The transport properties of a random velocity field with Kolmogorov spectrum and time correlations defined along Lagrangian trajectories are analyzed. The analysis is carried on in the limit of short correlation times, as a perturbation theory in the ratio, scale by scale, of the eddy decay and turn-over time. Various quantities such as the Batchelor constant and the dimensionless constants entering the expression for particle relative and self-diffusion are given in terms of this ratio and of the Kolmogorov constant. Particular attention is paid to particles with finite inertia. The self-diffusion properties of a particle with Stokes time longer than the Kolmogorov time are determined, verifying on an analytical example the dimensional results of [nlin.CD/0103018]. Expressions for the fluid velocity Lagrangian correlations and correlation times along a solid particle trajectory, are provided in several parameter regimes, including the infinite Stokes time limit corresponding to Eulerian correlations. The concentration fluctuation spectrum and the non-ergodic properties of a suspension of heavy particles in a turbulent flow, in the same regime, are analyzed. The concentration spectrum is predicted to obey, above the scale of eddies with lifetime equal to the Stokes time, a power law with universal -4/3 exponent, and to be otherwise independent of the nature of the turbulent flow. A preference of the solid particle to lie in less energetic regions of the flow is observed.

The Working Group I contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) provides a comprehensive assessment of the physical science basis of climate change. It considers in situ and remote observations; paleoclimate information; understanding of climate drivers and physical, chemical, and biological processes and feedbacks; global and regional climate modelling; advances in methods of analyses; and insights from climate services. It assesses the current state of the climate; human influence on climate in all regions; future climate change including sea level rise; global warming effects including extremes; climate information for risk assessment and regional adaptation; limiting climate change by reaching net zero carbon dioxide emissions and reducing other greenhouse gas emissions; and benefits for air quality. The report serves policymakers, decision makers, stakeholders, and all interested parties with the latest policy-relevant information on climate change. Available as Open Access on Cambridge Core.

We present GPQA, a challenging dataset of 448 multiple-choice questions written by domain experts in biology, physics, and chemistry. We ensure that the questions are high-quality and extremely difficult: experts who have or are pursuing PhDs in the corresponding domains reach 65% accuracy (74% when discounting clear mistakes the experts identified in retrospect), while highly skilled non-expert validators only reach 34% accuracy, despite spending on average over 30 minutes with unrestricted access to the web (i.e., the questions are"Google-proof"). The questions are also difficult for state-of-the-art AI systems, with our strongest GPT-4 based baseline achieving 39% accuracy. If we are to use future AI systems to help us answer very hard questions, for example, when developing new scientific knowledge, we need to develop scalable oversight methods that enable humans to supervise their outputs, which may be difficult even if the supervisors are themselves skilled and knowledgeable. The difficulty of GPQA both for skilled non-experts and frontier AI systems should enable realistic scalable oversight experiments, which we hope can help devise ways for human experts to reliably get truthful information from AI systems that surpass human capabilities.

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/