Miracle-dz/UniFAR-FTUs huggingface dataset:
Temporal Alignment of Longitudinal Microbiome Data

A major challenge in working with longitudinal data when studying some temporal process is the fact that differences in pace and dynamics might overshadow similarities between processes. In the case of longitudinal microbiome data, this may hinder efforts to characterize common temporal trends across individuals or to harness temporal information to better understand the link between the microbiome and the host. One possible solution to this challenge lies in the field of “temporal alignment” – an approach for optimally aligning longitudinal samples obtained from processes that may vary in pace. In this work we investigate the use of alignment-based analysis in the microbiome domain, focusing on microbiome data from infants in their first years of life. Our analyses center around two main use-cases: First, using the overall alignment score as a measure of the similarity between microbiome developmental trajectories, and showing that this measure can capture biological differences between individuals. Second, using the specific matching obtained between pairs of samples in the alignment to highlight changes in pace and temporal dynamics, showing that it can be utilized to predict the age of infants based on their microbiome and to uncover developmental delays. Combined, our findings serve as a proof-of-concept for the use of temporal alignment as an important and beneficial tool in future longitudinal microbiome studies.
C²-CapsViT: Cross-Context and Cross-Scale Capsule Vision Transformers for Remote Sensing Image Scene Classification

Accurately interpreting image contents plays a vital role in many Earth observation tasks. This letter constructs a novel cross-context and cross-scale capsule vision transformer (C2-CapsViT) architecture to serve for remote sensing image scene classification. First, employed with a multicontext patch embedding strategy, the token representation quality is greatly boosted to encode different-context feature semantics. Second, designed with a multiscale transformer block, different-grained long-range global feature interactions and different-type feature self-attentions are concurrently exploited to promote the feature encoding quality. Moreover, by combining the convolution and transformer structures, local and global feature semantics are effectively fused to direct accurate predictions. The C2-CapsViT is elaborately verified on three scene classification datasets. Both quantitative evaluations and comparative analyses prove its competitive capability and advanced performance.
Application of Markov Model to Estimate Individual Condition Parameters for Transformers

This paper presents a study to estimate individual condition parameters of the transformer population based on Markov Model (MM). The condition parameters under study were hydrogen (H2), methane (CH4), acetylene (C2H2), ethylene (C2H4), ethane (C2H6), carbon monoxide (CO), carbon dioxide (CO2), dielectric breakdown voltage, interfacial tension, colour, acidity, water content, and 2-furfuraldehyde (2-FAL). First, the individual condition parameter of the transformer population was ranked and sorted based on recommended limits as per IEEE Std. C57. 104-2008 and IEEE Std. C57.106-2015. Next, the mean for each of the condition parameters was computed and the transition probabilities for each condition parameters were obtained based on non-linear optimization technique. Next, the future states probability distribution was computed based on the MM prediction model. Chi-square test and percentage of absolute error analysis were carried out to find the goodness-of-fit between predicted and computed condition parameters. It is found that estimation for majority of the individual condition parameter of the transformer population can be carried out by MM. The Chi-square test reveals that apart from CH4 and C2H4, the condition parameters are outside the rejection region that indicates agreement between predicted and computed values. It is also observed that the lowest and highest percentages of differences between predicted and computed values of all the condition parameters are 81.46% and 98.52%, respectively.
Fluorescence Detection of Cancer Stem Cell Markers Using a Sensitive Nano-Aptamer Sensor

Antigen CD133 is a glycoprotein present on the surface of cancer stem cells (CSCs), which is a key molecule to regulate the fate of stem cells and a functional marker of stem cells. Herein, a novel fluorescence “turn-on” nano-aptamer sensor for quantifying CD133 was designed using hybridization between CD133-targeted aptamers and partially complementary paired RNA (ssRNA), which were modified on the surface of quantum dots (QDs) and gold nanoparticles (AuNPs), respectively. Owing to the hybridization of aptamers and ssRNA, the distance between QDs and AuNPs was shortened, which caused fluorescence resonance energy transfer (FRET) between them, and the florescence of QDs was quenched by AuNPs. When CD133 competitively replaced ssRNA and was bound to aptamers, AuNPs-ssRNA could be released, which led to a recovery of fluorescent signals of QDs. The increase in the relative value of fluorescence intensity was investigated to linearly correlate with the CD133 concentration in the range of 0–1.539 μM, and the detection limit was 6.99 nM. In confocal images of A549 cells, the CD133 aptamer sensor was further proved applicable in lung cancer cell samples with specificity, precision, and accuracy. Compared with complicated methods, this study provided a fresh approach to develop a highly sensitive and selective detection sensor for CSC markers.
Space-filling designs for multi-layer nested factors

Abstract This article considers computer experiments where levels for continuous factors are selected in sequential order with the level selected for one factor directly affecting the range of possible levels for the nested factor and, so on, for a finite number of factors. In addition, we assume that the nested relationships between the factors have no closed form solution. We propose an approach for constructing a multilayer nested factor design or multi-NFD for short. This space-filling design approach takes advantage of the maximin criterion and can be analyzed using a standard Gaussian process model. While the multi-NFD approach can be adapted for future computer experiments involving factor relationships of this type, we present results from a particular aerospace computer simulation study.
Metabolic Fingerprint Analysis of Cytochrome b5-producing E. coli N4830-1 Using FT-IR Spectroscopy

Optimization of recombinant protein expression in bacteria is an important task in order to increase protein yield while maintaining the structural fidelity of the product. In this study, we employ Fourier transform infrared (FT-IR) spectroscopy as a high throughput metabolic fingerprinting approach to optimize and monitor cytochrome b5 (CYT b5) production in Escherichia coli N4830-1, as the heterologous host. Cyt b5 was introduced as a plasmid with between 0 and 6 copies under a strong promoter. The FT-IR spectroscopy results combined with multivariate chemometric analysis illustrated discriminations among culture conditions as well as revealing features that correlated to the different cytb5 gene copy numbers. The second derivative of the FT-IR spectral data allowed for the quantitative detection of Cyt b5 directly inside the intact cells without the need for extraction, and highlighted changes in protein secondary structure that was directly correlated to the cytb5 gene copy number and protein content, and was in complete agreement with quantitative findings of standard traditional techniques such as SDS–PAGE and western blot analysis.
A generation of synthesized trajectories of free moving objects for testing a set of continuous spatio‐temporal queries

Testing vessel surveillance monitoring queries requires synthesized trajectory generation data of free‐moving objects. This article presents a method for generating trajectories of free moving objects with approach, u‐turn, detour, and hover patterns to verify a set of continuous queries expressed in spatiotemporal conditions. We present an algorithm that generates simplified paths, moving patterns, and inertia‐based trajectories to make various types of trajectory patterns for a given spatiotemporal query. The experimental results show that the generated synthesized trajectory dataset can validate given spatiotemporal queries with different velocities, trajectory patterns, and travel paths.
Immunogenicity of Mycobacterial Extracellular Vesicles Isolated From Host-Related Conditions Informs About Tuberculosis Disease Status

Tuberculosis (TB) still represents a major global health problem affecting over 10 million people worldwide. The gold-standard procedures for TB diagnosis are culture and nucleic acid amplification techniques. In this context, both lipoarabinomannan (LAM) urine test and rapid molecular tests have been major game changers. However, the low sensitivity of the former and the cost and the prohibitive infrastructure requirements to scale-up in endemic regions of the latter, make the improvement of the TB diagnostic landscape a priority. Most forms of life produce extracellular vesicles (EVs), including bacteria despite differences in bacterial cell envelope architecture. We demonstrated that Mycobacterium tuberculosis (Mtb), the causative agent of TB, produces EVs in vitro and in vivo as part of a sophisticated mechanism to manipulate host cellular physiology and to evade the host immune system. In a previous serology study, we showed that the recognition of several mycobacterial extracellular vesicles (MEV) associated proteins could have diagnostic properties. In this study, we pursued to expand the capabilities of MEVs in the context of TB diagnostics by analyzing the composition of MEVs isolated from Mtb cultures submitted to iron starvation and, testing their immunogenicity against a new cohort of serum samples derived from TB+ patients, latent TB-infected (LTBI) patients and healthy donors. We found that despite the stringent condition imposed by iron starvation, Mtb reduces the number of MEV associated proteins relative to iron sufficient conditions. In addition, TB serology revealed three new MEV antigens with specific biomarker capacity. These results suggest the feasibility of developing a point-of-care (POC) device based on selected MEV-associated proteins.
Exploring the Role of Physical Activity in Mediating the Association between Educational Level and Health-Related Quality of Life in an Adult Lifespan Sample from Madeira Island

Background: People more socioeconomically vulnerable tend to have a poorer health-related quality of life (HRQoL). Studies are trying to analyse the factors that may condition this relationship, including physical activity (PA), which may influence the relationship between socioeconomic status (SES) and HRQoL. This study aimed to analyse the relationship between SES and HRQoL through specific domains of PA. Methods: A total of 381 adults (≥18 years) from the Autonomous Region of Madeira completed the measurements. Mediation analyses using bootstrapping methods adjusted for confounding variables were performed to relate SES and HRQoL; the latter was evaluated using the physical component score (PCS) of the SF-12, the mental component score (MCS) of the SF-12, and the total score in the SF-12 questionnaire (SF-12 score), through physical activity. Results: Educational level was positively related to PCS and SF-12 score. Leisure-time PA (not including sports PA) and PA at work, as single mediating variables, did not mediate the relationship between SES and HRQoL. The total PA suppressed the socioeconomic gradient of HRQoL by 8–10%, 39–46%, and 15–16%, respectively, for the PCS, MCS, and the SF-12 score; sports PA mediated the relationship by 13–16%, 50%, and 15–21%, respectively. Conclusions: The results suggest that sports PA contributes to reducing the socioeconomic gradient of HRQoL.
Efficient Algorithms for Synthesizing Probing Waveforms With Desired Spectral Shapes

We consider the design of constant-modulus probing waveforms to improve the spectral compatibility of radar systems with the congested radio frequency environments. We seek to synthesize radar probing waveforms with desired spectral shapes. We use a weighted least-squares fitting approach to formulate the spectral shaping problem. We introduce two algorithms to tackle the optimization problem we encounter. Both algorithms are devised based on cyclic approaches and have guaranteed convergence of the objective values. Moreover, the proposed algorithms can be implemented via fast Fourier transforms and, hence, are computationally efficient. Furthermore, we extend the proposed algorithms to deal with peak-to-average-power ratio and similarity constraints, which are desirable in some radar applications. Finally, we provide several numerical examples to demonstrate the effectiveness of the proposed algorithms.
Ear recognition system using random forest and histograms of oriented gradients techniques

In recent years, systems of ear recognition are considered a significant topic of research in the biometrics field. In such systems, the models of machine learning represent a principal part in order to recognise humans’ identities by using their ear images. In this paper, a system of ear recognition is proposed by using random forest (RF) and histograms of oriented gradients (HOG) techniques. The HOG is used to extract features from ear images. Subsequently, these extracted features will be fed to the RF classifier to classify the ear images with respect to the classes. In this study, the ear images have been selected from the Indian Institute of Technology Delhi, second version (IITD II). The performance of the proposed system has evaluated by using different evaluation measures such as accuracy, specificity, and G-mean. The experimental results show that the proposed system for ear recognition obtains accuracy up to 99.69%. Furthermore, this system archives 99.84% and 80.78% for specificity and G-mean, respectively. The proposed system has the ability to identify persons through their ear images effectively.
An intelligence model for detection of PCOS based on k‐means coupled with LS‐SVM

Polycystic ovary syndrome (PCOS) is a hormonal disorder that affects women at an early age. Manual detection of PCOS is a challenging task for specialists, however, detection of PCOS as quick and accurate could save the lives of millions of women over the world. Current studies use high dimension features which leads to a low estimation accuracy, and high execution time. However, in this article, we develop a new intelligence system to classify PCOS based on k‐means coupled with a LS‐SVM (K‐M‐SVM) using a lower number of features. The original dataset is preprocessed and then k‐means is applied to select the most powerful features based on Euclidean distance to classify PCOS. It was found that the k‐means cluster had a high potential in selection the most influential features and eliminating the poor ones. As a result, a total of six features are chosen to represent PCOS data from the original features. The selected feature set are fed to the LS‐SVM to classify them into healthy and no healthy segments. Our findings showed that the proposed model (K‐M‐SVM) outperformed the state of the art, and it gained an accuracy of 99%.
Simulative Evaluation of a Joint-Cartesian Hybrid Motion Mapping for Robot Hands Based on Spatial In-Hand Information

Two sub-problems are typically identified for the replication of human finger motions on artificial hands: the measurement of the motions on the human side and the mapping method of human hand movements (primary hand) on the robotic hand (target hand). In this study, we focus on the second sub-problem. During human to robot hand mapping, ensuring natural motions and predictability for the operator is a difficult task, since it requires the preservation of the Cartesian position of the fingertips and the finger shapes given by the joint values. Several approaches have been presented to deal with this problem, which is still unresolved in general. In this work, we exploit the spatial information available in-hand, in particular, related to the thumb-finger relative position, for combining joint and Cartesian mappings. In this way, it is possible to perform a large range of both volar grasps (where the preservation of finger shapes is more important) and precision grips (where the preservation of fingertip positions is more important) during primary-to-target hand mappings, even if kinematic dissimilarities are present. We therefore report on two specific realizations of this approach: a distance-based hybrid mapping, in which the transition between joint and Cartesian mapping is driven by the approaching of the fingers to the current thumb fingertip position, and a workspace-based hybrid mapping, in which the joint–Cartesian transition is defined on the areas of the workspace in which thumb and fingertips can get in contact. The general mapping approach is presented, and the two realizations are tested. In order to report the results of an evaluation of the proposed mappings for multiple robotic hand kinematic structures (both industrial grippers and anthropomorphic hands, with a variable number of fingers), a simulative evaluation was performed.
Yield Response of Spring Maize under Future Climate and the Effects of Adaptation Measures in Northeast China

Agriculture production has been found to be the most sensitive sector to climate change. Northeast China (NEC) is one of the world’s major regions for spring maize production and it has been affected by climate change due to increases in temperature and decreases in sunshine hours and precipitation levels over the past few decades. In this study, the CERES-Maize model-v4.7 was adopted to assess the impact of future climatic change on the yield of spring maize in NEC and the effect of adaptation measures in two future periods, the 2030s (2021 to 2040) and the 2050s (2041 to 2060) relative to the baseline (1986 to 2005) under RCP4.5 and RCP8.5 scenarios. The results showed that increased temperatures and the decreases in both the precipitation level and sunshine hours in the NEC at six representative sites in the 2030s and 2050s periods based on RCP4.5 and RCP8.5 climate scenarios would shorten the maize growth durations by (1–38 days) and this would result in a reduction in maize yield by (2.5–26.4%). Adaptation measures, including altered planting date, supplemental irrigation and use of cultivars with longer growth periods could offset some negative impacts of yield decrease in maize. For high-temperature-sensitive cultivars, the adoption of early planting, cultivar change and adding irrigation practices could lead to an increase in maize yield by 23.7–43.6% and these measures were shown to be effective adaptation options towards reducing yield loss from climate change. The simulation results exhibited the effective contribution of appropriate adaptation measures in eliminating the negative impact of future climate change on maize yield.
Fast and Efficient Method for Optical Coherence Tomography Images Classification Using Deep Learning Approach

The use of optical coherence tomography (OCT) in medical diagnostics is now common. The growing amount of data leads us to propose an automated support system for medical staff. The key part of the system is a classification algorithm developed with modern machine learning techniques. The main contribution is to present a new approach for the classification of eye diseases using the convolutional neural network model. The research concerns the classification of patients on the basis of OCT B-scans into one of four categories: Diabetic Macular Edema (DME), Choroidal Neovascularization (CNV), Drusen, and Normal. Those categories are available in a publicly available dataset of above 84,000 images utilized for the research. After several tested architectures, our 5-layer neural network gives us a promising result. We compared them to the other available solutions which proves the high quality of our algorithm. Equally important for the application of the algorithm is the computational time, which is reduced by the limited size of the model. In addition, the article presents a detailed method of image data augmentation and its impact on the classification results. The results of the experiments were also presented for several derived models of convolutional network architectures that were tested during the research. Improving processes in medical treatment is important. The algorithm cannot replace a doctor but, for example, can be a valuable tool for speeding up the process of diagnosis during screening tests.
Estimation of Aortic Stiffness with Bramwell–Hill Equation: A Comparative Analysis with Carotid–Femoral Pulse Wave Velocity

Aortic stiffness is an important clinical parameter for predicting cardiovascular events. Carotid–femoral pulse wave velocity (cf-PWV) has been proposed for performing this evaluation non-invasively; however, it requires dedicated equipment and experienced operators. We explored the possibility of measuring aortic stiffness using ultrasound scans of the abdominal aorta coupled with the Bramwell–Hill equation. Healthy subjects were investigated; measurements of cf-PWV were taken by arterial tonometry and aortic systo-diastolic pressure difference was estimated using a validated model. Pulsatility of an abdominal tract of aorta was assessed by automated processing of ultrasound scans. Through a Bland–Altmann analysis, we found large biases when estimating each parameter by applying the Bramwell–Hill equation to the measured values of the other two paramters (bias, ± 1.96 SD; PWV, about 2.1 ± 2.5 m/s; pulsatility, 12 ± 14%; pressure jump, 47 ± 55 mmHg). These results indicate that the two measures are not interchangeable, and that a large part of the bias is attributable to blood pressure estimation. Further studies are needed to identify the possible sources of bias between cf-PWV and aortic pulsatility.
Optimal Control of Wireless Powered Edge Computing System for Balance Between Computation Rate and Energy Harvested

Wireless powered edge computing system (WPECS) enhances the computing power and extends the lifetime of wireless devices (WDs). This paper studies the WPECS with multiple WDs, in which the access point (AP) provides some transmission channels which differ from each other in the channel gain, and the WD powered through the wireless power transfer (WPT) technology has some indivisible tasks and adopts binary task-offloading actions. More energy harvested strengthens the WDs with more computing power, while corresponding to more energy consumption. Therefore, how to make the optimal tradeoff between computation rate and energy harvested arises as an interesting issue. To address this issue, this paper first formulates the switch process of transmission channel as a constrained Markov decision process (CMDP), and then proposed an effective algorithm to maximize the sum of computation rates of all WDs in terms of task data bits computed, within the required level of accumulative energy harvested. Theoretical analysis, simulations and field experiments jointly document and illustrate its performance. Note to Practitioners—This paper addresses the interesting tradeoff between computation rate and energy harvested in a wireless powered edge computing system that operates in the environments with limited available energy. It helps to improve the operation efficiency of the edge computing systems in the area of Internet of Things (IoT) or Cyber-Physical Systems (CPS) that employ wireless power transfer technology to power the wireless devices through the access point over the air to maximize the sum of computations rates of all WDs in terms of task data bits computed, while keeping the accumulative energy harvested within a range. Simulations and experimental investigations show that the solution proposed here outperforms existing solutions.