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The meaning of the word style depends on its context. While actions have already been quite studied for a while, style in human body motion is a growing topic of interest. In the context of animation, style is crucial as it brings realism and expressiveness to the motion of a character. Even though it is undoubtedly a key element in motions, its definition and the use of the word style in itself, among research works, lack consensus. Achieving realistic motions is tedious. It requires either a large motion capture dataset or the considerable work of artist animators. The lack of consistent style data is thus a challenge. Stylistic motion generation is quite studied in order to overcome this issue. This paper focuses on the study of style in human body motion from 3D human body skeletal data. It establishes a taxonomy of definitions of style, describes the data that have been used up until now, introduces key notions about motion capture data as well as machine learning, and presents approaches about style recognition, person identification through their style and motion style generation. [ABSTRACT FROM AUTHOR]

Graph networks can model data observed across different levels of biological systems that span from population graphs (with patients as network nodes) to molecular graphs that involve omics data. Graph-based approaches have shed light on decoding biological processes modulated by complex interactions. This paper systematically reviews graph-based analysis methods of Graph Signal Processing (GSP), Graph Neural Networks (GNNs) and graph topology inference, and their applications to biological data. This work focuses on the algorithms of graph-based approaches and the constructions of graph-based frameworks that are adapted to a broad range of biological data. We cover the Graph Fourier Transform and the graph filter developed in GSP, which provides tools to investigate biological signals in the graph domain that can potentially benefit from the underlying graph structures. We also review the node, graph, and interaction oriented applications of GNNs with inductive and transductive learning manners for various biological targets. As a key component of graph analysis, we provide a review of graph topology inference methods that incorporate assumptions for specific biological objectives. Finally, we discuss the biological application of graph analysis methods within this exhaustive literature collection, potentially providing insights for future research in biological sciences. [ABSTRACT FROM AUTHOR]

Event sequences are central to the analysis of data in domains that range from biology and health, to logfile analysis and people's everyday behavior. Many visualization tools have been created for such data, but people are error-prone when asked to judge the similarity of event sequences with basic presentation methods. This article describes an experiment that investigates whether local and global alignment techniques improve people's performance when judging sequence similarity. Participants were divided into three groups (basic versus local versus global alignment), and each participant judged the similarity of 180 sets of pseudo-randomly generated sequences. Each set comprised a target, a correct choice and a wrong choice. After training, the global alignment group was more accurate than the local alignment group (98 versus 93 percent correct), with the basic group getting 95 percent correct. Participants’ response times were primarily affected by the number of event types, the similarity of sequences (measured by the Levenshtein distance) and the edit types (nine combinations of deletion, insertion and substitution). In summary, global alignment is superior and people's performance could be further improved by choosing alignment parameters that explicitly penalize sequence mismatches. [ABSTRACT FROM AUTHOR]

Deterministic asynchronous Boolean networks play a crucial role in modeling and analysis of gene regulatory networks. In this paper, we focus on a typical type of deterministic asynchronous Boolean networks called deterministic generalized asynchronous random Boolean networks (DGARBNs). We first formulate the extended state transition graph, which captures the whole dynamics of a DGARBN and paves potential ways to analyze this DGARBN. We then propose two SMT-based methods for attractor detection and optimal control of DGARBNs. These methods are implemented in a JAVA tool called DABoolNet. Two experiments are designed to highlight the scalability of the proposed methods. We also formally state and prove several relations between DGARBNs and other models including deterministic asynchronous models, block-sequential Boolean networks, generalized asynchronous random Boolean networks, and mixed-context random Boolean networks. Several case studies are presented to show the applications of our methods. [ABSTRACT FROM AUTHOR]

Analysis of gene similarity not only can provide information on the understanding of the biological roles and functions of a gene, but may also reveal the relationships among various genes. In this paper, we introduce a novel idea of mining similar aspects from a gene information network, i.e., for a given gene pair, we want to know in which aspects (meta paths) they are most similar from the perspective of the gene information network. We defined a similarity metric based on the set of meta paths connecting the query genes in the gene information network and used the rank of similarity of a gene pair in a meta path set to measure the similarity significance in that aspect. A minimal set of gene meta paths where the query gene pair ranks the highest is a similar aspect, and the similar aspect of a query gene pair is far from trivial. We proposed a novel method, SCENARIO, to investigate minimal similar aspects. Our empirical study on the gene information network, constructed from six public gene-related databases, verified that our proposed method is effective, efficient, and useful. [ABSTRACT FROM AUTHOR]

We examine the problem of uncovering communities in complex real-world networks whose elements and their respective associations manifest as streams of data. Community detection is applied in emerging computational environments and concerns critical applications in diverse areas including social computing, web analysis, IoT and biology. Despite the already expended related research efforts, the task of revealing the community structure of massive and rapidly-evolving networks remains very challenging. More specifically, there is an emerging need for online approaches that ingest graph data as a stream. In this paper, we propose a streaming-graph community-detection algorithm that expands seed-sets of nodes to communities. We consider an online setting and process a stream of edges while aiming to form communities on-the-fly using partial knowledge of the graph structure. We use space-efficient structures to maintain very limited information regarding the nodes of the graph and the sought communities, so as to effectively process large scale networks. In addition to our novel streaming approach, we develop a technique that increases the accuracy of our algorithm considerably and additionally propose a new clustering algorithm that allows for automatically deriving the size of the communities we seek to detect. Using ground-truth communities for a wide range of large real-word and synthetic networks, our experimental evaluation shows that our approach does achieve accuracy comparable, and oftentimes better, to the state-of-the-art non-streaming community detection algorithms. More importantly, we attain significant improvements in both execution time and memory requirements. [ABSTRACT FROM AUTHOR]

Motivation: In bioinformatics, complex cellular modeling and behavior simulation to identify significant molecular interactions is considered a relevant problem. Traditional methods model such complex systems using single and binary network. However, this model is inadequate to represent biological networks as different sets of interactions can simultaneously take place for different interaction constraints (such as transcription regulation and protein interaction). Furthermore, biological systems may exhibit varying interaction topologies even for the same interaction type under different developmental stages or stress conditions. Therefore, models which consider biological systems as solitary interactions are inaccurate as they fail to capture the complex behavior of cellular interactions within organisms. Identification and counting of recurrent motifs within a network is one of the fundamental problems in biological network analysis. Existing methods for motif counting on single network topologies are inadequate to capture patterns of molecular interactions that have significant changes in biological expression when identified across different organisms that are similar, or even time-varying networks within the same organism. That is, they fail to identify recurrent interactions as they consider a single snapshot of a network among a set of multiple networks. Therefore, we need methods geared towards studying multiple network topologies and the pattern conservation among them. Contributions: In this paper, we consider the problem of counting the number of instances of a user supplied motif topology in a given multilayer network. We model interactions among a set of entities (e.g., genes)describing various conditions or temporal variation as multilayer networks. Thus a separate network as each layer shows the connectivity of the nodes under a unique network state. Existing motif counting and identification methods are limited to single network topologies, and thus cannot be directly applied on multilayer networks. We apply our model and algorithm to study frequent patterns in cellular networks that are common in varying cellular states under different stress conditions, where the cellular network topology under each stress condition describes a unique network layer. Results: We develop a methodology and corresponding algorithm based on the proposed model for motif counting in multilayer networks. We performed experiments on both real and synthetic datasets. We modeled the synthetic datasets under a wide spectrum of parameters, such as network size, density, motif frequency. Results on synthetic datasets demonstrate that our algorithm finds motif embeddings with very high accuracy compared to existing state-of-the-art methods such as G-tries, ESU (FANMODE)and mfinder. Furthermore, we observe that our method runs from several times to several orders of magnitude faster than existing methods. For experiments on real dataset, we consider Escherichia coli (E. coli)transcription regulatory network under different experimental conditions. We observe that the genes selected by our method conserves functional characteristics under various stress conditions with very low false discovery rates. Moreover, the method is scalable to real networks in terms of both network size and number of layers. [ABSTRACT FROM AUTHOR]

MiRNA-disease association prediction plays an important role in identifying human disease-related miRNAs. This approach is helpful not only to formulate individualized diagnosis schemes, but also to understand the pathogenesis of diseases. Many studies have focused on enhancing the prediction performance using explicit side information, such as miRNA functional similarity and disease semantic similarity. The existing approaches, however, often ignore the higher-order implicit proximity among miRNAs and diseases. To this end, in this paper, we first propose a novel approach HOP_MDA (Higher-Order Proximity based MiRNA and Disease Association Prediction) for predicting potential association between miRNA and disease. Both explicit interaction information and implicit higher-order proximity information between miRNA and disease are encoded with different order proximity matrices which are weightily combined into a parameterized prediction matrix. A supervised learning approach based on the known miRNAs-disease associations is proposed to determine the optimal weight parameters. The prediction matrix is then used to achieve effective prediction. Additionally, a higher-order proximity approximation technique (HOPA_MDA) is presented to make more efficient predictions. 5-fold cross validation is used to evaluate the performance of our proposed method. The average AUC values of HOPA_MDA for two real datasets are 0.921+/-0.002 and 0.944+/-0.0015, respectively. Our method can also predict potential miRNAs specific to new diseases with no known related miRNAs. [ABSTRACT FROM AUTHOR]

This interdisciplinary work focuses on the interest of a new auto-encoder for supervised classification of live cell populations growing in a thermostated imaging station and acquired by a Quantitative Phase Imaging (QPI) camera. This type of camera produces interferograms that have to be processed to extract features derived from quantitative linear retardance and birefringence measurements. QPI is performed on living populations without any manipulation or treatment of the cells. We use the efficient new autoencoder classification method instead of the classical Douglas-Rachford method. Using this new supervised autoencoder, we show that the accuracy of the classification of the cells present in the mitotic phase of the cell cycle is very high using QPI features. This is a very important finding since we demonstrate that it is now possible to very precisely follow cell growth in a non-invasive manner, without any bias. No dye or any kind of markers are necessary for this live monitoring. Any studies requiring analysis of cell growth or cellular response to any treatment could benefit from this new approach by simply monitoring the proportion of cells entering mitosis in the studied cell population. [ABSTRACT FROM AUTHOR]

This research investigates the potential of in vivo learning to enhance visual representation learning for image-based person re-identification (re-ID). Compared to traditional self-supervised learning (which require external data), the introduced in vivo learning utilizes supervisory labels generated from pedestrian images to improve re-ID accuracy without relying on external data sources. Three carefully designed in vivo learning tasks, leveraging statistical regularities within images, are proposed without the need for laborious manual annotations. These tasks enable feature extractors to learn more comprehensive and discriminative person representations by jointly modeling various aspects of human biological structure information, contributing to enhanced re-ID performance. Notably, the method seamlessly integrates with existing re-ID frameworks, requiring minimal modifications and no additional data beyond the existing training set. Extensive experiments on diverse datasets, including Market1501, CUHK03-NP, Celeb-reID, Celeb-reid-light, PRCC, and LTCC, demonstrate substantial enhancements in rank-1 precision compared to state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Effective communication is vital for academic project success, particularly in multidisciplinary teams with diverse backgrounds and disciplines. Misunderstandings can arise from differing interpretations of terms, which may go unnoticed. VisDict aims to bridge this gap by creating a visual dictionary within a science gateway to facilitate clear communication between workflow providers and domain researchers. This innovative approach translates computational science concepts into fields like physics and biology. This article delves into our method for building the visual dictionary, the insights gained from curating initial entries, and future plans for automated expansion and illustration of relevant terms. [ABSTRACT FROM AUTHOR]

In this study, we improve the existing model for force distribution over the muscles by considering reflex excitation as a nonvoluntary mechanism of our neuromuscular system. The improved model can explain the large difference between biological torque and experimentally optimized assistive torque profiles. Accordingly, we hypothesize that the “nonvoluntary nature of reflexive excitation highly restricts biological torque compensation”. The proposed model can also potentially characterize co-activation behavior in antagonistic muscles. Using our improved model, we introduce a well-posed framework to optimize the exoskeleton torque profile by metabolic rate minimization. Methods: To support our hypothesis and the proposed method, we utilize two experimental datasets for exoskeleton torque optimization; passive and active ankle exoskeletons. First, we use the passive exoskeleton dataset to identify the parameters of our model; i.e., reflex gains. Then, to validate the proposed model, the identified parameters are used to optimize the exoskeleton torque profile for the second experimental study. Limitations: It is assumed that joint kinematic and reflex gains are fixed with and without exoskeleton. Results: 74% of biological torque at the ankle joint cannot be experimentally compensated and the existing models can only explain that 17% of the biological torque is uncompensable. Our improved model can explain that 58% of biological torque is uncompensable (but still 16% remains unexplained). This achievement provides support for our hypothesis and shows undeniable contribution of reflex excitation for exoskeleton torque profile optimization. [ABSTRACT FROM AUTHOR]

Biopharmaceutical production systems and processes are vulnerable to cyberattacks from sophisticated adversaries. Therefore, it is imperative to start building biopharmaceutical manufacturing systems that offer verifiable formalism and transform the current state of security across all production stages. [ABSTRACT FROM AUTHOR]

One of the key challenges in systems biology is to derive gene regulatory networks (GRNs) from complex high-dimensional sparse data. Bayesian networks (BNs) and dynamic Bayesian networks (DBNs) have been widely applied to infer GRNs from gene expression data. GRNs are typically sparse but traditional approaches of BN structure learning to elucidate GRNs often produce many spurious (false positive) edges. We present two new BN scoring functions, which are extensions to the Bayesian Information Criterion (BIC) score, with additional penalty terms and use them in conjunction with DBN structure search methods to find a graph structure that maximises the proposed scores. Our BN scoring functions offer better solutions for inferring networks with fewer spurious edges compared to the BIC score. The proposed methods are evaluated extensively on auto regressive and DREAM4 benchmarks. We found that they significantly improve the precision of the learned graphs, relative to the BIC score. The proposed methods are also evaluated on three real time series gene expression datasets. The results demonstrate that our algorithms are able to learn sparse graphs from high-dimensional time series data. The implementation of these algorithms is open source and is available in form of an R package on GitHub at https://github.com/HamdaBinteAjmal/DBN4GRN , along with the documentation and tutorials. [ABSTRACT FROM AUTHOR]

Uncovering genotype-phenotype relationships is a fundamental challenge in genomics. Gene prioritization is an important step for this endeavor to make a short manageable list from a list of thousands of genes coming from high-throughput studies. Network propagation methods are promising and state of the art methods for gene prioritization based on the premise that functionally related genes tend to be close to each other in the biological networks. Recently, we introduced PhenoGeneRanker, a network-propagation algorithm for multiplex heterogeneous networks. PhenoGeneRanker allows multi-layer gene and phenotype networks. It also calculates empirical p values of gene and phenotype ranks using random stratified sampling of seeds of genes and phenotypes based on their connectivity degree in the network. In this study, we introduce the PhenoGeneRanker Bioconductor package and its application to multi-omics rat genome datasets to rank hypertension disease-related genes and strains. We showed that PhenoGeneRanker performed better to rank hypertension disease-related genes using multiplex gene networks than aggregated gene networks. We also showed that PhenoGeneRanker performed better to rank hypertension disease-related strains using multiplex phenotype network than single or aggregated phenotype networks. We performed a rigorous hyperparameter analysis and, finally showed that Gene Ontology (GO) enrichment of statistically significant top-ranked genes resulted in hypertension disease-related GO terms. [ABSTRACT FROM AUTHOR]

Since spiking neural networks (SNNs) can effectively simulate the information processing mechanism of the biological cortex, they are expected to bridge the gap between neuroscience and machine learning. The hardware simulation of large-scale SNNs requires a simple and versatile silicon neuron model framework. In this article, a spiking neuron circuit as the core device of SNNs is presented. The proposed neuron circuit can mimic the dynamics of different types of biological neurons by adjusting the bias voltage. In order to facilitate the implementation of the spiking neuron circuit based on complementary metal-oxide-semiconductor (CMOS) and reduce the overhead of the circuit area, a modified Mihalas–Niebur (MN) mathematical model is adopted. The improved MN model is biologically plausible and can still successfully display all dynamic behaviors observed in biology. The function of the proposed neuron circuit has been verified by the phase diagram analysis method. The simulation results show the designed neuron circuit can successfully replicate 15 of the 20 firing patterns exhibited by the biological cortex, which proves that the neuron can act as a universal spiking neuron in very large-scale integrated circuit (VLSI) neuromorphic networks. [ABSTRACT FROM AUTHOR]

Identifying biomarkers of heterogeneous complex diseases has always been one of the focuses in medical research. In previous studies, the powerful network propagation methods have been applied to finding marker genes related to specific diseases, but existing methods are mostly based on a single network, which may be greatly affected by the incompleteness of the network and the ignorance of a large amount of information about physical and functional interactions between biological components. Other methods that directly integrate multiple types of interactions into an aggregate network have the risks that different types of data may conflict with each other and the characteristics and topologies of each individual network are lost. Meanwhile, biomarkers used in clinical trials should have the characteristics of small quantity and strong discriminate ability. In this study, we developed a multiplex network-based dual ranking framework (DualRank) for heterogeneous complex disease analysis. We applied the proposed method to heterogeneous complex diseases for diagnosis, prognosis, and classification. The results showed that DualRank outperformed competing methods and could identify biomarkers with the small quantity, great prediction performance (average AUC = 0.818) and biological interpretability. [ABSTRACT FROM AUTHOR]

An enzyme-free and label-free fluorescent DNA aptasensor was constructed with computer assistance based on thermodynamic deviation driving interchain reactions. In this work, in the presence of target acetamiprid, the released trigger strand C-apt could open hairpin Hp1, which in turn triggered the strand displacement reaction and catalyzed the self-assembly of hairpins Hp1 and Hp2, so that the guanine base rich stem in Hp2 was opened. In the presence of K+ and NMM, the G-rich moiety could form a G-quadruplex and emit strong fluorescence at a specific excitation wavelength. The proposed strategy enables sensitive detection of acetamiprid at concentrations as low as 54.3 pM. Most importantly, computer-assisted analysis of the thermodynamic properties of nucleic acid strands and simulation of the reaction process and conditions of the proposed model before conducting biological experiments theoretically proves this strategy feasible and may simplify subsequent biological experiments. In addition, basic molecular logic gates, including OR and AND, were constructed based on this detection principle, and simulation tests and biological experiments were performed. The final results show that this strategy can not only have some applications in the field of food safety and environmental monitoring, but also provide a certain way for the development of molecular logic computing. [ABSTRACT FROM AUTHOR]

In the last two decades, the micromanipulation technique has been widely utilized in industrial and biological fields. However, existing micromanipulation methods can hardly meet all the demands in both fields. In this article, we present an all-purpose magnetically driven micromanipulation system with designed contact and noncontact modes for numerous industrial and biological applications, respectively. In the system, a micropipette containing the ferromagnetic beads driven by themagnetic field presentshigh-precision and high-speed motions. With another micropipette, this two-finger microhand can efficiently perform the chopstick-like grasp motion by physical contact. The vibration-based active release relying on the high-speed motion can help release the targets adhering to the end-effector. Besides, local whirling flow surrounding the micropipette induced by high-speed circular motion can trap, transport, and rotate microtargets without contact like a hydrodynamic tweezer to operate micro bio-targets without any damages. In experiments, microbeads are grasped and releasedefficiently in the desired positions using the chopstick-like two-finger microhand mode, and the arrays of a hexagon and “TMECH” have been assembled. The hydrodynamic tweezer mode is adopted to trap, transport, and rotate the microbeads and further applied in the operations of the mouse egg cells. We believe the proposed magnetic micromanipulation system, as an all-purpose tool at the microscale, holds great potential in industrial and biological applications. [ABSTRACT FROM AUTHOR]

A deep understanding of the underlying resistive switching mechanism for the implementation of volatile memristive properties is regarded as of great importance for enhancing their performance. Along these lines, a 2-D dynamical model is introduced to interpret the whole memristive pattern within the bilayer configuration, as well as the crucial of the dense layer of the Pt nanoparticles (NPs) on the local thermal distribution. Moreover, the probabilistic leaky-integrate-and-fire (LIF) neuron properties were simulated by considering a simple $\textit {RC}$ circuit in order to perform Bayesian extrapolation within a spiking neural network. A classification application is consequently demonstrated by using the liver tumor dataset. The advantageous capabilities of the stochastic-based spike neural networks (SNNs) are highlighted in striking contrast with the conventional artificial neural networks (ANNs), as well as the deterministic-based SNNs, in terms of prediction accuracy and power consumption. [ABSTRACT FROM AUTHOR]

Synthetic morphogenesis is a new engineering discipline, in which cells are genetically engineered to make designed shapes and structures. At least in this early phase of the field, devices tend to make use of natural shape-generating processes that operate in embryonic development but invoke them artificially at times and in orders of a technologist’s choosing. This requires the construction of genetic control, sequencing, and feedback systems that have close parallels to electronic design, which is one reason the field may be of interest to readers of IEEE journals. The other reason is that synthetic morphogenesis allows the construction of two-way interfaces, especially optogenetic and optoelectronic, between the living and the electronic, allowing unprecedented information flow and control between the two types of “machines.” This review introduces synthetic morphogenesis, illustrates what has been achieved, drawing parallels wherever possible between biology and electronics, and looks forward to likely next steps and challenges to be overcome. [ABSTRACT FROM AUTHOR]

Learning is commonplace in organisms such as ourselves and even in organisms as far distant as the bee and the octopus. Such learning is implemented by brains, or neuronal networks, and has been extensively studied within ethology, psychology, cognitive science, and neuroscience. Whether learning also takes place in nonneuronal settings has remained a matter of sustained controversy, too often dominated by ideological views. In this survey, I will explain how learning can be rigorously interpreted as a form of information processing and then explore the evidence for whether learning also takes place in organismal contexts outside the brain, such as physiology, development, and individual cells. I will try to explain why it is important to build bridges in this way between cognitive science and systems biology, why concepts and methods from various branches of engineering may be helpful in this task, and what the eventual impact may be on how we think about the organism. [ABSTRACT FROM AUTHOR]

Over the past 25 years, there has been an unparalleled increase in understanding of cancer biology. This transformation is exemplified by Hanahan and Weinberg’s decision in 2011 to expand their original Hallmarks of Cancer from six traits to ten! At the same time, mathematical modeling has emerged as a natural tool for unraveling the complex processes that contribute to the initiation and progression of cancer, for testing hypotheses about experimental and clinical observations and assisting with the development of new approaches for improving its treatment. This article starts by reviewing some of the earliest models of tumor growth and tumor responses to radiotherapy. Following Hanahan and Weinberg’s lead, attention then focuses on how closer collaboration with cancer scientists and access to experimental data are stimulating the development of new and increasingly detailed models that account, for example, for tumor–immune interactions and immunotherapy. The article concludes by discussing the ways in which mathematical modeling is being integrated with experimental and clinical studies, and outlining how this could improve disease diagnosis and the delivery of effective personalized treatments to cancer patients. As such, this article serves as an introduction to mathematical modeling of cancer and its treatments, suitable for researchers seeking to enter the field. [ABSTRACT FROM AUTHOR]

Synthetic biologists engineered the first genetic toggle switch and clock at the turn of the 21st century. Since the development of these first gene circuits, methods for circuit design and construction have improved dramatically, narrowing the gap between concept and proof-of-principle implementation. Engineered genetic constructs have started to leave the lab for the real world where they are being used in applications, including medicine, biosensing, and industrial chemical production. The field of synthetic gene circuits has also grown from focusing on single, isolated circuits to designing complex systems that operate across multiple populations in carefully engineered consortia. In addition, design methods have progressively moved toward including detailed models of the interactions between the host genome and the synthetic gene circuits that it contains in order to better predict circuit dynamics. This article will review some of the most recent advances in gene circuit design and implementation, with a focus on synthetic gene circuits being applied to address real-world problems. [ABSTRACT FROM AUTHOR]

Ammonia is a common contaminant in municipalities where human waste causes nitrification of local water bodies. In mining, ammonia contamination occurs as a byproduct of biological water treatment, from the use of ammonium nitrate fuel oil in explosives, and from the exposure of ammonia rich soils during the excavation process. In particular, gold mine effluent represents a significant source of ammonia and nitrogen-based contaminants. Current biological and abiotic treatment processes are difficult to employ at the scale required at mine sites due to the high operating costs, or are limited in effectiveness due to a lack of natural resources required to facilitate the treatment. This article evaluates the use of electrooxidation as a cost effective alternative to treating ammonia-laden wastewater in mining applications. Two mixed metal oxide electrodes are assessed in this article: IrO $_{2}$ /Ti and RuO $_{2}$ /Ti anodes. A Monte Carlo simulation is performed to determine a probabilistic range of capital and operating expenditure for a mining operation deploying an electrooxidation wastewater treatment system. The lowest capital cost of operating the electrochemical treatment occurs at a current density of 200 A/m $^{2}$ , where the number of cells required for treatment is minimized. [ABSTRACT FROM AUTHOR]

Dysfunction of miRNAs has an important relationship with diseases by impacting their target genes. Identifying disease-related miRNAs is of great significance to prevent and treat diseases. Integrating information of genes related miRNAs and/or diseases in calculational methods for miRNA-disease association studies is meaningful because of the complexity of biological mechanisms. Therefore, in this study, we propose a novel method based on tensor decomposition, termed TDMDA, to integrate multi-type data for identifying pathogenic miRNAs. First, we construct a three-order association tensor to express the associations of miRNA-disease pairs, the associations of miRNA-gene pairs, and the associations of gene-disease pairs simultaneously. Then, a tensor decomposition-based method with auxiliary information is applied to reconstruct the association tensor for predicting miRNA-disease associations, and the auxiliary information includes biological similarity information and adjacency information. The performance of TDMDA is compared with other advanced methods under 5-fold cross-validations. The experimental results indicate the TDMDA is a competitive method. [ABSTRACT FROM AUTHOR]

For continuous monitoring of cardiovascular diseases, this article presents a novel framework for heart sound acquisition. The proposed approach uses compressed sensing for signal sampling, and a two-stage reconstruction is developed for reconstruction. The first stage aims to give a tentative recovered signal, on which a peak detection technique is developed to identify whether there is a peak in current segment and, if so, its location. With such information, an adaptive dictionary is selected for the second round reconstruction. Because the selected dictionary is adaptive to the morphology of current frame, the signal reconstruction performance is consequently promoted. Experiment results indicate that a satisfactory performance can be obtained when the frame length is 256 and the signal morphology is divided into 16 categories. Furthermore, the proposed algorithm is compared with a series of counterparts, and the results well demonstrate the advantages of our proposal, especially at high compression ratios. [ABSTRACT FROM AUTHOR]

Adaptive temperature control is proposed to increase the nonthermal exposure level of in vitro experiments. By regulating an air temperature to cancel the exposure-induced temperature elevation in cells, the maximum specific absorption rate (SAR) is at least doubled to trigger and characterize nonthermal effects. The air temperature is prescribed by an iteration algorithm to balance the maximum and minimum cell temperature levels while preventing them from exceeding a nonthermal threshold. Adaptive temperature control is applied to a 900-MHz exposure setup based on a TEM cavity. An air temperature regulator was installed to the TEM cavity loaded with cell monolayers in two 35-mm-diameter Petri dishes. SAR and temperature distributions in the culture medium were calculated by the finite-difference time-domain algorithm and validated by $\vert S_{11}\vert $ and temperature measurements. Using a properly regulated air temperature, adaptive temperature control suppressed the temperature variances in cells within the $0.1~^{\circ }\text{C}$ threshold through a 60-min exposure with a 6.10-W/kg average SAR. The SAR is 2.43 times as much as that of conventional temperature control using a stable air temperature. An air temperature regulator for adaptive temperature control is a simple addition to upgrade a conventional exposure setup and increase the exposure level in search of nonthermal effects. [ABSTRACT FROM AUTHOR]

MicroRNAs (miRNAs) have been proved to play critical roles in diverse biological processes, including the human disease development process. Exploring the potential associations between miRNAs and diseases can help us better understand complex disease mechanisms. Given that traditional biological experiments are expensive and time-consuming, computational models can serve as efficient means to uncover potential miRNA-disease associations. This study presents a new computational model based on variational graph auto-encoder with matrix factorization (VGAMF) for miRNA-disease association prediction. More specifically, VGAMF first integrates four different types of information about miRNAs into an miRNA comprehensive similarity network and two types of information about diseases into a disease comprehensive similarity network, respectively. Then, VGAMF gets the non-linear representations of miRNAs and diseases, respectively, from those two comprehensive similarity networks with variational graph auto-encoders. Simultaneously, a non-negative matrix factorization is conducted on the miRNA-disease association matrix to get the linear representations of miRNAs and diseases. Finally, a fully connected neural network combines linear and non-linear representations of miRNAs and diseases to get the final predicted association score for all miRNA-disease pairs. In the 10-fold cross-validation experiments, VGAMF achieves an average AUC of 0.9280 on HMDD v2.0 and 0.9470 on HMDD v3.2, which outperforms other competing methods. Besides, the case studies on colon cancer and esophageal cancer further demonstrate the effectiveness of VGAMF in predicting novel miRNA-disease associations. [ABSTRACT FROM AUTHOR]

One of the key elements for early diagnosis of acute and severe diseases is the development of ultra-sensitive detection methods, which are required to analyze the pathological state of the human body from trace amounts of substances in blood or urine. Surface plasmon resonance (SPR) optical fiber biosensors, particularly those based on tilted fiber Bragg gratings (TFBG), have emerged in recent years as a novel solution for in-situ biomedical detection. TFBGs can sensitively detect the physical and chemical interactions between biomolecules in real-time by sensing minute changes in refractive index, even in vivo. The TFBG-based SPR method can achieve rapid and accurate analysis of biological samples by demodulating the wavelength, intensity, phase and polarization state of the optical spectrum. In addition, TFBG-based SPR fiber optic sensors are capable of simultaneously detecting the absolute or relative values of multiple parameters. This effectively eliminates the interference from the ambient environment and ensures the stability and reliability of the sensor. [ABSTRACT FROM AUTHOR]

Manufacturing system digitalization has recently surged due to Industry 4.0 developments. The interaction between manufacturing and computer science disciplines has driven a new generation of cyber-physical production systems. Smart factories can collect and process a large amount of data, and this data must be used meaningfully to raise manufacturing efficiency and sustainability. We posit, that in the future, improvements can be achieved through biointelligent manufacturing systems. These systems combine technological innovations, digitization, and artificial intelligence with principles derived from nature and biotechnology. Biological transformations of manufacturing systems hold many future possibilities. We provide a general overview of manufacturing system biological transformation and propose key areas for practical application in organizations. [ABSTRACT FROM AUTHOR]

Numerous biological studies of body and/or caudal fin fish have powerfully identified that the fish body's passive properties, such as stiffness and its distribution, contribute to the fish's outstanding swimming performance. In this study, we develop a free-swimming tensegrity fish robot (TenFiBot) to help scholars experimentally improve their understanding of the role of stiffness distribution in fish-like swimming. We detail the overall structure and parameters of TenFiBot, which can achieve body stiffness distribution with high stiffness controllability over a wide range. The effective variable stiffness principle is based on antagonistic stiffness that results from the prestress of tension structures in a kinematically singular configuration. We validate the effectiveness of variable stiffness for one variable-stiffness tensegrity joint (VSTJ) and demonstrate that the virtual rotational centers are always in the acceptable range during stiffness variation. We exemplify some cases of the stiffness distribution to verify the validity of body stiffness variation. The experimental results show that swimming performance, such as swimming velocity, varied dramatically when the stiffness distribution was varied for the robotic fish. The swimming performance of the robotic fish can be enhanced tremendously by changing the stiffness distribution. Moreover, the VSTJs in the fish body have great potential in constructing compliant robotic arms and snake-like robots. [ABSTRACT FROM AUTHOR]