Miracle-dz/UniFAR-FTUs huggingface dataset:
Differentiation of human adipose-derived stem cells into beating cardiomyocytes

Human adipose‐derived stem cells (ASCs) may differentiate into cardiomyocytes and this provides a source of donor cells for tissue engineering. In this study, we evaluated cardiomyogenic differentiation protocols using a DNA demethylating agent 5‐azacytidine (5‐aza), a modified cardiomyogenic medium (MCM), a histone deacetylase inhibitor trichostatin A (TSA) and co‐culture with neonatal rat cardiomyocytes. 5‐aza treatment reduced both cardiac actin and TropT mRNA expression. Incubation in MCM only slightly increased gene expression (1.5‐ to 1.9‐fold) and the number of cells co‐expressing nkx2.5/sarcomeric α‐actin (27.2%versus 0.2% in control). TSA treatment increased cardiac actin mRNA expression 11‐fold after 1 week, which could be sustained for 2 weeks by culturing cells in cardiomyocyte culture medium. TSA‐treated cells also stained positively for cardiac myosin heavy chain, α‐actin, TropI and connexin43; however, none of these treatments produced beating cells. ASCs in non‐contact co‐culture showed no cardiac differentiation; however, ASCs co‐cultured in direct contact co‐culture exhibited a time‐dependent increase in cardiac actin mRNA expression (up to 33‐fold) between days 3 and 14. Immunocytochemistry revealed co‐expression of GATA4 and Nkx2.5, α‐actin, TropI and cardiac myosin heavy chain in CM‐DiI labelled ASCs. Most importantly, many of these cells showed spontaneous contractions accompanied by calcium transients in culture. Human ASC (hASC) showed synchronous Ca2+ transient and contraction synchronous with surrounding rat cardiomyocytes (106 beats/min.). Gap junctions also formed between them as observed by dye transfer. In conclusion, cell‐to‐cell interaction was identified as a key inducer for cardiomyogenic differentiation of hASCs. This method was optimized by co‐culture with contracting cardiomyocytes and provides a potential cardiac differentiation system to progress applications for cardiac cell therapy or tissue engineering.
The Use of Lymphocyte Function–Associated Antigen (Lfa)-1–Deficient Mice to Determine the Role of Lfa-1, Mac-1, and α4 Integrin in the Inflammatory Response of Neutrophils

After injury or infection, neutrophils rapidly migrate from the circulation into tissues by means of an orderly progression of adhesion receptor engagements. Neutrophils have been previously considered to use selectins exclusively to roll on vessels before an adhesion step mediated by the β2 integrins, lymphocyte function–associated antigen (LFA)-1, and Mac-1. Here we use LFA-1−/− mice, function blocking monoclonal antibodies, and intravital microscopy to investigate the roles of LFA-1, Mac-1, and α4 integrins in neutrophil recruitment in vivo. For the first time, we show that LFA-1 makes a contribution to neutrophil rolling by stabilizing the transient attachment or tethering phase of rolling. In contrast, Mac-1 does not appear to be important for either rolling or firm adhesion, but instead contributes to emigration from the vessel. Blocking Mac-1 in the presence of LFA-1 significantly reduces emigration, suggesting cooperation between these two integrins. Low levels of α4β1 integrin can be detected on neutrophils from LFA-1+/+ and −/− mice. These cells make use of α4β1 during the rolling phase, particularly in the absence of LFA-1. Thus LFA-1 and α4β1, together with the selectins, are involved in the rolling phase of neutrophil recruitment, and, in turn, affect the later stages of the transmigration event.
Can We Use Neurocognition to Predict Repetition of Self-Harm, and Why Might This Be Clinically Useful? A Perspective

Over 800,000 people die by suicide each year globally, with non-fatal self-harm 20 times more common. With each episode of self-harm, the risks of future self-harm and suicide increase, as well as personal and healthcare costs. Therefore, early delineation of those at high risk of future self-harm is important. Historically, research has focused on clinical and demographic factors, but risk assessments based on these have low sensitivity to predict repetition. Various neurocognitive factors have been associated with self-harming behavior, but it is less certain if we can use these factors clinically (i) as risk markers to predict future self-harm and (ii) to become therapeutic targets for interventions. Recent systematic reviews and meta-analyses of behavioral tasks and fMRI studies point to an emerging hypothesis for neurocognition in self-harm: an underactive pre-frontal cortex is unable to respond appropriately to non-emotional stimuli, or inhibit a hyperactive emotionally-/threat-driven limbic system. However, there is almost no imaging data examining repetition of self-harm. Extrapolating from the non-repetition data, there may be several potential neurocognitive targets for interventions to prevent repeat self-harm: cognitive training; pharmacological regimes to promote non-emotional neurocognition; or other techniques, such as repetitive transcranial magnetic stimulation. Hence, there is an urgent need for imaging studies examining repetition and to test specific hypotheses. Until we investigate the functional neurocognitive basis underlying repetition of self-harm in a systematic manner using second-generational imaging techniques, we will be unable to inform third-generational imaging and potential future clinical applications.
Asymmetric Functioning of Dimeric Metabotropic Glutamate Receptors Disclosed by Positive Allosteric Modulators*

The recent discovery of positive allosteric modulators (PAMs) for G-protein-coupled receptors open new possibilities to control a number of physiological and pathological processes. Understanding the mechanism of action of such compounds will provide new information on the activation process of these important receptors. Within the last 10 years, a number of studies indicate that G-protein-coupled receptors can form dimers, but the functional significance of this phenomenon remains elusive. Here we used the metabotropic glutamate receptors as a model, because these receptors, for which PAMs have been identified, are constitutive dimers. We used the quality control system of the GABAB receptor to generate metabotropic glutamate receptor dimers in which a single subunit binds a PAM. We show that one PAM/dimer is sufficient to enhance receptor activity. Such a potentiation can still be observed if the subunit unable to bind the PAM is also made unable to activate G-proteins. However, the PAM acts as a non-competitive antagonist when it binds in the subunit that cannot activate G-proteins. These data are consistent with a single heptahelical domain reaching the active state per dimer during receptor activation.
Delay-based Congestion Control : Sampling and Correlation Issues Revisited

In this paper we revisit the commonly voiced concern that low correlation between measured delay and networ k congestion means that delay may be fundamentally flawed as a signal for congestion control. Our main contribution is to demonstrate that in fact what matters for congestion control is the aggregate behaviour of the flows sharing a link. Hence, perhaps somewhat surprisingly, while any given single flow may measure delay which is only weakly correlated with network congestion, this is not in itself an obstacle to congestion c ontrol.
Organizational Dynamics Programs 1-4-2007 Effects of ESOP Adoption and Employee Ownership : Thirty years of Research and Experience

An important, but little reported development in US business has been increasing numbers of employees with ownership rights in the corporation with an increasingly large economic value. Most comes through Employee Stock Ownership Plans (ESOPs), which were established in 1974 partly as a response to anticipated shortfalls in Social Security, but also with the hope of invigorating the economy and distributing the benefits of capitalism more widely through broad-based business ownership. Experience and research indicate that ESOPs and employee ownership more generally do accomplish these aims, but large knowledge gaps remain. Research does confirm that individual employee-owners benefit from ESOPs. Equity comes on top of, not in place of, other compensation. Employee ownership is also associated with considerably greater employment stability and, in firms that simultaneously increase worker participation in decision making, the result is increased job satisfaction, organizational commitment, identification, motivation, and workplace participation. High profile cases accentuate potential risks through lack of diversification, but most employeeowners are less vulnerable than counterparts. Research confirms also that employee ownership, on average, leads to increased firm productivity, profitability, and longevity. Evidence suggests that combining employee ownership with increased employee participation may generate astounding returns on investment. Little is known, however, about management of employee owned firms and few projects even attempt to justify societal claims. Economists, managers, and financiers remain skeptical of employee ownership, and few studies directly counter their concerns. Problems associated with employee ownership go unstudied. For all the extent and appeal of employee ownership, it is on the fringe of both social consciousness and the academic literature. Employee ownership is one of the few issues on which the political left and right can agree, and is thereby capable of attracting strong support across the US political spectrum. Recent concerns about social security solvency suggest further inducements to widening ESOPs. Given this opportunity, increased knowledge can help promote employee ownership, help ensure its wise adoption and successful implementation, and intelligently influence public policy.
Model selection approaches for non-linear system identification: a review

The identification of non-linear systems using only observed finite datasets has become a mature research area over the last two decades. A class of linear-in-the-parameter models with universal approximation capabilities have been intensively studied and widely used due to the availability of many linear-learning algorithms and their inherent convergence conditions. This article presents a systematic overview of basic research on model selection approaches for linear-in-the-parameter models. One of the fundamental problems in non-linear system identification is to find the minimal model with the best model generalisation performance from observational data only. The important concepts in achieving good model generalisation used in various non-linear system-identification algorithms are first reviewed, including Bayesian parameter regularisation and models selective criteria based on the cross validation and experimental design. A significant advance in machine learning has been the development of the support vector machine as a means for identifying kernel models based on the structural risk minimisation principle. The developments on the convex optimisation-based model construction algorithms including the support vector regression algorithms are outlined. Input selection algorithms and on-line system identification algorithms are also included in this review. Finally, some industrial applications of non-linear models are discussed.
Strategies to Improve Reliability in Routing Overlay Networks with Selfish Nodes

Routing overlay networks use the current Internet infrastructure to provide support for a wide range of applications, such as content distribution, multiplayer games, search and web services. Recent studies have suggested that those networks may contain selfish nodes, i.e., nodes that develop their strategies considering only their own objectives and interests, regardless of the optimal global behavior. Extremely egoistic nodes may even decide not to give their own resources back to the network (free-riders), making overlay service unavailable to every node that depends on them. In this paper, we approach the problem of increasing the reliability of overlay services by detecting and removing free-riders from the selfish overlay network. We use a game-theoretic approach to evaluate the mechanism, and we show that service reliability can be increased to up 83% even when 70% of the network is formed by free-riders. Our mechanism is also fairly simple and can be efficiently distributed.
INTRACELLULAR SURVIVAL OF STAPHYLOCOCCI

A tissue culture procedure is described which permits the quantitative evaluation of the intracellular survival of staphylococci within leucocytes. Staphylococcus aureus survived, but did not multiply, within neutrophils and monocytes of normal rabbits. The same was true of normal human blood leucocytes. Staphylococcus albus on the other hand was destroyed by these cells under the same conditions. Rat monocytes destroyed S. aureus and S. albus with equal facility. Although most experiments were carried out in the presence of 50 µg. streptomycin/ml. , similar results were obtained without the use of this antibiotic. The applications of the tissue culture procedure with regard to studies on virulence and immunity in staphylococcal disease are discussed.
Feature Subset Selection Algorithm for Elevated Dimensional Data By using Fast Cluster

A b s t r a c t - Feature selection involves recognizing a subset of the majority helpful features that produces attuned results as the unique set of features. Feature selection algorithm can be evaluated from mutually efficiency and effectiveness points of vision. FAST algorithm is Proposed and then experimentally evaluated in this paper. FAST algorithm mechanism considering two steps. In the primary step, features are separated into clusters by means of graph-theoretic clustering methods. In the subsequent step, the majority delegate feature that is robustly connected to target classes is chosen from each cluster form a subset of features. The Features in unusual clusters are relatively self-governing; the clustering-based approach of FAST has a elevated possibility of producing a subset of useful features. in the direction of guarantee to the efficiency of FAST, we implement the efficient minimum-spanning tree clustering technique. general experiments are approved to contrast FAST and some delegate feature selection algorithms, namely, FCBF, ReliefF, CFS, Consist, and FOCUS-SF, by admiration to four types of famous classifiers, specifically, the probability-based Naive Bayes, the tree-based C4.5, the instance-based IB1, and the rule-based RIPPER and following feature selection.
Roles of Voltage-Gated Tetrodotoxin-Sensitive Sodium Channels NaV1.3 and NaV1.7 in Diabetes and Painful Diabetic Neuropathy

Diabetes mellitus (DM) is a common chronic medical problem worldwide; one of its complications is painful peripheral neuropathy, which can substantially erode quality of life and increase the cost of management. Despite its clinical importance, the pathogenesis of painful diabetic neuropathy (PDN) is complex and incompletely understood. Voltage-gated sodium channels (VGSCs) link many physiological processes to electrical activity by controlling action potentials in all types of excitable cells. Two isoforms of VGSCs, NaV1.3 and NaV1.7, which are encoded by the sodium voltage-gated channel alpha subunit 3 and 9 (Scn3A and Scn9A) genes, respectively, have been identified in both peripheral nociceptive neurons of dorsal root ganglion (DRG) and pancreatic islet cells. Recent advances in our understanding of tetrodotoxin-sensitive (TTX-S) sodium channels NaV1.3 and NaV1.7 lead to the rational doubt about the cause–effect relation between diabetes and painful neuropathy. In this review, we summarize the roles of NaV1.3 and NaV1.7 in islet cells and DRG neurons, discuss the link between DM and painful neuropathy, and present a model, which may provide a starting point for further studies aimed at identifying the mechanisms underlying diabetes and painful neuropathy.
Locational Marginal Pricing based Smart Charging of Plug-In Hybrid Vehicle Fleets

The electrification of transportation is becoming an increasingly important topic due to geopolitical concerns regarding the supply of oil and strategies against climate change. Plug-in-hybrid electric vehicles (PHEVs), which rely mainly on electricity for propulsion and are equipped additionally with a combustion engine to bridge larger distances, would play a key role. However the introduction of battery vehicles poses significant challenges to the supply, transmission and distribution of electricity. Previous studies have focused on the impact of PHEVs on electric utilities [1, 2, 3], highlighting the importance of a controlled or ”smart-charging” scheme. In the absence of a controlled charging scheme, vehicles would typically start charging when arriving at a location where charging is possible, e.g. home or workplace. This would accentuate the already existing peaks in electricity demand potentially leading to overloaded network assets and increasing generation costs. Vehicles being parked most of the time, load shifting is often possible: There is a certain time frame where the demand of these loads can be considered to be elastic. Simple dual tariff schemes could help shift the load initially, but wouldn’t be viable at larger penetrations as new demand peaks could be created at the beginning of the low-tariff period. A large scale introduction of battery vehicles requires schemes with endogenous prices where the impact of the new load on electricity prices is taken into account. The smart charging scheme proposed here is based on day-ahead Locational Marginal Pricing, where fleet aggregators would participate in electricity markets placing demand bids. Charging is scheduled so as to minimize generation costs taking into account network constraints and the battery state of charge (SOC) constraints of the vehicles, which are given by their individual driving behavior. A multi-agent transport simulation called MATSim is used to forecast the driving patterns of individual vehicles [4]. The optimization framework adopted is an Optimal Power Flow (OPF): that is a welfare maximizing dispatch of loads and generators taking into account network constraints. This is an appropriate tool as the interdependencies between supply, demand and infrastructure are taken into account simultaneously. Thus the impact of PHEVs on the electricity market and network assets can be assessed. The proposed method has been tested with case studies on the Swiss system (comprising supply and demand of electricity and the transmission grid). The results show that this method leads to reduced generation costs and system stress. Moreover it can be used for system planning purposes as the impact of PHEV load on the different network assets can be assessed quantitatively. This paper is structured as follows: First the simulation model will be illustrated in the following section. The results of the case studies are shown in section 3. Finally section 4 concludes the paper.
Morphologically distinct microtubule ends in the mitotic centrosome of Caenorhabditis elegans

During mitosis, the connections of microtubules (MTs) to centrosomes and kinetochores are dynamic. From in vitro studies, it is known that the dynamic behavior of MTs is related to the structure of their ends, but we know little about the structure of MT ends in spindles. Here, we use high-voltage electron tomography to study the centrosome- and kinetochore-associated ends of spindle MTs in embryonic cells of the nematode, Caenorhabditis elegans. Centrosome-associated MT ends are either closed or open. Closed MT ends are more numerous and are uniformly distributed around the centrosome, but open ends are found preferentially on kinetochore-attached MTs. These results have structural implications for models of MT interactions with centrosomes.
A 2.4-GHz 18-mW Two-Point Delta-Sigma Modulation Transmitter for IEEE 802.15.4

A 2.4-GHz two-point modulation transmitter (TX), designed for IEEE 802.15. 4 (ZigBee) applications, is reported in this paper. The TX is based on a delta-sigma fractional-/V PLL to reduce chip area and power consumption. In addition, the chosen architecture prevents the transmission data rate from being limited by the PLL bandwidth. To alleviate the non-linearity problems of a conventional fractional-N PLL, linearization techniques are adopted. The TX is designed to operate in the 2.4-GHz ISM band, and is capable of delivering a date rate more than 2 Mbps. Implemented in the TSMC 0.18-mum CMOS process, the TX consumes 18 mW under a 1.4-V supply voltage.
On the observed and modeled development of Hurricane Earl ( 2010 ) during rapid intensification

Forecasting tropical cyclone (TC) intensification remains difficult despite research efforts to improve numerical weather prediction models. This study aims to examine Hurricane Earl’s rapid intensification in order to gain a better understanding of the observed and modeled intensification process. 113 dropwindsondes were analyzed before, during, and after RI in the eye, eyewall, and outer rainband to study the evolution of the thermodynamic and dynamic vertical profiles at these locations. Afterword, modeled vertical thermodynamic and wind profiles were analyzed with the dropwindsonde data to determine how the Hurricane Forecast Research and Forecasting (HWRF) System modeled the rapid intensification of Hurricane Earl. Thereafter, idealized warm core structures were analyzed to develop a theory as to how the structure of the warm core affects the minimum sea level pressure (MSLP). This theory was compared to Hurricane Earl’s warm core. Results show the development of a subsidence-induced dry layer in the eye and a saturated column in the eyewall. Additionally, neither the difference in the observed and modeled temperature, wind speed, or wind direction statistically influenced the difference in the observed and modeled MSLP. Furthermore, the depth of the warm core was determined as the most important factor in altering the MSLP of a TC, with the large depth of Hurricane Earl’s warm core contributing to its RI.
A Multi-modal Platform for Semantic Music Analysis: Visualizing Audio-and Score-Based Tension

Musicologists, music cognition scientists and others have long studied music in all of its facets. During the last few decades, research in both score and audio technology has opened the doors for automated, or (in many cases) semi-automated analysis. There remains a big gap, however, between the field of audio (performance) and score-based systems. In this research, we propose a web-based Interactive system for Multi-modal Music Analysis (IMMA), that provides musicologists with an intuitive interface for a joint analysis of performance and score. As an initial use-case, we implemented a tension analysis module in the system. Tension is a semantic characteristic of music that directly shapes the music experience and thus forms a crucial topic for researchers in musicology and music cognition. The module includes methods for calculating tonal tension (from the score) and timbral tension (from the performance). An audio-to-score alignment algorithm based on dynamic time warping was implemented to automate the synchronization between the audio and score analysis. The resulting system was tested on three performances (violin, flute, and guitar) of Paganini's Caprice No. 24 and four piano performances of Beethoven's Moonlight Sonata. We statistically analyzed the results of tonal and timbral tension and found correlations between them. A clustering algorithm was implemented to find segments of music (both within and between performances) with similar shape in their tension curve. These similar segments are visualized in IMMA. By displaying selected audio and score characteristics together with musical score following in sync with the performance playback, IMMA offers a user-friendly intuitive interface to bridge the gap between audio and score analysis.