Miracle-dz/UniFAR-FTUs huggingface dataset:
The Garlic Allelochemical Diallyl Disulfide Affects Tomato Root Growth by Influencing Cell Division, Phytohormone Balance and Expansin Gene Expression

Diallyl disulfide (DADS) is a volatile organosulfur compound derived from garlic (Allium sativum L.), and it is known as an allelochemical responsible for the strong allelopathic potential of garlic. The anticancer properties of DADS have been studied in experimental animals and various types of cancer cells, but to date, little is known about its mode of action as an allelochemical at the cytological level. The current research presents further studies on the effects of DADS on tomato (Solanum lycopersicum L.) seed germination, root growth, mitotic index, and cell size in root meristem, as well as the phytohormone levels and expression profile of auxin biosynthesis genes (FZYs), auxin transport genes (SlPINs), and expansin genes (EXPs) in tomato root. The results showed a biphasic, dose-dependent effect on tomato seed germination and root growth under different DADS concentrations. Lower concentrations (0.01–0.62 mM) of DADS significantly promoted root growth, whereas higher levels (6.20–20.67 mM) showed inhibitory effects. Cytological observations showed that the cell length of root meristem was increased and that the mitotic activity of meristematic cells in seedling root tips was enhanced at lower concentrations of DADS. In contrast, DADS at higher concentrations inhibited root growth by affecting both the length and division activity of meristematic cells. However, the cell width of the root meristem was not affected. Additionally, DADS increased the IAA and ZR contents of seedling roots in a dose-dependent manner. The influence on IAA content may be mediated by the up-regulation of FZYs and PINs. Further investigation into the underlying mechanism revealed that the expression levels of tomato EXPs were significantly affected by DADS. The expression levels of EXPB2 and beta-expansin precursor were increased after 3 d, and those of EXP1, EXPB3 and EXLB1 were increased after 5 d of DADS treatment (0.41 mM). This result suggests that tomato root growth may be regulated by multiple expansin genes at different developmental stages. Therefore, we conclude that the effects of DADS on the root growth of tomato seedlings are likely caused by changes associated with cell division, phytohormones, and the expression levels of expansin genes.
Learning Analytics to Identify Students At-risk in MOOCs

MOOCs (Massive Open Online Courses) have revolutionized the way we learn. But a major challenge faced by MOOCs is the high dropout rates among students at various stages of the course. It is important to identify the students at risk in terms of those who are likely to drop out at some stage of the course and those who might not meet the required passing criteria. In this paper, a model is proposed to identify learners who are at risk in an online course using association analysis which is done with dataset of students who have taken the course previously. Identifying such learners who are at risk and providing them real time feedback would help the students to be more aware of their current performance and to improve upon it. Several experiments were conducted to test the performance of the proposed work and the results show that the proposed model identifies 90% of the learners who are at risk. Out of 4,74,977 students, 4,32,502 were identified as at risk students based on the experimental analysis.
Representation of interval timing by temporally scalable firing patterns in rat prefrontal cortex

Significance The ability to estimate time interval in the order of seconds is important for animal behaviors. However, how the brain estimates the passage of time remains mysterious. In the current study, we trained rats to estimate two different time intervals and recorded activities of single neurons from the medial prefrontal cortex (mPFC). We found that some PFC neurons showed activity changes during time estimation by the rat, with the same profile that was temporally scaled by a factor proportional to the estimated time intervals. Local cooling of mPFC slowed the time estimated by the rat. Thus, PFC neuronal activity contributes to time estimation, and temporal scaling of neuronal activity may be a circuit mechanism for estimating different time intervals. Perception of time interval on the order of seconds is an essential component of cognition, but the underlying neural mechanism remains largely unknown. In rats trained to estimate time intervals, we found that many neurons in the medial prefrontal cortex (PFC) exhibited sustained spiking activity with diverse temporal profiles of firing-rate modulation during the time-estimation period. Interestingly, in tasks involving different intervals, each neuron exhibited firing-rate modulation with the same profile that was temporally scaled by a factor linearly proportional to the instructed intervals. The behavioral variability across trials within each task also correlated with the intertrial variability of the temporal scaling factor. Local cooling of the medial PFC, which affects neural circuit dynamics, significantly delayed behavioral responses. Thus, PFC neuronal activity contributes to time perception, and temporally scalable firing-rate modulation may reflect a general mechanism for neural representation of interval timing.
Candidate Selection and Organ Allocation in Liver Transplantation

Optimal candidate selection and organ allocation should offer liver transplantation to those who are sufficiently sick to justify the procedure but not too sick to benefit from it, in an order determined by patients' projected survival benefit, matching organs of sufficiently good quality to the appropriate recipients. Significant steps have been made in recent years toward devising selection and allocation criteria based on more objective and evidence-based definitions of candidate disease severity, transplant futility, organ quality, and appropriate donor-recipient matching. However, much work remains to be done in the future.
Spoken Document Retrieval With Unsupervised Query Modeling Techniques

Ever-increasing amounts of publicly available multimedia associated with speech information have motivated spoken document retrieval (SDR) to be an active area of intensive research in the speech processing community. Much work has been dedicated to developing elaborate indexing and modeling techniques for representing spoken documents, but only little to improving query formulations for better representing the information needs of users. The latter is critical to the success of a SDR system. In view of this, we present in this paper a novel use of a relevance language modeling framework for SDR. It not only inherits the merits of several existing techniques but also provides a principled way to render the lexical and topical relationships between a query and a spoken document. We further explore various ways to glean both relevance and non-relevance cues from the spoken document collection so as to enhance query modeling in an unsupervised fashion. In addition, we also investigate representing the query and documents with different granularities of index features to work in conjunction with the various relevance and/or non-relevance cues. Empirical evaluations performed on the TDT (Topic Detection and Tracking) collections reveal that the methods derived from our modeling framework hold good promise for SDR and are very competitive with existing retrieval methods.
Modeling Organic Chemistry and Planning Organic Synthesis

Organic Synthesis is a computationally challenging practical problem concerned with constructing a target molecule from a set of initially available molecules via chemical reactions. This paper demonstrates how organic synthesis can be formulated as a planning problem in Artificial Intelligence, and how it can be explored using the state-of-the-art domain independent planners. To this end, we develop a methodology to represent chemical molecules and generic reactions in PDDL 2.2, a version of the standardized Planning Domain Definition Language popular in AI. In our model, derived predicates define common functional groups and chemical classes in chemistry, and actions correspond to generic chemical reactions. We develop a set of benchmark problems. Since PDDL is supported as an input language by many modern planners, our benchmark can be subsequently useful for empirical assessment of the performance of various state-of-the-art planners.
Joint Sensing Adaptation and Resource Allocation for Cognitive Radio with Imperfect Sensing

In cognitive radio networks, imperfect spectrum sensing causes harmful interferences or low utilization of unused bands, depending on an access policy of the secondary system. Since these losses have a trade-off relationship, it is hard to decide proper sensing and access policies for the licensed bands. To deal with this problem, we propose a joint algorithm for sensing adaptation and opportunistic resource allocation. The joint optimization problem minimizes the total expected cost of the losses and utilities which are likely generated for the secondary system. To protect the primary system and ensure the secondary system's utility, we employ an average interference threshold constraint and a QoS constraint for the secondary system. Two types of average secondary utilities are considered for the QoS constraint; one is averaged over only transmitted frames and the other is averaged over total frames. Based on energy detection, we derive feasible ranges of sensing threshold to satisfy the constraints. Simulation results show that the proposed scheme dynamically adapts sensing threshold depending on the network environment and minimizes the total cost of the secondary system. In addition, we investigate the performance of the proposed scheme with the two types of the secondary utility.
The dimensions of corporate entrepreneurship and the performance of established organization

Recently, the issues of corporate entrepreneurship have evoked interest not only from academics, but also from business practitioners and policy makers. This interest stems from the recognition of the advantage that can be gained from corporate entrepreneurship activities. This paper analyses the effect of corporate entrepreneurship (intrapreneurship) dimensions on the financial performance of intrapreneurship companies of established Malaysian state government-linked corporation namely, Jcorp Group, a Johor state government-linked corporation. In this paper, there are four dimensions of corporate entrepreneurship being studied; (1) pro-activeness, (2) risk-taking, (3) innovations and (4) self-renewal. In addition, this paper also explores the moderating effects of resource availability, supportive organizat ional structure, and rewards on the relationsh ip between corporate entrepreneurship dimensions and company performance. The findings of this study show that pro-activeness has a positive and significant impact on financial performance of the company, and resource availability, supportive organizational structure and rewards do moderate the relationship between pro-activeness and financial performance. In contrast, we also found that risk-taking does not have a direct effect on financial performance of the company. However, resource availability, supportive organizational structure and rewards are shown to moderate the relationship between risk-taking and financial performa nce. Meanwhile, for innovation and self-renewal, we found that both are negatively related to financial performance. Further analysis shows that although all moderating factors were positively related with these two corporate entrepreneurship dimensions, but they are not significant .
1 EX / P 209 First-Principles Model-based Closed-loop Control of the Current Profile Dynamic Evolution on DIII -

Abstract. Recent experiments performed at DIII-D represent the first successful application of firstprinciples-driven, model-based, closed-loop magnetic profile control in a tokamak. For ITER to be capable of operating in advanced tokamak operating regimes, characterized by a high fusion gain, good plasma confinement, magnetohydrodynamic stability, and a noninductively driven plasma current, for extended periods of time, several challenging plasma control problems still need to be solved. For instance, setting up a suitable toroidal current density profile, which will require active closed-loop profile control, is key for one possible advanced operating scenario characterized by noninductive sustainment of the plasma current and steady-state operation. The current profile evolution in tokamaks is related to the poloidal magnetic flux profile evolution, which is modeled in normalized cylindrical coordinates using a nonlinear partial di↵erential equation referred to as the magnetic di↵usion equation. For control design purposes, the magnetic di↵usion equation has been combined with empirical correlations obtained from physical observations and experimental data from DIII-D for the electron temperature, plasma resistivity, and noninductive current drive to develop a control-oriented model of the system valid for low confinement mode discharges. This first-principles-driven model was used to synthesize a combined feedforward + feedback control scheme to drive the current profile to a desired target profile. Static and dynamic plasma response models were integrated into the design of the feedback controllers, and as a result, the model-based controllers know in which direction to actuate the system to generate a desired response in the profile evolution thanks to the embedded physics. Therefore, the need for trial-and-error tuning of the control scheme is eliminated, which is desirable for application on ITER where discharges are at a premium. A general framework for real-time feedforward + feedback control of magnetic and kinetic plasma profiles was implemented in the DIII-D Plasma Control System, and experimental results are presented to demonstrate the ability of the first-principles-driven, model-based feedback controllers to control the current profile evolution. These results demonstrate the applicability of this control scheme to active, closed-loop current profile control in advanced operating scenarios on ITER.
Hypersensitivity in Borderline Personality Disorder during Mindreading

Background One of the core symptoms of borderline personality disorder (BPD) is the instability in interpersonal relationships. This might be related to existent differences in mindreading between BPD patients and healthy individuals. Methods We examined the behavioural and neurophysiological (fMRI) responses of BPD patients and healthy controls (HC) during performance of the ‘Reading the Mind in the Eyes’ test (RMET). Results Mental state discrimination was significantly better and faster for affective eye gazes in BPD patients than in HC. At the neurophysiological level, this was manifested in a stronger activation of the amygdala and greater activity of the medial frontal gyrus, the left temporal pole and the middle temporal gyrus during affective eye gazes. In contrast, HC subjects showed a greater activation in the insula and the superior temporal gyri. Conclusion These findings indicate that BPD patients are highly vigilant to social stimuli, maybe because they resonate intuitively with mental states of others.
Learning to teach generative meaning-making through multimodal inquiry

This classroom narrative describes the transformation of one beginning English/language arts (ELA) teacher's perspective and practice as the teacher enacted multimodal inquiry activities that were the focus of both traditional and action research projects. Drawing on field notes, transcripts of classroom discourse, and student-produced artefacts, the authors illustrate the ways in which the teacher's practice integrated the language of research with the language of practice in ELA as the teacher sought to incorporate multimodal inquiry activities into his daily curriculum. The process of collaboration that is described in this narrative represents one way in which beginning teachers and teacher educators might overcome what Mary Kennedy (1999) has called the "problem of enactment"—the challenge that many beginning teachers have translating into their own classroom practice pedagogical perspectives encountered in the research highlighted in teacher education programs. The article recounts how one teacher worked through the problem of enactment over two semesters of research and practice as he negotiated theoretical principles and practical dilemmas related to multimodal inquiry during a 12 th -grade literature course in the eastern United States. The paper closes with implications for the integration of research and practice in ELA.
Characterization of denaturation and renaturation of DNA for DNA hybridization

Objectives The present study was designed to systematically characterize the denaturation and the renaturation of double stranded DNA (dsDNA), which is suitable for DNA hybridization. Methods A series of physical and chemical denaturation methods were implemented on well-defined 86-bp dsDNA fragment. The degree of each denaturation was measured and the most suitable denaturation method was determined. DNA renaturation tendency was also investigated for the suggested denaturation method. Results Heating, beads mill, and sonication bath did not show any denaturation for 30 minutes. However probe sonication fully denatured DNA in 5 minutes. 1 mol/L sodium hydroxide (alkaline treatment) and 60% dimethyl sulfoxide (DMSO) treatment fully denatured DNA in 2-5 minutes. Conclusions Among all the physical methods applied, the direct probe sonication was the most effective way to denature the DNA fragments. Among chemical methods, 60% DMSO was the most adequate denaturation method since it does not cause full renaturation during DNA hybridization.
A Fast Algorithm for Learning Overcomplete Dictionary for Sparse Representation Based on Proximal Operators

We present a fast, efficient algorithm for learning an overcomplete dictionary for sparse representation of signals. The whole problem is considered as a minimization of the approximation error function with a coherence penalty for the dictionary atoms and with the sparsity regularization of the coefficient matrix. Because the problem is nonconvex and nonsmooth, this minimization problem cannot be solved efficiently by an ordinary optimization method. We propose a decomposition scheme and an alternating optimization that can turn the problem into a set of minimizations of piecewise quadratic and univariate subproblems, each of which is a single variable vector problem, of either one dictionary atom or one coefficient vector. Although the subproblems are still nonsmooth, remarkably they become much simpler so that we can find a closed-form solution by introducing a proximal operator. This leads to an efficient algorithm for sparse representation. To our knowledge, applying the proximal operator to the problem with an incoherence term and obtaining the optimal dictionary atoms in closed form with a proximal operator technique have not previously been studied. The main advantages of the proposed algorithm are that, as suggested by our analysis and simulation study, it has lower computational complexity and a higher convergence rate than state-of-the-art algorithms. In addition, for real applications, it shows good performance and significant reductions in computational time.
Job Strain and Tobacco Smoking: An Individual-Participant Data Meta-Analysis of 166 130 Adults in 15 European Studies

Background Tobacco smoking is a major contributor to the public health burden and healthcare costs worldwide, but the determinants of smoking behaviours are poorly understood. We conducted a large individual-participant meta-analysis to examine the extent to which work-related stress, operationalised as job strain, is associated with tobacco smoking in working adults. Methodology and Principal Findings We analysed cross-sectional data from 15 European studies comprising 166 130 participants. Longitudinal data from six studies were used. Job strain and smoking were self-reported. Smoking was harmonised into three categories never, ex- and current. We modelled the cross-sectional associations using logistic regression and the results pooled in random effects meta-analyses. Mixed effects logistic regression was used to examine longitudinal associations. Of the 166 130 participants, 17% reported job strain, 42% were never smokers, 33% ex-smokers and 25% current smokers. In the analyses of the cross-sectional data, current smokers had higher odds of job strain than never-smokers (age, sex and socioeconomic position-adjusted odds ratio: 1.11, 95% confidence interval: 1.03, 1.18). Current smokers with job strain smoked, on average, three cigarettes per week more than current smokers without job strain. In the analyses of longitudinal data (1 to 9 years of follow-up), there was no clear evidence for longitudinal associations between job strain and taking up or quitting smoking. Conclusions Our findings show that smokers are slightly more likely than non-smokers to report work-related stress. In addition, smokers who reported work stress smoked, on average, slightly more cigarettes than stress-free smokers.
Studying Those Who Study Us: Diana Forsythe and the Importance of Interpretive Research in Informatics

Diana Forsythe was a pioneering anthropologist who raised innovative and unsettling questions about the role of information technology in life and work, and differences in perspective between designers and users of technology. Diana was known for her tenacious defense of the integrity of ethnographic research, and for her enthusiastic examination of the “culture of no culture” 1,2 that is American science and biomedicine. We examine Diana’s ongoing influence and relevance from the varied perspectives of four distinguished informatics researchers.
Parallel Real-Time Tasks, as Viewed by WCET Analysis and Task Scheduling Approaches

With the advent of multi-core platforms, research in the field of hard real-time has recently considered parallel software, from the perspective of both worst-case execution time (WCET) and task schedulability (or worst-case response time, WCRT) analyses. These two areas consider task models that are not completely identical and sometimes make different assumptions. This paper draws a brief overview of the state of the art in the timing analysis of parallel tasks and tries to identify points of convergence and divergence between the existing approaches.
Bayesian and hybrid Cramer-Rao bounds for QAM dynamical phase estimation

In this paper, we study Bayesian and hybrid Cramer-Rao bounds for the dynamical phase estimation of QAM modulated signals. We present the analytical expressions for the various CRBs. This avoids the calculation of any matrix inversion and thus greatly reduces the computation complexity. Through simulations, we also illustrate the behaviors of the BCRB and of the HCRB with the signal-to-noise ratio.
Determination of Ultra-trace Metal Impurities in High-purity Cadmium Using Inductively Coupled Plasma Mass Spectrometry after Matrix Separation with Anion Exchange Resin

The analytical method for ultra-trace metal impurities at μg kg^−1 level in high-purity Cd was examined by inductively coupled plasma mass spectrometry (ICP-MS) combined with matrix separation by Bio-Rad AG MP-1M anion exchange resin. After the separation of Cd, the metal impurities such as Li, In, Cr, Mn, Fe, Co, Ni, Cu, Ga, Sr, Ba and Pb were measured by an ICP-quadrupole mass spectrometer (ICP-QMS) and ICP-sector field mass spectrometer (ICP-SFMS). From the comparison of measured results, it was evaluated that the analytical sensitivity by ICP-SFMS was 10 times higher than ICP-QMS. In addition, ICP-SFMS could obtain determined values of Li and Fe that could not be determined by ICP-QMS. These results suggest the ICP-SFMS combined with matrix separation by anion exchange resin could be utilized for the determination of ultra-trace metal impurities in high-purity materials for the assessment of the purity of the materials