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As newly recognized environmental pollutants, microplastics (MPs, ≤5 mm in length) have been reported in various human tissues and fluids, including the spleen, liver, heart, blood and blood clots, raising global concerns about their impact on human health. This study investigated the characteristics of MPs in intravenous infusion and the removal of MPs from infusion products by infusion sets fitted with different filters using micro-Fourier Transform Infrared Spectroscopy. MPs were detected in infusion products, with an average abundance of 1.24 ± 1.44 items/unit (2.91 ± 3.91 items/L). The primary types of MPs identified were fragmented particles of polyethene and polypropylene, ranging in size from 15-100 µm. Internal filters in infusion sets played a crucial role in removing MPs, particularly fibrous ones, resulting in a reduction in both abundance and particle size of MPs in the human body. Moreover, this study conducted a general assessment of intravenous microplastic exposure among hospital patients and estimated the global per-person input of MPs via intravenous administration. It is an opportunity for us to gain a deeper understanding of MPs in intravenous infusion and provides guides selecting infusion devices, increasing awareness of associated health risks.

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Thanks to the mass adoption of internet and mobile devices, users of the social media can seamlessly and spontaneously connect with their friends, followers and followees. Consequently, social media networks have gradually become the major venue for broadcasting and relaying information, and is casting great influences on the people in many aspects of their daily lives. Thus locating those influential users in social media has become crucially important for the successes of many viral marketing, cyber security, politics, and safety-related applications. In this study, we address the problem through solving the tiered influence and activation thresholds target set selection problem, which is to find the seed nodes that can influence the most users within a limited time frame. Both the minimum influential seeds and maximum influence within budget problems are considered in this study. Besides, this study proposes several models exploiting different requirements on seed nodes selection, such as maximum activation, early activation and dynamic threshold. These time-indexed integer program models suffer from the computational difficulties due to the large numbers of binary variables to model influence actions at each time epoch. To address this challenge, this paper designs and leverages several efficient algorithms, i.e., Graph Partition, Nodes Selection, Greedy algorithm, recursive threshold back algorithm and two-stage approach in time, especially for large-scale networks. Computational results show that it is beneficial to apply either the breadth first search or depth first search greedy algorithms for the large instances. In addition, algorithms based on node selection methods perform better in the long-tailed networks.

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Patient stratification is a clinically important task because it allows us to establish and develop efficient treatment strategies for particular groups of patients. Molecular subtypes have been successfully defined using transcriptomic profiles, and they are used effectively in clinical practice, e.g., PAM50 subtypes of breast cancer. Survival prediction contributed to understanding diseases and also identifying genes related to prognosis. It is desirable to stratify patients considering these two aspects simultaneously. However, there are no methods for patient stratification that consider molecular subtypes and survival outcomes at once. Here, we propose a methodology to deal with the problem. A genetic algorithm is used to select a gene set from transcriptome data, and their expression quantities are utilized to assign a risk score to each patient. The patients are ordered and stratified according to the score. A gene set was selected by our method on a breast cancer cohort (TCGA-BRCA), and we examined its clinical utility using an independent cohort (SCAN-B). In this experiment, our method was successful in stratifying patients with respect to both molecular subtype and survival outcome. We demonstrated that the orders of patients were consistent across repeated experiments, and prognostic genes were successfully nominated. Additionally, it was observed that the risk score can be used to evaluate the molecular aggressiveness of individual patients.

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In the case of bladder cancer, carcinoma in situ (CIS) is known to have poor diagnosis. However, there are not enough studies that examine the biomarkers relevant to CIS development. Omics experiments generate data with tens of thousands of descriptive variables, e.g., gene expression levels. Often, many of these descriptive variables are identified as somehow relevant, resulting in hundreds or thousands of relevant variables for building models or for further data analysis. We analyze one such dataset describing patients with bladder cancer, mostly non-muscle-invasive (NMIBC), and propose a novel approach to feature selection. This approach returns high-quality features for prediction and yet allows interpretability as well as a certain level of insight into the analyzed data. As a result, we obtain a small set of seven of the most-useful biomarkers for diagnostics. They can also be used to build tests that avoid the costly and time-consuming existing methods. We summarize the current biological knowledge of the chosen biomarkers and contrast it with our findings.

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This work deals with a generalization of the minimum Target Set Selection (TSS) problem, a key algorithmic question in information diffusion research due to its potential commercial value. Firstly proposed by Kempe et al., the TSS problem is based on a linear threshold diffusion model defined on an input graph with node thresholds, quantifying the hardness to influence each node. The goal is to find the smaller set of items that can influence the whole network according to the diffusion model defined. This study generalizes the TSS problem on networks characterized by many-to-many relationships modeled via hypergraphs. Specifically, we introduce a linear threshold diffusion process on such structures, which evolves as follows. Let H=(V,E) be a hypergraph. At the beginning of the process, the nodes in a given set S⊆V are influenced. Then, at each iteration, (i) the influenced hyperedges set is augmented by all edges having a sufficiently large number of influenced nodes; (ii) consequently, the set of influenced nodes is enlarged by all the nodes having a sufficiently large number of already influenced hyperedges. The process ends when no new nodes can be influenced. Exploiting this diffusion model, we define the minimum Target Set Selection problem on hypergraphs (TSSH). Being the problem NP-hard (as it generalizes the TSS problem), we introduce four heuristics and provide an extensive evaluation on real-world networks.

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Support vector machine (SVM) is a robust machine learning method and is widely used in classification. However, the traditional SVM training methods may reveal personal privacy when the training data contains sensitive information. In the training process of SVMs, working set selection is a vital step for the sequential minimal optimization-type decomposition methods. To avoid complex sensitivity analysis and the influence of high-dimensional data on the noise of the existing SVM classifiers with privacy protection, we propose a new differentially private working set selection algorithm (DPWSS) in this paper, which utilizes the exponential mechanism to privately select working sets. We theoretically prove that the proposed algorithm satisfies differential privacy. The extended experiments show that the DPWSS algorithm achieves classification capability almost the same as the original non-privacy SVM under different parameters. The errors of optimized objective value between the two algorithms are nearly less than two, meanwhile, the DPWSS algorithm has a higher execution efficiency than the original non-privacy SVM by comparing iterations on different datasets. To the best of our knowledge, DPWSS is the first private working set selection algorithm based on differential privacy.

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Task set selection is facilitated when people expect a partner to perform the same task, suggesting that the features of the partner's performance are represented. However, it is unclear how similar the partner's reactions must be to promote compatibility effects: does a partner have to imitate subjects' specific actions or is it enough to perform the same task while responding to different stimuli with different actions? This present study investigated this question in a joint picture-word interference paradigm. Subjects either named pictures or read words, and a partner responded by performing the same or the competing task. In Experiment 1, the partner used the same picture-word combinations as the subject and thus compatible trials implied a complete imitation. Compatibility benefits were observed. In Experiment 2, the partner performed the same or the competing task on different stimuli, producing different actions. Compatibility effects were absent. To test whether this indicates that an overlap in abstract task features is insufficient or resulted from excessive task difficulty, Experiment 3 replicated Experiment 2 with a smaller stimulus set. Compatibility benefits were found. Taken together, the results suggest that a partner's abstract task can be represented and affect task set selection processes even without an overlap in stimulus-response mappings.

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Hopfield neural networks are useful for solving certain constrained set-selection problems. We establish that the vector fields associated with general networks of this type can be combined to produce a new network that solves the corresponding combination of set-selection/constraint problems, provided a relatively simple condition is satisfied. That is, we establish that just this one condition needs to be verified in order to be able to combine such networks. We introduce some generalizations of networks that exist in the literature, and, to demonstrate the usefulness of the work, we combine these networks to solve two well-known grid-based math puzzles (i.e. constraint problems): Kakuro and Akari (called Cross Sums and Light Up in North America). We present examples to illustrate the evolution of the solution process. We find that the difficulty rating of a Kakuro puzzle is strongly connected to the number of iterations used by the neural network solver.

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Current pharmaceutical formulation development still strongly relies on the traditional trial-and-error methods of pharmaceutical scientists. This approach is laborious, time-consuming and costly. Recently, deep learning has been widely applied in many challenging domains because of its important capability of automatic feature extraction. The aim of the present research is to apply deep learning methods to predict pharmaceutical formulations. In this paper, two types of dosage forms were chosen as model systems. Evaluation criteria suitable for pharmaceutics were applied to assess the performance of the models. Moreover, an automatic dataset selection algorithm was developed for selecting the representative data as validation and test datasets. Six machine learning methods were compared with deep learning. Results showed that the accuracies of both two deep neural networks were above 80% and higher than other machine learning models; the latter showed good prediction of pharmaceutical formulations. In summary, deep learning employing an automatic data splitting algorithm and the evaluation criteria suitable for pharmaceutical formulation data was developed for the prediction of pharmaceutical formulations for the first time. The cross-disciplinary integration of pharmaceutics and artificial intelligence may shift the paradigm of pharmaceutical research from experience-dependent studies to data-driven methodologies.

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The computational cost of quantum chemical methods grows rapidly with increasing level of theory and basis set size. At increasing costs, higher accuracies can be reached, forcing a compromise between cost and accuracy for most molecular systems. Heats of reaction, however, are mostly determined by a subset of atoms that experience significant bonding and/or electronic changes. To exploit this fact, the Stepwise Basis Builder (SBB) algorithm selectively adds basis functions to reactive atoms and maintains small basis sets on spectator atoms. This article introduces the SBB algorithm and how it chooses a basis for each atom, predicts calculation errors, and uses these predicted errors to reach target levels of accuracy. Benchmarks show SBB heats of reaction and activation barriers converge to values consistent with higher-quality calculations using a greatly reduced number of basis functions. © 2018 Wiley Periodicals, Inc.

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Computed tomography (CT) is the method of choice for diagnosing ureteral stones - kidney stones that obstruct the ureter. The purpose of this study is to develop a computer aided detection (CAD) algorithm for identifying a ureteral stone in thin slice CT volumes. The challenge in CAD for urinary stones lies in the similarity in shape and intensity of stones with non-stone structures and how to efficiently deal with large high-resolution CT volumes. We address these challenges by using a Convolutional Neural Network (CNN) that works directly on the high resolution CT volumes. The method is evaluated on a large data base of 465 clinically acquired high-resolution CT volumes of the urinary tract with labeling of ureteral stones performed by a radiologist. The best model using 2.5D input data and anatomical information achieved a sensitivity of 100% and an average of 2.68 false-positives per patient on a test set of 88 scans.

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Long-term genomic selection (GS) requires strategies that balance genetic gain with population diversity, to sustain progress for traits under selection, and to keep diversity for future breeding. In a simulation model for a recurrent selection scheme, we provide the first head-to-head comparison of two such existing strategies: genomic optimal contributions selection (GOCS), which limits realized genomic relationship among selection candidates, and weighted genomic selection (WGS), which upscales rare allele effects in GS. Compared to GS, both methods provide the same higher long-term genetic gain and a similar lower inbreeding rate, despite some inherent limitations. GOCS does not control the inbreeding rate component linked to trait selection, and, therefore, does not strike the optimal balance between genetic gain and inbreeding. This makes it less effective throughout the breeding scheme, and particularly so at the beginning, where genetic gain and diversity may not be competing. For WGS, truncation selection proved suboptimal to manage rare allele frequencies among the selection candidates. To overcome these limitations, we introduce two new set selection methods that maximize a weighted index balancing genetic gain with controlling expected heterozygosity (IND-HE) or maintaining rare alleles (IND-RA), and show that these outperform GOCS and WGS in a nearly identical way. While requiring further testing, we believe that the inherent benefits of the IND-HE and IND-RA methods will transfer from our simulation framework to many practical breeding settings, and are therefore a major step forward toward efficient long-term genomic selection.

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Recent studies highlight the influence of non-conscious information on task-set selection. However, it has not yet been tested whether this influence depends on conscious settings, as some theoretical models propose. In a series of three experiments, we explored whether non-conscious abstract cues could bias choices between a semantic and a perceptual task. In Experiment 1, we observed a non-conscious influence on task-set selection even when perceptual priming and cue-target compound confounds did not apply. Experiments 2 and 3 showed that, under restrictive conditions of visibility, cues only biased task selection when the conscious task-setting mindset led participants to search for information during the time period of the cue. However, this conscious strategy did not modulate the effect found when a subjective measure of consciousness was used. Altogether, our results show that the configuration of the conscious mindset determines the potential bias of non-conscious information on task-set selection.

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After reviewing set selection and memory model dynamical system neural networks, we introduce a neural network model that combines set selection with partial memories (stored memories on subsets of states in the network). We establish that feasible equilibria with all states equal to ± 1 correspond to answers to a particular set theoretic problem. We show that KenKen puzzles can be formulated as a particular case of this set theoretic problem and use the neural network model to solve them; in addition, we use a similar approach to solve Sudoku. We illustrate the approach in examples. As a heuristic experiment, we use online or print resources to identify the difficulty of the puzzles and compare these difficulties to the number of iterations used by the appropriate neural network solver, finding a strong relationship.

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