MERLIon CCS Challenge: A English-Mandarin code-switching child-directed speech corpus for language identification and diarization

To enhance the reliability and robustness of language identification (LID) and language diarization (LD) systems for heterogeneous populations and scenarios, there is a need for speech processing models to be trained on datasets that feature diverse language registers and speech patterns. We present the MERLIon CCS challenge, featuring a first-of-its-kind Zoom video call dataset of parent-child shared book reading, of over 30 hours with over 300 recordings, annotated by multilingual transcribers using a high-fidelity linguistic transcription protocol. The audio corpus features spontaneous and in-the-wild English-Mandarin code-switching, child-directed speech in non-standard accents with diverse language-mixing patterns recorded in a variety of home environments. This report describes the corpus, as well as LID and LD results for our baseline and several systems submitted to the MERLIon CCS challenge using the corpus.Comment: Accepted by Interspeech 2023, 5 pages, 2 figures, 3 table

International genome-wide meta-analysis identifies new primary biliary cirrhosis risk loci and targetable pathogenic pathways.

Primary biliary cirrhosis (PBC) is a classical autoimmune liver disease for which effective immunomodulatory therapy is lacking. Here we perform meta-analyses of discovery data sets from genome-wide association studies of European subjects (n=2,764 cases and 10,475 controls) followed by validation genotyping in an independent cohort (n=3,716 cases and 4,261 controls). We discover and validate six previously unknown risk loci for PBC (Pcombined<5 × 10(-8)) and used pathway analysis to identify JAK-STAT/IL12/IL27 signalling and cytokine-cytokine pathways, for which relevant therapies exist

An update on the recent advances and discovery of novel tubulin colchicine binding inhibitors

Microtubules, formed by α- and β-tubulin heterodimer, are considered as a major target to prevent the proliferation of tumor cells. Microtubule-targeted agents have become increasingly effective anticancer drugs. However, due to the relatively sophisticated chemical structure of taxane and vinblastine, their application has faced numerous obstacles. Conversely, the structure of colchicine binding site inhibitors (CBSIs) is much easier to be modified. Moreover, CBSIs have strong antiproliferative effect on multidrug-resistant tumor cells and have become the mainstream research orientation of microtubule-targeted agents. This review focuses mainly on the recent advances of CBSIs during 2017–2022, attempts to depict their biological activities to analyze the structure–activity relationships and offers new perspectives for designing next generation of novel CBSIs

MERLIon CCS Challenge: A English-Mandarin code-switching child-directed speech corpus for language identification and diarization

To enhance the reliability and robustness of language identification (LID) and language diarization (LD) systems for heterogeneous populations and scenarios, there is a need for speech processing models to be trained on datasets that feature diverse language registers and speech patterns. We present the MERLIon CCS challenge, featuring a first-of-its-kind Zoom video call dataset of parent-child shared book reading, of over 30 hours with over 300 recordings, annotated by multilingual transcribers using a high-fidelity linguistic transcription protocol. The audio corpus features spontaneous and in-the-wild English-Mandarin code-switching, child-directed speech in non-standard accents with diverse language-mixing patterns recorded in a variety of home environments. This report describes the corpus, as well as LID and LD results for our baseline and several systems submitted to the MERLIon CCS challenge using the corpus.Biomaterials & Tissue BiomechanicsSignal Processing System

Prototype Small-Animal PET-CT Imaging System for Image-Guided Radiation Therapy

Molecular imaging is becoming essential for precision targeted radiation therapy, yet progress is hindered from a lack of integrated imaging and treatment systems. We report the development of a prototype positron emission tomography (PET) scanner integrated into a commercial cone beam computed tomography (CBCT) based small animal irradiation system for molecular-image-guided, targeted external beam radiation therapy. The PET component consists of two rotating Hamamatsu time-of-flight PET modules positioned with a bore diameter of 101.6 mm and a radial field-of-view of 53.1 mm. The measured energy resolution after linearity correction at 511 KeV was 12.9% and the timing resolution was 283.6 ps. The measured spatial resolutions at the field-of-view center and 5 mm off the radial center were 2.6 mm × 2.6 mm × 1.6 mm and 2.6 mm × 2.6 mm × 2.7 mm respectively. 18F-Fluorodeoxyglucose-based PET imaging of a NEMA NU 4-2008 phantom resolved cylindrical volumes with diameters as small as 3 mm. To validate the system in-vivo, we performed 64Cu-DOTA-M5A PET and computed tomography (CT) imaging of carcinoembryonic antigen (CEA)-positive colorectal cancer in athymic nude mice and compared the results with a commercially available Siemens Inveon PET/CT system. The prototype PET system performed comparably to the Siemens system for identifying the location, size, and shape of tumors. Regions of heterogeneous 64Cu-DOTA-M5A uptake were observed. Using 64Cu-DOTA-M5A PET and CT images, a Monte Carlo-based radiation treatment plan was created to escalate the dose to the 64Cu-DOTA-M5A-based, highly active, biological target volume while largely sparing the normal tissue. Results demonstrate the feasibility of molecular-image-guided treatment plans using the prototype theranostic system

AI-Based Automated Lipomatous Tumor Segmentation in MR Images: Ensemble Solution to Heterogeneous Data.

Deep learning (DL) has been proposed to automate image segmentation and provide accuracy, consistency, and efficiency. Accurate segmentation of lipomatous tumors (LTs) is critical for correct tumor radiomics analysis and localization. The major challenge of this task is data heterogeneity, including tumor morphological characteristics and multicenter scanning protocols. To mitigate the issue, we aimed to develop a DL-based Super Learner (SL) ensemble framework with different data correction and normalization methods. Pathologically proven LTs on pre-operative T1-weighted/proton-density MR images of 185 patients were manually segmented. The LTs were categorized by tumor locations as distal upper limb (DUL), distal lower limb (DLL), proximal upper limb (PUL), proximal lower limb (PLL), or Trunk (T) and grouped by 80%/9%/11% for training, validation and testing. Six configurations of correction/normalization were applied to data for fivefold-cross-validation trainings, resulting in 30 base learners (BLs). A SL was obtained from the BLs by optimizing SL weights. The performance was evaluated by dice-similarity-coefficient (DSC), sensitivity, specificity, and Hausdorff distance (HD95). For predictions of the BLs, the average DSC, sensitivity, and specificity from the testing data were 0.72 [Formula: see text] 0.16, 0.73 [Formula: see text] 0.168, and 0.99 [Formula: see text] 0.012, respectively, while for SL predictions were 0.80 [Formula: see text] 0.184, 0.78 [Formula: see text] 0.193, and 1.00 [Formula: see text] 0.010. The average HD95 of the BLs were 11.5 (DUL), 23.2 (DLL), 25.9 (PUL), 32.1 (PLL), and 47.9 (T) mm, whereas of SL were 1.7, 8.4, 15.9, 2.2, and 36.6&nbsp;mm, respectively.&nbsp;The proposed method could improve the segmentation accuracy and mitigate the performance instability and data heterogeneity aiding the differential diagnosis of LTs in real clinical situations