A survey of dystocia in the Boxer breed

BACKGROUND: Dystocia occurs more commonly in some breeds of dogs than others. The Boxer breed is one of the highrisk breeds for whelping problems. The aim of this study was to document some reproductive parameters and the frequency of dystocia in Boxers. METHODS: Two questionnaires were sent to the breeders of Boxers in Sweden during 1994 to 1997. Data from 253 whelpings and 1671 pups was received, which constitutes 56.5% of all Boxer litters registered with the Swedish Kennel Club during these years. Data was analysed using Chi-square test, and Fischer's exact test. RESULTS: Dystocia occurred in 32% of the individual bitches, and in 27.7% of all the whelpings. Caesarian section was performed in 22.8% of all the whelpings and in 80.1% of the cases of dystocia. Medical treatment was tried in 20 cases but was successful only in 5 (25%). The dystocia was of maternal origin in 68.6% and of fetal origin in 28.6% of cases. The most common reasons for dystocia were primary uterine inertia (60%) and malpresentation of the fetus (26%). Dystocia increased with increasing age of the bitch from four years of age. Average litter size was 6.6 (± 2.2) pups born, and 5.0 (± 2.1) pups registered. Pup mortality was 24%. Stillbirths accounted for 6.1% of the pup deaths and 1% died in the neonatal period, while 15.6% of the pups were euthanised, the majority because they had disqualifying white coat colour. Cryptorchidism was observed in 9.8% of the male pups born and in 13.4% of the male pups that were registered. CONCLUSION: The Boxer suffers a high frequency of dystocia, mainly due to uterine inertia, but also fetal malpresentation. Breeders should be adviced to include easy whelpings in their breeding program

Extracellular membrane vesicles from Limosilactobacillus reuteri strengthen the intestinal epithelial integrity, modulate cytokine responses and antagonize activation of TRPV1

Bacterial extracellular membrane vesicles (MV) are potent mediators of microbe-host signals, and they are not only important in host-pathogen interactions but also for the interactions between mutualistic bacteria and their hosts. Studies of MV derived from probiotics could enhance the understanding of these universal signal entities, and here we have studied MV derived from Limosilactobacillus reuteri DSM 17938 and BG-R46. The production of MV increased with cultivation time and after oxygen stress. Mass spectrometry-based proteomics analyses revealed that the MV carried a large number of bacterial cell surface proteins, several predicted to be involved in host-bacteria interactions. A 5 '-nucleotidase, which catalyze the conversion of AMP into the signal molecule adenosine, was one of these and analysis of enzymatic activity showed that L. reuteri BG-R46 derived MV exhibited the highest activity. We also detected the TLR2 activator lipoteichoic acid on the MV. In models for host interactions, we first observed that L. reuteri MV were internalized by Caco-2/HT29-MTX epithelial cells, and in a dose-dependent manner decreased the leakage caused by enterotoxigenic Escherichia coli by up to 65%. Furthermore, the MV upregulated IL-1 beta and IL-6 from peripheral blood mononuclear cells (PBMC), but also dampened IFN-gamma and TNF-alpha responses in PBMC challenged with Staphylococcus aureus. Finally, we showed that MV from the L. reuteri strains have an antagonistic effect on the pain receptor transient receptor potential vanilloid 1 in a model with primary dorsal root ganglion cells from rats. In summary, we have shown that these mobile nanometer scale MV reproduce several biological effects of L. reuteri cells and that the production parameters and selection of strain have an impact on the activity of the MV. This could potentially provide key information for development of innovative and more efficient probiotic products

Comparison of sequencing-based methods to profile DNA methylation and identification of monoallelic epigenetic modifications.

Analysis of DNA methylation patterns relies increasingly on sequencing-based profiling methods. The four most frequently used sequencing-based technologies are the bisulfite-based methods MethylC-seq and reduced representation bisulfite sequencing (RRBS), and the enrichment-based techniques methylated DNA immunoprecipitation sequencing (MeDIP-seq) and methylated DNA binding domain sequencing (MBD-seq). We applied all four methods to biological replicates of human embryonic stem cells to assess their genome-wide CpG coverage, resolution, cost, concordance and the influence of CpG density and genomic context. The methylation levels assessed by the two bisulfite methods were concordant (their difference did not exceed a given threshold) for 82% for CpGs and 99% of the non-CpG cytosines. Using binary methylation calls, the two enrichment methods were 99% concordant and regions assessed by all four methods were 97% concordant. We combined MeDIP-seq with methylation-sensitive restriction enzyme (MRE-seq) sequencing for comprehensive methylome coverage at lower cost. This, along with RNA-seq and ChIP-seq of the ES cells enabled us to detect regions with allele-specific epigenetic states, identifying most known imprinted regions and new loci with monoallelic epigenetic marks and monoallelic expression

The DCDC2 deletion is not a risk factor for dyslexia

Dyslexia is a specific impairment in learning to read and has strong heritability. An intronic deletion within the DCDC2 gene, with ~8% frequency in European populations, is increasingly used as a marker for dyslexia in neuroimaging and behavioral studies. At a mechanistic level, this deletion has been proposed to influence sensory processing capacity, and in particular sensitivity to visual coherent motion. Our re-assessment of the literature, however, did not reveal strong support for a role of this specific deletion in dyslexia. We also analyzed data from five distinct cohorts, enriched for individuals with dyslexia, and did not identify any signal indicative of associations for the DCDC2 deletion with reading-related measures, including in a combined sample analysis (N=526). We believe we conducted the first replication analysis for a proposed deletion effect on visual motion perception and found no association (N=445 siblings). We also report that the DCDC2 deletion has a frequency of 37.6% in a cohort representative of the general population recruited in Hong Kong (N=220). This figure, together with a lack of association between the deletion and reading abilities in this cohort, indicates the low likelihood of a direct deletion effect on reading skills. Therefore, on the basis of multiple strands of evidence, we conclude that the DCDC2 deletion is not a strong risk factor for dyslexia. Our analyses and literature re-evaluation are important for interpreting current developments within multidisciplinary studies of dyslexia and, more generally, contribute to current discussions about the importance of reproducibility in science

Mass balance of the Greenland Ice Sheet from 1992 to 2018

In recent decades, the Greenland Ice Sheet has been a major contributor to global sea-level rise1,2, and it is expected to be so in the future3. Although increases in glacier flow4–6 and surface melting7–9 have been driven by oceanic10–12 and atmospheric13,14 warming, the degree and trajectory of today’s imbalance remain uncertain. Here we compare and combine 26 individual satellite measurements of changes in the ice sheet’s volume, flow and gravitational potential to produce a reconciled estimate of its mass balance. Although the ice sheet was close to a state of balance in the 1990s, annual losses have risen since then, peaking at 335 ± 62 billion tonnes per year in 2011. In all, Greenland lost 3,800 ± 339 billion tonnes of ice between 1992 and 2018, causing the mean sea level to rise by 10.6 ± 0.9 millimetres. Using three regional climate models, we show that reduced surface mass balance has driven 1,971 ± 555 billion tonnes (52%) of the ice loss owing to increased meltwater runoff. The remaining 1,827 ± 538 billion tonnes (48%) of ice loss was due to increased glacier discharge, which rose from 41 ± 37 billion tonnes per year in the 1990s to 87 ± 25 billion tonnes per year since then. Between 2013 and 2017, the total rate of ice loss slowed to 217 ± 32 billion tonnes per year, on average, as atmospheric circulation favoured cooler conditions15 and as ocean temperatures fell at the terminus of Jakobshavn Isbræ16. Cumulative ice losses from Greenland as a whole have been close to the IPCC’s predicted rates for their high-end climate warming scenario17, which forecast an additional 50 to 120 millimetres of global sea-level rise by 2100 when compared to their central estimate

Mass balance of the Greenland and Antarctic ice sheets from 1992 to 2020

Ice losses from the Greenland and Antarctic ice sheets have accelerated since the 1990s, accounting for a significant increase in the global mean sea level. Here, we present a new 29-year record of ice sheet mass balance from 1992 to 2020 from the Ice Sheet Mass Balance Inter-comparison Exercise (IMBIE). We compare and combine 50 independent estimates of ice sheet mass balance derived from satellite observations of temporal changes in ice sheet flow, in ice sheet volume, and in Earth's gravity field. Between 1992 and 2020, the ice sheets contributed 21.0±1.9g€¯mm to global mean sea level, with the rate of mass loss rising from 105g€¯Gtg€¯yr-1 between 1992 and 1996 to 372g€¯Gtg€¯yr-1 between 2016 and 2020. In Greenland, the rate of mass loss is 169±9g€¯Gtg€¯yr-1 between 1992 and 2020, but there are large inter-annual variations in mass balance, with mass loss ranging from 86g€¯Gtg€¯yr-1 in 2017 to 444g€¯Gtg€¯yr-1 in 2019 due to large variability in surface mass balance. In Antarctica, ice losses continue to be dominated by mass loss from West Antarctica (82±9g€¯Gtg€¯yr-1) and, to a lesser extent, from the Antarctic Peninsula (13±5g€¯Gtg€¯yr-1). East Antarctica remains close to a state of balance, with a small gain of 3±15g€¯Gtg€¯yr-1, but is the most uncertain component of Antarctica's mass balance. The dataset is publicly available at 10.5285/77B64C55-7166-4A06-9DEF-2E400398E452 (IMBIE Team, 2021)

Automatisk sportanalyssystem för bordtennis med bildigenkänningsmetoder

This study presents several machine learning models tasked with gathering information about what happens in a table tennis match. This information includes the ball's position, identifying serves, and other events such as ball hits and bounces. The TTNet backbone demonstrated superior performance across all tasks, including inference time. The Classification Head achieved high accuracy (94.6%), F1 score (97.2%), and RMSE value (0.014) for ball detection, accurately predicting ball positions. The TTNet model with the Simple Event Head showed promise for event detection (97.5% accuracy, 80.4% F1 score). However, the dataset is heavily imbalanced and the models would probably benefit from a more balanced training set. Serve detection performance was subpar for all models (best F1 score: 42.1%), requiring significant dataset and model reconstruction. All top-performing models achieved fast inference speeds (<4 ms) on various devices, enabling real-time operation even on smartphones