Miracle-dz/UniFAR-FTUs huggingface dataset:
Combining transcranial magnetic stimulation and FMRI to examine the default mode network.

The default mode network is a group of brain regions that are active when an individual is not focused on the outside world and the brain is at "wakeful rest. " It is thought the default mode network corresponds to self-referential or "internal mentation". It has been hypothesized that, in humans, activity within the default mode network is correlated with certain pathologies (for instance, hyper-activation has been linked to schizophrenia and autism spectrum disorders whilst hypo-activation of the network has been linked to Alzheimer's and other neurodegenerative diseases. As such, noninvasive modulation of this network may represent a potential therapeutic intervention for a number of neurological and psychiatric pathologies linked to abnormal network activation. One possible tool to effect this modulation is Transcranial Magnetic Stimulation: a non-invasive neurostimulatory and neuromodulatory technique that can transiently or lastingly modulate cortical excitability (either increasing or decreasing it) via the application of localized magnetic field pulses. In order to explore the default mode network's propensity towards and tolerance of modulation, we will be combining TMS (to the left inferior parietal lobe) with functional magnetic resonance imaging (fMRI). Through this article, we will examine the protocol and considerations necessary to successfully combine these two neuroscientific tools.
Pain Relativity in Motor Control

Motivational theories of pain highlight its role in people’s choices of actions that avoid bodily damage. By contrast, little is known regarding how pain influences action implementation. To explore this less-understood area, we conducted a study in which participants had to rapidly point to a target area to win money while avoiding an overlapping penalty area that would cause pain in their contralateral hand. We found that pain intensity and target-penalty proximity repelled participants’ movement away from pain and that motor execution was influenced not by absolute pain magnitudes but by relative pain differences. Our results indicate that the magnitude and probability of pain have a precise role in guiding motor control and that representations of pain that guide action are, at least in part, relative rather than absolute. Additionally, our study shows that the implicit monetary valuation of pain, like many explicit valuations (e.g., patients’ use of rating scales in medical contexts), is unstable, a finding that has implications for pain treatment in clinical contexts.
Gonadal Hormones Affect Spine Synaptic Density in the CA1 Hippocampal Subfield of Male Rats

The effects of androgen on the density of spine synapses on pyramidal neurons in the CA1 area of the hippocampus were studied in male rats. Gonadectomy (GDNX) had no significant effect on the number of CA1 pyramidal cells but reduced CA1 spine synapse density by almost 50% (to 0.468 ± 0.018 spine synapses/μm3) compared with sham-operated controls (0.917 ± 0.06 spine synapses/μm3). Treatment of GDNX rats with testosterone propionate (500 μg/d, s.c., 2 d) increased spine synapse density to levels (1.01 ± 0.026 spine synapses/μm3) comparable with intact males. A similar increase in synapse density (1.013 ± 0.05 spine synapses/μm3) was observed in GDNX animals after treatment with dihydrotestosterone (DHT) (500 μg/d, s.c., 2 d) but not after estradiol (10 μg/d, s.c., 2 d; 0.455 ± 0.02 spine synapse/μm3). These data indicate that testosterone is important for maintenance of normal spine synapse density in the CA1 region of the male rat hippocampus. The comparable responses to testosterone and the non-aromatizable androgen DHT, coupled with the lack of response to estradiol, suggest that testosterone acts directly on hippocampal androgen receptors rather than indirectly via local estrogen biosynthesis.
Cognitive and behavioral rating measures of executive function as predictors of academic outcomes in children

ABSTRACT Interrelations of two measurement methods (cognitive versus behavioral ratings) for executive function (EF) were examined and related to reading comprehension and math calculations in fourth and fifth grade students (n = 93) in the context of a diverse urban student population. Relations among measures within four EF processes (working memory, planning, inhibition and shifting) were modest; relations to academics were stronger. EF measures contributed to both academic outcomes even in the context of relevant covariates (age, language and educational program). Working memory was particularly important for reading comprehension across measurement type. Cognitive measures from all EF processes, particularly inhibition and planning, and behavioral ratings of working memory were important for math.
A Novel Framework Based on Word-of-Mouth Mining for non-Prosumer Decision Support

The deeper penetration of business-to-consumer e-commerce requires that customer decision support systems (CDSS) serve a wider range of users. However, a significant weakness of existing e-shopping assistance programs is their inability to aid non-professional consumers (non-prosumers) in buying highly differentiated products. This paper proposes a novel framework that infers product recommendations with minimal information input. At the heart of the proposed framework is the featureusage map (FUM), a Bayesian network-based model that encodes the correlations among a product’s technical specifications and its suitability in terms of its using scenario (usage). It also incorporates a query-based lazy learning mechanism that elicits a product’s rating score from product reviews and constructs its corresponding FUM in an on-demand manner. This mechanism allows the knowledge base to be enriched incrementally, with no need for an exhaustive repository of FUMs pertaining to all possible usage queries a user may invoke. The effectiveness of the proposed framework is evaluated through an empirical user study. The results show that the framework is able to effectively derive product ratings based on specified usage. Moreover, this rating information can also be incorporated into a conventional buying guide system to deliver purchase decision support for non-prosumers.
Image demosaicking by nonlocal adaptive thresholding

Single sensor digital color cameras capture only one of the three primary colors at each pixel and full color image is formed by interpolating all other missing color samples at each pixel. This process is color demosaicking. Most demosaicking algorithms assume the existence of high local spectral redundancy in estimating the missing color samples. However, for images with sharp color transitions and high color saturation, such an assumption may be invalid and visually unpleasant demosaicking errors will occur. In this paper the image non-local redundancy is used to improve the local color reproduction result. For that First, multiple local directional estimates of a missing color sample are computed and fused according to local gradients. Then nonlocal pixels similar to the estimated pixel are searched to enhance the local estimate. For that an adaptive thresholding method rather than the commonly used nonlocal means filtering is implemented to improve the local estimate. Results demonstrate that the local directional interpolation and nonlocal adaptive thresholding (LDI-NAT) method outperforms than other CDM methods in reconstructing the edges and reducing color interpolation artifacts, leading to higher visual quality of reproduced color images.
Tonotopic Variation in the Calcium Dependence of Neurotransmitter Release and Vesicle Pool Replenishment at Mammalian Auditory Ribbon Synapses

The mammalian cochlea is specialized to recognize and process complex auditory signals with remarkable acuity and temporal precision over a wide frequency range. The quality of the information relayed to the auditory afferent fibers mainly depends on the transfer characteristics of inner hair cell (IHC) ribbon synapses. To investigate the biophysical properties of the synaptic machinery, we measured changes in membrane capacitance (ΔCm) in low-frequency (apical region, ∼300 Hz) and high-frequency (basal, ∼30 kHz) gerbil IHCs maintained in near physiological conditions (1.3 mm extracellular Ca2+ and body temperature). With maturation, the Ca2+ efficiency of exocytosis improved in both apical and basal IHCs and was more pronounced in the latter. Prehearing IHCs showed a similar Ca2+ cooperativity of exocytosis despite the smaller ΔCm in apical cells. After maturation, ΔCm in high-frequency IHCs increased linearly with the Ca2+ current, whereas, somewhat surprisingly, the relationship was significantly more nonlinear in low-frequency cells. This tonotopic difference seemed to be correlated with ribbon synapse morphology (spherical in apical and ellipsoid in basal IHCs) but not with the expression level of the proposed Ca2+ sensor otoferlin or the spatial coupling between Ca2+ channels and active zones. Repetitive stimulation of adult IHCs showed that vesicle pool refilling could become rate limiting for vesicle release, with high-frequency IHCs able to sustain greater release rates. Together, our findings provide the first evidence for a tonotopic difference in the properties of the synaptic machinery in mammalian IHCs, which could be essential for fine-tuning their receptor characteristics during sound stimulation.
The zCOSMOS Redshift Survey: How group environment alters global downsizing trends

We took advantage of the wealth of information provided by the first ~10000 galaxies of the zCOSMOS-bright survey and its group catalogue to study the complex interplay between group environment and galaxy properties. The classical indicator F_blue (fraction of blue galaxies) proved to be a simple but powerful diagnostic tool. We studied its variation for different luminosity and mass selected galaxy samples. Using rest-frame B-band selected samples, the groups galaxy population exhibits significant blueing as redshift increases, but maintains a lower F_blue with respect both to the global and the isolated galaxy population. However moving to mass selected samples it becomes apparent that such differences are largely due to the biased view imposed by the B-band luminosity selection, being driven by the population of lower mass, bright blue galaxies for which we miss the redder, equally low mass, counterparts. By focusing the analysis on narrow mass bins such that mass segregation becomes negligible we find that only for the lowest mass bin explored (logMass = 10.8 are already in place at z ~ 1 and do not exhibit any strong environmental dependence, possibly originating from so-called 'nature'/internal mechanisms. In contrast, for lower galaxy masses and redshifts lower than z ~ 1, we observe the emergence in groups of a population of 'nurture' red galaxies: slightly deviating from the trend of the downsizing scenario followed by the global galaxy population, and more so with cosmic time. These galaxies exhibit signatures of group-related secular physical mechanisms directly influencing galaxy evolution.
A Method for Discovering Common Patterns from Two RNA Secondary Structures and its Application to Structural Repeat Detection

We propose an ab initio method, named DiscoverR, for finding common patterns from two RNA secondary structures. The method works by representing RNA secondary structures as ordered labeled trees and performs tree pattern discovery using an efficient dynamic programming algorithm. DiscoverR is able to identify and extract the largest common substructures from two RNA molecules having different sizes without prior knowledge of the locations and topologies of these substructures. We also extend DiscoverR to find repeated regions in an RNA secondary structure, and apply this extended method to detect structural repeats in the 3'-untranslated region of a protein kinase gene. We describe the biological significance of a repeated hairpin found by our method, demonstrating the usefulness of the method. DiscoverR is implemented in Java; a jar file including the source code of the program is available for download at http://bioinformatics.njit. edu/DiscoverR.
The role of high-throughput technologies in clinical cancer genomics

Cancer is a genetic disease driven by both heritable and somatic alterations in DNA, which underpin not only oncogenesis but also progression and eventual metastasis. The major impetus for elucidating the nature and function of somatic mutations in cancer genomes is the potential for the development of effective targeted anticancer therapies. Over the last decade, high-throughput technologies have allowed us unprecedented access to a host of cancer genomes, leading to an influx of new information about their pathobiology. The challenge now is to integrate such emerging information into clinical practice to achieve tangible benefits for cancer patients. This review examines the roles array-based comparative genomic hybridization and next-generation sequencing are playing in furthering our understanding of both hematological and solid-organ tumors. Furthermore, the authors discuss the current challenges in translating the role of these technologies from bench to bedside.
Neural Mechanisms of Foraging

Looking for Greener Pastures Humans, like other animals, have evolved to forage. Brain-imaging studies by Kolling et al. (p. 95) suggest that activity in the dorsal anterior cingulate cortex supplies a continuous signal of environmental richness predicted by foraging theory. The signal exhibits a frame of reference that is tied to the key foraging decision of whether to engage with the current choice or to search for alternatives. The same strategy is used when humans are making other types of decisions. In contrast, the ventromedial prefrontal cortex, a brain region that lacks any signals pertinent to foraging, encodes choice values in a manner uninfluenced by environmental richness. A brain signal in the dorsal anterior cingulate cortex tracks the average value of a person’s environment. Behavioral economic studies involving limited numbers of choices have provided key insights into neural decision-making mechanisms. By contrast, animals’ foraging choices arise in the context of sequences of encounters with prey or food. On each encounter, the animal chooses whether to engage or, if the environment is sufficiently rich, to search elsewhere. The cost of foraging is also critical. We demonstrate that humans can alternate between two modes of choice, comparative decision-making and foraging, depending on distinct neural mechanisms in ventromedial prefrontal cortex (vmPFC) and anterior cingulate cortex (ACC) using distinct reference frames; in ACC, choice variables are represented in invariant reference to foraging or searching for alternatives. Whereas vmPFC encodes values of specific well-defined options, ACC encodes the average value of the foraging environment and cost of foraging.
ALK: a tyrosine kinase target for cancer therapy

The anaplastic lymphoma kinase (ALK) gene plays an important physiologic role in the development of the brain and can be oncogenically altered in several malignancies, including non-small-cell lung cancer (NSCLC) and anaplastic large cell lymphomas (ALCL). Most prevalent ALK alterations are chromosomal rearrangements resulting in fusion genes, as seen in ALCL and NSCLC. In other tumors, ALK copy-number gains and activating ALK mutations have been described. Dramatic and often prolonged responses are seen in patients with ALK alterations when treated with ALK inhibitors. Three of these—crizotinib, ceritinib, and alectinib—are now FDA approved for the treatment of metastatic NSCLC positive for ALK fusions. However, the emergence of resistance is universal. Newer ALK inhibitors and other targeting strategies are being developed to counteract the newly emergent mechanism(s) of ALK inhibitor resistance. This review outlines the recent developments in our understanding and treatment of tumors with ALK alterations.
Aspect Refinement Ð Unifying AOP and Stepwise Refinement

Stepwise refinement (SWR) is fundamental to software engineering. As aspectoriented programming (AOP) is gaining momentum in software development, aspects should be considered in the light of SWR. In this paper, we elaborate the notion of aspect refinement that unifies AOP and SWR at the architectural level. To reflect this unification to the programming language level, we present an implementation technique for refining aspects based on mixin composition along with a set of language mechanisms for refining all kinds of structural elements of aspects in a uniform way (methods, pointcuts, advice). To underpin our proposal, we contribute a fully functional compiler on top of AspectJ, present a non-trivial, medium-sized case study, and derive a set of programming guidelines.
Cancelable biometrics for finger vein recognition

Cancelable biometrics is one of the possible solutions to security and privacy problems in biometrics-based recognition systems. In this paper we propose the use of two classical transformations, block re-mapping and image warping, for the definition of cancelable biometrics from finger vein pattern images. Specifically, the impact on matching performance of the employed distortions, as well as the effects of their parameters' selection, are here investigated. An analysis of the renewability of the employed approaches is also provided. Performance comparable with what achieved in the unprotected approach can be reached in case of block re-mapping transformation, which is also able to provide renewability.
Pacific Asia Conference on Information Systems ( PACIS ) 2014 EFFICIENT APPROXIMATION FOR LARGE-SCALE KERNEL CLUSTERING ANALYSIS

Kernel k-means is useful for performing clustering on nonlinearly separable data. The kernel k-means is hard to scale to large data due to the quadratic complexity. In this paper, we propose an approach which utilizes the low-dimensional feature approximation of the Gaussian kernel function to capitalize a fast linear k-means solver to perform the nonlinear kernel k-means. This approach takes advantage of the efficiency of the linear solver and the nonlinear partitioning ability of the kernel clustering. The experimental results show that the proposed approach is much more efficient than a normal kernel kmeans solver and achieves similar clustering performance.
The Role of Local Backrub Motions in Evolved and Designed Mutations

Amino acid substitutions in protein structures often require subtle backbone adjustments that are difficult to model in atomic detail. An improved ability to predict realistic backbone changes in response to engineered mutations would be of great utility for the blossoming field of rational protein design. One model that has recently grown in acceptance is the backrub motion, a low-energy dipeptide rotation with single-peptide counter-rotations, that is coupled to dynamic two-state sidechain rotamer jumps, as evidenced by alternate conformations in very high-resolution crystal structures. It has been speculated that backrubs may facilitate sequence changes equally well as rotamer changes. However, backrub-induced shifts and experimental uncertainty are of similar magnitude for backbone atoms in even high-resolution structures, so comparison of wildtype-vs.-mutant crystal structure pairs is not sufficient to directly link backrubs to mutations. In this study, we use two alternative approaches that bypass this limitation. First, we use a quality-filtered structure database to aggregate many examples for precisely defined motifs with single amino acid differences, and find that the effectively amplified backbone differences closely resemble backrubs. Second, we directly apply a provably-accurate, backrub-enabled protein design algorithm to idealized versions of these motifs, and discover that the lowest-energy computed models match the average-coordinate experimental structures. These results support the hypothesis that backrubs participate in natural protein evolution and validate their continued use for design of synthetic proteins.
Self-Relevant Disgust and Self-Harm Urges in Patients with Borderline Personality Disorder and Depression: A Pilot Study with a Newly Designed Psychological Challenge

Background Borderline personality disorder (BPD) is a common psychiatric condition associated with self-harm. Self-harm is poorly understood and there is currently no treatment for acute presentations with self-harm urges. Objectives By using a new task (Self-relevant Task; SRT), to explore emotions related to one's own person (PERSON task) and body (BODY task), to study the correlations of these emotions, specifically disgust, with self-harm urge level changes, and to test the task's potential to be developed into an experimental model of self-harming for treatment trials. Methods 17 BPD patients, 27 major depressive disorder (MDD) patients, and 25 healthy volunteers performed the SRT. Emotion labels were extracted from task narratives and disgust and self-harm urge level changes measured by visual analogue scales. We used validated rating scales to measure symptom severity. Results The SRT was effective at inducing negative emotions and self-harm urge changes. Self-harm urge changes correlated with borderline symptom severity. Post-task disgust levels on the visual analogue scales were higher in BPD patients than in healthy controls in the PERSON task, and higher than in both control groups in the BODY task. Changes in disgust levels during the task were significantly greater in the patient groups. Post-task disgust levels or changes in disgust were not associated with self-harm urge changes (except the latter in MDD in the PERSON task), but self-harm urge changes and disgust (but no other emotion) narrative labels were on a whole sample level. Conclusion Although associations with the analogue scale measures were not significant, self-disgust reported in the narrative of patients may be associated with a higher probability of self-harm urges. Further research with larger sample sizes is needed to confirm this relationship and to examine whether reducing self-disgust could reduce self-harm urges. The SRT was effective and safe, and could be standardized for experimental studies.