Miracle-dz/UniFAR-FTUs huggingface dataset:
Unique and shared roles of the posterior parietal and dorsolateral prefrontal cortex in cognitive functions

The dorsolateral prefrontal cortex (PFC) and posterior parietal cortex (PPC) are two parts of a broader brain network involved in the control of cognitive functions such as working-memory, spatial attention, and decision-making. The two areas share many functional properties and exhibit similar patterns of activation during the execution of mental operations. However, neurophysiological experiments in non-human primates have also documented subtle differences, revealing functional specialization within the fronto-parietal network. These differences include the ability of the PFC to influence memory performance, attention allocation, and motor responses to a greater extent, and to resist interference by distracting stimuli. In recent years, distinct cellular and anatomical differences have been identified, offering insights into how functional specialization is achieved. This article reviews the common functions and functional differences between the PFC and PPC, and their underlying mechanisms.
Phospholipid Mediated Activation of Calcium Dependent Protein Kinase 1 (CaCDPK1) from Chickpea: A New Paradigm of Regulation

Phospholipids, the major structural components of membranes, can also have functions in regulating signaling pathways in plants under biotic and abiotic stress. The effects of adding phospholipids on the activity of stress-induced calcium dependent protein kinase (CaCDPK1) from chickpea are reported here. Both autophosphorylation as well as phosphorylation of the added substrate were enhanced specifically by phosphatidylcholine and to a lesser extent by phosphatidic acid, but not by phosphatidylethanolamine. Diacylgylerol, the neutral lipid known to activate mammalian PKC, stimulated CaCDPK1 but at higher concentrations. Increase in Vmax of the enzyme activity by these phospholipids significantly decreased the Km indicating that phospholipids enhance the affinity towards its substrate. In the absence of calcium, addition of phospholipids had no effect on the negligible activity of the enzyme. Intrinsic fluorescence intensity of the CaCDPK1 protein was quenched on adding PA and PC. Higher binding affinity was found with PC (K½ = 114 nM) compared to PA (K½ = 335 nM). We also found that the concentration of PA increased in chickpea plants under salt stress. The stimulation by PA and PC suggests regulation of CaCDPK1 by these phospholipids during stress response.
Characterization of hematologic malignancies based on discrete orthogonal moments

During the last decade leukemia and lymphomas have been a hot topic in the biomedical area. Their diagnosis is a time-consuming task that, in many cases, delays treatments. On the other hand, discrete orthogonal moments (DOMs) are a tool recently introduced in biomedical image analysis. Here, we propose a combination of DOMs to help in the diagnosis of leukemia and lymphomas. We classify the IICBU2008-lymphoma dataset that includes three hematologic malignancies: chronic lymphocytic leukemia, follicular lymphoma, and mantle cell lymphoma. Our methodology analyzes such diseases in the hema-toxylin and eosin color space. We also include feature analysis to preserve the most discriminating characteristics of the malignant tissues. Finally, the classification of the samples is performed with kernel Fisher discriminant analysis. The accuracy is 93.85%. The results show the proposal could be useful in different biomedical applications.
Direct Causal Networks for the Study of Transcranial Magnetic Stimulation Effects on Focal Epileptiform Discharges

BACKGROUND Transcranial magnetic stimulation (TMS) can have inhibitory effects on epileptiform discharges (EDs) of patients with focal seizures. However, the brain connectivity before, during and after EDs, with or without the administration of TMS, has not been extensively explored. OBJECTIVE To investigate the brain network of effective connectivity during ED with and without TMS in patients with focal seizures. METHODS For the effective connectivity a direct causality measure is applied termed partial mutual information from mixed embedding (PMIME). TMS-EEG data from two patients with focal seizures were analyzed. Each EEG record contained a number of EDs in the majority of which TMS was administered over the epileptic focus. As a control condition, sham stimulation over the epileptogenic zone or real TMS at a distance from the epileptic focus was also performed. The change in brain connectivity structure was investigated from the causal networks formed at each sliding window. CONCLUSION The PMIME could detect distinct changes in the network structure before, within, and after ED. The administration of real TMS over the epileptic focus, in contrast to sham stimulation, terminated the ED prematurely in a node-specific manner and regained the network structure as if it would have terminated spontaneously.
Carcinoma-Derived Interleukin-8 Disorients Dendritic Cell Migration Without Impairing T-Cell Stimulation

Background Interleukin-8 (IL-8, CXCL8) is readily produced by human malignant cells. Dendritic cells (DC) both produce IL-8 and express the IL-8 functional receptors CXCR1 and CXCR2. Most human colon carcinomas produce IL-8. IL-8 importance in malignancies has been ascribed to angiogeneis promotion. Principal Findings IL-8 effects on human monocyte-derived DC biology were explored upon DC exposure to recombinant IL-8 and with the help of an IL-8 neutralizing mAb. In vivo experiments were performed in immunodeficient mice xenografted with IL-8-producing human colon carcinomas and comparatively with cell lines that do not produce IL-8. Allogenic T lymphocyte stimulation by DC was explored under the influence of IL-8. DC and neutrophil chemotaxis were measured by transwell-migration assays. Sera from tumor-xenografted mice contained increasing concentrations of IL-8 as the tumors progress. IL-8 production by carcinoma cells can be modulated by low doses of cyclophosphamide at the transcription level. If human DC are injected into HT29 or CaCo2 xenografted tumors, DC are retained intratumorally in an IL-8-dependent fashion. However, IL-8 did not modify the ability of DC to stimulate T cells. Interestingly, pre-exposure of DC to IL-8 desensitizes such cells for IL-8-mediated in vitro or in vivo chemoattraction. Thereby DC become disoriented to subsequently follow IL-8 chemotactic gradients towards malignant or inflamed tissue. Conclusions IL-8 as produced by carcinoma cells changes DC migration cues, without directly interfering with DC-mediated T-cell stimulation.
THE MECHANICAL CHARACTERIZATION AND IN VIVO EVALUATION OF POROUS TiNi AS GRAFT MATERIAL

To obtain TiNi foams with interconnected pores that have surface quality necessary for bone growth in addition to required mechanical performance, sintering with the space holder technique was employed in this study, which aimed to evaluate the bone healing process of TiNi graft materials. For this purpose, processed TiNi foams with three different porosities were placed into the created defects in the femur of rats. Moreover, the mechanical properties of the processed TiNi foams were conducted via monotonic compression tests in order to evaluate mechanical biocompatibility.
The c-SMAC

T cells integrate and transduce the key signals necessary to mount an appropriate immune response. To do this, they rely on both secreted factors as well as physical cell-cell contact. Much attention has focused on the organization of proteins at the contact area between a T cell and an antigen-presenting cell, known as the immunological synapse. It has been shown in vitro that proteins segregate into two distinct regions within this contact area, a central area referred to as the c-SMAC, where the T cell receptor and associated signaling molecules are enriched, and a peripheral region called the p-SMAC containing LFA-1 and the scaffolding protein talin. Whether or not these structures form in vivo and how they function in T cell activation remain issues of great interest. Here, we review recently published work and propose several possible functions for the role of the c-SMAC in T cell activation.
Mining the Modern Code Review Repositories: A Dataset of People, Process and Product

In this paper, we present a collection of Modern Code Review data for five open source projects. The data showcases mined data from both an integrated peer review system and source code repositories. We present an easy–to–use andricher data structure to retrieve the 1. ) People 2. ) Process and 3. ) Product aspects of the peer review. This paperpresents the extraction methodology, the dataset structure, and a collection of database dumps.
RPM: Representative Pattern Mining for Efficient Time Series Classification

Time series classication is an important problem that has received a great amount of attention by researchers and practitioners in the past two decades. In this work, we propose a novel algorithm for time series classication based on the discovery of class-specic representative patterns. We dene representative patterns of a class as a set of subsequences that has the greatest discriminative power to distinguish one class of time series from another. Our approach rests upon two techniques with linear complexity: symbolic discretization of time series, which generalizes the structural patterns, and grammatical inference, which automatically nds recurrent correlated patterns of variable length, producing an initial pool of common patterns shared by many instances in a class. From this pool of candidate patterns, our algorithm selects the most representative patterns that capture the class specicities, and that can be used to eectively discriminate between time series classes. Through an exhaustive experimental evaluation we show that our algorithm is competitive in accuracy and speed with the stateof-the-art classication techniques on the UCR time series repository, robust on shifted data, and demonstrates excellent performance on real-world noisy medical time series.
Mathematical Modeling of Social Games

Human computation is a technique that makes use of human abilities for computation to solve problems. Social games use the power of the Internet game players to solve human computation problems. In previous works, many social games were proposed and were quite successful, but no formal framework exists for designing social games in general. A formal framework is important because it lists out the design elements of a social game, the characteristics of a human computation problem, and their relationships. With a formal framework, it simplifies the way to design a social game for a specific problem. In this paper, our contributions are: (1) formulate a formal model on social games, (2) analyze the framework and derive some interesting properties based on model’s interactions, (3) illustrate how some current social games can be realized with the proposed formal model, and (4) describe how to design a social game for solving a specific problem with the use of the proposed formal model. This paper presents a set of design guidelines derived from the formal model and demonstrates that the model can help to design a social game for solving a specific problem in a formal and structural way.
Impact of Glaucoma and Dry Eye on Text-Based Searching

Purpose We determine if visual field loss from glaucoma and/or measures of dry eye severity are associated with difficulty searching, as judged by slower search times on a text-based search task. Methods Glaucoma patients with bilateral visual field (VF) loss, patients with clinically significant dry eye, and normally-sighted controls were enrolled from the Wilmer Eye Institute clinics. Subjects searched three Yellow Pages excerpts for a specific phone number, and search time was recorded. Results A total of 50 glaucoma subjects, 40 dry eye subjects, and 45 controls completed study procedures. On average, glaucoma patients exhibited 57% longer search times compared to controls (95% confidence interval [CI], 26%–96%, P < 0.001), and longer search times were noted among subjects with greater VF loss (P < 0.001), worse contrast sensitivity (P < 0.001), and worse visual acuity (P = 0.026). Dry eye subjects demonstrated similar search times compared to controls, though worse Ocular Surface Disease Index (OSDI) vision-related subscores were associated with longer search times (P < 0.01). Search times showed no association with OSDI symptom subscores (P = 0.20) or objective measures of dry eye (P > 0.08 for Schirmer's testing without anesthesia, corneal fluorescein staining, and tear film breakup time). Conclusions Text-based visual search is slower for glaucoma patients with greater levels of VF loss and dry eye patients with greater self-reported visual difficulty, and these difficulties may contribute to decreased quality of life in these groups. Translational Relevance Visual search is impaired in glaucoma and dry eye groups compared to controls, highlighting the need for compensatory strategies and tools to assist individuals in overcoming their deficiencies.
A randomized trial of naltrexone for smoking cessation.

AIMS To evaluate the efficacy and safety of orally administered naltrexone, alone or in combination with nicotine patches, as a treatment for cigarette smoking. DESIGN Randomized, partially-blinded, 2 x 2 factorial trial using naltrexone (active vs. placebo) and nicotine patches (active vs. none). PARTICIPANTS One hundred cigarette smokers. INTERVENTION Twelve weeks of either placebo-only, naltrexone-only, placebo with nicotine patches or naltrexone with nicotine patches. The naltrexone dose was 50 mg taken once daily, and the nicotine patch dose was 21 mg/24-hour for the first 8 weeks and 14 mg/24-hour for the remaining 4 weeks. Brief behavioral intervention was provided at each visit. MEASUREMENTS One-week point-prevalence smoking abstinence rates confirmed by an expired air carbon monoxide level of 8 parts per million (ppm) or less, daily cigarette smoking and cigarette craving. FINDINGS At the end of treatment, there was no effect of naltrexone on smoking abstinence. The smoking abstinence rates were 19% and 22% for the placebo only and naltrexone only treatment groups, respectively, and 48% and 46% for the placebo with nicotine patch and naltrexone with nicotine patch groups, respectively. However, the effect of the nicotine patch at this time was significant (p = 0.006), but not at the 6-month follow-up. No significant effect of naltrexone was observed on daily cigarette smoking on cigarette craving during the study. CONCLUSIONS The opioid antagonist naltrexone was not found to be effective for smoking cessation and had no significant effect on daily cigarette consumption or craving. The results of the present study provide no support for the use of naltrexone, alone or in combination with nicotine patches, as a therapeutic treatment for smoking cessation.
Operating articulated objects based on experience

Many tasks that would be of benefit to users in domestic environments require that robots manipulate articulated objects such as doors and drawers. In this paper, we present a novel approach that simultaneously estimates the kinematic model of an articulated object based on the trajectory described by the robot's end effector, and uses this model to predict the future trajectory of the end effector. One advantage of our approach is that the robot can directly use these predictions to generate an equilibrium point control path for operating the mechanism. Additionally, our approach can improve these predictions based on previously learned articulation models. We have implemented and tested our approach on a real mobile manipulator. Through 40 trials, we show that the robot can reliably open various household objects, including cabinet doors, sliding doors, office drawers, and a dishwasher. Furthermore, we demonstrate that using the information from previous interactions as a prior significantly improves the prediction accuracy.
Plant source water apportionment using stable isotopes: A comparison of simple linear, two‐compartment mixing model approaches

Plant source water identification using stable isotopes is now a common practice in ecohydrological process investigations. Notwithstanding, little critical evaluation of the approaches for source apportionment have been conducted. Here, we present a critical evaluation of the main methods used for source apportionment between vadose and saturated zone water: simple mass balance and Bayesian mixing models. We leverage new isotope stem water samples from a diverse set of tree species in a strikingly uniform terrain and soil conditions at the Christchurch Botanic Garden, New Zealand. Our results show that using δ2H alone in a simple, two‐source mass balance approach leads to erroneous results, particularly an apparent overestimation of groundwater contribution to xylem. Alternatively, using both δ2H and δ18O in a Bayesian inference framework improves the source water estimates and is more useful than the simple mass balance approach, particularly when soil and groundwater contributions are relatively disproportionate. We suggest that plant source water quantification methods should take into consideration the possible effects of 2H/1H fractionation. The Bayesian inference approach used here may be less sensitive to 2H/1H fractionation effects than simple mass balance methods.
Nyström Method vs Random Fourier Features: A Theoretical and Empirical Comparison

Both random Fourier features and the Nystrom method have been successfully applied to efficient kernel learning. In this work, we investigate the fundamental difference between these two approaches, and how the difference could affect their generalization performances. Unlike approaches based on random Fourier features where the basis functions (i.e., cosine and sine functions) are sampled from a distribution independent from the training data, basis functions used by the Nystrom method are randomly sampled from the training examples and are therefore data dependent. By exploring this difference, we show that when there is a large gap in the eigen-spectrum of the kernel matrix, approaches based on the Nystrom method can yield impressively better generalization error bound than random Fourier features based approach. We empirically verify our theoretical findings on a wide range of large data sets.
Boosting for Efficient Model Selection for Syntactic Parsing

We present an efficient model selection method using boosting for transition-based constituency parsing. It is designed for exploring a high-dimensional search space, defined by a large set of feature templates, as for example is typically the case when parsing morphologically rich languages. Our method removes the need to manually define heuristic constraints, which are often imposed in current state-of-the-art selection methods. Our experiments for French show that the method is more efficient and is also capable of producing compact, state-of-the-art models.
Finite Volume Methods for the Two-fluid MHD Equations

Two fluid (TF) ideal magnetohydrodynamics (MHD) equations are a generalized form of the ideal MHD equations in which the electrons and ions are considered as separate species. The design of efficient numerical schemes for the these equations is complicated on account of the non-linearities and the presence of stiff source terms, particularly for realistic charge to mass ratios. We design novel finite volume schemes based on an implicit-explicit (IMEX) time stepping routine. The special structure of the twofluid MHD equations enable us to split the source terms carefully in order to ensure that only local (in each cell) equations need to be solved at each time step. Furthermore, these equations are solved exactly. Benchmark numerical experiments are presented to illustrate the efficiency of new approach.
Variable Resolution Discretization for High-Accuracy Solutions of Optimal Control Problems

State abstraction is of central importance in remforcement learning and Markov Decision Processes. This paper studies the case of variable resolution state abstraction for continuous-state, deterministic dynamic control problems in which near-optimal policies are required. We describe variable resolution policy and value function representations based on Kuhn triangulations embedded in a kd-tree. We then consider top-down approaches to choosing which cells to split in order to generate improved policies. We begin with local approaches based on value function properties and policy properties that use only features of individual cells in making splitting choices. Later, by introducing two new non-local measures, influence and variance, we derive a splitting criterion that allows one cell to efficiently take into account its impact on other cells when deciding whether to split. We evaluate the performance of a variety of splitting criteria on many benchmark problems (published on the web), paying careful attention to their number-of-cells versus closeness-to-optimality tradeoff curves.
Infrequent Weighted Itemset Mining Using Frequent Pattern Growth

Itemset mining has been an active area of research due to its successful application in various data mining scenarios including finding association rules. Frequent weighted itemsets represent correlations frequently holding in data in which items may weight differently. Infrequent Itemset mining is a variation of frequent itemset mining where it finds the uninteresting patterns i.e., it finds the data items which occurs very rarely. This seminar tackles the issue of discovering rare and weighted itemsets, i.e., the infrequent weighted itemset (IWI) mining problem. Two algorithms that perform IWI and Minimal IWI mining efficiently are presented. This new algorithm is based on the pattern- growth paradigm to find minimally infrequent itemsets. A minimally infrequent itemset has no subset which is also infrequent.
Proteomics‐driven progress in neurodegeneration research

Proteomics technologies have been widely used in the investigation of neurodegenerative and psychiatric disorders, and in particular in the detection of differences between healthy individuals and patients suffering from such diseases. Thus, brain and cerebrospinal fluid (CSF) samples from patients with Alzheimer's disease, Down syndrome, Pick's disease, Parkinson's disease, schizophrenia, and other disorders as well as brain and CSF from animals serving as models of neurological disorders have been analyzed by proteomics. 2‐DE followed by MALDI‐TOF‐MS has been mainly applied as this proteomics approach provides the possibility of convenient quantification of protein levels and detection of post‐translational modifications. About 330 unique proteins with deranged levels and modifications have been detected by proteomics approaches to be related to neurodegeneration and psychiatric disorders. They are mainly involved in metabolism pathways, cytoskeleton formation, signal transduction, guidance, detoxification, transport, and conformational changes. In this article, we provide a summary of the major contributions of proteomics technologies in the study of neurodegenerative and psychiatric diseases, in particular, in the detection of changes in protein levels and modifications related to these disorders.