Miracle-dz/UniFAR-FTUs huggingface dataset:
Cell-Based Double-Screening Method to Identify a Reliable Candidate for Osteogenesis-Targeting Compounds

Small-molecule compounds strongly affecting osteogenesis can form the basis of effective therapeutic strategies in bone regenerative medicine. A cell-based high-throughput screening system might be a powerful tool for identifying osteoblast-targeting candidates; however, this approach is generally limited with using only one molecule as a cell-based sensor that does not always reflect the activation of the osteogenic phenotype. In the present study, we used the MC3T3-E1 cell line stably transfected with the green fluorescent protein (GFP) reporter gene driven by a fragment of type I collagen promoter (Col-1a1GFP-MC3T3-E1) to evaluate a double-screening system to identify osteogenic inducible compounds using a combination of a cell-based reporter assay and detection of alkaline phosphatase (ALP) activity. Col-1a1GFP-MC3T3-E1 cells were cultured in an osteogenic induction medium after library screening of 1280 pharmacologically active compounds (Lopack1280). After 7 days, GFP fluorescence was measured using a microplate reader. After 14 days of osteogenic induction, the cells were stained with ALP. Library screening using the Col-1a1/GFP reporter and ALP staining assay detected three candidates with significant osteogenic induction ability. Furthermore, leflunomide, one of the three detected candidates, significantly promoted new bone formation in vivo. Therefore, this double-screening method could identify candidates for osteogenesis-targeting compounds more reliably than conventional methods.
Forwarding in Energy-Constrained Wireless Information Centric Networks

Information Centric Networks (ICNs) have been considered one of the most promising candidates to overcome the disadvantages of host-centric architectures when applied to IoT networks, having the potential to address the challenges of a smart city. One of the foundations of a smart city is its sensory capacity, which is obtained through devices associated with the IoT concept. The more sensors spread out, the greater the ability to sense the city. However, such a scale demands high energy requirements and an effective improvement in the energy management is unavoidable. To improve the energy management, we are proposing an efficient forwarding scheme in energy-constrained wireless ICNs. To achieve this goal, we consider the type of devices, their internal energy and the network context, among other parameters. The proposed forwarding strategy extends and adapts concepts of ICNs, by means of packet domain analysis, neighbourhood evaluation and node sleeping and waking strategies. The proposed solution takes advantage of the neighbourhood to be aware of the moments to listen and forward packets in order to consistently address mobility, improving the quality of content delivery. The evaluation is performed by simulation with real datasets of urban mobility, one from the lagoon of “Ria de Aveiro” and the other from a vehicular network in the city of Porto. The results show that the proposed forwarding scheme resulted in significant improvements in network content availability, in the overall energy saving and, consequently, in the network lifetime.
Performance Models in Robotics with a Use Case on SLAM

The performance of a software component implementing a robotic functionality depends on many factors ranging from the system configuration (e.g., available sensors and robot kinematics) to the operating environment, passing by the component configuration parameters. A naive approach to model the performance of such a software component is to measure its performance on every possible combination of such variables. However, this is not possible as the number of combinations would not be tractable, considering also that multiple measurements should be performed for each of them. To make the problem tractable we propose to sample a relatively small number of combinations, conduct experiments for each of them, and from these results estimate a statistical model of the software component performance which we call component performance model. A performance model allows comparison of different components implementing the same functionality to determine the best one to be used in a given setting and its optimal configuration. Moreover, performance models of multiple functionalities may be composed to predict the performance of an entire system at design-time. Besides the general framework to extract performance models here we present an operational use case in Simultaneous Localization and Mapping (SLAM).
PENGARUH LATIHAN KELINCAHAN TEHADAP KEMAMPUAN MENGGIRING PEMAIN SEPAK BOLA KLUB LEPARISSA AMALATU KABUPATEN SERAM BAGIAN BARAT

Football is a game that requires technique, cohesiveness and cooperation. Mastery of basic techniques will reflect the skill level of the football player concerned. Physical skills in question are strength, speed, agility, flexibility, balance, and endurance. Agility training is very important, because what is needed when dribbling is how athletes are able to avoid themselves from opposing opponents while still controlling and maintaining the ball without losing balance, with the research location at the Alba Batu Mera club. This study uses a quantitative method with a field experiment approach with a one-grop test-post test research design. Based on the results of hypothesis testing in this study, it is known that there is an effect of agility training on the ability of the Alba Batu Merah Ambon club soccer player. This shows that agility training can have a strong influence on the ability to dribble very well so that it can be explained that agility training is an alternative that can be used to improve the ability to dribble.
Recent Advances in the Synthesis of Complex Macromolecular Architectures Based on Poly(N-vinyl pyrrolidone) and the RAFT Polymerization Technique

Recent advances in the controlled RAFT polymerization of complex macromolecular architectures based on poly(N-vinyl pyrrolidone), PNVP, are summarized in this review article. Special interest is given to the synthesis of statistical copolymers, block copolymers, and star polymers and copolymers, along with graft copolymers and more complex architectures. In all cases, PNVP is produced via RAFT techniques, whereas other polymerization methods can be employed in combination with RAFT to provide the desired final products. The advantages and limitations of the synthetic methodologies are discussed in detail.
MisRoBÆRTa: Transformers versus Misinformation

Misinformation is considered a threat to our democratic values and principles. The spread of such content on social media polarizes society and undermines public discourse by distorting public perceptions and generating social unrest while lacking the rigor of traditional journalism. Transformers and transfer learning proved to be state-of-the-art methods for multiple well-known natural language processing tasks. In this paper, we propose MisRoBÆRTa, a novel transformer-based deep neural ensemble architecture for misinformation detection. MisRoBÆRTa takes advantage of two state-of-the art transformers, i.e., BART and RoBERTa, to improve the performance of discriminating between real news and different types of fake news. We also benchmarked and evaluated the performances of multiple transformers on the task of misinformation detection. For training and testing, we used a large real-world news articles dataset (i.e., 100,000 records) labeled with 10 classes, thus addressing two shortcomings in the current research: (1) increasing the size of the dataset from small to large, and (2) moving the focus of fake news detection from binary classification to multi-class classification. For this dataset, we manually verified the content of the news articles to ensure that they were correctly labeled. The experimental results show that the accuracy of transformers on the misinformation detection problem was significantly influenced by the method employed to learn the context, dataset size, and vocabulary dimension. We observe empirically that the best accuracy performance among the classification models that use only one transformer is obtained by BART, while DistilRoBERTa obtains the best accuracy in the least amount of time required for fine-tuning and training. However, the proposed MisRoBÆRTa outperforms the other transformer models in the task of misinformation detection. To arrive at this conclusion, we performed ample ablation and sensitivity testing with MisRoBÆRTa on two datasets.
Integrating Energy Management and Lean Manufacturing

There is a close relationship between energy efficiency and lean manufacturing. Lean focuses on the continuous elimination of non-value added activities and waste in a manufacturing process. Energy management focuses on the continuous elimination of wasted energy in a manufacturing process. This paper will focus on industrial facilities that participated in a Power Smart 1 Lean initiative with Manitoba Hydro. The objective of this service is to leverage lean principles by capitalizing on the synergies between lean manufacturing and energy management to increase the incorporation of energy efficiency into a manufacturing plant. Case studies are presented showing the resulting electric and gas saving opportunities from identifying and reducing wasted energy. Examples are presented to show the incidental energy savings realized by facilities that have used lean to improve productivity. Finally, case studies are discussed which demonstrate the utilization of lean approaches and tools with parallels to energy management.
Aortic Stiffness and Pulsatile Pressures as Potential Mediators of Chronic Kidney Disease Induced Impaired Diastolic Function

Purpose We assessed whether aortic stiffness and pulsatile pressures can mediate chronic kidney disease (CKD)-associated impaired diastolic function. Participants and Methods In 276 black Africans including 46 CKD (19 non-dialysis; 27 dialysis) and 230 control subjects, pulse wave velocity (PWV) estimated aortic stiffness and pulsatile pressures (forward and backward wave pressure, central systolic blood pressure (CSBP) and pulse pressure (CPP)) were determined by applanation tonometry; e’ as an index of left ventricular active relaxation and E/e’ as a measure of left ventricular filling pressure or passive relaxation were evaluated by echocardiography. Results In age, sex, traditional cardiovascular risk factor and mean arterial pressure (MAP) adjusted regression models, CKD was inversely associated with e’ (p = 0.03) and directly with E/e’ (p < 0.01). The CKD-e’ relationship was attenuated and no longer significant (p = 0.31) upon additional adjustment for aortic PWV but not pulsatile pressures (p = 0.03–0.05). In product of coefficient mediation analysis, PWV accounted for 47.6% of the CKD-e’ association. CSBP (22.9%) and CPP (18.6%) but not PWV (11.3%) accounted for a significant and relevant proportion of the CKD-E/e’ relationship. However, CKD remained strongly associated with E/e’ independent of aortic function measures (p < 0.01). Treatable covariates that were or tended to be consistently associated with diastolic function included MAP (p < 0.01) and diabetes (p = 0.02–0.07) for the CKD-e’ and CKD-E/e’ relations, respectively. Conclusion Aortic stiffness rather than pulsatile pressures mediates CKD-related impaired left ventricular active relaxation. By contrast, aortic pulsatile pressures (and not stiffness) contribute to CKD-related left ventricular filling pressures but do not fully account for the respective association.
Continuous Speech for Improved Learning Pathological Voice Disorders

Goal: Numerous studies had successfully differentiated normal and abnormal voice samples. Nevertheless, further classification had rarely been attempted. This study proposes a novel approach, using continuous Mandarin speech instead of a single vowel, to classify four common voice disorders (i.e. functional dysphonia, neoplasm, phonotrauma, and vocal palsy). Methods: In the proposed framework, acoustic signals are transformed into mel-frequency cepstral coefficients, and a bi-directional long-short term memory network (BiLSTM) is adopted to model the sequential features. The experiments were conducted on a large-scale database, wherein 1,045 continuous speech were collected by the speech clinic of a hospital from 2012 to 2019. Results: Experimental results demonstrated that the proposed framework yields significant accuracy and unweighted average recall improvements of 78.12–89.27% and 50.92–80.68%, respectively, compared with systems that use a single vowel. Conclusions: The results are consistent with other machine learning algorithms, including gated recurrent units, random forest, deep neural networks, and LSTM.The sensitivities for each disorder were also analyzed, and the model capabilities were visualized via principal component analysis. An alternative experiment based on a balanced dataset again confirms the advantages of using continuous speech for learning voice disorders.
From Monolithic Models to Agile Micromodels

: This paper proposes a new modeling approach that allows to split classical monolithic class models into a system of small micromodels. The events of such a system are modeled through an additional Event Storming. This new approach facilitates evolution as Event Storming uses events with relatively simple structures and new events may be added easily. Similarly, sets of micromodels are more evolveable as changes remain local to only those micromodels that are concerned by the particular subset of changed events. Different micromodels communicate via a small set of dedicated “interface" events, which again aids evolution.
The Aircraft Landing Problem

) x ji (cid:5) t j , i , j (cid:1) …, The problem studied in the paper is an air traffic problem on the airport runway. The goal is finding an aircraft landing sequence that meets the time window for the particular aircraft and at the same time the separation times between two aircraft, which is necessary for the security of landings. The integer programming formulations and the relationship to the traveling salesman problem with cumulative costs are shown.
Condition Monitoring System of Wind Turbine Generators

The development and implementation of the condition monitoring systems (CMS) play a significant role in overcoming the number of failures in the wind turbine generators that result from the harsh operation conditions, such as over temperature, particularly when turbines are deployed offshore. In order to increase the reliability of the wind energy industry, monitoring the operation conditions of wind generators is essential to detect the immediate faults rapidly and perform appropriate preventative maintenance. CMS helps to avoid failures, decrease the potential shutdowns while running, reduce the maintenance and operation costs and maintain wind turbines protected. The knowledge of wind turbine generators’ faults, such as stator and rotor inter-turn faults, is indispensable to perform the condition monitoring accurately, and assist with maintenance decision making. Many techniques are utilized to avoid the occurrence of failures in wind turbine generators. The majority of the previous techniques that are applied to monitor the wind generator conditions are based on electrical and mechanical concepts and theories. An advanced CMS can be implemented by using a variety of different techniques and methods to confirm the validity of the obtained electrical and mechanical condition monitoring
Better Confidence Intervals for a Binomial Proportion

The parameter is also called binomial proportion. In practice the value of the parameter is usually unknown and must be estimated from a sample. Let X be a number of successes in a random sample of size n. The maximum likelihood estimator for from the sample is p X/n. This estimator is unbiased and consistent. The 100! (1 )% two-sided confidence interval for parameter is an interval pL, pU such as P(pL pU) 1 , where (1 ) is the desired confidence coefficient, (0,1).
Non-planar Surface Shape Reconstruction from a Point Cloud in the Context of Muscles Attachments Estimation

Knowledge of muscle attachments on bones is essential for musculoskeletal modelling. A muscle attachment is often represented by points (in 3D) obtained by a manual digitisation system during dissection. Although this representation suffices for many purposes, sophisticated musculoskeletal models commonly require representing a muscle attachment by a surface patch or at least by a closed boundary curve. In this paper, therefore, we propose an approach to automatic shape reconstruction from such point sets. It is based on iso-contour extraction from a scalar field of distances to geodetics connecting the pairs of points (from the input set) as identified by a state-of-the-art algorithm for 2D curve reconstruction running on the input points transformed to 2D. We investigated the performance of 15 existing state-of-the-art algorithms with public implementations on the TLEM 2.0 data set of muscle attachments. The best results were obtained for the lenz algorithm with just one unacceptable reconstruction when standard projection onto a best-fit plane was used to transform the input 3D points to 2D. The second algorithm was α-shape with three unacceptable reconstructions, whereas in this case, the multidimensional scaling technique was exploited to transform the points.
APS2-ACCESS-C2: the first Australian operational NWP convection-permitting model

The Australian Bureau of Meteorology’s ‘Australian Parallel Suite’ (APS) operational numerical weather prediction regional Australian Community Climate and Earth-System Simulator (ACCESS) city-based system (APS1 ACCESS-C1) was updated in August 2017 with the com-missioning of the APS2 ACCESS-C2. ACCESS-C2 runs over six regional domains. Significant upgrade changes included implementation of Unified Model 8.2 code; nesting in the 12 km resolution APS2 ACCESS-R2 regional model; and, importantly, an increased horizontal resolution from 4 to 1.5 km, enabling C2 to become the first Australian operational convection-permitting model (CPM). Traditional rainfall verification metrics and Fractions Skill Score show C2 forecast skill over ACCESS-C domains in summer and winter was generally, and in many cases, significantly better than C1. Case studies showed that C2 forecasts had better-detailed wind and precipitation fields, particularly at longer forecast ranges and higher rain rates. The improvements in C2 forecasts were principally due to its CPM ability to simulate high temporal and spatial resolution features, which continue to be of great interest to forecasters. C2 also laid the groundwork for the present day APS3 ACCESS-C forecast C3 and ensemble CE3 models and further development of higher resolution (down to 300 m) fire weather and urban models.
Hippocampal Over-Expression of Cyclooxygenase-2 (COX-2) Is Associated with Susceptibility to Stress-Induced Anhedonia in Mice

The phenomenon of individual variability in susceptibility/resilience to stress and depression, in which the hippocampus plays a pivotal role, is attracting increasing attention. We investigated the potential role of hippocampal cyclooxygenase-2 (COX-2), which regulates plasticity, neuroimmune function, and stress responses that are all linked to this risk dichotomy. We used a four-week-long chronic mild stress (CMS) paradigm, in which mice could be stratified according to their susceptibility/resilience to anhedonia, a key feature of depression, to investigate hippocampal expression of COX-2, a marker of microglial activation Iba-1, and the proliferation marker Ki67. Rat exposure, social defeat, restraints, and tail suspension were used as stressors. We compared the effects of treatment with either the selective COX-2 inhibitor celecoxib (30 mg/kg/day) or citalopram (15 mg/kg/day). For the celecoxib and vehicle-treated mice, the Porsolt test was used. Anhedonic (susceptible) but not non-anhedonic (resilient) animals exhibited elevated COX-2 mRNA levels, increased numbers of COX-2 and Iba-1-positive cells in the dentate gyrus and the CA1 area, and decreased numbers of Ki67-positive cells in the subgranular zone of the hippocampus. Drug treatment decreased the percentage of anhedonic mice, normalized swimming activity, reduced behavioral despair, and improved conditioned fear memory. Hippocampal over-expression of COX-2 is associated with susceptibility to stress-induced anhedonia, and its pharmacological inhibition with celecoxib has antidepressant effects that are similar in size to those of citalopram.
Combinatorial Auctions for Selling Network Capacity

The paper presents modeling of combinatorial auctions in network economy. It is an integration of two characteristic elements of today’s economic reality, the network economy and auction mechanisms. The network economy is a term for today’s global relationship among economic subjects characterized by massive connectivity. Network industries play a crucial role in modern life. Today’s network systems provide the infrastructure and foundation for the functioning of societies and economies. Auctions are important market mechanisms for the allocation of goods and services. Auctions are preferred often to other common processes because they are open, quite fair, easy to understand by participants, and lead to economically efficient outcomes. Their popularity is also caused by expansion of e-commerce. Design of auctions (see [7]) is a multidisciplinary effort made of contributions from economics, operations research, computer science, and other disciplines.
A stochastic approach to rhotic variation in Spanish codas

A stochastic analysis using aerodynamic and temporal variables is presented to explain tap/trill variation in Spanish codas. Simultaneous intraoral pressure (Po), translingual flow (F) and acoustic signals were obtained for two native speakers of Peninsular Spanish performing a rate-controlled reiterative task. Data were analyzed using linear mixed effects models fitted with random effects for speaker and repetition. The results show that tongue height and tongue anteriority of the preceding vowel affect the timing and aerodynamic parameters of the rhotic onset gesture, but the phonological specifications of the following consonants do not. Continuous variables were fit into a Bayesian logit regression model using noninformative priors which shows that tap/trill variation in coda can be predicted in part from aerodynamic and temporal parameters. Such a proposal obviates the need to resort to positional constraints to explain tap/trill variation in codas.
Adaptive Design and Analysis Via Partitioning Trees for Emulation of a Complex Computer Code

Abstract Computer models are used as replacements for physical experiments in a large variety of applications. Nevertheless, direct use of the computer model for the ultimate scientific objective is often limited by the complexity and cost of the model. Gaussian process regression has been the almost ubiquitous choice for a fast statistical emulator for such a computer model, due to its flexible form and analytical expressions for measures of predictive uncertainty. However, even this statistical emulator can be computationally intractable for large designs, due to computing time increasing with the cube of the design size. Multiple methods have been proposed for addressing this problem. We discuss several of them, and compare their predictive and computational performance in several scenarios. We propose solving this problem using a new method, adaptive design and analysis via partitioning trees (ADAPT). The new approach is motivated by the idea that most computer models are only complex in particular regions of the input space. By taking a data-adaptive approach to the development of a design, and choosing to partition the space in the regions of highest variability, we obtain a higher density of points in these regions and hence accurate prediction. Supplemental files for this article are available online.