Miracle-dz/UniFAR-FTUs huggingface dataset:
Molecular circuit for approximate maximum a posteriori demodulation of concentration modulated signals

This paper builds on our earlier work on designing demodulators for diffusion-based molecular communication using a Markovian approach. The demodulation filters take the form of an ordinary differential equation which computes log-posteriori probability of observing a transmission symbol given the continuous history of receptor activities. This work considers the realisation of these demodulation filters using molecular circuits, i.e. a set of chemical reactions. There are two key issues to overcome. First, these filters require the computation of a derivative. Second, these filters require a model of the expected signal and one needs to derive a method to encode this expected signal. In this paper, we use time-scale separation to handle the derivative computation. For the case where the transmitter uses rectangular pulses as its transmitting symbols, we show how the expected signal can be encoded. We show that the output of the derived molecular circuit is approximately equal to the log-posteriori probability calculated by the demodulation filter if the log-posteriori probability is positive.
Effect of structural design on the pullout strength of suture anchors for rotator cuff repair

Various types of suture anchor designs are currently available for rotator cuff repair. The purpose of our study was to investigate the pullout strength of such anchors based on their structural design and the predominant geometric design factors affecting the pullout strength using finite element analysis. Finite element models were constructed using five cadaveric humeri and ten suture anchors with different designs. The pullout strength and distribution of bone stress around the anchor at three different directions of the applied force (0°, 45°, and 75°) were analyzed. The following geometric factors of suture anchor design were computed and their correlations with pullout strength assessed: Overall length, minor, and major diameters; number of threads; height of thread; distance between threads; helix angle; contact surface area between the anchor threads and surrounding bone; contact surface area between the cylindrical portion of the anchor; and surrounding bone and total contact surface area between the anchor and surrounding bone. The pullout strength and distribution of bone stress around the anchor varied according to the suture anchor designs and the direction of the applied force, respectively. The pullout strength had a strong positive correlation with the contact surface area between the anchor threads and surrounding bone, overall length, and the number and height of threads. This study demonstrated that suture anchor designs with increased contact surface area between the anchor threads and surrounding bone, overall length, and the number and height of threads can enhance the pullout strength during rotator cuff repair. © 2018 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 36:3318–3327, 2018.
Black Phosphorus/Platinum Heterostructure: A Highly Efficient Photocatalyst for Solar‐Driven Chemical Reactions

A 2D black phosphorus/platinum heterostructure (Pt/BP) is developed as a highly efficient photocatalyst for solar‐driven chemical reactions. The heterostructure, synthesized by depositing BP nanosheets with ultrasmall (≈1.1 nm) Pt nanoparticles, shows strong Pt–P interactions and excellent stability. The Pt/BP heterostructure exhibits obvious P‐type semiconducting characteristics and efficient absorption of solar energy extending into the infrared region. Furthermore, during light illumination, accelerated charge separation is observed from Pt/BP as manifested by the ultrafast electron migration (0.11 ps) detected by a femtosecond pump‐probe microscopic optical system as well as efficient electron accumulation on Pt revealed by in situ X‐ray photoelectron spectroscopy. These unique properties result in remarkable performance of Pt/BP in typical hydrogenation and oxidation reactions under simulated solar light illumination, and its efficiency is much higher than that of other common Pt catalysts and even much superior to that of conventional thermal catalysis. The 2D Pt/BP heterostructure has enormous potential in photochemical reactions involving solar light and the results of this study provide insights into the design of next‐generation high‐efficiency photocatalysts.
Preferred Provider Relationships Between Medicare Advantage Plans and Skilled Nursing Facilities Reduce Switching Out of Plans: An Observational Analysis

Unlike traditional Medicare, Medicare Advantage (MA) plans contract with specific skilled nursing facilities (SNFs). Patients treated in an MA plan’s preferred SNF may benefit from enhanced coordination and have a lower likelihood of switching out of their plan. Using 2011-2014 Medicare enrollment data, the Medicare Healthcare Effectiveness Data and Information Set, and the Minimum Data Set, we examined Medicare enrollees who were newly admitted to SNFs in 2012-2013. We used the Centers for Medicare & Medicaid Services star rating to distinguish between MA plans and show how SNF concentration experienced by patients varies between patients in plans with different star ratings. We found that highly rated MA plans steer their patients to a smaller number of SNFs, and these patients are less likely to switch out of their plans. Strengthening the MA plan–SNF relationship may lower disenrollment rates for SNF beneficiaries, imparting benefits to both patients and payers.
Some reflections on combining meta‐analysis and structural equation modeling

Meta‐analysis and structural equation modeling (SEM) are 2 of the most prominent statistical techniques employed in the behavioral, medical, and social sciences. They each have their own well‐established research communities, terminologies, statistical models, software packages, and journals (Research Synthesis Methods and Structural Equation Modeling: A Multidisciplinary Journal). In this paper, I will provide some personal reflections on combining meta‐analysis and SEM in the forms of meta‐analytic SEM and SEM‐based meta‐analysis. The critical contributions of Becker (1992), Shadish (1992), and Viswesvaran and Ones (1995) in the early development of meta‐analytic SEM are highlighted. Another goal of the paper is to illustrate how meta‐analysis can be extended and integrated with other techniques to address new research questions such as the analysis of Big Data. I hope that this paper may stimulate more research development in the area of combining meta‐analysis and SEM.
Bicuspid aortic valve with critical coarctation of the aorta: single- or two-stage operation?

Background A bicuspid aortic valve (BAV) with critical coarctation of the aorta (CoA) is a rare but dangerous congenital cardiac malformation. The aim of this study was to compare the benefits of a single- versus two-stage operation for patients of BAV with CoA. Methods We retrospectively evaluated 20 patients of BAV and CoA who underwent surgery from 2000 to 2016 in our center. Eight patients underwent a single-stage procedure through a median sternotomy approach, while 12 patients underwent two-stage operation. Results The patients' baseline characteristics were similar between the two groups, and the overall mortality rate was 0%. No significant differences were found in the postoperative pressure gradient (P=0.64), use of implants (P=0.81), reoperation for bleeding, phrenic nerve injury, blood transfusion (P=1.00), or thromboembolic events between the two groups. However, patients in the single-stage group sustained less surgical trauma and ICU stay hours (P<0.01) than those in the two-stage group. Conclusions Performance of a single-stage operation was feasible for patients of BAV with CoA. It was a safe and optimal choice with less surgical trauma compared with a two-stage operation.
Seeing Colors: Learning Semantic Text Encoding for Classification

The question we answer with this work is: can we convert a text document into an image to exploit best image classification models to classify documents? To answer this question we present a novel text classification method which converts a text document into an encoded image, using word embedding and capabilities of Convolutional Neural Networks (CNNs), successfully employed in image classification. We evaluate our approach by obtaining promising results on some well-known benchmark datasets for text classification. This work allows the application of many of the advanced CNN architectures developed for Computer Vision to Natural Language Processing. We test the proposed approach on a multi-modal dataset, proving that it is possible to use a single deep model to represent text and image in the same feature space.
Job-exposure matrices addressing lifestyle to be applied in register-based occupational health studies

Objectives Information about lifestyle factors in register-based occupational health studies is often not available. The objective of this study was therefore to develop gender, age and calendar-time specific job-exposure matrices (JEMs) addressing five selected lifestyle characteristics across job groups as a tool for lifestyle adjustment in register-based studies. Methods We combined and harmonised questionnaire and interview data on lifestyle from several Danish surveys in the time period 1981–2013 for 264 054 employees registered with a DISCO-88 code (the Danish version of International Standard Classification of Occupations (ISCO)-88) in a nationwide register-based Danish Occupational Cohort. We modelled the probability of specified lifestyles in mixed models for each level of the four-digit DISCO code with age and sex as fixed effects and assessed variation in terms of intraclass correlation coefficients (ICCs) and exposure-level percentile ratios across jobs for six different time periods from 1981 through 2013. Results The ICCs were overall low (0.26%–7.05%) as the within-job group variation was large relative to the between job group variation, but across jobs the calendar period-specific ratios between highest and lowest predicted levels were ranging from 1.2 to 6.9, and for the 95%/1% and the 75%/5% percentile ratios ranges were 1.1–2.8 and 1.1–1.6, respectively, thus indicating substantial contrast for some lifestyle exposures and some occupations. Conclusions The lifestyle JEMs may prove a useful tool for control of lifestyle-related confounding in register-based occupational health studies where lacking information on individual lifestyle factors may compromise internal validity.
Cancerous esophageal stenosis before treatment was significantly correlated to poor prognosis of patients with esophageal cancer: a meta-analysis.

Background Cancerous esophageal stenosis encountered during endoscopic ultrasonography before treatment was observed in about 30% of esophageal cancer patients. Since the pT stage in TNM classification measures only the depth of infiltration but not the growth to esophagus, it is interesting to know whether tumor growth into the esophagus (and eventual stenosis) is of added value in prognostic assessment. However, the impact of esophageal stenosis on survival of esophageal cancer patient remains unclear. Therefore, we conducted a meta-analysis focusing on current topic for the first time. Methods A systematic literature search in PubMed and EMBASE was conducted to identify relevant studies up to 14 March 2018. Data of 5-year overall survival (OS) was extracted and analysed. Results A total of five cohort studies consisting of 1,282 patients (278 patients with cancerous esophageal stenosis before treatment and 1,004 patients without) with esophageal cancer treated with surgery, chemoradiotherapy, or palliative therapy were included for analysis. Meta-analysis showed that patients with esophageal stenosis had significantly lower 5-year OS [22.3% and 33.0%, respectively; risk ratio (RR) =1.21; 95% CI, 1.11-1.32; P<0.001; I2=27.1%] than those without. No heterogeneity or publication bias was observed during analysis. Conclusions Patients with cancerous esophageal stenosis identified by endoscopy before treatment had significantly poorer survival than those without. High-quality studies with appropriate adjustments for confounding factor are needed to confirm the findings.
In vivo detection of optically-evoked opioid peptide release

Though the last decade has seen accelerated advances in techniques and technologies to perturb neuronal circuitry in the brain, we are still poorly equipped to adequately dissect endogenous peptide release in vivo. To this end we developed a system that combines in vivo optogenetics with microdialysis and a highly sensitive mass spectrometry-based assay to measure opioid peptide release in freely moving rodents.
Guiding ultrasonic waves across blood vessels to support intra-body networking

Intra Body Area Networks (IBANs) will allow revolutionary applications in the field of smart and connected healthcare systems. So far, research has focused on networking between implanted devices via conventional ionizing RF waves which have not yet been proved to be safe for human health. In the last years, novel networking approaches employing ultrasonic waves have been proposed showing that, in principle, the approach is viable. However, the major limitation of the proposed solutions is related to the limited transmission range. Starting from this, we propose to use blood vessels as a waveguided ultrasonic transmission medium and demonstrate its effectiveness in making IBANs more performing in terms of communication distance, as compared to diffusive ultrasonic communications across human tissues.
Screening for Lactobacillus plantarum Strains That Possess Organophosphorus Pesticide-Degrading Activity and Metabolomic Analysis of Phorate Degradation

This work performed a large scale assessment for organophosphorus pesticides (OPPs) degradation activity of 121 Lactobacillus (L.) plantarum strains. Six L. plantarum strains (P9, IMAU80110, IMAU40100, IMAU10585, IMAU10209, and IMAU80070) were found to possess high capacity of degrading three commonly used OPPs, namely dimethoate, phorate, and omethoate; and they were selected for more detailed characterization. Moreover, the three OPPs were mainly detected in the culture supernatants but not in the cell extracts, further confirming that the OPPs were degraded rather than absorbed by the cells. Among the six selected strains, P9 was most tolerant to gastrointestinal juices and bile. We thus used ultra-high performance liquid chromatography electron spray ionization coupled with time-of-flight mass spectrometry (UPLC/ESI-Q-TOF/MS) to generate the metabolomic profiles of the strain P9 growing in MRS medium with and without containing phorate. By using orthogonal partial least squares discriminant analysis, we identified some potential phorate-derived degradative products. This work has identified novel lactic acid bacteria resources for application in pesticide degradation. Our results also shed light on the phorate degradation mechanism by L. plantarum P9.
Mitochondrial-targeted penetrating peptide delivery for cancer therapy

ABSTRACT Introduction: Mitochondria are promising targeting organelles for anticancer strategies; however, mitochondria are difficult for antineoplastic drugs to recognize and bind. Mitochondria-penetrating peptides (MPPs) are unique tools to gain access to the cell interior and deliver a bioactive cargo into mitochondria. MPPs have combined or delivered a variety of antitumor cargoes and obviously inhibited the tumor growth in vivo and in vitro. MPPs create new opportunities to develop new treatments for cancer. Areas covered: We review the target sites of mitochondria and the target-penetration mechanism of MPPs, different strategies, and various additional strategies decorated MPPs for tumor cell mitochondria targeting, the decorating mattes including metabolism molecules, RNA, DNA, and protein, which exploited considered as therapeutic combined with MPPs and target in human cancer treatment. Expert opinion/commentary: Therapeutic selectivity that preferentially targets the mitochondrial abnormalities in cancer cells without toxic impact on normal cells still need to be deepen. Moreover, it needs appropriate study designs for a correct evaluation of the target delivery outcome and the degradation rate of the drug in the cell. Generally, it is optimistic that the advances in mitochondrial targeting drug delivery by MPPs plasticity outlined here will ultimately help to the discovery of new approaches for the prevention and treatment of cancers.
Molecular dynamics simulations investigate the pathway of substrate entry active site of rhomboid protease

Abstract Rhomboid proteases can catalyze peptide bond cleavage and participate in abundant biological processes encompassing all branches of life; however, the pathway for substrate entry into its active site remains ambiguous. Here, the two possible pathways are preliminarily determined through molecular dynamics: One pathway is between Tm2 and Tm5, and the other is between Loop3 and Loop5. Then, the umbrella sampling simulations are performed to investigate the more feasible pathway for substrate entry. The results show that free energy barriers along the two pathways are similar; in the pathway 1, Trp236 and Trp157 as pivotal residues are responsible for the rotation of substrate in the binding process; in the pathway 2, among some important residues, the residue His150 plays an important role in substrate entry. Further, combining with previous experiment results, it is concluded that the substrate is inclined to enter into the active site along pathway 2. Our results are important for further understanding the function and catalysis mechanism of rhomboid proteases. Communicated by Ramaswamy H. Sarma
Bioinspired Straight Walking Task-Space Planner

Although the attention on bipedal locomotion has increased over the last decades, robots are still far behind compared to human locomotor abilities. Their performance limitations can be partially attributed to the hardware, but the primary constrain has been the poor understanding of bipedal dynamics. Based on the recently developed model of potential energy for bipedal structures, this work proposes a task-space planner for human-like straight locomotion. The proposed architecture is based on potential energy model and employs locomotor strategies obtained from human data as a reference for optimal behaviour. The model generates CoM trajectory, foot swing trajectory and the base of support from the knowledge of the desired speed, initial posture, height, weight, number of steps and the angle between the foot and the ground during heel-strike. The data show that the proposed architecture can generate behaviour in line with human walking strategies for both the CoM and the foot swing. Although the planned trajectory is not smooth compared to human trajectories, the proposed model significantly reduces the error in the estimation of the CoM vertical trajectory. Moreover, being the planner able to generate a single stride in less than 140 ms and sequences of 10 strides in less than 600 ms, it allows an online task-space planning for locomotion. Lastly, the proposed architecture is also supported by analogies with current theories on human motor control of locomotion.
Learning Data-adaptive Nonparametric Kernels

Traditional kernels or their combinations are often not sufficiently flexible to fit the data in complicated practical tasks. In this paper, we present a Data-Adaptive Nonparametric Kernel (DANK) learning framework by imposing an adaptive matrix on the kernel/Gram matrix in an entry-wise strategy. Since we do not specify the formulation of the adaptive matrix, each entry in it can be directly and flexibly learned from the data. Therefore, the solution space of the learned kernel is largely expanded, which makes DANK flexible to adapt to the data. Specifically, the proposed kernel learning framework can be seamlessly embedded to support vector machines (SVM) and support vector regression (SVR), which has the capability of enlarging the margin between classes and reducing the model generalization error. Theoretically, we demonstrate that the objective function of our devised model is gradient-Lipschitz continuous. Thereby, the training process for kernel and parameter learning in SVM/SVR can be efficiently optimized in a unified framework. Further, to address the scalability issue in DANK, a decomposition-based scalable approach is developed, of which the effectiveness is demonstrated by both empirical studies and theoretical guarantees. Experimentally, our method outperforms other representative kernel learning based algorithms on various classification and regression benchmark datasets.
Factors influencing developmental delay among young children in poor rural China: a latent variable approach

Objectives The aims of the study were to determine the prevalence of suspected developmental delay in children living in poor areas of rural China and to investigate factors influencing child developmental delay. Design A community-based, cross-sectional survey was conducted. Eighty-three villages in Shanxi and Guizhou Provinces, China. Participants A total of 2514 children aged 6–35 months and their primary caregivers. Outcome measures Suspected child developmental delay was evaluated using the Ages & Stages Questionnaires-Chinese version. Caregivers’ education and age, wealth index, child feeding index, parent-child interaction, number of books and Zung Self-Rating Depression Scale were reported by the primary caregivers. Haemoglobin levels were measured using a calibrated, automated analyser. Birth weight was obtained from medical records. Results Overall, 35.7% of the surveyed children aged 6–35 months demonstrated suspected developmental delay. The prevalence of suspected developmental delay was inversely associated with age, with the prevalence among young children aged 6–11 months being almost double that of children aged 30–35 months (48.0% and 22.8%, respectively). Using a structural equation model, it was demonstrated that caregiver’s care and stimulus factors and child’s haemoglobin level were directly correlated, while caregiver’s sociodemographic factors were indirectly associated with suspected developmental delay. Conclusions The prevalence of suspected developmental delay is high in poor rural areas of China, and appropriate interventions to improve child development are needed.
Maximum Entropy Principle Analysis in Network Systems with Short-time Recordings

In many realistic systems, maximum entropy principle (MEP) analysis provides an effective characterization of the probability distribution of network states. However, to implement the MEP analysis, a sufficiently long-time data recording in general is often required, e.g., hours of spiking recordings of neurons in neuronal networks. The issue of whether the MEP analysis can be successfully applied to network systems with data from short-time recordings has yet to be fully addressed. In this work, we investigate relationships underlying the probability distributions, moments, and effective interactions in the MEP analysis and then show that, with short-time recordings of network dynamics, the MEP analysis can be applied to reconstructing probability distributions of network states that is much more accurate than the one directly measured from the short-time recording. Using spike trains obtained from both Hodgkin-Huxley neuronal networks and electrophysiological experiments, we verify our results and demonstrate that MEP analysis provides a tool to investigate the neuronal population coding properties for short-time recordings.