Mug-STAN: Adapting Image-Language Pretrained Models for General Video Understanding

Large-scale image-language pretrained models, e.g., CLIP, have demonstrated remarkable proficiency in acquiring general multi-modal knowledge through web-scale image-text data. Despite the impressive performance of image-language models on various image tasks, how to effectively expand them on general video understanding remains an area of ongoing exploration. In this paper, we investigate the image-to-video transferring from the perspective of the model and the data, unveiling two key obstacles impeding the adaptation of image-language models: non-generalizable temporal modeling and partially misaligned video-text data. To address these challenges, we propose Spatial-Temporal Auxiliary Network with Mutual-guided alignment module (Mug-STAN), a simple yet effective framework extending image-text model to diverse video tasks and video-text data.Specifically, STAN adopts a branch structure with decomposed spatial-temporal modules to enable generalizable temporal modeling, while Mug suppresses misalignment by introducing token-wise feature aggregation of either modality from the other. Extensive experimental results verify Mug-STAN significantly improves adaptation of language-image pretrained models such as CLIP and CoCa at both video-text post-pretraining and finetuning stages. With our solution, state-of-the-art zero-shot and finetuning results on various downstream datasets, including MSR-VTT, DiDeMo, LSMDC, Kinetics-400, Something-Something-2, HMDB-51, UCF- 101, and AVA, are achieved. Moreover, by integrating pretrained Mug-STAN with the emerging multimodal dialogue model, we can realize zero-shot video chatting. Codes are available at https://github.com/farewellthree/STA

Equipment Development for Simultaneously Bifacial Plating Metallization in Bifacial Solar Cells

For silicon solar cells, screen-printing has long been favored by the manufacturers due to its simplicity and high throughput. Over decades of technology advancement, screen-printing has almost pushed to its material and equipment limits (e.g. high-aspect ratio of finger, material impacts on cell etc.). However, in recent years, the increasing silver prices, reducing wafer thickness, and emerging new cell designs continue introducing challenges to this mature technology. Those issues have provided incentives to seek metallization alternatives. Among various metallization techniques, Ni/Cu plated contacts could be the most promising candidate due to its scaling up potential. However, there has been a delay of market share increase versus the ITRPV predication. The lack of readily available appropriate equipment and process standard could be the key reason to cause this. Here, we presented the developing UNSW bifacial plating technology. The special designed industrial prototype has greatly improved the plating uniformity and product stability. Moreover, double sides of the cell could be simultaneously plated thereby effectively reducing the processing time. In this paper, the design consideration and engineering solutions were plainly reviewed. The robust cell metallization process has been demonstrated on 6-inch industrial bifacial PERC precursors and primary intergraded with hydrogenation technology successfully. Despite that, this plating prototype could also be applied on other cell structures such as TOPCon, HJT and tandem structure. This innovative technology would remarkably optimize the processing engineering of plating metallization and may promote the utilization of plated contacts in solar cells manufacturing

Inferring the Individual Psychopathologic Deficits With Structural Connectivity in a Longitudinal Cohort of Schizophrenia.

The prediction of schizophrenia-related psychopathologic deficits is exceedingly important in the fields of psychiatry and clinical practice. However, objective association of the brain structure alterations to the illness clinical symptoms is challenging. Although, schizophrenia has been characterized as a brain dysconnectivity syndrome, evidence accounting for neuroanatomical network alterations remain scarce. Moreover, the absence of generalized connectome biomarkers for the assessment of illness progression further perplexes the prediction of long-term symptom severity. In this paper, a combination of individualized prediction models with quantitative graph theoretical analysis was adopted, providing a comprehensive appreciation of the extent to which the brain network properties are affected over time in schizophrenia. Specifically, Connectome-based Prediction Models were employed on Structural Connectivity (SC) features, efficiently capturing individual network-related differences, while identifying the anatomical connectivity disturbances contributing to the prediction of psychopathological deficits. Our results demonstrated distinctions among widespread cortical circuits responsible for different domains of symptoms, indicating the complex neural mechanisms underlying schizophrenia. Furthermore, the generated models were able to significantly predict changes of symptoms using SC features at follow-up, while the preserved SC features suggested an association with improved positive and overall symptoms. Moreover, cross-sectional significant deficits were observed in network efficiency and a progressive aberration of global integration in patients compared to healthy controls, representing a group-consensus pathological map, while supporting the dysconnectivity hypothesis

A potential therapeutic drug for osteoporosis: prospect for osteogenic LncRNAs

Long non-coding RNAs (LncRNAs) play essential roles in multiple physiological processes including bone formation. Investigators have revealed that LncRNAs regulated bone formation through various signaling pathways and micro RNAs (miRNAs). However, several problems exist in current research studies on osteogenic LncRNAs, including sophisticated techniques, high cost for in vivo experiment, as well as low homology of LncRNAs between animal model and human, which hindered translational medicine research. Moreover, compared with gene editing, LncRNAs would only lead to inhibition of target genes rather than completely knocking them out. As the studies on osteogenic LncRNA gradually proceed, some of these problems have turned osteogenic LncRNA research studies into slump. This review described some new techniques and innovative ideas to address these problems. Although investigations on osteogenic LncRNAs still have obtacles to overcome, LncRNA will work as a promising therapeutic drug for osteoporosis in the near future

Cloning and System Analysis of Genes Controlling Grain Yield Using Ear Leaf in Maize

Maize is a leading food, feed and bioenergy crop in the U.S. and the world, while grain yield is the most important to all cereals, including maize, wheat, rice and sorghum. Since genes are the keys to comprehensively understand and effectively manipulate traits of agronomic importance, over 1,500 genes controlling grain yield have been recently cloned in maize and rice, of which over 98% were cloned from maize developing ear shoot in our laboratory. These genes have allowed deciphering of the molecular mechanisms underlying quantitative genetics and grain yield, and development of a gene-based breeding system in maize, but little is known about how they regulate grain yield spatially in different plant tissues. Here we report cloning, validation and systems analysis of 703 genes controlling maize grain yield (ZmGY_el) from ear leaf, another plant part significantly contributing to grain yield. We show that each ZmGY_el gene contributes to grain yield by 9.0% - 37.1% and 63.0% of them led to increased grain yield and 37.0% resulted in decreased grain yield, when turned on or up-regulated in ear leaf. Comparative analysis reveals that different sets of genes contribute to grain yield in different tissues, even though some of the genes contribute to grain yield in both tissues. Furthermore, we have further formulated the molecular mechanisms of grain yield using the genes cloned from ear leaf. These results have provided new insights into the molecular mechanisms underlying grain yield, thus providing new knowledge and toolkits for effective manipulation of grain yield in maize and other cereal crops

Functional and Evolutionary Dynamics of Genes Involved in Drought Tolerance in Loblolly Pine (Pinus Taeda L.)

Drought, a major threat to the health and productivity of both natural ecosystems and agriculture, is expected to increase in frequency and intensity across many regions as a consequence of climate change and repurposing of natural water resources. Loblolly pine (Pinus taeda L.) represents a major forest species across the southeastern US due to its widespread distribution, ecological prominence, and extensive utilization for industrial production. Thus, developing loblolly varieties with increased tolerance to aridity is a major goal of the forest industry. However, this will require a significant leap forward in our understanding of the genetic basis of drought tolerance in loblolly. The main goal of this project is to generate genomic resources and bioinformatic approaches to identify genes, regulatory regions, and genetic variants involved in drought tolerance in loblolly pine. In the first component, I analyzed transcriptomic (RNA-seq) data from two loblolly genotypes with divergent tolerance to aridity. I identified more than 4,000 drought-related transcripts in response to drought in the root of Pinus taeda. Genotype x Environment (GxE) interactions were prevalent, suggesting that very different cohorts of genes are influenced by drought in the tolerant vs. sensitive loblolly genotypes. In the second part, I identified nearly 9,500 unique sites representing 24 clusters of Transcription Factor Binding Sites (TFBSs) in the promoter region of 1,386 DRTs. All of the 24 TFBSs share homology with known motifs in flowering plants. A total of 1,046 unique DRTs linked to 16 TFBSs were associated with 213 overrepresented non-redundant GO terms, most of which are related to processes known to be involved in drought tolerance. In the third component of my research, I integrated the transcriptome data with extensive genetic variant (SNP) datasets in loblolly to determine the evolutionary dynamics associated with DRTs. I found that DRTs share higher rates of adaptive evolution and contain a higher than expected number of SNPs associated with aridity than other genes. Overall, these findings will assist the sustained effort to develop varieties of loblolly pine that can better sustain the projected increase in aridity along with the range of this key forest species

Cloning and System Analysis of Genes Controlling Grain Yield Using Ear Leaf in Maize

Maize is a leading food, feed and bioenergy crop in the U.S. and the world, while grain yield is the most important to all cereals, including maize, wheat, rice and sorghum. Since genes are the keys to comprehensively understand and effectively manipulate traits of agronomic importance, over 1,500 genes controlling grain yield have been recently cloned in maize and rice, of which over 98% were cloned from maize developing ear shoot in our laboratory. These genes have allowed deciphering of the molecular mechanisms underlying quantitative genetics and grain yield, and development of a gene-based breeding system in maize, but little is known about how they regulate grain yield spatially in different plant tissues. Here we report cloning, validation and systems analysis of 703 genes controlling maize grain yield (ZmGY_el) from ear leaf, another plant part significantly contributing to grain yield. We show that each ZmGY_el gene contributes to grain yield by 9.0% - 37.1% and 63.0% of them led to increased grain yield and 37.0% resulted in decreased grain yield, when turned on or up-regulated in ear leaf. Comparative analysis reveals that different sets of genes contribute to grain yield in different tissues, even though some of the genes contribute to grain yield in both tissues. Furthermore, we have further formulated the molecular mechanisms of grain yield using the genes cloned from ear leaf. These results have provided new insights into the molecular mechanisms underlying grain yield, thus providing new knowledge and toolkits for effective manipulation of grain yield in maize and other cereal crops