Chip-based photonic radar for high-resolution imaging

Radar is the only sensor that can realize target imaging at all time and all weather, which would be a key technical enabler for future intelligent society. Poor resolution and large size are two critical issues for radars to gain ground in civil applications. Conventional electronic radars are difficult to address both issues especially in the relatively low-frequency band. In this work, we propose and experimentally demonstrate, for the first time to the best of our knowledge, a chip-based photonic radar based on silicon photonic platform, which can implement high resolution imaging with very small footprint. Both the wideband signal generator and the de-chirp receiver are integrated on the chip. A broadband photonic imaging radar occupying the full Ku band is experimentally established. A high precision range measurement with a resolution of 2.7 cm and an error of less than 2.75 mm is obtained. Inverse synthetic aperture (ISAR) imaging of multiple targets with complex profiles are also implemented.Comment: 4 pages, 6figure

LLM4DyG: Can Large Language Models Solve Problems on Dynamic Graphs?

In an era marked by the increasing adoption of Large Language Models (LLMs) for various tasks, there is a growing focus on exploring LLMs' capabilities in handling web data, particularly graph data. Dynamic graphs, which capture temporal network evolution patterns, are ubiquitous in real-world web data. Evaluating LLMs' competence in understanding spatial-temporal information on dynamic graphs is essential for their adoption in web applications, which remains unexplored in the literature. In this paper, we bridge the gap via proposing to evaluate LLMs' spatial-temporal understanding abilities on dynamic graphs, to the best of our knowledge, for the first time. Specifically, we propose the LLM4DyG benchmark, which includes nine specially designed tasks considering the capability evaluation of LLMs from both temporal and spatial dimensions. Then, we conduct extensive experiments to analyze the impacts of different data generators, data statistics, prompting techniques, and LLMs on the model performance. Finally, we propose Disentangled Spatial-Temporal Thoughts (DST2) for LLMs on dynamic graphs to enhance LLMs' spatial-temporal understanding abilities. Our main observations are: 1) LLMs have preliminary spatial-temporal understanding abilities on dynamic graphs, 2) Dynamic graph tasks show increasing difficulties for LLMs as the graph size and density increase, while not sensitive to the time span and data generation mechanism, 3) the proposed DST2 prompting method can help to improve LLMs' spatial-temporal understanding abilities on dynamic graphs for most tasks. The data and codes will be open-sourced at publication time

Combining Karhunen–Loève expansion and stochastic modeling for probabilistic delineation of well capture zones in heterogeneous aquifers

The delineation of well capture zones (WCZs), particularly for water supply wells, is of utmost importance to ensure water quality. This task requires a comprehensive understanding of the aquifer’s hydrogeological parameters for precise delineation. However, the inherent uncertainty associated with these parameters poses a significant challenge. Traditional deterministic methods bear inherent risks, emphasizing the demand for more resilient and probabilistic techniques. This study introduces a novel approach that combines the Karhunen–Loève expansion (KLE) technique with stochastic modeling to probabilistically delineate well capture zones in heterogeneous aquifers. Through numerical examples involving moderate and strong heterogeneity, the effectiveness of KLE dimension reduction and the reliability of stochastic simulations are explored. The results show that increasing the number of KL-terms significantly improves the statistical attributes of the samples. When employing more KL-terms, the statistical properties of the hydraulic conductivity field outperform those of cases with fewer KL-terms. Notably, particularly in scenarios of strong heterogeneity, achieving a convergent probabilistic WCZs map requires a greater number of KL-terms and stochastic simulations compared to cases with moderate heterogeneity

A review of common statistical methods for dealing with multiple pollutant mixtures and multiple exposures

Traditional environmental epidemiology has consistently focused on studying the impact of single exposures on specific health outcomes, considering concurrent exposures as variables to be controlled. However, with the continuous changes in environment, humans are increasingly facing more complex exposures to multi-pollutant mixtures. In this context, accurately assessing the impact of multi-pollutant mixtures on health has become a central concern in current environmental research. Simultaneously, the continuous development and optimization of statistical methods offer robust support for handling large datasets, strengthening the capability to conduct in-depth research on the effects of multiple exposures on health. In order to examine complicated exposure mixtures, we introduce commonly used statistical methods and their developments, such as weighted quantile sum, bayesian kernel machine regression, toxic equivalency analysis, and others. Delineating their applications, advantages, weaknesses, and interpretability of results. It also provides guidance for researchers involved in studying multi-pollutant mixtures, aiding them in selecting appropriate statistical methods and utilizing R software for more accurate and comprehensive assessments of the impact of multi-pollutant mixtures on human health

New insights into autophagy in inflammatory subtypes of asthma

Asthma is a heterogeneous airway disease characterized by airway inflammation and hyperresponsiveness. Autophagy is a self-degrading process that helps maintain cellular homeostasis. Dysregulation of autophagy is involved in the pathogenesis of many diseases. In the context of asthma, autophagy has been shown to be associated with inflammation, airway remodeling, and responsiveness to drug therapy. In-depth characterization of the role of autophagy in asthma can enhance the understanding of the pathogenesis, and provide a theoretical basis for the development of new biomarkers and targeted therapy for asthma. In this article, we focus on the relationship of autophagy and asthma, and discuss its implications for asthma pathogenesis and treatment

Myasthenia gravis-like syndrome induced by expression of interferon gamma in the neuromuscular junction.

Abnormal humoral responses toward motor end plate constituents in muscle induce myasthenia gravis (MG). To study the etiology of this disease, and whether it could be induced by host defense molecules, we examined the consequences of interferon (IFN) gamma production within the neuromuscular junction of transgenic mice. The transgenic mice exhibited gradually increasing muscular weakness, flaccid paralysis, and functional disruption of the neuromuscular junction that was reversed after administration of an inhibitor of acetylcholinesterase, features which are strikingly similar to human MG. Furthermore, histological examination revealed infiltration of mononuclear cells and autoantibody deposition at motor end plates. Immunoprecipitation analysis indicated that a previously unidentified 87-kD target antigen was recognized by sera from transgenic mice and also by sera from the majority of human MG patients studied. These results suggest that expression of IFN-gamma at motor end plates provokes an autoimmune humoral response, similar to human MG, thus linking the expression of this factor with development of this disease

Recurrent mucinous carcinoma with sarcomatoid and sarcomatous mural nodules: a case report and literature review

Ovarian mucinous tumors with sarcomatous mural nodules are rare. Sarcomatous nodules have a bad prognosis. Its diagnosis and treatment are controversial.It is still controversial whether malignant mural nodules represent a dedifferentiated form of mucinous tumors or collisional tumors. This is a case report of a 32-year-old female diagnosed with ovarian mucinous tumor recurred as a mucinous carcinoma combined with sarcomatoid and undifferentiated sarcoma mural nodules after surgery and chemotherapy. The primary lesion did not have a sarcomatous component after comprehensive sampling and repeated review, while the recurrent lesion had a predominantly sarcomatous component. The patient received a second operation and postoperative chemotherapy plus Anlotinib with no progression at 16 months of follow-up. Primary mucinous carcinoma and sarcomatous mural nodules revealed the same K-RAS mutation(c.35G>T, pG12V), TP53 mutation (c.817C>T, p.R273C), MLL2 mutation(c.13450C>T, p.R4484) and NF1 mutation(c.7876A>G, p.S2626G). We present a comprehensive analysis on morphologic characteristics, molecular detection results, clinical management, and prognosis of ovarian mucinous tumors with mural nodules of sarcomatoid and undifferentiated sarcoma. Mutation sharing between primary mucinous carcinoma and recurrent sarcomatous nodules supports monoclonal origin of primary and recurrent tumors, suggesting a tendency for sarcomatous differentiation during the progression of epithelial tumors. Malignant mural nodules represent dedifferentiation in mucinous ovarian tumors rather than collision of two different tumor types. Therefore, it is imperative to conduct comprehensive sampling, rigorous clinical examination, and postoperative follow-up in order to thoroughly evaluate all mural nodules of ovarian mucinous tumors due to their potential for malignancy and sarcomatous differentiation

Gli Transcriptional Activity Is Essential for Kras-Induced Pancreatic Tumorigenesis and Regulates IKBKE/NF-κ\kappaB Activity in the Tumor Epithelium

Pancreatic ductal adenocarcinoma (PDAC), one of the most aggressive human malignances, is thought to be initiated by KRAS activation. Here we find that transcriptional activation mediated by the Gli family of transcription factors, although dispensable for pancreatic development, is required for Kras-induced proliferation and survival in primary pancreatic epithelial cells in culture, and Kras-driven pancreatic intraepithelial neoplasia and PDAC formation in vivo. Further, ectopic Gli1 activation in the mouse pancreas accelerates Kras-driven tumor formation, underscoring the importance of Gli transcription factors in pancreatic tumorigenesis. Interestingly, we demonstrate Gli-stimulated IKBKE (IKK$\varepsilon$)/nuclear factor-$\kappa$B (NF-$\kappa$B) activity in pancreatic cancer cells in culture and in vivo, and show that this activity is a critical downstream mediator for Gli-dependent PDAC cell transformation and survival. Together, these studies demonstrate for the first time the requirement for Gli in Kras- dependent pancreatic epithelial transformation, implicate a novel mechanism of Gli-NF-$\kappa$B oncogenic activation, and provide genetic evidence supporting the therapeutic targeting of Gli activity in pancreatic cancer.Molecular and Cellular Biolog

Gene expression signature of atypical breast hyperplasia and regulation by SFRP1

BACKGROUND: Atypical breast hyperplasias (AH) have a 10-year risk of progression to invasive cancer estimated at 4-7%, with the overall risk of developing breast cancer increased by ~ 4-fold. AH lesions are estrogen receptor alpha positive (ERalpha+) and represent risk indicators and/or precursor lesions to low grade ERalpha+ tumors. Therefore, molecular profiles of AH lesions offer insights into the earliest changes in the breast epithelium, rendering it susceptible to oncogenic transformation. METHODS: In this study, women were selected who were diagnosed with ductal or lobular AH, but no breast cancer prior to or within the 2-year follow-up. Paired AH and histologically normal benign (HNB) tissues from patients were microdissected. RNA was isolated, amplified linearly, labeled, and hybridized to whole transcriptome microarrays to determine gene expression profiles. Genes that were differentially expressed between AH and HNB were identified using a paired analysis. Gene expression signatures distinguishing AH and HNB were defined using AGNES and PAM methods. Regulation of gene networks was investigated using breast epithelial cell lines, explant cultures of normal breast tissue and mouse tissues. RESULTS: A 99-gene signature discriminated the histologically normal and AH tissues in 81% of the cases. Network analysis identified coordinated alterations in signaling through ERalpha, epidermal growth factor receptors, and androgen receptor which were associated with the development of both lobular and ductal AH. Decreased expression of SFRP1 was also consistently lower in AH. Knockdown of SFRP1 in 76N-Tert cells resulted altered expression of 13 genes similarly to that observed in AH. An SFRP1-regulated network was also observed in tissues from mice lacking Sfrp1. Re-expression of SFRP1 in MCF7 cells provided further support for the SFRP1-regulated network. Treatment of breast explant cultures with rSFRP1 dampened estrogen-induced progesterone receptor levels. CONCLUSIONS: The alterations in gene expression were observed in both ductal and lobular AH suggesting shared underlying mechanisms predisposing to AH. Loss of SFRP1 expression is a significant regulator of AH transcriptional profiles driving previously unidentified changes affecting responses to estrogen and possibly other pathways. The gene signature and pathways provide insights into alterations contributing to AH breast lesions