Lighting up NeRF via Unsupervised Decomposition and Enhancement

Neural Radiance Field (NeRF) is a promising approach for synthesizing novel views, given a set of images and the corresponding camera poses of a scene. However, images photographed from a low-light scene can hardly be used to train a NeRF model to produce high-quality results, due to their low pixel intensities, heavy noise, and color distortion. Combining existing low-light image enhancement methods with NeRF methods also does not work well due to the view inconsistency caused by the individual 2D enhancement process. In this paper, we propose a novel approach, called Low-Light NeRF (or LLNeRF), to enhance the scene representation and synthesize normal-light novel views directly from sRGB low-light images in an unsupervised manner. The core of our approach is a decomposition of radiance field learning, which allows us to enhance the illumination, reduce noise and correct the distorted colors jointly with the NeRF optimization process. Our method is able to produce novel view images with proper lighting and vivid colors and details, given a collection of camera-finished low dynamic range (8-bits/channel) images from a low-light scene. Experiments demonstrate that our method outperforms existing low-light enhancement methods and NeRF methods.Comment: ICCV 2023. Project website: https://whyy.site/paper/llner

A new multivariate empirical mode decomposition method for improving the performance of SSVEP-based brain–computer interface

Objective: Accurate and efficient detection of steady-state visual evoked potentials (SSVEP) in electroencephalogram (EEG) is essential for the related brain–computer interface (BCI) applications. Approach: Although the canonical correlation analysis (CCA) has been applied extensively and successfully to SSVEP recognition, the spontaneous EEG activities and artifacts that often occur during data recording can deteriorate the recognition performance. Therefore, it is meaningful to extract a few frequency sub-bands of interest to avoid or reduce the influence of unrelated brain activity and artifacts. This paper presents an improved method to detect the frequency component associated with SSVEP using multivariate empirical mode decomposition (MEMD) and CCA (MEMD-CCA). EEG signals from nine healthy volunteers were recorded to evaluate the performance of the proposed method for SSVEP recognition. Main results: We compared our method with CCA and temporally local multivariate synchronization index (TMSI). The results suggest that the MEMD-CCA achieved significantly higher accuracy in contrast to standard CCA and TMSI. It gave the improvements of 1.34%, 3.11%, 3.33%, 10.45%, 15.78%, 18.45%, 15.00% and 14.22% on average over CCA at time windows from 0.5 s to 5 s and 0.55%, 1.56%, 7.78%, 14.67%, 13.67%, 7.33% and 7.78% over TMSI from 0.75 s to 5 s. The method outperformed the filter-based decomposition (FB), empirical mode decomposition (EMD) and wavelet decomposition (WT) based CCA for SSVEP recognition. Significance: The results demonstrate the ability of our proposed MEMD-CCA to improve the performance of SSVEP-based BCI

Neuromotor Noise, Error Tolerance and Velocity-Dependent Costs in Skilled Performance

In motor tasks with redundancy neuromotor noise can lead to variations in execution while achieving relative invariance in the result. The present study examined whether humans find solutions that are tolerant to intrinsic noise. Using a throwing task in a virtual set-up where an infinite set of angle and velocity combinations at ball release yield throwing accuracy, our computational approach permitted quantitative predictions about solution strategies that are tolerant to noise. Based on a mathematical model of the task expected results were computed and provided predictions about error-tolerant strategies (Hypothesis 1). As strategies can take on a large range of velocities, a second hypothesis was that subjects select strategies that minimize velocity at release to avoid costs associated with signal- or velocity-dependent noise or higher energy demands (Hypothesis 2). Two experiments with different target constellations tested these two hypotheses. Results of Experiment 1 showed that subjects chose solutions with high error-tolerance, although these solutions also had relatively low velocity. These two benefits seemed to outweigh that for many subjects these solutions were close to a high-penalty area, i.e. they were risky. Experiment 2 dissociated the two hypotheses. Results showed that individuals were consistent with Hypothesis 1 although their solutions were distributed over a range of velocities. Additional analyses revealed that a velocity-dependent increase in variability was absent, probably due to the presence of a solution manifold that channeled variability in a task-specific manner. Hence, the general acceptance of signal-dependent noise may need some qualification. These findings have significance for the fundamental understanding of how the central nervous system deals with its inherent neuromotor noise

Clarifying confusions over carbon conclusions: antecedent soil carbon drives gains realised following intervention

Carbon removals associated with incremental gains in soil organic carbon (SOC) at scale have enormous potential to mitigate global warming, yet confusion over contexts that elicit SOC accrual abound. Here, we examine how bespoke interventions (through irrigation, fertiliser, crop type and rotations), antecedent SOC levels and soil type impact on long-term SOC accrual and greenhouse gas (GHG) emissions. Using a whole farm systems modelling approach informed using participatory research, we discovered an inverse relationship between antecedent SOC stocks and SOC gains realised following intervention, with greater initial SOC levels resulting in lower ex poste change in SOC. We found that SOC accrual was greatest for clays and least for sands, although changes in SOC in sandy loam soils were also low. Diversified whole farm adaptations – implemented through inclusion of grain legumes within wheat/canola crop rotations – were more conducive to improvement in SOC stocks, followed by Intensified systems (implemented through greater rates of irrigation, farm areas under irrigation, nitrogen fertiliser and inclusion of rice and maize in crop rotations). Adaptations that Simplified farm systems by reducing irrigation and fertiliser use resulted in the lowest SOC accrual. In most cases, long-term SOC stocks fell when SOC at the outset was greater than 4–5%, regardless of intervention made, soil or crop type, crop rotation, production system or climate. We contend that (1) management interventions primarily impacted SOC in the soil surface (0–30 cm) and had de minimus impact on deep SOC stocks (30–100 cm), (2) crop rotations including wheat, canola and faba beans were more conducive to improvement in SOC stocks, (3) scenarios with high status quo SOC had little impact on crop productivity, and not necessarily the lowest GHG emissions intensity, (4) productivity and GHG emissions intensity were largely a function of the quantum of nitrogenous fertiliser added, rather than SOC stocks, and (5) aspirations for improving SOC are likely to be futile if antecedent SOC stocks are already high (4–5 %). We conclude that potential for improving SOC stocks exists in contexts where antecedent stocks are low (<1%), which may include regions with land degradation, chronic erosion and/ or other constraints to vegetative ground cover that could be sustainably and consistently alleviated

Sustainable intensification with irrigation raises farm profit despite climate emergency

Societal Impact Statement Despite comprising a small proportion of global agricultural land use, irrigated agriculture is enormously important to the global agricultural economy. Burgeoning food demand driven by population growth—together with reduced food supply caused by the climate crisis—is polarising the existing tension between water used for agricultural production versus that required for environmental conservation. We show that sustainable intensification via more diverse crop rotations, more efficient water application infrastructure and greater farm area under irrigation is conducive to greater farm business profitability under future climates. Summary &bull; Research aimed at improving crop productivity often does not account for the complexity of real farms underpinned by land-use changes in space and time. &bull; Here, we demonstrate how a new framework—WaterCan Profit—can be used to elicit such complexity using an irrigated case study farm with four whole-farm adaptation scenarios (Baseline, Diversified, Intensified and Simplified) with four types of irrigated infrastructure (Gravity, Pipe & Riser, Pivot and Drip). &bull; Without adaptation, the climate crisis detrimentally impacted on farm profitability due to the combination of increased evaporative demand and increased drought frequency. Whole-farm intensification—via greater irrigated land use, incorporation of rice, cotton and maize and increased nitrogen fertiliser application—was the only adaptation capable of raising farm productivity under future climates. Diversification through incorporation of grain legumes into crop rotations significantly improved profitability under historical climates; however, profitability of this adaptation declined under future climates. Simplified systems reduced economic risk but also had lower long-term economic returns. &bull; We conclude with four key insights: (1) When assessing whole-farm profit, metrics matter: Diversified systems generally had higher profitability than Intensified systems per unit water, but not per unit land area; (2) gravity-based irrigation infrastructure required the most water, followed by sprinkler systems, whereas Drip irrigation used the least water; (3) whole-farm agronomic adaptation through management and crop genotype had greater impact on productivity compared with changes in irrigation infrastructure; and (4) only whole-farm intensification was able to raise profitability under future climates

Characterization of antigenic variants of hepatitis C virus in immune evasion

<p>Abstract</p> <p>Background</p> <p>Antigenic variation is an effective way by which viruses evade host immune defense leading to viral persistence. Little is known about the inhibitory mechanisms of viral variants on CD4 T cell functions.</p> <p>Results</p> <p>Using sythetic peptides of a HLA-DRB1*15-restricted CD4 epitope derived from the non-structural (NS) 3 protein of hepatitis C virus (HCV) and its antigenic variants and the peripheral blood mononuclear cells (PBMC) from six HLA-DRB1*15-positive patients chronically infected with HCV and 3 healthy subjects, the <it>in vitro </it>immune responses and the phenotypes of CD4⁺CD25⁺cells of chronic HCV infection were investigated. The variants resulting from single or double amino acid substitutions at the center of the core region of the Th1 peptide not only induce failed T cell activation but also simultaneously up-regulate inhibitory IL-10, CD25^-TGF-β⁺Th3 and CD4⁺IL-10⁺Tr1 cells. In contrast, other variants promote differentiation of CD25⁺TGF-β⁺Th3 suppressors that attenuate T cell proliferation.</p> <p>Conclusions</p> <p>Naturally occuring HCV antigenic mutants of a CD4 epitope can shift a protective peripheral Th1 immune response into an inhibitory Th3 and/or Tr1 response. The modulation of antigenic variants on CD4 response is efficient and extensive, and is likely critical in viral persistence in HCV infection.</p

Seismic Response of Resilient Bridges with SMA-Based Rocking ECC-Reinforced Piers

Post-earthquake investigation shows that numerous reinforced concrete (RC) bridges were demolished because of large residual displacements. Improving the self-centering capability and hence resilience of these bridges located in earthquake-prone regions is essential. In this regard, a resilient bridge system incorporating engineered cementitious composites (ECC) reinforced piers and shape memory alloy (SMA) energy dissipation components, i.e., SMA washers, is proposed to enhance its resilience when subjected to strong earthquakes. This study commences with a detailed introduction of the resilient SMA-washer-based rocking bridge system with ECC-reinforced piers. Subsequently, a constitutive model of the ECC material is implemented into OpenSees and the constitutive model is validated by test data. The working principle and constitutive model of the SMA washers are also introduced. A series of dynamic analysis on the conventional and resilient rocking bridge systems with ECC-reinforced piers under a suite of ground motions at E1 and E2 earthquake levels are conducted. The analysis results indicate that the resilient rocking bridge system with ECC-reinforced piers has superior resilience and damage control capacities over the conventional one

MicroRNA Biosensors for Early Detection of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the main pathological type of liver cancer. Due to its insidious onset and the lack of specific early markers, HCC is often diagnosed at an advanced stage, and the survival rate of patients with partial liver resection is low. Non-coding RNAs (ncRNAs) have emerged as valuable biomarkers for HCC detection, with microRNAs (miRNAs) being a particularly relevant class of short ncRNAs. MiRNAs play a crucial role in gene expression regulation and can serve as biomarkers for early HCC detection. However, the detection of miRNAs poses a significant challenge due to their small molecular weight and low abundance. In recent years, biosensors utilizing electrochemical, optical, and electrochemiluminescent strategies have been developed to address the need for simple, rapid, highly specific, and sensitive miRNA detection. This paper reviews the recent advances in miRNA biosensors and discusses in detail the probe types, electrode materials, sensing strategies, linear ranges, and detection limits of the sensors. These studies are expected to enable early intervention and dynamic monitoring of tumor changes in HCC patients to improve their prognosis and survival status