Search

Application-oriented papers provide an important way to invigorate and cross-pollinate the visualization field, but the exact criteria for judging an application paper's merit remain an open question. This article builds on a panel at the 2016 IEEE Visualization Conference entitled "Application Papers: What Are They, and How Should They Be Evaluated?" that sought to gain a better understanding of prevalent views in the visualization community. This article surveys current trends that favor application papers, reviews the benefits and contributions of this paper type, and discusses their assessment in the review process. It concludes with recommendations to ensure that the visualization community is more inclusive to application papers. [ABSTRACT FROM PUBLISHER]

Lithium represents the main drug for the treatment of Bipolar Disorder (BD) as it acts as a mood stabilizer. However, because of its narrow therapeutic window, psychiatric patients are obliged to frequent tests in hospital to control their lithium blood concentration during therapy. In this paper, we fabricate for the first time a complete non-invasive system for the Therapeutic Drug Monitoring (TDM) of lithium in sweat in people suffering from BD. The wearable electrochemical sensing platform includes a paper fluidics and a stable Reference Electrode (RE). Lithium detection is based on the use of a potentiometric Ion-Selective Electrode (ISE) with a nanostructured solid contact in order to increase the potential stability of the sensor. A chemical chlorination combined with the use of a PVC membrane doped with an Ionic Liquid (IL) is used to minimize possible potential drifts of the RE. The Nernstian response (56.8±3.9 mV /decade) of the system in the range of clinical interest is then proved in aqueous solution and in sweat. The integration of the sensing platform with a paper fluidics transporting fresh sweat sample to the sensing area is also considered. The already tested sweat is collected in a reservoir made of slow adsorbing paper. The complete wearable system is then successfully tested in a simulated setup by using a mannequin. The measured potential is well compared to the predicted ones in the therapeutic window of lithium drug, proving the good sensing capability in artificial sweat. [ABSTRACT FROM AUTHOR]

Drug-induced liver injury describes the adverse effects of drugs that damage the liver. Life-threatening results were also reported in severe cases. Therefore, liver toxicity is an important assessment for new drug candidates. These reports are documented in research papers that contain preliminary in vitro and in vivo experiments. Conventionally, data extraction from publications relies on resource-demanding manual labeling, which restricts the efficiency of the information extraction. The development of natural language processing techniques enables the automatic processing of biomedical texts. Herein, based on around 28,000 papers (titles and abstracts) provided by the Critical Assessment of Massive Data Analysis challenge, this study benchmarked model performances on filtering liver-damage-related literature. Among five text embedding techniques, the model using term frequency-inverse document frequency (TF-IDF) and logistic regression outperformed others with an accuracy of 0.957 on the validation set. Furthermore, an ensemble model with similar overall performances was developed with a logistic regression model on the predicted probability given by separate models with different vectorization techniques. The ensemble model achieved a high accuracy of 0.954 and an F1 score of 0.955 in the hold-out validation data in the challenge. Moreover, important words in positive/negative predictions were identified via model interpretation. The prediction reliability was quantified with conformal prediction, which provides users with a control over the prediction uncertainty. Overall, the ensemble model and TF-IDF model reached satisfactory classification results, which can be used by researchers to rapidly filter literature that describes events related to liver injury induced by medications. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of shaping nicotine’s administration trajectory for maximization of its cardio-accelerating effects. Pharmacologists explain the interaction of this drug with the human body using a two-compartment model. One compartment, in the model, describes the concentration dynamics of nicotine in the plasma, while the other captures buildup of resistance to the drug’s effects. Researchers have employed this model and shown that a periodic delivery trajectory results in an average efficacy level superior to the best achievable steady-state performance. The literature has also studied finding the optimal shape of this periodic drug delivery trajectory and offers different solution methods. While the existing methods depend on the exact knowledge of the drug’s model, the human body’s metabolism varies from one patient to another, affecting the shape of the maximum efficacy trajectory. This creates a need for online solution methods. This paper addresses this issue by developing two online solution algorithms for the problem: 1) a model-reference, gradient-descent-based regulator that considers parameter uncertainty and 2) a model-free extremum seeking control scheme. The latter controller is shown to converge very slowly. Still, it is included in this paper as a completely model-free option and as a benchmark. In contrast, the first controller converges more quickly, and its convergence to the optimal solution is proved using Floquet theory. Finally, this paper demonstrates the performance of the two methods in a simulation study, and also compares them against the deterministic solutions from the literature. [ABSTRACT FROM PUBLISHER]

In this paper, we study an automatic hypothesis generation (HG) problem, which refers to the discovery of meaningful implicit connections between scientific terms, including but not limited to diseases, chemicals, drugs, and genes extracted from databases of biomedical publications. Most prior studies of this problem focused on the use of static information of terms and largely ignored the temporal dynamics of scientific term relations. Even when the dynamics were considered in a few recent studies, they learned the representations for the scientific terms, rather than focusing on the term-pair relations. Since the HG problem is to predict term-pair connections, it is not enough to know with whom the terms are connected, it is more important to know how the connections have been formed (in a dynamic process). We formulate this HG problem as a future connectivity prediction in a dynamic attributed graph. The key is to capture the temporal evolution of node-pair (term-pair) relations. We propose an inductive edge (node-pair) embedding method named T-PAIR, utilizing both the graphical structure and node attribute to encode the temporal node-pair relationship. We demonstrate the efficiency of the proposed model on three real-world datasets, which are three graphs constructed from Pubmed papers published until 2019 in Neurology, Immunotherapy, and Virology, respectively. Evaluations were conducted on predicting future term-pair relations between millions of seen terms (in the transductive setting), as well as on the relations involving unseen terms (in the inductive setting). Experiment results and case study analyses show the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

Molecular communication is an emerging communication paradigm for biological nanomachines. It allows biological nanomachines to communicate through exchanging molecules in an aqueous environment and to perform collaborative tasks through integrating functionalities of individual biological nanomachines. This paper develops the layered architecture of molecular communication and describes research issues that molecular communication faces at each layer of the architecture. Specifically, this paper applies a layered architecture approach, traditionally used in communication networks, to molecular communication, decomposes complex molecular communication functionality into a set of manageable layers, identifies basic functionalities of each layer, and develops a descriptive model consisting of key components of the layer for each layer. This paper also discusses open research issues that need to be addressed at each layer. In addition, this paper provides an example design of targeted drug delivery, a nanomedical application, to illustrate how the layered architecture helps design an application of molecular communication. The primary contribution of this paper is to provide an in-depth architectural view of molecular communication. Establishing a layered architecture of molecular communication helps organize various research issues and design concerns into layers that are relatively independent of each other, and thus accelerates research in each layer and facilitates the design and development of applications of molecular communication. [ABSTRACT FROM PUBLISHER]

This paper proposes a leader–follower-based model of mobile molecular communication networks for target detection applications. The proposed model divides the application functionalities of molecular communication networks into two types of mobile bio-nanomachine: leader and follower bio-nanomachines. Leader bio-nanomachines distribute in the environment to detect a target and create an attractant gradient around the target. Follower bio-nanomachines move according to the attractant gradient established by leader bio-nanomachines; they approach the target and perform necessary functionalities, such as releasing drug molecules. This paper develops mathematical expressions for the proposed model, describes wet laboratory experiments designed to estimate model parameters, and performs biologically realistic computer simulation experiments to evaluate the performance of the proposed model. The main contributions of this paper are to demonstrate the functional division of molecular communication networks, which will facilitate the design and development of molecular communication networks. Furthermore, insight into the application-level performance of molecular communication networks will be provided based on the proposed model. [ABSTRACT FROM PUBLISHER]

This paper validates two approximate dynamic programming approaches on a maritime interdiction problem involving the allocation of multiple heterogeneous assets over a large area of responsibility to interdict multiple drug smugglers using heterogeneous types of transportation on the sea with varying contraband weights. The asset allocation is based on a probability of activity surface, which represents spatio-temporal target activity obtained by integrating intelligence data on drug smuggler whereabouts/waypoints for contraband transportation, behavior models, and meteorological and oceanographic information. We validate the proposed architectural and algorithmic concepts via several realistic mission scenarios. We conduct sensitivity analyses to quantify the robustness and proactivity of our approach, as well as to measure the value of information used in the allocation process. The contributions of this paper have been transitioned to and are currently being tested by Joint Interagency Task Force—South, an organization tasked with providing the initial line of defense against drug trafficking in the East Pacific and Caribbean Oceans. [ABSTRACT FROM AUTHOR]

In high-throughput drug screening applications, as manual drug sample delivery is time-consuming and error-prone, there is an urgent need for accurate and efficient drug sample delivery biochip for large-scale microwell arrays. This paper proposes a new microfluidic biochip architecture, where drugs are automatically prepared with different concentration values, and then delivered into multiple microwells. For large-scale drug sample delivery biochips, the routing of drug sample delivery channels is a very challenging task without effective routing solutions. This paper proposes an ultrafast rule-based escape routing method, called URBER, to address the large-scale routing of drug sample delivery channels, which scales well in both runtime and memory even for a very large problem size. URBER runs very fast because it routes channels based on a set of predefined rules, which avoids runtime consumed in solution space exploration. All benchmarks for 30 ${\le }~{N}$ , ${M} {\le }~100$ have been tested, where ${N}$ and ${M}$ are the number of columns and rows of the terminal array. Among these benchmarks, about ~91.9% are routed with optimal solutions, and the runtime is order of magnitudes faster than optimal min-cost flow-based methods (speedup is from ~600 to ~340 k). Specifically, for all benchmarks with (${M}/{N}$) ${\in }$ ((3/4), (4/3)), optimal routing solutions are always obtained. URBER also shows promise of routing large-scale designs with up to 500 k terminals efficiently. [ABSTRACT FROM AUTHOR]

This paper focuses on constructing genotypic predictors for antiretroviral drug susceptibility of HIV. To this end, a method to recover the largest elements of an unknown vector in a least squares problem is developed. The proposed method introduces two novel ideas. The first idea is a novel forward stepwise selection procedure based on the magnitude of the estimates of the candidate variables. To implement this newly introduced procedure, we revise Tikhonov regularisation from a sparse representations’ perspective. This analysis leads us to the second novel idea in the paper, which is the development of a new method to recover the largest elements of the unknown vector in the least squares problem. The method implements a sequence of Tikhonov regularisation problems which aim to recover the largest of the remaining elements of the unknown vector. Additionally, we derive sufficient conditions that ensure the recovery of the largest elements of the unknown vector. We perform numerical studies using simulated data and data from the Stanford HIV resistance database. The performance of the proposed method is compared against a state-of-the-art method. [ABSTRACT FROM AUTHOR]

How do alliances lead to knowledge growth and learning for parent firms? Prior research focuses on knowledge inflows from a partner when a firm enters into an alliance. In this paper, we examine factors that enable firms to make better use of their own knowledge and capabilities as a consequence of alliances. To demonstrate the latter dimension of knowledge growth, we depart from prior research that has focused on cross citations of patents between partners, and instead a study self-citation activity. We examine the effect of four factors on self-citations, each of which reflects distinct learning mechanisms and cooperative dynamics, namely knowledge inflows from the alliance partner, knowledge outflows to the alliance partner, partner characteristics, and alliance governance. Results suggest that knowledge inflows from the partner were positively associated with changes in self-citations in alliance domains, whereas knowledge outflows to the partner had a positive impact on changes in self-citations for the smaller firm in the alliance. In contrast, firm specific knowledge possessed by the partner and equity governance had a negative impact on changes in self-citations in alliance domains. This paper develops a fuller understanding of the learning effects of alliances by highlighting that apart from better utilization of the partner's capabilities, alliances also lead to enhanced utilization of the firm's own knowledge and capabilities in further innovative activity. [ABSTRACT FROM AUTHOR]

Drug delivery is one of the most important issues in the treatment of cancer and surviving the patient. Recently, with a combination of mathematical models of the tumor growth and control theory, optimal drug delivery can be planned, individually. The goal is reducing the tumor volume with minimum side effects on the patient. One of the most important challenges of the modeling is considering the drug resistance, which may lead to failure of the treatment. In this paper, a mathematical model is proposed for describing the growth dynamics of the melanoma tumor cells. It is assumed that the melanoma cancer is treated with Notch signaling pathway inhibitors of the cancer stem cells. The model parameters are identified based on experimental data obtained from 13 male nude mice with an induced melanoma cancer involved in a dual antiplatelet therapy (DAPT) program. The mathematical model is used to determine if DAPT can reduce the growth rate of the tumor. Then an optimal drug delivery plan for the treatment of every animal model is presented, individually using finite-set model predictive control method. The results show that the proposed model can estimate the drug's effect on the treatment of melanoma cancer. [ABSTRACT FROM AUTHOR]

Drug recommendation task based on the deep learning model has been widely studied and applied in the health care field in recent years. However, the accuracy of drug recommendation models still needs to be improved. In addition, the existing recommendation models either give only one recommendation (however, there may be a variety of drug combination options in practice) or can not provide the confidence level of the recommended result. To fill these gaps, a Drug Recommendation model based on Message Propagation neural network (denoted as DRMP) is proposed in this paper. Then, the Drug-Drug Interaction (DDI) knowledge is introduced into the proposed model to reduce the DDI rate in recommended drugs. Finally, the proposed model is extended to Bayesian Neural Network (BNN) to realize multiple recommendations and give the confidence of each recommendation result, so as to provide richer information to help doctors make decisions. Experimental results on public data sets show that the proposed model is superior to the best existing models. [ABSTRACT FROM AUTHOR]

This paper aims to devise efficient combination chemotherapy schedules that determine the dosages of drugs administered to cancer patients with drug resistance that, as a Gordian knot to cancer chemotherapy, may weaken the efficacy of chemotherapy. To characterize cell growth, we use the existing cell cycle-specific model, in which the mechanism of acquired drug resistance is incorporated. Subsequently, the determination of the optimal chemotherapy schedule for the patients is formulated as a nonlinear optimization problem, with the objective of minimizing not only the quantity of tumor cells but also the posttreatment chemotherapy-induced toxicity. To overcome the difficulty in finding a satisfactory solution to the problem due to its nonlinear nature, we develop a memetic algorithm (MA) with an advanced local search strategy. The efficiency of the proposed MA is validated by comparison with other state-of-the-art methods. In addition, we compare the best found solution to the problem in the presence of drug resistance with that in the absence of drug resistance. The resultant findings reveal that drug resistance is a crucial factor in the determination of chemotherapy schedules. Note to Practitioners—The efficacy of chemotherapy is limited by drug toxicity and is weakened by drug resistance. This paper deals with the problem of optimization of chemotherapy schedules with mathematical modeling and heuristic algorithms and is the first to consider the drug specificity, drug combination, and drug resistance simultaneously. The proposed algorithm is flexible. It can be applied to other optimization problems with proper adaptations. The experimental results suggest that the best found solution to the problem in the absence of drug resistance may, however, not remain as the best found solution, or even become an infeasible one, in the presence of drug resistance. Existing clinical regimens may be improved with the assistance of our method that aims to optimize the chemotherapy schedule in the presence of drug resistance. The parameters of our model should be refined with experimental and clinical data to confer more benefits on real treatments in clinical practice in the future. [ABSTRACT FROM AUTHOR]

Multi-electrode arrays (MEAs) are a candidate technology to screen cardiotoxicity in vitro because they enable noninvasive recording of cardiac beating rate, electrical field potential duration, and other parameters. In this paper, we present an active MEA chip featuring 16 384 electrodes, 1024 simultaneous readout channels, and 64 stimulation units (SUs) to enable six different cell-interfacing modalities: extracellular and intracellular recording, current and voltage stimulation, fast impedance motoring, and impedance spectroscopy (IS). The chip was designed and fabricated in a 0.13- $\mu \text{m}$ Al CMOS technology, and the electrodes were fabricated on top of the silicon substrate using biocompatible TiN. Measurement results show a total input-referred noise of $7.5 \pm 0.6~\mu \text{V}_{\mathrm{rms}}$ and $12.0 \pm 2.4~\mu \text{V}_{\mathrm {rms}}$ for the action potential and full bands, respectively, with a total power consumption of 95 mW for 1024 readout channels. We have experimentally validated the CMOS MEA in in vitro experiments, demonstrating all the different modalities. This novel platform will potentially enable high-throughput electrical activity monitoring and drug screening of cardiomyocytes. [ABSTRACT FROM AUTHOR]

This paper deals with an abstraction of a unified problem of drug discovery and pathogen identification. Pathogen identification involves the identification of disease-causing biomolecules. Drug discovery involves finding chemical compounds, called lead compounds, that bind to pathogenic proteins and eventually inhibit the function of the protein. In this paper, the lead compounds are abstracted as inhibitors, pathogenic proteins as defectives, and the mixture of “ineffective” chemical compounds and non-pathogenic proteins as normal items. A defective could be immune to the presence of an inhibitor in a test. So, a test containing a defective is positive if it does not contain its “associated” inhibitor. The goal of this paper is to identify the defectives, inhibitors, and their “associations” with high probability, or in other words, learn the immune defectives graph (IDG) efficiently through group tests. We propose a probabilistic non-adaptive pooling design, a probabilistic two-stage adaptive pooling design, and decoding algorithms for learning the IDG. For the two-stage adaptive-pooling design, we show that the sample complexity of the number of tests required to guarantee recovery of the inhibitors, defectives, and their associations with high probability, i.e., the upper bound, exceeds the proposed lower bound by a logarithmic multiplicative factor in the number of items. To be precise, lower and upper bounds of $\Omega ((r+d)\log n + rd)$ and O(rd \log n)$ tests, respectively, are identified for classifying r$ inhibitors and d$ defectives amongst n tests) exceeds the proposed lower bound (given by \max \{\Omega ((r+d)\log n + rd),\Omega (({r^{2}}/{\log r})\log n),\Omega (d^{2})\} tests) by at most a logarithmic multiplicative factor in the number of items. [ABSTRACT FROM AUTHOR]

For the past decades, computational methods have been developed to predict various interactions in biological problems. Usually these methods treated the predicting problems as semi-supervised problem or positive-unlabeled(PU) learning problem. Researchers focused on the prediction of unlabeled samples and hoped to find novel interactions in the datasets they collected. However, most of the computational methods could only predict a small proportion of undiscovered interactions and the total number was unknown. In this paper, we developed an estimation method with deep learning to calculate the number of undiscovered interactions in the unlabeled samples, derived its asymptotic interval estimation, and applied it to the compound synergism dataset, drug-target interaction(DTI) dataset and MicroRNA-disease interaction dataset successfully. Moreover, this method could reveal which dataset contained more undiscovered interactions and would be a guidance for the experimental validation. Furthermore, we compared our method with some mixture proportion estimators and demonstarted the efficacy of our method. Finally, we proved that AUC and AUPR were related with the number of undiscovered interactions, which was regarded as another evaluation indicator for the computational methods. [ABSTRACT FROM AUTHOR]

The identification of drug side-effects is considered to be an important step in drug design, which could not only shorten the time but also reduce the cost of drug development. In this paper, we investigate the relationship between the potential side-effects of drug candidates and their chemical structures. The preliminary Regularized Regression (RR) model for drug side-effects prediction has promising features in the efficiency of model training and the existence of a closed form solution. It performs better than other state-of-the-art methods, in terms of minimum accuracy and average accuracy. In order to dig inside how drug structure will associate with side effect, we further propose weighted GTS (Generalized T-Student Kernel: WGTS) SVM model from a structural risk minimization perspective. The SVM model proposed in this paper provides a better understanding of drug side-effects in the process of drug development. The usefulness of the WGTS model lies in the superior performance in a cross validation setting on 888 approved drugs with 1385 side-effects profiling from SIDER database. This work is expected to shed light on intriguing studies that predict potential un-identifying side-effects and suggest how we can avoid drug side-effects by the removal of some distinguished chemical structures. [ABSTRACT FROM AUTHOR]

With the advent of nanotechnology, concepts related to the Internet of Things, such as the Internet of NanoThings and Internet of Bio-NanoThings (IoBNT) have also emerged in the classical literature. The main concern of this paper is the IoBNT, which projects the prospective application domain where the activities of very tiny, biocompatible, and non-intrusive devices operating in an in-body nanonetwork can be monitored and controlled through the Internet. In this paper, we present an illustrative scenario and system model of an IoBNT for application in an advanced healthcare delivery system. To address one of the major challenges of the IoBNT, we present an exemplary architecture and model of a bio-cyber interface for connecting the conventional electromagnetic-based Internet to the biochemical signaling-based bionanonetwork. The bio-cyber interface is designed and modeled by employing biological concepts, such as the responsiveness of certain biomolecules to thermal and light stimuli, and the bioluminescence phenomenon of some biochemical reactions. The analysis in this paper focuses on the system that comprises the bio-cyber interface and the information propagation network of the blood vessel that leads to the in-body nanonetwork location. The effects of the system and design parameters associated with the IoBNT models presented are numerically evaluated. [ABSTRACT FROM AUTHOR]

Due to the rising cases of a prescribed drug overdose and mishandling of the medication container there is an urgent need to devise a smart technological solution to assist this issue. One of the possible solutions to the above issue is the use of low-cost pressure sensor integrated with the medicine container whose data can be communicated to the dedicated smartphone Android application. Once the patient taps the medicine out, the pressure sensor sends the data to the smartphone which can be monitored by the patient’s family and the sensor can then be discarded. In this work, a simple all paper based pressure sensor to monitor drug dosage is fabricated using Molybdenum disulfide (MoS2) grown on paper with an interdigitated electrode drawn using graphite pencil on paper as electrodes. The sensitivity of the fabricated pressure sensor was found to be 0.32 kPa−1 which is comparable and even better than the pressure sensor fabricated using sophisticated techniques. Repeatability of the sensor over 2000 cycles indicates an excellent long-term stability. Bending cycle studies over 1000 cycles observes a negligible change in the performance suggesting the high reliability of the sensor. The fabricated pressure sensor was integrated successfully in the medicine package to monitor the real time pills intake by a patient which shows high promise for its use in health care industry. The successful demonstration of the smart drug dosage system is a step ahead in health care industry which opens up many avenues of research in the similar field. [ABSTRACT FROM AUTHOR]

Objective: The goal of this paper was to optimize robust PID control for propofol anesthesia in children aged 5–10 years to improve performance, particularly to decrease the time of induction of anesthesia while maintaining robustness. Methods: We analyzed results of a previous study conducted by our group to identify opportunities for system improvement. Allometric scaling was introduced to reduce the interpatient variability and a new robust PID controller was designed using an optimization-based method. We evaluated this optimized design in a clinical study involving 16 new cases. Results: The optimized controller design achieved the performance predicted in simulation studies in the design stage. Time of induction of anesthesia was median [Q1, Q3] 3.7 [2.3, 4.1] min and the achieved global score was 13.4 [9.9, 16.8]. Conclusion: Allometric scaling reduces the interpatient variability in this age group and allows for improved closed-loop performance. The uncertainty described by the model set, the predicted closed-loop responses, and the predicted robustness margins are realistic. The system meets the design objectives of improved speed of induction of anesthesia while maintaining robustness and improving clinically relevant system behavior. Significance: Control system optimization and ongoing system improvements are essential to the development of a clinically relevant commercial device. This paper demonstrates the validity of our approach, including system modeling, controller optimization, and pre-clinical testing in simulation. [ABSTRACT FROM AUTHOR]

This paper describes the design and evaluation of a controller for multi-input single-output (MISO) propofol-remifentanil anesthesia, guided by feedback from a measure of depth-of-hypnosis (DOH). DOH monitors are commonly used in clinical practice to guide anesthetic dosing, however, there is currently no widely accepted nociception monitor to guide remifentanil (analgesic) infusion. Variability in the DOH measure has been associated with insufficient analgesia, and feasibility of closed-loop control of both propofol and remifentanil infusion using DOH feedback has been demonstrated. However, DOH variability does not provide a measure of analgesia in the absence of stimulation. Consequently, control of the opioid-hypnotic balance is lost in control systems relying on DOH feedback alone. The proposed design overcomes this limitation by introducing a second, indirect control objective. This paper defines clinical design specifications to achieve adequate anesthesia in a wide range of clinical cases, proposes a modification of the habituating control framework, and presents methods to translate the clinical objectives into control objectives within this framework. The developed design methodology provides a controller that: 1) meets the clinical objectives; 2) is robust to interpatient variability, both in single-input single-output and MISO operation; 3) is robust to nonlinear drug interactions; 4) gives the user control of the opioid-hypnotic balance in the absence of stimulation and in the presence of input saturation; and 5) improves disturbance rejection following nociceptive stimulation. The MISO system performed as designed in 80 clinical cases. [ABSTRACT FROM AUTHOR]

This paper demonstrates the ability of mach- ine learning approaches to identify a few genes among the 23,398 genes of the human genome to experiment on in the laboratory to establish new drug mechanisms. As a case study, this paper uses MDA-MB-231 breast cancer single-cells treated with the antidiabetic drug metformin. We show that mixture-model-based unsupervised methods with validation from hierarchical clustering can identify single-cell subpopulations (clusters). These clusters are characterized by a small set of genes (1% of the genome) that have significant differential expression across the clusters and are also highly correlated with pathways with anticancer effects driven by metformin. Among the identified small set of genes associated with reduced breast cancer incidence, laboratory experiments on one of the genes, CDC42, showed that its downregulation by metformin inhibited cancer cell migration and proliferation, thus validating the ability of machine learning approaches to identify biologically relevant candidates for laboratory experiments. Given the large size of the human genome and limitations in cost and skilled resources, the broader impact of this work in identifying a small set of differentially expressed genes after drug treatment lies in augmenting the drug-disease knowledge of pharmacogenomics experts in laboratory investigations, which could help establish novel biological mechanisms associated with drug response in diseases beyond breast cancer. [ABSTRACT FROM AUTHOR]

Nano-materials are highly exploited to enhance sensing performance of electrochemical electrodes by increasing the electro-active surface area (EASA). In this paper, an analytical calculation of the deposited EASA is presented and experimentally validated by comparing two kinds of nano-structured electrochemical sensors modified with multi-walled carbon nano-tubes (MWCNTs) and fullerenes. Further, the two differently nano-structured electrodes, prepared with equal optimum EASA, have been adopted as novel approach in the detection of paracetamol (APAP) and midazolam (MZ), two largely adopted and complementary sedative drugs. We have used the novel approach to let emerge the different nano-scale structures’ sensing performance at the same equivalent EASA. The analytical method for EASA calculation has been recently validated by our group on different nano-materials; hence the aim of the actual work is to present, for the first time, its efficient applicability also in comparing the performance of different allotropes of the same nature (carbon-based) in drug sensing, e.g., MZ and APAP. This paper also includes the comparison in undiluted human serum (HS), proposed here as complex bio-sample, to confirm the validity of our novel approach close to real applications. With respect to fullerenes, the MWCNTs-modified electrodes obtained the best limit of detection values in HS detection, as 0.3518 \mu \textM and 0.5367 mm for APAP and MZ, respectively. Scanning electron microscopy analysis also confirmed the higher quality of MWCNTs-based surface in terms of homogeneity and denser coverage of the surface with respect to fullerene-based one, with a better-packed aggregate of assembled structures. [ABSTRACT FROM PUBLISHER]

Motivation: To minimize the accelerating amount of time invested on the biomedical literature search, numerous approaches for automated knowledge extraction have been proposed. Relation extraction is one such task where semantic relations between the entities are identified from the free text. In the biomedical domain, extraction of regulatory pathways, metabolic processes, adverse drug reaction or disease models necessitates knowledge from the individual relations, for example, physical or regulatory interactions between genes, proteins, drugs, chemical, disease or phenotype. Results: In this paper, we study the relation extraction task from three major biomedical and clinical tasks, namely drug-drug interaction, protein-protein interaction, and medical concept relation extraction. Towards this, we model the relation extraction problem in a multi-task learning (MTL)framework, and introduce for the first time the concept of structured self-attentive network complemented with the adversarial learning approach for the prediction of relationships from the biomedical and clinical text. The fundamental notion of MTL is to simultaneously learn multiple problems together by utilizing the concepts of the shared representation. Additionally, we also generate the highly efficient single task model which exploits the shortest dependency path embedding learned over the attentive gated recurrent unit to compare our proposed MTL models. The framework we propose significantly improves over all the baselines (deep learning techniques)and single-task models for predicting the relationships, without compromising on the performance of all the tasks. [ABSTRACT FROM AUTHOR]

This paper considers the problem of adaptive group testing for isolating up to $k$ defective items from a population of size $n$. There exist restrictions or preferences which determine how the items can be pooled for testing. A graphical model formalizes the pooling restrictions and preferences. Such graph-constrained group testing is investigated in three settings: populations with defectives, populations facing the potential presence of inhibitors, and populations with community structures. Adaptive group testing frameworks are provided for each setting. In populations without inhibitors, existing non adaptive frameworks can isolate the defective items perfectly with $\Theta(k \log n/k)$ number of tests, where is the $\beta$ -mixing time of a random walk over the underlying graph. This paper provides a two-stage framework that can perfectly isolate up to $k$ defective items for a regular graph using $\Theta(k2 \log n k + k)$ number of tests, thus achieving an approximate gain of a factor of $k$ over the non-adaptive frameworks. This twostage framework's principles are extended to community-structured graphs and graphs with up to $r$ inhibitor items. In particular, when inhibitors are present in the graph, a four-stage group testing framework is proposed. The results show that in the regime $r= O(k)$ for a fully connected graph, $\Theta ((k+r)\log n/(k+r) + r\log n)$ tests are sufficient for isolating the defective items. This matches the corresponding necessary condition on tests which scales $(k+r)\log n$. The adaptive graphconstrained group testing framework is also empirically evaluated. [ABSTRACT FROM AUTHOR]

The term “In Silico Trial” indicates the use of computer modelling and simulation to evaluate the safety and efficacy of a medical product, whether a drug, a medical device, a diagnostic product or an advanced therapy medicinal product. Predictive models are positioned as new methodologies for the development and the regulatory evaluation of medical products. New methodologies are qualified by regulators such as FDA and EMA through formal processes, where a first step is the definition of the Context of Use (CoU), which is a concise description of how the new methodology is intended to be used in the development and regulatory assessment process. As In Silico Trials are a disruptively innovative class of new methodologies, it is important to have a list of possible CoUs highlighting potential applications for the development of the relative regulatory science. This review paper presents the result of a consensus process that took place in the InSilicoWorld Community of Practice, an online forum for experts in in silico medicine. The experts involved identified 46 descriptions of possible CoUs which were organised into a candidate taxonomy of nine CoU categories. Examples of 31 CoUs were identified in the available literature; the remaining 15 should, for now, be considered speculative. [ABSTRACT FROM AUTHOR]

It is crucial for doctors to fully understand the interaction between drugs in prescriptions, especially when a patient takes multiple medications at the same time during treatment. The purpose of drug drug interaction (DDI) extraction is to automatically obtain the interaction between drugs from biomedical literature. Current state-of-the-art approaches for DDI extraction task are based on artificial intelligence and natural language processing. While such existing DDI extraction methods can provide more knowledge and enhance the performance through external resources such as biomedical databases or ontologies, due to the difficulty of updating, these external resources are delayed. In fact, user generated content (UGC) is another kind of external medical resources that can be quickly updated. We are trying to use UGC resources to provide more available information for our deep learning DDI extraction method. In this paper, we present a DDI extraction approach through a new attention mechanism called full-attention which can combine the UGC information with contextual information. We conducted a series of experiments on the DDI 2013 Evaluation dataset to evaluate our method. Experiments show improved performance compared with the state of the art and UGC-DDI model achieves a competitive F-score of 0.712. [ABSTRACT FROM AUTHOR]

The prediction of Drug-Target Interaction (DTI) is an important research direction in bioinformatics as it greatly shortens the development cycle of new drugs. State-of-the-art computational methods for DTI prediction adopt a binary classification framework. The supervision is incomplete, i.e. only a small amount of DTIs are known and treated as positive instances, while the rest are unknown and treated as negative. Two severe problems occur in such a framework: (1) the number of negative samples is overwhelming and (2) a negative label cannot rule out the possibility of a positive drug-target interaction. In this paper, we address the problem of learning from incomplete labels in DTI prediction. The key assumption here is that labels are missing not at random. For example, negative DTI labels are more likely to be missing because biomedical researchers prioritize to study DTIs that are more likely to be positive. We introduce a novel probabilistic model, factorization with non-random missing labels (FNML). It models the generative process for the DTI labels (i.e. the labels are positive or negative) and responses (i.e. the labels are observed or missing). In particular, the probability of observing or missing a label is associated with the sign of the label. In order to further reduce prediction variance and improve prediction accuracy on highly imbalanced DTI datasets, we present FNML-EN, an ensemble scheme which is designed specifically for FNML model. We conduct comprehensive experiments on the latest DTI database, demonstrating the superior and robust performance of the proposed models. [ABSTRACT FROM AUTHOR]

A large number of techniques have been developed to investigate magnetic nanoparticles for biomedical engineering applications to drug-delivery systems. In this paper, we carried out research into a new drug-release technology using liposomal nanoparticles equipped with magnetic controls. Exposures to magnetic fields were carried out using a permanent magnet (0.25 T) or an air-cored coil system (40 mT). Under exposure to the static field, anticancer flutamide (FM) in the liposomal membrane was excited by an emission wavelength of 325 nm. To compare the drug-release potentials with magnetic field effects on the behavior of FM in the model membranes, we measured the spectral changes with absorbance at 314, 260, and 370 nm after the excitation. Judging from the overall result of our liposomes prepared in this paper, up to 20 min after the excitation, the gradual increases in the drug-releases were relatively consistent with an S-T level intersystem crossing for a radical pair mechanism in equilibrium. Concerning the releases with magnetic field effects, the release rates obtained using a homogenous field of 0.25 T were more extensive on the order of 67%–75%, than those obtained at 0 T. Our system may provide the best possible balance between clinical performance and low invasivity. We believe the idea that magnetic fields are extremely suitable for the gentle control of chemical reactions. [ABSTRACT FROM AUTHOR]

Adverse drug-drug interaction (ADDI) is a significant life-threatening issue, posing a leading cause of hospitalizations and deaths in healthcare systems. This paper proposes a unified Multi-Attribute Discriminative Representation Learning (MADRL) model for ADDI prediction. Unlike the existing works that equally treat features of each attribute without discrimination and do not consider the underlying relationship among drugs, we first develop a regularized optimization problem based on CUR matrix decomposition for joint representative drug and discriminative feature selection such that the selected drugs and features can well approximate the original feature spaces and the critical factors discriminative to ADDIs can be properly explored. Different from the existing models that ignore the consistent and unique properties among attributes, a Generative Adversarial Network (GAN) framework is then designed to capture the inter-attribute shared and intra-attribute specific representations of adverse drug pairs for exploiting their consensus and complementary information in ADDI prediction. Meanwhile, MADRL is compatible with any kind of attributes and capable of exploring their respective effects on ADDI prediction. An iterative algorithm based on the alternating direction method of multipliers is developed for optimization. Experiments on publicly available dataset demonstrate the effectiveness of MADRL when compared with eleven baselines and its six variants. [ABSTRACT FROM AUTHOR]

Predicting treatment medicines is a key task in many intelligent healthcare systems. Prediction of treatment medicines can assist doctors in making informed prescription decisions for patients according to their Electronic Health Records (EHRs). However, predicting treatment medicines is a challenging task due to the following reasons: (1) heterogeneous nature of EHR data that typically includes laboratory results, treatment records, disease conditions, and demographic information; (2) complex correlations among EHR sequences, including inter-correlations between sequences and temporal intra-correlations within each sequence; (3) temporal dynamics of these correlations changing with disease progression. In this paper, we predict treatment medicines for patients with dual adaptive sequential networks (DASNet). Specifically, DASNet is designed with three components. First, a decomposed adaptive long short-term memory network (DA-LSTM) is designed to capture the intra- and inter-correlations in multiple heterogeneous temporal sequences. Then, we develop an attentive meta learning network (AT-MetaNet) to learn dynamic weight parameters for DA-LSTM, thus enabling it to model various correlation structures. Finally, we employ an attentive fusion network (AT-FuNet) to incorporate historical information and collectively fuse representation embeddings of heterogeneous data to predict treatment medicines. Our results on the public MIMIC-III dataset covering 11 medical conditions demonstrate that the proposed end-to-end model can achieve the state-of-the-art prediction performance while providing clinically useful insights. [ABSTRACT FROM AUTHOR]

In Silico Trials methodologies will play a growing and fundamental role in the development and de-risking of new medical devices in the future. While the regulatory pathway for Digital Patient and Personal Health Forecasting solutions is clear, it is more complex for In Silico Trials solutions, and therefore deserves a deeper analysis. In this position paper, we investigate the current state of the art towards the regulatory system for in silico trials applied to medical devices while exploring the European regulatory system toward this topic. We suggest that the European regulatory system should start a process of innovation: in principle to limit distorted quality by different internal processes within notified bodies, hence avoiding that the more innovative and competitive companies focus their attention on the needs of other large markets, like the USA, where the use of such radical innovations is already rapidly developing. [ABSTRACT FROM AUTHOR]

The papers in this special section focus on artificial intelligence-drive applications for synthetic biology that promote human well being. Synthetic biology is an important branch of biological science, which is different and even completely opposite from traditional research direction on biology. It is obvious that synthetic biology will promote the next biotechnology revolution. At present, related researches are not limited in the painstaking splicing of genes, but have begun to construct genetic codes in order to construct new organisms using synthetic genetic factors. Specially, it is estimated that synthetic biology will have excellent application prospects in many fields, including the production of more effective vaccines, new drugs, biology based manufacturing, the production of sustainable energy, the biological treatment of environmental pollution, and biosensors that can detect toxic chemicals. In this way, synthetic biology is expected to make rapid progress in the next few years. [ABSTRACT FROM AUTHOR]

This paper considers the scenario of a targeted drug delivery system, which consists of deploying a number of biological nanomachines close to a biological target (e.g., a tumor), able to deliver drug molecules in the diseased area. Suitably located transmitters are designed to release a continuous flow of drug molecules in the surrounding environment, where they diffuse and reach the target. These molecules are received when they chemically react with compliant receptors deployed on the receiver surface. In these conditions, if the release rate is relatively high and the drug absorption time is significant, congestion may happen, essentially at the receiver site. This phenomenon limits the drug absorption rate and makes the signal transmission ineffective, with an undesired diffusion of drug molecules elsewhere in the body. The original contribution of this paper consists of a theoretical analysis of the causes of congestion in diffusion-based molecular communications. For this purpose, it is proposed a reception model consisting of a set of pure loss queuing systems. The proposed model exhibits an excellent agreement with the results of a simulation campaign made by using the Biological and Nano-Scale communication simulator version 2 (BiNS2), a well-known simulator for molecular communications, whose reliability has been assessed through in vitro experiments. The obtained results can be used in rate control algorithms to optimally determine the optimal release rate of molecules in drug delivery applications. [ABSTRACT FROM PUBLISHER]

In recent years, as online social networks continue to grow in size, estimating node features, such as sociodemographics, preferences and health status, in a scalable and reliable way has become a primary research direction in social network mining. Although many techniques have been developed for estimating various node features, quantifying uncertainty in such estimations has received little attention. Furthermore, most existing methods study networks parametrically, which limits insights about necessary quantity of queried data, reliable feature estimation, and estimator uncertainty. Uncertainty quantification is critical for answering key questions, such as, given a limited availability of social network data, how much data should be queried from the network?, and which node features can be learned reliably? More importantly, how can we evaluate uncertainty of our estimators? Uncertainty quantification is not equivalent to network sampling but constitutes a key complementary concept to sampling and the associated reliability analysis. To our knowledge, this paper is the first work that sheds light on uncertainty quantification and uncertainty propagation in social network feature mining. We propose a novel non-parametric bootstrap method for uncertainty analysis of node features in social network mining, derive its asymptotic properties, and demonstrate its effectiveness with extensive experiments. Furthermore, we develop a new metric based on dispersion of estimations, enabling analysts to assess how much more information is needed for increasing prediction reliability based on the estimated uncertainty. We demonstrate the effectiveness of our new uncertainty quantification methodology with extensive experiments on real life social networks, and a case study of mental health on Twitter. [ABSTRACT FROM AUTHOR]

Blood vessels are flow-induced diffusive molecular channels equipped with transport mechanisms across their walls for conveying substances between the organs in the body. Mathematical modeling of the blood vessel as a molecular transport channel can be used for the characterization of the underlying processes and higher-level functions in the circulatory system. Besides, the mathematical model can be utilized for designing and realizing nano-scale molecular communication systems for healthcare applications including drug delivery systems. In this paper, a continuous-time Markov chain framework is proposed to simply model active transport mechanisms e.g. transcytosis, across the single-layered endothelial cells building the inner vessel wall. Correspondingly, a general homogeneous boundary condition over the vessel wall is introduced. Coupled with the derived boundary condition, the flow-induced diffusion problem in an ideal vessel structure with a cylindrical shape is accurately formulated which takes into account variation in all three dimensions. The corresponding concentration Green’s function is analytically derived in terms of a convergent infinite series. Particle-based simulation results confirm the proposed analysis. Also, the effects of system parameters on the concentration Green’s function are examined. [ABSTRACT FROM AUTHOR]

In this paper, a multiple transmitter local drug delivery system associated with encapsulated drug transmitters is investigated. One of the limitations of drug delivery systems is the reservoir capacity. In order to improve the lifetime of drug transmitting nanomachines, and, hence, the longevity of drug delivery scenario, the system is associated with encapsulated drug transmitters. Encapsulated drugs are incapable of reaction with the environment unless they are unpacked in a drug transmitter nanomachine. Therefore, far-reaching transmitters do not have harmful effects on the healthy parts of the body. The advantage of this protocol is to increase the time interval between consecutive administrations without increased toxicity. As a result, it improves the mental health of patients and reduces the costs of treatment. The lifetime of this drug delivery system depends on the distribution and topology of encapsulated drug transmitters rather than their rates. Finally, a lower bound is derived on the expected lifetime of a Poisson distributed random network of nanomachines. [ABSTRACT FROM AUTHOR]

This paper presents a fully integrated CMOS multimodality joint sensor/stimulator array with 1024 pixels for real-time holistic cellular characterization and drug screening. The proposed system consists of four pixel groups and four parallel signal-conditioning blocks. Every pixel group contains 16 × 16 pixels, and each pixel includes one \text28\,\mu \textm\times \text28\,\mu \textm gold-plated electrode, four \text12\,\mu \textm\times \text12\,\mu \textm photodiodes, and in-pixel circuits, within a \text58\,\mu \textm\times \text58\,\mu \textm pixel footprint. Each pixel supports real-time extracellular potential recording, optical detection, charge-balanced biphasic current stimulation, and cellular impedance measurement for the same cellular sample. The proposed system is fabricated in a standard 130-nm CMOS process. Rat cardiomyocytes are successfully cultured on-chip. Measured high-resolution optical opacity images, extracellular potential recordings, biphasic current stimulations, and cellular impedance images demonstrate the unique advantages of the system for holistic cell characterization and drug screening. Furthermore, this paper demonstrates the use of optical detection on the on-chip cultured cardiomyocytes to real-time track their cyclic beating pattern and beating rate. [ABSTRACT FROM PUBLISHER]

Drug repositioning/repurposing is a very important approach towards identifying novel treatments for diseases in drug discovery. Recently, large-scale biological datasets are increasingly available for pharmaceutical research and promote the development of drug repositioning, but efficiently utilizing these datasets remains challenging. In this paper, we develop a novel multimodal framework, termed GraphPK (Graph-based Prior Knowledge) for improving in silico drug repositioning via using the prior knowledge from a drug knowledge graph. First, we construct a knowledge graph by integrating relevant bio-entities (drugs, diseases, etc.) and associations/interactions among them, and apply the knowledge graph embedding technique to extract prior knowledge of drugs and diseases. Moreover, we make use of the known drug-disease association, and obtain known association-based features from an association bipartite graph through graph embedding, and also take into account biological domain features, i.e., drug chemical structures and disease semantic similarity. Finally, we design a multimodal neural network to combine three types of features from the knowledge graph, the known associations and the biological domain, and build the prediction model for predicting drug-disease associations. Massive experiments show that our method outperforms other state-of-the-art methods in terms of most metrics, and the ablation analysis regarding the three types of features reveals that prior knowledge from knowledge graphs can not only lift the predictive power of in silico drug repositioning, but also enhance the model's robustness to different scenarios. The results of case studies offer support that GraphPK has the potential for actual use. [ABSTRACT FROM AUTHOR]

The detection of drug-drug interactions (DDIs) is a crucial task for drug safety surveillance, which provides effective and safe co-prescriptions of multiple drugs. Since laboratory researches are often complicated, costly and time-consuming, it's urgent to develop computational approaches to detect drug-drug interactions. In this paper, we conduct a comprehensive review of state-of-the-art computational methods falling into three categories: literature-based extraction methods, machine learning-based prediction methods and pharmacovigilance-based data mining methods. Literature-based extraction methods detect DDIs from published literature using natural language processing techniques; machine learning-based prediction methods build prediction models based on the known DDIs in databases and predict novel ones; pharmacovigilance-based data mining methods usually apply statistical techniques on various electronic data to detect drug-drug interaction signals. We first present the taxonomy of drug-drug interaction detection methods and provide the outlines of three categories of methods. Afterwards, we respectively introduce research backgrounds and data sources of three categories, and illustrate their representative approaches as well as evaluation metrics. Finally, we discuss the current challenges of existing methods and highlight potential opportunities for future directions. [ABSTRACT FROM AUTHOR]

Insomnia is a common public health problem and an open biomedical research topic. Insomnia results in various health problems, including memory decline, decreases concentration and weakens problem-solving ability. The insufficient sleep also leads to skin ageing, heart disease, high blood pressure, arrhythmia and stroke. While it remains as a global health concern, sleep quality improvement using modern technologies, such as machine learning, classification technologies, virtual reality (VR), becomes an open and hot research problem. These modern technologies offer new curing solutions under certain conditions. In this paper, we present a sleeping-aid system with a single-channel electroencephalogram (EEG) sleep stage classification algorithm to improve the sleep quality. The sleeping-aid system promotes machine learning integrated VR and multimedia technology for sleep improvement. Ninety participants were invited to test on three different systems with 3D VR, 2D video, and music only. An adequate stimulus of audio-vision can be a complement of the drug treatment. The experimental results showed that the proposed method demonstrated superior performance over existing methods. [ABSTRACT FROM AUTHOR]

As nanotechnology advances, it is possible to build nanomachines to conduct tasks on the nano-scale. Since the EM wave, the traditional communication medium does not apply in nano-scale, Molecular Communication (MC) has been recently given increasing attention for its excellent biocompatibility and low energy consumption within the human body. Diffusion channel-based MC(DbMC) is mainly studied in the aquatic environment. However, due to the randomness of diffusion, DbMC suffers high losses, low propagation speed, and limited communication range. Directional Molecular Communication(DMC) is proposed by introducing chemotaxis. DMC can significantly improve the efficiency of molecular communication because it keeps molecules moving along a predetermined path. It is particularly suitable in the scenario of target tracking related to some applications such as drug delivery. In this paper, we have proposed a novel massive beacon coordinates system model to aid target tracking. Beacons in this system navigate nanomachines, and the beacon system can uniquely determine their position coordinates. Each nanomachine carries a lot of bacteria carrier (E.coli) to share information. Information is encoded in DNA molecules and transferred to other nanomachines by bacteria carriers. With the help of bacteria carriers, nanomachines can share their current position information with others to realize cooperated fast target tracking. We have evaluated its performance in target tracking through simulation by comparison with the diffusion-based model. Some key factors that may influence target tracking are also taken into consideration. [ABSTRACT FROM AUTHOR]

Searching similar graphs in graph databases for a query graph has attracted extensive attention recently. Existing works on graph similarity queries are threshold based approaches which return graphs with distances to the query smaller than a given threshold. However, in many applications the number of answer graphs for the same threshold can vary significantly for different queries. In this paper, we study the problem of finding top- $k$ k most similar graphs for a query under the distance measure based on maximum common subgraph (MCS). Since computing MCS is NP-hard, we devise a novel framework to prune unqualified graphs based on the lower bounds of graph distance, and accordingly derive four lower bounds with different tightness and computational cost for pruning. To further reduce the number of MCS computations, we also propose an improved framework based on both lower and upper bounds, and derive three new upper bounds. To support efficient pruning, we design three indexes with different tradeoffs between pruning power and construction cost. To accelerate the index construction, we explore bound relaxation techniques, based on which approximate indexes can be efficiently built. We conducted extensive performance studies on real-life graph datasets to validate the effectiveness and efficiency of our approaches. [ABSTRACT FROM AUTHOR]

Objective: In this paper, we present an original decision support algorithm to assist the anesthesiologists delivery of drugs to maintain the optimal Depth of Anesthesia (DoA). Methods: Derived from a Transform Predictive State Representation algorithm (TPSR), our model learned by observing anesthesiologists in practice. This framework, known as apprenticeship learning, is particularly useful in the medical field as it is not based on an exploratory process – a prohibitive behavior in healthcare. The model only relied on the four commonly monitored variables: Heart Rate (HR), the Mean Blood Pressure (MBP), the Respiratory Rate (RR) and the concentration of anesthetic drug (AAFi). Results: Thirty-one patients have been included. The performances of the model is analyzed with metrics derived from the Hamming distance and cross entropy. They demonstrated that low rank dynamical system had the best performances on both predictions and simulations. Then, a confrontation of our agent to a panel of six real anesthesiologists demonstrated that $\boldsymbol{95.7}$ % of the actions were valid. Conclusion: These results strongly support the hypothesis that TPSR based models convincingly embed the behavior of anesthesiologists including only four variables that are commonly assessed to predict the DoA. Significance: The proposed novel approach could be of great help for clinicians by improving the fine tuning of the DoA. Furthermore, the possibility to predict the evolutions of the variables would help preventing side effects such as low blood pressure. A tool that could autonomously help the anesthesiologist would thus improve safety-level in the surgical room. [ABSTRACT FROM AUTHOR]

Targeted drug delivery system is believed as one of the most promising solutions for cancer treatment due to its low-dose requirement and less side effects. However, both passive targeting and active targeting rely on systemic blood circulation and diffusion, which is actually not the real “active” drug delivery. In this paper, an ant-behavior inspired nanonetwork composing of intelligent nanomachines is proposed. A big intelligent nanomachine take small intelligent nanomachines and drugs to the vicinity of of the tumor area. The small intelligent nanomachines can coordinate with each other to find the most effective path to the tumor cell for drug transportation. The framework and mechanism of this cooperative network are proposed. The route finding algorithm is presented. The convergence performance is analytically analyzed where the influence of the factors such as molecule degradation rate, home-destination distance, number of small nanomachines to the convergence is presented. Finally the simulation results validate the effectiveness of the proposed mechanism and analytical analysis. [ABSTRACT FROM AUTHOR]

Feasibility of closed-loop propofol anesthesia has been demonstrated in clinical trials; however, no closed-loop system is currently routinely used in clinical practice. Data from closed-loop clinical trials provide valuable information for system evaluation prior to market approval, including outlier behavior. To exploit this valuable data, closed-loop identification in the presence of nonzero disturbances is required. Disturbances due to stimulation are not zero mean and will cause bias in models identified from clinical data. In this paper, we use multiple-input single-output (MISO) closed-loop identification to take the response to disturbances during induction of anesthesia into account. It is shown that direct MISO output-error identification introduces limited bias for this specific closed-loop identification problem and the use of more complex closed-loop identification methods does not improve model accuracy. Using this approach, we identify and validate a set of models that describes both the blood pressure and the depth-of-hypnosis response to propofol infusion for patients at risk of cardiovascular suppression. Quantification of both these responses for the same patients provides a valuable basis for the design and evaluation of constrained control systems. [ABSTRACT FROM AUTHOR]

Ontologies are formal representations of concepts and complex relationships among them. They have been widely used to capture comprehensive domain knowledge in areas such as biology and medicine, where large and complex ontologies can contain hundreds of thousands of concepts. Especially due to the large size of ontologies, visualisation is useful for authoring, exploring and understanding their underlying data. Existing ontology visualisation tools generally focus on the hierarchical structure, giving much less emphasis to non-hierarchical associations. In this paper we present OntoPlot, a novel visualisation specifically designed to facilitate the exploration of all concept associations whilst still showing an ontology's large hierarchical structure. This hybrid visualisation combines icicle plots, visual compression techniques and interactivity, improving space-efficiency and reducing visual structural complexity. We conducted a user study with domain experts to evaluate the usability of OntoPlot, comparing it with the de facto ontology editor Protégé. The results confirm that OntoPlot attains our design goals for association-related tasks and is strongly favoured by domain experts. [ABSTRACT FROM AUTHOR]

Microfluidic chips are now being increasingly used for fast and cost-effective implementation of biochemical protocols. Sample preparation involves dilution and mixing of fluids in certain ratios, which are needed for most of the protocols. On a digital microfluidic biochip (DMFB), these tasks are usually automated as a sequence of droplet mix-split steps. In the most widely used (1:1) mix-split operation for DMFBs, two equal-volume droplets are mixed followed by a split operation, which, ideally, should produce two daughter-droplets of equal volume (balanced splitting). However, because of uncertain variabilities in fluidic operations, the outcome of droplet-split operations often becomes erroneous, i.e., they may cause unbalanced splitting. As a result, the concentration factor (CF) of each constituent fluid in the mixture may become erroneous during sample preparation. All traditional approaches aimed to recover from such errors deploy on-chip sensors to detect possible volumetric imbalance, and adopt either checkpointing-based rollback or roll-forward techniques. Most of them suffer from significant overhead in terms of assay-completion time, reactant-cost, and uncertainties in termination due to randomly occurring split-errors. In this paper, we propose a new approach to accurate dilution preparation on a DMFB that is oblivious to volumetric split-errors. It does not need any sensor and can handle multiple split-errors, deterministically. The proposed method is customized for each target-CF based on the criticality of split-errors in each mix-split step. Simulation experiments on various test-cases demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]