Miracle-dz/UniFAR-FTUs huggingface dataset:
Does the combination of exercise and cognitive training improve working memory in older adults? A systematic review and meta-analysis

Background Cognitive functioning is dependent on working memory and a decline in working memory is the main cause of cognitive aging. Many studies have suggested that physical exercise or cognitive intervention can effectively improve working memory in the elderly. However, it is still unknown whether a combination of exercise and cognitive training (CECT) is more effective than either intervention alone. The present systematic review and meta-analysis were undertaken to evaluate the effect of CECT on working memory in the elderly. Methods The review was registered in the International Prospective Systematic Review (PROSPERO, CRD42021290138). Systematic searches were conducted on Web of Science, Elsevier Science, PubMed and Google Scholar. The data were extracted according to the PICOS framework. Comprehensive meta-analysis (CMA) software was used to perform the meta-analysis, moderator analysis and publication bias testing. Results The current meta-analysis included 21 randomized controlled trials (RCT). Results showed that CECT had a significantly greater impact on working memory in older adults compared to no intervention groups (SMD = 0.29, 95% CI [0.14–0.44], p < 0.01), with no significant difference between CECT and exercise (SMD = 0.16, 95% CI [−0.04–0.35], p = 0.12) or cognitive intervention alone (SMD = 0.08, 95% CI [−0.13–0.30], p = 0.44). Furthermore, the positive effect of CECT was moderated by intervention frequency and cognitive state. Conclusions The CECT can effectively improve working memory of older adults, but the effect of CECT compared to single intervention needs to be further explored.
Nurses Transitioning to Primary Health Care in Australia: A Practice Improvement Initiative

Introduction Current nursing programs provide little pre-registration information and clinical exposure to primary health care nursing practice. Newly graduated nurses and those transitioning to primary health care report disorientation and confusion. Limited knowledge about the ideal elements of a program to support transitioning nurses is an imperative to understand how best to support nurses during this period. The peak body for nurses in primary health care was funded to develop and implement a 1-year transition to practice improvement initiative for nurses in Australia about to be employed in or who are new to primary health care. This quality improvement evaluation study aimed to determine the impact of the transition program on participants’ nursing knowledge, skills, and confidence, and their overall satisfaction with the program. Methods A mixed methods evaluation of the project utilized data from online pre- and post-participation surveys, mentor meetings, and field notes. Descriptive statistics and paired t-tests were employed for quantitative items. Free text and field notes were subject to broad thematic analysis. Findings from qualitative and quantitative data were triangulated. Results Ninety-three nurse participants and 62 mentors were recruited. There were statistically significant differences between mean summed post-completion and pre-completion self-assessment rating scores for knowledge, skills, and confidence. Program elements were well received. The orientation workshop, mentoring, and education activities were identified as most influential in supporting transition. The self-assessment framework was identified as least influential. Conclusion A 12-month transition program was effective in building the knowledge, skills, and confidence of participants to deliver comprehensive and effective nursing services.
A Multi-Hop QoS-Aware and Predicting Link Quality Estimation (PLQE) Routing Protocol for Reliable WBSN

Technological advancements and miniaturization of sensor have made it possible to use tiny size sensors nodes to monitor patients’ physiological data in real-time at very low cost. The network of such small size sensor nodes is often called wireless body sensor networks (WBSNs). The patient’s data is highly sensitive which required to reach reliably and timely at the destination node. However, short range wireless nodes and postural body movements cause frequent topological changes and recurrent network partitioning. The network partitioning leads to delayed and significant loss of critical patient’s data such as Electrocardiogram (ECG), Electroencephalogram (EEG) and Electromyogram (EMG). Therefore, QoS requirement of WBSNs in terms of reliability, end-to-end delay and throughput are highly compromise. We in this paper propose a Predicting routing protocol PLQE to address reliable data transmission and network partitioning due to node mobility. The PLQE dynamically determines the efficient links by using beta probability density function, link quality and link delay estimation (LDE). The PLQE is composed of link reliability factor (LRF) and expected probability Indicator (EPI) to obtain most often updated status of the links to figure out stable and reliable end to end route to the destination node. The simulation results confirm the higher performance of the proposed scheme against state-of-the-art routing protocols in terms of packet delivery ratio, end to end delay, throughput and normalized routing load.
Structure-Based Design and Pharmacophore-Based Virtual Screening of Combinatorial Library of Triclosan Analogues Active against Enoyl-Acyl Carrier Protein Reductase of Plasmodium falciparum with Favourable ADME Profiles

Cost-effective therapy of neglected and tropical diseases such as malaria requires everlasting drug discovery efforts due to the rapidly emerging drug resistance of the plasmodium parasite. We have carried out computational design of new inhibitors of the enoyl-acyl carrier protein reductase (ENR) of Plasmodium falciparum (PfENR) using computer-aided combinatorial and pharmacophore-based molecular design. The Molecular Mechanics Poisson–Boltzmann Surface Area (MM-PBSA) complexation QSAR model was developed for triclosan-based inhibitors (TCL) and a significant correlation was established between the calculated relative Gibbs free energies of complex formation (∆∆Gcom) between PfENR and TCL and the observed inhibitory potencies of the enzyme (IC50exp) for a training set of 20 known TCL analogues. Validation of the predictive power of the MM-PBSA QSAR model was carried out with the generation of 3D QSAR pharmacophore (PH4). We obtained a reasonable correlation between the relative Gibbs free energy of complex formation ∆∆Gcom and IC50exp values, which explained approximately 95% of the PfENR inhibition data: pIC50exp=−0.0544×∆∆Gcom+6.9336,R2=0.95. A similar agreement was established for the PH4 pharmacophore model of the PfENR inhibition (pIC50exp=0.9754×pIC50pre+0.1596, R2=0.98). Analysis of enzyme–inhibitor binding site interactions suggested suitable building blocks to be used in a virtual combinatorial library of 33,480 TCL analogues. Structural information derived from the complexation model and the PH4 pharmacophore guided us through in silico screening of the virtual combinatorial library of TCL analogues to finally identify potential new TCL inhibitors effective at low nanomolar concentrations. Virtual screening of the library by PfENR-PH4 led to a predicted IC50pre value for the best inhibitor candidate as low as 1.9 nM. Finally, the stability of PfENR-TCLx complexes and the flexibility of the active conformation of the inhibitor for selected top-ranking TCL analogues were checked with the help of molecular dynamics. This computational study resulted in a set of proposed new potent inhibitors with predicted antimalarial effects and favourable pharmacokinetic profiles that act on a novel pharmacological target, PfENR.
Hypercontractile Cardiac Phenotype in Mice with Migraine-Associated Mutation in the Na+,K+-ATPase α2-Isoform

Two α-isoforms of the Na+,K+-ATPase (α1 and α2) are expressed in the cardiovascular system, and it is unclear which isoform is the preferential regulator of contractility. Mice heterozygous for the familial hemiplegic migraine type 2 (FHM2) associated mutation in the α2-isoform (G301R; α2+/G301R mice) have decreased expression of cardiac α2-isoform but elevated expression of the α1-isoform. We aimed to investigate the contribution of the α2-isoform function to the cardiac phenotype of α2+/G301R hearts. We hypothesized that α2+/G301R hearts exhibit greater contractility due to reduced expression of cardiac α2-isoform. Variables for contractility and relaxation of isolated hearts were assessed in the Langendorff system without and in the presence of ouabain (1 µM). Atrial pacing was performed to investigate rate-dependent changes. The α2+/G301R hearts displayed greater contractility than WT hearts during sinus rhythm, which was rate-dependent. The inotropic effect of ouabain was more augmented in α2+/G301R hearts than in WT hearts during sinus rhythm and atrial pacing. In conclusion, cardiac contractility was greater in α2+/G301R hearts than in WT hearts under resting conditions. The inotropic effect of ouabain was rate-independent and enhanced in α2+/G301R hearts, which was associated with increased systolic work.
Cohesive Dynamics of M-agents with Finite-Distributed Delays on Time Scales

In this paper, we investigate a swarm model based on attraction/repulsion functions with finite-distributed communication delays on time scales. To get the main consequence of this model, we introduce a mean-value theorem of integrals on T and a lemma to estimate the exponential function on time scales T. Under the aforementioned preparations, we study the cohesiveness of the swarm based on Lyapunov stability method and theory of time scales. We show that all the members of the swarm will eventually enter into a bounded hyperball in finite time and estimate the upperbound time of forming the cohesive cluster. In the end, simulation results are presented to illustrate the effectiveness of our results.
On Multivariate Markov Chains for Common and Non-Common Objects in Multiple Networks

Node importance or centrality evaluation is an important methodology for network analysis. In this paper, we are interested in the study of objects appearing in several networks. Such common objects are important in network-network interac- tions via object-object interactions. The main contribution of this paper is to model multiple networks where there are some common objects in a multivariate Markov chain framework, and to develop a method for solving common and non-common ob- jects' stationary probability distributions in the networks. The stationary probability distributions can be used to evaluate the importance of common and non-common ob- jects via network-network interactions. Our experimental results based on examples of co-authorship of researchers in different conferences and paper citations in differ- ent categories have shown that the proposed model can provide useful information for researcher-researcher interactions in networks of different conferences and for paper- paper interactions in networks of different categories. AMS subject classifications: 60J10, 91D30, 05C81
Real-time 3D ultrafast shear wave absolute vibro-elastography

3D ultrasound elastography has the potential to be a fast and accurate approach for estimating tissue shear modulus from the wave equation. One of the drawbacks of 3D imaging is the long data acquisition time which reduces practicality and can introduce artifacts from unwanted tissue motion such as from breathing. This paper presents a novel imaging technique for real-time 3D data collection with a volume rate of 2000 volumes/s and total acquisition time of 0.05 s. Plane wave imaging is used with Shear Wave Absolute Vibro-Elastography (S-WAVE) where an external mechanical vibration source generates multi-frequency shear waves in tissue. A matrix array transducer is used to collect volumes of radio frequency (RF) data. Axial, lateral and elevational displacements are then estimated over 3D volumes. The curl of the displacements is used in a local frequency estimation technique to estimate elasticity in the acquired volumes. The S-WAVE excitation frequency is substantially extended up to 500 Hz by using a high volume rate imaging. Therefore, the method can be applied on a wide range of applications such as prostate, breast, liver, and thyroid elastography. Results on liver fibrosis phantoms using several excitation frequencies are presented and compared with the manufacturer elasticity values for validation. The estimated elasticities by the proposed method fall within the manufacturer’s elasticity range for all frequencies.
Finding sequential patterns with TF-IDF metrics in health-care databases

Abstract Finding frequent sequential patterns has been defined as finding ordered list of items that occur more times in a database than a user defined threshold. For big and dense databases that contain really long sequences and large itemset such as medical case histories, algorithm proposed on this idea of counting the occurrences output enourmous number of highly redundant frequent sequences, and are therefore simply impractical. Therefore, there is a need for algorithm that perform frequent pattern search and prefiltering simultaneously. In this paper, we propose an algorithm that reinterprets the term support on text mining basis. Experiments show that our method not only eliminates redundancy among the output sequences, but it scales much better with huge input data sizes. We apply our algorithm for mining medical databases: what diagnoses are likely to lead to a certain future health condition.
Characterizing the development and drivers of 2021 Western US drought

Understanding the drivers of the major 2021 drought conditions across the Western U.S. (WUS) is important to develop future resilience and adaptation measures. While evapotranspiration (ET) tends to increase in a warming climate when water is available, areas with low precipitation would evaporate less water than expected, as seen in the 2021 drought. This study examines the relative roles of higher temperatures and lower precipitation, as well as anthropogenic forcing (e.g. increased greenhouse gases and land-use land-cover change) to the 2021 drought across the WUS. Using observations, the anomalously dry 2021 soil moisture is mainly tied to precipitation deficit, rather than higher temperatures, suggesting that an increase in ET in response to higher temperatures (i.e. saturation deficit) depends on water availability. Therefore, high temperatures may play only a secondary role in driving the 2021 drought condition across the WUS. Additionally, a suite of variable infiltration capacity model experiments confirms that the reduced precipitation in 2021 has caused negative soil moisture anomalies. Based on the Coupled Model Intercomparison Project Phase 6 experiments, anthropogenic forcing dramatically increases the risk of the extreme 2021 dryness, with risk ratio being 73.91, 12.78 and 25.81 for temperature, precipitation, and soil moisture respectively. Therefore, the extreme drought is not explained by natural forcing (e.g. solar irradiance and volcanic eruption) alone. Rather, anthropogenic forcing (e.g. increased greenhouse gases and land-use land-cover change) has increased the risk of this drought condition by approximately 26 times in terms of soil moisture compared with a world without this forcing.
Inductively-coupled plasma discharge for use in high-energy-density science experiments

Inductively-coupled plasma discharges are well-suited as plasma sources for experiments in fundamental high-energy density science, which require large volume and stable plasmas. For example, experiments studying particle beam-plasma instabilities and the emergence of coherent macroscopic structures — which are key for modelling emission from collisionless shocks present in many astrophysical phenomena. A meter-length, table-top, inductive radio-frequency discharge has been constructed for use in a high-energy density science experiment at CERN which will study plasma instabilities of a relativistic electron-positron beam. In this case, a large volume is necessary for the beam to remain inside the plasma as it diverges to centimeter-scale diameters during the tens-of-centimeters of propagation needed for instabilities to develop. Langmuir probe measurements of the plasma parameters show that plasma can be stably sustained in the discharge with electron densities exceeding 1011 cm-3. The discharge has been assembled using commercially-available components, making it an accessible option for commissioning at a University laboratory.
Hybrid cavity polaritons in a ZnO-perovskite microcavity

We realize a Fabry-Perot hybrid microcavity containing an association of a ZnO thin layer and of a layer of the two-dimensional layered perovskite 5-methyl-2-furanmethanamonium lead bromide (MFMPB). From angle-resolved reflectivity experiments performed at low temperature of 5 K, we show that this hybrid cavity works in the strong-coupling regime and that the lower, middle, and upper polariton branches are observed. We show that the middle polariton branch (MPB) contains a significant component of the cavity photon and both of the two exciton species.
A Novel Hybrid Post-Weighting Digital Predistortion in mMIMO Under Crosstalk

—In a hybrid beamforming, a single digital predistortion (DPD) is inefﬁcient to address all the nonlinearities over a subarray of power ampliﬁers (PAs) with underlying crosstalk in a massive multiple-input multiple-output (mMIMO) transmitter. In this context, the proposed work describes a novel hybrid post-weighting (PW) scheme. Here, it extends the competence of one trained DPD to all PAs exclusively via following PW block associated with optimal coefﬁcients along the basis functions of the DPD. Consequently, it reduces the nonlinear radiation signiﬁcantly in a wide range of azimuth directions to the transmitter.
Representation Learning with Multi-Step Inverse Kinematics: An Efficient and Optimal Approach to Rich-Observation RL

We study the design of sample-efficient algorithms for reinforcement learning in the presence of rich, high-dimensional observations, formalized via the Block MDP problem. Existing algorithms suffer from either 1) computational intractability, 2) strong statistical assumptions that are not necessarily satisfied in practice, or 3) suboptimal sample complexity. We address these issues by providing the first computationally efficient algorithm that attains rate-optimal sample complexity with respect to the desired accuracy level, with minimal statistical assumptions. Our algorithm, MusIK, combines systematic exploration with representation learning based on multi-step inverse kinematics, a learning objective in which the aim is to predict the learner's own action from the current observation and observations in the (potentially distant) future. MusIK is simple and flexible, and can efficiently take advantage of general-purpose function approximation. Our analysis leverages several new techniques tailored to non-optimistic exploration algorithms, which we anticipate will find broader use.
On Parallel k-Center Clustering

We consider the classic k-center problem in a parallel setting, on the low-local-space Massively Parallel Computation (MPC) model, with local space per machine of O (nδ), where δ ∈ (0,1) is an arbitrary constant. As a central clustering problem, the k-center problem has been studied extensively. Still, until very recently, all parallel MPC algorithms have been requiring Ω(k) or even Ω(knδ) local space per machine. While this setting covers the case of small values of k, for a large number of clusters these algorithms require large local memory, making them poorly scalable. The case of large k,k ≥ Ω(nδ), has been considered recently for the low-local-space MPC model by Bateni et al. (2021), who gave an O (log log n)-round MPC algorithm that produces k(1 + ο (1)) centers whose cost has multiplicative approximation of O (log log log n). In this paper we extend the algorithm of Bateni et al. and design a low-local-space MPC algorithm that in O (log log n) rounds returns a clustering with k(1 + ο(1)) clusters that is an O (log*n)-approximation for k-center.
Human-Robot Skill Transfer with Enhanced Compliance via Dynamic Movement Primitives

Finding an efficient way to adapt robot trajectory is a priority to improve overall performance of robots. One approach for trajectory planning is through transferring human-like skills to robots by Learning from Demonstrations (LfD). The human demonstration is considered the target motion to mimic. However, human motion is typically optimal for human embodiment but not for robots because of the differences between human biomechanics and robot dynamics. The Dynamic Movement Primitives (DMP) framework is a viable solution for this limitation of LfD, but it requires tuning the second-order dynamics in the formulation. Our contribution is introducing a systematic method to extract the dynamic features from human demonstration to auto-tune the parameters in the DMP framework. In addition to its use with LfD, another utility of the proposed method is that it can readily be used in conjunction with Reinforcement Learning (RL) for robot training. In this way, the extracted features facilitate the transfer of human skills by allowing the robot to explore the possible trajectories more efficiently and increasing robot compliance significantly. We introduced a methodology to extract the dynamic features from multiple trajectories based on the optimization of human-likeness and similarity in the parametric space. Our method was implemented into an actual human-robot setup to extract human dynamic features and used to regenerate the robot trajectories following both LfD and RL with DMP. It resulted in a stable performance of the robot, maintaining a high degree of human-likeness based on accumulated distance error as good as the best heuristic tuning.
Photoacoustic Digital Tooth and Image Reconstruction of Tooth Root

Accurate tooth detection and imaging are crucial for diagnosing dental diseases and planning orthodontic procedures. Reconstruction activities on the tooth root surface can lead to alveolar bone atrophy and hinder tooth movement during therapy. The current golden-standard imaging modality for teeth is oral and maxillofacial cone beam CT (CBCT), which is, however, radioactive. To address this issue, we propose the use of photoacoustic (PA) imaging, an emerging radiation-free modality, for tooth root imaging. We verify its feasibility through simulation experiments based on a digital tooth phantom and modify the image reconstruction algorithms to address artifacts caused by the heterogeneous acoustic velocity.