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Microbial communities along mucosal surfaces throughout the digestive tract are hypothesized as risk factors for impaired glucose regulation and the development of clinical cardiometabolic disease. We investigated whether baseline measures of subgingival microbiota predicted fasting plasma glucose (FPG) longitudinally. The Oral Infections, Glucose Intolerance and Insulin Resistance Study (ORIGINS) enrolled 230 diabetes-free adults (77% female) aged 20 to 55 y (mean ± SD, 34 ± 10 y) from whom baseline subgingival plaque and longitudinal FPG were measured. DNA was extracted from subgingival plaque, and V3 to V4 regions of the 16S rRNA gene were sequenced. FPG was measured at baseline and again at 2 y; glucose change was defined as follow-up minus baseline. Multivariable linear models regressed 2-y glucose change onto baseline measures of community diversity and abundances of 369 individual taxa. A microbial dysbiosis index (MDI) summarizing top individual taxa associated with glucose change was calculated and used in regression models. Models were adjusted for age, sex, race/ethnicity, education, smoking status, body mass index, and baseline glucose levels. Statistical significance was based on the false discovery rate (FDR;

A true monolithic infrared photonics platform is within reach if strain and bandgap energy can be independently engineered in SiGeSn semiconductors. Herein, we investigate the structural and optoelectronic properties of a 1.5 μm-thick Si0.06Ge0.90Sn0.04 layer that is nearly lattice-matched to a Ge on Si substrate. Atomic-level studies demonstrate high crystalline quality and uniform composition and show no sign of short-range ordering and clusters. Room-temperature spectroscopic ellipsometry and transmission measurements show direct bandgap absorption at 0.83 eV and a reduced indirect bandgap absorption at lower energies. Si0.06Ge0.90Sn0.04 photoconductive devices operating at room temperature exhibit dark current and spectral responsivity (1 A/W below 1.5 μm wavelengths) similar to Ge on Si devices, with the advantage of a near-infrared bandgap tunable by alloy composition. These results underline the relevance of SiGeSn semiconductors in implementing a group IV material platform for silicon-integrated infrared optoelectronics. [ABSTRACT FROM AUTHOR]

There is an increasing need for silicon-compatible high-bandwidth extended-short wave infrared (e-SWIR) photodetectors (PDs) to implement cost-effective and scalable optoelectronic devices. These systems are quintessential to address several technological bottlenecks in detection and ranging, surveillance, ultrafast spectroscopy, and imaging. In fact, current e-SWIR high-bandwidth PDs are predominantly made of III–V compound semiconductors and thus are costly and suffer a limited integration on silicon besides a low responsivity at wavelengths exceeding 2.3 μm. To circumvent these challenges, Ge1−xSnx semiconductors have been proposed as building blocks for silicon-integrated high-speed e-SWIR devices. Herein, this study demonstrates vertical all-GeSn PIN PDs consisting of p-Ge0.92Sn0.08/i-Ge0.91Sn0.09/n-Ge0.89Sn0.11 and p-Ge0.91Sn0.09/i-Ge0.88Sn0.12/n-Ge0.87Sn0.13 heterostructures grown on silicon following a step-graded temperature-controlled epitaxy protocol. The performance of these PDs was investigated as a function of the device diameter in the 10–30 μm range. The developed PD devices yield a high bandwidth of 12.4 GHz at a bias of 5 V for a device diameter of 10 μm. Moreover, these devices show a high responsivity of 0.24 A/W, a low noise, and a 2.8 μm cutoff wavelength, thus covering the whole e-SWIR range. [ABSTRACT FROM AUTHOR]

We assessed the behavioral context of calls produced by blue whales Balaenoptera musculus off the California coast based on acoustic, behavioral, and dive data obtained through acoustic recording tags, sex determination from tissue sampling, and coordinated visual and acoustic observations. Approximately one-third of 38 monitored blue whales vocalized, with sounds categorized into 3 types: (1) low-frequency pulsed A and tonal B calls, in either rhythmic repetitive song sequences or as intermittent, singular calls; (2) downswept D calls; and (3) highly variable amplitude-or frequency-modulated calls. Clear patterns of behavior, sex, and group size are evident for some call types. Only males were documented producing AB calls, with song produced by lone, traveling blue whales, and singular AB calls were more typically produced by whales in pairs; D calls were heard from both sexes during foraging, commonly from individuals within groups. The sex bias evident in AB callers suggests that these calls probably play a role in reproduction, even though the calls are produced year-round. All calls are produced at shallow depth, and calling whales spend more time at shallow depths than non-calling whales, suggesting that a cost may be incurred during D calling, as less time is spent feeding at deeper depths. This relationship between calling and depth may predict the traveling behavior of singing blue whales, as traveling whales do not typically dive to deep depths and therefore would experience little extra energetic cost related to the production of long repetitive song bouts while moving between foraging areas.

Motivated by an application in high‐throughput genomics and metabolomics, we propose a novel and fully data‐driven approach for estimating large block structured sparse covariance matrices in the case where the number of variables is much larger than the number of samples without limiting ourselves to block diagonal matrices. Our approach consists in approximating such a covariance matrix by the sum of a low‐rank sparse matrix and a diagonal matrix. Our methodology also can deal with matrices for which the block structure appears only if the columns and rows are permuted according to an unknown permutation. Our technique is implemented in the R package BlockCov which is available from the Comprehensive R Archive Network (CRAN) and from GitHub. In order to illustrate the statistical and numerical performance of our package some numerical experiments are provided as well as a thorough comparison with alternative methods. Finally, our approach is applied to the use of 'multi‐omic' approaches for studying seed quality. [ABSTRACT FROM AUTHOR]

Background: Wearable resistance (WR) training is a modality that allows athletes to perform loaded sport-specific movements to develop force and power outputs. The acute responses by which WR works is still relatively unknown, and the effects of WR load and location of the load has not yet been examined. Objectives: To investigate the acute neuromuscular and stride characteristic responses to different wearable resistance (WR) loads and placements on the calf muscles during high-speed running. Methods: Ten well-trained subjects completed a workout of ten sets of three 10s runs at 18km.h-1 (20s of rest between runs and one min between sets). Five conditions were tested: (1) unloaded control, (2) bilateral 0.75 vs. 1.5% body mass (BM) loading on the distal posterior calf, (3) bilateral proximal vs. distal loading of 1.5% BM positioned posteriorly, (4) bilateral anterior vs. posterior loading of 1.5% BM positioned distally, (5) unilateral loading of 1.5% BM on the distal posterior calf. Data were collected using Electromyography (EMG) and back-mounted GPS-embedded accelerometers. Magnitude of differences of within athlete and between muscle comparisons were calculated using effect sizes (ES) ± 90% confidence limits (CL). Results: No substantial differences in accelerometry data were observed between any of the loaded conditions and the control. EMG activity was lower for proximal loading compared to the control for the gluteus maximus (ES±90%CL; -0.72±0.41), vastus lateralis (-0.89±0.47) and vastus medialis (VM) (-0.97±0.46). Anterior loading induced substantially lower EMG activity for the semitendinosus (-0.70±0.48) and VM (-0.64±0.39) muscles compared with the control. EMG activity of the VM (-0.73±0.46) muscle was also substantially lower for posterior loading compared to the control. Unilateral loading induced no substantial differences in EMG activity between the loaded and unloaded legs. Conclusion: This preliminary study has provided a rationale for the performance of further investigations into the effects of WR lower limb loading on stride characteristics and EMG activity from a chronic standpoint using a larger population. [ABSTRACT FROM AUTHOR]

The L858R point mutation of exon 21 of EGFR accounts for up to 45% of EGFR-mutated lung adenocarcinomas (LACs). EGFR (L858R) mutation-specific antibody has been shown to have high positive predictive value (99%-100%). HER2 amplification is known to be a mechanism of acquired resistance to erlotinib. False-positive staining of EGFR (L858R) by immunohistochemistry (IHC) has been reported in ER+, HER-amplified breast carcinomas, but not in LAC. We identified 2 LACs positive for both EGFR L858R (2-3+) and HER2 (3+) by IHC. Targeted gene sequencing was performed to confirm the underlying molecular abnormality and to uncover potential cross-reactivity. Patient 1 was a 57-year-old man with stage IV LAC. Staging node biopsy showed metastatic adenocarcinoma diffusely positive for EGFR L858R by IHC. Sequencing revealed no EGFR (L858R) mutation, but amplification of HER2. HER2 was also 3+ positive by IHC. Patient 2 was a 52-year-old man with progression while taking erlotinib for LAC with EGFR exon 19 deletion. Rebiopsy of the primary tumor showed focal strong positivity for EGFR (L858R) by IHC, despite lack of EGFR (L858R) mutation by sequencing. HER2 was 3+ by IHC in the same cells that were EGFR L858R positive by IHC. We demonstrate 2 examples of false positivity for EGFR L858R by IHC. Structural homology between HER2 and EGFR might partially explain the cross-reactivity; however, further studies are needed to elucidate the frequency and mechanism of this event., C. Leduc; N. Rekhtman; W. Travis; A. Moreira; M. Arcila; M. Ladanyi. Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New [...]

Summary: Motivated by an application to the clustering of milking kinetics of dairy goats, we propose a novel approach for functional data clustering. This issue is of growing interest in precision livestock farming, which is largely based on the development of data acquisition automation and on the development of interpretative tools to capitalize on high throughput raw data and to generate benchmarks for phenotypic traits. The method that we propose in the paper falls in this context. Our methodology relies on a piecewise linear estimation of curves based on a novel regularized change‐point‐estimation method and on the k‐means algorithm applied to a vector of coefficients summarizing the curves. The statistical performance of our method is assessed through numerical experiments and is thoroughly compared with existing experiments. Our technique is finally applied to milk emission kinetics data with the aim of a better characterization of interanimal variability and towards a better understanding of the lactation process. [ABSTRACT FROM AUTHOR]