure of Borderline Personality Features in Adolescents

Objective: Dialectical Behavior Therapy (DBT), developed by Marsha Linehan for adults with suicidal behaviors and borderline personality disorder, has been successfully extended to adolescent populations. In addition, recent literature has discussed the wisdom of considering a diagnosis of borderline personality disorder (BPD) in teens under the age of 18. Linehan conceptualizes BPD as a disorder primarily of the emotion regulation system, and in particular, problems with regulation of emotions, impulses, relationships, and self. Thus, her skills training component of Dialectical Behavior Therapy (DBT) consists of modules targeting these problem areas: emotion regulation, distress tolerance, interpersonal effectiveness, and mindfulness. Despite the proliferation of research supporting the efficacy of DBT, no measures specifically address the four core areas targeted in DBT skills training in adolescents. The Life Problems Inventory was developed to assess the four core BPD problem areas as described by Linehan and as targeted in DBT skills training. Method: The Life Problems Inventory, a 60-item self-report instrument, was developed to assess Emotion Dysregulation, Impulsivity, Interpersonal Chaos, and Confusion about Self. The present study evaluates the psychometric properties of this instrument examining internal consistency, convergent validity, and criterion validity in an adolescent outpatient population (N = 195). Results: The LPI was found to be internally consistent, to demonstrate convergent validity to related constructs, and to distinguish diagnostic samples. Conclusions: Findings from this work 1) contribute to the self-report assessment of BPD features in adolescents according to Linehan’s conceptualization, and 2) demonstrate the psychometric properties of a clinical outcome measure for the skills training component of DBT.
Generating object hypotheses in natural scenes through human-robot interaction

We propose a method for interactive modeling of objects and object relations based on real-time segmentation of video sequences. In interaction with a human, the robot can perform multi-object segmentation through principled modeling of physical constraints. The key contribution is an efficient multi-labeling framework, that allows object modeling and disambiguation in natural scenes. Object modeling and labeling is done in a real-time segmentation system, to which hypotheses and constraints denoting relations between objects can be added incrementally. Through instructions such as key presses or spoken words, a scene can be segmented in regions corresponding to multiple physical objects. The approach solves some of the difficult problems related to disambiguation of objects merged due to their direct physical contact. Results show that even a limited set of simple interactions with a human operator can substantially improve segmentation results.
Analysis and definition of morphological descriptors for automatic detection of epileptiform events in EEG signals with artificial neural networks

This study proposes to analyze morphological characteristics of electroencephalogram (EEG) signals in order to define a representation of epileptiform events that can distinguish them from other events occurring in the signal. Despite the existence of several studies on parameterization of EEG signals, particularly for automatic detection of paroxysms related to epilepsy, it was necessary to create a new set of parameters that reveal specific morphological characteristics pertaining to these events, since during the automatic detection process they may get mixed up if only conventional descriptors are used. The proposed parameters are fed to artificial neural networks and the individual and collective contribution of each parameter was evaluated by statistical process. The proposed method achieved automatic detection with a 90% success rate and sensitivity and specificity between 90% and 95%.
A Histogram shifting-based blind reversible data hiding method with a histogram peak estimator

This paper proposes a simple but efficient method which is free from memorizing side information, for histogram shifting-based reversible data hiding (HS-RDH). To hide data into an image, a HS-RDH method modifies particular pixel values in the image, based on its tonal distribution, and the method not only extracts hidden data but also recovers the original image from the distorted image which conveys hidden data. Most of HS-RDH methods excepting a few methods should memorize a set of image-dependent side information. The proposed method is free from memorizing side information by introducing two mechanisms; one is estimation of a side information based on a simple statistic and the other is hiding a part of side information as well as data to the image. This approach makes the proposed method is superior to the conventional blind methods in terms of the quality of images which carry hidden data.
Correlation between ST Elevation and Q Waves on the Predischarge Electrocardiogram and the Extent and Location of MIBI Perfusion Defects in Anterior Myocardial Infarction

Background: The common electrocardiographic subclassification of anterior acute myocardial infarction (AMI) is not reliable in presenting the exact location of the infarct. We investigated the relationship between predischarge electrocardiographic patterns and the extent and location of perfusion defects in 55 patients with first anterior AMI.
Analysis of Radiation From a Line Source in a Grounded Dielectric Slab Covered by a Metal Strip Grating

In this paper, we present a study of the radiation from a line source located inside a grounded dielectric slab covered by a metal strip grating. The array scanning method was applied to enable an efficient ldquounit cellrdquo formulation. The numerical solution was obtained with the use of the spectral method of moments. Both TM and TE cases were solved. The effects of the geometrical parameters on the propagation of guided modes and their excitation are studied.
Regulation of dendrite growth and maintenance by exocytosis

ABSTRACT Dendrites lengthen by several orders of magnitude during neuronal development, but how membrane is allocated in dendrites to facilitate this growth remains unclear. Here, we report that Ras opposite (Rop), the Drosophila ortholog of the key exocytosis regulator Munc18-1 (also known as STXBP1), is an essential factor mediating dendrite growth. Neurons with depleted Rop function exhibit reduced terminal dendrite outgrowth followed by primary dendrite degeneration, suggestive of differential requirements for exocytosis in the growth and maintenance of different dendritic compartments. Rop promotes dendrite growth together with the exocyst, an octameric protein complex involved in tethering vesicles to the plasma membrane, with Rop–exocyst complexes and exocytosis predominating in primary dendrites over terminal dendrites. By contrast, membrane-associated proteins readily diffuse from primary dendrites into terminals, but not in the reverse direction, suggesting that diffusion, rather than targeted exocytosis, supplies membranous material for terminal dendritic growth, revealing key differences in the distribution of materials to these expanding dendritic compartments. Highlighted Article: This study characterizes the interaction between Drosophila Rop and the exocyst in control of dendritic morphogenesis, and demonstrates how membrane is allocated in dendrites to facilitate growth.
Breastfeeding and maternal cardiovascular risk factors and outcomes: A systematic review

Background There is growing evidence that breastfeeding has short- and long-term cardiovascular health benefits for mothers. The objectives of this systematic review were to examine the association between breastfeeding and maternal cardiovascular risk factors and outcomes that have not previously been synthesized systematically, including metabolic syndrome, hypertension and cardiovascular disease. Methods and findings This systematic review meets PRISMA guidelines. The MEDLINE, EMBASE and CINAHL databases were systematically searched for relevant publications of any study design from the earliest publication date to March 2016. The reference lists from selected articles were reviewed, and forward and backward referencing were conducted. The methodological quality of reviewed articles was appraised using validated checklists. Twenty-one studies meeting the inclusion criteria examined the association between self-reported breastfeeding and one or more of the following outcomes: metabolic syndrome/metabolic risk factors (n = 10), inflammatory markers/adipokines (n = 2), hypertension (n = 7), subclinical cardiovascular disease (n = 2), prevalence/incidence of cardiovascular disease (n = 3) and cardiovascular disease mortality (n = 2). Overall, 19 studies (10 cross-sectional/retrospective, 9 prospective) reported significant protective effects of breastfeeding, nine studies (3 cross-sectional/retrospective, 5 prospective, 1 cluster randomized controlled trial) reported non-significant findings and none reported detrimental effects of breastfeeding. In most studies reporting significant associations, breastfeeding remained associated with both short- and long-term maternal cardiovascular health risk factors/outcomes, even after covariate adjustment. Findings from several studies suggested that the effects of breastfeeding may diminish with age and a dose-response association between breastfeeding and several metabolic risk factors. However, further longitudinal studies, including studies that measure exclusive breastfeeding, are needed to confirm these findings. Conclusions The evidence from this review suggests that breastfeeding is associated with cardiovascular health benefits. However, results should be interpreted with caution as the evidence gathered for each individual outcome was limited by the small number of observational studies. Additional prospective studies are needed. PROSPERO registration number CRD42016047766.
An analytic model for full spectral sky-dome radiance

We present a physically-based analytical model of the daytime sky. Based on the results of a first-principles brute force simulation of radiative transfer in the atmosphere, we use the same general approach of fitting basis function coefficients to radiance data as the Perez and Preetham models do. However, we make several modifications to this process, which together significantly improve the rendition of sunsets and high atmospheric turbidity setups -- known weak points of the Preetham model. Additionally, our model accounts for ground albedo, and handles each spectral component independently. The latter property makes it easily extensible to the near ultraviolet range of the spectrum, so that the daylight appearance of surfaces that include optical brighteners can be properly predicted. Due to its similar mathematical properties, the new model can be used as a drop-in replacement of the Preetham model.
Magnetic Resonance Fingerprinting with Combined Gradient- and Spin-echo Echo-planar Imaging: Simultaneous Estimation of T1, T2 and T2* with integrated-B1 Correction

Purpose To introduce a novel magnetic-resonance fingerprinting (MRF) framework with single-shot echo-planar imaging (EPI) readout to simultaneously estimate tissue T2, T1 and T2*, and integrate B1 correction. Methods Spin-echo EPI is combined with gradient-echo EPI to achieve T2 estimation as well as T1 and T2* quantification. In the dictionary matching step, the GE-EPI data segment provides estimates of tissue T1 and T2* with additional B1 information, which are then incorporated into the T2-matching step that uses the SE-EPI data segment. In this way, biases in T2 and T2* estimates do not affect each other. Results An excellent correspondence was found between our T1, T2, and T2* estimates and results obtained from standard approaches in both phantom and human scans. In the phantom scan, a linear relationship with R2>0.96 was found for all parameter estimates. The maximum error in the T2 estimate was found to be below 6%. In the in-vivo scan, similar contrast was noted between MRF and standard approaches, and values found in a small region of interest (ROI) located in the grey matter (GM) were in line with previous measurements (T2MRF=88±7ms vs T2Ref=89±11ms, T1MRF=1153±154ms vs T1Ref=1122±52ms, T2*MRF=56±4ms vs T2*Ref=53±3ms). Conclusion Adding a spin echo data segment to EPI based MRF allows accurate and robust measurements of T2, T1 and T2* relaxation times. This MRF framework is easier to implement than spiral-based MRF. It doesn’t suffer from undersampling artifacts and seems to require a smaller dictionary size that can fasten the reconstruction process.
Relative Position Estimation of Detected Robots for Formation Control

In this paper, we address a relative position estimation problem for a line formation of multiple robots. The existing study assumed that the light of all distance sensors hits other robots, which does not always happen. Therefore, we propose a method for estimating the relative positions of the detected robots which is eﬀective even if the light of distance sensors does not always hit the robots. We consider using information on the motion of the robots obtained by wireless communication. To fuse the information from the distance sensors and wireless sensors, the Extended Kalman Filter is employed. Finally, we verify the eﬀectiveness of this method by a simulation.
Multi-channel Wind Noise Reduction Using the Corcos Model

Outdoor recordings of speech are often corrupted by wind noise, which is difficult to reduce due to its high non-stationarity. In this work, a multi-channel wind noise reduction method is presented, based on a joint estimation of the speech and wind noise power spectral densities. In contrast to existing methods that assume uncorrelated wind noise, the estimation phase is performed exploiting the spatial characteristics of wind noise measured by closely-spaced microphones. Here the characteristics are approximated by a fluid-dynamics model, termed the Corcos model. An additional contribution is the employment of a frequency dependent trade-off parameter in the reduction phase, which depends on the ratio of the difference-signal power to the sum-signal power of a sub-set of two microphones. In particular, the trade-off parameter of the parametric multi-channel Wiener filter is used to control the trade-off between noise reduction and speech distortion. The proposed method can be used also to reduce spatially uncorrelated wind noise. An evaluation in terms of speech quality and signal-to-noise ratio improvements is carried out to compare the proposed method to an existing multichannel wind noise reduction method, using recorded and simulated wind noise samples.
Highly sensitive and scalable AAO-based nano-fibre SERS substrate for sensing application

Well-ordered periodic nanostructures are excellent substrates for many surface-enhanced Raman spectroscopy (SERS) applications. Conventional fabrication approaches such as high precision electron beam lithography or focused ion beam produce high resolution nano-features with great reproducibility at the expense of low throughput. In this work, a highly sensitive and scalable AAO-nano-fibre (ANF) SERS substrate is demonstrated by optimising the second anodisation time of the standard two-step anodisation of aluminium and performing an additional wet etching step on the resulting AAO substrate. The optimised ANF substrate exhibits SERS sensitivity that surpasses the AAO nanoholes and the metal-film-on-nanoparticles substrates. A detection limit of 0.1 nM is achieved with a signal-to-noise ratio of 2.6–3 using a low excitation power of 0.1 mW. The ANF substrate exhibits an enhancement factor of 9.28 × 106 and a standard deviation of no more than 8%. The results indicate that the highly sensitive and scalable ANF substrate is a promising substrate for commercial SERS application.
Detecting dynamic association among twitter topics

Over the last few years, Twitter is increasingly becoming an important source of up-to-date topics about what is happening in the world. In this paper, we propose a dynamic topic association detection model to discover relations between Twitter topics, by which users can gain insights into richer information about topics of interest. The proposed model utilizes a time constrained method to extract event-based spatio-temporal topic association, and constructs a dynamic temporal map to represent the obtained result. Experimental results show the improvement of the proposed model compared to static spatio-temporal method and co-occurrence method.
Experience Report: A Safety Engineering Tool Supporting Error Model Creation and Visualization

In this paper, we present a novel software tool called AVL Safety Extensions which is part of a tool framework for model-based automotive safety engineering. The tool framework supports a tool dependent methodology (TDM) which covers the left-hand V-model phases of ISO 26262-3 and ISO 26262-4 and requires the use of the language SSML (System Safety Modeling Language). The AVL Safety Extensions support safety engineers applying the TDM by creating consistent and complete work products and by simplifying and automating workflow steps. We present the AVL Safety Extensions in the context of the tool framework, the language SSML and the TDM focusing on the AVL Safety Extensions' capabilities for error model creation and visualization supporting safety analysis techniques such as FTA (Fault Tree Analysis) and FMEA (Failure Modes and Effects Analysis). Moreover, we illustrate the applicability of the presented approach using an industrial case study of hybrid electric vehicle development.
Toward Convergence in Job Schedulers forParallel

The space of job schedulers for parallel supercomputers is rather fragmented, because diierent researchers tend to make diierent assumptions about the goals of the scheduler, the information that is available about the workload, and the operations that the scheduler may perform. We argue that by identifying these assumptions explicitly, it is possible to reach a level of convergence. For example, it is possible to unite most of the diierent assumptions into a common framework by associating a suitable cost function with the execution of each job. The cost function reeects knowledge about the job and the degree to which it ts the goals of the system. Given such cost functions, scheduling is done to maximize the system's proot.
Strain and temperature dependence of the insulating phases of VO2 near the metal-insulator transition

In addition to its metal-insulator transition (MIT), VO2 exhibits a rich phase behavior of insulating monoclinic (M1,M2) and triclinic (T) phases. By using micro-Raman spectroscopy and independent control of temperature and uniaxial strain in individual single-crystal microbeams, we map these insulating phases with their associated structural changes as represented by their respective phonon frequencies. The competition between these structural forms is dictated by the internal strain due to differing lattice constants, the experimentally applied external strain, and the temperature-dependent phase stability. We identify the nature of the triclinic phase as a continuously distorted variant of the M1 monoclinic phase, while a discontinuous transition into the M2 phase occurs from both the M1 and T phases. The results suggest that understanding the driving forces that determine the interplay between M1, M2, and T phases near the MIT could be critical for the identification of the underlying mechanism behind the MIT itself.
EFFECTS OF DIFFERENT TYPES OF BIOCHAR ON BASIC PROPERTIES AND BACTERIAL COMMUNITIES OF BLACK SOIL

In order to explore the effects of biomass carbon on the basic properties of black soil and bacterial diversity, in this study, the combination of indoor culture and high-throughput sequencing was used to analyze the nutrient changes and bacterial communities of black soil after adding biochar. The results showed that compared with CK, soil pH, organic matter and available potassium increased significantly after adding biochar (P < 0.05), and catalase, invertase, urease, and phosphatase increased substantially, while polyphenol oxidase decreased. A total of 17 phyla, 51 known genera were detected by high-throughput sequencing, among which Proteobacteria was the dominant phyla. The dominant genera including Sphingomonas, Mizugakiibacter, Bacillus, Marmoricola, Rhodanobacter, and Streptomyces. From the analysis of bacterial diversity, it can be concluded that the bacteria ace, chao1, Shannon and Simpson index of black soil increased after adding biochar, compared with CK in redundancy analysis (RDA), AN, NO3-N, NH4-N had a greater impact on microbial communities. The microbial functional bacteria AOA and cbbl were significantly correlated with pH, SOM, AN, NH4-N, and NO3-N (p < 0.05). Therefore, the addition of biochar can not only improve soil nutrients but also plays an important role in maintaining the diversity of soil bacterial communities.
IMPACTS OF LONG-TERM FERTILIZATION ON BACTERIAL COMMUNITY STRUCTURE, SOIL MICROBIAL BIOMASS, AND GRAIN YIELDS IN RED PADDY SOIL IN CHINA

Bacteria play essential roles in soil ecosystems. This study aims to investigate rice rhizosphere bacterial communities, soil nutrition, and grain yield affected by long-term (30 years) fertilization practices. Four fertilizations were applied to experimental red paddy soils: no fertilizer (NF), chemical fertilizer (CF), harvested residue returned to soil (HRR), and 60% organic manure plus chemical fertilizer (OCF). Bacterial communities were then assessed by means of denaturing gradient gel electrophoresis (DGGE) targeting 16S rRNA genes. Results showed that the bacterial community structure of OCF was similar to HRR, which was the most diverse and stable compared to the other treatments. Moreover, CF’s effects were similar to NF, which was markedly less diverse. Eight bacterial phyla were identified by sequence analysis of the DGGE bands from rhizosphere inhabitants in the red paddy soils. In addition, soil microbial biomass C and N were significantly higher in HRR and OCF than in CF and NF. Furthermore, double rice grain yield and dry matter in OCF plots were significantly highest (P < 0.05). Long-term treatment of organic matter containing fertilizers promoted rhizospheric bacterial diversity and soil quality. The combination of inorganic and organic fertilizers (OCF) improved grain yields more than solely inorganic (CF) or organic (HRR) fertilizers.
Nitrogen utilization of potato genotypes and expression analysis of genes controlling nitrogen assimilation

Introduction Significant differences in nitrogen use efficiency (NUE) were detected previously among potato cultivars. Exploration of the genetic background may facilitate the breeding of cultivars with highly effective nitrogen use. Methods Expression of NUE genes was analyzed at three different N-supply levels in five potato genotypes. Correlations of NUE gene expressions and agronomical parameters with such indices as the nitrogen uptake efficiency, nitrogen utilization efficiency, NUE, and harvest indices were analyzed. Results The correlations between expression level of the nitrate-reductase, nitrite-reductase, ammonium transporter, and asparagine synthase genes and different agronomically important parameters were detected. Discussion Our results contribute to more rational, genotype-dependent nitrogen use in potato production and have relevance in breeding of new cultivars with better nitrogen utilization, as well as in production of seed potato.