Dirac cohomology, branching laws and Wallach modules

The idea of using Dirac cohomology to study branching laws was initiated by Huang, Pandzi\'c and Zhu in 2013 [HPZ]. One of their results says that the Dirac cohomology of $\pi$ completely determines $\pi|_{K}$, where $\pi$ is any irreducible unitarizable highest weight $(\mathfrak{g}, K)$ module. This paper aims to develop this idea for the exceptional Lie groups $E_{6(-14)}$ and $E_{7(-25)}$: we recover the $K$-spectrum of the Wallach modules from their Dirac cohomology.Comment: 17 pages, 4 figures, 5 table

Enhancing Signal Recognition Accuracy in Delay-Based Optical Reservoir Computing: A Comparative Analysis of Training Algorithms

To improve the accuracy of signal recognition in delay-based optical reservoir computing (RC) systems, this paper proposes the use of nonlinear algorithms at the output layer to replace traditional linear algorithms for training and testing datasets and apply them to the identification of frequency-modulated continuous wave (FMCW) LiDAR signals. This marks the inaugural use of the system for the identification of FMCW LiDAR signals. We elaborate on the fundamental principles of a delay-based optical RC system using an optical-injected distributed feedback laser (DFB) laser and discriminate four FMCW LiDAR signals through this setup. In the output layer, three distinct training algorithms—namely linear regression, support vector machine (SVM), and random forest—were employed to train the optical reservoir. Upon analyzing the experimental results, it was found that regardless of the size of the dataset, the recognition accuracy of the two nonlinear training algorithms was superior to that of the linear regression algorithm. Among the two nonlinear algorithms, the Random Forest algorithm had a higher recognition accuracy than SVM when the sample size was relatively small

Ligustrazine Inhibits the Migration and Invasion of Renal Cell Carcinoma

Ligustrazine is a Chinese herb (Chuanxiong) approved for use as a medical drug in China. Recent evidence suggests that ligustrazine has promising antitumor properties. Our preliminary results showed that ligustrazine could inhibit the growth of human renal cell carcinoma (RCC) cell lines. However, the complicated molecular mechanism has not been fully revealed. Therefore, the purpose of this study to investigate the mechanism of ligustrazine resistance in human RCC cells. Cell proliferation, migration, invasion, and colony-formation ability of RCC cells A498 were detected by MTT assay, clonal formation rates, and transwell chamber assay in vitro. The expression of epithelial–mesenchymal transition (EMT)–related proteins were analyzed using western blot test. The effect of ligustrazine on the growth of A498 cells in nude mice was investigated in vivo. Our results showed that ligustrazine could significantly inhibit the proliferation, migration, and invasion of A498 both in vivo and vitro. Western blot analysis showed that the expressions of EMT-related, N-cadherin, snail, and slug proteins were significantly decreased in A498 in the ligustrazine treatment group. This study indicated that ligustrazine could significantly inhibit the malignant biological behaviors of RCC cell lines, possibly by inhibiting the EMT process

Spatial-temporal analysis of malaria and the effect of environmental factors on its incidence in Yongcheng, China, 2006–2010

BACKGROUND: In 2003, Plasmodium vivax malaria has re-emerged in central eastern China including Yongcheng prefecture, Henan Province, where no case has been reported for eleven years. Our goals were to detect the space-time distribution pattern of malaria and to determine significant environmental variables contributing to malaria incidence in Yongcheng from 2006 to 2010, thus providing scientific basis for further optimizing current malaria surveillance and control programs. METHODS: This study examined the spatial and temporal heterogeneities in the risk of malaria and the influencing factors on malaria incidence using geographical information system (GIS) and time series analysis. Univariate analysis was conducted to estimate the crude correlations between malaria incidence and environmental variables, such as mosquito abundance and climatic factors. Multivariate analysis was implemented to construct predictive models to explore the principal environmental determinants on malaria epidemic using a Generalized Estimating Equation (GEE) approach. RESULTS: Annual malaria incidence at town-level decreased from the north to south, and monthly incidence at prefecture-level demonstrated a strong seasonal pattern with a peak from July to November. Yearly malaria incidence had a visual spatial association with yearly average temperature. Moreover, the best-fit temporal model (model 2) (QIC = 16.934, P<0.001, R(2) = 0.818) indicated that significant factors contributing to malaria incidence were maximum temperature at one month lag, average humidity at one month lag, and malaria incidence of the previous month. CONCLUSIONS: Findings supported the effects of environment factors on malaria incidence and indicated that malaria control targets should vary with intensity of malaria incidence, with more public resource allocated to control the source of infections instead of large scale An. sinensis control when malaria incidence was at a low level, which would benefit for optimizing the malaria surveillance project in China and some other countries with unstable or low malaria transmission

Trimodal Therapy: A Tumor Vascularâ Targeted Interlocking Trimodal Nanosystem That Induces and Exploits Hypoxia (Adv. Sci. 8/2018)

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/145503/1/advs201870048.pd

A Tumor Vascularâ Targeted Interlocking Trimodal Nanosystem That Induces and Exploits Hypoxia

Vascularâ targeted photodynamic therapy (VTP) is a recently approved strategy for treating solid tumors. However, the exacerbated hypoxic stress makes tumor eradication challenging with such a single modality approach. Here, a new graphene oxide (GO)â based nanosystem for rationally designed, interlocking trimodal cancer therapy that enables VTP using photosensitizer verteporfin (VP) (1) with codelivery of banoxantrone dihydrochloride (AQ4N) (2), a hypoxiaâ activated prodrug (HAP), and HIFâ 1α siRNA (siHIFâ 1α) (3) is reported. The VTPâ induced aggravated hypoxia is highly favorable for AQ4N activation into AQ4 (a topoisomerase II inhibitor) for chemotherapy. However, the hypoxiaâ induced HIFâ 1α acts as a â hidden brake,â through downregulating CYP450 (the dominant HAPâ activating reductases), to substantially hinder AQ4N activation. siHIFâ 1α is rationally adopted to suppress the HIFâ 1α expression upon hypoxia and further enhance AQ4N activation. This trimodal nanosystem significantly delays the growth of PCâ 3 tumors in vivo compared to the control nanoparticles carrying VP, AQ4N, or siHIFâ 1α alone or their pairwise combinations. This multimodal nanoparticle design presents, the first example exploiting VTP to actively induce hypoxia for enhanced HAP activation. It is also revealed that HAP activation is still insufficient under hypoxia due to the hidden downregulation of the HAPâ activating reductases (CYP450), and this can be well overcome by GO nanoparticleâ mediated siHIFâ 1α intervention.Vascularâ targeted photodynamic therapy (VTP) is integrated with hypoxiaâ activated prodrug (AQ4N) and HIFâ 1α siRNA (siHIFâ 1α) for interlocking trimodal therapy. The VTPâ induced aggravated hypoxia is exploited for efficient AQ4N activation for chemotherapy. HIFâ 1α induced by hypoxia acts as a â hidden brake,â through downregulating CYP450 reductases, to hinder AQ4N activation. siHIFâ 1α is rationally adopted to suppress HIFâ 1α expression upon VTP to enhance AQ4N activation.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/145505/1/advs661-sup-0001-S1.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/145505/2/advs661.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/145505/3/advs661_am.pd

Integrated three-stage decentralized scheduling for virtual power plants: A model-assisted multi-agent reinforcement learning method

Virtual power plant (VPP) emerges as a promising integration and aggregation technology that facilitates the utilization of massive flexible demand-side resources (DSRs). However, non-negligible modeling errors and high-dimensional uncertainties involved in DSR aggregation threaten the delivery reliability and cost-effectiveness of VPP operation. To address this problem, this study proposes an integrated three-stage scheduling framework for VPPs and develops a model-assisted multi-agent reinforcement learning (MARL) approach. In the proposed framework, the VPP scheduling problem is formulated as a decentralized partially observable Markov Decision Process (Dec-POMDP), which depicts the complex interaction process among the three stages (bidding, re-dispatching and disaggregation). The interactions are evaluated by a comprehensive reward function, incorporating the trading and operation costs, as well as imbalance penalties. To enable decentralized decision-making, a model-assisted multi-agent proximal policy optimization (MA2PPO) algorithm is proposed, which trains a separate actor network for each aggregator. Additionally, the MA2PPO is augmented with a model-assisted safety decision-making method to accelerate the training process. Numerical simulation results verify that the proposed method enhances the delivery reliability and cost-effectiveness of the VPP, while achieving faster convergence time compared with purely model-free MARL methods