The Minimum Effective Training Dose Required for 1RM Strength in Powerlifters

The aim of this multi-experiment paper was to explore the concept of the minimum effective training dose (METD) required to increase 1-repetition-maximum (1RM) strength in powerlifting (PL) athletes. The METD refers to the least amount of training required to elicit meaningful increases in 1RM strength. A series of five studies utilising mixed methods, were conducted using PL athletes & coaches of all levels in an attempt to better understand the METD for 1RM strength. The studies of this multi-experiment paper are: an interview study with elite PL athletes and highly experienced PL coaches (n = 28), an interview and survey study with PL coaches and PL athletes of all levels (n = 137), two training intervention studies with intermediate-advanced PL athletes (n = 25) and a survey study with competitive PL athletes of different levels (n = 57). PL athletes looking to train with a METD approach can do so by performing ~3–6 working sets of 1–5 repetitions each week, with these sets spread across 1–3 sessions per week per powerlift, using loads above 80% 1RM at a Rate of Perceived Exertion (RPE) of 7.5–9.5 for 6–12 weeks and expect to gain strength. PL athletes who wish to further minimize their time spent training can perform autoregulated single repetition sets at an RPE of 9–9.5 though they should expect that strength gains will be less likely to be meaningful. However, the addition of 2–3 back-off sets at ~80% of the single repetitions load, may produce greater gains over 6 weeks while following a 2-3-1 squat-bench press-deadlift weekly training frequency. When utilizing accessory exercises in the context of METD, PL athletes typically utilize 1–3 accessory exercises per powerlift, at an RPE in the range of 7–9 and utilize a repetition range of ~6–10 repetitions.

Algorithmic extraction of smartphone accelerometer-derived mechano-biological descriptors of resistance exercise is robust to changes in intensity and velocity

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.

“Just One More Rep!” – Ability to Predict Proximity to Task Failure in Resistance Trained Persons

In resistance training, the use of predicting proximity to momentary task failure (MF, i.e., maximum effort), and repetitions in reserve scales specifically, is a growing approach to monitoring and controlling effort. However, its validity is reliant upon accuracy in the ability to predict MF which may be affected by congruence of the perception of effort compared with the actual effort required. The present study examined participants with at least 1 year of resistance training experience predicting their proximity to MF in two different experiments using a deception design. Within each experiment participants performed four trials of knee extensions with single sets (i.e., bouts of repetitions) to their self-determined repetition maximum (sdRM; when they predicted they could not complete the next repetition if attempted and thus would reach MF if they did) and MF (i.e., where despite attempting to do so they could not complete the current repetition). For the first experiment (n = 14) participants used loads equal to 70% of a one repetition maximum (1RM; i.e., the heaviest load that could be lifted for a single repetition) performed in a separate baseline session. Aiming to minimize participants between day variability in repetition performances, in the second separate experiment (n = 24) they used loads equal to 70% of their daily isometric maximum voluntary contraction (MVC). Results suggested that participants typically under predicted the number of repetitions they could perform to MF with a meta-analytic estimate across experiments of 2.0 [95%CIs 0.0 to 4.0]. Participants with at least 1 year of resistance training experience are likely not adequately accurate at gauging effort in submaximal conditions. This suggests that perceptions of effort during resistance training task performance may not be congruent with the actual effort required. This has implications for controlling, programming, and manipulating the actual effort in resistance training and potentially on the magnitude of desired adaptations such as improvements in muscular hypertrophy and strength.

Repetition of Computer Security Warnings Results in Differential Repetition Suppression Effects as Revealed With Functional MRI

Computer users are often the last line of defense in computer security. However, with repeated exposures to system messages and computer security warnings, neural and behavioral responses show evidence of habituation. Habituation has been demonstrated at a neural level as repetition suppression where responses are attenuated with subsequent repetitions. In the brain, repetition suppression to visual stimuli has been demonstrated in multiple cortical areas, including the occipital lobe and medial temporal lobe. Prior research into the repetition suppression effect has generally focused on a single repetition and has not examined the pattern of signal suppression with repeated exposures. We used complex, everyday stimuli, in the form of images of computer programs or security warning messages, to examine the repetition suppression effect across repeated exposures. The use of computer warnings as stimuli also allowed us to examine the activation of learned fearful stimuli. We observed widespread linear decreases in activation with repeated exposures, suggesting that repetition suppression continues after the first repetition. Further, we found greater activation for warning messages compared to neutral images in the anterior insula, pre-supplemental motor area, and inferior frontal gyrus, suggesting differential processing of security warning messages. However, the repetition suppression effect was similar in these regions for both warning messages and neutral images. Additionally, we observed an increase of activation in the default mode network with repeated exposures, suggestive of increased mind wandering with continuing habituation.

Improved cortical surface reconstruction using sub-millimeter resolution MPRAGE by image denoising

Automatic cerebral cortical surface reconstruction is a useful tool for cortical anatomy quantification, analysis and visualization. Recently, the Human Connectome Project and several studies have shown the advantages of using T1-weighted magnetic resonance (MR) images with sub-millimeter isotropic spatial resolution instead of the standard 1-millimeter isotropic resolution for improved accuracy of cortical surface positioning and thickness estimation. Nonetheless, sub-millimeter resolution images are noisy by nature and require averaging multiple repetitions to increase the signal-to-noise ratio for precisely delineating the cortical boundary. The prolonged acquisition time and potential motion artifacts pose significant barriers to the wide adoption of cortical surface reconstruction at sub-millimeter resolution for a broad range of neuroscientific and clinical applications. We address this challenge by evaluating the cortical surface reconstruction resulting from denoised single-repetition sub-millimeter T1-weighted images. We systematically characterized the effects of image denoising on empirical data acquired at 0.6 mm isotropic resolution using three classical denoising methods, including denoising convolutional neural network (DnCNN), block-matching and 4-dimensional filtering (BM4D) and adaptive optimized non-local means (AONLM). The denoised single-repetition images were found to be highly similar to 6-repetition averaged images, with a low whole-brain averaged mean absolute difference of ∼0.016, high whole-brain averaged peak signal-to-noise ratio of ∼33.5 dB and structural similarity index of 0.92, and minimal gray matter–white matter contrast loss (2% to 9%). The whole-brain mean absolute discrepancies in gray–white surface placement, gray–CSF surface placement and cortical thickness estimation were lower than 165 μm, 155 μm and 145 μm—sufficiently accurate for most applications. The denoising performance is equivalent to averaging ∼2.5 repetitions of the data in terms of image similarity, and 1.6–2.2 repetitions in terms of the cortical surface placement accuracy. The scan-rescan precision of the cortical surface positioning and thickness estimation was lower than 170 μm. Our unique dataset and systematic characterization support the use of denoising methods for improved cortical surface reconstruction sub-millimeter resolution.

The study of correlation between productivity and morphobiological traits of the collection peas samples

The current paper has presented the study results of correlation between productivity and morphobiological traits of the collection peas samples. The study was carried out at the FSBSI “AgriculturalResearchCenter“Donskoy”, located in the southern part of theRostovregion. The purpose of the study was to analyze the correlation between the quantitative traits of collection peas samples and their productivity. The study of the pea collection was conducted in the collection nursery of pea in 2017–2019, in accordance with the IPI methodology for the study of legumes (1975), the methodology of the State Commission for Variety Testing of Agricultural Crops (2019) and the methodology of a fi trial (2012). The objects of the study were 100 samples of pea of domestic and foreign breeding taken from the world collection of VIGR named after N. I. Vavilov. The variety “Aksaysky Usaty5”was used as a standard variety. The forecrop was winter wheat. The sowing of the collection samples was carried out in the third decade of March with the SSFK-7 seeder, with a seeding rate of million germinating seeds per1 ha, with a row width of15 cm. The plots contained seven rows. The plot area was5 m2 with a single repetition. The registration plot was0.25 m2. There has been identifi that in 2017-2019 the average productivity ranged from 1.12 t/ha (“Omega”,Moldova) to 2.82 t/ha (“Kazanets”, Rossiya) with average 2.13 t/ha in the collection. The most productive samples were plants with a plant height of 45–55 cm, an average number of beans 2.7–3.3 pcs/plant, 1000 seed weight of 210–270 g, a vegetation period of 69–73 days, and seed weight per plant of 3.3–4.3 g, an average number of seeds 13–19 pcs/plant. The study results of correlation between productivity and morphobiological traits of the collection peas samples are going to be used as an initial material in further breeding work.

Increasing N200 Potentials Via Visual Stimulus Depicting Humanoid Robot Behavior

Achieving recognizable visual event-related potentials plays an important role in improving the success rate in telepresence control of a humanoid robot via N200 or P300 potentials. The aim of this research is to intensively investigate ways to induce N200 potentials with obvious features by flashing robot images (images with meaningful information) and by flashing pictures containing only solid color squares (pictures with incomprehensible information). Comparative studies have shown that robot images evoke N200 potentials with recognizable negative peaks at approximately 260[Formula: see text]ms in the frontal and central areas. The negative peak amplitudes increase, on average, from [Formula: see text]V, induced by flashing the squares, to [Formula: see text]V, induced by flashing the robot images. The data analyses support that the N200 potentials induced by the robot image stimuli exhibit recognizable features. Compared with the square stimuli, the robot image stimuli increase the average accuracy rate by 9.92%, from 83.33% to 93.25%, and the average information transfer rate by 24.56[Formula: see text]bits/min, from 72.18[Formula: see text]bits/min to 96.74 bits/min, in a single repetition. This finding implies that the robot images might provide the subjects with more information to understand the visual stimuli meanings and help them more effectively concentrate on their mental activities.

Rules Rule! Brain Activity Dissociates the Representations of Stimulus Contingencies with Varying Levels of Complexity

The significance of stimuli is linked not only to their nature but also to the sequential structure in which they are embedded, which gives rise to contingency rules. Humans have an extraordinary ability to extract and exploit these rules, as exemplified by the role of grammar and syntax in language. To study the brain representations of contingency rules, we recorded ERPs and event-related optical signal (EROS; which uses near-infrared light to measure the optical changes associated with neuronal responses). We used sequences of high- and low-frequency tones varying according to three contingency rules, which were orthogonally manipulated and differed in processing requirements: A Single Repetition rule required only template matching, a Local Probability rule required relating a stimulus to its context, and a Global Probability rule could be derived through template matching or with reference to the global sequence context. ERP activity at 200–300 msec was related to the Single Repetition and Global Probability rules (reflecting access to representations based on template matching), whereas longer-latency activity (300-450 msec) was related to the Local Probability and Global Probability rules (reflecting access to representations incorporating contextual information). EROS responses with corresponding latencies indicated that the earlier activity involved the superior temporal gyrus, whereas later responses involved a fronto-parietal network. This suggests that the brain can simultaneously hold different models of stimulus contingencies at different levels of the information processing system according to their processing requirements, as indicated by the latency and location of the corresponding brain activity.