Increased force and elastic energy storage are not the mechanisms that improve jump performance with accentuated eccentric loading during a constrained vertical jump.

Accentuated eccentric loading (AEL) involves higher load applied during the eccentric phase of a stretch-shortening cycle movement, followed by a sudden removal of load before the concentric phase. Previous studies suggest that AEL enhances human countermovement jump performance, however the mechanism is not fully understood. Here we explore whether isolating additional load during the countermovement is sufficient to increase ground reaction force, and hence elastic energy stored, at the start of the upward movement and whether this leads to increased jump height or power generation. We conducted a trunk-constrained vertical jump test on a custom-built device to isolate the effect of additional load while controlling for effects of squat depth, arm swing, and coordination. Twelve healthy, recreationally active adults (7 males, 5 females) performed maximal jumps without AEL, followed by randomised AEL conditions prescribed as a percentage of body mass (10%, 20%, and 30%), before repeating jumps without AEL. No significant changes in vertical ground reaction force at the turning point were observed. High load AEL conditions (20% and 30% body weight) led to slight reductions in jump height, primarily due to decreased hip joint and centre of mass work. AEL conditions did not alter peak or integrated activation levels of the knee extensor muscles. The constrained movement task used here, which excluded potential contributions of trunk motion, arm swing, rate of descent, squat depth, and point of load application, allows the conclusion that increased elastic energy return is not the primary mechanism for potentiating effects of AEL on jump performance.

Benchmarking a Molecular Flake Model on the Road to Programmable Graphene-Based Single-Atom Catalysts.

Single-atom catalysts (SACs) of embedding an active metal in nitrogen-doped graphene are emergent catalytic materials in various applications. The rational design of efficient SACs necessitates an electronic and mechanistic understanding of those materials with reliable quantum mechanical simulations. Conventional computational methods of modeling SACs involve using an infinite slab model with periodic boundary condition, limiting to the selection of generalized gradient approximations as the exchange correlation (XC) functional within density functional theory (DFT). However, these DFT approximations suffer from electron self-interaction error and delocalization error, leading to errors in predicted charge-transfer energetics. An alternative strategy is using a molecular flake model, which carved out the important catalytic center by cleaving C-C bonds and employing a hydrogen capping scheme to saturate the innocent dangling bonds at the molecular boundary. By doing so, we can afford more accurate hybrid XC functionals, or even high-level correlated wavefunction theory, to study those materials. In this work, we compared the structural, electronic, and catalytic properties of SACs simulated using molecular flake models and periodic slab models with first-row transition metals as the active sites. Molecular flake models successfully reproduced structural properties, including both global distortion and local metal-coordination environment, as well as electronic properties, including spin magnetic moments and metal partial charges, for all transition metals studied. In addition, we calculated CO binding strength as a descriptor for electrochemical CO2 reduction reactivity and noted qualitatively similar trends between two models. Using the computationally efficient molecular flake models, we investigated the effect of tuning Hartree-Fock exchange in a global hybrid functional on the CO binding strength and observed system-dependent sensitivities. Overall, our calculations provide valuable insights into the development of accurate and efficient computational tools to simulate SACs.

Musculoskeletal simulations to examine the effects of accentuated eccentric loading (AEL) on jump height.

Background: During counter movement jumps, adding weight in the eccentric phase and then suddenly releasing this weight during the concentric phase, known as accentuated eccentric loading (AEL), has been suggested to immediately improve jumping performance. The level of evidence for the positive effects of AEL remains weak, with conflicting evidence over the effectiveness in enhancing performance. Therefore, we proposed to theoretically explore the influence of implementing AEL during constrained vertical jumping using computer modelling and simulation and examined whether the proposed mechanism of enhanced power, increased elastic energy storage and return, could enhance work and power. Methods: We used a simplified model, consisting of a ball-shaped body (head, arm, and trunk), two lower limb segments (thigh and shank), and four muscles, to simulate the mechanisms of AEL. We adjusted the key activation parameters of the muscles to influence the performance outcome of the model. Numerical optimization was applied to search the optimal solution for the model. We implemented AEL and non-AEL conditions in the model to compare the simulated data between conditions. Results: Our model predicted that the optimal jumping performance was achieved when the model utilized the whole joint range. However, there was no difference in jumping performance in AEL and non-AEL conditions because the model began its push-off at the similar state (posture, fiber length, fiber velocity, fiber force, tendon length, and the same activation level). Therefore, the optimal solution predicted by the model was primarily driven by intrinsic muscle dynamics (force-length-velocity relationship), and this coupled with the similar model state at the start of the push-off, resulting in similar push-off performance across all conditions. There was also no evidence of additional tendon-loading effect in AEL conditions compared to non-AEL condition. Discussion: Our simplified simulations did not show improved jump performance with AEL, contrasting with experimental studies. The reduced model demonstrates that increased energy storage from the additional mass alone is not sufficient to induce increased performance and that other factors like differences in activation strategies or movement paths are more likely to contribute to enhanced performance.

Disparities in Biomarker Testing and Clinical Trial Enrollment Among Patients With Lung, Breast, or Colorectal Cancers in the United States.

PURPOSE: Comprehensive tumor biomarker testing is a fundamental step in the selection of highly effective molecularly driven therapies for a variety of solid tumors. The primary objective of this study was to examine racial differences in biomarker testing and clinical trial participation in the United States using a real-world database. METHODS: Patients in a real-world deidentified database diagnosed with advanced/metastatic non-small-cell lung cancer (NSCLC), metastatic colorectal cancer (CRC), or metastatic breast cancer were eligible. Biomarker testing and clinical trial participation was compared between Black and White racial groups using chi-squared test and stepwise logistic regression controlling for baseline covariates. RESULTS: A total of 23,488 patients met eligibility criteria. Next-generation sequencing (NGS) testing rates differed significantly between White versus Black race before first-line therapy (36.6% v 29.7%, P < .0001) and at any given time (54.7% v 43.8%, P < .0001) in the nonsquamous NSCLC cohort. Similar disparities in NGS testing rates at any time during the study were observed among patients with CRC (White 51.6%; Black 41.8%, P < .0001). No differences were observed in the breast cancer cohort. Patients of Black race were less likely to be treated in a clinical trial in the overall NSCLC cohort when compared with White counterparts (3.9% v 2.1%, P = .0002). A statistically significant relationship between biomarker/NGS testing and clinical trial enrollment was observed in all cohorts (P < .003) after adjusting for covariates. CONCLUSION: In a real-world database, significant disparities in NGS-based testing rates were observed between Black and White races in NSCLC and CRC. NGS and any biomarker testing were both associated with trial enrollment in all cohorts. There is a need for interventions to promote access to comprehensive testing for patients with advanced/metastatic tumors.

A Human-Centered Machine-Learning Approach for Muscle-Tendon Junction Tracking in Ultrasound Images.

Biomechanical and clinical gait research observes muscles and tendons in limbs to study their functions and behaviour. Therefore, movements of distinct anatomical landmarks, such as muscle-tendon junctions, are frequently measured. We propose a reliable and time efficient machine-learning approach to track these junctions in ultrasound videos and support clinical biomechanists in gait analysis. In order to facilitate this process, a method based on deep-learning was introduced. We gathered an extensive dataset, covering 3 functional movements, 2 muscles, collected on 123 healthy and 38 impaired subjects with 3 different ultrasound systems, and providing a total of 66864 annotated ultrasound images in our network training. Furthermore, we used data collected across independent laboratories and curated by researchers with varying levels of experience. For the evaluation of our method a diverse test-set was selected that is independently verified by four specialists. We show that our model achieves similar performance scores to the four human specialists in identifying the muscle-tendon junction position. Our method provides time-efficient tracking of muscle-tendon junctions, with prediction times of up to 0.078 seconds per frame (approx. 100 times faster than manual labeling). All our codes, trained models and test-set were made publicly available and our model is provided as a free-to-use online service on https://deepmtj.org/.

Evolutionary computation for discovery of composite transcription factor binding sites.

Previous research demonstrated the use of evolutionary computation for the discovery of transcription factor binding sites (TFBS) in promoter regions upstream of coexpressed genes. However, it remained unclear whether or not composite TFBS elements, commonly found in higher organisms where two or more TFBSs form functional complexes, could also be identified by using this approach. Here, we present an important refinement of our previous algorithm and test the identification of composite elements using NFAT/AP-1 as an example. We demonstrate that by using appropriate existing parameters such as window size, novel-scoring methods such as central bonusing and methods of self-adaptation to automatically adjust the variation operators during the evolutionary search, TFBSs of different sizes and complexity can be identified as top solutions. Some of these solutions have known experimental relationships with NFAT/AP-1. We also indicate that even after properly tuning the model parameters, the choice of the appropriate window size has a significant effect on algorithm performance. We believe that this improved algorithm will greatly augment TFBS discovery.

Drug Repositioning by Mining Adverse Event Data in ClinicalTrials.gov.

The protocol below describes an in silico method for drug repositioning (drug repurposing). The data source is ClinicalTrials.gov , which contains about a quarter of a million clinical studies. Mining such rich and clean clinical summary data could be helpful to many health-related researches. Described here is a method that utilizes serious adverse event data to identify potential new uses of drugs and dietary supplements (repositioning).

Catalytic synthesis of functionalized (polar and non-polar) polyolefin block copolymers.

Herein, we report a methodology for the synthesis of polyolefin containing block-copolymers using a catalytic postpolymerization modification strategy. The most common polyolefin grades are converted into macroinitiators using a cross-metathesis reaction. These functionalized polyolefins are then used to initiate living: coordinative ring opening polymerization of lactide, anionic ring opening polymerization of epoxide, and radical polymerization of styrene to yield the corresponding block copolymers. The high activity of the catalysts employed in the different steps offers improved practicality for scalable synthesis.

Analysis of pathway activity in primary tumors and NCI60 cell lines using gene expression profiling data.

To determine cancer pathway activities in nine types of primary tumors and NCI60 cell lines, we applied an in silica approach by examining gene signatures reflective of consequent pathway activation using gene expression data. Supervised learning approaches predicted that the Ras pathway is active in approximately 70% of lung adenocarcinomas but inactive in most squamous cell carcinomas, pulmonary carcinoids, and small cell lung carcinomas. In contrast, the TGF-beta, TNF-alpha, Src, Myc, E2F3, and beta-catenin pathways are inactive in lung adenocarcinomas. We predicted an active Ras, Myc, Src, and/or E2F3 pathway in significant percentages of breast cancer, colorectal carcinoma, and gliomas. Our results also suggest that Ras may be the most prevailing oncogenic pathway. Additionally, many NCI60 cell lines exhibited a gene signature indicative of an active Ras, Myc, and/or Src, but not E2F3, beta-catenin, TNF-alpha, or TGF-beta pathway. To our knowledge, this is the first comprehensive survey of cancer pathway activities in nine major tumor types and the most widely used NCI60 cell lines. The "gene expression pathway signatures" we have defined could facilitate the understanding of molecular mechanisms in cancer development and provide guidance to the selection of appropriate cell lines for cancer research and pharmaceutical compound screening.

Systematic drug repositioning through mining adverse event data in ClinicalTrials.gov.

Drug repositioning (i.e., drug repurposing) is the process of discovering new uses for marketed drugs. Historically, such discoveries were serendipitous. However, the rapid growth in electronic clinical data and text mining tools makes it feasible to systematically identify drugs with the potential to be repurposed. Described here is a novel method of drug repositioning by mining ClinicalTrials.gov. The text mining tools I2E (Linguamatics) and PolyAnalyst (Megaputer) were utilized. An I2E query extracts "Serious Adverse Events" (SAE) data from randomized trials in ClinicalTrials.gov. Through a statistical algorithm, a PolyAnalyst workflow ranks the drugs where the treatment arm has fewer predefined SAEs than the control arm, indicating that potentially the drug is reducing the level of SAE. Hypotheses could then be generated for the new use of these drugs based on the predefined SAE that is indicative of disease (for example, cancer).

The Value of Implementing Multidisciplinary Perinatal Care Conference in the Private Practice Setting.

Objective The objective of this study was to estimate the impact of multidisciplinary (Multi-D) perinatal care conference (PCC) implementation in the private practice setting. Methods After the initial 12-month period following implementation of the monthly PCC by private maternal-fetal medicine and neonatology practitioners, conference attendees were asked to completed a modified version of the Attitudes Toward Health Care Teams Scale, involving 19 questions assessing their attitudes and opinions toward Multi-D team care on a five-point Likert's scale. Results Of the 51 average attendees to the PCC, 82.3% completed the survey. A majority of respondents agreed that Multi-D team care resulted in improved care for patients and family, was not overly complex to coordinate, and resulted in significant job satisfaction and improved medical knowledge. Conclusion Multi-D care is an effective approach to the complicated needs of maternal-fetal medicine patients which may lead to improved patient and family outcomes, high provider satisfaction, and can easily be implemented and utilized within a private practice or community hospital setting.

Comparative analysis and integrative classification of NCI60 cell lines and primary tumors using gene expression profiling data.

BACKGROUND: NCI60 cell lines are derived from cancers of 9 tissue origins and have been invaluable in vitro models for cancer research and anti-cancer drug screen. Although extensive studies have been carried out to assess the molecular features of NCI60 cell lines related to cancer and their sensitivities to more than 100,000 chemical compounds, it remains unclear if and how well these cell lines represent or model their tumor tissues of origin. Identification and confirmation of correct origins of NCI60 cell lines are critical to their usage as model systems and to translate in vitro studies into clinical potentials. Here we report a direct comparison between NCI60 cell lines and primary tumors by analyzing global gene expression profiles. RESULTS: Comparative analysis suggested that 51 of 59 cell lines we analyzed represent their presumed tumors of origin. Taking advantage of available clinical information of primary tumor samples used to generate gene expression profiling data, we further classified those cell lines with the correct origins into different subtypes of cancer or different stages in cancer development. For example, 6 of 7 non-small cell lung cancer cell lines were classified as lung adenocarcinomas and all of them were classified into late stages in tumor progression. CONCLUSION: Taken together, we developed and applied a novel approach for systematic comparative analysis and integrative classification of NCI60 cell lines and primary tumors. Our results could provide guidance to the selection of appropriate cell lines for cancer research and pharmaceutical compound screenings. Moreover, this gene expression profile based approach can be generally applied to evaluate experimental model systems such as cell lines and animal models for human diseases.

Identification and expression of novel isoforms of human stromal cell-derived factor 1.

Stromal Cell-derived factor 1 (SDF-1) is a CXC chemokine that binds to the CXCR4 receptor. Recent publication indicates that the SDF-1/CXCR4 signaling pathway plays a pivotal role during development and in many patho-physiological conditions including hematopoiesis, blood vessel formation, cancer metastasis, angiogenesis and HIV infection. Two human SDF-1 isoforms, SDF-1alpha and SDF-1beta, have been reported to date. Here we report the identification of four additional human SDF-1 isoforms derived from alternative splicing events, SDF-1gamma, SDF-1delta, SDF-1epsilon and SDF-1phi. These SDF-1 splice variants all share the same first three exons but contain different fourth exons. The human SDF-1 gene spans over 88 kilobase-pairs on chromosome 10. Using the semi-quantitative RT-PCR method, we determined the tissue distribution of these SDF-1 isoforms. SDF-1alpha and SDF-1beta share similar expression patterns and the highest expression were detected in liver, pancreas and spleen. SDF-1gamma seems to be the human orthologue of recently isolated rat SDF-1gamma, and its expression was only detected in the heart. SDF-1delta expression can be detected in several adult tissues but the highest expression was detected in fetal liver. When transfected into HEK293 cells, all the SDF-1 isoforms can be detected as secreted proteins in the cell culture media. The conditioned media from transfected cells can stimulate cell migration in a CXCR4-dependent manner. These data suggest that the novel SDF-1 splice variants encode functional proteins.

A flexible integration and visualisation system for biomarker discovery.

Biological data have accumulated at an unprecedented pace as a result of improvements in molecular technologies. However, the translation of data into information, and subsequently into knowledge, requires the intricate interplay of data access, visualisation and interpretation. Biological data are complex and are organised either hierarchically or non-hierarchically. For non-hierarchically organised data, it is difficult to view relationships among biological facts. In addition, it is difficult to make changes in underlying data storage without affecting the visualisation interface. Here, we demonstrate a platform where non-hierarchically organised data can be visualised through the application of a customised hierarchy incorporating medical subject headings (MeSH) classifications. This platform gives users flexibility in updating and manipulation. It can also facilitate fresh scientific insight by highlighting biological impacts across different hierarchical branches. An example of the integration of biomarker information from the curated Proteome database using MeSH and the StarTree visualisation tool is presented.

Cloning and functional characterization of the rabbit C-C chemokine receptor 2.

BACKGROUND: CC-family chemokine receptor 2 (CCR2) is implicated in the trafficking of blood-borne monocytes to sites of inflammation and is implicated in the pathogenesis of several inflammatory diseases such as rheumatoid arthritis, multiple sclerosis and atherosclerosis. The major challenge in the development of small molecule chemokine receptor antagonists is the lack of cross-species activity to the receptor in the preclinical species. Rabbit models have been widely used to study the role of various inflammatory molecules in the development of inflammatory processes. Therefore, in this study, we report the cloning and characterization of rabbit CCR2. Data regarding the activity of the CCR2 antagonist will provide valuable tools to perform toxicology and efficacy studies in the rabbit model. RESULTS: Sequence alignment indicated that rabbit CCR2 shares 80 % identity to human CCR2b. Tissue distribution indicated that rabbit CCR2 is abundantly expressed in spleen and lung. Recombinant rabbit CCR2 expressed as stable transfectants in U-937 cells binds radiolabeled 125I-mouse JE (murine MCP-1) with a calculated Kd of 0.1 nM. In competition binding assays, binding of radiolabeled mouse JE to rabbit CCR2 is differentially competed by human MCP-1, -2, -3 and -4, but not by RANTES, MIP-1alpha or MIP-1beta. U-937/rabbit CCR2 stable transfectants undergo chemotaxis in response to both human MCP-1 and mouse JE with potencies comparable to those reported for human CCR2b. Finally, TAK-779, a dual CCR2/CCR5 antagonist effectively inhibits the binding of 125I-mouse JE (IC50 = 2.3 nM) to rabbit CCR2 and effectively blocks CCR2-mediated chemotaxis. CONCLUSION: In this study, we report the cloning of rabbit CCR2 and demonstrate that this receptor is a functional chemotactic receptor for MCP-1.

A statistical analysis of the TRANSFAC database.

Transcription factors are key regulatory elements that control gene expression. The TRANSFAC database represents the largest repository for experimentally derived transcription factor binding sites (TFBS). Understanding TFBS, which are typically conserved during evolution, helps us identify genomic regions related to human health and disease, and regions that might be predictive of patient outcomes. Here we present a statistical analysis of all TFBS in the TRANSFAC database. Our analysis suggests that current definition of TFBS core regions in TRANSFAC should be re-examined so as to capture a more precise notion of "cores." We offer insight into more appropriate definitions of TFBS consensus sequences and core regions. These revised definitions provide a better understanding of the nature of transcription factor-DNA binding and assist with developing algorithms for de novo TFBS discovery as well as finding novel variants of known TFBS.

Expression levels of protein kinase C-alpha in non-small-cell lung cancer.

Current treatments of non-small-cell lung cancer (NSCLC) are inadequate and new therapies are being developed that target specific cellular signaling proteins associated with tumor growth. One potential target is protein kinase C (PKC)-alpha, a signaling molecule with an important role in cell regulation and proliferation. The present study examines the expression levels of PKC-alpha in NSCLC to better understand the distribution of PKC-alpha in NSCLC. We analyzed tumor specimens from an independent tumor tissue bank to determine PKC-alpha protein and messenger RNA gene expression in NSCLC. In addition, we used publicly available gene expression array data to further understand PKC-a-associated gene expression profiles in NSCLC. We found that PKC-alpha is highly expressed in < or = 20% of patients with NSCLC. We also found that PKC-alpha was preferentially expressed in adenocarcinoma compared with squamous cell carcinoma of the lung.

Post hoc subgroup analysis of the HEART2D trial demonstrates lower cardiovascular risk in older patients targeting postprandial versus fasting/premeal glycemia.

OBJECTIVE: To identify the Hyperglycemia and Its Effect After Acute Myocardial Infarction on Cardiovascular Outcomes in Patients With Type 2 Diabetes Mellitus (HEART2D) trial subgroups with treatment difference. RESEARCH DESIGN AND METHODS: In 1,115 type 2 diabetic patients who had suffered from an acute myocardial infarction (AMI), the HEART2D trial compared two insulin strategies targeting postprandial or fasting/premeal glycemia on time until first cardiovascular event (cardiovascular death, nonfatal MI, nonfatal stroke, coronary revascularization, or hospitalization for acute coronary syndrome). The HEART2D trial ended prematurely for futility. We used the classification and regression tree (CART) to identify baseline subgroups with potential treatment differences. RESULTS: CART estimated the age of >65.7 years to best predict the difference in time to first event. In the subgroup aged>65.7 years (prandial, n=189; basal, n=210), prandial patients had a significantly longer time to first event and a lower proportion experienced a first event (n=56 [29.6%] vs. n=85 [40.5%]; hazard ratio 0.69 [95% CI 0.49-0.96]; P=0.029), despite similar A1C levels. CONCLUSIONS: Older type 2 diabetic AMI survivors may have a lower risk for a subsequent cardiovascular event with insulin targeting postprandial versus fasting/premeal glycemia.

Control protocol for robust in vitro glial scar formation around microwires: essential roles of bFGF and serum in gliosis.

Previously, we reported an in vitro cell culture model that recreates many of the hallmarks of glial scarring around electrodes used for recording in the brain; however, the model lacked the reproducibility necessary to establish a useful characterization tool. This methods paper describes a protocol, modeled on protocols typically used to culture neural stem/precursor cells, that generates a predictable positive control of an intense scarring reaction. Six independent cell culture variables (growth media, seeding density, bFGF addition day, serum concentration in treatment media, treatment day, and duration of culture) were varied systematically and the resulting scars were quantified. The following conditions were found to give the highest level of scarring: Neurobasal medium supplemented with B27, 10% fetal bovine serum at treatment, 10 ng/ml b-FGF addition at seeding and at treatment, treatment at least 6 days after seeding and scar growth of at least 5 days. Seeding density did not affect scarring as long as at least 500,000 cells were seeded per well, but appropriate media, bFGF, and serum were essential for significant scar formation-insights that help validate the in vitro-based approach to understanding glial scarring. With the control protocol developed in this study producing a strong, reproducible glial scarring positive control with every dissection, this culture model is suitable for the in vitro study of the mechanisms behind glial scarring and neuroelectrode failure.

Murine maternal cell microchimerism: analysis using real-time PCR and in vivo imaging.

In humans, maternal cells are present in the affected tissues of children with inflammatory myopathy, scleroderma, and neonatal lupus. It is unknown if maternal cell microchimerism (MCM) contributes to the pathology of disease. We sought to understand the factors that affect MCM to serve as a baseline for future mechanistic studies. Using a mouse model, we bred female mice transgenic for the luciferase (Luc) reporter gene to wild-type (WT) males. The WT offspring were sacrificed at various postnatal ages. DNA was extracted from multiple organs, and real-time PCR amplification was used to quantify Luc transgene as a marker for maternally derived cells. Sensitivity was one to two transgenic cells per 100,000 WT cells. MCM was noted in 85% of mice and 45% of tissues assayed. The average quantity of MCM was 158 maternal cells per 100,000 neonatal cells. The organs displaying the highest frequency and quantity of MCM were heart and lung (P < 0.001). Postnatal age up to 21 days did not appear to affect levels of MCM (P = 0.47), whereas increasing parity may increase levels of MCM. The data show that MCM is a common occurrence in healthy newborn mice, that it is present in their major organs, and that there are organ specific differences. This may represent differential migration of maternal cells or varying receptivity of specific fetal organs to microchimerism. Pregnancy history appears to play a role in maternal cell trafficking. The role of MCM in pregnancy and disease pathogenesis remains to be elucidated.