Miracle-dz/UniFAR-FTUs huggingface dataset:
An angiogenic laminin site and its antagonist bind through the a v b 3 and a 5 b 1 integrins

Angiogenesis is important for wound healing, tumor growth, and metastasis. Endothelial cells differentiate into capillary-like structures on a laminin1-rich matrix (Matrigel). We previously identified 20 angiogenic sites on laminin-1 (a1b1g1) by screening 559 overlapping synthetic peptides. C16, the most potent g1 chain peptide, blocked laminin-1-mediated adhesion and was the only g1 chain peptide to block attachment to both collagen I and fibronectin. This suggested that C16 was acting via a receptor common to these substrates. We demonstrated that C16 is angiogenic in vivo. Affinity chromatography identified the integrins a5b1 and avb3 as surface receptors. Blocking antibodies confirmed the role of these receptors in C16 adhesion. C16 does not contain an RGD sequence and, as expected, an RGD-containing peptide did not block C16 adhesion nor did C16 act via MAP kinase phosphorylation. Furthermore, we identified a C16 scrambled sequence, C16S, which antagonizes the angiogenic activity of bFGF and of C16 by binding to the same receptors. Because the laminin g1 chain is ubiquitous in most tissues, C16 is likely an important functional site. Since the biological activity of C16 is blocked by a scrambled peptide, C16S may serve as an anti-angiogenic therapeutic agent. —Ponce, M. L. , Nomizu, M. , Kleinman, H. K. An angiogenic laminin site and its antagonist bind through the avb3 and a5b1 integrins. FASEB J. 15, 1389–1397 (2001)
Region graph based text extraction from outdoor images

A graph-based texts extraction scheme using region similarities is proposed. In graph-based text extraction method, region graph is generated based on the extracted character element regions from edge images. The edge weights of the graph are defined by their representative colors, positions, area sizes and aspect ratios of their bounding boxes. The MST is obtained from the graph, since sets of similar regions are the most significant factors in texts extraction techniques. Then, texts blocks with small edge weights are obtained from the MST. Finally, texts regions which satisfy texts conditions are extracted from these subsets. The proposed method extracts texts under complex shadows and low contrast images.
On the Portability and Tuning of SupervisedWord Sense Disambiguation

This report describes a set of experiments carried out to explore the portability of alternative supervised Word Sense Disambiguation algorithms. The aim of the work is threefold: rstly, studying the performance of these algorithms when tested on a diierent corpus from that they were trained on; secondly, exploring their ability to tune to new domains, and thirdly, demonstrating empirically that the LazyBoosting algorithm outperforms state-of-the-art supervised WSD algorithms in both previous situations.
Oxidative stress and ferritin levels in haemodialysis patients.

BACKGROUND Increased oxidative stress (OS) and inflammation are associated with atherosclerotic coronary artery disease in haemodialysis (HD) patients. Ferritin may have other effects in addition to its role in storing intracellular iron. This study was performed to determine any relationships between markers of OS, nutrition and inflammation in HD patients with normal and high ferritin levels. METHODS Our cohort comprised 34 maintenance dialysis patients on erythropoietin therapy and 22 healthy controls. HD patients were divided into two groups: 17 with normal (<800 ng/ml) and 17 with high (>800 ng/ml) ferritin levels, and we measured lipid profile, albumin, highly sensitive C-reactive protein (hsCRP), anti-oxidant enzymes [whole blood glutathione peroxidase (Gpx), serum superoxide dismutase (SOD), paraoxonase, arylestherase (AE) and total anti-oxidant status (TAOC)], anti-oxidants (vitamin C) and lipid peroxidation products [red blood cell malondialdehyde (RBC MDA)]. RESULTS Compared with controls, the HD patients had higher serum urea, blood pressure, triglyceride, hsCRP, RBC MDA, SOD and TAOC values and lower albumin, low-density lipoprotein cholesterol, apolipoprotein AI, paraoxonase, AE and whole blood Gpx activities. Serum vitamin C, uric acid, apolipoprotein B, total- and high-density lipoprotein cholesterol, apolipoprotein B MDA, and lymphocyte levels in the HD patients with normal and high ferritin levels were similar. The OS markers of HD patients did not differ, whether or not they received intravenous iron supplementation or had transferrin saturations < 50% or > or = 50%. CONCLUSION HD patients are in a higher oxidative state, which results in the reduction of total anti-oxidant capacity and also have an increased inflammation status. We could not find a relationship between ferritin level and OS markers in HD patients receiving erythropoietin.
Concepts and Measures of Agency

Agency is inescapably plural in both concept and measurement. In Senâ€™s account of agency, 1) agency is exercised with respect to goals the person values; 2) agency includes effective power as well as direct control; 3) agency may advance well-being or may address other-regarding goals; 4) to identify agency also entails an assessment of the value of the agentâ€™s goals; 5) the agentâ€™s responsibility for a state of affairs should be incorporated into his or her evaluation of it. This chapter refracts the literature on agency measurement through the first four of these characteristics, showing how particular survey-based measures of individual agency elucidate or obscure each distinction. It also observes that existing measures used in development tend to focus on control but not effective freedom, on goals the agent has reason to value rather than goals she values, and on own rather than other-regarding agency. The literature on measurement also raises a number of very relevant issues for the conceptual approach.
Ligand Diversity of Human and Chimpanzee CYP3A4: Activation of Human CYP3A4 by Lithocholic Acid Results from Positive Selection

For currently unknown reasons, the evolution of CYP3A4 underwent acceleration in the human lineage after the split from chimpanzee. We investigated the significance of this event by comparing Escherichia coli-expressed CYP3A4 from humans, chimpanzee, and their most recent common ancestor. The expression level of chimpanzee CYP3A4 was ∼50% of the human CYP3A4, whereas ancestral CYP3A4 did not express in E. coli. Steady-state kinetic analysis with 7-benzyloxyquinoline, 7-benzyloxy-4-(trifluoromethyl)coumarin (7-BFC), and testosterone showed no significant differences between human and chimpanzee CYP3A4. Upon addition of α-naphthoflavone (25 μM), human CYP3A4 showed a slightly decreased substrate concentration at which 50% of the maximal rate Vmax is reached for 7-BFC, whereas chimpanzee CYP3A4 showed a >2-fold increase. No significant differences in inhibition/activation were found for a panel of 43 drugs and endogenous compounds, suggesting that the wide substrate spectrum of human CYP3A4 precedes the human-chimpanzee split. A striking exception was the hepatotoxic secondary bile acid lithocholic acid, which at saturation caused a 5-fold increase in 7-BFC debenzylation by human CYP3A4 but not by chimpanzee CYP3A4. Mutagenesis of human CYP3A4 revealed that at least four of the six amino acids positively selected in the human lineage contribute to the activating effect of lithocholic acid. In summary, the wide functional conservation between chimpanzee and human CYP3A4 raises the prospect that phylogenetically more distant primate species such as rhesus and squirrel monkey represent suitable models of the human counterpart. Positive selection on the human CYP3A4 may have been triggered by an increased load of dietary steroids, which led to a novel defense mechanism against cholestasis.
Discovering Functional Communities in Social Media

We propose an approach for discovering functional communities in social media by identifying groups of users who interact with similar content, represented as dense biclusters in a user-content matrix. We present a heuristic algorithm to efficiently search the space of possible co-clusterings for one which maximizes the value of a given metric, along with a new class of co-clustering metrics that are more suitable for this task than existing metrics. We evaluate our approach using synthetic and real-world datasets.
Multiple Criteria Combinatorial Auction: A B2B Allocation Mechanism for Substitute Goods

We present business-to-business electronic auction mechanism as an alternative to sales usually accomplished through negotiation. The mechanism allows bids that are incompletely specified yet provide an evaluative framework to facilitate the allocation of complementary goods when substitutes are available. The auction form allows the consumer to create a unique combination of goods upon which to place a multiple criteria bid. Bidders are given the flexibility to change and or modify their bids and bundles until a stopping criterion has been reached. The allocation to bidders requires solving a complex combinatorial problem in realtime. Since the proposed integer program model may become computationally intractable as the number of bidders increase, we present simplifying heuristics to make the problem manageable on a large scale. (Combinatorial Auction, Integer Programming, Constraint Programming, Multi-criteria Auction, Greedy Algorithm, Advertising)
Gradual Programming : Bridging the Semantic Gap ( Position Paper )

There is no perfect programming language. Programmers must write code conforming to the idiosyncrasies of a programming language. Thus, there is often a disconnect between the intent of the developer and the meaning of the program. This semantic gap has a negative effect on programmer productivity, software reliability, and execution efficiency. In this position paper, we argue that in order to address this semantic gap, we must drastically rethink how we develop software.
Simple and efficient scheduling scheme in sensor networks

The design of data communication protocols in wireless sensor networks often take into account real-time delivery of reports of the mission critical applications. The sensed data need to be delivered with some real-time constraints, such as end-to-end deadlines. Since the data communication model is different from the traditional wireless ad hoc networks or any centralized systems, different solutions have been proposed, such as real-time sensitive routing protocols, data packet prioritization, and real-time scheduling. Most of these solutions prioritize packets at the MAC layer according to their deadlines and distances to the sink. Packet prioritization by itself cannot completely support real-time data communication requirements. In our work, we develop Simple and Efficient Scheduling Scheme in Sensor Networks (SES-SN), in which packet scheduling, queuing as well as routing are considered. This scheme efficiently utilizes the limited energy and available memory resources of sensor nodes and also has a significant impact on the success of real-time sensor data communication and avoids collision.
S-GOODNESS FOR LOW-RANK MATRIX RECOVERY, TRANSLATED FROM SPARSE SIGNAL RECOVERY

Low-rank matrix recovery (LMR) is a rank minimization problem subject to linear equality constraints, and it arises in many fields such as signal and image processing, statistics, computer vision, system identification and control. This class of optimization problems is generally NP-hard. A popular approach replaces the rank function with the nuclear norm of the matrix variable. In this paper, we extend and characterize the concept of s-goodness for a sensing matrix in sparse signal recovery (proposed by Juditsky and Nemirovski [Math Program, 2011]) to linear transformations in LMR. Utilizing the two characteristic s-goodness constants, γs and γ̂s, of a linear transformation, we derive necessary and sufficient conditions for a linear transformation to be s-good. Moreover, we establish the equivalence of s-goodness and the null space properties. Therefore, s-goodness is a necessary and sufficient condition for exact s-rank matrix recovery via the nuclear norm minimization.
Self-similar collapse with cooling and heating in an expanding universe

We derive self-similar solutions including cooling and heating in an Einstein-de Sitter universe, and investigate the effects of cooling and heating on the gas density and temperature distributions. We assume that the cooling rate has a power-law dependence on the gas density and temperature, A ρ A T B , and that the heating rate is r pT. The values of A and B are chosen by requiring that the cooling time is proportional to the Hubble time in order to obtain similarity solutions. In the region where the cooling rate is greater than the heating rate, a cooling inflow is established, and the gas is compressed and heats up. Because the compression is greater in the inner region than in the outer region, the temperature becomes an increasing profile toward the centre. In particular, when a large infall velocity is produced due to an enormous energy loss, the slope of the density approaches a value that depends on A, B, and the velocity slope, and the slope of the temperature approaches -1. On the other hand, in the region where the heating rate is greater than the cooling rate, the infall velocity is suppressed, compression of the gas is weakened, and the gas cools down. The slope of the density becomes shallow due to suppression of the contraction, and the temperature is lower than that without heating. The self-similar collapse presented here gives insights to the effects of cooling and heating on the gas distributions in galaxies and clusters of galaxies.
Adapting an online learning system for school mathematics to mathematics teacher education

Recent Australian Institute of Teaching and School Leadership (AITSL) standards for primary mathematics teacher education highlight the significance of mathematical content knowledge for graduating teachers. Online learning systems have become popular for school mathematics in recent years. As the development and maintenance of such systems requires more resources than a teacher education faculty is likely to have available, this study identifies ways of adapting a commercial resource intended for schools to one that is appropriate for teacher education. The study reported is based on the experiences of the authors in building Cambridge HOTmaths into the design of two early undergraduate units for pre-service primary mathematics teachers at Murdoch University over 2011 and 2012. Students are provided with a licence for personal use of the online learning system, and a small mark was allocated to reflect individual student’s engagement with the online materials, partly to recognise the significant learning of mathematics involved, but also to provide an inducement to use it. The software is used periodically in plenary lectures and in workshops, according to individual teacher preferences and practices; specific advice for HOTmaths use is provided in weekly learning guides accessed online by all students. Differences between school use and teacher education use are described in the paper. Data on the extent to which students have made use of HOTmaths are presented, together with student feedback regarding the value of the online experience. Different approaches taken by teaching staff to the use of HOTmaths are also reported. Together, the evidence suggests that there is considerable merit in adapting this resource for use in primary mathematics teacher education.
Windthrow Detection in European Forests with Very High-Resolution Optical Data

With climate change, extreme storms are expected to occur more frequently. These storms can cause severe forest damage, provoking direct and indirect economic losses for forestry. To minimize economic losses, the windthrow areas need to be detected fast to prevent subsequent biotic damage, for example, related to beetle infestations. Remote sensing is an efficient tool with high potential to cost-efficiently map large storm affected regions. Storm Niklas hit South Germany in March 2015 and caused widespread forest cover loss. We present a two-step change detection approach applying commercial very high-resolution optical Earth Observation data to spot forest damage. First, an object-based bi-temporal change analysis is carried out to identify windthrow areas larger than 0.5 ha. For this purpose, a supervised Random Forest classifier is used, including a semi-automatic feature selection procedure; for image segmentation, the large-scale mean shift algorithm was chosen. Input features include spectral characteristics, texture, vegetation indices, layer combinations and spectral transformations. A hybrid-change detection approach at pixel-level subsequently identifies small groups of fallen trees, combining the most important features of the previous processing step with Spectral Angle Mapper and Multivariate Alteration Detection. The methodology was evaluated on two test sites in Bavaria with RapidEye data at 5 m pixel resolution. The results regarding windthrow areas larger than 0.5 ha were validated with reference data from field visits and acquired through orthophoto interpretation. For the two test sites, the novel object-based change detection approach identified over 90% of the windthrow areas (≥0.5 ha). The red edge channel was the most important for windthrow identification. Accuracy levels of the change detection at tree level could not be calculated, as it was not possible to collect field data for single trees, nor was it possible to perform an orthophoto validation. Nevertheless, the plausibility and applicability of the pixel-based approach is demonstrated on a second test site.
Direct, Differential Effects of Tamoxifen, 4-Hydroxytamoxifen, and Raloxifene on Cardiac Myocyte Contractility and Calcium Handling

Tamoxifen (Tam), a selective estrogen receptor modulator, is in wide clinical use for the treatment and prevention of breast cancer. High Tam doses have been used for treatment of gliomas and cancers with multiple drug resistance, but long QT Syndrome is a side effect. Tam is also used experimentally in mice for inducible gene knockout in numerous tissues, including heart; however, the potential direct effects of Tam on cardiac myocyte mechanical function are not known. The goal of this study was to determine the direct, acute effects of Tam, its active metabolite 4-hydroxytamoxifen (4OHT), and related drug raloxifene (Ral) on isolated rat cardiac myocyte mechanical function and calcium handling. Tam decreased contraction amplitude, slowed relaxation, and decreased Ca2+ transient amplitude. Effects were primarily observed at 5 and 10 μM Tam, which is relevant for high dose Tam treatment in cancer patients as well as Tam-mediated gene excision in mice. Myocytes treated with 4OHT responded similarly to Tam-treated cells with regard to both contractility and calcium handling, suggesting an estrogen-receptor independent mechanism is responsible for the effects. In contrast, Ral increased contraction and Ca2+ transient amplitudes. At 10 μM, all drugs had a time-dependent effect to abolish cellular contraction. In conclusion, Tam, 4OHT, and Ral adversely and differentially alter cardiac myocyte contractility and Ca2+ handling. These findings have important implications for understanding the Tam-induced cardiomyopathy in gene excision studies and may be important for understanding effects on cardiac performance in patients undergoing high-dose Tam therapy.
A graph matching method based on probe assignments

In this paper, a graph matching method and a distance between attributed graphs are defined. Both approaches are based on graph probes. Probes can be seen as features exctracted from a given graph. They represent a local information. According two graphs G1,G2, the univalent mapping can be expressed as the minimum-weight probe matching between G1 and G2 with respect to a cost function.
Incremental recursive fisher linear discriminant for online feature extraction

In this paper, we propose a new online feature extraction algorithm called Incremental Recursive Fisher Linear Discriminant (IRFLD). In the conventional Linear Discriminant Analysis (LDA), the number of discriminant vectors is limited to the number of classes minus one due to the rank of a between-class covariance matrix. However, the proposed IRFLD can remove this limitation. That is, an arbitrary number of discriminant vectors up to input dimensions can be obtained to construct a feature space. In the proposed IRFLD, the Pang et al.'s Incremental Linear Discriminant Analysis (ILDA) is extended such that effective discriminant vectors are recursively searched for the complementary space of a conventional discriminant space. In addition, a suitable number of effective discriminant vectors are automatically determined using a cross-validation method, where several representative training data are held as validation data and they are updated using the k-means clustering whenever a chunk of new training data are given. The performance of IRFLD is evaluated for 5 benchmark data sets. The experimental results show that the final classification accuracies of IRFLD are always better than those of ILDA. We also reveal that this performance improvement is attained by adding discriminant vectors in a complementary discriminant space.
ALMA antenna

The Atacama Large Millimeter Array (ALMA) is a millimeter and submillimeter astronomical observatory being built in northern Chile. The array consists of a 12-m array (50 12-m antennas) and of the Atacama compact array (4 12-m antennas and 12 7-m antennas). The ALMA antennas are required to meet very stringent technical specification such as pointing/tracking accuracy, fast motion capability, and surface accuracy etc. under operating conditions. The paper describes the design features and summarizes major performance of the ALMA antennas.
Ensemble Neural Network Strategy for Predicting Credit Default Evaluation

Credit default constitutes a cornerstone for the banking industry. Effects of credit default play an important role in banking industry. Decisions concerning credits granting are one of the most crucial issues in an everyday banks' policy. Well-allocated credits may become one of the biggest sources of profits for any financial organizations. The key problem consists of distinguishing healthy (that surely repay) and delinquent (that likely default) credit applicants. Credit risk evaluation is an important and interesting management science problem in financial analysis. In this paper, the architecture used the theory of artificial neural networks and business rules to correctly determine whether a customer is default or not. The Feed-forward back propagation neural network is used to predict the credit default. The results of applying the artificial neural networks methodology to classify credit risk based upon selected parameters show abilities of the network to learn the patterns.
Higher wavelet entropy in the EEG with mild cognitive impairment patients

In this study, 8 elderly mild cognitive impairment (MCI) patients and 17 normal controls participated in a cognition task during which they were asked to judge whether the color of two graphics is matched or not. We calculated the wavelet entropy (WE) of the event-related EEG signal to explore the difference of complexity between MCI patients and normal controls. The results revealed that 1) MCI patients had a higher disordered bioelectric activity at medial-center, medial-posterior and right-posterior than the normal controls; 2) greater entropy was found in anterior brain compared with other two regions in normal controls in medial hemisphere. While the MCI patients had the smallest entropy in anterior brain in right hemisphere under the match task; 3) The normal controls had higher entropy in medial-anterior and the MCI patients had lower entropy in right-anterior during the color-match task.