The novel COVID-19 outbreak has affected more than 200 countries and territories as of March 2020. Given that patients with cancer are generally more vulnerable to infections, systematic analysis of diverse cohorts of patients with cancer affected by COVID-19 is needed. We performed a multicenter study including 105 patients with cancer and 536 age-matched noncancer patients confirmed with COVID-19. Our results showed COVID-19 patients with cancer had higher risks in all severe outcomes. Patients with hematologic cancer, lung cancer, or with metastatic cancer (stage IV) had the highest frequency of severe events. Patients with nonmetastatic cancer experienced similar frequencies of severe conditions to those observed in patients without cancer. Patients who received surgery had higher risks of having severe events, whereas patients who underwent only radiotherapy did not demonstrate significant differences in severe events when compared with patients without cancer. These findings indicate that patients with cancer appear more vulnerable to SARS-COV-2 outbreak.SIgnIfICAnCe: Because this is the first large cohort study on this topic, our report will provide muchneeded information that will benefit patients with cancer globally. As such, we believe it is extremely important that our study be disseminated widely to alert clinicians and patients.

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The fungal family Clavicipitaceae includes plant symbionts and parasites that produce several psychoactive and bioprotective alkaloids. The family includes grass symbionts in the epichloae clade (Epichloë and Neotyphodium species), which are extraordinarily diverse both in their host interactions and in their alkaloid profiles. Epichloae produce alkaloids of four distinct classes, all of which deter insects, and some—including the infamous ergot alkaloids—have potent effects on mammals. The exceptional chemotypic diversity of the epichloae may relate to their broad range of host interactions, whereby some are pathogenic and contagious, others are mutualistic and vertically transmitted (seed-borne), and still others vary in pathogenic or mutualistic behavior. We profiled the alkaloids and sequenced the genomes of 10 epichloae, three ergot fungi (Claviceps species), a morning-glory symbiont (Periglandula ipomoeae), and a bamboo pathogen (Aciculosporium take), and compared the gene clusters for four classes of alkaloids. Results indicated a strong tendency for alkaloid loci to have conserved cores that specify the skeleton structures and peripheral genes that determine chemical variations that are known to affect their pharmacological specificities. Generally, gene locations in cluster peripheries positioned them near to transposon-derived, AT-rich repeat blocks, which were probably involved in gene losses, duplications, and neofunctionalizations. The alkaloid loci in the epichloae had unusual structures riddled with large, complex, and dynamic repeat blocks. This feature was not reflective of overall differences in repeat contents in the genomes, nor was it characteristic of most other specialized metabolism loci. The organization and dynamics of alkaloid loci and abundant repeat blocks in the epichloae suggested that these fungi are under selection for alkaloid diversification. We suggest that such selection is related to the variable life histories of the epichloae, their protective roles as symbionts, and their associations with the highly speciose and ecologically diverse cool-season grasses.

Numerous short-lived and highly reactive oxygen species (ROS) such as O 2 · -(superoxide), · OH (hydroxyl radical), and H 2 O 2 (hydrogen peroxide) are continuously generated in vivo. Depending upon concentration, location and intracellular conditions, ROS can cause toxicity or act as signaling molecules. The cellular levels of ROS are controlled by antioxidant enzymes and small molecule antioxidants. As major antioxidant enzymes, superoxide dismutases (SODs), including copper-zinc superoxide dismutase (Cu/ZnSOD), manganese superoxide dismutase (MnSOD) and extracellular superoxide dismutase (ECSOD), play a crucial role in scavenging O 2 · -. This review focuses on the regulation of the genes (sods) coding for these enzymes with an emphasis on human genes. Current knowledge about sods structure and their regulation is summarized and depicted as diagrams.Studies to date on genes coding for Cu/ZnSOD (sod1) are mostly focused on alteration in the coding region and their associations with Amyotrophic Lateral Sclerosis (ALS). Evaluation of nucleotide sequences reveals that regulatory elements of the sod2 gene reside in both the non-coding and coding regions. Changes associated with sod2 lead to alteration in expression levels as well as protein function. We also discuss the structural basis for the changes in SOD expression associated with pathological conditions and where more work is needed to establish the relationship between SODs and diseases.

Two-dimensional (2D) heterostructured materials, combining the collective advantages of individual building blocks and synergistic properties, have spurred great interest as a new paradigm in materials science. The family of 2D transition-metal carbides and nitrides, MXenes, has emerged as an attractive platform to construct functional materials with enhanced performance for diverse applications. Here, we synthesized 2D MoS -on-MXene heterostructures through in situ sulfidation of Mo TiC T MXene. The computational results show that MoS -on-MXene heterostructures have metallic properties. Moreover, the presence of MXene leads to enhanced Li and Li S adsorption during the intercalation and conversion reactions. These characteristics render the as-prepared MoS -on-MXene heterostructures stable Li-ion storage performance. This work paves the way to use MXene to construct 2D heterostructures for energy storage applications.

Abstract-For robotic vehicles to navigate safely and efficiently in pedestrian-rich environments, it is important to model subtle human behaviors and navigation rules (e.g., passing on the right). However, while instinctive to humans, socially compliant navigation is still difficult to quantify due to the stochasticity in people's behaviors. Existing works are mostly focused on using feature-matching techniques to describe and imitate human paths, but often do not generalize well since the feature values can vary from person to person, and even run to run. This work notes that while it is challenging to directly specify the details of what to do (precise mechanisms of human navigation), it is straightforward to specify what not to do (violations of social norms). Specifically, using deep reinforcement learning, this work develops a time-efficient navigation policy that respects common social norms. The proposed method is shown to enable fully autonomous navigation of a robotic vehicle moving at human walking speed in an environment with many pedestrians.

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We address the task of jointly determining what a person is doing and where they are looking based on the analysis of video captured by a headworn camera. To facilitate our research, we first introduce the EGTEA Gaze+ dataset. Our dataset comes with videos, gaze tracking data, hand masks and action annotations, thereby providing the most comprehensive benchmark for First Person Vision (FPV). Moving beyond the dataset, we propose a novel deep model for joint gaze estimation and action recognition in FPV. Our method describes the participant's gaze as a probabilistic variable and models its distribution using stochastic units in a deep network. We further sample from these stochastic units, generating an attention map to guide the aggregation of visual features for action recognition. Our method is evaluated on our EGTEA Gaze+ dataset and achieves a performance level that exceeds the state-of-the-art by a significant margin. More importantly, we demonstrate that our model can be applied to larger scale FPV dataset-EPIC-Kitchens even without using gaze, offering new state-of-the-art results on FPV action recognition.