Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance validity on the Stroop Color and Word Test in a mixed forensic and patient sample

Abstract Objective: Assessment of performance validity is an essential component of neuropsychological evaluation. Performance validity indicators contained within cognitive ability measures are particularly advantageous, as they do not require additional test administration time or material, and allow for assessment of validity across multiple cognitive domains over the course of an evaluation. The present study examined the classification accuracy of the Stroop Color and Word Test (Stroop) Word, Color, and Color–Word trials in detecting invalid performance in a mixed forensic and patient sample. Method: Archival data from a mixed-diagnosis sample were analyzed, consisting of 558 (Mage=40.8; Meducation=15.3) individuals referred for neuropsychological evaluation in clinical or forensic contexts, who completed a test battery that included the Stroop and at least two performance validity tests (PVTs). Examinees were assigned to Valid or Invalid performance groups based on PVT outcomes. Results: Word and Color scores were found to have excellent classification accuracy, whereas Color–Word yielded acceptable classification accuracy. Sensitivity and specificity values are presented for a range of cutoff scores, as are positive and negative predictive values for setting-specific base rates of invalid performance. Conclusions: Performances on the Stroop Color and Word Test, particularly the Word and Color trials, can be useful in detecting invalid performance in a mixed patient and forensic sample. Clinical implications are discussed.
Self-Management of Type 2 Diabetes in Adult Haitian Immigrants: A Qualitative Study

Introduction: A large body of literature exists on self-management of type 2 diabetes (T2D) in a variety of populations. However, research is limited on how Haitian immigrants self-manage their T2D despite a prevalence of 6.9% in Haiti. The purpose of this study was to explore and describe the lived experience of adult Haitian immigrants managing T2D living in the United States. Methodology: Moustakas’s phenomenological approach guided this qualitative study. Adult Haitian immigrants diagnosed with T2D for at least 1 year were interviewed. Individual interviews were audio-recorded, transcribed verbatim, uploaded into NVivo, and analyzed using Moustakas’s existential data analysis process. Results: We interviewed 16 participants (mean age 56;12 females; an average of 11 years living in the United States; mean hemoglobin A1c 8.1%). Four themes emerged: self-reliance, spirituality, nostalgia for home, and a desire for positive patient–provider relationships. Cultural influences and health beliefs may affect individual self-management of T2D in this population. Conclusions: These results may assist clinicians in identifying factors that contribute to suboptimal self-management in Haitian immigrants and help patients reach glycemic control. Culturally competent assessment and interventions for Haitian immigrants with T2D may not be provided without considering these four themes.
Deep-Learning-Enabled Automatic Optical Inspection for Module-Level Defects in LCD

Liquid crystal display (LCD) defects detection on module level is increasingly important for flat-panel displays (FPD) industry to increase the production capacity via machine vision technology. However, it is an overwhelmingly challenging issue due to various difficulties. This article discloses a practical automatic optical inspection (AOI) system consisting of hardware structure and software algorithm to detect module-level defects. The AOI system is the core component to build a distributed integrated inspection system with the help of the Internet of Things (IoT). Starting from the analysis of the challenges encountered in module-level defects inspection, a delicate photograph scheme is proposed to reveal different kinds of defects. In order to robustly work on the module-level defects detection with complex situations, a novel framework based on YOLOV3 detection unit is proposed in this article, including the preprocessing module, detection module, defects definition module, and interferences elimination module. To the best of our knowledge, this is the first work that designs a practical AOI system for module-level defects detection. In order to demonstrate the effectiveness of the proposed method, extensive experiments have been conducted on the manufacturing lines. The evaluation of the detection performance of the AOI system in comparison with a manual scheme indicates that the proposed system is practical for module-level defects detection. Currently, the proposed system has been deployed in a real-world LCD manufacturing line from a major player in the world.
Cytotoxicity of biodegradable magnesium alloy WE43 to tumor cells in vitro: Bioresorbable implants with antitumor activity?

In this study, a degradable magnesium alloy WE43 (Mg-3.56%Y-2.20%Nd-0.47%Zr) was used as a research object. To refine its microstructure from the initial homogenized one, the alloy was subjected to severe plastic deformation (SPD) by equal channel angular pressing (ECAP). The data presented show that coincubation of tumor LNCaP and MDA-MB-231 cells with the WE43 alloy in the homogenized and the ECAP-processed states led to a decrease in their viability and proliferation. An increase in the concentration of Annexin V(+) cells during coincubation with samples in both microstructural states investigated was also observed. This is associated with the induction of apoptosis in the cell culture through contact with the samples. Concurrently, a significant drop in the concentration of Bcl-2(+) cells occurred. It was established that ECAP led to an enhancement of the cytotoxic activity of the alloy against tumor cells. This study demonstrated that alloy WE43 can be considered as a promising candidate for application in orthopedic implants in clinical oncology, where it could play a double role of a mechanically stable, yet bioresorbable, scaffold with local antitumor activity. © 2019 Wiley Periodicals, Inc. J Biomed Mater Res Part B, 2019.
A Study of Core Utilization and Residency in Heterogeneous Smart Phone Architectures

In recent years, the smart phone platform has seen a rise in the number of cores and the use of heterogeneous clusters as in the Qualcomm Snapdragon, Apple A10 and the Samsung Exynos processors. This paper attempts to understand characteristics of mobile workloads, with measurements on heterogeneous multicore phone platforms with big and little cores. It answers questions such as the following: (i) Do smart phones need multiple cores of different types (eg: big or little)? (ii) Is it energy-efficient to operate with more cores (with less time) or fewer cores even if it might take longer? (iii)What are the best frequencies to operate the cores considering energy efficiency? (iv) Do mobile applications need out-of-order speculative execution cores with complex branch prediction? (v) Is IPC a good performance indicator for early design tradeoff evaluation while working on mobile processor design? Using Geekbench and more than 3 dozen Android applications, and the Workload Automation tool from ARM, we measure core utilization, frequency residencies, and energy efficiency characteristics on two leading edge smart phones. Many characteristics of smartphone platforms are presented, and architectural implications of the observations as well as design considerations for future mobile processors are discussed. A key insight is that multiple big and complex cores are beneficial both from a performance as well as an energy point of view in certain scenarios. It is seen that 4 big cores are utilized during application launch and update phases of applications. Similarly, reboot using all 4 cores at maximum performance provides latency advantages. However, it consumes higher power and energy, and reboot with 2 cores was seen to be more energy efficient than reboot with 1 or 4 cores. Furthermore, inaccurate branch prediction is seen to result in up to 40% mis-speculated instructions in many applications, suggesting that it is important to improve the accuracy of branch predictors in mobile processors. While absolute IPCs are observed to be a poor predictor of benchmark scores, relative IPCs are useful for estimating the impact of microarchitectural changes on benchmark scores.
Brain Activity While Waiting to Steer a Car: An fMRI Study

In this paper, we report a novel approach to automobile development based on the understanding of the brain mechanism. We aim to realize fun to drive applying the neuroscientific findings of affective states. There are few studies about it, therefore using newly developed experimental unit, we are examining participants' brain activity affected by the load of steering operation task in the MRI scanner. We focused on the brain activity while waiting to steer, assuming that it reflects participants' affective state in preparation of the upcoming steering task. We observed activations in the anterior cingulate and insular cortices comprising the salience network, which is one of the intrinsic brain networks and involved in preparation for an upcoming task. Intriguingly, when waiting for the steering task having larger reaction force, the anterior cingulate cortex, known to be related to anxiety, was more active. Moreover, waiting for the condition in which the steering had to be operated with only the non-dominant arm, the amygdala, known to be associated with negative emotion, was significantly activated. Thus, our results newly show that the steering reaction force and the hand used for steering operation may influence brain activity while waiting for the upcoming steering task.
Study on the influencing factors of online learning effect based on decision tree and recursive feature elimination

With the popularity of online learning, more and more researchers have attached great importance to the relationship between learning effect and influence factors in the online courses. In literature works, Logistic Stepwise Regression algorithm is the most used method. But this method has limitation in run time especially when the dimension of data is large. Besides that, it can't rank the importance of factors. Aiming at the above shortcomings, this paper proposes a novel approach to analyze the influencing factors of online learning, which is based on the combination of decision tree and recursive feature elimination. Firstly, the feature sorting algorithm is based on decision tree to conduct the preliminary screening, which is to form a candidate feature set. Then, recursive feature elimination is used to rank the candidate features by their importance. At this stage, Logistic Regression (LR), Support Vector Machine (SVM) and Decision Tree (DT) models are used separately to obtain each collating sequence of importance. By averaging these collating sequences, the final importance ranking of candidate features is achieved. Finally, an experiment is carried out on the Open University Learning Analytics dataset, and the results show that learning behavior has an important impact on the learning effect. Positive learning behaviors can lead to better learning effect.
Wireless Computer Vision Using Commodity Radios

We introduce the design and implementation of BackCam, a low-power wireless camera sensor platform that supports continuous realtime vision applications, all using commodity radios. In the lowest power mode, our camera board consumes only 9.7mW and continuously transmits images for over one month on two AA batteries. We introduce a novel power management system that incorporates input from the camera itself to increase battery life up to 62%. Using images and system metadata as input, we designed a feedback system between the sensor and the gateway. This allows dynamic vision application requirements to be met while consuming as little power as possible. For example, our system can temporarily increase the resolution after an object of interest is detected, then reduce it again after it has disappeared. This increases the accuracy of simplistic facial recognition by at least 25% compared to operating constantly in the lowest power mode. We implement communications using a full-duplex WiFi backscatter radio, ensuring compatibility with commodity WiFi devices. We also designed an efficient data streaming and compression pipeline straight from the camera to the backscatter transmitter, allowing us to minimize latency and avoid expensive memory writes. We deployed BackCam in a real office environment, and as a proof-of-concept, implemented basic realtime face detection and recognition. CCS CONCEPTS • Networks → Sensor networks.
Utilization of four galactans by
 Bacteroides thetaiotaomicron
 A4 based on transcriptome

The interaction between gut microbiota and polysaccharide is being paid more and more attention. Galactan is a kind of polysaccharide mainly composed of galactose, and it has been shown to play a significant role in regulating gut microbiota. Bacteroides thetaiotaomicron is considered as the best degrader of polysaccharides. The purpose of this paper was to investigate the utilization of four different galactans, including agarose, carrageenan, arabinogalactan, and glucofucogalactan, by our own isolated B. thetaiotaomicron A4. Samples of cultures grown on either four galactans groups or control groups were collected. The change of OD 600 , pH, and short-chain fatty acids (SCFAs) duringfermentationwere determined, andgrowthcurve and transcriptome of B. thetaiotaomicron A4 were studied. B. thetaiotaomicron A4 could utilize the four galac-tansandgrewwellonthem,withcarrageenanbeingmostutilized,followedbyarabino- galactan,glucofucogalactan,andagarose.SCFAs(mainlyaceticacidandpropionicacid) producedalongwiththedecreasedpHduringfermentation.Alargenumberofgenesof B. thetaiotaomicron A4 were upregulated and functioned in different pathways during the degradation of the four galactans. The carbohydrate metabolism-related pathways of B. thetaiotaomicron A4 were enriched after feeding the four galactans, although the specificpathwaysweredifferentamongfourgalactansgroups.Thedifferentstructural characteristicsoffourgalactansrequiredthat B.thetaiotaomicron A4couldexcretecor-respondingenzymestodegradethem.Theseresultshelptounderstandtheinteraction between galactans and gut microbe.
Soliton metamorphosis dynamics in ultrafast fiber lasers

The transitions from the incoherent noise to the coherent soliton have been fully revealed in ultrafast lasers. However, the soliton transformation between different coherent states, termed as soliton metamorphosis, remains an attractive yet uncharted territory. Here, we reveal the ultrafast dynamics of the soliton metamorphosis via single-shot spectroscopy in a specially designed fiber laser capable of emitting fast-switchable dissipative solitons and stretched pulses. It is demonstrated that the soliton metamorphosis is a consecutive evolution process including the self-phase modulation stage, pulse split stage, and transient stretched pulse stage. Particularly, the long-period pulse breathing and the spectral period doubling appear in the forepart and middle part of the last stage. The metamorphosis dynamics and soliton properties are substantiated by numerical simulation based on a three-step model. This work not only unveils the transient evolution physics of the pulse in soliton metamorphosis, but also provides a simple and effective way to control operations of ultrafast lasers.
Mind games: A temporal sentiment analysis of the political messages of the Internet Research Agency on Facebook and Twitter

This study examines the temporal dynamics of emotional appeals in Russian campaign messages used in the 2016 election. Communications on two giant social media platforms, Facebook and Twitter, are analyzed to assess emotion in message content and targeting that may have contributed to influencing people. The current study conducts both computational and qualitative investigations of the Internet Research Agency’s (IRA) emotion-based strategies across three different dimensions of message propagation: the platforms themselves, partisan identity as targeted by the source, and social identity in politics, using African American identity as a case. We examine (1) the emotional flows along the campaign timeline, (2) emotion-based strategies of the Russian trolls that masked left- and right-leaning identities, and (3) emotion in messages projecting to or about African American identity and representation. Our findings show sentiment strategies that differ between Facebook and Twitter, with strong evidence of negative emotion targeting Black identity.
Distributed type‐2 fuzzy adaptive control for heterogeneous nonlinear multiagent systems

A novel type‐2 fuzzy adaptive consensus control is studied for a class of heterogeneous multiagent systems with identical or nonidentical dimensions. At first, the similar composite structure for a class of heterogeneous multiagent systems with identical or nonidentical dimensions is introduced. Some similar parameters among each agent are defined that can be obtained by calculating the proposed similar properties. Second, according to the graph theory and Lyapunov stability, a novel distributed type‐2 fuzzy adaptive control with similar parameters is constructed for the consensus of nonlinear heterogeneous multiagents with different dimensions, in which all signals can converge to small zero fields. Finally, two simulation examples are addressed for the considered systems to show the effectiveness of the control scheme, and some merits of the control approach are also demonstrated by comparison of similar control.
The DELLA Proteins Influence the Expression of Cytokinin Biosynthesis and Response Genes During Nodulation

The key event that initiates nodule organogenesis is the perception of bacterial signal molecules, the Nod factors, triggering a complex of responses in epidermal and cortical cells of the root. The Nod factor signaling pathway interacts with plant hormones, including cytokinins and gibberellins. Activation of cytokinin signaling through the homeodomain-containing transcription factors KNOX is essential for nodule formation. The main regulators of gibberellin signaling, the DELLA proteins are also involved in regulation of nodule formation. However, the interaction between the cytokinin and gibberellin signaling pathways is not fully understood. Here, we show in Pisum sativum L. that the DELLA proteins can activate the expression of KNOX and BELL transcription factors involved in regulation of cytokinin metabolic and response genes. Consistently, pea la cry-s (della1 della2) mutant showed reduced ability to upregulate expression of some cytokinin metabolic genes during nodulation. Our results suggest that DELLA proteins may regulate cytokinin metabolism upon nodulation.
The Role of Electrospun Fiber Scaffolds in Stem Cell Therapy for Skin Tissue Regeneration

Stem cell therapy has emerged as one of the topics in tissue engineering where undifferentiated and multipotent cells are strategically placed/ injected in tissue structure for cell regeneration. Over the years, stem cells have shown promising results in skin repairs for non-healing and/or chronic wounds. The addition of the stem cells around the wound site promotes signaling pathways for growth factors that regulate tissue reconstruction. However, injecting stem cells around the wound site has its drawbacks, including cell death due to lack of microenvironment cues. This particular issue is resolved when biomaterial scaffolds are involved in the cultivation and mechanical support of the stem cells. In this review, we describe the current models of stem cell therapy by injections and those that are done through cell cultures using electrospun fiber scaffolds. Electrospun fibers are considered as an ideal candidate for cell cultures due to their surface properties. Through the control of fiber morphology and fiber structure, cells are able to proliferate and differentiate into keratinocytes for skin tissue regeneration. Furthermore, we provide another perspective of using electrospun fibers and stem cells in a layer-by-layer structure for skin substitutes (dressing). Finally, electrospun fibers have the potential to incorporate bioactive agents to achieve controlled release properties, which is beneficial to the survival of the delivered stem cells or the recruitment of the cells. Overall, our work illustrates that electrospun fibers are ideal for stem cell cultures while serving as cell carriers for wound dressing materials.
The SPB-Box Transcription Factor AaSPL2 Positively Regulates Artemisinin Biosynthesis in Artemisia annua L.

Artemisinin, an important compound produced by Artemisia annua, is the active ingredient in the treatment of malaria. Jasmonic acid, one of the phytohormones, is an important elicitor of artemisinin biosynthesis by enhancing transcription levels of transcription factors. SPL transcription factors are plant-specific transcription factors of plant growth, development, and secondary metabolism regulation. However, to date, the SPL transcription factors that regulate artemisinin biosynthesis is currently unclear. Here, we show that an SPL transcription factor can positively regulate artemisinin biosynthesis by binding to the promoter of artemisinin biosynthetic pathway genes. We screened AaSPL2 by gene expression profiles analysis in 14 SPL transcription factors. We demonstrated that AaSPL2 can activate the promoter of DBR2 by dual-LUC assy. Moreover, in the AaSPL2 overexpression plants, the artemisinin content was increased by 33–86%, and in the AaSPL2 -RNAi transgenic plants, artemisinin content was decreased by 33–65%. These data suggest that AaSPL2 and DBR2 interact with a “GTAC” cis-element in the DBR2 promoter, mediating the transcriptional activation of DBR2 in response to JA and resulting in the improvement on artemisinin content.
A data management method for databases using hybrid storage systems

When applications require high I/O performance, solid-state drives (SSDs) are often preferable because they perform better than traditional hard-disk drives (HDDs). Therefore, database system response time can be improved by moving frequently used data to SSDs. However, because capacity is both more limited and more expensive for SSDs than it is for HDDs, there is a need to determine which data are most appropriate to migrate from HDDs to SSDs. In this paper, we propose a data management method for databases using SSDs by considering an integrated mechanism and a migration rule for performing migrations between HDDs and SSDs. The proposed method can provide database systems with high I/O performance by properly moving high-priority data to SSDs with fast access capabilities while maintaining the low-priority data on HDDs, which have lower costs. The results of experiments show that the proposed method can achieve the goal while requiring only small amounts of SSD capacity.
Enhanced Satellite Imaging Algorithm Classifier using Convolutional Neural modified Support Vector Machine (CNMSVM)

The development of Enhanced Satellite Imaging Algorithm is a continuous effort to achieve further advancement in the area of image processing and recognition through the use of artificial intelligence theories and applications. This study utilized a convolutional neural modified support vector machine aimed at satellite image map classification via new model. The dataset comes from Kaggle using satellite images of the Amazon Rainforest to train multilabel classifier. The result was evaluated using F2 score which achieved at least 0.91412 by training CNMSVM proposed model.