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Background: It was shown that single repetition, contraction-phase specific and total time-under-tension (TUT) can be extracted reliably and validly from smartphone accelerometer-derived data of resistance exercise machines using user-determined resistance exercise velocities at 60% one repetition maximum (1-RM). However, it remained unclear how robust the extraction of these mechano-biological descriptors is over a wide range of movement velocities (slow- versus fast-movement velocity) and intensities (30% 1-RM versus 80% 1-RM) that reflect the interindividual variability during resistance exercise., Objective: In this work, we examined whether the manipulation of velocity or intensity would disrupt an algorithmic extraction of single repetitions, contraction-phase specific and total TUT., Methods: Twenty-seven participants performed four sets of three repetitions of their 30% and 80% 1-RM with velocities of 1 s, 2 s, 6 s and 8 s per repetition, respectively. An algorithm extracted the number of repetitions, single repetition, contraction-phase specific and total TUT. All exercises were video-recorded. The video recordings served as the gold standard to which algorithmically-derived TUT was compared. The agreement between the methods was examined using Limits of Agreement (LoA). The Pearson correlation coefficients were used to calculate the association, and the intraclass correlation coefficient (ICC 2.1) examined the interrater reliability., Results: The calculated error rate for the algorithmic detection of the number of single repetitions derived from two smartphones accelerometers was 1.9%. The comparison between algorithmically-derived, contraction-phase specific TUT against video, revealed a high degree of correlation (r > 0.94) for both exercise machines. The agreement between the two methods was high on both exercise machines, intensities and velocities and was as follows: LoA ranged from -0.21 to 0.22 seconds for single repetition TUT (2.57% of mean TUT), from -0.24 to 0.22 seconds for concentric contraction TUT (6.25% of mean TUT), from -0.22 to 0.24 seconds for eccentric contraction TUT (5.52% of mean TUT) and from -1.97 to 1.00 seconds for total TUT (5.13% of mean TUT). Interrater reliability for single repetition, contraction-phase specific TUT was high (ICC > 0.99)., Conclusion: Neither intensity nor velocity disrupts the proposed algorithmic data extraction approach. Therefore, smartphone accelerometers can be used to extract scientific mechano-biological descriptors of dynamic resistance exercise with intensities ranging from 30% to 80% of the 1-RM with velocities ranging from 1 s to 8 s per repetition, respectively, thus making this simple method a reliable tool for resistance exercise mechano-biological descriptors extraction., Competing Interests: Kieser Training AG provided support in the form of salaries for author DA, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. ieffects AG provided support in the form of salaries for author BA, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Advances in machine learning and deep learning methods, together with the increasing availability of large-scale pharmacological, genomic, and chemical datasets, have created opportunities for identifying potentially useful relationships within biochemical networks. Knowledge embedding models have been found to have value in detecting knowledge-based correlations among entities, but little effort has been made to apply them to networks of biochemical entities. This is because such networks tend to be unbalanced and sparse, and knowledge embedding models do not work well on them. However, to some extent, the shortcomings of knowledge embedding models can be compensated for if they are used in association with graph embedding. In this paper, we combine knowledge embedding and graph embedding to represent biochemical entities and their relations as dense and low-dimensional vectors. We build a cascade learning framework which incorporates semantic features from the knowledge embedding model, and graph features from the graph embedding model, to score the probability of linking. The proposed method performs noticeably better than the models with which it is compared. It predicted links and entities with an accuracy of 93%, and its average hits@10 score has an average of 8.6% absolute improvement compared with original knowledge embedding model, 1.1% to 9.7% absolute improvement compared with other knowledge and graph embedding algorithm. In addition, we designed a meta-path algorithm to detect path relations in the biomedical network. Case studies further verify the value of the proposed model in finding potential relationships between diseases, drugs, genes, treatments, etc. Amongst the findings of the proposed model are the suggestion that VDR (vitamin D receptor) may be linked to prostate cancer. This is backed by evidence from medical databases and published research, supporting the suggestion that our proposed model could be of value to biomedical researchers., Competing Interests: The authors have declared that no competing interests exist.

The global population growth of the bed bug, Cimex lectularius (L.), is attributed to their cryptic behavior, diverse insecticide resistance mechanisms, and lack of public awareness. Bed bug control can be challenging and typically requires chemical and non-chemical treatments. One common non-chemical method for bed bug management is thermal remediation. However, in certain instances, bed bugs are known to survive heat treatments. Bed bugs may be present after a heat treatment due to (i) abiotic factors associated with the inability to achieve lethal temperatures in harborage areas for a sufficient time period, (ii) re-infestation from insects that escaped to cooler areas during a heat treatment or (iii) development of physiological resistance that allows them to survive heat exposure. Previous research has investigated the optimal temperature and exposure time required for either achieving complete mortality or sublethally affecting their growth and development. However, no research has examined bed bug populations for their ability to develop resistance to heat exposure and variation in thermo-tolerance between different bed bug strains. The goals of this study were: i) to determine if bed bugs could be selected for heat resistance under a laboratory selection regime, and ii) to determine if bed bug populations with various heat exposure histories, insecticide resistance profiles, and geographic origins have differential temperature tolerances using two heat exposure techniques (step-function and ramp-function). Selection experiments found an initial increase in bed bug survivorship; however, survivorship did not increase past the fourth generation. Sublethal exposure to heat significantly reduced bed bug feeding and, in some cases, inhibited development. The step-function exposure technique revealed non-significant variation in heat tolerance between populations and the ramp-function exposure technique provided similar results. Based on these study outcomes, the ability of bed bugs to develop heat resistance appears to be limited., Competing Interests: The authors have declared that no competing interests exist. Merit-based student scholarship from the commercial funder J. T. Eaton and Company does not alter the authors’ adherence to PLOS ONE policies on sharing data and materials.

Three stereoisomeric inhibitors of Pin1: (2R,5S)-, (2S,5R)- and (2S,5S)-Ac-pSer-Ψ[(Z)CH = C]-pipecolyl(Pip)-2-(2-naphthyl)ethylamine 1, that mimic L-pSer-D-Pro, D-pSer-L-Pro, and D-pSer-D-Pro amides respectively, were synthesized by a 13-step route. The newly formed stereogenic centers in the pipecolyl ring were introduced by Luche reduction, followed by stereospecific [2,3]-Still-Wittig rearrangement. The (Z)- to (E)-alkene ratio in the rearrangements were consistently 5.5 to 1. The stereochemistry at the original Ser α-carbon controlled the stereochemistry of the Luche reduction, but it did not affect the stereochemical outcome of the rearrangement, which consistently gave the (Z)-alkene. The epimerized by-product, (2S,5S)-10, resulting from the work-up after Na/NH3 debenzylation of (2S,5R)-9, was carried on to the (2S,5S)-1 isomer. Compound (2S,5S)-10 was resynthesized from the Luche reduction by-product, (2R,3R)-3, and the stereochemistry was confirmed by comparison of the optical rotations. The IC50 values for (2R,5S)-1, (2S,5R)-1 and (2S,5S)-1 Pin1 inhibition were: 52, 85, and 140 μM, respectively.

No non-invasive test exists for forearm exercise that allows identification of power-time relationship parameters (W', critical power) and thereby identification of the heavy-severe exercise intensity boundary and scaling of aerobic metabolic exercise intensity. The aim of this study was to develop a maximal effort handgrip exercise test to estimate forearm critical force (fCF; force analog of power) and establish its repeatability and validity. Ten healthy males (20-43 years) completed two maximal effort rhythmic handgrip exercise tests (repeated maximal voluntary contractions (MVC); 1 s contraction-2 s relaxation for 600 s) on separate days. Exercise intensity was quantified via peak contraction force and contraction impulse. There was no systematic difference between test 1 and 2 for fCF(peak) force (p = 0.11) or fCF(impulse) (p = 0.76). Typical error was small for both fCF(peak force) (15.3 N, 5.5%) and fCF(impulse) (15.7 N ⋅ s, 6.8%), and test re-test correlations were strong (fCF(peak force), r = 0.91, ICC = 0.94, p<0.01; fCF(impulse), r = 0.92, ICC = 0.95, p<0.01). Seven of ten subjects also completed time-to-exhaustion tests (TTE) at target contraction force equal to 10%fCF(peak force). TTE predicted by W' showed good agreement with actual TTE during the TTE tests (r = 0.97, ICC = 0.97, P<0.01; typical error 0.98 min, 12%; regression fit slope = 0.99 and y intercept not different from 0, p = 0.31). MVC did not predict fCF(peak force) (p = 0.37), fCF(impulse) (p = 0.49) or W' (p = 0.15). In conclusion, the poor relationship between MVC and fCF or W' illustrates the serious limitation of MVC in identifying metabolism-based exercise intensity zones. The maximal effort handgrip exercise test provides repeatable and valid estimates of fCF and should be used to normalize forearm aerobic metabolic exercise intensity instead of MVC.