Multi-spatial-mode effects in squeezed-light-enhanced interferometric gravitational wave detectors

Proposed near-future upgrades of the current advanced interferometric gravitational wave detectors include the usage of frequency dependent squeezed light to reduce the current sensitivity-limiting quantum noise. We quantify and describe the degradation effects that spatial mode-mismatches between optical resonators have on the squeezed field. These mode-mismatches can to first order be described by scattering of light into second-order Gaussian modes. As a demonstration of principle, we also show that squeezing the second-order Hermite-Gaussian modes $\mathrm{HG}_{02}$ and $\mathrm{HG}_{20}$, in addition to the fundamental mode, has the potential to increase the robustness to spatial mode-mismatches. This scheme, however, requires independently optimized squeeze angles for each squeezed spatial mode, which would be challenging to realise in practise.Comment: 10 pages, 12 figure

Resistance to immune checkpoint inhibitors in advanced lung cancer: Clinical characteristics, potential prognostic factors and next strategy

BackgroundImmune checkpoint inhibitors (ICIs) have shown unprecedented clinical benefit in cancer immunotherapy and are rapidly transforming the practice of advanced lung cancer. However, resistance routinely develops in patients treated with ICIs. We conducted this retrospective study to provide an overview on clinical characteristics of ICI resistance, optimal treatment beyond disease progression after prior exposure to immunotherapy, as well as potential prognostic factors of such resistance.Methods190 patients diagnosed with unresectable lung cancer who received at least one administration of an anti-programmed cell death 1 (PD-1)/anti-programmed cell death-ligand 1(PD-L1) at any treatment line at Zhongshan Hospital Fudan University between Sep 2017 and December 2019 were enrolled in our study. Overall survival (OS) and progression-free survival (PFS) were analyzed. Levels of plasma cytokines were evaluated for the prognostic value of ICI resistance.ResultsWe found that EGFR/ALK/ROS1 mutation and receiving ICI treatment as second-line therapy were risk factors associated with ICI resistance. Patients with bone metastasis at baseline had a significantly shorter PFS1 time when receiving initial ICI treatment. Whether or not patients with oligo-progression received local treatment seemed to have no significant effect on PFS2 time. Systemic therapies including chemotherapy and anti-angiogenic therapy rather than continued immunotherapy beyond ICI resistance had significant effect on PFS2 time. TNF, IL-6 and IL-8 were significantly elevated when ICI resistance. Lower plasma TNF level and higher plasma IL-8 level seemed to be significantly associated with ICI resistance. A nomogram was established to prognosis the clinical outcome of patients treated with ICIs.ConclusionPatients with EGFR/ALK/ROS1 mutation, or those receiving ICI treatment as second-line therapy had higher risk of ICI resistance. Patients with bone metastasis had poor prognosis during immunotherapy. For those patients with oligo-progression after ICI resistance, combination with local treatment did not lead to a significantly longer PFS2 time. Chemotherapy and anti-angiogenic therapy rather than continued immunotherapy beyond ICI resistance had significant effect on PFS2 time. Levels of plasma cytokines including TNF, IL-6 and IL-8 were associated with ICI resistance

AD-AutoGPT: An Autonomous GPT for Alzheimer's Disease Infodemiology

In this pioneering study, inspired by AutoGPT, the state-of-the-art open-source application based on the GPT-4 large language model, we develop a novel tool called AD-AutoGPT which can conduct data collection, processing, and analysis about complex health narratives of Alzheimer's Disease in an autonomous manner via users' textual prompts. We collated comprehensive data from a variety of news sources, including the Alzheimer's Association, BBC, Mayo Clinic, and the National Institute on Aging since June 2022, leading to the autonomous execution of robust trend analyses, intertopic distance maps visualization, and identification of salient terms pertinent to Alzheimer's Disease. This approach has yielded not only a quantifiable metric of relevant discourse but also valuable insights into public focus on Alzheimer's Disease. This application of AD-AutoGPT in public health signifies the transformative potential of AI in facilitating a data-rich understanding of complex health narratives like Alzheimer's Disease in an autonomous manner, setting the groundwork for future AI-driven investigations in global health landscapes.Comment: 20 pages, 4 figure

Iminodisuccinic Acid Relieved Cadmium Stress in Rapeseed Leaf by Affecting Cadmium Distribution and Cadmium Chelation with Pectin

Rapeseed (Brassica napus L.) is a nutritious vegetable, while cadmium (Cd) pollution threatens the growth, productivity, and food security of rapeseed. By studying the effects of iminodisuccinic acid (IDS), an easily biodegradable and environmental friendly chelating agent, on Cd distribution at the organ and cellular level, we found IDS promoted dry matter accumulation of rapeseed and increased the contents of photosynthetic pigment in leaves. Inhibited root-shoot Cd transport resulted in higher activity of antioxidant enzymes and decreased hydrogen peroxide (H2O2) and malondialdehyde (MDA) accumulation in leaves, which indicated that IDS contributed to alleviating Cd-caused oxidative damage in leaf cells. Additionally, IDS increased Cd subcellular distribution in cell wall (CW), especially in covalently bound pectin (CSP), and relieved Cd toxicity in organelle of leaves. IDS also enhanced demethylation of CSP. The Cd content in CSP, demethylation degree, and pectin methylesterase activity of CSP increased by 37.95%, 13.34%, and 13.16%, respectively, while IDS did not change the contents of different CW components. The improved Cd fixation in leaf CW was mainly attributed to enhance demethylation of covalently bound pectin (CSP) and Cd chelation with CSP

Responses of Cell Wall Components to Low Nitrogen in Rapeseed Roots

Rapeseed (Brassica napus L.) is a major oil crop in China, with the world’s largest planted area and total yield. Rapeseed has a high demand for nitrogen (N), and nitrogen deficiency in soil is an important limiting factor for rapeseed production. However, rapeseed responds to N deprivation by regulating its own morphology, structure, and physiology. We carried out the current experiment by utilizing low N (LN: 0.3 mM NO3−) and normal N (CK: 6.0 mM NO3−) treatments using Brassica napus as the experimental material. The study results showed that low N induced root elongation in rapeseed, and the root length of LN treatment was 2.37 times that of HN treatment. The dry matter of roots also significantly increased due to low N treatment. Meanwhile, low N treatment decreased photosynthetic pigment (including chlorophyll a, chlorophyll b, and carotenoids) contents and dry mass accumulation of leaves. A higher root/shoot ratio and N physiological efficiency were observed under low N treatment. The changes in cell wall components (pectin, cellulose, hemicellulose, and lignin), related enzymes, and genes’ transcription levels in roots were determined and the results suggested that low N promoted the demethylation of ion-bound pectin (ISP) and covalently bound pectin (CSP), the content of CSP and cellulose. The promoted pectin methylesterase (PME) activity, inhibited pectin and cellulose degradation enzymes, and up/downregulation of related genes also confirming the results of cell wall components. The low N-increased demethylation degree of pectin and content of pectin and cellulose in cell walls was conducive to cell wall loosening and cell wall synthesis during cell division and elongation, ultimately promoting root-adaptive elongation. The study revealed a possible mechanism in which the alteration of cell wall component content and structure participates in cell elongation and expansion, which directly induces root elongation under N deficiency. The successful implementation of this research may be conducive to facilitating the development of rapeseed cultivars with high N use efficiency through root-based genetic improvements and improving plant adaptability to low N

Flash Flood Forecasting Using Support Vector Regression Model in a Small Mountainous Catchment

Flash floods in mountainous catchments are often caused by the rainstorm, which may result in more severe consequences than plain area floods due to less timescale and a fast-flowing front of water and debris. Flash flood forecasting is a huge challenge for hydrologists and managers due to its instantaneity, nonlinearity, and dependency. Among different methods of flood forecasting, data-driven models have become increasingly popular in recent years due to their strong ability to simulate nonlinear hydrological processes. This study proposed a Support Vector Regression (SVR) model, which is a powerful artificial intelligence-based model originated from statistical learning theory, to forecast flash floods at different lead times in a small mountainous catchment. The lagged average rainfall and runoff are identified as model input variables, and the time lags associated with the model input variables are determined by the hydrological concept of the time of response. There are 69 flash flood events collected from 1984 to 2012 in a mountainous catchment in China and then used for the model training and testing. The contribution of the runoff variables to the predictions and the phase lag of model outputs are analyzed. The results show that: (i) the SVR model has satisfactory predictive performances for one to three-hours ahead forecasting; (ii) the lagged runoff variables have a more significant effect on the predictions than the rainfall variables; and (iii) the phase lag (time difference) of prediction results significantly exists in both two- and three-hours-ahead forecasting models, however, the input rainfall information can assist in mitigating the phase lag of peak flow

Uncertainty Quantification in Machine Learning Modeling for Multi-Step Time Series Forecasting: Example of Recurrent Neural Networks in Discharge Simulations

As a revolutionary tool leading to substantial changes across many areas, Machine Learning (ML) techniques have obtained growing attention in the field of hydrology due to their potentials to forecast time series. Moreover, a subfield of ML, Deep Learning (DL) is more concerned with datasets, algorithms and layered structures. Despite numerous applications of novel ML/DL techniques in discharge simulation, the uncertainty involved in ML/DL modeling has not drawn much attention, although it is an important issue. In this study, a framework is proposed to quantify uncertainty contributions of the sample set, ML approach, ML architecture and their interactions to multi-step time-series forecasting based on the analysis of variance (ANOVA) theory. Then a discharge simulation, using Recurrent Neural Networks (RNNs), is taken as an example. Long Short-Term Memory (LSTM) network, a state-of-the-art DL approach, was selected due to its outstanding performance in time-series forecasting, and compared with simple RNN. Besides, novel discharge forecasting architecture is designed by combining the expertise of hydrology and stacked DL structure, and compared with conventional design. Taking hourly discharge simulations of Anhe (China) catchment as a case study, we constructed five sample sets, chose two RNN approaches and designed two ML architectures. The results indicate that none of the investigated uncertainty sources are negligible and the influence of uncertainty sources varies with lead-times and discharges. LSTM demonstrates its superiority in discharge simulations, and the ML architecture is as important as the ML approach. In addition, some of the uncertainty is attributable to interactions rather than individual modeling components. The proposed framework can both reveal uncertainty quantification in ML/DL modeling and provide references for ML approach evaluation and architecture design in discharge simulations. It indicates uncertainty quantification is an indispensable task for a successful application of ML/DL

Therapeutic window of globular adiponectin against cerebral ischemia in diabetic mice: the role of dynamic alteration of adiponectin/adiponectin receptor expression

Photograph of the Ponca Chief Standing Bear Monument

Increased nitrogen use efficiency via amino acid remobilization from source to sink organs in Brassica napus

Nitrogen (N) is an essential plant growth nutrient whose coordinated distribution from source to sink organs is crucial for seed development and overall crop yield. We compared high and low N use efficiency (NUE) Brassica napus (rapeseed) genotypes. Metabonomics and transcriptomics revealed that leaf senescence induced by N deficiency promoted amino acid allocation from older to younger leaves in the high-NUE genotype at the vegetative growth stage. Efficient source to sink remobilization of amino acids elevated the numbers of branches and pods per plant under a N-deficiency treatment during the reproductive stage. A 15N tracer experiment confirmed that more amino acids were partitioned into seeds from the silique wall during the pod stage in the high-NUE genotype, owing mainly to variation in genes involved in organic N transport and metabolism. We suggest that the greater amino acid source-to-sink allocation efficiency during various growth stages in the high-NUE genotype resulted in higher yield and NUE under N deficiency. These findings support the hypothesis that strong amino acid remobilization in rapeseed leads to high yield, NUE, and harvest index