Abstract
As more countries commit to emissions reductions by midcentury to curb anthropogenic climate change, decarbonization of the electricity sector becomes a first-order task in reaching this ...goal. Renewables, particularly wind and solar power, will be predominant components of this transition. How availability of the wind and solar resource will change in the future in response to regional climate changes is an important and underdiscussed topic of the decarbonization process. Here, we study changes in potential for wind power in China and India, evaluating prospectively until the year 2060. To do this, we study a downscaled, high-resolution multimodel ensemble of CMIP5 models under high and low emissions scenarios. While there is some intermodel variability, we find that spatial changes are generally consistent across models, with decreases of up to 965 (a 1% change) and 186 TWh (a 2% change) in annual electricity generation potential for China and India, respectively. Compensating for the declining resource are weakened seasonal and diurnal variabilities, allowing for easier large-scale wind power integration. We conclude that while the ensemble indicates available wind resource over China and India will decline slightly in the future, there remains enormous potential for significant wind power expansion, which must play a major role in carbon neutral aspirations.
The development of immunotherapy has changed the treatment landscape of advanced kidney renal clear cell carcinoma (KIRC), offering patients more treatment options. Cuproptosis, a novel cell death ...mode dependent on copper ions and mitochondrial respiration has not yet been studied in KIRC. We assembled a comprehensive cohort of The Cancer Genome Atlas (TCGA)-KIRC and GSE29609, performed cluster analysis for typing twice using seven cuproptosis-promoting genes (CPGs) as a starting point, and assessed the differences in biological and clinicopathological characteristics between different subtypes. Furthermore, we explored the tumor immune infiltration landscape in KIRC using ESTIMATE and single-sample gene set enrichment analysis (ssGSEA) and the potential molecular mechanisms of cuproptosis in KIRC using enrichment analysis. We constructed a cuproptosis score (CUS) using the Boruta algorithm combined with principal component analysis. We evaluated the impact of CUS on prognosis, targeted therapy, and immunotherapy in patients with KIRC using survival analysis, the predictions from the Cancer Immunome Atlas database, and targeted drug susceptibility analysis. We found that patients with high CUS levels show poor prognosis and efficacy against all four immune checkpoint inhibitors, and their immunosuppression may depend on
. However, the high-CUS group showed higher sensitivity to sunitinib, axitinib, and elesclomol. Sunitinib monotherapy may reverse the poor prognosis and result in higher progression free survival. Then, we identified two potential CPGs and verified their differential expression between the KIRC and the normal samples. Finally, we explored the effect of the key gene
on the proliferation of KIRC cells and confirmed the presence of cuproptosis in KIRC cells. We developed a targeted therapy and immunotherapy strategy for advanced KIRC based on CUS. Our findings provide new insights into the relationship among cuproptosis, metabolism, and immunity in KIRC.
Leaf area index (LAI) and leaf chlorophyll content (Cab) are two important indicators of vegetation growth. Due to the high-coupling of spectral signals of leaf area and chlorophyll content, ...simultaneous retrieval of LAI and Cab from remotely sensed date is always challenging. In this paper, an approach for joint estimation of grassland LAI and Cab from unmanned aerial vehicle (UAV) hyperspectral data was proposed. Firstly, based on a PROSAIL model, 15 typical hyperspectral vegetation indices (VIs) were calculated and analyzed to identify optimal VIs for LAI and Cab estimation. Secondly, four pairs of VIs were established and their discreteness was also calculated for building a two-dimension matrix. Thirdly, a two-layer VI matrix was generated to determine the relationship of VIs with LAI values and Cab values. Finally, LAI and Cab were jointly retrieved according to the cells of the two-layer matrix. The retrieval reduced the cross-influence between LAI and Cab. Compared with the VI empirical model and the single-layer VI matrix, the accuracy of LAI and Cab retrieved from UAV hyperspectral data based on the two-layer VI matrix was significantly improved (for LAI: R2 = 0.73, RMSE = 0.91 m2/m2 and u(SD) = 0.82 m2/m2; for Cab: R2 = 0.79, RMSE = 11.7 μg/cm2 and u(SD) = 10.84 μg/cm2). The proposed method has the potential for rapid retrieval of LAI and Cab from hyperspectral data. As a method similar to look-up table, the two-layer matrix can be used directly for LAI and Cab estimation without the need for prior measurements for training.
Large-scale and multidimensional spatiotemporal data sets are becoming ubiquitous in many real-world applications such as monitoring urban traffic and air quality. Making predictions on these time ...series has become a critical challenge due to not only the large-scale and high-dimensional nature but also the considerable amount of missing data. In this paper, we propose a Bayesian temporal factorization (BTF) framework for modeling multidimensional time series-in particular spatiotemporal data-in the presence of missing values. By integrating low-rank matrix/tensor factorization and vector autoregressive (VAR) process into a single probabilistic graphical model, this framework can characterize both global and local consistencies in large-scale time series data. The graphical model allows us to effectively perform probabilistic predictions and produce uncertainty estimates without imputing those missing values. We develop efficient Gibbs sampling algorithms for model inference and model updating for real-time prediction and test the proposed BTF framework on several real-world spatiotemporal data sets for both missing data imputation and multi-step rolling prediction tasks. The numerical experiments demonstrate the superiority of the proposed BTF approaches over existing state-of-the-art methods.
Abstract
In-memory computing may enable multiply-accumulate (MAC) operations, which are the primary calculations used in artificial intelligence (AI). Performing MAC operations with high capacity in ...a small area with high energy efficiency remains a challenge. In this work, we propose a circuit architecture that integrates monolayer MoS
2
transistors in a two-transistor–one-capacitor (2T-1C) configuration. In this structure, the memory portion is similar to a 1T-1C Dynamic Random Access Memory (DRAM) so that theoretically the cycling endurance and erase/write speed inherit the merits of DRAM. Besides, the ultralow leakage current of the MoS
2
transistor enables the storage of multi-level voltages on the capacitor with a long retention time. The electrical characteristics of a single MoS
2
transistor also allow analog computation by multiplying the drain voltage by the stored voltage on the capacitor. The sum-of-product is then obtained by converging the currents from multiple 2T-1C units. Based on our experiment results, a neural network is ex-situ trained for image recognition with 90.3% accuracy. In the future, such 2T-1C units can potentially be integrated into three-dimensional (3D) circuits with dense logic and memory layers for low power in-situ training of neural networks in hardware.
This paper presents a collaborative route discovery method that leverages the experience and preferences of taxi drivers in urban areas. The proposed method is mainly comprised of two phases: ...collaborative preference discovery (CPD) and intelligent driver network generation (IDNG). In the first phase, given an origin-destination (O-D) pair and provided that the cluster is a road segment set within a time-reachable range, we propose CPD which involves cluster-to-cluster retrieval to capture the top-k routes that are not only frequently traversed by taxis but also neighboring to the O-D pair. In the second phase, to support route computation, an IDNG algorithm is devised to generate an experiential graph for each specific O-D pair. In empirical studies, using the period-based experiential route database, sensitivity analysis is employed to select optimal parameters of intelligent driver networks. The results demonstrate that the routes recommended by our collaborative method are much more reliable than those of the shortest-path method with respect to the variance of travel time. Moreover, the recommended routes are traversed more frequently than those of the fastest-path and the shortest-path methods, while the travel time and route lengths of our routes are approximately equal to those of the conventional methods.
Owing to the melting and healing properties of thermoplastic resin, additive manufacturing or 3D printing is considered one of the most promising technologies for fiber-reinforced thermoplastic ...composites. However, manufacturing defects are still the main concern, which significantly limits the application of 3D-printed composite structures. To gain an insight into the effects of different processing parameters on the typical manufacturing defects, a micro-scale analysis was carried out via Micro-CT technology on the 3D-printed continuous carbon fiber-reinforced polylactic acid (PLA) composite specimens. The bias distribution of the fiber in the deposited filament was found. Moreover, when the feed rate of the filament was reduced from 100% to 50%, the a/b value was closer to 3.33, but the porosity increased from 7.077% to 25.352%. When the layer thickness was 0.2 mm, the increased nozzle pressure reduced the porosity but also increased the risk of fiber bundle breakage. The research provides an effective approach for analyzing the micro-structure of 3D printed composite structures and thus offers guidance for the processing control.
Universal visual quantitative chemical detection technology has emerged as an increasingly crucial tool for convenient testing with immediate results in the fields of environmental assessment, ...homeland security, clinical drug testing and health care, particularly in resource-limited settings. Here, we show a host-guest liquid gating mechanism to translate molecular interface recognition behavior into visually quantifiable detection signals. Quantitative chemical detection is achieved, which has obvious advantages for constructing a portable, affordable, on-site sensing platform to enable the visual quantitative testing of target molecules without optical/electrical equipment. Experiments and theoretical calculations confirm the specificity and scalability of the system. This mechanism can also be tailored by the rational design of host-guest complexes to quantitatively and visually detect various molecules. With the advantages of versatility and freedom from additional equipment, this detection mechanism has the potential to revolutionize environmental monitoring, food safety analysis, clinical drug testing, and more.
Candida albicans is the most common fungal pathogen in humans, and most diseases produced by C. albicans are associated with biofilms. We previously developed nylon-3 polymers with potent activity ...against planktonic C. albicans and excellent C. albicans versus mammalian cell selectivity. Here we show that these nylon-3 polymers have strong and selective activity against drug-resistant C. albicans in biofilms, as manifested by inhibition of biofilm formation and by killing of C. albicans in mature biofilms. The best nylon-3 polymer (poly-βNM) is superior to the antifungal drug fluconazole for all three strains examined. This polymer is slightly less effective than amphotericin B (AmpB) for two strains, but the polymer is superior against an AmpB-resistant strain.
Abstract
To investigate the pathogenesis of a congenital form of hepatic fibrosis, human hepatic organoids were engineered to express the most common causative mutation for Autosomal Recessive ...Polycystic Kidney Disease (ARPKD). Here we show that these hepatic organoids develop the key features of ARPKD liver pathology (abnormal bile ducts and fibrosis) in only 21 days. The ARPKD mutation increases collagen abundance and thick collagen fiber production in hepatic organoids, which mirrors ARPKD liver tissue pathology. Transcriptomic and other analyses indicate that the ARPKD mutation generates cholangiocytes with increased TGFβ pathway activation, which are actively involved stimulating myofibroblasts to form collagen fibers. There is also an expansion of collagen-producing myofibroblasts with markedly increased PDGFRB protein expression and an activated STAT3 signaling pathway. Moreover, the transcriptome of ARPKD organoid myofibroblasts resemble those present in commonly occurring forms of liver fibrosis. PDGFRB pathway involvement was confirmed by the anti-fibrotic effect observed when ARPKD organoids were treated with PDGFRB inhibitors. Besides providing insight into the pathogenesis of congenital (and possibly acquired) forms of liver fibrosis, ARPKD organoids could also be used to test the anti-fibrotic efficacy of potential anti-fibrotic therapies.
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