Audio Contrastive based Fine-tuning

Audio classification plays a crucial role in speech and sound processing tasks with a wide range of applications. There still remains a challenge of striking the right balance between fitting the model to the training data (avoiding overfitting) and enabling it to generalise well to a new domain. Leveraging the transferability of contrastive learning, we introduce Audio Contrastive-based Fine-tuning (AudioConFit), an efficient approach characterised by robust generalisability. Empirical experiments on a variety of audio classification tasks demonstrate the effectiveness and robustness of our approach, which achieves state-of-the-art results in various settings.Comment: Under revie

Evaluation of a village-based digital health kiosks program: A protocol for a cluster randomized clinical trial

Background To address disparities in healthcare quality and access between rural and urban areas in China, reforms emphasize strengthening primary care and digital health utilization. Yet, evidence on digital health approaches in rural areas is lacking. Objective This study will evaluate the effectiveness of Guangdong Second Provincial General Hospital's Digital Health Kiosk program, which uses the Dingbei telemedicine platform to connect rural clinicians to physicians in upper-level health facilities and provide access to artificial intelligence-enabled diagnostic support. We hypothesize that our interventions will increase healthcare utilization and patient satisfaction, decrease out-of-pocket costs, and improve health outcomes. Methods This cluster randomized control trial will enroll clinics according to a partial factorial design. Clinics will be randomized to either a control arm with clinician medical training, a second arm additionally receiving Dingbei telemedicine training, or a third arm with monetary incentives for patient visits conducted through Dingbei plus all prior interventions. Clinics in the second and third arm will then be orthogonally randomized to a social marketing arm that targets villager awareness of the kiosk program. We will use surveys and Dingbei administrative data to evaluate clinic utilization, revenue, and clinician competency, as well as patient satisfaction and expenses. Results We have received ethical approval from Guangdong Second Provincial General Hospital (IRB approval number: GD2H-KY IRB-AF-SC.07-01.1), Peking University (IRB00001052-21007), and the University of North Carolina at Chapel Hill (323385). Study enrollment began April 2022. Conclusions This study has the potential to inform future telemedicine approaches and assess telemedicine as a method to address disparities in healthcare access. Trial registration number: ChiCTR210005387

SciMMIR:Benchmarking Scientific Multi-modal Information Retrieval

Multi-modal information retrieval (MMIR) is a rapidly evolving field, where significant progress, particularly in image-text pairing, has been made through advanced representation learning and cross-modality alignment research. However, current benchmarks for evaluating MMIR performance in image-text pairing within the scientific domain show a notable gap, where chart and table images described in scholarly language usually do not play a significant role. To bridge this gap, we develop a specialised scientific MMIR (SciMMIR) benchmark by leveraging open-access paper collections to extract data relevant to the scientific domain. This benchmark comprises 530K meticulously curated image-text pairs, extracted from figures and tables with detailed captions in scientific documents. We further annotate the image-text pairs with two-level subset-subcategory hierarchy annotations to facilitate a more comprehensive evaluation of the baselines. We conducted zero-shot and fine-tuning evaluations on prominent multi-modal image-captioning and visual language models, such as CLIP and BLIP. Our analysis offers critical insights for MMIR in the scientific domain, including the impact of pre-training and fine-tuning settings and the influence of the visual and textual encoders. All our data and checkpoints are publicly available at https://github.com/Wusiwei0410/SciMMIR

Establishing chromosomal design-build-test-learn through a synthetic chromosome and its combinatorial reconfiguration

Chromosome-level design-build-test-learn cycles (chrDBTLs) allow systematic combinatorial reconfiguration of chromosomes with ease. Here, we established chrDBTL with a redesigned synthetic Saccharomyces cerevisiae chromosome XV, synXV. We designed and built synXV to harbor strategically inserted features, modified elements, and synonymously recoded genes throughout the chromosome. Based on the recoded chromosome, we developed a method to enable chrDBTL: CRISPR-Cas9-mediated mitotic recombination with endoreduplication (CRIMiRE). CRIMiRE allowed the creation of customized wild-type/synthetic combinations, accelerating genotype-phenotype mapping and synthetic chromosome redesign. We also leveraged synXV as a "build-to-learn" model organism for translation studies by ribosome profiling. We conducted a locus-to-locus comparison of ribosome occupancy between synXV and the wild-type chromosome, providing insight into the effects of codon changes and redesigned features on translation dynamics in vivo. Overall, we established synXV as a versatile reconfigurable system that advances chrDBTL for understanding biological mechanisms and engineering strains. </p

Debugging and consolidating multiple synthetic chromosomes reveals combinatorial genetic interactions

The Sc2.0 project is building a eukaryotic synthetic genome from scratch. A major milestone has been achieved with all individual Sc2.0 chromosomes assembled. Here, we describe the consolidation of multiple synthetic chromosomes using advanced endoreduplication intercrossing with tRNA expression cassettes to generate a strain with 6.5 synthetic chromosomes. The 3D chromosome organization and transcript isoform profiles were evaluated using Hi-C and long-read direct RNA sequencing. We developed CRISPR Directed Biallelic URA3-assisted Genome Scan, or ‘‘CRISPR D-BUGS,’’ to map phenotypic variants caused by specific designer modifications, known as ‘‘bugs.’’ We first fine-mapped a bug in synthetic chromosome II (synII) and then discovered a combinatorial interaction associated with synIII and synX, revealing an unexpected genetic interaction that links transcriptional regulation, inositol metabolism, and tRNASer CGA abundance. Finally, to expedite consolidation, we employed chromosome substitution to incorporate the largest chromosome (synIV), thereby consolidating &gt;50% of the Sc2.0 genome in one strain </p