Miracle-dz/UniFAR-FTUs huggingface dataset:
Matrix Metalloproteinase-8 Is Expressed in Rheumatoid Synovial Fibroblasts and Endothelial Cells

Neutrophil collagenase (matrix metalloproteinase-8 or MMP-8) is regarded as being synthesized exclusively by polymorphonuclear neutrophils (PMN). However, in vivo MMP-8 expression was observed in mononuclear fibroblast-like cells in the rheumatoid synovial membrane. In addition, we detected MMP-8 mRNA expression in cultured rheumatoid synovial fibroblasts and human endothelial cells. Up-regulation of MMP-8 was observed after treatment of the cells with either tumor necrosis factor-α (10 ng/ml) or phorbol 12-myristate 13-acetate (10 nm). Western analysis showed a similar regulation at the protein level. The size of secreted MMP-8 was 50 kDa, which is about 30 kDa smaller than MMP-8 from PMN. Conditioned media from rheumatoid synovial fibroblasts contained both type I and II collagen degrading activity. However, degradation of type II collagen, but not that of type I collagen, was completely inhibited by 50 μm doxycycline, suggesting specific MMP-8 activity. In addition, doxycycline down-regulated MMP-8 induction, at both the mRNA and protein levels. Thus MMP-8 exerts markedly wider expression in human cells than had been thought previously, implying that PMN are not the only source of cartilage degrading activity at arthritic sites. The inhibition of both MMP-8 activity and synthesis by doxycycline provides an incentive for further studies on the clinical effects of doxycycline in the treatment of rheumatoid arthritis.
Interaction of Veratrum nigrum with Panax ginseng against Obesity: A Sang-ban Relationship

Obesity has become a major health threat in developed countries. However, current medications for obesity are limited because of their adverse effects. Interest in natural products for the treatment of obesity is thus rapidly growing. Korean Medicine (KM) is characterized by the wide use of herbal formulas. However, the combination rule of herbal formulas in KM lacks experimental evidence. According to Shennong's Classic of Materia Medica, the earliest book of herbal medicine, Veratrum nigrum (VN) has antagonistic features against Panax ginseng (PG), and the PG-VN pair is strictly forbidden. In this study, we have shown the effects of PG, VN, and their combination on obesity in high-fat (HF) diet-induced obese mice and in 3T3-L1 cells. PG, VN, and PG-VN combination significantly reduced weight gain and the fat pad weight in HF diet-induced obese mice. They also significantly decreased lipid accumulation and the expressions of two major adipogenesis factors, PPARγ and C/EBPα, in 3T3-L1 cells. In addition, the PG-VN combination had synergistic effects compared with the mixture of extracts of PG and VN on inhibition of PPARγ and C/EBPα expressions at lower doses. These results indicate a new potential anti-obese pharmacotherapy and also provide scientific evidence supporting the usage of herbal combinations instead of mixtures in KM.
Synthesis of emotional expressions specific to facial structure

Imposing expressions on expression-neutral human face images is an interesting application of human-computer-interaction, animation, entertainment and other such fields. The objective of this paper is to impose one of the six prototypic emotional expressions i.e., Joy, Surprise, Disgust, Fear, Anger and Sorrow to a given expression-neutral face image. For this, we first establish individual models for each of the six prototypic expressions. This model is independent of the shape and texture i.e., identity of the subjects in the training video sequences. Given an intensity of a particular expression, we find the changes in the shape and texture due to a particular expression from the derived models. These changes are added to the automatically annotated test image on which the expression is to be imposed. The major contributions of the paper are: (1) Developing a method for finding facial structure specific changes of a subject for imposing a particular expression and (2) Establishing a nonlinear relationship between the expression intensity and the corresponding facial changes. The experimental results show that the proposed method is better compared to another related method. The proposal is also good at preserving the identity of the subject while imposing a given expression on the expression-neutral face image.
An empirical study on the performance of hash table

Hash table is a valuable data structure that is expected to provide constant amortized access time. Although there are a lot of researches on hashing, it seems there is no enough practical study on its stability with large data set. In this paper, we conducted a few experiments to study the performance of hashing with a large set of data and compared the results of different collision approaches. Our experiments revealed a few new phenomena. The experiment results leans to close addressing than open addressing by a huge edge and deem linear probing impractical due to its low performance. When items are randomly distributed with keys in a large space, different hash algorithms might produce similar performance. Increasing randomness in keys does not help hash table performance either. These discoveries might be able to provide heuristics to programmers on how to design software products using hash tables.
Motivasi untuk Berhenti Merokok pada MahasiswaPerokok Berat ditinjau dari Self Efficacy danPengetahuan Bahaya Rokok

Penelitian ini bertujuan untuk mengetahui adanya hubungan antara self efficacy dan pengetahuan akan bahaya rokok dengan motivasi berhenti merokok pada mahasiswa perokok berat di Semarang. Kriteria populasi yang digunakan dalam penelitian ini adalah seorang perokok berat dengan rata-rata mengkonsumsi > 15 batang rokok / hari serta masih aktif berstatus sebagai mahasiswa. Jumlah sampel yang diperoleh dalam penelitian ini ada 33 mahasiswa. Adapun hipotesis mayor dalam penelitian ini adalah terdapat hubungan antara self efficacy dan pengetahuan bahaya rokok terhadap motivasi untuk berhenti merokok pada perokok berat, sedangkan hipotesis minor berbunyi (1) Terdapat hubungan positif antara self efficacy dengan motivasi berhenti merokok dan (2) Terdapat hubungan positif antara pengetahuan bahaya rokok dengan motivasi berhenti merokok. Teknik analisa data yang digunakan adalah analisis regresi berganda untuk hipotesis mayor dan korelasi product moment untuk hipotesis minor. Hasil penelitian yang diperoleh menunjukkan bahwa (1) ada hubungan yang signifikan antara self efficacy dan pengetahuan akan bahaya rokok dengan motivasi berhenti merokok pada perokok berat dengan R = 0,855; F = 40,737 ( p < 0,01 ) (2) ada hubungan positif yang signifikan antara self-efficacy dengan motivasi berhenti merokok dimana nilai , (3) ada hubungan postif yang signifikan antara pengetahuan bahaya rokok dengan motivasi berhenti merokok dimana nilai
Cluster tree based multi-label classification for protein function prediction

Automatically assigning functions for unknown proteins is a key task in computational biology. Proteins in nature have multiple classes according to the functions they perform. Many efforts have been made to cast the protein function prediction into a multi-label learning problem. This paper proposes a novel Cluster Tree based Multi-label Learning algorithm (CTML) for protein function prediction. The main idea is to compute a set of predictive labels associated at each node for multi-label prediction by using the k-means clustering techniques and the predictive functions via the learning data at the nodes. With the propagation of the predictive labels from the root node to the leaf node, the correlations between labels can be preserved. Experimental results on benchmark data (genbase and yeast datasets) show that the proposed CTML algorithm is effective in predicting protein functions. Moreover, the classification performance of the CTML algorithm is competitive against the other baseline multi-label learning algorithms.
An Effective Approach to Continuous User Authentication for Touch Screen Smart Devices

Due to the rapid increase in the use of personal smart devices, more sensitive data is stored and viewed on these smart devices. This trend makes it easier for attackers to access confidential data by physically compromising (including stealing) these smart devices. Currently, most personal smart devices employ one of the one-time user authentication schemes, such as four-to-six digits, fingerprint or pattern-based schemes. These authentication schemes are often not good enough for securing personal smart devices because the attackers can easily extract all the confidential data from the smart device by breaking such schemes, or by keeping the authenticated session open on a physically compromised smart device. In addition, existing re-authentication or continuous authentication techniques for protecting personal smart devices use centralized architecture and require servers at a centralized location to train and update the learning model used for continuous authentication, which impose additional communication overhead. In this paper, an approach is presented to generating and updating the authentication model on the user's smart device with user's gestures, instead of a centralized server. There are two major advantages in this approach. One is that this approach continuously learns and authenticates finger gestures of the user in the background without requiring the user to provide specific gesture inputs. The other major advantage is to have better authentication accuracy by treating uninterrupted user finger gestures over a short time interval as a single gesture for continuous user authentication.
Title : Tight regulation of the unfolded protein sensor Ire 1 by its 1 intramolecularly antagonizing subdomain 2 3 4

Authors: Rubwad Mathuranyanon, Tomoko Tsukamoto, Asumi Takeuchi, 5 Yuki Ishiwata-Kimata, Yuichi Tuchiya, Kenji Kohno, and Yukio Kimata 6 7 Affiliation: Graduate School of Biological Sciences, Nara Institute of Science and 8 Technology, 8916-5 Takayama, Ikoma , Nara 630-0192, Japan 9 10 Corresponding author: Yukio Kimata (kimata@bs.naist.jp) 11 12 Running title: Tight regulation of Ire1 13 14 Abbreviations; CSSR: core stress-sensing region, DTT: dithiothreitol, ER: endoplasmic 15 reticulum, IDR intrinsically disordered region, MBP: maltose binding protein, MFY: 16 M229A/F285A/Y301A, NUCR: N-terminal unconserved region, RT-PCR: reverse 17 transcription PCR, UPR: unfolded protein response, UPRE: UPR element. 18 19 Key words; unfolded protein response, stress response, endoplasmic reticulum, 20 intrinsically disordered region, molecular chaperone, misfolded protein 21 22 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed. © 2015. Published by The Company of Biologists Ltd.
Safe base-station repositioning in wireless sensor networks

In wireless sensor networks (WSNs), large populations of tiny sensor nodes probe the environment and report the collected data to a base-station(s) for processing. Due to the resource constraints, WSNs pose numerous and non-conventional design challenges compared to contemporary communication networks. One of the performance optimization techniques that have been recently proposed is to dynamically relocate the base-station closer to the sensors involved in heavy packet traffic in order to increase energy efficiency, packet delivery ratio and timeliness. In this paper, we argue that such relocation may jeopardize the base-station since it often moves the base-station too close to danger zones. We present STEER, an efficient safety-oriented relocation algorithm for base-stations that strives to protect the base-station while maintaining good network performance. The effectiveness of our algorithm is validated in a simulated environment
Agriculture modifies the seasonal decline of breeding success in a tropical wild bird population

Summary Habitat conversion for agriculture is a major driver of biodiversity loss, but our understanding of the demographic processes involved remains poor. We typically investigate the impacts of agriculture in isolation even though populations are likely to experience multiple, concurrent changes in the environment (e.g. land and climate change). Drivers of environmental change may interact to affect demography, but the mechanisms have yet to be explored fully in wild populations. Here, we investigate the mechanisms linking agricultural land use with breeding success using long‐term data for the formerly Critically Endangered Mauritius kestrel Falco punctatus, a tropical forest specialist that also occupies agricultural habitats. We specifically focused on the relationship between breeding success, agriculture and the timing of breeding because the latter is sensitive to changes in climatic conditions (spring rainfall) and enables us to explore the interactive effects of different (land and climate) drivers of environmental change. Breeding success, measured as egg survival to fledging, declines seasonally in this population, but we found that the rate of this decline became increasingly rapid as the area of agriculture around a nest site increased. If the relationship between breeding success and agriculture was used in isolation to estimate the demographic impact of agriculture, it would significantly under‐estimate breeding success in dry (early) springs and over‐estimate breeding success in wet (late) springs. Analysis of prey delivered to nests suggests that the relationship between breeding success and agriculture might be due, in part, to spatial variation in the availability of native, arboreal geckos. Synthesis and applications. Agriculture modifies the seasonal decline in breeding success in this population. As springs are becoming wetter in our study area and since the kestrels breed later in wetter springs, the impact of agriculture on breeding success will become worse over time. Our results suggest that forest restoration designed to reduce the detrimental impacts of agriculture on breeding may also help reduce the detrimental effects of breeding late due to wetter springs. Our results therefore highlight the importance of considering the interactive effects of environmental change when managing wild populations.
Decoding the Contents of Working Memory Tracks Strategy and Performance in Prospective Remembering

Theories of prospective memory (PM) posit that it can be subserved either by working memory (WM) or episodic memory (EM). Testing and refining these multiprocess theories of PM requires a way of tracking participants9 reliance on WM versus EM. Here we use multi-voxel pattern analysis (MVPA) to derive a trial-by-trial measure of WM use in prospective memory. We manipulated strategy demands by varying the degree of proactive interference (which hurts EM) and the memory load required to perform the secondary task (which hurts WM). Our MVPA measures showed 1) greater WM use and 2) a trial-by-trial correlation between WM use and PM behavior for the condition in which participants were pushed to rely more on WM. Finally, we also showed that MVPA measures of WM use are not redundant with other behavioral measures: in the condition in which participants were pushed more to rely on WM, using neural and behavioral measures together led to better prediction of PM accuracy than either type of measure on its own.
Analysis and Synthesis of Palestrina-Style Counterpoint Using Markov Chains

This paper presents a novel approach to computergenerated Palestrina-style counterpoint using probabilistic Markov Chains. It is shown how Markov Chains adequately capture the rules of species counterpoint and how they can be used to synthesize species counterpoint given a cantus firmus. It is also shown how such rules can be inferred from given counterpoint examples.
Selecting Sample Preparation Workflows for Mass Spectrometry-Based Proteomic and Phosphoproteomic Analysis of Patient Samples with Acute Myeloid Leukemia

Global mass spectrometry (MS)-based proteomic and phosphoproteomic studies of acute myeloid leukemia (AML) biomarkers represent a powerful strategy to identify and confirm proteins and their phosphorylated modifications that could be applied in diagnosis and prognosis, as a support for individual treatment regimens and selection of patients for bone marrow transplant. MS-based studies require optimal and reproducible workflows that allow a satisfactory coverage of the proteome and its modifications. Preparation of samples for global MS analysis is a crucial step and it usually requires method testing, tuning and optimization. Different proteomic workflows that have been used to prepare AML patient samples for global MS analysis usually include a standard protein in-solution digestion procedure with a urea-based lysis buffer. The enrichment of phosphopeptides from AML patient samples has previously been carried out either with immobilized metal affinity chromatography (IMAC) or metal oxide affinity chromatography (MOAC). We have recently tested several methods of sample preparation for MS analysis of the AML proteome and phosphoproteome and introduced filter-aided sample preparation (FASP) as a superior methodology for the sensitive and reproducible generation of peptides from patient samples. FASP-prepared peptides can be further fractionated or IMAC-enriched for proteome or phosphoproteome analyses. Herein, we will review both in-solution and FASP-based sample preparation workflows and encourage the use of the latter for the highest protein and phosphorylation coverage and reproducibility.
Graph-based code selection techniques for embedded processors

Code selection is an important task in code generation for programmable processors, where the goal is to find an efficient mapping of machine-independent intermediate code to processor-specific machine instructions. Traditional approaches to code selection are based on tree parsing which enables fast and optimal code selection for intermediate code given as a set of data-flow trees. While this approach is generally useful in compilers for general-purpose processors, it may lead to poor code quality in the case of embedded processors. The reason is that the special architectural features of embedded processors require performing code selection on data-flow graphs, which are a more general representation of intermediate code. In this paper, we present data-flow graph-based code selection techniques for two architectural families of embedded processors: media processors with support for SIMD instructions and fixed-point DSPs with irregular data paths. Both techniques exploit the fact that, in the area of embedded systems, high code quality is a much more important goal than high compilation speed. We demonstrate that certain architectural features can only be utilized by graph-based code selection, while in other cases this approach leads to a significant increase in code quality as compared to tree-based code selection.
HIDDEN MARKOV INDEPENDENT COMPONENT ANALYSIS AS A MEASURE OF COUPLING IN MULTIVARIATE FINANCIAL TIME SERIES

Modelling the dynamics of financial markets has been an area of active research in recent years. This paper presents a time series analysis model which can be used to infer patterns within financial data, in order to better understan d the dynamics of financial markets. The focus of the paper is on finding causal and time-scale relationships between financial time series. Wavelets are used to extract useful time-scale information from financial data at different frequencies and mutual information between time series is used as the canonical measure of coupling. A Hidden Markov Independent Component Analysis (HMICA) model is used to infer a series of hidden states and it is shown that these hidden states are indicative of changes in mutual information between time series at various different time scales.
ACUT: Out-Of-Core Delaunay Triangulation of Large Terrain Data Sets

In the last couple of years, very detailed highresolution terrain data sets have become available thanks to new acquisition techniques, e.g., the airborne laser scanning. Such data sets contain, typically, several millions of points and, therefore, several gigabytes are required just to store them, which disallows their loading into the memory of a common computer. In this paper, we propose a novel out-of-core technique for construction of the Delaunay triangulation of such large data sets. It is based on the method of incremental insertion with flipping that is simple, robust and can be easily extended for weights of points, constraints, etc. The proposed technique was tested on various data sets with sizes up to 128M points on a commodity hardware (P4 3.2GHz, 2GB RAM, 250GB SATA disk). The largest data set was processed in about 2.5 hours.
A family of normalized least mean fourth algorithms and their tracking analysis

In this work, a family of normalized least mean fourth algorithms is presented. Unlike the LMF algorithm, the convergence behavior of these algorithms is independent of the input data correlation statistics. Moreover, the tracking analysis of these algorithms is carried out in the presence of two sources of nonstationarites (carrier frequency offset between transmitter and receiver, and random variations in the environment). Simulation results agree very closely with theory and an enhancement in performance is obtained through the use of these techniques over the LMF algorithm. the step size.
An ANN-GA Semantic Rule-Based System to Reduce the Gap Between Predicted and Actual Energy Consumption in Buildings

This paper addresses the endemic problem of the gap between predicted and actual energy performance in public buildings. A system engineering approach is used to characterize energy performance factoring in building intrinsic properties, occupancy patterns, environmental conditions, as well as available control variables and their respective ranges. Due to the lack of historical data, a theoretical simulation model is considered. A semantic mapping process is proposed using principle component analysis (PCA) and multi regression analysis (MRA) to determine the governing (i.e., most sensitive) variables to reduce the energy gap with a (near) real-time capability. Further, an artificial neural network (ANN) is developed to learn the patterns of this semantic mapping, and is used as the cost function of a genetic algorithm (GA)-based optimization tool to generate optimized energy saving rules factoring in multiple objectives and constraints. Finally, a novel rule evaluation process is developed to evaluate the generated energy saving rules, their boundaries, and underpinning variables. The proposed solution has been tested on both a simulation platform and a pilot building – a care home in the Netherlands. Validation results suggest an average 25% energy reduction while meeting occupants’ comfort conditions.