A randomized algorithm for the QR decomposition-based approximate SVD

Matrix decomposition is a very important mathematical tool in numerical linear algebra for data processing. In this paper, we introduce a new randomized matrix decomposition algorithm, which is called randomized approximate SVD based on Qatar Riyal decomposition (RCSVD-QR). Our method utilize random sampling and the OR decomposition to address a serious bottlenck associated with classical SVD. RCSVD-QR gives satisfactory convergence speed as well as accuracy as compared to those state-of-the-art algorithms. In addition, we provides an estimate for the expected approximation error in Frobenius norm. Numerical experiments verify these claims.Comment: 6 pages,6 figure

Multicolor stimulated Raman scattering microscopy

Stimulated Raman scattering (SRS) microscopy has opened up a wide range of biochemical imaging applications by probing a particular Raman-active molecule vibrational mode in the specimen. However, the original implementation with picosecond pulse excitation can only realize rapid chemical mapping with a single Raman band. Here we present a novel SRS microscopic technique using a grating-based pulse shaper for excitation and a grating-based spectrograph for detection to achieve simultaneous multicolor SRS imaging with high sensitivity and high acquisition speeds. In particular, we use a linear combination of the measured $CH_2$ and $CH_3$ stretching signals to map the distributions of protein and lipid contents simultaneously.Chemistry and Chemical Biolog

Exploring Speech Enhancement for Low-resource Speech Synthesis

High-quality and intelligible speech is essential to text-to-speech (TTS) model training, however, obtaining high-quality data for low-resource languages is challenging and expensive. Applying speech enhancement on Automatic Speech Recognition (ASR) corpus mitigates the issue by augmenting the training data, while how the nonlinear speech distortion brought by speech enhancement models affects TTS training still needs to be investigated. In this paper, we train a TF-GridNet speech enhancement model and apply it to low-resource datasets that were collected for the ASR task, then train a discrete unit based TTS model on the enhanced speech. We use Arabic datasets as an example and show that the proposed pipeline significantly improves the low-resource TTS system compared with other baseline methods in terms of ASR WER metric. We also run empirical analysis on the correlation between speech enhancement and TTS performances.Comment: Submitted to ICASSP 202

The Pawnee earthquake as a result of the interplay among injection, faults and foreshocks

The Pawnee M5.8 earthquake is the largest event in Oklahoma instrument recorded history. It occurred near the edge of active seismic zones, similar to other M5+ earthquakes since 2011. It ruptured a previously unmapped fault and triggered aftershocks along a complex conjugate fault system. With a high-resolution earthquake catalog, we observe propagating foreshocks leading to the mainshock within 0.5 km distance, suggesting existence of precursory aseismic slip. At approximately 100 days before the mainshock, two M ≥ 3.5 earthquakes occurred along a mapped fault that is conjugate to the mainshock fault. At about 40 days before, two earthquakes clusters started, with one M3 earthquake occurred two days before the mainshock. The three M ≥ 3 foreshocks all produced positive Coulomb stress at the mainshock hypocenter. These foreshock activities within the conjugate fault system are near-instantaneously responding to variations in injection rates at 95% confidence. The short time delay between injection and seismicity differs from both the hypothetical expected time scale of diffusion process and the long time delay observed in this region prior to 2016, suggesting a possible role of elastic stress transfer and critical stress state of the fault. Our results suggest that the Pawnee earthquake is a result of interplay among injection, tectonic faults, and foreshocks

WaterScenes: A Multi-Task 4D Radar-Camera Fusion Dataset and Benchmark for Autonomous Driving on Water Surfaces

Autonomous driving on water surfaces plays an essential role in executing hazardous and time-consuming missions, such as maritime surveillance, survivors rescue, environmental monitoring, hydrography mapping and waste cleaning. This work presents WaterScenes, the first multi-task 4D radar-camera fusion dataset for autonomous driving on water surfaces. Equipped with a 4D radar and a monocular camera, our Unmanned Surface Vehicle (USV) proffers all-weather solutions for discerning object-related information, including color, shape, texture, range, velocity, azimuth, and elevation. Focusing on typical static and dynamic objects on water surfaces, we label the camera images and radar point clouds at pixel-level and point-level, respectively. In addition to basic perception tasks, such as object detection, instance segmentation and semantic segmentation, we also provide annotations for free-space segmentation and waterline segmentation. Leveraging the multi-task and multi-modal data, we conduct numerous experiments on the single modality of radar and camera, as well as the fused modalities. Results demonstrate that 4D radar-camera fusion can considerably enhance the robustness of perception on water surfaces, especially in adverse lighting and weather conditions. WaterScenes dataset is public on https://waterscenes.github.io

Gene Co-Expression Networks Restructured Gene Fusion in Rhabdomyosarcoma Cancers

Rhabdomyosarcoma is subclassified by the presence or absence of a recurrent chromosome translocation that fuses the FOXO1 and PAX3 or PAX7 genes. The fusion protein (FOXO1-PAX3/7) retains both binding domains and becomes a novel and potent transcriptional regulator in rhabdomyosarcoma subtypes. Many studies have characterized and integrated genomic, transcriptomic, and epigenomic differences among rhabdomyosarcoma subtypes that contain the FOXO1-PAX3/7 gene fusion and those that do not; however, few investigations have investigated how gene co-expression networks are altered by FOXO1-PAX3/7. Although transcriptional data offer insight into one level of functional regulation, gene co-expression networks have the potential to identify biological interactions and pathways that underpin oncogenesis and tumorigenicity. Thus, we examined gene co-expression networks for rhabdomyosarcoma that were FOXO1-PAX3 positive, FOXO1-PAX7 positive, or fusion negative. Gene co-expression networks were mined using local maximum Quasi-Clique Merger (lmQCM) and analyzed for co-expression differences among rhabdomyosarcoma subtypes. This analysis observed 41 co-expression modules that were shared between fusion negative and positive samples, of which 17/41 showed significant up- or down-regulation in respect to fusion status. Fusion positive and negative rhabdomyosarcoma showed differing modularity of co-expression networks with fusion negative (n = 109) having significantly more individual modules than fusion positive (n = 53). Subsequent analysis of gene co-expression networks for PAX3 and PAX7 type fusions observed 17/53 were differentially expressed between the two subtypes. Gene list enrichment analysis found that gene ontology terms were poorly matched with biological processes and molecular function for most co-expression modules identified in this study; however, co-expressed modules were frequently localized to cytobands on chromosomes 8 and 11. Overall, we observed substantial restructuring of co-expression networks relative to fusion status and fusion type in rhabdomyosarcoma and identified previously overlooked genes and pathways that may be targeted in this pernicious disease

Anticonvulsant activities of α-asaronol ((E)-3'-hydroxyasarone), an active constituent derived from α-asarone.

BACKGROUND: Epilepsy is one of chronic neurological disorders that affects 0.5-1.0% of the world's population during their lifetime. There is a still significant need to develop novel anticonvulsant drugs that possess superior efficacy, broad spectrum of activities and good safety profile. METHODS: α-Asaronol and two current antiseizure drugs (α-asarone and carbamazepine (CBZ)) were assessed by in vivo anticonvulsant screening with the three most employed standard animal seizure models, including maximal electroshock seizure (MES), subcutaneous injection-pentylenetetrazole (PTZ)-induced seizures and 3-mercaptopropionic acid (3-MP)-induced seizures in mice. Considering drug safety evaluation, acute neurotoxicity was assessed with minimal motor impairment screening determined in the rotarod test, and acute toxicity was also detected in mice. RESULTS: In our results, α-asaronol displayed a broad spectrum of anticonvulsant activity (ACA) and showed better protective indexes (PI = 11.11 in MES, PI = 8.68 in PTZ) and lower acute toxicity (LD50 = 2940 mg/kg) than its metabolic parent compound (α-asarone). Additionally, α-asaronol displayed a prominent anticonvulsant profile with ED50 values of 62.02 mg/kg in the MES and 79.45 mg/kg in the sc-PTZ screen as compared with stiripentol of ED50 of 240 mg/kg and 115 mg/kg in the relevant test, respectively. CONCLUSION: The results of the present study revealed α-asaronol can be developed as a novel molecular in the search for safer and efficient anticonvulsants having neuroprotective effects as well as low toxicity. Meanwhile, the results also suggested that α-asaronol has great potential to develop into another new aromatic allylic alcohols type anticonvulsant drug for add-on therapy of Dravet's syndrome

A pan-kidney cancer study identifies subtype specific perturbations on pathways with potential drivers in renal cell carcinoma

Background: Renal cell carcinoma (RCC) is a complex disease and is comprised of several histological subtypes, the most frequent of which are clear cell renal cell carcinoma (ccRCC), papillary renal cell carcinoma (PRCC) and chromophobe renal cell carcinoma (ChRCC). While lots of studies have been performed to investigate the molecular characterizations of different subtypes of RCC, our knowledge regarding the underlying mechanisms are still incomplete. As molecular alterations are eventually reflected on the pathway level to execute certain biological functions, characterizing the pathway perturbations is crucial for understanding tumorigenesis and development of RCC. Methods: In this study, we investigated the pathway perturbations of various RCC subtype against normal tissue based on differential expressed genes within a certain pathway. We explored the potential upstream regulators of subtype-specific pathways with Ingenuity Pathway Analysis (IPA). We also evaluated the relationships between subtype-specific pathways and clinical outcome with survival analysis. Results: In this study, we carried out a pathway-based analysis to explore the mechanisms of various RCC subtypes with TCGA RNA-seq data. Both commonly altered pathways and subtype-specific pathways were detected. To identify the distinctive characteristics of each subtype, we focused on subtype-specific perturbed pathways. Specifically, we observed that some of the altered pathways were regulated by several recurrent upstream regulators which presenting different expression patterns among distinct RCC subtypes. We also noticed that a large number of perturbed pathways were controlled by the subtype-specific upstream regulators. Moreover, we also evaluated the relationships between perturbed pathways and clinical outcome. Prognostic pathways were identified and their roles in tumor development and progression were inferred. Conclusions: In summary, we evaluated the relationships among pathway perturbations, upstream regulators and clinical outcome for differential subtypes in RCC. We hypothesized that the alterations of common upstream regulators as well as subtype-specific upstream regulators work together to affect the downstream pathway perturbations and drive cancer initialization and prognosis. Our findings not only increase our understanding of the mechanisms of various RCC subtypes, but also provide targets for personalized therapeutic intervention

Clinical effects of 3D printing-assisted posterolateral incision in the treatment of ankle fractures involving the posterior malleolus

ObjectiveTo explore the clinical outcomes of a 3D printing-assisted posterolateral approach for the treatment of ankle fractures involving the posterior malleolus.MethodsA total of 51 patients with ankle fractures involving the posterior malleolus admitted to our hospital from January 2018 to December 2019 were selected. The patients were divided into 3D printing group (28 cases) and control group (23 cases). 3D printing was performed for ankle fractures, followed by printing of a solid model and simulation of the operation on the 3D model. The operation was then performed according to the preoperative plan, including open reduction and internal fixation via the posterolateral approach with the patient in the prone position. Routine x-ray and CT examinations of the ankle joint were performed, and ankle function was evaluated using the American Foot and Ankle Surgery Association (AOFAS) ankle-hindfoot score.ResultsAll patients underwent x-ray and CT examinations. All fractures healed clinically, without loss of reduction or failure of internal fixation. Good clinical effects were achieved in both groups of patients. The operation time, intraoperative blood loss and intraoperative fluoroscopy frequency in the 3D printing group were significantly less than those in the control group (p < 0.05). There was no significant difference between the two groups in the anatomical reduction rate of fractures or the incidence of surgical complications (p > 0.05).ConclusionThe 3D printing-assisted posterolateral approach is effective in the treatment of ankle fractures involving the posterior malleolus. The approach can be well planned before the operation, is simple to perform, yields good fracture reduction and fixation, and has good prospects for clinical application

Scaling Speech Technology to 1,000+ Languages

Expanding the language coverage of speech technology has the potential to improve access to information for many more people. However, current speech technology is restricted to about one hundred languages which is a small fraction of the over 7,000 languages spoken around the world. The Massively Multilingual Speech (MMS) project increases the number of supported languages by 10-40x, depending on the task. The main ingredients are a new dataset based on readings of publicly available religious texts and effectively leveraging self-supervised learning. We built pre-trained wav2vec 2.0 models covering 1,406 languages, a single multilingual automatic speech recognition model for 1,107 languages, speech synthesis models for the same number of languages, as well as a language identification model for 4,017 languages. Experiments show that our multilingual speech recognition model more than halves the word error rate of Whisper on 54 languages of the FLEURS benchmark while being trained on a small fraction of the labeled data