Neuromuscular adaptations to different set configurations during a periodized power training block in elite junior Judokas

Although the impacts of traditional sets (TS) versus cluster (CL) sets resistance training have been broadly explored among recreationally trained populations, no studies have previously compared these set configurations among elite Judokas. Twenty-two elite male and female Judokas (age = 17.5 ± 1.2 years) performed identical periodized 4-week hypertrophy and strength blocks (8 weeks in total). Following this, for the final 4-week power training block, the cohort was separated into either TS (n = 11) or CL (n = 11) set structures. CL were prescribed by including 45-second intra-set rest every two repetitions. One-repetition maximum (1RM) and peak barbell velocities of the back squat and bench press, and countermovement (CMJ) jump height were assessed before and following each 4-week mesocycle. Significant strength and power improvements were observed after the 4-week hypertrophy training block (1RM bench press = Δ 3.82 kg, ES [95 % CI] = 1.34 [0.76, 1.93], p \u3c 0.001; 1RM squat = Δ 4.71 kg, ES = 0.52 [0.07, 0.96], p = 0.024; CMJ height = Δ 0.54 cm, ES = 0.62 [0.16, 1.07], p = 0.008) and after the 4-week maximal strength training block (1RM bench press = Δ 1.5 kg, ES = 0.68 [0.21, 1.41], p = 0.004; 1RM squat = Δ 5.47 kg, ES = 0.61 [0.15, 1.06], p = 0.010; CMJ height = Δ 0.45 cm, ES = 0.71 [0.23, 1.17], p = 0.003). However, no time × group differences were observed between the TS and CL groups following the 4-week power training block. Though traditional periodized resistance training improved neuromuscular qualities of elite junior Judokas, no between-group neuromuscular differences using either TS or CL suggests that both methods may be used as part of periodized training programs

Ordering block copolymers

A method for ordering block copolymers including forming a first layer having a first preference mode; and providing a reactive agent in selected regions of the first layer that modifies the selected regions to a second preference mode, wherein the selected regions define other regions of the first layer retaining the first preference mode thereby forming an alignment layer for block copolymers.Board of Regents, University of Texas Syste

Digital Orthopaedics: A Glimpse Into the Future in the Midst of a Pandemic.

BackgroundThe response to COVID-19 catalyzed the adoption and integration of digital health tools into the health care delivery model for musculoskeletal patients. The change, suspension, or relaxation of Medicare and federal guidelines enabled the rapid implementation of these technologies. The expansion of payment models for virtual care facilitated its rapid adoption. The authors aim to provide several examples of digital health solutions utilized to manage orthopedic patients during the pandemic and discuss what features of these technologies are likely to continue to provide value to patients and clinicians following its resolution.ConclusionThe widespread adoption of new technologies enabling providers to care for patients remotely has the potential to permanently change the expectations of all stakeholders about the way care is provided in orthopedics. The new era of Digital Orthopaedics will see a gradual and nondisruptive integration of technologies that support the patient's journey through the successful management of their musculoskeletal disease

Subglacial roughness of the Greenland Ice Sheet: relationship with contemporary ice velocity and geology

The subglacial environment of the Greenland Ice Sheet (GrIS) is poorly constrained both in its bulk properties, for example geology, the presence of sediment, and the presence of water, and interfacial conditions, such as roughness and bed rheology. There is, therefore, limited understanding of how spatially heterogeneous subglacial properties relate to ice-sheet motion. Here, via analysis of 2 decades of radio-echo sounding data, we present a new systematic analysis of subglacial roughness beneath the GrIS. We use two independent methods to quantify subglacial roughness: first, the variability in along-track topography – enabling an assessment of roughness anisotropy from pairs of orthogonal transects aligned perpendicular and parallel to ice flow and, second, from bed-echo scattering – enabling assessment of fine-scale bed characteristics. We establish the spatial distribution of subglacial roughness and quantify its relationship with ice flow speed and direction. Overall, the beds of fast-flowing regions are observed to be rougher than the slow-flowing interior. Topographic roughness exhibits an exponential scaling relationship with ice surface velocity parallel, but not perpendicular, to flow direction in fast-flowing regions, and the degree of anisotropy is correlated with ice surface speed. In many slow-flowing regions both roughness methods indicate spatially coherent regions of smooth beds, which, through combination with analyses of underlying geology, we conclude is likely due to the presence of a hard flat bed. Consequently, the study provides scope for a spatially variable hard- or soft-bed boundary constraint for ice-sheet models

Ketogenic diet modifies the gut microbiota in a murine model of autism spectrum disorder

BackgroundGastrointestinal dysfunction and gut microbial composition disturbances have been widely reported in autism spectrum disorder (ASD). This study examines whether gut microbiome disturbances are present in the BTBR(T + tf/j) (BTBR) mouse model of ASD and if the ketogenic diet, a diet previously shown to elicit therapeutic benefit in this mouse model, is capable of altering the profile.FindingsJuvenile male C57BL/6 (B6) and BTBR mice were fed a standard chow (CH, 13 % kcal fat) or ketogenic diet (KD, 75 % kcal fat) for 10-14 days. Following diets, fecal and cecal samples were collected for analysis. Main findings are as follows: (1) gut microbiota compositions of cecal and fecal samples were altered in BTBR compared to control mice, indicating that this model may be of utility in understanding gut-brain interactions in ASD; (2) KD consumption caused an anti-microbial-like effect by significantly decreasing total host bacterial abundance in cecal and fecal matter; (3) specific to BTBR animals, the KD counteracted the common ASD phenotype of a low Firmicutes to Bacteroidetes ratio in both sample types; and (4) the KD reversed elevated Akkermansia muciniphila content in the cecal and fecal matter of BTBR animals.ConclusionsResults indicate that consumption of a KD likely triggers reductions in total gut microbial counts and compositional remodeling in the BTBR mouse. These findings may explain, in part, the ability of a KD to mitigate some of the neurological symptoms associated with ASD in an animal model

Initial validation of a novel method of presurgical language localization through functional connectivity (fcMRI)

OBJECTIVE: Neurosurgery is potentially curative in chronic epilepsy but can only be offered to patients if the surgical risk to language is known. Clinical functional magnetic resonance imaging (fMRI) is an ideal, noninvasive method for localizing language cortex yet remains to be validated for this purpose. We have recently presented a novel method for localizing language cortex. Here we present a preliminary evaluation of this method’s validity. We hypothesized language regions identified using this novel method would demonstrate stronger functional connectivity than randomly generated set of proximal networks. METHOD: fMRI data were collected from sixteen temporal lobe patients (12 left) being evaluated for epilepsy surgery at UCLA (mean age 38.9 [sd 11.4]; 6 female; per Wada 14 left language dominant, 1 right, 1 mixed). Language maps were generated using a recently standardized method relying on a conjunction of language tasks (e.g., visual object naming; auditory naming; reading) to identify known language regions (Broca’s area; inferior and superior Wernicke’s Areas; Angular gyrus; Basal Temporal Language Area; Exner’s Area; and Supplementary Speech Area). With activations defined as network nodes, mean network connectivity was compared via permutation tests with alternate (i) fully random and (ii) proximal random networks. Mean network connectivity was determined in independently-acquired motor fMRI datasets (9 foot, 16 hand, 14 tongue). FINDINGS: 77% (30/39) of clinician-derived language networks exhibited mean connectivity greater than fully random networks (p\u3c0.05). Similarly, 69% (27/39) of clinician-derived language networks exhibited mean connectivity greater than proximal random networks (p\u3c0.05). Further analysis of networks not passing the permutation test suggests that low connectivity of non-valid networks may be driven not by low connectivity across all nodes, but by individual nodes that may not actually possess membership within the network. CONCLUSIONS: This study provides preliminary validity for a novel, clinician-based approach to mapping language cortex pre-surgery. This complements our recent work showing this method is reliable, and supports a proposed study comparing fMRI language maps using this technique with the results of direct stimulation mapping

AAPM medical physics practice guideline 10.a.: Scope of practice for clinical medical physics.

The American Association of Physicists in Medicine (AAPM) is a nonprofit professional society whose primary purposes are to advance the science, education, and professional practice of medical physics. The AAPM has more than 8000 members and is the principal organization of medical physicists in the United States. The AAPM will periodically define new practice guidelines for medical physics practice to help advance the science of medical physics and to improve the quality of service to patients throughout the United States. Existing medical physics practice guidelines will be reviewed for the purpose of revision or renewal, as appropriate, on their fifth anniversary or sooner. Each medical physics practice guideline (MPPG) represents a policy statement by the AAPM, has undergone a thorough consensus process in which it has been subjected to extensive review, and requires the approval of the Professional Council. The medical physics practice guidelines recognize that the safe and effective use of diagnostic and therapeutic radiation requires specific training, skills, and techniques as described in each document. As the review of the previous version of AAPM Professional Policy (PP)-17 (Scope of Practice) progressed, the writing group focused on one of the main goals: to have this document accepted by regulatory and accrediting bodies. After much discussion, it was decided that this goal would be better served through a MPPG. To further advance this goal, the text was updated to reflect the rationale and processes by which the activities in the scope of practice were identified and categorized. Lastly, the AAPM Professional Council believes that this document has benefitted from public comment which is part of the MPPG process but not the AAPM Professional Policy approval process. The following terms are used in the AAPM's MPPGs: Must and Must Not: Used to indicate that adherence to the recommendation is considered necessary to conform to this practice guideline. Should and Should Not: Used to indicate a prudent practice to which exceptions may occasionally be made in appropriate circumstances

Structural Topic Models for Open-Ended Survey Responses

Collection and especially analysis of open-ended survey responses are relatively rare in the discipline and when conducted are almost exclusively done through human coding. We present an alternative, semiautomated approach, the structural topic model (STM) (Roberts, Stewart, and Airoldi 2013; Roberts et al. 2013), that draws on recent developments in machine learning based analysis of textual data. A crucial contribution of the method is that it incorporates information about the document, such as the author's gender, political affiliation, and treatment assignment (if an experimental study). This article focuses on how the STM is helpful for survey researchers and experimentalists. The STM makes analyzing open-ended responses easier, more revealing, and capable of being used to estimate treatment effects. We illustrate these innovations with analysis of text from surveys and experiments