Metabolic Impacts of Using Nitrogen and Copper-Regulated Promoters to Regulate Gene Expression in Neurospora crassa.

The filamentous fungus Neurospora crassa is a long-studied eukaryotic microbial system amenable to heterologous expression of native and foreign proteins. However, relatively few highly tunable promoters have been developed for this species. In this study, we compare the tcu-1 and nit-6 promoters for controlled expression of a GFP reporter gene in N. crassa. Although the copper-regulated tcu-1 has been previously characterized, this is the first investigation exploring nitrogen-controlled nit-6 for expression of heterologous genes in N. crassa. We determined that fragments corresponding to 1.5-kb fragments upstream of the tcu-1 and nit-6 open reading frames are needed for optimal repression and expression of GFP mRNA and protein. nit-6 was repressed using concentrations of glutamine from 2 to 20 mM and induced in medium containing 0.5-20 mM nitrate as the nitrogen source. Highest levels of expression were achieved within 3 hr of induction for each promoter and GFP mRNA could not be detected within 1 hr after transfer to repressing conditions using the nit-6 promoter. We also performed metabolic profiling experiments using proton NMR to identify changes in metabolite levels under inducing and repressing conditions for each promoter. The results demonstrate that conditions used to regulate tcu-1 do not significantly change the primary metabolome and that the differences between inducing and repressing conditions for nit-6 can be accounted for by growth under nitrate or glutamine as a nitrogen source. Our findings demonstrate that nit-6 is a tunable promoter that joins tcu-1 as a choice for regulation of gene expression in N. crassa

Object Detection in Videos with Tubelet Proposal Networks

Object detection in videos has drawn increasing attention recently with the introduction of the large-scale ImageNet VID dataset. Different from object detection in static images, temporal information in videos is vital for object detection. To fully utilize temporal information, state-of-the-art methods are based on spatiotemporal tubelets, which are essentially sequences of associated bounding boxes across time. However, the existing methods have major limitations in generating tubelets in terms of quality and efficiency. Motion-based methods are able to obtain dense tubelets efficiently, but the lengths are generally only several frames, which is not optimal for incorporating long-term temporal information. Appearance-based methods, usually involving generic object tracking, could generate long tubelets, but are usually computationally expensive. In this work, we propose a framework for object detection in videos, which consists of a novel tubelet proposal network to efficiently generate spatiotemporal proposals, and a Long Short-term Memory (LSTM) network that incorporates temporal information from tubelet proposals for achieving high object detection accuracy in videos. Experiments on the large-scale ImageNet VID dataset demonstrate the effectiveness of the proposed framework for object detection in videos.Comment: CVPR 201

Optimum Operating Conditions Confirmation and Effectiveness Analysis Based on Research of the Coagulation and Precipitation Integrated Process

AbstractAiming at the increasing small-scale water supply projects, the increasingly serious pollution of the water resource and stringent water quality standards, the coagulation and precipitation integrated process on the basis of quiescent precipitation was proposed in this study. By experiments in the integrated reactor, the optimum process operating conditions were confirmed. It is verified that the optimal dosage of PAC was 16mg/L in the optimum temperature and pH range. The repeated utilization volume of the floc mud from the former precipitation period was the same as 6% of the water volume in the next processing period, and the corresponding optimal dosage of PAC was 8mg/L with 50% reduction of the flocculants dosage, while the residual turbidity was less than 1.0NTU, which could reach the standard after simple filtration and disinfection procedure. With low energy consumption, little land occupation, low cost, high efficiency of the water production and strong anti shock loading capability, this process could guarantee the safety of drinking water supply, and deserve popularization and application

DDColor: Towards Photo-Realistic Image Colorization via Dual Decoders

Image colorization is a challenging problem due to multi-modal uncertainty and high ill-posedness. Directly training a deep neural network usually leads to incorrect semantic colors and low color richness. While transformer-based methods can deliver better results, they often rely on manually designed priors, suffer from poor generalization ability, and introduce color bleeding effects. To address these issues, we propose DDColor, an end-to-end method with dual decoders for image colorization. Our approach includes a pixel decoder and a query-based color decoder. The former restores the spatial resolution of the image, while the latter utilizes rich visual features to refine color queries, thus avoiding hand-crafted priors. Our two decoders work together to establish correlations between color and multi-scale semantic representations via cross-attention, significantly alleviating the color bleeding effect. Additionally, a simple yet effective colorfulness loss is introduced to enhance the color richness. Extensive experiments demonstrate that DDColor achieves superior performance to existing state-of-the-art works both quantitatively and qualitatively. The codes and models are publicly available at https://github.com/piddnad/DDColor.Comment: ICCV 2023; Code: https://github.com/piddnad/DDColo

RSFNet: A White-Box Image Retouching Approach using Region-Specific Color Filters

Retouching images is an essential aspect of enhancing the visual appeal of photos. Although users often share common aesthetic preferences, their retouching methods may vary based on their individual preferences. Therefore, there is a need for white-box approaches that produce satisfying results and enable users to conveniently edit their images simultaneously. Recent white-box retouching methods rely on cascaded global filters that provide image-level filter arguments but cannot perform fine-grained retouching. In contrast, colorists typically employ a divide-and-conquer approach, performing a series of region-specific fine-grained enhancements when using traditional tools like Davinci Resolve. We draw on this insight to develop a white-box framework for photo retouching using parallel region-specific filters, called RSFNet. Our model generates filter arguments (e.g., saturation, contrast, hue) and attention maps of regions for each filter simultaneously. Instead of cascading filters, RSFNet employs linear summations of filters, allowing for a more diverse range of filter classes that can be trained more easily. Our experiments demonstrate that RSFNet achieves state-of-the-art results, offering satisfying aesthetic appeal and increased user convenience for editable white-box retouching.Comment: Accepted by ICCV 202

Speaker adaptation of a multilingual acoustic model for cross-language synthesis

Several studies have shown promising results in adapting DNN-based acoustic models as a mechanism to transfer characteristics from pre-trained models. One such example is speaker adaptation using a small amount of data, where fine-tuning has helped train models that extrapolate well to diverse linguistic contexts that are not present in the adaptation data. In the current work, our objective is to synthesize speech in different languages using the target speaker's voice, regardless of the language of their data. To achieve this goal, we create a multilingual model using a corpus that consists of recordings from a large number of monolingual and a few bilingual speakers in multiple languages. The model is then adapted using the target speaker's recordings in a language other than the target language. We also explore if additional adaptation data from a native speaker of the target language improves the performance. The subjective evaluation shows that the proposed approach of cross-language speaker adaptation is able to synthesize speech in the target language, in the target speaker's voice, without data spoken by the target speaker in that language. Also, extra data from a native speaker of the target language can improve model performance