Miracle-dz/UniFAR-FTUs huggingface dataset:
Angiogenesis , Metastasis , and the Cellular Microenvironment Disseminated Prostate Cancer Cells Can Instruct Hematopoietic Stem and Progenitor Cells to Regulate Bone Phenotype

Prostate cancer metastases and hematopoietic stem cells (HSC) frequently home to the bonemarrow, where they compete to occupy the sameHSC niche. We have also shown that under conditions of hematopoietic stress, HSCs secrete the bone morphogenetic proteins (BMP)-2 and BMP-6 that drives osteoblastic differentiation from mesenchymal precursors. As it is not known, we examined whether metastatic prostate cancer cells can alter regulation of normal bone formation by HSCs and hematopoietic progenitor cells (HPC). HSC/HPCs isolated frommice bearing nonmetastatic and metastatic tumor cells were isolated and their ability to influence osteoblastic and osteoclastic differentiation was evaluated.When the animals were inoculated with the LNCaP C4-2B cell line, which produces mixed osteoblastic and osteolytic lesions in bone, HPCs, but not HSCs, were able to induced stromal cells to differentiate down an osteoblastic phenotype. Part of themechanism responsible for this activity was the production of BMP-2. On the other hand, when the animals were implanted with PC3 cells that exhibits predominantly osteolytic lesions in bone, HSCs derived from these animals were capable of directly differentiating into tartrate-resistant acid phosphatase–positive osteoclasts through an interleukin-6–mediated pathway. These studies for the first time identify HSC/HPCs as novel targets for future therapy involved in the bone abnormalities of prostate cancer. Mol Cancer Res; 10(3); 282–92. 2012 AACR.
Detection and Association Based Multi-target Tracking in Surveillance Video

The Multiple Target Tracking (MTT) problem is one of the fundamental challenges in computer vision. In this paper, we propose a feasible detection and association based MTT system which uses a modified Deformable Part-Based Model (DPM) to generate detection results and then links detections into track lets to further form long trajectories. We first describe our modified DPM algorithm which could automatically discovery optimal object part configurations to improve detection performance. Next to tackle the MTT problem, e.g., Associating detections under imperfect detector identifications, severe occlusions and interferences between objects, etc conditions, we introduce an EM-like inference algorithm that alternatively optimizes the Trajectory Models (TM) for all the targets and the Maximum A Posterior (MAP) solution of the Markov Random Field(MRF) model. At the E-step, we update the TM based on the inference result of the current MRF model, and at the M-step, we use the up-to-date TM to re-compute the probabilities in the MRF model to re-fine the MAP solution. As shown by our experimental results, the presented detection and association based MTT system leads to satisfactory performance.
Dual time point method for the quantification of irreversible tracer kinetics: A reference tissue approach applied to [18F]-FDOPA brain PET

The Patlak graphical analysis (PGAREF) for quantification of irreversible tracer binding with a reference tissue model was approximated by a dual time point imaging approach (DTPREF). The DTPREF was applied to 18 [18F]-FDOPA brain scans using the occipital cortex as reference region (DTPOCC) and compared to both PGAOCC and striatal-to-occipital ratios (SOR). Pearson correlation analysis and Bland–Altman plots showed an excellent correlation and good agreement between DTPOCC and PGAOCC, while correlations between SOR and PGAOCC were consistently lower. Linear discriminant analysis (LDA) demonstrated a similar performance for all methods in differentiating patients with Parkinson’s disease (PD) from healthy controls (HC). Specifically for [18F]-FDOPA brain imaging, these findings validate DTPOCC as an approximation for PGAOCC, providing the same quantitative information while reducing the acquisition time to two short static scans. For PD patients, this approach can greatly improve patient comfort while reducing motion artifacts and increasing image quality. In general, DTPREF can improve the clinical applicability of tracers with irreversible binding characteristics when a reference tissue is available.
Application of genomics-assisted breeding for generation of climate resilient crops: progress and prospects

Climate change affects agricultural productivity worldwide. Increased prices of food commodities are the initial indication of drastic edible yield loss, which is expected to increase further due to global warming. This situation has compelled plant scientists to develop climate change-resilient crops, which can withstand broad-spectrum stresses such as drought, heat, cold, salinity, flood, submergence and pests, thus helping to deliver increased productivity. Genomics appears to be a promising tool for deciphering the stress responsiveness of crop species with adaptation traits or in wild relatives toward identifying underlying genes, alleles or quantitative trait loci. Molecular breeding approaches have proven helpful in enhancing the stress adaptation of crop plants, and recent advances in high-throughput sequencing and phenotyping platforms have transformed molecular breeding to genomics-assisted breeding (GAB). In view of this, the present review elaborates the progress and prospects of GAB for improving climate change resilience in crops, which is likely to play an ever increasing role in the effort to ensure global food security.
Equivalence of four descriptions of generalized line graphs

A new characterization of generalized line graphs, analogous to that of line graphs found by Van Rooij and Wilf [Acta Math Acad Sci Hungar 16 (1965), 263–269] is obtained. By a cycle of implications, we settle the equivalence of the definition of generalized line graph given by Hoffman [Combinatorial Structures and their Applications, Gordon and Breach, NY, 1970, pp. 173–176], its description by vectorial representation [P. J. Cameron et al., Algebra 43 (1976), 305–327], the current characterization and the one involving minimal nongeneralized line graphs [D. Cvetković et al., J Graph Theory 5(4) (1981), 385–399; D. Cvetković et al., Des Codes Cryptogr 34(2–3) (2005), 229–240; S. B. Rao et al., Combinatorics and Graph Theory, Lecture Notes 885, Springer, Berlin, 1981, pp. 459–472; G. R. Vijayakumar, J Combin Inform System Sci 9(3) (1984), 182–192]. This process includes a short method of finding the aforementioned graphs, analogous to the method found by Beineke [J Combin Theory 9 (1970), 129–135] to obtain all minimal nonline graphs. © 2010 Wiley Periodicals, Inc. J Graph Theory 67: 27‐33, 2011
Approaches to Vaccination Among Populations in Areas of Conflict

Vaccination is an important and cost-effective disease prevention and control strategy. Despite progress in vaccine development and immunization delivery systems worldwide, populations in areas of conflict (hereafter, “conflict settings”) often have limited or no access to lifesaving vaccines, leaving them at increased risk for morbidity and mortality related to vaccine-preventable disease. Without developing and refining approaches to reach and vaccinate children and other vulnerable populations in conflict settings, outbreaks of vaccine-preventable disease in these settings may persist and spread across subnational and international borders. Understanding and refining current approaches to vaccinating populations in conflict and humanitarian emergency settings may save lives. Despite major setbacks, the Global Polio Eradication Initiative has made substantial progress in vaccinating millions of children worldwide, including those living in communities affected by conflicts and other humanitarian emergencies. In this article, we examine key strategic and operational tactics that have led to increased polio vaccination coverage among populations living in diverse conflict settings, including Nigeria, Somalia, and Pakistan, and how these could be applied to reach and vaccinate populations in other settings across the world.
Adiponectin in renal disease: relationship to phenotype and genetic variation in the gene encoding adiponectin.

BACKGROUND The prevalence of cardiovascular disease (CVD) and inflammation is high in patients with end-stage renal disease (ESRD). Adiponectin is an adipocytokine that may have significant anti-inflammatory and anti-atherosclerotic effects. Low adiponectin levels have previously been found in patients with high risk for CVD. METHODS In a cohort of 204 (62% males) ESRD patients aged 52 +/- 1 years the following parameters were studied: presence of CVD, body composition, plasma adiponectin (N= 107), cholesterol, triglycerides, HDL-cholesterol, serum leptin, high-sensitivity C-reactive protein (hs-CRP), urinary albumin excretion (UAE), and single-nucleotide polymorphisms (SNPs) in the apM1 gene at positions -11391, -11377, 45, and 276. Thirty-six age- (52 +/- 2 years) and gender-matched (64% males) healthy subjects served as control subjects. RESULTS Markedly (P < 0.0001) elevated median plasma adiponectin levels were observed in ESRD patients (22.2 microg/mL), especially type 1 diabetic patients (36.8 microg/mL), compared to control subjects (12.2 microg/mL). Log plasma adiponectin correlated to visceral fat mass (R=-0.29; P < 0.01) and Log hs-CRP (R=-0.26; P < 0.01). In a stepwise (forward followed by backward) multiple regression model only type-1 diabetes (P < 0.001) and visceral fat mass (P < 0.05) were independently associated with plasma adiponectin levels. The adiponectin gene -11377 C/C genotype was associated with a lower prevalence of CVD (25 vs. 42%) compared to the G/C genotype. CONCLUSION The present cross-sectional study demonstrates that, whereas genetic variations seem to have a minor impact on circulating adiponectin levels, lower visceral fat mass and type 1 diabetes mellitus are associated with elevated plasma adiponectin levels in ESRD patients. Furthermore, low levels of adiponectin are associated with inflammation in ESRD.
Mitigating Data Sparsity Using Similarity Reinforcement-Enhanced Collaborative Filtering

The data sparsity problem has attracted significant attention in collaborative filtering-based recommender systems. To alleviate data sparsity, several previous efforts employed hybrid approaches that incorporate auxiliary data sources into recommendation techniques, like content, context, or social relationships. However, due to privacy and security concerns, it is generally difficult to collect such auxiliary information. In this article, we focus on the pure collaborative filtering methods without relying on any auxiliary data source. We propose an improved memory-based collaborative filtering approach enhanced by a novel similarity reinforcement mechanism. It can discover potential similarity relationships between users or items by making better use of known but limited user-item interactions, thus to extract plentiful historical rating information from similar neighbors to make more reliable and accurate rating predictions. This approach integrates user similarity reinforcement and item similarity reinforcement into a comprehensive framework and lets them enhance each other. Comprehensive experiments conducted on several public datasets demonstrate that, in the face of data sparsity, our approach achieves a significant improvement in prediction accuracy when compared with the state-of-the-art memory-based and model-based collaborative filtering algorithms.
Lightweight Cloud Application Sandboxing

Modern cloud applications can quickly grow to an elaborate and intricate tangle of services. In this scenario, paying attention to security aspects is important to mitigate the impact of incidents. Indeed, several research works and industrial standards recommend the integration of least privilege policies to prevent disruptions such as file system tampering. Unfortunately, technologies like containers virtualize file system resources with a volume-based approach, which may be overly coarse.In this work we address this problem proposing an approach that restrict application access to file system resources with a resource-based granularity. To this end, we develop a flexible and intuitive tool that relies on instrumentation to collect, merge, and audit the activity traces generated by any application component. We then demonstrate how this information is used to create fine-grained access policies, and introduce sandboxing using recent kernel security modules, strengthening the security boundary of the whole application. In the experimental evaluation we showcase the mitigation capabilities associated with our approach, and the low performance footprint. The proposal is associated with an open source implementation.
Conflict-Tolerant Real-Time Specifications in Metric Temporal Logic

A framework based on the notion of ``conflict-tolerance'' was proposed in [DG08, DGRS08] as a compositional methodology for developing and reasoning about systems that comprise multiple independent controllers. A central notion in this framework is that of a ``conflict-tolerant'' specification for a controller. In this work we propose a way of defining conflict-tolerant real-time specifications in Metric Interval Temporal Logic (MITL). We call our logic CT-MITL for Conflict-Tolerant MITL. We then give a clock optimal ``delay-then-extend'' construction for building a timed transition system for monitoring past-MITL formulas. We show how this monitoring transition system can be used to solve the associated verification and synthesis problems for CT-MITL.
A Conceptual Framework of Academic Well-Being

The organisational transformation in higher education institutions has troubled academics well-being, mostly where there is insufficient care and support from leaders to simplify the change. The study aims to establish the conceptual framework of academic well-being in Buddhist Colleges in Indonesia. Qualitative results designate that stress and conflict as significant consequences of organizational change; with other adverse effects including reduced academic freedom, family life, health, and distracted career prospects. As a result, reduction in academic satisfaction and well-being has occured. However, these contrary consequences were happened in a short term only. In addition, the idea of diminishing marginal suggests that academics resisted to change firstly but then believed the changes will create a better circumstance at a certain period. The conceptual framework is to help building academic well-being concepts in Buddhist College in Indonesia.Organizational Change, Academic Well-Being, Conflict, Academic Satisfaction
[Prognostic factors in breast cancer].

Prognostic factors are clinical and pathological features that give information in estimating the likely clinical outcome of an individual suffering from cancer. The author gives a short review of the most important prognostic factors in breast cancer. 376 breast cancer cases of a ten year interval in a county hospital are summarized. Traditional clinico-pathological parameters i.e. TNM and steroid receptor status are discussed. The more common karyotipic, oncogene and tumor suppressor gene alterations are outlined in the study. Methods for their detection are presented and their value in prognostication is reviewed. Emphasis was laid on steroid receptors, c-erpB-2, p53 and bcl-2 alterations. Genes responsible for heritable forms of increased breast cancer risk are briefly reviewed.
Chronic imipramine treatment-induced changes in acetylcholinesterase (EC 3.1.1.7) activity in discrete rat brain regions.

Cholinergic as well as monoaminergic neurotransmission seems to be involved in the etiology of affective disorders. Chronic treatment with imipramine, a classical antidepressant drug, induces adaptive changes in monoaminergic neurotransmission. In order to identify possible changes in cholinergic neurotransmission we measured total, membrane-bound and soluble acetylcholinesterase (Achase) activity in several rat brain regions after chronic imipramine treatment. Changes in Achase activity would indicate alterations in acetylcholine (Ach) availability to bind to its receptors in the synaptic cleft. Male rats were treated with imipramine (20 mg/kg, i.p.) for 21 days, once a day. Twenty-four hours after the last dose the rats were sacrificed and homogenates from several brain regions were prepared. Membrane-bound Achase activity (nmol thiocholine formed min-1 mg protein-1) after chronic imipramine treatment was significantly decreased in the hippocampus (control = 188.8 +/- 19.4, imipramine = 154.4 +/- 7.5, P < 0.005) and striatum (control = 850.9 +/- 59.6, imipramine = 742.5 +/- 34.7, P < 0.005). A small increase in total Achase activity was observed in the medulla oblongata and pons. No changes in enzyme activity were detected in the thalamus or total cerebral cortex. Since the levels of Achase seem to be enhanced through the interaction between Ach and its receptors, a decrease in Achase activity may indicate decreased Ach release by the nerve endings. Therefore, our data indicate that cholinergic neurotransmission is decreased after chronic imipramine treatment which is consistent with the idea of an interaction between monoaminergic and cholinergic neurotransmission in the antidepressant effect of imipramine.
Recent advances in the management of resistant hypertension

And suddenly, following the preliminary results of renal denervation and carotid baroreceptor stimulation, a big interest in resistant hypertension rose, and all interventionists, many of them with no previous experience with hypertension, fell in love with hypertension and especially resistant hypertension. In the European Society of Hypertension/International Society of Hypertension (ESH/ISH) 2014 Joint Hypertension meeting in Athens, there were no more than four to five sessions related to resistant hypertension and renal denervation, while in the 2014 EuroPCR meeting there were more than 60 renal denervation sessions! In light of the growing scientific interest in the treatment of this patient group, an update on the treatment available and some concerns regarding the definition and treatment of resistant hypertension is presented.
Trading Dynamics in Decentralized Markets with Adverse Selection

We study a dynamic, decentralized lemons market with one-time entry and characterize its set of equilibria. Our framework offers a theory of how “frozen” markets suffering from adverse selection recover or “thaw” over time endogenously; given an initial fraction of lemons, our model delivers sharp predictions about the length of time it takes for the market to recover, and how prices and the composition of assets in the market behave over this horizon. We use our framework to analyze a form of government intervention introduced during the recent financial crisis in order to help unfreeze the market for asset-backed securities. We find that, depending on the fraction of lemons in the market, such an intervention can speed up or slow down market recovery. More generally, our analysis highlights that the success of an intervention in a lemons market depends on both its size and duration.
Power Prediction in Large Scale Multiple Testing: A Fourier Approach

A problem that is frequently found in large-scale multiple testing is that, in the present stage of experiment (e.g. gene microarray, functional MRI), the signals are so faint that it is impossible to attain a desired level of testing power, and one has to enroll more samples in the follow-up experiment. Suppose we are going to enlarge the sample size by n times in the follow-up experiment, where n > 1 is not necessary an integer. A problem of great interest is, given data based on the current stage of experiment, how to predict the testing power after the sample size is enlarged by n times. We consider test z-scores and model the test statistics in the current experiment as Xj ∼ N(μj , 1), 1 ≤ j ≤ p. We propose a Fourier approach to predicting the testing power after n replicates. The approach produces a very accurate prediction for moderately large values of n ( n ≤ 7). Finally, we discuss potential applications of this method on real data with emphasis on gene microarray data. Acknowledgement : I would like to thank Peter Huggins for his valuable discussions.
STABLE LOCOMOTION OF FEEDFORWARD CONTROLLED ONE-LEGGED ROBOT

INTRODUCTION An accepted model for understanding the dynamics of legged locomotion is the simple spring-mass model introduced by Blickhan [1] and further researched by Seyfarth, et. al. [3]. In reality, however, humans and animals use segmented legs for locomtion instead of simple springs. The combination of both in a motor-driven mechanical application should therefore be more biologically inspired. The aim of this study was to investigate, wether an actuated mechanical system allows stable locomotion without sensory feedback.
Object Segmentation with Membrane Computing ⋆

Membrane computing is a new class of distributed and parallel computing models, which is inspired by the structure and functioning of living cells as well as tissues or higher order biological structures. In this paper, a novel infrared object segmentation method with membrane computing is proposed. It is based on a specially designed cell-like P system, which is used to obtain the optimal parameters quickly. On several infrared images, the performance of the proposed method is compared with those of the existing entropy-based object segmentation methods as well as genetic algorithm (GA)-based and ant colony optimization (ACO)-based methods.
Analytical modelling of shared buffer ATM switches with hot-spot pushout under bursty traffic

Asynchronous transfer mode (ATM) shared buffer switches have numerous advantages such as relatively high throughput and good buffer utilization, and they are increasingly favoured in recent VLSI switch designs. But under nonuniform traffic, the performance degrades dramatically due to the monopolization of the buffer by some favoured cells. To overcome this, restricted types of sharing and hot-spot pushout (HSPO) have been proposed, and the latter has been shown by simulation to perform better in all situations. We construct an analytical model for the first time, for evaluation of a shared buffer ATM switch with HSPO under bursty traffic. We use an urn model to approximate the effects of pushout, while keeping only four state-variables, and our results give a very good agreement with simulation.
A novel virtual world based HCI paradigm for multimedia scholarly communication

The sharing of academic knowledge through printed publications has been widely and successfully utilized for more than a hundred years. However, the need to process huge amounts of data in scientific analysis and communicate its results to the scientific community has presented a big challenge for researchers in the data-intensive era. In addition to providing accurate and flexible graphical representations of data, the entire research process should ideally be made verifiable by peers. While web-based tools have been proposed to address this problem, most of them lack important features for scientific work such as real-time collaboration, powerful multimedia visualization and interaction, and environment persistency. Therefore, in this paper, we will propose the use of virtual world technology to address these issues. In particular, we will give an overview of the different methods that we have implemented in the context of multimedia interaction, which is considered the most critical factor in the development of virtual worlds as sound platforms for human-centered multimedia systems.