An outbreak of betacoronavirus severe acute respiratory syndrome (SARS)-CoV-2 began in Wuhan, China in December 2019. COVID-19, the disease associated with SARS-CoV-2 infection, rapidly spread to produce a global pandemic. We report development of a rapid (<40 min), easy-to-implement and accurate CRISPR–Cas12-based lateral flow assay for detection of SARS-CoV-2 from respiratory swab RNA extracts. We validated our method using contrived reference samples and clinical samples from patients in the United States, including 36 patients with COVID-19 infection and 42 patients with other viral respiratory infections. Our CRISPR-based DETECTR assay provides a visual and faster alternative to the US Centers for Disease Control and Prevention SARS-CoV-2 real-time RT–PCR assay, with 95% positive predictive agreement and 100% negative predictive agreement. SARS-CoV-2 in patient samples is detected in under an hour using a CRISPR-based lateral flow assay.

There has recently been a dramatic renewal of interest in hadron spectroscopy and charm physics. This renaissance has been driven in part by the discovery of a plethora of charmonium-like XYZ states at BESIII and B factories, and the observation of an intriguing proton-antiproton threshold enhancement and the possibly related X(1835) meson state at BESIII, as well as the threshold measurements of charm mesons and charm baryons. We present a detailed survey of the important topics in tau-charm physics and hadron physics that can be further explored at BESIII during the remaining operation period of BEPCII. This survey will help in the optimization of the data-taking plan over the coming years, and provides physics motivation for the possible upgrade of BEPCII to higher luminosity.

Genome editing has been a transformative force in the life sciences and human medicine, offering unprecedented opportunities to dissect complex biological processes and treat the underlying causes of many genetic diseases. CRISPR-based technologies, with their remarkable efficiency and easy programmability, stand at the forefront of this revolution. In this Review, we discuss the current state of CRISPR gene editing technologies in both research and therapy, highlighting limitations that constrain them and the technological innovations that have been developed in recent years to address them. Additionally, we examine and summarize the current landscape of gene editing applications in the context of human health and therapeutics. Finally, we outline potential future developments that could shape gene editing technologies and their applications in the coming years.

We report a successful observation of pressure-induced superconductivity in a topological compound Bi2Te3 with Tc of ∼3 K between 3 to 6 GPa. The combined high-pressure structure investigations with synchrotron radiation indicated that the superconductivity occurred at the ambient phase without crystal structure phase transition. The Hall effects measurements indicated the hole-type carrier in the pressure-induced superconducting Bi2Te3 single crystal. Consequently, the first-principles calculations based on the structural data obtained by the Rietveld refinement of X-ray diffraction patterns at high pressure showed that the electronic structure under pressure remained topologically nontrivial. The results suggested that topological superconductivity can be realized in Bi2Te3 due to the proximity effect between superconducting bulk states and Dirac-type surface states. We also discuss the possibility that the bulk state could be a topological superconductor.

Polymer science has applications in biomedical engineering, prosthetics, surgical implants, and prospective pharmaceutical excipients for drug delivery. “Intelligent or Smart Polymers” are created for drug targeting either by derivatization of natural polymers or controlled radical polymerization of electrolytes. Their mode of action is governed by the environmental stimuli viz. temperature, pH, ionic concentration, magnetism, and so on. pH‐responsive polymers, because of their self‐assembling behavior, alter their solubility, conformation, surface activity, and hydrophilicity when exposed to a specific pH. The physiological pH varies from acidic nuclei to alkaline cytoplasm and highly acidic gastric juice to slightly alkaline plasma; thus, various polymers are under study for delivering small molecules, genes, peptides, enzymes, growth factors, and antibodies. The non‐invasive drug delivery routes like oral, ocular, nasal, pulmonary, transdermal, and rectal routes can be explored for targeting recombinant proteins, monoclonal antibodies, and small molecules with particular emphasis on the individual's physiological and pathological state. Further, these polymers can be designed into various architectures like dendrimers, liposomes, micelles, and metallic nanoparticles that can serve as drug reservoirs for sustaining drug release. The challenges in this field are the selection of biocompatible polymers with ease of synthesis and scale‐up, ensuring effective drug‐loading, and stability aspects, producing robust pharmacological data, and timely regulatory approvals. This review exclusively explores the physicochemical characteristics of pH‐responsive polymers, their categorization, various architectural entities, recent studies and patents, and their emerging applications concerning specific diseases.

Abstract. Aerosol acidity plays a key role in secondary aerosol formation. The high-temporal-resolution PM2.5 pH and size-resolved aerosol pH in Beijing were calculated with ISORROPIA II. In 2016–2017, the mean PM2.5 pH (at relative humidity (RH) > 30 %) over four seasons was 4.5±0.7 (winter) > 4.4±1.2 (spring) > 4.3±0.8 (autumn) > 3.8±1.2 (summer), showing moderate acidity. In coarse-mode aerosols, Ca2+ played an important role in aerosol pH. Under heavily polluted conditions, more secondary ions accumulated in the coarse mode, leading to the acidity of the coarse-mode aerosols shifting from neutral to weakly acidic. Sensitivity tests also demonstrated the significant contribution of crustal ions to PM2.5 pH. In the North China Plain (NCP), the common driving factors affecting PM2.5 pH variation in all four seasons were SO42-, TNH3 (total ammonium (gas + aerosol)), and temperature, while unique factors were Ca2+ in spring and RH in summer. The decreasing SO42- and increasing NO3- mass fractions in PM2.5 as well as excessive NH3 in the atmosphere in the NCP in recent years are the reasons why aerosol acidity in China is lower than that in Europe and the United States. The nonlinear relationship between PM2.5 pH and TNH3 indicated that although NH3 in the NCP was abundant, the PM2.5 pH was still acidic because of the thermodynamic equilibrium between NH4+ and NH3. To reduce nitrate by controlling ammonia, the amount of ammonia must be greatly reduced below excessive quantities.

We present flow Q-learning (FQL), a simple and performant offline reinforcement learning (RL) method that leverages an expressive flow-matching policy to model arbitrarily complex action distributions in data. Training a flow policy with RL is a tricky problem, due to the iterative nature of the action generation process. We address this challenge by training an expressive one-step policy with RL, rather than directly guiding an iterative flow policy to maximize values. This way, we can completely avoid unstable recursive backpropagation, eliminate costly iterative action generation at test time, yet still mostly maintain expressivity. We experimentally show that FQL leads to strong performance across 73 challenging state- and pixel-based OGBench and D4RL tasks in offline RL and offline-to-online RL. Project page: https://seohong.me/projects/fql/

We report a study of the processes of e^{+}e^{-}→K^{+}D_{s}^{-}D^{*0} and K^{+}D_{s}^{*-}D^{0} based on e^{+}e^{-} annihilation samples collected with the BESIII detector operating at BEPCII at five center-of-mass energies ranging from 4.628 to 4.698 GeV with a total integrated luminosity of 3.7  fb^{-1}. An excess of events over the known contributions of the conventional charmed mesons is observed near the D_{s}^{-}D^{*0} and D_{s}^{*-}D^{0} mass thresholds in the K^{+} recoil-mass spectrum for events collected at sqrt[s]=4.681  GeV. The structure matches a mass-dependent-width Breit-Wigner line shape, whose pole mass and width are determined as (3982.5_{-2.6}^{+1.8}±2.1)  MeV/c^{2} and (12.8_{-4.4}^{+5.3}±3.0)  MeV, respectively. The first uncertainties are statistical and the second are systematic. The significance of the resonance hypothesis is estimated to be 5.3  σ over the contributions only from the conventional charmed mesons. This is the first candidate for a charged hidden-charm tetraquark with strangeness, decaying into D_{s}^{-}D^{*0} and D_{s}^{*-}D^{0}. However, the properties of the excess need further exploration with more statistics.

We study community detection in the contextual stochastic block model arXiv:1807.09596 [cs.SI], arXiv:1607.02675 [stat.ME]. In arXiv:1807.09596 [cs.SI], the second author studied this problem in the setting of sparse graphs with high-dimensional node-covariates. Using the non-rigorous cavity method from statistical physics, they conjectured the sharp limits for community detection in this setting. Further, the information theoretic threshold was verified, assuming that the average degree of the observed graph is large. It is expected that the conjecture holds as soon as the average degree exceeds one, so that the graph has a giant component. We establish this conjecture, and characterize the sharp threshold for detection and weak recovery.

We describe a strategy for identifying the universe of research publications relevant to the application and development of artificial intelligence. The approach leverages the arXiv corpus of scientific preprints, in which authors choose subject tags for their papers from a set defined by editors. We compose a functional definition of AI relevance by learning these subjects from paper metadata, and then inferring the arXiv-subject labels of papers in larger corpora: Clarivate Web of Science, Digital Science Dimensions, and Microsoft Academic Graph. This yields predictive classification $F_1$ scores between .75 and .86 for Natural Language Processing (cs.CL), Computer Vision (cs.CV), and Robotics (cs.RO). For a single model that learns these and four other AI-relevant subjects (cs.AI, cs.LG, stat.ML, and cs.MA), we see precision of .83 and recall of .85. We evaluate the out-of-domain performance of our classifiers against other sources of topic information and predictions from alternative methods. We find that a supervised solution can generalize to identify publications that belong to the high-level fields of study represented on arXiv. This offers a method for identifying AI-relevant publications that updates at the pace of research output, without reliance on subject-matter experts for query development or labeling.