Heterogeneous Multi-task Learning for Human Pose Estimation with Deep Convolutional Neural Network

We propose an heterogeneous multi-task learning framework for human pose estimation from monocular image with deep convolutional neural network. In particular, we simultaneously learn a pose-joint regressor and a sliding-window body-part detector in a deep network architecture. We show that including the body-part detection task helps to regularize the network, directing it to converge to a good solution. We report competitive and state-of-art results on several data sets. We also empirically show that the learned neurons in the middle layer of our network are tuned to localized body parts

Polychlorinated Biphenyls (PCBs) Enhance Metastatic Properties of Breast Cancer Cells by Activating Rho-Associated Kinase (ROCK)

Background: Polychlorinated biphenyls (PCBs) are a family of structurally related chlorinated aromatic hydrocarbons. Numerous studies have documented a wide spectrum of biological effects of PCBs on human health, such as immunotoxicity, neurotoxocity, estrogenic or antiestrogenic activity, and carcinogensis. The role of PCBs as etiologic agents for breast cancer has been intensively explored in a variety of in vivo, animal and epidemiologic studies. A number of investigations indicated that higher levels of PCBs in mammary tissues or sera correlated to breast cancer risk, and PCBs might be implicated in advancing breast cancer progression. Methodology/Principal Findings: In the current study, we for the first time report that PCBs greatly promote the ROCK activity and therefore increase cell motility for both non-metastatic and metastatic human breast cancer cells in vitro. In the in vivo study, PCBs significantly advance disease progression, leading to enhanced capability of metastatic breast cancer cells to metastasize to bone, lung and liver. Additionally, PCBs robustly induce the production of intracellular reactive oxygen species (ROS) in breast cancer cells; ROS mechanistically elevate ROCK activity. Conclusions/Significance: PCBs enhance the metastatic propensity of breast cancer cells by activating the ROCK signaling, which is dependent on ROS induced by PCBs. Inhibition of ROCK may stand for a unique way to restrain metastases in breast cancer upon PCB exposure

Escaping Saddle Points in Heterogeneous Federated Learning via Distributed SGD with Communication Compression

We consider the problem of finding second-order stationary points of heterogeneous federated learning (FL). Previous works in FL mostly focus on first-order convergence guarantees, which do not rule out the scenario of unstable saddle points. Meanwhile, it is a key bottleneck of FL to achieve communication efficiency without compensating the learning accuracy, especially when local data are highly heterogeneous across different clients. Given this, we propose a novel algorithm Power-EF that only communicates compressed information via a novel error-feedback scheme. To our knowledge, Power-EF is the first distributed and compressed SGD algorithm that provably escapes saddle points in heterogeneous FL without any data homogeneity assumptions. In particular, Power-EF improves to second-order stationary points after visiting first-order (possibly saddle) points, using additional gradient queries and communication rounds only of almost the same order required by first-order convergence, and the convergence rate exhibits a linear speedup in terms of the number of workers. Our theory improves/recovers previous results, while extending to much more tolerant settings on the local data. Numerical experiments are provided to complement the theory.Comment: 27 page

Strain engineering and biosensor development for efficient biofuel production by Saccharomyces cerevisiae

Metabolic engineering of Saccharomyces cerevisiae is an attractive approach to enhance the production of cellulosic ethanol, fatty alcohols and other advanced biofuels. Production of cellulosic ethanol from lignocelluloses has attracted a lot of interest and significant improvement has been made to construct and optimize the recombinant S. cerevisiae strains capable of converting glucose or pentose sugars into ethanol. Unfortunately, pentose sugars, which constitute up to 30% of biomass hydrolysate, cannot be co-utilized simultaneously with glucose by recombinant S. cerevisiae strains. Great efforts have been made to improve the co-utilization efficiency of sugars derived from lignocellulose hydrolysates. A lot of research has been carried out to lower the effect of glucose repression that leads to inefficient pentose sugars utilization in the presence of glucose, but it remains challenging to overcome this issue by depletion of genes involved in transcriptional regulation or optimization of pentose sugar transportation and utilization. To overcome the glucose repression problem in S. cerevisiae, we designed a strategy to construct a S. cerevisiae strain capable of simultaneously utilizing cellobiose and xylose derived from lignocellulose. The high efficiency pathway containing a cellobiose transporter and a β-glucosidase enables fast cellobiose utilization and ethanol production, and glucose repression is avoided by the intracellular utilization of cellobiose. Distinguished from existing glucose derepression methods, glucose utilization is not impaired, while xylose utilization is improved because of the synergistic effects. To optimize the cellobiose utilization efficiency, the functional role of an important enzyme in glucose conversion, aldose 1-epimerase (AEP), was investigated. AEP is supposed to maintain the intracellular equilibrium of α-glucose and β-glucose when the spontaneous conversion between the two glucose anomers is not sufficient. However, the heterologous cellobiose utilization pathway results in excess β-glucose accumulation and lowers the rate of glucose glycolysis, which limits efficient utilization of cellobiose in engineered S. cerevisiae strains. We found three AEP candidates (Gal10, Yhr210c and Ynr071c) in S. cerevisiae and investigated their function in cellobiose utilization. Deletion of Gal10 led to complete loss of both AEP activity and cell growth on cellobiose, while complementation restored the AEP activity and cell growth. In addition, deletion of YHR210C or YNR071C resulted in improved cellobiose utilization. These results suggest that the intracellular mutarotation of β-glucose to α-glucose might be a rate controlling step and Gal10 plays a crucial role in cellobiose fermentation by engineered S. cerevisiae., The production of advanced biofuels, such as higher alcohols, fatty acid derived fuels, and hydrocarbons, is considered to be a better fuel alternative solution. Because their physiochemical properties are more compatible with the current gasoline-based infrastructure than ethanol. However, compared to current progress in ethanol production, a lot more efforts are needed to make these advanced biofuels commercially available. Recent efforts in advanced biofuels synthesis have been focused on the design, construction and optimization of pathways and strains, but detection becomes the bottleneck step that hinders high-throughput screening. Genetic biosensors convert chemical concentrations into detectable fluorescence signal via transcriptional regulation, and may serve as an important tool for screening and cell sorting. We have constructed a genomic sensor that correlates intracellular malonyl-CoA concentration to a fluorescence signal by transcriptional regulation. Malonyl-CoA is the building block for the biosynthesis of fatty acids, 3-hydroxypropionic acid, polyketides, and flavonoids, which can either be used directly or be used as a precursor for the production of biofuels and value-added chemicals. The sensor was combined with a genome wide mutant library in S. cerevisiae, and used to screen for mutants with higher productivity of malonyl-CoA, thus improving the downstream production of the reporter chemical, 3-hydroxypropionic acid. The constructed malonyl-CoA sensors can also be employed as control elements in order to modulate gene expression of biosynthetic pathways of important compounds that are of particular interest to the pharmaceutical and biofuel industries. The development of transcriptional-regulation based sensors relies on the discovery and identification of transcription factors and operators, which are usually heterologous to the platform microorganism. We explored a novel strategy to discover multiple sensors by transcriptional profiling. The strategy utilizes the native regulation mechanisms in S. cerevisiae, minimizes extrinsic manipulation and screens for multiple metabolite-responsive promoters with various transcription activities in a short time. A proof-of-concept sensor targeting acetyl-CoA was established and validated and the development of more sensors is in progress. This strategy provides an innovative approach for metabolite monitoring and pathway control.

Vote2Cap-DETR++: Decoupling Localization and Describing for End-to-End 3D Dense Captioning

3D dense captioning requires a model to translate its understanding of an input 3D scene into several captions associated with different object regions. Existing methods adopt a sophisticated "detect-then-describe" pipeline, which builds explicit relation modules upon a 3D detector with numerous hand-crafted components. While these methods have achieved initial success, the cascade pipeline tends to accumulate errors because of duplicated and inaccurate box estimations and messy 3D scenes. In this paper, we first propose Vote2Cap-DETR, a simple-yet-effective transformer framework that decouples the decoding process of caption generation and object localization through parallel decoding. Moreover, we argue that object localization and description generation require different levels of scene understanding, which could be challenging for a shared set of queries to capture. To this end, we propose an advanced version, Vote2Cap-DETR++, which decouples the queries into localization and caption queries to capture task-specific features. Additionally, we introduce the iterative spatial refinement strategy to vote queries for faster convergence and better localization performance. We also insert additional spatial information to the caption head for more accurate descriptions. Without bells and whistles, extensive experiments on two commonly used datasets, ScanRefer and Nr3D, demonstrate Vote2Cap-DETR and Vote2Cap-DETR++ surpass conventional "detect-then-describe" methods by a large margin. Codes will be made available at https://github.com/ch3cook-fdu/Vote2Cap-DETR

Effects of Fiscal Decentralization on Garbage Classifications

China has been promoting garbage classification in its rural areas, yet it lacks financial appropriation and fiscal decentralization to support waste processing projects. Though the existing literature has suggested fiscal decentralization strategies between different local government levels, few of the studies ascertain garbage classification efficiency from a quantitative perspective. To bridge the gap, this study examines the optimal fiscal decentralization strategies for garbage classification. It uses an optimization model while considering decision makers’ requirements regarding the fund allocation amounts at different government levels and the classification ratios in villages as constraints and decisions, respectively. A three-stage heuristic algorithm is applied to determine optimal landfill locations and efficient classification ratios for the garbage processing system in rural China, with an analytical discussion on the propositions and properties of the model. Our analytical results suggest that 1) the theoretically optimal solution is conditionally achievable, 2) the applied algorithm can achieve the optimal solution faster when the relationship between governance costs and classification ratios reaches some mathematical conditions, and 3) there is always a potential for increasing the retained funds between different government levels or for reducing the total appropriation from the county government. The numerical experiment on a primary dataset from 12 towns and 143 villages in the Pingyuan county of Guangdong province, China, does not only affirm the qualitative results, but it also provides insights into the difficulties encountered during the implementation of the garbage classification policy in China’s rural areas

Cross-modal subspace learning with scheduled adaptive margin constraints

This work has been partially funded by the CMU Portugal research project GoLocal Ref. CMUP-ERI/TIC/0046/2014, by the H2020 ICT project COGNITUS with the grant agreement no 687605 and by the FCT project NOVA LINCS Ref. UID/CEC/04516/2019. We also gratefully acknowledge the support of NVIDIA Corporation with the donation of the GPUs used for this research.Cross-modal embeddings, between textual and visual modalities, aim to organise multimodal instances by their semantic correlations. State-of-the-art approaches use maximum-margin methods, based on the hinge-loss, to enforce a constant margin m, to separate projections of multimodal instances from different categories. In this paper, we propose a novel scheduled adaptive maximum-margin (SAM) formulation that infers triplet-specific constraints during training, therefore organising instances by adaptively enforcing inter-category and inter-modality correlations. This is supported by a scheduled adaptive margin function, that is smoothly activated, replacing a static margin by an adaptively inferred one reflecting triplet-specific semantic correlations while accounting for the incremental learning behaviour of neural networks to enforce category cluster formation and enforcement. Experiments on widely used datasets show that our model improved upon state-of-the-art approaches, by achieving a relative improvement of up to approximate to 12.5% over the second best method, thus confirming the effectiveness of our scheduled adaptive margin formulation.publishersversionpublishe

Pharmacological effects and mechanisms of paeonol on antitumor and prevention of side effects of cancer therapy

Cancer represents one of the leading causes of mortality worldwide. Conventional clinical treatments include radiation therapy, chemotherapy, immunotherapy, and targeted therapy. However, these treatments have inherent limitations, such as multidrug resistance and the induction of short- and long-term multiple organ damage, ultimately leading to a significant decrease in cancer survivors’ quality of life and life expectancy. Paeonol, a nature active compound derived from the root bark of the medicinal plant Paeonia suffruticosa, exhibits various pharmacological activities. Extensive research has demonstrated that paeonol exhibits substantial anticancer effects in various cancer, both in vitro and in vivo. Its underlying mechanisms involve the induction of apoptosis, the inhibition of cell proliferation, invasion and migration, angiogenesis, cell cycle arrest, autophagy, regulating tumor immunity and enhanced radiosensitivity, as well as the modulation of multiple signaling pathways, such as the PI3K/AKT and NF-κB signaling pathways. Additionally, paeonol can prevent adverse effects on the heart, liver, and kidneys induced by anticancer therapy. Despite numerous studies exploring paeonol’s therapeutic potential in cancer, no specific reviews have been conducted. Therefore, this review provides a systematic summary and analysis of paeonol’s anticancer effects, prevention of side effects, and the underlying mechanisms involved. This review aims to establish a theoretical basis for the adjunctive strategy of paeonol in cancer treatment, ultimately improving the survival rate and enhancing the quality of life for cancer patients