Miracle-dz/UniFAR-FTUs huggingface dataset:
Inducible T‐cell co‐stimulator (ICOS) and ICOS ligand are novel players in the multiple‐myeloma microenvironment

The inducible T‐cell co‐stimulator (ICOS) is a T‐cell receptor that, once bound to ICOS ligand (ICOSL) expressed on several cell types including the B‐cell lineage, plays a decisive role in adaptive immunity by regulating the interplay between B and T cells. In addition to its immunomodulatory functions, we have shown that ICOS/ICOSL signalling can inhibit the activity of osteoclasts, unveiling a novel mechanism of lymphocyte–bone cells interactions. ICOS and ICOSL can also be found as soluble forms, namely sICOS and sICOSL. Here we show that: (i) levels of sICOS and sICOSL are increased in multiple myeloma (MM) compared to monoclonal gammopathy of undetermined significance and smouldering MM; (ii) levels of sICOS and sICOSL variably correlate with several markers of tumour burden; and (iii) sICOS levels tend to be higher in Durie–Salmon stage II/III versus stage I MM and correlate with overall survival as an independent variable. Moreover, surface ICOS and ICOSL are expressed in both myeloma cells and normal plasma cells, where they probably regulate different functional stages. Finally, ICOSL triggering inhibits the migration of myeloma cell lines in vitro and the growth of ICOSL+ MOPC‐21 myeloma cells in vivo. These results suggest that ICOS and ICOSL represent novel markers and therapeutic targets for MM.
Long Range Gesture Recognition in Robot Environments with a Single Camera

This paper proposes a vision-based human's arm gesture recognition method for human robot interaction particularly at a long distance where speech information is not available. We define four meaningful arm gestures for a long range interaction. The proposed method is able to recognize the defined gestures only with 320x240 pixel-sized low-resolution input images captured from a single camera at a long distance, approximately five meters distance from the camera. In addition, the system differentiates the target gestures from the users' normal actions that occur in daily life without any constraints. For human detection at a long distance, the proposed approach combines results from mean-shift color tracking, short-and long-range face detection, and omega shape detection. The system then detects arm blocks by using a background subtraction method with a background updating module and recognizes the target gestures based on the information about region, periodical motion, and shape of the arm blocks. From the experiments on a realistic and large database, a recognition rate of 97.235% is achieved, which is a sufficiently practical level for various robot applications based on human’s gestures.
Research on Particle Swarm Optimsiation and its Application in Neural Network

This paper based on the PSO algorithm is a neural network model, and with other learning algorithm, and the results show that the performance comparisons are based on improved PSO algorithm two perceptron networks have higher classification accuracy and strong generalization ability. Particle Swarm Optimization (PSO) as an emerging evolutionary algorithm fast convergence speed, robustness, global search ability, and does not need the help of the characteristics of the problem itself (such as gradient). Combination of PSO and neural network PSO algorithm to optimize the connection weights of the neural network can be used to overcome the problem of BP neural network can not only play the generalization ability of the neural network, but also can improve the convergence rate of the neural network and learning capacity.
Elevation Dependent Management of the Edwards Aquifer: Linked Mathematical and Dynamic Programming Approach

This study addresses the dynamic issue of optimum pumping and optimum target ending aquifer elevation. A stochastic dynamic programming (DP) approach is used to address this optimal elevation question. The analytical approach relies on the generation of stochastic state transition information from a stochastic nonlinear, static programming model of the aquifer. Methodologically, the model linkage allowed us to handle a large number of potential state variables by collapsing them into a simple ending elevation state variable in the DP.
The Voter Engagement Model: Preparing the Next Generation of Social Workers for Political Practice

ABSTRACT Although voting is a fundamental mechanism through which Americans engage with their democracy, social workers often do not see it as a valid component of practice. Voting is an important source of political power and provides a means to ensure equitable representation. Educating social work students on how they can encourage voter engagement within practice is an important step to rectifying this missed opportunity. This article describes the implementation of a voter engagement model in social work education. Pre- and post-tests from 475 participants demonstrate the efficacy of this model in increasing social work students’ perceived importance of voting to their practice, their likelihood of voting in future elections, and their likelihood of engaging others in voting in future elections.
Towards Repeatable, Scalable Graphene Integrated Micro-Nano Electromechanical Systems (MEMS/NEMS)

The demand for graphene-based devices is rapidly growing but there are significant challenges for developing scalable and repeatable processes for the manufacturing of graphene devices. Basic research on understanding and controlling growth mechanisms have recently enabled various mass production approaches over the past decade. However, the integration of graphene with Micro-Nano Electromechanical Systems (MEMS/NEMS) has been especially challenging due to performance sensitivities of these systems to the production process. Therefore, ability to produce graphene-based devices on a large scale with high repeatability is still a major barrier to the commercialization of graphene. In this review article, we discuss the merits of integrating graphene into Micro-Nano Electromechanical Systems, current approaches for the mass production of graphene integrated devices, and propose solutions to overcome current manufacturing limits for the scalable and repeatable production of integrated graphene-based devices.
HIV Drug Resistance in Kenyan Infants Diagnosed With HIV and Their Mothers

The presence and type of HIV drug resistance mutations among 5 infants diagnosed with HIV were assessed and compared with their mothers’ viral mutations. Mother and infant blood samples were sequenced and screened for HIV drug resistance mutations using the Stanford HIV Sequence Database. Three of 5 (60%) mother-infant pairs harbored HIV drug resistance mutations.
A blocker-tolerant RF front end with harmonic-rejecting N-path filtering

A 0.2-1 GHz RF front end with a harmonic-rejecting N-path filter (HR-NPF) features tunable narrow band filtering as well as high attenuation at the 3rd and 5th order LO harmonics at the LNA output. The B1dB-CP is -2.4dBm at a 20MHz offset, and remains high at the 3rd and 5th LO harmonics thanks to the HR-NPF configuration. The reverse isolation of the LNA helps to keep the LO emission below -90dBm. The two-stage harmonic rejection approach offers >51dB harmonic rejection ratio at the 3rd and 5th LO harmonics without calibration. The front end consumes 26-32mA while occupying an area of 0.77mm2.
Cardiac reconstruction and analysis from high resolution CT images

OF THE DISSERTATION Cardiac Reconstruction and Analysis from High Resolution CT Images by Mingchen Gao Dissertation Director: Professor Dimitris N. Metaxas Heart disease is a major cause of mortality worldwide. Detecting/diagnosing such diseases in their early stages is critical, and heavily depends on non-invasive imaging methods, e.g., computed tomography (CT) and magnetic resonance imaging (MRI). High resolution cardiac CT imaging technology has the resolution to reveal the complex endocardial structures of the left ventricle, such as the trabeculae and the papillary muscles. However, the development of suitable methods for the quantitative analysis of these dense data sources has lagged greatly behind the development of the imaging methods themselves. As a result, in clinical practice, and in much of the research that uses these imaging data, the quantitative analysis of cardiac function has largely been confined to the calculation of simple measures of global function, such as the ejection fraction, while local function being just qualitatively assessed. Therefore there is a large amount of the functional information potentially available from cardiac images essentially untouched. In this thesis, for the first time, we extract clinical meaningful endocardial information of the left ventricle (LV) from high resolution CT images. The reconstructed result captured the fine detailed structures, which are extremely challenging to segment due to their delicate and complex nature in both geometry and topology. Our
Approach to COVID-19 time series data using deep learning and spectral analysis methods

This article focuses on the application of deep learning and spectral analysis to epidemiology time series data, which has recently piqued the interest of some researchers. The COVID-19 virus is still mutating, particularly the delta and omicron variants, which are known for their high level of contagiousness, but policymakers and governments are resolute in combating the pandemic's spread through a recent massive vaccination campaign of their population. We used extreme machine learning (ELM), multilayer perceptron (MLP), long short-term neural network (LSTM), gated recurrent unit (GRU), convolution neural network (CNN) and deep neural network (DNN) methods on time series data from the start of the pandemic in France, Russia, Turkey, India, United states of America (USA), Brazil and United Kingdom (UK) until September 3, 2021 to predict the daily new cases and daily deaths at different waves of the pandemic in countries considered while using root mean square error (RMSE) and relative root mean square error (rRMSE) to measure the performance of these methods. We used the spectral analysis method to convert time (days) to frequency in order to analyze the peaks of frequency and periodicity of the time series data. We also forecasted the future pandemic evolution by using ELM, MLP, and spectral analysis. Moreover, MLP achieved best performance for both daily new cases and deaths based on the evaluation metrics used. Furthermore, we discovered that errors for daily deaths are much lower than those for daily new cases. While the performance of models varies, prediction and forecasting during the period of vaccination and recent cases confirm the pandemic's prevalence level in the countries under consideration. Finally, some of the peaks observed in the time series data correspond with the proven pattern of weekly peaks that is unique to the COVID-19 time series data.
CO2 AND TEMPERATURE EFFECTS ON EVAPOTRANSPIRATION AND IRRIGATED AGRICULTURE

A sensitivity analysis of potential evapotranspiration (PET) rates under both CO\d2 and air temperature changes was conducted. PET was modeled with the Penman-Monteith equation so that the effects of atmospheric CO\d2 concentrations on plant stomatal resistance, and the effects of temperature on land-surface–atmosphere water vapor exchanges were explicitly taken into account. A root-zone soil-water balance was performed using a physically based soil-crop-climate model to analyze the sensitivity of soil moisture to changes in atmospheric temperature and CO\d2 concentrations, and the effects of CO\d2 fertilization on plant photosynthesis and crop yield. A wide spectrum of directional climate change scenarios were analyzed, including both a 3°C increase and a 3°C decrease in air temperature, and both a 50 and a 100% increase in atmospheric CO\d2 concentrations. An additive crop yield model and an optimal irrigation scheduling model were used to maximize agricultural benefits by maximizing crop yield and minimizing irrigation costs. The model was applied to an irrigated potato crop in the San Luis Valley of Colorado.
Schistosoma mansoni Adult Worm Protective and Diagnostic Proteins in n-Butanol Extracts Revealed by Proteomic Analysis

The S. mansoni adult worm n-butanol extract (Sm-AWBE) has been previously shown to contain specific S. mansoni antigens that have been used for immunodiagnosis of schistosomiasis in solid phase alkaline phosphatase immunoassay (APIA) and western blot (WB) analyses. Sm-AWBE was also used in immunoprotection studies against a fatal live-cercariae challenge in experimental mouse vaccination (~43% protection). The Sm-AWBE fraction was prepared by mixing adult worm membranous suspensions with aqueous-saturated n-butanol, centrifuging and recovering n-butanol-resistant proteins in the aqueous phase. Here we report a preliminary identification of Sm-AWBE protein components as revealed from a qualitative proteomic study after processing Sm-AWBE by 1D-gel electrophoresis, in-gel and in-solution tryptic digestions, and mass spectrometry analyses. We identified 33 proteins in Sm-AWBE, all previously known S. mansoni proteins and antigens; among them, immunomodulatory proteins and proteins mostly involved in host–parasite interactions. About 81.8% of the identified Sm-AWBE proteins are antigenic. STRING analysis showed a set of Sm-AWBE proteins configuring a small network of interactive proteins and a group of proteins without interactions. Functional groups of proteins included muscle contraction, antioxidant, GPI-anchored phosphoesterases, regulatory 14-3-3, various enzymes and stress proteins. The results widen the possibilities to design novel antigen combinations for better diagnostic and immunoprotective strategies for schistosomiasis control.
Review of Learning 2.0 Practices: Study on the Impact of Web 2.0 Innovations on Education and Training in Europe

Over the last few years, web 2.0 or social computing applications like blogs, wikis, photo- and video-sharing sites, as well as online social networking sites and virtual worlds, have seen unprecedented take up. This has changed the way people access, manage and exchange knowledge, and the way they connect and interact. Younger people especially are using these tools and services as a natural way of extending their personal relations and as a means of keeping in touch with friends. This trend is accompanied by the emergence of structurally different learning styles, especially among young people. As a result, living, learning and working patterns have already changed significantly and are expected to change even more dramatically in the future. Education and training systems need innovative ways of fostering new skills for new jobs, taking into account the changing living, working and learning patterns in a digital society. So far, however, education and training systems have not, on the whole, reacted to these changes. Neither schools nor universities have seized the potential of digital media for enhancing learning and addressing their learners? needs. Due to the novelty of social computing, take up in education and training is still in an experimental phase. There are various diverse small-scale projects and initiatives all over Europe, which try to exploit social computing for a multitude of learning purposes, but research on enabling and disabling factors is scarce. This study is part of a collaboration project between the European Commission's Joint Research Centre (JRC-IPTS) and its Directorate General for Education and Culture (DG EAC). The objective is to investigate the innovative and inclusive potential of social computing applications in formal education by reviewing current practice. The report identifies, structures and analyses existing Learning 2.0 practice in Europe with a view to generating evidence on the impact of social computing for learning and its potential in promoting innovation and inclusion. It combines a review of research on Learning 2.0 with the collection of experience and good practice from a broad variety of cases.
Determination and allocation of typical load profiles to the eligible consumers

Information concerning the consumer's load profile (LP) in a deregulated environment is of initial importance for load balancing purposes and for billing the consumers, which deviated from contracted schedules. Establishment of a system that enables consumers, without the quarter-hourly load measurements, to participate at the retail market, requires the typical load profiles (TLP) of the various types of consumers. Furthermore, simple and straightforward method for the TLP assignment to the particular eligible consumers should be established. In the paper, a methodology for consumer's load profiles classification is presented. The measured load profiles (MLP) are classified by hierarchic clustering method and fuzzy logic. Probability neural network is used to assign the TLP to the particular group of consumers. The results demonstrate that the proposed method is efficient for and assigning TLP to the consumers.
Generalization of the subspace-based array shape estimations

This work describes a nonplane wave signal model for array shape estimation in ocean environments. Though existing array shape estimation methods, which utilize reference sources, show good performance in the environment where plane wave assumption is satisfied, their performances are degraded significantly in more realistic nonplane wave situation. The proposed subspace-based array shape estimation method, which generalizes the conventional subspace fitting method, works very well for both plane wave and nonplane wave environments. The Cramer-Rao lower bound is investigated to analyze the theoretical lower bound of the variance of the estimation. The simulation results indicate that the proposed method correctly estimates the shape of perturbed array regardless of reference source location.
Family-managed firms, external sources of knowledge and innovation

ABSTRACT This study examines the influence of external sources of knowledge on innovation in a sample of Spanish manufacturing firms during the period 2002–2015. By differentiating firms according to their family status, our results reveal that the probability of engaging in product and process innovations is higher for family-managed firms involved in R&D cooperation with a varied group of partners. Additionally, our findings provide support for the role of R&D-oriented family-managed firms in easing the flow of knowledge and information for innovation at regional level (i.e. family firm-led knowledge spillovers). Because of their unique social capital and embeddedness in the local setting, family-managed firms contribute to creating a favourable regional environment for innovation for all co-located firms. However, this result holds only when the geographical concentration of R&D-oriented family-managed firms is coupled with technological proximity. Theoretical, policy and managerial implications are discussed.
Noise Immunity of Multilevel Pulse-Modulated Signals Propagating along a Dispersive Optical Path

A fiber-optic transmission system for multi-level pulse amplitude modulated signals with direct photodetection on the receiving side is considered. A mathematical model for the transmission of M-level PAM signals through the dispersion optical path based on the Gaussian approximation of the transmitted pulses has been developed. The criterion of the noise immunity evaluation is the narrowing of the eye closure in the receiver. Formulas that allow estimating the power penalty due to fiber dispersion, depending on the levels, duration and duty cycle of optical pulses, as well as the parameters of the optical fiber are derived. Comparative estimates of the noise immunity of transmission PAM −4 and PAM-2 signals with bit rates of 12.5 Gbps, 25 Gbps, 50 Gbps, 100 Gbps, 200 Gbps, 400 Gbps over a standard optical fiber at a wavelength of 1550 nm are given. In comparison with the transmission of PAM-2 signals, the use of PAM-4 signals is preferable at a signal transmission rate of more than 25 Gbit/s and power penalties of more than 2.5 dB, provided that the bit error correction technology is used in the transceiver equipment, which halves the bandwidth requirements for optoelectronic components.
Modal Identification of Ultralow-Frequency Flexible Structures Based on Digital Image Correlation Method

Traditional modal testing has difficulty accurately identifying the ultralow-frequency modes of flexible structures. Ultralow-frequency excitation and vibration signal acquisition are two main obstacles. Aiming at ultralow-frequency modal identification of flexible structures, a modal testing method based on Digital Image Correlation method and Eigensystem Realization Algorithm is proposed. Considering impulse and shaker excitation are difficult to make generate ultralow-frequency vibration of structures, the initial displacement is applied to the structure for excitation. The ultralow-frequency accelerometer always has a large mass, which will change the dynamics performance of the flexible structure, so a structural vibration response was obtained through the Digital Image Correlation method. After collecting the free-decay vibration signal, the ultralow-frequency mode of the structure was identified by using the Eigensystem Realization Algorithm. Ground modal tests were conducted to verify the proposed method. Firstly, a solar wing structure was adopted, from which it was concluded that the signal acquisition using Digital Image Correlation method had high feasibility and accuracy. Secondly, an ultralow-frequency flexible cantilever beam structure which had the theoretical solution was employed to verify the proposed method and the theoretical fundamental frequency of the structure was 0.185 Hz. Results show that the Digital Image Correlation method can effectively measure the response signal of the ultralow-frequency flexible structure, and obtain the dynamics characteristics.
Electro-energetics of Biventricular, Septal and Conduction System Pacing

Abnormal electrical activation of the ventricles creates abnormalities in cardiac mechanics. Local contraction patterns, as reflected by strain, are not only out of phase, but also show opposing length changes in early and late activated regions. Consequently, the efficiency of cardiac pump function (the amount of stroke work generated by a unit of oxygen consumed), is approximately 30% lower in dyssynchronous than in synchronous hearts. Maintaining good cardiac efficiency appears important for long-term outcomes. Biventricular, left ventricular septal, His bundle and left bundle branch pacing may minimise the amount of pacing-induced dyssynchrony and efficiency loss when compared to conventional right ventricular pacing. An extensive animal study indicates maintenance of mechanical synchrony and efficiency during left ventricular septal pacing and data from a few clinical studies support the idea that this is also the case for left bundle branch pacing and His bundle pacing. This review discusses electro-mechanics and mechano-energetics under the various paced conditions and provides suggestions for future research.