Abstract: False Negative Tests for SARS-CoV-2 Infection Diagnostic testing for SARS-CoV-2 will help in safely reopening the country, but only if tests are highly accurate. Several steps need to be taken by m...

Abstract: Detection of microbial DNA is an evolutionarily conserved mechanism that alerts the host immune system to mount a defense response to microbial infections. However, this detection mechanism also poses a challenge to the host as to how to distinguish foreign DNA from abundant self-DNA. Cyclic guanosine monophosphate (GMP)-adenosine monophosphate (AMP) synthase (cGAS) is a DNA sensor that triggers innate immune responses through production of the second messenger cyclic GMP-AMP (cGAMP), which binds and activates the adaptor protein STING. However, cGAS can be activated by double-stranded DNA irrespective of the sequence, including self-DNA. Although how cGAS is normally kept inactive in cells is still not well understood, recent research has provided strong evidence that genomic DNA damage leads to cGAS activation to stimulate inflammatory responses. This review summarizes recent findings on how genomic instability and DNA damage trigger cGAS activation and how cGAS serves as a link from DNA damage to inflammation, cellular senescence, and cancer.

Abstract: We show how to turn any classifier that classifies well under Gaussian noise into a new classifier that is certifiably robust to adversarial perturbations under the $\ell_2$ norm. This "randomized smoothing" technique has been proposed recently in the literature, but existing guarantees are loose. We prove a tight robustness guarantee in $\ell_2$ norm for smoothing with Gaussian noise. We use randomized smoothing to obtain an ImageNet classifier with e.g. a certified top-1 accuracy of 49% under adversarial perturbations with $\ell_2$ norm less than 0.5 (=127/255). No certified defense has been shown feasible on ImageNet except for smoothing. On smaller-scale datasets where competing approaches to certified $\ell_2$ robustness are viable, smoothing delivers higher certified accuracies. Our strong empirical results suggest that randomized smoothing is a promising direction for future research into adversarially robust classification. Code and models are available at this http URL.

Abstract: Importance: Coronavirus disease 2019 (COVID-19) continues to spread rapidly worldwide. Neutralizing antibodies are a potential treatment for COVID-19. Objective: To determine the effect of bamlanivimab monotherapy and combination therapy with bamlanivimab and etesevimab on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral load in mild to moderate COVID-19. Design, Setting, and Participants: The BLAZE-1 study is a randomized phase 2/3 trial at 49 US centers including ambulatory patients (N = 613) who tested positive for SARS-CoV-2 infection and had 1 or more mild to moderate symptoms. Patients who received bamlanivimab monotherapy or placebo were enrolled first (June 17-August 21, 2020) followed by patients who received bamlanivimab and etesevimab or placebo (August 22-September 3). These are the final analyses and represent findings through October 6, 2020. Interventions: Patients were randomized to receive a single infusion of bamlanivimab (700 mg [n = 101], 2800 mg [n = 107], or 7000 mg [n = 101]), the combination treatment (2800 mg of bamlanivimab and 2800 mg of etesevimab [n = 112]), or placebo (n = 156). Main Outcomes and Measures: The primary end point was change in SARS-CoV-2 log viral load at day 11 (±4 days). Nine prespecified secondary outcome measures were evaluated with comparisons between each treatment group and placebo, and included 3 other measures of viral load, 5 on symptoms, and 1 measure of clinical outcome (the proportion of patients with a COVID-19-related hospitalization, an emergency department [ED] visit, or death at day 29). Results: Among the 577 patients who were randomized and received an infusion (mean age, 44.7 [SD, 15.7] years; 315 [54.6%] women), 533 (92.4%) completed the efficacy evaluation period (day 29). The change in log viral load from baseline at day 11 was -3.72 for 700 mg, -4.08 for 2800 mg, -3.49 for 7000 mg, -4.37 for combination treatment, and -3.80 for placebo. Compared with placebo, the differences in the change in log viral load at day 11 were 0.09 (95% CI, -0.35 to 0.52; P = .69) for 700 mg, -0.27 (95% CI, -0.71 to 0.16; P = .21) for 2800 mg, 0.31 (95% CI, -0.13 to 0.76; P = .16) for 7000 mg, and -0.57 (95% CI, -1.00 to -0.14; P = .01) for combination treatment. Among the secondary outcome measures, differences between each treatment group vs the placebo group were statistically significant for 10 of 84 end points. The proportion of patients with COVID-19-related hospitalizations or ED visits was 5.8% (9 events) for placebo, 1.0% (1 event) for 700 mg, 1.9% (2 events) for 2800 mg, 2.0% (2 events) for 7000 mg, and 0.9% (1 event) for combination treatment. Immediate hypersensitivity reactions were reported in 9 patients (6 bamlanivimab, 2 combination treatment, and 1 placebo). No deaths occurred during the study treatment. Conclusions and Relevance: Among nonhospitalized patients with mild to moderate COVID-19 illness, treatment with bamlanivimab and etesevimab, compared with placebo, was associated with a statistically significant reduction in SARS-CoV-2 viral load at day 11; no significant difference in viral load reduction was observed for bamlanivimab monotherapy. Further ongoing clinical trials will focus on assessing the clinical benefit of antispike neutralizing antibodies in patients with COVID-19 as a primary end point. Trial Registration: ClinicalTrials.gov Identifier: NCT04427501.

Abstract: In this work, a microwave approach is developed to rapidly synthesize ultralong zinc pyrovanadate (Zn3 V2 O7 (OH)2 ·2H2 O, ZVO) nanowires with a porous crystal framework. It is shown that our synthesis strategy can easily be extended to fabricate other metal pyrovanadate compounds. The zinc pyrovanadate nanowires show significantly improved electrochemical performance when used as intercalation cathode for aqueous zinc-ion battery. Specifically, the ZVO cathode delivers high capacities of 213 and 76 mA h g-1 at current densities of 50 and 3000 mA g-1 , respectively. Furthermore, the Zn//ZVO cells show good cycling stability up to 300 cycles. The estimated energy density of this Zn cell is ≈214Wh kg-1 , which is much higher than commercial lead-acid batteries. Significant insight into the Zn-storage mechanism in the pyrovanadate cathodes is presented using multiple analytical methods. In addition, it is shown that our prototype device can power a 1.5 V temperature sensor for at least 24 h.

Abstract: Flexible non-metal oxygen electrodes fabricated from phosphorus-doped graphitic carbon nitride nano-flowers directly grown on carbon-fiber paper exhibit high activity and stability in reversibly catalyzing oxygen reduction and evolution reactions, which is a result of N, P dual action, enhanced mass/charge transfer, and high active surface area The performance is comparable to that of the state-of-the-art transition-metal, noble-metal, and non-metal catalysts Remarkably, the flexible nature of these oxygen electrodes allows their use in folded and rolled-up forms, and directly as cathodes in Zn–air batteries, featuring low charge/discharge overpotential and long lifetime

Abstract: We combine electromagnetic (EM) and gravitational wave (GW) information on the binary neutron star (NS) merger GW170817 in order to constrain the radii $R_{\rm ns}$ and maximum mass $M_{\rm max}$ of NSs. GW170817 was followed by a range of EM counterparts, including a weak gamma-ray burst (GRB), kilonova (KN) emission from the radioactive decay of the merger ejecta, and X-ray/radio emission consistent with being the synchrotron afterglow of a more powerful off-axis jet. The type of compact remnant produced in the immediate merger aftermath, and its predicted EM signal, depend sensitively on the high-density NS equation of state (EOS). For a soft EOS which supports a low $M_{\rm max}$, the merger undergoes a prompt collapse accompanied by a small quantity of shock-heated or disk wind ejecta, inconsistent with the large quantity $\gtrsim 10^{-2}M_{\odot}$ of lanthanide-free ejecta inferred from the KN. On the other hand, if $M_{\rm max}$ is sufficiently large, then the merger product is a rapidly-rotating supramassive NS (SMNS), which must spin-down before collapsing into a black hole. A fraction of the enormous rotational energy necessarily released by the SMNS during this process is transferred to the ejecta, either into the GRB jet (energy $E_{\rm GRB}$) or the KN ejecta (energy $E_{\rm ej}$), also inconsistent with observations. By combining the total binary mass of GW170817 inferred from the GW signal with conservative upper limits on $E_{\rm GRB}$ and $E_{\rm ej}$ from EM observations, we constrain the likelihood probability of a wide-range of previously-allowed EOS. These two constraints delineate an allowed region of the $M_{\rm max}-R_{\rm ns}$ parameter space, which once marginalized over NS radius places an upper limit of $M_{\rm max} \lesssim 2.17M_{\odot}$ (90\%), which is tighter or arguably less model-dependent than other current constraints.

Abstract: The potential for the gut microbiota to affect health has a particular relevance for older individuals. This is because the microbiota may modulate aging-related changes in innate immunity, sarcopaenia, and cognitive function, all of which are elements of frailty. Both cell culture-dependent and -independent studies show that the gut microbiota of older people differs from that of younger adults. There is no chronological threshold or age at which the composition of the microbiota suddenly alters; rather, changes occur gradually with time. Our detailed analyses have separated the microbiota into groups associated with age, long-term residential care, habitual diet, and degree of retention of a core microbiome. We are beginning to understand how these groups change with aging and how they relate to clinical phenotypes. These data provide a framework for analyzing microbiota-health associations, distinguishing correlation from causation, identifying microbiota interaction with physiological aging processes, and developing microbiota-based health surveillance for older adults.

Abstract: After three decades of intense and fundamental research on metal–organic frameworks (MOFs), is there anything left to say or to explain? The synthesis and properties of MOFs have already been comprehensively described elsewhere. It is time, however, to prove the nature of their true usability: technological applications based on these extended materials require development and implementation as a natural consequence of the up-to-known intensive research focused on their design and preparation. The current large number of reviews on MOFs emphasizes practical strategies to develop novel networks with varied crystal size, shape and topology, being mainly devoted to academic concerns. The present survey intends to push the boundaries and summarise the state-of-the-art on the preparation of promising (multi)functional MOFs in worldwide laboratories and their use as materials for industrial implementation. This review starts, on the one hand, to describe several tools and striking examples of remarkable and recent (multi)functional MOFs exhibiting outstanding properties (e.g., in gas adsorption and separation, selective sorption of harmful compounds, heterogeneous catalysis, luminescent and corrosion protectants). On the other hand, and in a second part, it intends to use these examples of MOFs to incite scientists to move towards the transference of knowledge from the laboratories to the industry. Within this context, we exhaustively review the many efforts of several worldwide commercial companies to bring functional MOFs towards the daily use, analysing the various patents and applications reported to date. Overall, this review goes from the very basic concepts of functional MOF engineering and preparation ending up in their industrial production on a large scale and direct applications in society.

Abstract: BACKGROUND: As the coronavirus disease pandemic spread across the United States and protective measures to mitigate its impact were enacted, parents and children experienced widespread disruptions in daily life. Our objective with this national survey was to determine how the pandemic and mitigation efforts affected the physical and emotional well-being of parents and children in the United States through early June 2020. METHODS: In June 2020, we conducted a national survey of parents with children age RESULTS: Since March 2020, 27% of parents reported worsening mental health for themselves, and 14% reported worsening behavioral health for their children. The proportion of families with moderate or severe food insecurity increased from 6% before March 2020 to 8% after, employer-sponsored insurance coverage of children decreased from 63% to 60%, and 24% of parents reported a loss of regular child care. Worsening mental health for parents occurred alongside worsening behavioral health for children in nearly 1 in 10 families, among whom 48% reported loss of regular child care, 16% reported change in insurance status, and 11% reported worsening food security. CONCLUSIONS: The coronavirus disease pandemic has had a substantial tandem impact on parents and children in the United States. As policy makers consider additional measures to mitigate the health and economic effects of the pandemic, they should consider the unique needs of families with children.

Abstract: The emergence of a pandemic affecting the respiratory system can result in a significant demand for face masks. This includes the use of cloth masks by large sections of the public, as can be seen ...

Abstract: In recent years, stem cell therapy has become a very promising and advanced scientific research topic. The development of treatment methods has evoked great expectations. This paper is a review focused on the discovery of different stem cells and the potential therapies based on these cells. The genesis of stem cells is followed by laboratory steps of controlled stem cell culturing and derivation. Quality control and teratoma formation assays are important procedures in assessing the properties of the stem cells tested. Derivation methods and the utilization of culturing media are crucial to set proper environmental conditions for controlled differentiation. Among many types of stem tissue applications, the use of graphene scaffolds and the potential of extracellular vesicle-based therapies require attention due to their versatility. The review is summarized by challenges that stem cell therapy must overcome to be accepted worldwide. A wide variety of possibilities makes this cutting edge therapy a turning point in modern medicine, providing hope for untreatable diseases.

Abstract: Background The relationship between the presence of antibodies to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the risk of subsequent reinfection remains unclear. Metho...

Abstract: Complex three-dimensional (3D) structures in biology (e.g., cytoskeletal webs, neural circuits, and vasculature networks) form naturally to provide essential functions in even the most basic forms of life. Compelling opportunities exist for analogous 3D architectures in human-made devices, but design options are constrained by existing capabilities in materials growth and assembly. We report routes to previously inaccessible classes of 3D constructs in advanced materials, including device-grade silicon. The schemes involve geometric transformation of 2D micro/nanostructures into extended 3D layouts by compressive buckling. Demonstrations include experimental and theoretical studies of more than 40 representative geometries, from single and multiple helices, toroids, and conical spirals to structures that resemble spherical baskets, cuboid cages, starbursts, flowers, scaffolds, fences, and frameworks, each with single- and/or multiple-level configurations.

Abstract: When two sheets of graphene are stacked at a small twist angle, the resulting flat superlattice minibands are expected to strongly enhance electron-electron interactions. Here, we present evidence that near three-quarters ([Formula: see text]) filling of the conduction miniband, these enhanced interactions drive the twisted bilayer graphene into a ferromagnetic state. In a narrow density range around an apparent insulating state at [Formula: see text], we observe emergent ferromagnetic hysteresis, with a giant anomalous Hall (AH) effect as large as 10.4 kilohms and indications of chiral edge states. Notably, the magnetization of the sample can be reversed by applying a small direct current. Although the AH resistance is not quantized, and dissipation is present, our measurements suggest that the system may be an incipient Chern insulator.

Abstract: This study sought to identify factors associated with depression, anxiety, and PTSD symptomatology in U.S. young adults (18-30 years) during the COVID-19 pandemic. This cross-sectional online study assessed 898 participants from April 13, 2020 to May 19, 2020, approximately one month after the U.S. declared a state of emergency due to COVID-19 and prior to the initial lifting of restrictions across 50 U.S. states. Respondents reported high levels of depression (43.3%, PHQ-8 scores ≥ 10), high anxiety scores (45.4%, GAD-7 scores ≥ 10), and high levels of PTSD symptoms (31.8%, PCL-C scores ≥ 45). High levels of loneliness, high levels of COVID-19-specific worry, and low distress tolerance were significantly associated with clinical levels of depression, anxiety, and PTSD symptoms. Resilience was associated with low levels of depression and anxiety symptoms but not PTSD. Most respondents had high levels of social support; social support from family, but not from partner or peers, was associated with low levels of depression and PTSD. Compared to Whites, Asian Americans were less likely to report high levels across mental health symptoms, and Hispanic/Latinos were less likely to report high levels of anxiety. These factors provide initial guidance regarding the clinical management for COVID-19-related mental health problems.

Abstract: Artificial intelligence has seen several breakthroughs in recent years, with games often serving as milestones. A common feature of these games is that players have perfect information. Poker, the quintessential game of imperfect information, is a long-standing challenge problem in artificial intelligence. We introduce DeepStack, an algorithm for imperfect-information settings. It combines recursive reasoning to handle information asymmetry, decomposition to focus computation on the relevant decision, and a form of intuition that is automatically learned from self-play using deep learning. In a study involving 44,000 hands of poker, DeepStack defeated, with statistical significance, professional poker players in heads-up no-limit Texas hold’em. The approach is theoretically sound and is shown to produce strategies that are more difficult to exploit than prior approaches.

Abstract: CONTEXT: The optimal pharmacologic treatment for early convulsive status epilepticus is unclear. OBJECTIVE: To analyze efficacy, tolerability and safety data for anticonvulsant treatment of children and adults with convulsive status epilepticus and use this analysis to develop an evidence-based treatment algorithm. DATA SOURCES: Structured literature review using MEDLINE, Embase, Current Contents, and Cochrane library supplemented with article reference lists. STUDY SELECTION: Randomized controlled trials of anticonvulsant treatment for seizures lasting longer than 5 minutes. DATA EXTRACTION: Individual studies were rated using predefined criteria and these results were used to form recommendations, conclusions, and an evidence-based treatment algorithm. RESULTS: A total of 38 randomized controlled trials were identified, rated and contributed to the assessment. Only four trials were considered to have class I evidence of efficacy. Two studies were rated as class II and the remaining 32 were judged to hav...

Abstract: Despite decades of research, efforts to directly target KRAS have been challenging. MRTX849 was identified as a potent, selective, and covalent KRASG12C inhibitor that exhibits favorable drug-like properties, selectively modifies mutant cysteine 12 in GDP-bound KRASG12C and inhibits KRAS-dependent signaling. MRTX849 demonstrated pronounced tumor regression in 17 of 26 (65%) of KRASG12C-positive cell line- and patient-derived xenograft models from multiple tumor types and objective responses have been observed in KRASG12C-positive lung and colon adenocarcinoma patients. Comprehensive pharmacodynamic and pharmacogenomic profiling in sensitive and partially resistant non-clinical models identified mechanisms implicated in limiting anti-tumor activity including KRAS nucleotide cycling and pathways that induce feedback reactivation and/or bypass KRAS dependence. These factors included activation of RTKs, bypass of KRAS dependence, and genetic dysregulation of cell cycle. Combinations of MRTX849 with agents that target RTKs, mTOR, or cell cycle demonstrated enhanced response and marked tumor regression in several tumor models, including MRTX849-refractory models.

Abstract: SALib contains Python implementations of commonly used global sensitivity analysis methods, including Sobol (Sobol’ 2001, Andrea Saltelli (2002), Andrea Saltelli et al. (2010)), Morris (Morris 1991 ...

Abstract: We present mass models of the Milky Way created to fit observational constraints and to be consistent with expectations from theoretical modelling. The method used to create these models is that demonstrated in our previous study, and we improve on those models by adding gas discs to the potential, considering the effects of allowing the inner slope of the halo density profile to vary, and including new observations of maser sources in the Milky Way amongst the new constraints. We provide a best-fitting model, as well as estimates of the properties of the Milky Way. Under the assumptions in our main model, we find that the Sun is R0 = 8.20 ± 0.09 kpc from the Galactic Centre, with the circular speed at the Sun being v0 = 232.8 ± 3.0 kms-1; and that the Galaxy has a total stellar mass of (54.3 ± 5.7) × 109 M⊙, a total virial mass of (1.30 ± 0.30) × 1012M⊙ and a local dark-matter density of 0.40 ± 0.04 GeV cm-3, where the quoted uncertainties are statistical. These values are sensitive to our choice of priors and constraints. We investigate systematic uncertainties, which in some cases may be larger. For example, if we weaken our prior on R0, we find it to be 7.97 ± 0.15 kpc and that v0 = 226.8 ± 4.2 kms-1.We find that most of these properties, including the local dark-matter density, are remarkably insensitive to the assumed power-law density slope at the centre of the dark-matter halo. We find that it is unlikely that the local standard of rest differs significantly from that found under assumptions of axisymmetry. We have made code to compute the force from our potential, and to integrate orbits within it, publicly available. (Less)

Abstract: With the transition of facial expression recognition (FER) from laboratory-controlled to challenging in-the-wild conditions and the recent success of deep learning techniques in various fields, deep neural networks have increasingly been leveraged to learn discriminative representations for automatic FER. Recent deep FER systems generally focus on two important issues: overfitting caused by a lack of sufficient training data and expression-unrelated variations, such as illumination, head pose and identity bias. In this paper, we provide a comprehensive survey on deep FER, including datasets and algorithms that provide insights into these intrinsic problems. First, we describe the standard pipeline of a deep FER system with the related background knowledge and suggestions of applicable implementations for each stage. We then introduce the available datasets that are widely used in the literature and provide accepted data selection and evaluation principles for these datasets. For the state of the art in deep FER, we review existing novel deep neural networks and related training strategies that are designed for FER based on both static images and dynamic image sequences, and discuss their advantages and limitations. Competitive performances on widely used benchmarks are also summarized in this section. We then extend our survey to additional related issues and application scenarios. Finally, we review the remaining challenges and corresponding opportunities in this field as well as future directions for the design of robust deep FER systems.

Abstract: Recently it was conjectured that a certain infinite-dimensional “diagonal” subgroup of BMS supertranslations acting on past and future null infinity ( and ) is an exact symmetry of the quantum gravity S-matrix, and an associated Ward identity was derived. In this paper we show that this supertranslation Ward identity is precisely equivalent to Weinberg’s soft graviton theorem. Along the way we construct the canonical generators of supertranslations at , including the relevant soft graviton contributions. Boundary conditions at the past and future of and a correspondingly modified Dirac bracket are required. The soft gravitons enter as boundary modes and are manifestly the Goldstone bosons of spontaneously broken supertranslation invariance.

Abstract: In this paper, we consider the potential of data transmission in a system with a massive number of radiating and sensing elements, thought of as a contiguous surface of electromagnetically active material. We refer to this as a large intelligent surface (LIS), which is a newly proposed concept and conceptually goes beyond contemporary massive MIMO technology. First, we consider capacities of single-antenna autonomous terminals communicating to the LIS where the entire surface is used as a receiving antenna array in a perfect line-of-sight propagation environment. Under the condition that the surface area is sufficiently large, the received signal after a matched-filtering operation can be closely approximated by a sinc-function-like intersymbol interference channel. Second, we analyze a normalized capacity measured per unit surface, for a fixed transmit power per volume unit with different terminal deployments. As terminal density increases, the limit of the normalized capacity [nats/s/Hz/volume-unit] achieved when wavelength $\lambda$ approaches zero is equal to half of the transmit power per volume unit divided by the noise spatial power spectral density. Third, we show that the number of independent signal dimensions that can be harvested per meter deployed surface is $2/\lambda$ for one-dimensional terminal deployment, and $\pi /\lambda ^2$ per square meter for two- and three-dimensional terminal deployments. Finally, we consider implementations of the LIS in the form of a grid of conventional antenna elements, and show that the sampling lattice that minimizes the surface area and simultaneously obtains one independent signal dimension for every spent antenna is the hexagonal lattice.

Abstract: Deep neural networks (DNN) have revolutionized the field of natural language processing (NLP). Convolutional neural network (CNN) and recurrent neural network (RNN), the two main types of DNN architectures, are widely explored to handle various NLP tasks. CNN is supposed to be good at extracting position-invariant features and RNN at modeling units in sequence. The state of the art on many NLP tasks often switches due to the battle between CNNs and RNNs. This work is the first systematic comparison of CNN and RNN on a wide range of representative NLP tasks, aiming to give basic guidance for DNN selection.