Identification of germline variation and somatic mutations is a major issue in human genetics. However, due to the limitations of DNA sequencing technologies and computational algorithms, our understanding of genetic variation and somatic mutations is far from complete. In the present study, we performed whole-genome sequencing using long-read sequencing technology (Oxford Nanopore) for 11 Japanese liver cancers and matched normal samples which were previously sequenced for the International Cancer Genome Consortium (ICGC). We constructed an analysis pipeline for the long-read data and identified germline and somatic structural variations (SVs). In polymorphic germline SVs, our analysis identified 8004 insertions, 6389 deletions, 27 inversions, and 32 intra-chromosomal translocations. By comparing to the chimpanzee genome, we correctly inferred events that caused insertions and deletions and found that most insertions were caused by transposons and Alu is the most predominant source, while other types of insertions, such as tandem duplications and processed pseudogenes, are rare. We inferred mechanisms of deletion generations and found that most non-allelic homolog recombination (NAHR) events were caused by recombination errors in SINEs. Analysis of somatic mutations in liver cancers showed that long reads could detect larger numbers of SVs than a previous short-read study and that mechanisms of cancer SV generation were different from that of germline deletions. Our analysis provides a comprehensive catalog of polymorphic and somatic SVs, as well as their possible causes. Our software are available at https//github.com/afujimoto/CAMPHOR and https//github.com/afujimoto/CAMPHORsomatic .Our analysis provides a comprehensive catalog of polymorphic and somatic SVs, as well as their possible causes. Our software are available at https//github.com/afujimoto/CAMPHOR and https//github.com/afujimoto/CAMPHORsomatic . PRESSURE 2 is a randomised evaluation of the clinical and cost-effectiveness of two types of mattress for the prevention of pressure ulcers (PUs). The primary clinical endpoint was time to development of a category ≥2 PU. The current 'gold standard' for PU identification is expert clinical assessment. Due to the mattress appearance, a blinded assessment of the endpoint is not possible. This poses a risk to the internal validity of the study. A possible approach is to use photographs of skin sites, with central blinded review. However, there are practical and scientific concerns including patients' consent to photographs, burden of data collection, photograph quality, data completeness and comparison of photographs to the current 'gold standard'. This paper reports the findings of the PRESSURE 2 photographic validation sub-study. Where consent was obtained, photographs were taken of all category ≥2 PUs on the first presentation to assess over-reporting, and for the assessment of under-reporting, a random s device trials. The reliability of central blinded expert photography was found to be 'very good' (PABAK). Photographs have been found to be an acceptable method of data validation for participants. Methods to improve the quality of photographs would increase the confidence in the assessments. ISRCTN Registry ISRCTN01151335 . Registered on 19 April 2013.ISRCTN Registry ISRCTN01151335 . Registered on 19 April 2013.The remarkable developments in computer vision and machine learning have changed the methodologies of many scientific disciplines. They have also created a new research field in wood science called computer vision-based wood identification, which is making steady progress towards the goal of building automated wood identification systems to meet the needs of the wood industry and market. Nevertheless, computer vision-based wood identification is still only a small area in wood science and is still unfamiliar to many wood anatomists. To familiarize wood scientists with the artificial intelligence-assisted wood anatomy and engineering methods, we have reviewed the published mainstream studies that used or developed machine learning procedures. This review could help researchers understand computer vision and machine learning techniques for wood identification and choose appropriate techniques or strategies for their study objectives in wood science. Platelet-rich plasma (PRP) has been used to favor anterior cruciate ligament (ACL) healing after reconstruction surgeries. However, clinical data are still inconclusive and subjective about PRP. check details Thus, we propose a quantitative method to demonstrate that PRP produced morphological structure changes. Thirty-four patients undergoing ACL reconstruction surgery were evaluated and divided into control group (sixteen patients) without PRP application and experiment group (eighteen patients) with intraoperative application of PRP. Magnetic resonance imaging (MRI) scans were performed 3 months after surgery. We used Matlab® and machine learning (ML) in Orange Canvas® to texture analysis (TA) features extraction. Experienced radiologists delimited the regions of interest (RoIs) in the T2-weighted images. Sixty-two texture parameters were extracted, including gray-level co-occurrence matrix and gray level run length. We used the algorithms logistic regression (LR), naive Bayes (NB), and stochastic gradient descent (SGD). The accuracy of the classification with NB, LR, and SGD was 83.3%, 75%, 75%, respectively. For the area under the curve, NB, LR, and SGD presented values of 91.7%, 94.4%, 75%, respectively. In clinical evaluations, the groups show similar responses in terms of improvement in pain and increase in the IKDC index (International Knee Documentation Committee) and Lysholm score indices differing only in the assessment of flexion, which presents a significant difference for the group treated with PRP. Here, we demonstrated quantitatively that patients who received PRP presented texture changes when compared to the control group. Thus, our findings suggest that PRP interferes with morphological parameters of the ACL. Protocol no. CAAE 56164316.6.0000.5411.Protocol no. CAAE 56164316.6.0000.5411.