Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effects of Perioperative Anesthesia and Analgesia on Immune Function in Patients Undergoing Breast Cancer Resection: A Prospective Randomized Study

Introduction: Perioperative anesthesia and analgesia exacerbate immunosuppression in immunocompromised cancer patients. The natural killer (NK) cell is a critical part of anti-tumor immunity. We compared the effects of two different anesthesia and analgesia methods on the NK cell cytotoxicity (NKCC) in patients undergoing breast cancer surgery. Methods: Fifty patients undergoing breast cancer resection were randomly assigned to receive propofol-remifentanil anesthesia with postoperative ketorolac analgesia (Propofol-ketorolac groups) or sevoflurane-remifentanil anesthesia with postoperative fentanyl analgesia (Sevoflurane-fentanyl group). The primary outcome was NKCC, which was measured before and 24 h after surgery. Post-surgical pain scores and inflammatory responses measured by white blood cell, neutrophil, and lymphocyte counts were assessed. Cancer recurrence or metastasis was evaluated with ultrasound and whole body bone scan every 6 months for 2 years after surgery. Results: The baseline NKCC (%) was comparable between the two groups (P = 0.082). Compared with the baseline value, NKCC (%) increased in the Propofol-ketorolac group [15.2 (3.2) to 20.1 (3.5), P = 0.048], whereas it decreased in the Sevoflurane-fentanyl group [19.5 (2.8) to 16.4 (1.9), P = 0.032]. The change of NKCC over time was significantly different between the groups (P = 0.048). Pain scores during 48 h after surgery and post-surgical inflammatory responses were comparable between the groups. One patient in the Sevoflurane-fentanyl group had recurrence in the contralateral breast and no metastasis was found in either group. Conclusions: Propofol anesthesia with postoperative ketorolac analgesia demonstrated a favorable impact on immune function by preserving NKCC compared with sevoflurane anesthesia and postoperative fentanyl analgesia in patients undergoing breast cancer surgery.
Using kinetic and kinematic parameters to explain changes in gait due to cognitive tasks

Although historically considered an automatic process, gait control has been shown to consume attentional demands, supported by the concept of dual-tasking with a motor and cognitive challenge. Identifying the impact of cognitive challenges on motor tasks in healthy younger and older adults could detect increased fall risk and lead to prevention strategies for at risk populations, such as the elderly or cognitively impaired. The impact on kinetics, kinematics, and spatiotemporal variability due to dual-tasking is currently unknown. The objective of this study is to identify how dualtasking impacts motor task performance in healthy younger and older adults. Eleven healthy younger (20.1 ± 1.9 years) and eleven healthy older (60.5 ± 9.4 years) adults first completed a neuropsychological assessment consisting of various cognitive tests to define a baseline measure of cognitive abilities. Three tasks were repeated during treadmill walking: a working memory task (Paced Auditory Serial Addition Test, PASAT), a visual attention and processing speed task (Symbol Digit Modalities Test, SDMT), and a visual attention and fine motor skills task (cellular phone dialing task, Phone). Three walking conditions were used for dual-tasking: self-selected, fast (120% of self-selected), and limp (induced by the instrumented split-belt treadmill with one belt moving at self-selected speed and one belt moving at fast speed). Kinetic, kinematic, and spatiotemporal parameters were recorded, and statistical analysis (ANOVA) was run separately for older and younger adults with cognitive test as the independent variable.
Diffusion Geodesic Path: A Common Feature for Automatic Image-Map Registration

The purpose of image-map registration is to revise the digital map included in geographic information system (GIS) with aerial image. The traditional method for this task requires the manual selection of features such as tie points in both the image and the map. Determining how to automatically extract common features between image and map is a difficult problem. In this study, we propose a novel framework of extracting the common feature named diffusion geodesic path (DGP) between image and map, and use it for image-map registration. In order to overcome the differences in presentations between image and map, we first construct two new edge images from the image and the map using the diffusion process in physics to diffuse the differences between them. Then, we extract several paths between feature points (corner points) on these new edge images using the geodesic. Finally, a part of the extracted paths are automatically selected and are used for registration. The experimental results implicate that our DGP is a common and robust feature between image and map, and it can provide sufficient information for automatic image-map registration. The whole framework works well on automatic image-map registration
SoftMC: A Flexible and Practical Open-Source Infrastructure for Enabling Experimental DRAM Studies

DRAM is the primary technology used for main memory in modern systems. Unfortunately, as DRAM scales down to smaller technology nodes, it faces key challenges in both data integrity and latency, which strongly affects overall system reliability and performance. To develop reliable and high-performance DRAM-based main memory in future systems, it is critical to characterize, understand, and analyze various aspects (e.g., reliability, latency) of existing DRAM chips. To enable this, there is a strong need for a publicly-available DRAM testing infrastructure that can flexibly and efficiently test DRAM chips in a manner accessible to both software and hardware developers. This paper develops the first such infrastructure, SoftMC (Soft Memory Controller), an FPGA-based testing platform that can control and test memory modules designed for the commonly-used DDR (Double Data Rate) interface. SoftMC has two key properties: (i) it provides flexibility to thoroughly control memory behavior or to implement a wide range of mechanisms using DDR commands, and (ii) it is easy to use as it provides a simple and intuitive high-level programming interface for users, completely hiding the low-level details of the FPGA. We demonstrate the capability, flexibility, and programming ease of SoftMC with two example use cases. First, we implement a test that characterizes the retention time of DRAM cells. Experimental results we obtain using SoftMC are consistent with the findings of prior studies on retention time in modern DRAM, which serves as a validation of our infrastructure. Second, we validate two recently-proposed mechanisms, which rely on accessing recently-refreshed or recently-accessed DRAM cells faster than other DRAM cells. Using our infrastructure, we show that the expected latency reduction effect of these mechanisms is not observable in existing DRAM chips, which demonstrates the usefulness of SoftMC in testing new ideas on existing memory modules. We discuss several other use cases of SoftMC, including the ability to characterize emerging non-volatile memory modules that obey the DDR standard. We hope that our open-source release of SoftMC fills a gap in the space of publicly-available experimental memory testing infrastructures and inspires new studies, ideas, and methodologies in memory system design.
The Spending and Debt Responses to Minimum Wage Increases

This paper presents evidence that spending increases more than income, and thus debt rises, in households with minimum wage workers following a minimum wage hike. Furthermore, we show that the size, timing, persistence, and composition of spending is inconsistent with the basic certainty equivalent life cycle model as well as simple “rule of thumb” models where consumption is equal to a fraction of current income. However, our findings are consistent with a model where households can borrow against part of the value of their durable goods. We obtain these results from four different datasets – the Consumer Expenditure Survey, Survey of Income and Program Participation, Current Population Survey, and administrative bank and credit bureau records. PRELIMINARY AND INCOMPLETE
Operating Characteristics of Brief Screens for Dementia in a Multicultural Population.

Operating characteristics of seven screens for dementia were compared across various groups for 795 persons who had received a criterion diagnostic evaluation. Area under the curve (AUC), based on receiver operating characteristics, was compared between and within scales as an indication of their efficiency. Differences in AUC were only 5% across all the scales for the entire sample, but increased to 11% across sociocultural groups and scales and to 20% across education groups and scales. Two scales (the Mini-Mental State Exam and the Short Portable Mental Status Questionnaire) misclassified most nondementias for the entire sample, and all scales misclassified most nondementias among persons with less than 5 years of education. Findings could support a recommendation that certain shorter scales be used because they perform as well as longer ones, are more consistent across cultural and educational groups, and can be more easily modified to improve performance in culturally diverse populations.
Caged Naloxone Reveals Opioid Signaling Deactivation Kinetics s

The spatiotemporal dynamics of opioid signaling in the brain remain poorly defined. Photoactivatable opioid ligands provide a means to quantitatively measure these dynamics and their underlying mechanisms in brain tissue. Although activation kinetics can be assessed using caged agonists, deactivation kinetics are obscured by slow clearance of agonist in tissue. To reveal deactivation kinetics of opioid signaling we developed a caged competitive antagonist that can be quickly photoreleased in sufficient concentrations to render agonist dissociation effectively irreversible. Carboxynitroveratrylnaloxone (CNV-NLX), a caged analog of the competitive opioid antagonist NLX, was readily synthesized from commercially available NLX in good yield and found to be devoid of antagonist activity at heterologously expressed opioid receptors. Photolysis in slices of rat locus coeruleus produced a rapid inhibition of the ionic currents evoked bymultiple agonists of them-opioid receptor (MOR), but not of a-adrenergic receptors, which activate the same pool of ion channels. Using the high-affinity peptide agonist dermorphin, we established conditions under which light-driven deactivation rates are independent of agonist concentration and thus intrinsic to the agonist-receptor complex. Under these conditions, some MOR agonists yielded deactivation rates that are limited by G protein signaling, whereas others appeared limited by agonist dissociation. Therefore, the choice of agonist determines which feature of receptor signaling is unmasked by CNV-NLX photolysis.
An Unsupervised Playfield Segmentation for Various Sport Videos

In this paper, we propose an unsupervised method of playfield segmentation for various sport videos. The method first applies local maximum clustering to gather feature samples into several clusters in the Cb-Cr plane. Next, a novel idea is developed to merge clusters into four color classes -- Red, Green, Blue and Grey. Finally, a simple scheme of region fusion eliminates small and unimportant areas. The experimental results indicate that the method effectively segments the playfield regions in various scenes of different sport videos.
Kinetic analysis of antibody-antigen interactions at a supported lipid monolayer.

Modified phospholipids possessing carboxyl head groups synthesized from phosphatidylethanolamine were incorporated into supported lipid monolayers on top of a thin gold film. A monoclonal antibody was chemically coupled to the modified lipids in these monolayers and the kinetics of antigen binding were determined by surface plasmon resonance. The binding could be analyzed using a conventional 1:1 binding algorithm and the derived kinetic and affinity constants were almost identical to those reported for the same interaction on a dextran hydrogel-based sensor chip. When an antigen was chemically coupled to a modified lipid monolayer, the binding of a monoclonal antibody to this surface was biphasic. A two-step algorithm describing the formation of a 1:2 antibody:antigen complex was developed which accurately described the data and enabled differentiation of the two binding steps. The binding was assayed varying both the concentration of antibody in solution and the density of antigen on the surface. The affinities determined by Scatchard analysis of equilibrium binding levels were similar to those values obtained from an ELISA.
Loitering Detection Based on Trajectory Analysis

Some potential danger, such as robbery, steal, and drug-dealing activity, can be avoided effectively by detecting loitering behavior in advance. In this paper, we propose a loitering detection algorithm by analyzing the trajectory of pedestrian. Firstly, we propose a trajectory extraction method combined with motion estimation and object detection to adapt to the tracking under a static camera scene. Then we judge the loitering behavior by analyzing the trajectory of object through the duration time and angles variation between the initial point and remaining points on the trajectory. The experiments were performed using several surveillance cameras. And experimental results show that the proposed method provides superior performance in terms of accuracy, robustness, and speed.
An integrated approach to bearing fault diagnostics and prognostics

This paper presents an integrated fault diagnostic and prognostic approach for bearing health monitoring and condition-based maintenance. The proposed scheme consists of three main components including principal component analysis (PCA), hidden Markov model (HMM), and an adaptive stochastic fault prediction model. The principal signal features extracted by PCA are utilized by HMM to generate a component health/degradation index, which is the input to an adaptive prognostics component for on-line remaining useful life prediction. The effectiveness of the scheme is shown by simulation studies using experimental vibration data obtained from a bearing health monitoring testbed.
CoMiC: Adapting a Short Answer Assessment System for Answer Selection

Open forum threads exhibit a great variability in the quality and quantity of the answers they attract, making it difficult to manually moderate and separate relevant from irrelevant content. The goal of SemEval 2015 Task 3 (Subtask A, English) is to build systems that automatically distinguish between relevant and irrelevant content in forum threads. We extend a short answer assessment system to build relations between forum questions and answers with respect to similarity, question type, and answer content. The features are used in a sequence classifier to account for the conversation character of threads. The performance of this approach is modest in comparison to the other task participants and also to the performance the system usually reaches in short answer assessment. However, the new features implemented for this task are a first step in developing more fine-grained question-answer features and identifying relevant answers.
Female participation in collective group defense in black howler monkeys (Alouatta pigra)

Many group‐living animals actively defend a home range against neighboring groups. In many of these societies, males are the primary participants during group defense, while female participation ranges from seldom to frequent. Among howler monkeys (Alouatta spp.), loud calls (i.e., howling) are often used in the context of intergroup spacing as a form of cooperative group defense. Males initiate and lead these howling bouts, but females occasionally participate as well. During a 28‐month study, I examined social and ecological factors influencing the participation of adult females in naturally occurring howling bouts of five multimale–multifemale groups of black howler monkeys (A. pigra) at Palenque National Park, Mexico. I calculated the percentage of time each female participated during howling bouts for which the participation of all resident females could be recorded ≥80% of the time (N = 287). At least one female was observed to participate in 53% of the vocal displays. Female participation was significantly greater during howling bouts that were part of visual intergroup encounters compared to spontaneous calls or calls in response to nearby calls when there was no visual contact with rival groups. Female calling behavior was not influenced by the presence of infants vulnerable to infanticide or by the proximity to food resources. Nonetheless, in four of the five study groups, one female called significantly more than the other resident female(s), suggesting that these females played a special role within the group's social dynamics, not previously recognized for this species. Am. J. Primatol. 77:595–604, 2015. © 2015 Wiley Periodicals, Inc.
A brain-computer interface (BCI) system based on auditory stream segregation

An auditory brain-computer interface (BCI) which detected event-related potential (ERP) elicited by selective attention to one of the tone streams was proposed. Each tone in two kinds of frequency oddball tone sequences with different tone frequency range was presented alternatively to subjects, and they were perceived by subjects as two kinds of segregated streams. Event-related potentials elicited by two kinds of deviant tones were classified by linear discriminant analysis (LDA) to find which streams subjects paid selective attention. By the experiments to six subjects, it was shown that this system could realize binary selection from two kinds of segregated tone streams.
A Mechanochemical Switch to Control Radical Intermediates

B12-dependent enzymes employ radical species with exceptional prowess to catalyze some of the most chemically challenging, thermodynamically unfavorable reactions. However, dealing with highly reactive intermediates is an extremely demanding task, requiring sophisticated control strategies to prevent unwanted side reactions. Using hybrid quantum mechanical/molecular mechanical simulations, we follow the full catalytic cycle of an AdoB12-dependent enzyme and present the details of a mechanism that utilizes a highly effective mechanochemical switch. When the switch is “off”, the 5′-deoxyadenosyl radical moiety is stabilized by releasing the internal strain of an enzyme-imposed conformation. Turning the switch “on,” the enzyme environment becomes the driving force to impose a distinct conformation of the 5′-deoxyadenosyl radical to avoid deleterious radical transfer. This mechanochemical switch illustrates the elaborate way in which enzymes attain selectivity of extremely chemically challenging reactions.
Journal of Theoretical and Applied Information Technology Time Series Modeling Using Artificial Neural Networks

An Artificial Intelligent technique exists in human brain for observation Of behavior of neurons in human brain. A neural network achieves the intelligent results by using massively parallel computations rather than by using rules or other logical structures. A set of elements begins by being randomly connected. Then network is trained to recognize a pattern by strengthening signals that lead to appropriate results and weakening incorrect or inefficient signals. An Artificial Neural Network(ANN) has procedural rules or formulas for only what kinds of input data the neural network can use to make an association with desired output. The approach of ANN has several advantages over conventional statistical and deterministicàpproaches. One of the most important algorithms of ANN is " Back Propagation Algorithm " (BPA) which learns by computing an error signal and than propagating the error backward throw the network. The BPA method is applied to statistical model(ARIMA) to test its efficiency and then applied to some actual geophysical data. The result of the analysis shows that the ANN is fast and efficient method for simulating/modeling large amount of data.
Title Page Full Title Epigenetic Changes in Bone Marrow Progenitor Cells Influence the Inflammatory Phenotype and Alter Wound Healing in Type 2 Diabetes Short Running Title Epigenetics Impair Diabetic Wound Healing Authors

Classically-activated (M1) macrophages are known to play a role in the development of chronic inflammation associated with impaired wound healing in Type 2 diabetes (T2D); however, the mechanism responsible for the dominant pro-inflammatory (M1) macrophage phenotype in T2D wounds is unknown. Since epigenetic enzymes can direct macrophage phenotypes, we assessed the role of histone methylation in bone marrow (BM) stem/progenitor cells in the programming of macrophages towards a pro-inflammatory phenotype. We have found that a repressive histone methylation mark, H3K27me3, is decreased at the promoter of the IL12 gene in BM progenitors and this epigenetic signature is passed down to wound macrophages in a murine model of glucose intolerance (diet-induced obese [DIO]). These epigenetically ‘pre-programmed’ macrophages result in poised macrophages in peripheral tissue and negatively impact wound repair. We found that in diabetic conditions, the H3K27 demethylase, Jmjd3, drives IL12 production in macrophages and that IL12 production can be modulated by inhibiting Jmjd3. Using human T2D tissue and murine models, we have identified a previously unrecognized mechanism by which macrophages are programmed towards a pro-inflammatory phenotype, establishing a pattern of unrestrained inflammation associated with non-healing wounds. Hence, histone demethylase inhibitor-based therapy may represent a novel treatment option for diabetic wounds. Page 2 of 39 Diabetes
DPCM-quantized block-based compressed sensing of images using Robbins Monro approach

Compressed Sensing or Compressive Sampling is the process of signal reconstruction from the samples obtained at a rate far below the Nyquist rate. In this work, Differential Pulse Coded Modulation (DPCM) is coupled with Block Based Compressed Sensing (CS) reconstruction with Robbins Monro (RM) approach. RM is a parametric iterative CS reconstruction technique. In this work extensive simulation is done to report that RM gives better performance than the existing DPCM Block Based Smoothed Projected Landweber (SPL) reconstruction technique. The noise seen in Block SPL algorithm is not much evident in this non-parametric approach. To achieve further compression of data, Lempel-Ziv-Welch channel coding technique is proposed.
Interoperability for Geospatial Analysis: a Semantics and Ontology-based Approach

Information extraction and integration from heterogeneous, autonomous data resources are major requirements for many spatial applications. Geospatial analysis for scientific discovery involves identification of relevant information resources, extraction and fusion of requisite subsets of the information, application of spatial analytical techniques and visualization of the results in an appropriate form. The motivating application domain underlying this research is marine environmental management, although the principles discussed apply to a wide range of scientific disciplines. The research discussed in the paper focuses on integration of data sources, data exploration and interactive data analysis. A knowledge base is used to capture the semantics of the spatial, temporal and thematic dimensions at a domain level, and the context-aware framework exploited to meet the requirements of a varied and distributed user community with differing objectives.