Prior work decoding linguistic meaning from imaging data has been largely limited to concrete nouns, using similar stimuli for training and testing, from a relatively small number of semantic categories. Here we present a new approach for building a brain decoding system in which words and sentences are represented as vectors in a semantic space constructed from massive text corpora. By efficiently sampling this space to select training stimuli shown to subjects, we maximize the ability to generalize to new meanings from limited imaging data. To validate this approach, we train the system on imaging data of individual concepts, and show it can decode semantic vector representations from imaging data of sentences about a wide variety of both concrete and abstract topics from two separate datasets. These decoded representations are sufficiently detailed to distinguish even semantically similar sentences, and to capture the similarity structure of meaning relationships between sentences.

The coronavirus disease 2019 (COVID-19) breaking out in late December 2019 is gradually being controlled in China, but it is still spreading rapidly in many other countries and regions worldwide. It is urgent to conduct prediction research on the development and spread of the epidemic. In this article, a hybrid artificial-intelligence (AI) model is proposed for COVID-19 prediction. First, as traditional epidemic models treat all individuals with coronavirus as having the same infection rate, an improved susceptible-infected (ISI) model is proposed to estimate the variety of the infection rates for analyzing the transmission laws and development trend. Second, considering the effects of prevention and control measures and the increase of the public's prevention awareness, the natural language processing (NLP) module and the long short-term memory (LSTM) network are embedded into the ISI model to build the hybrid AI model for COVID-19 prediction. The experimental results on the epidemic data of several typical provinces and cities in China show that individuals with coronavirus have a higher infection rate within the third to eighth days after they were infected, which is more in line with the actual transmission laws of the epidemic. Moreover, compared with the traditional epidemic models, the proposed hybrid AI model can significantly reduce the errors of the prediction results and obtain the mean absolute percentage errors (MAPEs) with 0.52%, 0.38%, 0.05%, and 0.86% for the next six days in Wuhan, Beijing, Shanghai, and countrywide, respectively. Index Terms-Coronavirus disease 2019 (COVID-19) prediction, epidemic model, hybrid artificial-intelligence (AI) model, natural language processing (NLP).

Background: Radiotherapy continues to be delivered uniformly without consideration of individual tumor characteristics. To advance toward more precise treatments in radiotherapy, we queried the lung computed tomography (CT)-derived feature space to identify radiation sensitivity parameters that can predict treatment failure and hence guide the individualization of radiotherapy dose. Methods: We used a cohort-based registry of 849 patients with cancer in the lung treated with high dose radiotherapy using stereotactic body radiotherapy. We input pre-therapy lung CT images into a multi-task deep neural network, Deep Profiler, to generate an image fingerprint that primarily predicts time to event treatment outcomes and secondarily approximates classical radiomic features. We validated our findings in an independent study population (n = 95). Deep Profiler was combined with clinical variables to derive iGray, an individualized dose that estimates treatment failure probability to be <5%. Findings: Radiation treatments in patients with high Deep Profiler scores fail at a significantly higher rate than in those with low scores. The 3-year cumulative incidences of local failure were 20.3% (95% CI: 16.0–24.9) and 5.7% (95% CI: 3.5–8.8), respectively. Deep Profiler independently predicted local failure (hazard ratio 1.65, 95% 1.02–2.66, p = 0.04). Models that included Deep Profiler and clinical variables predicted treatment failures with a concordance index of 0.72 (95% CI: 0.67–0.77), a significant improvement compared to classical radiomics or clinical variables alone (p = <0.001 and <0.001, respectively). Deep Profiler performed well in an external study population (n = 95), accurately predicting treatment failures across diverse clinical settings and CT scanner types (concordance index = 0.77 [95% CI: 0.69–0.92]). iGray had a wide dose range (21.1–277 Gy, BED), suggested dose reductions in 23.3% of patients and can be safely delivered in the majority of cases. Interpretation: Our results indicate that there are image-distinct subpopulations that have differential sensitivity to radiotherapy. The image-based deep learning framework proposed herein is the first opportunity to use medical images to individualize radiotherapy dose.

Arsenic poisoning is a global health problem. Chronic exposure to arsenic has been associated with the development of a wide range of diseases and health problems in humans. Arsenic exposure induces the generation of intracellular reactive oxygen species (ROS), which mediate multiple changes to cell behavior by altering signaling pathways and epigenetic modifications, or cause direct oxidative damage to molecules. Antioxidants with the potential to reduce ROS levels have been shown to ameliorate arsenic-induced lesions. However, emerging evidence suggests that constructive activation of antioxidative pathways and decreased ROS levels contribute to chronic arsenic toxicity in some cases. This review details the pathways involved in arsenic-induced redox imbalance, as well as current studies on prophylaxis and treatment strategies using antioxidants.

We describe a closed-loop brain-computer interface that re-ranks an image database by iterating between user generated 'interest' scores and computer vision generated visual similarity measures. The interest scores are based on decoding the electroencephalographic (EEG) correlates of target detection, attentional shifts and self-monitoring processes, which result from the user paying attention to target images interspersed in rapid serial visual presentation (RSVP) sequences. The highest scored images are passed to a semi-supervised computer vision system that reorganizes the image database accordingly, using a graph-based representation that captures visual similarity between images. The system can either query the user for more information, by adaptively resampling the database to create additional RSVP sequences, or it can converge to a 'done' state. The done state includes a final ranking of the image database and also a 'guess' of the user's chosen category of interest. We find that the closed-loop system's re-rankings can substantially expedite database searches for target image categories chosen by the subjects. Furthermore, better reorganizations are achieved than by relying on EEG interest rankings alone, or if the system were simply run in an open loop format without adaptive resampling.

High density lipoprotein (HDL) binds lipopolysaccharide (LPS or endotoxin) and neutralizes its toxicity. We investigated the function of Apolipoprotein A-I (ApoA-I), a major apolipoprotein in HDL, in this process. Mouse macrophages were incubated with LPS, LPS+ApoA-I, LPS+ApoA-I+LFF (lipoprotein-free plasma fraction d>1.210 g/ml), LPS+HDL, LPS+HDL+LFF, respectively. MTT method was used to detect the mortality of L-929 cells which were attacked by the release-out cytokines in LPS-activated macrophages. It was found that ApoA-I significantly decreased L-929 cells mortality caused by LPS treatment (LPS vs. LPS+ApoA-I, P<0.05) and this effect became even more significant when LFF was utilized (LPS vs. LPS+ApoA-I+LFF, P<0.01; LPS vs. LPS+HDL+LFF, P<0.01). There was no significant difference between LPS+ApoA-I+LFF and LPS+HDL+LFF treatment, indicating that ApoA-I was the main factor. We also investigated in vivo effects of ApoA-I on mouse mortality rate and survival time after LPS administration. We found that the mortality in LPS+ApoA-I group (20%) and in LPS+ApoA-I+LFF group (10%) was significantly lower than that in LPS group (80%) (P<0.05, P<0.01, respectively); the survival time was (43.20 +/- 10.13) h in LPS+ApoA-I group and (46.80 +/- 3.79) h in LPS+ApoA-I+LFF group, which were significantly longer than that in LPS group (16.25 +/- 17.28) h (P<0.01). We also carried out in vitro binding study to investigate the binding capacity of ApoA-I and ApoA-I+LFF to fluorescence labeled LPS (FITC-LPS). It was shown that both ApoA-I and ApoA-I+LFF could bind with FITC-LPS, however, the binding capacity of ApoA-I+LFF to FITC-LPS (64.47 +/- 8.06) was significantly higher than that of ApoA-I alone (24.35 +/- 3.70) (P<0.01). The results suggest that: (1) ApoA-I has the ability to bind with and protect against LPS; (2) LFF enhances the effect of ApoA-I; (3) ApoA-I is the major contributor for HDL anti-endotoxin function.

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