Miracle-dz/UniFAR-FTUs huggingface dataset:
Selection of Novel Peptides Homing the 4T1 CELL Line: Exploring Alternative Targets for Triple Negative Breast Cancer

The use of bacteriophages to select novel ligands has been widely explored for cancer therapy. Their application is most warranted in cancer subtypes lacking knowledge on how to target the cancer cells in question, such as the triple negative breast cancer, eventually leading to the development of alternative nanomedicines for cancer therapeutics. Therefore, the following study aimed to select and characterize novel peptides for a triple negative breast cancer murine mammary carcinoma cell line– 4T1. Using phage display, 7 and 12 amino acid random peptide libraries were screened against the 4T1 cell line. A total of four rounds, plus a counter-selection round using the 3T3 murine fibroblast cell line, was performed. The enriched selective peptides were characterized and their binding capacity towards 4T1 tissue samples was confirmed by immunofluorescence and flow cytometry analysis. The selected peptides (4T1pep1 –CPTASNTSC and 4T1pep2—EVQSSKFPAHVS) were enriched over few rounds of selection and exhibited specific binding to the 4T1 cell line. Interestingly, affinity to the human MDA-MB-231 cell line was also observed for both peptides, promoting the translational application of these novel ligands between species. Additionally, bioinformatics analysis suggested that both peptides target human Mucin-16. This protein has been implicated in different types of cancer, as it is involved in many important cellular functions. This study strongly supports the need of finding alternative targeting systems for TNBC and the peptides herein selected exhibit promising future application as novel homing peptides for breast cancer therapy.
International Stochastic Discount Factors and Covariance Risk

We consider an analytical framework that aims at investigating the impact of stochastic covariance and thus stochastic correlation between international economies. Using a Wishart pure diffusion process, we model the covariance matrix of country-specific stochastic discount factors (SDF) to incorporate stochastic volatility and unspanned stochastic covariance. We infer the dynamic structure of the SDFs and the time series of latent state variables by embedding a nonlinear filter in a quasi maximum likelihood estimation approach. Our findings suggest that at the outbreak of the financial crisis in the second half of the year 2008 stochastic discount factors face a sharp decrease in correlation associated with high levels of exchange rate volatility. After a short adaption period, local correlations stabilize at high positive levels. This indicates vanishing foreign currency risk premia which is in line with the breakdown of the carry trade phenomenon.
S-SCAM/MAGI-2 Is an Essential Synaptic Scaffolding Molecule for the GluA2-Containing Maintenance Pool of AMPA Receptors

Synaptic plasticity, the cellular basis of learning and memory, involves the dynamic trafficking of AMPA receptors (AMPARs) into and out of synapses. One of the remaining key unanswered aspects of AMPAR trafficking is the mechanism by which synaptic strength is preserved despite protein turnover. In particular, the identity of AMPAR scaffolding molecule(s) involved in the maintenance of GluA2-containing AMPARs is completely unknown. Here we report that the synaptic scaffolding molecule (S-SCAM; also called membrane-associated guanylate kinase inverted-2 and atrophin interacting protein-1) plays the critical role of maintaining synaptic strength. Increasing S-SCAM levels in rat hippocampal neurons led to specific increases in the surface AMPAR levels, enhanced AMPAR-mediated synaptic transmission, and enlargement of dendritic spines, without significantly effecting GluN levels or NMDA receptor (NMDAR) EPSC. Conversely, decreasing S-SCAM levels by RNA interference-mediated knockdown caused the loss of synaptic AMPARs, which was followed by a severe reduction in the dendritic spine density. Importantly, S-SCAM regulated synaptic AMPAR levels in a manner, dependent on GluA2 not GluA1, sensitive to N-ethylmaleimide-sensitive fusion protein interaction, and independent of activity. Further, S-SCAM increased surface AMPAR levels in the absence of PSD-95, while PSD-95 was dependent on S-SCAM to increase surface AMPAR levels. Finally, S-SCAM overexpression hampered NMDA-induced internalization of AMPARs and prevented the induction of long term-depression, while S-SCAM knockdown did not. Together, these results suggest that S-SCAM is an essential AMPAR scaffolding molecule for the GluA2-containing pool of AMPARs, which are involved in the constitutive pathway of maintaining synaptic strength.
Thermodynamic Functions of Dense Plasmas: Analytic Approximations for Astrophysical Applications

We briefly review analytic approximations of thermodynamic functions of fully ionized nonideal electronion plasmas, applicable in a wide range of plasma parameters, including the domains of nondegenerate and degenerate, nonrelativistic and relativistic electrons, weakly and strongly coupled Coulomb liquids, classical and quantum Coulomb crystals. We present improvements to previously published approximations. Our code for calculation of thermodynamic functions based on the reviewed approximations is made publicly available (© 2010 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
Automatic palpation for quantitative ultrasound elastography by visual servoing and force control

The precise location of tumors is an important step in surgical planning that can be obtained from mechanical properties of soft tissues. In this paper we propose a robotic-assisted palpation system that automatically moves an ultrasound probe to optimize the elastography process and improve the resulting elastogram. The main contribution of this work is the use of the elastography modality directly as input of the robot controller. Force measures are also considered in the probe control in order to automatically induce soft tissue deformation needed for real-time elastography imaging process. Moreover, an automatic exploration process is implemented to orient the probe to reach different views of a soft tissue target of interest. This allows to improve the elastogram quality of the element of interest by fusing the information observed from different positions.
A new point cloud simplification algorithm

We present a new technique for the simplification of pointsampled geometry without any prior surface reconstruction. Using Fast Marching farthest point sampling for implicit surfaces and point clouds [1], we devise a coarse-tofine uniform or feature-sensitive simplification algorithm with user-controlled density guarantee. The algorithm is computationally and memory efficient, easy to implement and inherently allows for the generation of progressive and multiresolution representations of the input point set.
Lazy multigram learning environment for automatic content recommendation systems

In this paper I present an environment and algorithm for lazy (incremental) construction of multigram profile models as part of IR (information retrieval) training and exploitation processes. N-grams are traditionally used for natural language text models, but they can be also successfully used for domain independent document classification. I am demonstrating results of a prototype utility which proves these ideas.
Behavioural issues in software engineering process modelling: a multi-paradigm approach

There is a growing consensus that if substantial software engineering productivity improvements are to be realised, much greater attention must be paid to the so-called "softer" (or people-centred) factors. A software engineering process modelling framework is presented. The focus of this work is on behavioural aspects. A feature of the framework is that user views, in a variety of modelling formalisms, may be extracted from a common, highly-abstracted conceptual model. This is consistent with an emerging view that the development of more formal models of organization and management theory (OMT) requires a "horses for courses" approach. That is, different theories are often best represented using different paradigms, with the conceptual model serving as a common reference point through which consistent user views may be derived and verified. Examples of user views, represented and implemented in Prolog and a systems dynamics modelling tool, are presented.
Food Insecurity, Poor Diet Quality, and Suboptimal Intakes of Folate and Iron Are Independently Associated with Perceived Mental Health in Canadian Adults

Background: To address nutrition-related population mental health data gaps, we examined relationships among food insecurity, diet quality, and perceived mental health. Methods: Stratified and logistic regression analyses of respondents aged 19–70 years from the Canadian Community Health Survey, Cycle 2.2 were conducted (n = 15,546). Measures included the Household Food Security Survey Module, diet quality (i.e., comparisons to the Dietary Reference Intakes, Healthy Eating Index), perceived mental health (poor versus good), sociodemographics, and smoking. Results: In this sample, 6.9% were food insecure and 4.5% reported poor mental health. Stratified analysis of food security and mental health status by age/gender found associations for poor diet quality, protein, fat, fibre, and several micronutrients (p-values < 0.05); those who were food insecure tended to have higher suboptimal intakes (p-values < 0.05). After adjustment for covariates, associations in relation to mental health emerged for food insecurity (OR = 1.60, 95% CI 1.45–1.71), poor diet quality (1.61, 95% CI 1.34–1.81), and suboptimal intakes of folate (OR = 1.58, 95% CI 1.17–1.90) and iron (OR = 1.45, 95% CI 1.23–1.88). Conclusions: Population approaches that improve food security and intakes of high quality diets may protect people from poor mental health.
Returns to Equity, Investment and Q: Evidence from the United Kingdom

Conventional wisdom has it that Tobin’s Q cannot help explain aggregate investment. This is puzzling, as recent evidence suggests the closely related user cost approach can do so. We do not attempt to explain this puzzle. Instead, we take an entirely different approach, not using the first-order conditions from the firm’s maximisation problem but instead exploiting the present-value expression for the firm’s value. The standard linearised present-value asset price decomposition suggests that Q should be able to predict other variables, such as stock returns. Using UK data we find that it has strong long-horizon predictive power for debt accumulation, stock returns and UK business investment. The correctly signed results on both returns and investment appear to be robust, and are supported by the commonly used and bootstrapped standard error corrections, as well as recently developed asymptotic corrections.
A Label-Free Impedance Immunosensor Using Screen-Printed Interdigitated Electrodes and Magnetic Nanobeads for the Detection of E. coli O157:H7

Escherichia coli O157:H7 is one of the leading bacterial pathogens causing foodborne illness. In this study, an impedance immunosensor based on the use of magnetic nanobeads and screen-printed interdigitated electrodes was developed for the rapid detection of E. coli O157:H7. Magnetic nanobeads coated with anti-E. coli antibody were mixed with an E. coli sample and used to isolate and concentrate the bacterial cells. The sample was suspended in redox probe solution and placed onto a screen-printed interdigitated electrode. A magnetic field was applied to concentrate the cells on the surface of the electrode and the impedance was measured. The impedance immunosensor could detect E. coli O157:H7 at a concentration of 104.45 cfu·mL−1 (~1400 bacterial cells in the applied volume of 25 μL) in less than 1 h without pre-enrichment. A linear relationship between bacteria concentration and impedance value was obtained between 104 cfu·mL−1 and 107 cfu·mL−1. Though impedance measurement was carried out in the presence of a redox probe, analysis of the equivalent circuit model showed that the impedance change was primarily due to two elements: Double layer capacitance and resistance due to electrode surface roughness. The magnetic field and impedance were simulated using COMSOL Multiphysics software.
Optimizing Squat Technique

There are many variations of the squat technique, including stance width, foot positioning, and squat depth. However, research indicates that the optimal squat technique is a wide stance (>= shoulder width) with natural foot positioning, unrestricted movement of the knees, and full depth while the lordotic curve of the lumbar spine is maintained with a forward or upward gaze.
Improving Parallelism of Recursive Stencil Computations without Sacrificing Cache Performance

The state-of-the-art "trapezoidal decomposition algorithm" for stencil computations on modern multicore machines use recursive divide-and-conquer (DAC) to achieve asymptotically optimal cache complexity cache-obliviously. But the same DAC approach restricts parallelism by introducing artificial dependencies among subtasks in addition to those arising from the defining stencil equations. As a result, the trapezoidal decomposition algorithm has suboptimal parallelism. In this paper we present a variant of the parallel trapezoidal decomposition algorithm called "cache-oblivious wavefront" (COW) that starts execution of recursive subtasks earlier than the start time prescribed by the original algorithm without violating any real dependencies implied by the underlying recurrences, and thus reducing serialization due to artificial dependencies. The reduction in serialization leads to an improvement in parallelism. Moreover, since we do not change the DAC-based decomposition of tasks used in the original algorithm, cache performance does not suffer. We provide experimental measurements of absolute running times, burdened span by Cilkview, and L1/L2 cache misses by PAPI to validate our claims.
An evidential Markovian model for data fusion and unsupervised image classification

The authors deal with the fusion of information and the classification of images supplied by several sensors. By intrinsic characteristics of each sensor, the provided information is usually defined on a different set of hypotheses, called frames of discernment. An adapted formalism is needed to compute the fusion process. We resolve this problem of multi-sensor image fusion and classification in an evidential framework which is well adapted for the combination of knowledge defined on different frames of discernment. We present two models for merging available information, a non contextual and a vectorial model which is defined by using a Markov chain structure to represent a priori knowledge associated to labelling image. In the Markovian approach, the Markovian property is preserved after fusion, which enables us to apply standard classification algorithms. We adopt an unsupervised context in which parameter estimation is done by using a mixture distribution algorithm, the ICE algorithm. We apply these models to satellite images.
Anomaly detection in power system control center critical infrastructures using rough classification algorithm

Power System Control Centers are moving toward distributed and decentralized operations. The use of information technology (IT) to achieve these goals produces vulnerabilities and security threats. To safeguard against the threat of cyber-attacks, service providers also need to maintain the accuracy, assurance and integrity of their interdependent data networks. This paper presents the results of the Anomaly Detection technique using Rough Sets Classification Algorithm for improving the security of Power System Control Centers in the Electric Power System Critical Infrastructure. The methodology presented can be used to identify attacks and failures and, also, for improving the confidence of the state estimation process. A test environment is implemented and the results for a 6 bus electrical power system are presented.
The Impact of financial risks on profitability of Malaysian commercial banks : 1996-2005

This paper examines the relationship between financial risks and profitability of the conventional and Islamic banks in Malaysia for the period between 1996 and 2005. The measures of profitability that have been used in the study are the return on equity (ROE) and return on assets (ROA) while the financial risks are credit risk, interest rate risk and liquidity risks. This study employs panel data regression analysis of Generalised Least Squares of fixed effects and random effects models. It was found that credit risk has a significant impact on ROA and ROE for the conventional as well as the Islamic banks. The relationship between interest rate risk and ROE were found to be weakly significant for the conventional banks and insignificant for the Islamic banks. The effect of interest rate risk on ROA is significant for the conventional banks. Liquidity risk was found to have an insignificant impact on both profitability measures. Keywords—Credit risk, interest rate risk, liquidity risk, market risk, profitability.
Persistence and Recovery for In-Memory NoSQL Services: A Measurement Study

NoSQL systems are deployed as the core components for delivering big data Web services today. With growing main memory capacity, we witness the growing interest and deployment of in-memory NoSQL services (IM-NoSQL), which are designed to maximize the utilization of DRAM for ultra low latency services. To address the volatility of DRAM for in-memory computing services, persistence and failure recovery are important functionality for IM-NoSQL. In this paper we report an extensive measurement study on the performance of persistence and recovery for IM-NoSQL. We evaluate the performance and effectiveness of several common mechanisms used for persistence and recovery in the presence of server crashes, such as snapshot and logging based approaches. Through this study, we are able to answer some of the most frequently asked questions in provisioning of IM-NoSQL services: (i) Can an IM-NoSQL system work effectively when the available memory is insufficient to load the whole dataset? (ii) What is the overhead of maintaining snapshot compared to logging? (iii) How fast an IM-NoSQL system can recover in the presence of failure? And (iv) how does an IM-NoSQL system respond to the different persistence models? We report our comprehensive measurement results on execution, persistence and recovery performance of Redis, a representative implementation of IM-NoSQL services.
Counterparty Risk in Financial Contracts: Should the Insured Worry About the Insurer?

We analyze the effect of counterparty risk on financial insurance contracts, using the case of credit risk transfer in banking. This paper posits a new moral hazard problem on the insurer side of the market, which causes the insured party to be exposed to excessive counterparty risk. We find that this counterparty risk can create an incentive for the insured party to reveal superior information about the likelihood of a claim. In particular, a unique separating equilibrium may exist, even in the absence of any costly signaling device.
Is the Taxonomic Composition of Landbird Communities in Mexico Predictable

Some bird families are more widespread than others both in geography and in habitat use (high-incidence fami- lies). The number of Mexican species in a bird family was found to be strongly correlated with the number of communi- ties that possess representatives of that family. This effect did not result from a higher probability of larger families hav- ing more widespread species or having species with broader diets or habitat preferences, nor from body size, abundance or clutch size, other factors which tend to correlate with the incidence of species. Instead, number of species per se (a trait of families) strongly influenced family incidence and thereby community composition. Therefore, community composition is influenced not only by ecological assembly rules at the species level, but also by the result of macroevolutionary proc- esses above the species level. This pattern may be related to species-area curves and provides an opportunity for coevolu- tion to occur even in situations in which species-specific coadaptation is not possible. This pattern increases the predict- ability of species composition of communities.
Specific Antibodies for the Detection of Alternaria Allergens and the Identification of Cross-Reactive Antigens in Other Fungi

Background: The mould Alternaria alternata is an important source of respiratory allergens. A. alternata extracts show great variations regarding allergenic potency. The aim of this study was to generate antibody probes specific for important Alternaria allergens and to use them to study allergen expression, depending on different culture conditions, as well as to search for cross-reactive allergens in other mould species. Methods: Synthetic peptides from antigenic regions of A. alternata allergens (Alt a 1, Alt a 2, Alt a 3, Alt a 6 and Alt a 8) were used to raise highly specific rabbit antibodies. These antibodies and IgE from allergic patients were used to detect allergens by immunoblotting in extracts of 4 A. alternata strains grown under varying culturing conditions, in commercial skin-prick extracts and in closely (Cladosporium herbarum and Aureobasidium pullulans) or distantly related (Aspergillus niger and Penicillium chrysogenum) mould species. Results: There was a wide variation of expression of the individual A. Alternata allergens, depending on the strain and culture conditions, but the antibody probes allowed us to distinguish strains and culture conditions with low and high allergen expression. In the commercial skin-prick solutions, varying levels of Alt a 1 were found, but no other allergens were detectable. Alt a 1 was identified as species-specific A. Alternata allergen, whereas Alt a 3, 6- and Alt a 8-cross-reactive antigens were found in C.herbarum and/or A. pullulans. Conclusions and Clinical Relevance: Peptide-specific antibodies are useful to analyze diagnostic and therapeutic mould extracts, to study the presence of A. Alternata allergens in biological samples and to search for cross-reactive allergens in other mould species.
Anterior Spinal Artery Syndrome: A Case Report

Nontraumatic spinal cord ischemia is uncommon, especially at the cervical spinal cord. We describe a 30-year-old man presenting with acute onset of quadriparesis and impaired sensation for pain and temperature after backing up the car. He was diagnosed noninvasively with magnetic resonance imaging (MRI) and electrophysiological studies. The image studies had revealed a high signal intensity lesion in the cord from C5 to the upper part of thoracic spinal cord (T2) on T2-weighted images. The nerve conduction velocity study (NCV) revealed non pick-up of bilateral F waves and median nerves of upper limbs with decreased compound muscle action potentials (CMAPs) of the bilateral ulnar nerves. Motor evoked potentials (MEP) after transcranial cortical stimulation revealed low amplitudes and abnormal central conduction time to the legs and arms, which was more severe on the upper limbs and especially on the left side. The patient regained most of the muscle power except extensor muscles of the elbow and wrist after conservative treatment.