Deep learning for seismic phase detection and picking in the aftershock zone of 2008 M_W 7.9 Wenchuan Earthquake

The increasing volume of seismic data from long-term continuous monitoring motivates the development of algorithms based on convolutional neural network (CNN) for faster and more reliable phase detection and picking. However, many less studied regions lack a significant amount of labeled events needed for traditional CNN approaches. In this paper, we present a CNN-based Phase-Identification Classifier (CPIC) designed for phase detection and picking on small to medium sized training datasets. When trained on 30,146 labeled phases and applied to one-month of continuous recordings during the aftershock sequences of the 2008 M_W 7.9 Wenchuan Earthquake in Sichuan, China, CPIC detects 97.5% of the manually picked phases in the standard catalog and predicts their arrival times with a five-times improvement over the ObsPy AR picker. In addition, unlike other CNN-based approaches that require millions of training samples, when the off-line training set size of CPIC is reduced to only a few thousand training samples the accuracy stays above 95%. The deployment of CPIC takes less than 12 h to pick arrivals in 31-day recordings on 14 stations. In addition to the catalog phases manually picked by analysts, CPIC finds more phases for existing events and new events missed in the catalog. Among those additional detections, some are confirmed by a matched filter method while others require further investigation. Finally, when tested on a small dataset from a different region (Oklahoma, US), CPIC achieves 97% accuracy after fine tuning only the fully connected layer of the model. This result suggests that the CPIC developed in this study can be used to identify and pick P/S arrivals in other regions with no or minimum labeled phases

Continuous Femoral Nerve Block versus Intravenous Patient Controlled Analgesia for Knee Mobility and Long-Term Pain in Patients Receiving Total Knee Replacement: A Randomized Controlled Trial

Objectives. To evaluate the comparative analgesia effectiveness and safety of postoperative continuous femoral nerve block (CFNB) with patient controlled intravenous analgesia (PCIA) and their impact on knee function and chronic postoperative pain. Methods. Participants were randomly allocated to receive postoperative continuous femoral nerve block (group CFNB) or intravenous patient controlled analgesia (group PCIA). Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores for knee and incidence of chronic postoperative pain at 3, 6, and 12 months postoperatively were compared. postoperative pain and salvage medication at rest or during mobilization 24 hours, 48 hours, and 7 days postoperatively were also recorded. Results. After discharge from the hospital and rehabilitation of joint function, patients in group CFNB reported significantly improved knee flexion and less incidence of chronic postoperative pain at 3 months and 6 months postoperatively (P<0.05). Analgesic rescue medications were significantly reduced in patients receiving CFNB (P<0.001 and P=0.031, resp.). Conclusion. With standardized rehabilitation therapy, continuous femoral nerve block analgesia reduced the incidence of chronic postoperative pain, improved motility of replaced joints, and reduced the dosages of rescue analgesic medications, suggesting a recovery-enhancing effect of peripheral nerve block analgesia

Somatic Mutational Landscape of Splicing Factor Genes and Their Functional Consequences across 33 Cancer Types

Hotspot mutations in splicing factor genes have been recently reported at high frequency in hematological malignancies, suggesting the importance of RNA splicing in cancer. We analyzed whole-exome sequencing data across 33 tumor types in The Cancer Genome Atlas (TCGA), and we identified 119 splicing factor genes with significant non-silent mutation patterns, including mutation over-representation, recurrent loss of function (tumor suppressor-like), or hotspot mutation profile (oncogene-like). Furthermore, RNA sequencing analysis revealed altered splicing events associated with selected splicing factor mutations. In addition, we were able to identify common gene pathway profiles associated with the presence of these mutations. Our analysis suggests that somatic alteration of genes involved in the RNA-splicing process is common in cancer and may represent an underappreciated hallmark of tumorigenesis

Regeneration of mature dermis by transplanted particulate acellular dermal matrix in a rat model of skin defect wound

Native mammalian extracellular matrix (ECM) has been made in various forms including particles, sheet and mesh which are appropriate for site-specific applications. The ECM particles are usually created by homogenization method and have a wider size distribution. This needs to be improved to produce more uniform ECM particles. In present study, we had successfully developed a method for preparing particulate acellular dermal matrix (PADM) in different gauges. The resultant PADM was approaching a rectangular parallelepiped or cubic shape, with a better or narrower size distribution than other ECM particles in previous reports. It also retained ultrastructure and functional molecules of native ECM. In vivo performances were evaluated after implantation of PADM in an acute full-thickness skin defect wound in rats. Histological analysis showed that allogeneic PADM used as dermal regeneration template could facilitate maturation and improving collagen bundle structure of regenerated dermis at the endpoint of 20 weeks post-surgery. The PADM could be used for further investigation in analyzing the impacts of cellularly and/or molecularly modified PADM on soft tissue regeneration