Sublethal Photothermal Stimulation with a Micropulse Laser Induces Heat Shock Protein Expression in ARPE-19 Cells

Purpose/Aim of the Study. Subthreshold micropulse diode laser photocoagulation is an effective treatment for macular edema. The molecular mechanisms underlying treatment success are poorly understood. Therefore, we investigated the effects of sublethal laser energy doses on a single layer of densely cultured ARPE-19 cells as a model of the human retinal pigment epithelium (RPE). Materials and Methods. A single layer of densely cultured human ARPE-19 cells was perpendicularly irradiated with a micropulse diode laser. Nonirradiated cells served as controls. Sublethal laser energy was applied to form a photocoagulation-like area in the cultured cell layers. Hsp70 expression was evaluated using quantitative polymerase chain reaction and immunocytochemistry. Results. Photocoagulation-like areas were successfully created in cultured ARPE-19 cell layers using sublethal laser energy with our laser irradiation system. Hsp70 mRNA expression in cell layers was induced within 30 min of laser irradiation, peaking at 3 h after irradiation. This increase was dependent on the number of laser pulses. Hsp70 upregulation was not observed in untreated cell layers. Immunostaining indicated that Hsp70 expression occurred concentrically around laser irradiation sites and persisted for 24 h following irradiation. Conclusion. Sublethal photothermal stimulation with a micropulse laser may facilitate Hsp70 expression in the RPE without inducing cellular damage

Automatic quantification of mammary glands on non-contrast X-ray CT by using a novel segmentation approach

ABSTRACT This paper describes a brand new automatic segmentation method for quantifying volume and density of mammary gland regions on non-contrast CT images. The proposed method uses two processing steps: (1) breast region localization, and (2) breast region decomposition to accomplish a robust mammary gland segmentation task on CT images. The first step detects two minimum bounding boxes of left and right breast regions, respectively, based on a machine-learning approach that adapts to a large variance of the breast appearances on different age levels. The second step divides the whole breast region in each side into mammary gland, fat tissue, and other regions by using spectral clustering technique that focuses on intra-region similarities of each patient and aims to overcome the image variance caused by different scan-parameters. The whole approach is designed as a simple structure with very minimum number of parameters to gain a superior robustness and computational efficiency for real clinical setting. We applied this approach to a dataset of 300 CT scans, which are sampled with the equal number from 30 to 50 years-old-women. Comparing to human annotations, the proposed approach can measure volume and quantify distributions of the CT numbers of mammary gland regions successfully. The experimental results demonstrated that the proposed approach achieves results consistent with manual annotations. Through our proposed framework, an efficient and effective low cost clinical screening scheme may be easily implemented to predict breast cancer risk, especially on those already acquired scans

WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing

Self-supervised learning (SSL) achieves great success in speech recognition, while limited exploration has been attempted for other speech processing tasks. As speech signal contains multi-faceted information including speaker identity, paralinguistics, spoken content, etc., learning universal representations for all speech tasks is challenging. To tackle the problem, we propose a new pre-trained model, WavLM, to solve full-stack downstream speech tasks. WavLM jointly learns masked speech prediction and denoising in pre-training. By this means, WavLM does not only keep the speech content modeling capability by the masked speech prediction, but also improves the potential to non-ASR tasks by the speech denoising. In addition, WavLM employs gated relative position bias for the Transformer structure to better capture the sequence ordering of input speech. We also scale up the training dataset from 60k hours to 94k hours. WavLM Large achieves state-of-the-art performance on the SUPERB benchmark, and brings significant improvements for various speech processing tasks on their representative benchmarks. The code and pre-trained models are available at https://aka.ms/wavlm.Comment: Submitted to the Journal of Selected Topics in Signal Processing (JSTSP

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements

Changes in salivary oxytocin levels and bonding disorder in women from late pregnancy to early postpartum: A pilot study.

OBJECTIVES:This pilot study aimed to 1) follow the longitudinal changes in the salivary oxytocin level of pregnant women from late pregnancy to early postpartum, 2) examine the factors related to these changes, and 3) clarify the association of these changes with mother-infant bonding. METHODS:This study used a longitudinal observational design and questionnaires to obtain objective and subjective data. For oxytocin evaluation, saliva samples were collected and their oxytocin levels were measured at 4-time points [i.e., 1) 36-37 gestation weeks, 2) 38-39 gestation weeks, 3) 1-2 days postpartum, 4) 4-5 days postpartum]. The oxytocin level was assayed in duplicates by enzyme-linked immunosorbent assay. Baseline data were evaluated using the Parental Bonding Instrument (25 items), State Trait Anxiety Inventory (20 items), and Center for Epidemiologic Studies Depression Scale. Postpartum data were evaluated using the Mother to Infant Bonding Scale Japanese Version (10 items), Maternity Blues Scale (13 items), and 'Fatigue after Childbirth' using the Visual Analogue Scale (VAS: 0-100 mm). RESULTS:The participants were 13 primiparas with a mean age of 33 years. They had no depression or anxiety at the baseline. Their mean salivary oxytocin levels significantly increased from late pregnancy (36-39 gestation weeks) up to 1 day postpartum and then decreased until 5 days postpartum. There was a negligible correlation between the bonding disorder and the salivary oxytocin level on the 5th day after childbirth. A moderate correlation was observed between the maternity blues score and the salivary oxytocin level. There was a significant negative correlation between the postpartum fatigue and the salivary oxytocin level 1 day and 5 days after childbirth. CONCLUSION:The mean salivary oxytocin levels significantly increased from the baseline up to 1 day postpartum and then decreased until 5 days postpartum. The salivary oxytocin level was moderately associated with maternity blues and significantly with postpartum fatigue

Changes in salivary oxytocin after inhalation of clary sage essential oil scent in term-pregnant women: a feasibility pilot study

Abstract Objectives This pilot study using a quasi-experimental design was conducted to evaluate the feasibility (i.e., limited efficacy, practicality, and acceptability) of our intervention protocol involving inhalation of the scent of clary sage essential oil by pregnant women and measurement of their preinhalation and postinhalation oxytocin levels. Results Participants were women of singleton pregnancies between 38 and 40 gestation weeks (N = 11). The experiment group (n = 5) inhaled the scent of clary sage essential oil diluted 50-fold with 10 mL of odorless propylene glycol for 20 min. Regarding limited efficacy, the oxytocin level 15 min postinhalation increased in 3 women and was unmeasurable in 2. The control group (n = 6) inhaled similarly without the 50-fold dilution of clary sage essential oil. Their oxytocin level increased in 2 women, decreased in 2, and was unmeasurable in 2. Uterine contraction was not observed in both groups. Regarding practicality, 3 of the 11 women could not collect sufficient saliva. The cortisol level decreased in both groups postinhalation. The protocol had no negative effects. Regarding acceptability, burden of the protocol was not observed. Trial registration The Clinical Trials Registry of University Hospital Medical Information Network in Japan—UMIN000017830. Registered:  June 8, 201