LERC: Coordinated Cache Management for Data-Parallel Systems

Memory caches are being aggressively used in today's data-parallel frameworks such as Spark, Tez and Storm. By caching input and intermediate data in memory, compute tasks can witness speedup by orders of magnitude. To maximize the chance of in-memory data access, existing cache algorithms, be it recency- or frequency-based, settle on cache hit ratio as the optimization objective. However, unlike the conventional belief, we show in this paper that simply pursuing a higher cache hit ratio of individual data blocks does not necessarily translate into faster task completion in data-parallel environments. A data-parallel task typically depends on multiple input data blocks. Unless all of these blocks are cached in memory, no speedup will result. To capture this all-or-nothing property, we propose a more relevant metric, called effective cache hit ratio. Specifically, a cache hit of a data block is said to be effective if it can speed up a compute task. In order to optimize the effective cache hit ratio, we propose the Least Effective Reference Count (LERC) policy that persists the dependent blocks of a compute task as a whole in memory. We have implemented the LERC policy as a memory manager in Spark and evaluated its performance through Amazon EC2 deployment. Evaluation results demonstrate that LERC helps speed up data-parallel jobs by up to 37% compared with the widely employed least-recently-used (LRU) policy

Research Progress on Intervention of Natural Products from Plants in Neurotoxicity of Acrylamide

Acrylamide (ACR) is a common toxic substance in foods, which can cause serious damage to human organs and systems, especially the nervous system. At present, there are no appropriate measures to prevent and treat ACR neurotoxicity. In recent years, it has been reported that some natural plant products with high safety for consumption, strong antioxidant activity and low cost can intervene in ACR-induced neurotoxicity. This paper mainly introduces the neurotoxicity of ACR, natural plant products that can intervene in ACR neurotoxicity, and the underlying mechanism of action in order to provide a theoretical reference and research ideas for the treatment of ACR neurotoxicity in multiple ways and though multiple targets, as well as the development and application of natural products against ACR neurotoxicity

Spinal disease diagnosis assistant based on MRI images using deep transfer learning methods

IntroductionIn light of the potential problems of missed diagnosis and misdiagnosis in the diagnosis of spinal diseases caused by experience differences and fatigue, this paper investigates the use of artificial intelligence technology for auxiliary diagnosis of spinal diseases.MethodsThe LableImg tool was used to label the MRIs of 604 patients by clinically experienced doctors. Then, in order to select an appropriate object detection algorithm, deep transfer learning models of YOLOv3, YOLOv5, and PP-YOLOv2 were created and trained on the Baidu PaddlePaddle framework. The experimental results showed that the PP-YOLOv2 model achieved a 90.08% overall accuracy in the diagnosis of normal, IVD bulges and spondylolisthesis, which were 27.5 and 3.9% higher than YOLOv3 and YOLOv5, respectively. Finally, a visualization of the intelligent spine assistant diagnostic software based on the PP-YOLOv2 model was created and the software was made available to the doctors in the spine and osteopathic surgery at Guilin People's Hospital.Results and discussionThis software automatically provides auxiliary diagnoses in 14.5 s on a standard computer, is much faster than doctors in diagnosing human spines, which typically take 10 min, and its accuracy of 98% can be compared to that of experienced doctors in the comparison of various diagnostic methods. It significantly improves doctors' working efficiency, reduces the phenomenon of missed diagnoses and misdiagnoses, and demonstrates the efficacy of the developed intelligent spinal auxiliary diagnosis software

The aza-Morita-Baylis-Hillman reaction of electronically and sterically deactivated substrates.

The aza-Morita–Baylis–Hillman (azaMBH) reaction has been studied for electronically and sterically deactivated Michael acceptors. It is found that electronically deactivated systems can be converted with electron-rich phosphanes and pyridines as catalysts equally well. For sterically deactivated systems clearly better catalytic turnover can be achieved with pyridine catalysts. This is in accordance with the calculated affinities of the catalysts towards different Michael-acceptors

LyricWhiz: Robust Multilingual Zero-shot Lyrics Transcription by Whispering to ChatGPT

We introduce LyricWhiz, a robust, multilingual, and zero-shot automatic lyrics transcription method achieving state-of-the-art performance on various lyrics transcription datasets, even in challenging genres such as rock and metal. Our novel, training-free approach utilizes Whisper, a weakly supervised robust speech recognition model, and GPT-4, today's most performant chat-based large language model. In the proposed method, Whisper functions as the "ear" by transcribing the audio, while GPT-4 serves as the "brain," acting as an annotator with a strong performance for contextualized output selection and correction. Our experiments show that LyricWhiz significantly reduces Word Error Rate compared to existing methods in English and can effectively transcribe lyrics across multiple languages. Furthermore, we use LyricWhiz to create the first publicly available, large-scale, multilingual lyrics transcription dataset with a CC-BY-NC-SA copyright license, based on MTG-Jamendo, and offer a human-annotated subset for noise level estimation and evaluation. We anticipate that our proposed method and dataset will advance the development of multilingual lyrics transcription, a challenging and emerging task.Comment: 9 pages, 2 figures, 5 tables, accepted by ISMIR 202

Distinct distribution and prognostic significance of molecular subtypes of breast cancer in Chinese women: a population-based cohort study

<p>Abstract</p> <p>Background</p> <p>Molecular classification of breast cancer is an important prognostic factor. The distribution of molecular subtypes of breast cancer and their prognostic value has not been well documented in Asians.</p> <p>Methods</p> <p>A total of 2,791 breast cancer patients recruited for a population-based cohort study were evaluated for molecular subtypes of breast cancer by immunohistochemical assays. Data on clinicopathological characteristics were confirmed by centralized pathology review. The average follow-up of the patients was 53.4 months. Overall and disease-free survival by molecular subtypes of breast cancer were evaluated.</p> <p>Results</p> <p>The prevalence of the luminal A, luminal B, human epidermal growth factor receptor 2 (HER2), and triple-negative subtypes were 48.6%, 16.7%, 13.7%, and 12.9%, respectively. The luminal A subtype was more likely to be diagnosed in older women (P = 0.03) and had a stronger correlation with favorable clinicopathological factors (smaller tumor size, lower histologic grade, and earlier TNM stage) than the triple-negative or HER2 subtypes. Women with triple-negative breast cancer had a higher frequency of family history of breast cancer than women with other subtypes (P = 0.048). The 5-year overall/disease-free survival percentages for the luminal A, luminal B, HER2, and triple-negative subtypes were 92.9%/88.6%, 88.6%/85.1%, 83.2%/79.1%, and 80.7%/76.0%, respectively. A similar pattern was observed in multivariate analyses. Immunotherapy was associated with improved overall and disease-free survival for luminal A breast cancer, but reduced disease-free survival (HR = 2.21, 95% CI, 1.09-4.48) for the HER2 subtype of breast cancer.</p> <p>Conclusions</p> <p>The triple-negative and HER2 subtypes were associated with poorer outcomes compared with the luminal A subtype among these Chinese women. The HER2 subtype was more prevalent in this Chinese population compared with Western populations, suggesting the importance of standardized HER2 detection and anti-HER2 therapy to potentially benefit a high proportion of breast cancer patients in China.</p

DSSylation, a novel protein modification targets proteins induced by oxidative stress, and facilitates their degradation in cells

Timely removal of oxidatively damaged proteins is critical for cells exposed to oxidative stresses; however, cellular mechanism for clearing oxidized proteins is not clear. Our study reveals a novel type of protein modification that may play a role in targeting oxidized proteins and remove them. In this process, DSS1 (deleted in split hand/split foot 1), an evolutionally conserved small protein, is conjugated to proteins induced by oxidative stresses in vitro and in vivo, implying oxidized proteins are DSS1 clients. A subsequent ubiquitination targeting DSS1-protein adducts has been observed, suggesting the client proteins are degraded through the ubiquitin-proteasome pathway. The DSS1 attachment to its clients is evidenced to be an enzymatic process modulated by an unidentified ATPase. We name this novel protein modification as DSSylation, in which DSS1 plays as a modifier, whose attachment may render target proteins a signature leading to their subsequent ubiquitination, thereby recruits proteasome to degrade them.Electronic supplementary materialThe online version of this article (doi:10.1007/s13238-013-0018-8) contains supplementary material, which is available to authorized users