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The Lyman continuum (LyC) cannot be observed at the epoch of reionization (z ≳ 6) owing to intergalactic H i absorption. To identify LyC emitters (LCEs) and infer the fraction of escaping LyC, astronomers have developed various indirect diagnostics of LyC escape. Using measurements of the LyC from the Low-redshift Lyman Continuum Survey (LzLCS), we present the first statistical test of these diagnostics. While optical depth indicators based on Lyα, such as peak velocity separation and equivalent width, perform well, we also find that other diagnostics, such as the [O iii]/[O ii] flux ratio and star formation rate surface density, predict whether a galaxy is an LCE. The relationship between these galaxy properties and the fraction of escaping LyC flux suggests that LyC escape depends strongly on H i column density, ionization parameter, and stellar feedback. We find that LCEs occupy a range of stellar masses, metallicities, star formation histories, and ionization parameters, which may indicate episodic and/or different physical causes of LyC escape.

Depth information is important for autonomous systems to perceive environments and estimate their own state. Traditional depth estimation methods, like structure from motion and stereo vision matching, are built on feature correspondences of multiple viewpoints. Meanwhile, the predicted depth maps are sparse. Inferring depth information from a single image (monocular depth estimation) is an ill-posed problem. With the rapid development of deep neural networks, monocular depth estimation based on deep learning has been widely studied recently and achieved promising performance in accuracy. Meanwhile, dense depth maps are estimated from single images by deep neural networks in an end-to-end manner. In order to improve the accuracy of depth estimation, different kinds of network frameworks, loss functions and training strategies are proposed subsequently. Therefore, we survey the current monocular depth estimation methods based on deep learning in this review. Initially, we conclude several widely used datasets and evaluation indicators in deep learning-based depth estimation. Furthermore, we review some representative existing methods according to different training manners: supervised, unsupervised and semi-supervised. Finally, we discuss the challenges and provide some ideas for future researches in monocular depth estimation.

The role of macroalgae in Blue Carbon assessments has been controversial, partially due to uncertainties about the fate of exported macroalgae. Available evidence suggests that macroalgae are exported to reach the open ocean and the deep sea. Nevertheless, this evidence lacks systematic assessment. Here, we provide robust evidence of macroalgal export beyond coastal habitats. We used metagenomes and metabarcodes from the global expeditions Tara Oceans and Malaspina 2010 Circumnavigation. We discovered macroalgae worldwide at up to 5,000 km from coastal areas. We found 24 orders, most of which belong to the phylum Rhodophyta. The diversity of macroalgae was similar across oceanic regions, although the assemblage composition differed. The South Atlantic Ocean presented the highest macroalgal diversity, whereas the Red Sea was the least diverse region. The abundance of macroalgae sequences attenuated exponentially with depth at a rate of 37.3% km−1, and only 24% of macroalgae available at the surface were expected to reach the seafloor at a depth of 4,000 m. Our findings indicate that macroalgae are exported across the open and the deep ocean, suggesting that macroalgae may be an important source of allochthonous carbon, and their contribution should be considered in Blue Carbon assessments.

A central challenge in microbial ecology is to understand the underlying mechanisms driving community assembly, particularly in the continuum of species sorting and dispersal limitation. However, little is known about the relative importance of species sorting and dispersal limitation in shaping marine microbial communities; especially, how they are related to organism types/traits and water depth. Here, we used variation partitioning and null model analysis to compare mechanisms driving bacterial and protist metacommunity dynamics at the basin scale in the East China Sea, based on MiSeq paired-end sequencing of 16S ribosomal DNA (rDNA) and 18S rDNA, respectively, in surface, deep chlorophyll maximum and bottom layers. Our analyses indicated that protist communities were governed more strongly by species sorting relative to dispersal limitation than were bacterial communities; this pattern was consistent across the three-depth layers, albeit to different degrees. Furthermore, we detected that bacteria exhibited wider habitat niche breadths than protists, whereas, passive dispersal abilities were not appreciably different between them. Our findings support the ‘size-plasticity’ hypothesis: smaller organisms (bacteria) are less environment filtered than larger organisms (protists), as smaller organisms are more likely to be plastic in metabolic abilities and have greater environmental tolerance.

Vascular complications following solid organ transplantation may lead to graft ischemia, dysfunction or loss. Imaging approaches can provide intermittent assessments of graft perfusion, but require highly skilled practitioners and do not directly assess graft oxygenation. Existing systems for monitoring tissue oxygenation are limited by the need for wired connections, the inability to provide real-time data or operation restricted to surface tissues. Here, we present a minimally invasive system to monitor deep-tissue O 2 that reports continuous real-time data from centimeter-scale depths in sheep and up to a 10-cm depth in ex vivo porcine tissue. The system is composed of a millimeter-sized, wireless, ultrasound-powered implantable luminescence O 2 sensor and an external transceiver for bidirectional data transfer, enabling deep-tissue oxygenation monitoring for surgical or critical care indications. The oxygenation of deep tissues is continuously measured using an ultrasound-powered wireless implant.

Consciousness can be defined by two components: arousal (wakefulness) and awareness (subjective experience). However, neurophysiological consciousness metrics able to disentangle between these components have not been reported. Here, we propose an explainable consciousness indicator (ECI) using deep learning to disentangle the components of consciousness. We employ electroencephalographic (EEG) responses to transcranial magnetic stimulation under various conditions, including sleep ( n  = 6), general anesthesia ( n  = 16), and severe brain injury ( n  = 34). We also test our framework using resting-state EEG under general anesthesia ( n  = 15) and severe brain injury ( n  = 34). ECI simultaneously quantifies arousal and awareness under physiological, pharmacological, and pathological conditions. Particularly, ketamine-induced anesthesia and rapid eye movement sleep with low arousal and high awareness are clearly distinguished from other states. In addition, parietal regions appear most relevant for quantifying arousal and awareness. This indicator provides insights into the neural correlates of altered states of consciousness. The authors propose an explainable consciousness indicator using deep learning to quantify arousal and awareness under sleep, anesthesia, and in patients with disorders of consciousness.

Stock market trends forecast is one of the most current topics and a significant research challenge due to its dynamic and unstable nature. The stock data is usually non-stationary, and attributes are non-correlative to each other. Several traditional Stock Technical Indicators (STIs) may incorrectly predict the stock market trends. To study the stock market characteristics using STIs and make efficient trading decisions, a robust model is built. This paper aims to build up an Evolutionary Deep Learning Model (EDLM) to identify stock trends’ prices by using STIs. The proposed model has implemented the Deep Learning (DL) model to establish the concept of Correlation-Tensor. The analysis of the dataset of three most popular banking organizations obtained from the live stock market based on the National Stock exchange (NSE) – India, a Long Short Term Memory (LSTM) is used. The datasets encompassed the trading days from the 17 of Nov 2008 to the 15 of Nov 2018. This work also conducted exhaustive experiments to study the correlation of various STIs with stock price trends. The model built with an EDLM has shown significant improvements over two benchmark ML models and a deep learning one. The proposed model aids investors in making profitable investment decisions as it presents trend-based forecasting and has achieved a prediction accuracy of 63.59%, 56.25%, and 57.95% on the datasets of HDFC, Yes Bank, and SBI, respectively. Results indicate that the proposed EDLA with a combination of STIs can often provide improved results than the other state-of-the-art algorithms.

Volcanic eruptions shape Earth’s surface and provide a window into deep Earth processes. How the primary asthenospheric melts form, pond and ascend through the lithosphere is, however, still poorly understood. Since 10 May 2018, magmatic activity has occurred offshore eastern Mayotte (North Mozambique channel), associated with large surface displacements, very-low-frequency earthquakes and exceptionally deep earthquake swarms. Here we present geophysical and marine data from the MAYOBS1 cruise, which reveal that by May 2019, this activity formed an 820-m-tall, ~5 km³ volcanic edifice on the seafloor. This is the largest active submarine eruption ever documented. Seismic and deformation data indicate that deep (>55 km depth) magma reservoirs were rapidly drained through dykes that intruded the entire lithosphere and that pre-existing subvertical faults in the mantle were reactivated beneath an ancient caldera structure. We locate the new volcanic edifice at the tip of a 50-km-long ridge composed of many other recent edifices and lava flows. This volcanic ridge is an extensional feature inside a wide transtensional boundary that transfers strain between the East African and Madagascar rifts. We propose that the massive eruption originated from hot asthenosphere at the base of a thick, old, damaged lithosphere. An ~5 km³ volcanic edifice offshore Mayotte formed between May 2018 and May 2019 by rapid magma intrusion through the entire lithosphere, according to an analysis of marine observations and geophysical data.

Human society is currently in the era of the knowledge economy, and quantitative measurement of the difficulty of product design provides a basis for enterprises to further implement rational resource allocation, which has important practical significance for enterprises. Based on the actual situation of the enterprise, this study aims to gain a deep understanding of the development process of toy products and establish a comprehensive evaluation index system suitable for evaluating the difficulty coefficient of the mechanical structure design of toy products. The study determined the evaluation indicators that affect the difficulty of mechanical structure design in the product development process through investigation and analysis methods and used the network analysis method to determine the weights of relevant evaluation indicators. Finally, the design difficulty coefficient of toy products was obtained by weighting. ANP comprehensively considered various factors while considering the mutual influence between them, describing and calculating the relationship between each factor, and conducting a comprehensive ranking. Several examples were conveniently calculated by using Super Decisions software, and the results showed that the application of ANP to evaluate the difficulty of mechanical structure design for toy products is reasonable.

While calcium imaging has become a mainstay of modern neuroscience, the spectral properties of current fluorescent calcium indicators limit deep-tissue imaging as well as simultaneous use with other probes. Using two monomeric near-infrared (NIR) fluorescent proteins (FPs), we engineered an NIR Förster resonance energy transfer (FRET)-based genetically encoded calcium indicator (iGECI). iGECI exhibits high levels of brightness and photostability and an increase up to 600% in the fluorescence response to calcium. In dissociated neurons, iGECI detects spontaneous neuronal activity and electrically and optogenetically induced firing. We validated the performance of iGECI up to a depth of almost 400 µm in acute brain slices using one-photon light-sheet imaging. Applying hybrid photoacoustic and fluorescence microscopy, we simultaneously monitored neuronal and hemodynamic activities in the mouse brain through an intact skull, with resolutions of ~3 μm (lateral) and ~25–50 μm (axial). Using two-photon imaging, we detected evoked and spontaneous neuronal activity in the mouse visual cortex, with fluorescence changes of up to 25%. iGECI allows biosensors and optogenetic actuators to be multiplexed without spectral crosstalk. A near-infrared fluorescent calcium indicator can be combined with other optogenetic tools in vivo without spectral crosstalk.