Optimized Merge Sort on Modern Commodity Multi-core CPUs

Sorting is a kind of widely used basic algorithms. As the high performance computing devices are increasingly common, more and more modern commodity machines have the capability of parallel concurrent computing. A new implementation of sorting algorithms is proposed to harness the power of newer SIMD operations and multi-core computing provided by modern CPUs. The algorithm is hybrid by optimized bitonic sorting network and multi-way merge. New SIMD instructions provided by modern CPUs are used in the bitonic network implementation, which adopted a different method to arrange data so that the number of SIMD operations is reduced. Balanced binary trees are used in multi-way merge, which is also different with former implementations. Efforts are also paid on minimizing data moving in memory since merge sort is a kind of memory-bound application. The performance evaluation shows that the proposed algorithm is twice as fast as the sort function in C++ standard library when only single thread is used. It also outperforms radix sort implemented in Boost library

TPH-2 polymorphisms interact with early life stress to influence response to treatment with antidepressant drugs

Background: Variation in genes implicated in monoamine neurotransmission may interact with environmental factors to influence antidepressant response. We aimed to determine how a range of single nucleotide polymorphisms in monoaminergic genes influence this response to treatment and how they interact with childhood trauma and recent life stress in a Chinese sample. An initial study of monoaminergic coding region single nucleotide polymorphisms identified significant associations of TPH2 and HTR1B single nucleotide polymorphisms with treatment response that showed interactions with childhood and recent life stress, respectively (Xu et al., 2012). Methods: A total of 47 further single nucleotide polymorphisms in 17 candidate monoaminergic genes were genotyped in 281 Chinese Han patients with major depressive disorder. Response to 6 weeks’ antidepressant treatment was determined by change in the 17-item Hamilton Depression Rating Scale score, and previous stressful events were evaluated by the Life Events Scale and Childhood Trauma Questionnaire-Short Form. Results: Three TPH2 single nucleotide polymorphisms (rs11178998, rs7963717, and rs2171363) were significantly associated with antidepressant response in this Chinese sample, as was a haplotype in TPH2 (rs2171363 and rs1487278). One of these, rs2171363, showed a significant interaction with childhood adversity in its association with antidepressant response. Conclusions: These findings provide further evidence that variation in TPH2 is associated with antidepressant response and may also interact with childhood trauma to influence outcome of antidepressant treatment

Geochemical characteristics of biogenic barium in sediments of the Antarctica Ross Sea and their indication for paleoproductivity

241-248In this paper, the biogenic Ba of Column R11 in the Antarctic Ross Sea and its implications to the paleo oceanographic productivity since the late of Late Quaternary were discussed, combined with the organic carbon, opal and biogenic calcium carbonate. The biogenic Ba contents ranged from 51.8 to 508.4 μg/g overall, exhibiting a gradually rising trend from the bottom to the top. It highly correlated both with TOC and opal, revealing that on one hand biogenic Ba can be used to study the change of productivity in the Ross Sea; and on the other hand, the marine productivity gradually increased since the late Pleistocene. The new productivity based on Francois model varied from 0.40 to 233.90 gC/(m2•a). The high values were mainly concentrated at the depth from 32 to 48 cm, but the new productivity values of the bottom were lower. It was inferred that the change in marine productivity in the Ross Sea was possibly affected by the ice cover since the late Pleistocene

A two-stage framework for optical coherence tomography angiography image quality improvement

IntroductionOptical Coherence Tomography Angiography (OCTA) is a new non-invasive imaging modality that gains increasing popularity for the observation of the microvasculatures in the retina and the conjunctiva, assisting clinical diagnosis and treatment planning. However, poor imaging quality, such as stripe artifacts and low contrast, is common in the acquired OCTA and in particular Anterior Segment OCTA (AS-OCTA) due to eye microtremor and poor illumination conditions. These issues lead to incomplete vasculature maps that in turn makes it hard to make accurate interpretation and subsequent diagnosis.MethodsIn this work, we propose a two-stage framework that comprises a de-striping stage and a re-enhancing stage, with aims to remove stripe noise and to enhance blood vessel structure from the background. We introduce a new de-striping objective function in a Stripe Removal Net (SR-Net) to suppress the stripe noise in the original image. The vasculatures in acquired AS-OCTA images usually exhibit poor contrast, so we use a Perceptual Structure Generative Adversarial Network (PS-GAN) to enhance the de-striped AS-OCTA image in the re-enhancing stage, which combined cyclic perceptual loss with structure loss to achieve further image quality improvement.Results and discussionTo evaluate the effectiveness of the proposed method, we apply the proposed framework to two synthetic OCTA datasets and a real AS-OCTA dataset. Our results show that the proposed framework yields a promising enhancement performance, which enables both conventional and deep learning-based vessel segmentation methods to produce improved results after enhancement of both retina and AS-OCTA modalities

Advanced deep learning models for phenotypic trait extraction and cultivar classification in lychee using photon-counting micro-CT imaging

IntroductionIn contemporary agronomic research, the focus has increasingly shifted towards non-destructive imaging and precise phenotypic characterization. A photon-counting micro-CT system has been developed, which is capable of imaging lychee fruit at the micrometer level and capturing a full energy spectrum, thanks to its advanced photon-counting detectors.MethodsFor automatic measurement of phenotypic traits, seven CNN-based deep learning models including AttentionUNet, DeeplabV3+, SegNet, TransUNet, UNet, UNet++, and UNet3+ were developed. Machine learning techniques tailored for small-sample training were employed to identify key characteristics of various lychee species.ResultsThese models demonstrate outstanding performance with Dice, Recall, and Precision indices predominantly ranging between 0.90 and 0.99. The Mean Intersection over Union (MIoU) consistently falls between 0.88 and 0.98. This approach served both as a feature selection process and a means of classification, significantly enhancing the study's ability to discern and categorize distinct lychee varieties.DiscussionThis research not only contributes to the advancement of non-destructive plant analysis but also opens new avenues for exploring the intricate phenotypic variations within plant species