Papers

While technology and trade have made modern food systems increasingly resilient to disruptions, it is unknown if human society could survive the most extreme threats to agriculture, such as from severe climate change or nuclear/biological warfare. One way that society could withstand such disruptions is to make food without agriculture. Here, I evaluate the feasibility of rapidly scaling up nonagricultural food production in response to a disaster. I find that even in an idealized worst-case scenario where all current agricultural production ends instantaneously and cannot be restarted, it may be possible for at least some countries to begin producing enough food without agriculture to feed their populations before existing food supplies run out. As a proof of principle, for the US, producing edible bacteria grown on natural gas appears to be one feasible option for quickly making food without agriculture. Feeding the US population this way would require about 16% of US annual natural gas production, 6% of US annual electricity consumption, and $530 billion (1.9% of US GDP) worth of food production facilities. Several key findings from this report remain uncertain, and if a disaster hits at the worst possible time with respect to annual variation in stored food supplies, there may not be enough time to scale up other food production options. However, this report suggests that some countries are close to, or may already be at, the point where they could survive just about any agricultural disaster, as long as modern industry remains intact. I find that there are several interventions that could more thoroughly close this window of vulnerability: designing and piloting alternative food production facilities in advance of a disaster, improving the nutritional quality of alternative foods through research and development, and increasing crop stock levels.

The plasma oscillations in high-mobility field-effect transistors (HEMTs) have emerged as a key physical mechanism for manipulating electromagnetic radiation in the sub-terahertz (sub-THz) and THz frequency ranges. These collective electron excitations can be excited and tuned electrically offering a compelling route to compact, integrable components for a wide range of next-generation technologies, including sixth-generation (6G) wireless networks, high-resolution biomedical and chemical spectroscopy, industrial process monitoring, and advanced security and defense systems. For these applications, plasmonic crystals – periodic arrays of many strongly coupled FET channels – are particularly promising. In this work, we report on a new class of collective excitations in plasmonic crystals termed rotonic plasmons, which arise at plasmonic mode crossings and exhibit a parabolic dispersion law reminiscent of soft-mode and roton-like spectra. We show that uniform gate modulation across plasmonic crystal unit cells induces periodic variations in the sheet carrier concentration and, consequently, in the plasma frequency. This time-periodic modulation drives nonlinear plasmonic parametric resonances enabling RF-to-THz conversion. By solving the generalized Mathieu equation with damping, we demonstrate that high-amplitude gate pumping enables frequency multiplication and, at cryogenic temperatures (77K) leads to parametric instabilities due to enhanced electron mobility. In plasmonic crystals with lower mobility, RF-to-THz conversion can instead be realized via periodic short-pulse excitation, a regime we introduce as Time-Domain Frequency Multiplication (TDFM). Investigation of AlGaN/GaN low-high plasmonic crystals confirm their potential as tunable, compact THz sources.

Chalcogenide-based optical phase change materials (OPCMs) exhibit a large contrast in refractive index when reversibly switched between their stable amorphous and crystalline states. OPCMs have rapidly gained attention due to their versatility as nonvolatile amplitude or phase modulators in various photonic devices. However, open challenges remain, such as achieving reliable response and transparency spanning into the visible spectrum, a combination of properties in which current broadband OPCMs (e.g., Ge2Sb2Se4Te1, Sb2Se3, or Sb2S3) fall short. Discovering novel materials or engineering existing ones is, therefore, crucial in extending the application scope of OPCMs. Here, we use magnetron co-sputtering to study the effects of Si doping into Sb2Se3. We employ ellipsometry, X-ray diffraction, Raman spectroscopy, and scanning and transmission electron microscopy to investigate the effects of Si doping on the optical properties and crystal structure and compare these results with those from first principles calculations. Moreover, we study the crystallization and melt-quenching of thin films via nano-differential scanning calorimetry (NanoDSC). Our experiments demonstrate that 20% Si doping increases the transparency window in both states, specifically to 800 nm (1.55 eV) in the amorphous phase, while reducing power consumption by lowering the melting temperature. However, this reduction comes at the cost of reducing the refractive index contrast between states and slowing the kinetics of the phase transition.

Chirped coherent Rayleigh-Brillouin scattering (CRBS) is a flow diagnostic technique that offers high signal-to-noise ratios and nanosecond temporal resolution. To extract information of dilute gas flow, experimental spectra must be compared with theoretical predictions derived from the Boltzmann equation. In this work, we develop a MATLAB code that deterministically solves the Boltzmann equation to compute CRBS spectra, enabling each line shape to be obtained in about one minute. We find that the CRBS spectrum is highly sensitive to the intermolecular potential, and that rapid chirping generates fine ripples around the Rayleigh peak along with spectral asymmetries.

This paper summarizes our work on the fabrication, characterization, and modeling of a high-temperature superconducting double racetrack (DRC) coil. Using an in-house developed winding system, a DRC was wound with ReBCO superconducting tape. The coil was first characterized under direct current to obtain its IV characteristic and its critical current. It was then characterized under alternating current (60 Hz and 120 Hz) to measure its alternating current losses (AC losses). These measurements were compared with a 3D finite element model and show reasonable agreement given the modeling assumptions. The coil exhibits good performance, did not undergo any degradation during winding, and can be used for further studies. This validates our fabrication process and our approach to superconducting coil characterization.

This study introduces an innovative ventilation system, which leverages an origami-inspired structure to improve and regulate natural airflow driven by the stack effect, with applications in underground mines and buildings. The proposed system retrofits chimneys and/or exhaust risers with expandable origami units that dynamically modulate their geometric features (height and vent area) to control buoyancy-driven ventilation independent of external wind or solar conditions. The efficacy of the proposed concept is evaluated using a three-dimensional computational model that focuses on how the geometric design of the deployable stack and varying atmospheric conditions affect the volumetric airflow through the stack in both its fully expanded and contracted states. The results show that the ventilation rate increases by up to 25% with each doubling of the height of the origami stack. % These findings highlight the potential of leveraging origami-inspired structures for adaptable and controllable energy-efficient ventilation, which is particularly beneficial in energy-intensive applications and/or remote, off-grid locations.

Public opinion on environmental issues remains polarized in many countries, posing a significant barrier to the implementation of effective policies. Behind this polarization, empirical studies have identified social susceptibility, personal prejudice, and personal experience as dominant factors in opinion formation on environmental issues. However, current coupled human-environment models have not yet incorporated all three factors in polarized populations. We developed a stylized coupled human-environment model to investigate how social susceptibility, personal prejudice, and personal experience shape opinion formation and the environment in polarized populations. Using analytical and numerical methods, we characterized the conditions under which polarization, consensus, opinion changes, and cyclic dynamics emerge depending on the costs of mitigation, environmental damage, and the factors influencing opinion formation. Our model shows that prejudice is the key driver of persistent polarization, with even slightly prejudiced populations maintaining indefinite polarization independent of their level of objectivity. We predict that polarization can be reduced by decreasing the role of prejudice or increasing the willingness to consider opposing opinions. Finally, our model shows that cost reduction methods are less effective at reducing environmental impact in prejudiced populations. Our model generates thresholds for when reducing costs or emissions is more useful depending on the factors which influence the population's opinion formation. Overall, our model provides a framework for investigating the importance of cognitive and social structures in determining human-environment dynamics.

Climate change is a global emergency, as was the COVID-19 pandemic. Why was our collective response to COVID-19 so much stronger than our response to the climate emergency, to date? We hypothesize that the answer has to do with the scale of the systems, and not just spatial and temporal scales but also the `altruistic scale' that measures whether an action must rely upon altruistic motives for it to be adopted. We treat COVID-19 and climate change as common pool resource problems that exemplify coupled human-environment systems. We introduce a framework that captures regimes of containment, mitigation, and failure to control. As parameters governing these three scales are varied, it is possible to shift from a COVID-like system to a climate-like system. The framework replicates both inaction in the case of climate change mitigation, as well as the faster response that we exhibited to COVID-19. Our cross-system comparison also suggests actionable ways that cooperation can be improved in large-scale common pool resources problems, like climate change. More broadly, we argue that considering scale and incorporating human-natural system feedbacks are not just interesting special cases within non-cooperative game theory, but rather should be the starting point for the study of altruism and human cooperation.

Over the last ten years, from 2015 to 2024, the day with the highest average number of births was on the 20th of July, while Christmas Day recorded the fewest. Apart from July, a noticeable concentration of births occurred in late September, reflecting conceptions that took place during the end of year holidays. Births tended to be less frequent on weekends and public holidays. Since the 1970s, France's “baby season” shifted from spring to summer. From 1975 to 2010, heat waves were most often followed by a dip in births 9 months later, in April or May. This effect became less pronounced in recent years, as heatwaves grew in frequency. Another striking change was the sharp decline, since the 1970s, of the traditional April baby boom among primary school teachers.

Mathematical models of infectious diseases are frequently used as a tool to support public health policy and decisions around implementation of interventions such as school closures. However, most publications on policy-relevant modelling lack an ethical framework and do not explicitly consider the ethical implications of the work. This creates a risk that unintended consequences of interventions are overlooked or that models are used to justify decisions that are inconsistent with public health ethics. In this article, we focus on the case study of school closures as a commonly modelled intervention against pandemic influenza, COVID-19, and other infectious disease threats. We briefly review some of the key concepts in public health ethics and describe approaches to modelling the effects of school closures. We then identify a series of ethical considerations involved in modelling school closures. These include accounting for population heterogeneity and inequalities; including a diversity of viewpoints and expertise in model design; considering the distribution of benefits and harms; and model transparency and contextualisation. We conclude with some recommendations to ensure that policy-relevant modelling is consistent with some key ethics values.

Aluminum is a widely used alloy, which is susceptible to fatigue failure. Characterizing fatigue performance for materials is extremely time and cost demanding, especially for high cycle data. To help mitigate this, a transfer learning based framework has been developed using Long short-term memory networks (LSTMs) in which a source LSTM model is trained based on pure axial fatigue data for Aluminum 7075-T6 alloy which is then transferred to predict high cycle torsional S-N curves. The framework was able to accurately predict Al torsional S-N curves for a much higher cycle range. It is the belief that this framework will help to drastically mitigate the cost of gathering fatigue characteristics for different materials and help prioritize tests with better cost and time constraints.

There is a growing need to evaluate Large Language Models (LLMs) on complex, high-impact, real-world tasks to assess their true readiness as reasoning agents. To address this gap, we introduce AgentCaster, a contamination-free framework employing multimodal LLMs end-to-end for the challenging, long-horizon task of tornado forecasting. Within AgentCaster, models interpret heterogeneous spatiotemporal data from a high-resolution convection-allowing forecast archive. We assess model performance over a 40-day period featuring diverse historical data, spanning several major tornado outbreaks and including over 500 tornado reports. Each day, models query interactively from a pool of 3,625 forecast maps and 40,125 forecast soundings for a forecast horizon of 12-36 hours. Probabilistic tornado-risk polygon predictions are verified against ground truths derived from geometric comparisons across disjoint risk bands in projected coordinate space. To quantify accuracy, we propose domain-specific TornadoBench and TornadoHallucination metrics, with TornadoBench highly challenging for both LLMs and domain expert human forecasters. Notably, human experts significantly outperform state-of-the-art models, which demonstrate a strong tendency to hallucinate and overpredict risk intensity, struggle with precise geographic placement, and exhibit poor spatiotemporal reasoning in complex, dynamically evolving systems. AgentCaster aims to advance research on improving LLM agents for challenging reasoning tasks in critical domains.

The ability to dynamically control the relative orientation of layers in two dimensional (2D) van der Waals (vdW) heterostructures represents a critical step toward the realization of reconfigurable nanoscale devices. Existing actuation methods often rely on mechanical contact, complex architectures, or extreme operating conditions, which limit their applicability and scalability. In this work, we present a proof-of-concept demonstration of contactless electrostatic actuation based on electron-beam-induced charge injection. By locally charging an insulating hexagonal boron nitride (hBN) flake on an electrically grounded graphene layer, we create an interfacial electric field that generates in-plane electrostatic torque and induces angular displacement. We validate the induced rotation through in-situ scanning electron microscopy (SEM) and twist-dependent Raman spectroscopy.

Recycled concrete aggregate (RCA) is recognized as a readily available, mechanically sufficient construction and demolition waste product that is suitable as a base course substitute for natural, virgin aggregate in pavement construction. Environmentally responsible applications of RCA must consider the high alkalinity, high pH leachate, and heavy metal leaching risks reported in the literature. The existing body of literature does not address discrepancies between field and laboratory measurements of RCA leachate pH, nor are there any existing studies of aged RCA leachate composition as a function of time. To consider the influence of contact time on RCA leachate, the present study evaluates recovered RCA base course samples from the Minnesota Road Research highway construction study site using modified batch test methodology. Leachate pH, alkalinity, and calcium ion (Ca2+) concentration were monitored for 24 hours to understand RCA leachate chemistry during the initial contact period. Leachate pH is high upon initial contact with water (pH > 10) and decreases over time as it reacts with atmospheric carbon dioxide. Calcium ion concentration increases rapidly in the initial contact period, then more gradually as calcium saturation is reached. Alkalinity stabilizes (50-65 mg CaCO3/L) after a dramatic increase during the initial contact period.

In this paper, we study two years of access card validation records from Bogotá's multimodal public transport system, comprising over 2.3 billion trips across bus rapid transit, feeder buses, dual-service buses, and an aerial cable network. From user trajectories constructed exclusively from access records, we derive motifs that reveal recurrent mobility patterns extending beyond simple two-location visits. This approach enables the construction of an integrated origin-destination (OD) matrix covering 2,828 urban zones. Similarity analysis using the Jensen-Shannon divergence confirms the temporal stability of mobility structures across semesters, despite infrastructure changes and fare policy adjustments. From the obtained OD matrices, we derive transition probabilities between zones and uncover a robust power-law relationship with geographical distance, consistent with Lévy flight dynamics. We validate our model using Monte Carlo simulations showing that reproduces both local and long-range displacements, with similar scaling exponents across time. These findings demonstrate that Bogotá's public transport mobility can be effectively modeled through Lévy processes, providing a novel framework for analyzing complex transportation systems based solely on user access records.

In this paper, we re-explore a simple textbook Landau model describing improper ferroelectricity and show that in the limit where both proper and improper instabilities exist and compete, improper ferroelectrics can display switching between multiple polarisation states. Using first principles calculations we highlight how the hexagonal tungsten bronze materials may be an archetypal case, with the possibility to switch between improper and proper phases. The resulting functional characteristics are akin to "ferrielectrics", with switching behaviour in the form of a triple hysteresis loop. Such functionality could be ideal for creating non-volatile multistate systems for use in memory devices or as a backbone for neuromorphic computing.

Simulations of optical quantum systems are essential for the development of quantum technologies. However, these simulations are often computationally intensive, especially when repeated evaluations are required for data fitting, parameter estimation, or real-time feedback. To address this challenge, we develop a Long Short-Term Memory neural network capable of replicating the output of these simulations with high accuracy and significantly reduced computational cost. Once trained, the surrogate model produces spectra in milliseconds, providing a speed-up of 5000x relative to traditional numerical solvers. We focus on applying this technique to Doppler-broadened Electromagnetically Induced Transparency in a ladder-type scheme for Rydberg-based sensing, achieving near-unity agreement with the physics solver for resonant and off-resonant regimes. We demonstrate the effectiveness of the LSTM model on this representative optical quantum system, establishing it as a surrogate tool capable of supporting real-time signal processing and feedback-based optimisation.

Si-PIN detectors can be microstructured to achieve angular-selective particle detection capabilities, which we call active Transverse Energy Filter (aTEF). The microstructuring consists of a honeycomb structure of deep hexagonally-shaped holes with active silicon side walls, while the bottom of the holes is made insensitive to ionizing radiation. The motivation for this kind of detector arises from the need to distinguish background electrons from signal electrons in a spectrometer of MAC-E filter type. We have demonstrated the angular-dependent detection efficiency of self-fabricated aTEF prototypes in a test setup using an angular-selective photoelectron source to illuminate the detector from various incidence angles.

Lubrication theory makes use of the assumptions of a long and thin fluid domain and a small scaled Reynolds number to formulate a linearized approximation to the Navier-Stokes equations. Extended lubrication theory aims to improve the model accuracy by relaxing these assumptions and including additional terms in the formulation. However, such models are sensitive to large surface gradients which lead the assumptions of the model to break down. In this paper, we present a formulation of extended lubrication theory, and compare our model with several existing models, along with the numerical solution to the Stokes equations. The error in pressure and velocity is characterized for a variety of fluid domain geometries. Our results indicate that the new solution is suitable for a wide range of geometries. The magnitude of surface variation and the length scale ratio are both important factors influencing the accuracy of the extended lubrication theory models.

The structure of road networks impacts various urban dynamics, from traffic congestion to environmental sustainability and access to essential services. Recent studies reveal that most roads are underutilized, faster alternative routes are often overlooked, and traffic is typically concentrated on a few corridors. In this article, we examine how road network structure, and in particular the presence of mobility attractors (e.g., highways and ring roads), shapes the counterpart to traffic concentration: route diversification. To this end, we introduce DiverCity, a measure that quantifies the extent to which traffic can potentially be distributed across multiple, loosely overlapping near-shortest routes. Analyzing 56 diverse global cities, we find that DiverCity is influenced by network characteristics and is associated with traffic efficiency. Within cities, DiverCity increases with distance from the city center before stabilizing in the periphery, but declines in the proximity of mobility attractors. We demonstrate that strategic speed limit adjustments on mobility attractors can increase DiverCity while preserving travel efficiency. We isolate the complex interplay between mobility attractors and DiverCity through simulations in a controlled setting, confirming the patterns observed in real-world cities. DiverCity provides a practical tool for urban planners and policymakers to optimize road network design and balance route diversification, efficiency, and sustainability. We provide an interactive platform (https://divercitymaps.github.io) to visualize the spatial distribution of DiverCity across all considered cities.

Flash droughts are increasingly occurring worldwide due to climate change, causing widespread socioeconomic and agricultural losses. However, timely and accurate flash drought forecasting remains challenging for operational forecast systems due to uncertain representations of interactive hydroclimate processes. Here, we introduce the Interpretable Transformer for Drought (IT-Drought), a deep learning model based on publicly available hydroclimate data. IT-Drought skillfully forecasts soil moisture up to 42 days and flash droughts up to 11 days ahead on average across the contiguous U.S., substantially outperforming existing forecast systems limited to 10-day skillful soil moisture forecasting and unable to forecast flash droughts. IT-Drought offers novel data-driven interpretability on flash drought onset, benchmarking the memory effects of soil moisture, and revealing heterogeneous impacts and effective time ranges of climatic conditions, predominantly radiation and temperature. The findings highlight the potential of leveraging IT-Drought for early warning and mechanistic investigation of flash droughts, ultimately supporting drought risk mitigation.

Analysing biological spiking neural network models with synaptic plasticity has proven to be challenging both theoretically and numerically. In a network with N all-to-all connected neurons, the number of synaptic connections is on the order of $N^2$, making these models computationally demanding. Furthermore, the intricate coupling between neuron and synapse dynamics, along with the heterogeneity generated by plasticity, hinder the use of classic theoretical tools such as mean-field or slow-fast analyses. To address these challenges, we introduce a new variable which we term a typical neuron X. Viewed as a post-synaptic neuron, X is composed of the activity state V , the time since its last spike S, and the empirical distribution $\xi$ of the triplet V , S and W (incoming weight) associated to the pre-synaptic neurons. In particular, we study a stochastic spike-timing-dependent plasticity (STDP) model of connection in a probabilistic Wilson-Cowan spiking neural network model, which features binary neural activity. Taking the large N limit, we obtain from the empirical distribution of the typical neuron a simplified yet accurate representation of the original spiking network. This mean-field limit is a piecewise deterministic Markov process (PDMP) of McKean-Vlasov type, where the typical neuron dynamics depends on its own distribution. We term this analysis McKean-Vlasov mean-field (MKV-MF). Our approach not only reduces computational complexity but also provides insights into the dynamics of this spiking neural network with plasticity. The model obtained is mathematically exact and capable of tracking transient changes. This analysis marks the first exploration of MKV-MF dynamics in a network of spiking neurons interacting with STDP.

Non-Hermitian quantum systems, governed by nonunitary evolution, offer powerful tools for manipulating quantum states through engineered loss. A prime example is coherent absorption, where quantum states undergo phase-dependent partial or complete absorption in a lossy medium. Here, we demonstrate a fully programmable implementation of nonunitary transformations that emulate coherent absorption of quantum light using a programmable integrated linear photonic circuit, with loss introduced via coupling to an ancilla mode [Phys. Rev. X 8, 021017; 2018]. Probing the circuit with a single-photon dual-rail state reveals phase-controlled coherent tunability between perfect transmission and perfect absorption. A two-photon NOON state input, by contrast, exhibits switching between deterministic single-photon and probabilistic two-photon absorption. Across a range of input phases and circuit configurations, we observe nonclassical effects such as anti-coalescence and bunching, along with continuous and coherent tuning of output Fock state probability amplitudes. Classical Fisher information analysis reveals phase sensitivity peaks of 1 for single-photon states and 3.4 for NOON states, the latter exceeding the shot-noise limit of 2 and approaching the Heisenberg limit of 4 for two-photon states. The experiment integrates quantum state generation, programmable photonic circuitry, and photon-number-resolving detection, establishing ancilla-assisted circuits as powerful tools for programmable quantum state engineering, filtering, multiplexed sensing, and nonunitary quantum simulation.

While in electromagnetism we have space-time symmetry, magnetoelectric (ME) processes are characterized by space-time symmetry breaking. Our goal is to show that quantum vacuum fields with both time reversal and space inversion symmetry breaking (so-called ME fields) can be observed in subwavelength regions of electromagnetic (EM) radiation. The sources of ME fields are ME meta-atoms, subwavelength elements made of magnetic insulators, where the dynamical ME behavior is due to modulations and topological coupling of magnetization and electric polarization. The interaction between EM and ME systems occurs through virtual structures, ME virtual photons. Experimentally, ME quantum vacuum states can be detected in a microwave cavity. The topic of these studies concerns fundamental aspects of ME quantum electrodynamics (MEQED).

Large sea spray drops - of up to 2mm in diameter - constitute one of the most uncertain factors controlling the intensification of hurricanes and severe storms because their generation mechanisms are not understood. Wave splashing produces among the largest spray drops, but observational data regarding these drops is difficult to obtain and hence cannot inform current modelling efforts. In this study, we instead propose a sea spray generation function (SSGF) for ocean wave splashing by assembling a model from first principles. First, we introduce the transverse collision of two cylindrical liquid rims as the basic mechanism for drop production. We characterize the resulting drop production in terms of three competing processes: ligament production and merging, drop generation by end-pinching, and gravity which arrests the mechanism. Second, we formulate a theoretical model which explains the drop size distributions produced by the colliding rims and test it against existing experimental and numerical data. Finally, the model can be developed into a full SSGF by incorporating sea state information with relatively few tuning parameters. The model is flexible and can be extended by including related effects such as finite droplet lifetime and secondary breakup. Altogether, our model suggests that wave splashing can efficiently produce numerous secondary droplets, challenging prior assumptions that it is an inefficient generation mechanism for sea spray.

Blood transfusion remains a cornerstone of modern medicine, saving countless lives daily. Yet the quality of transfused blood varies dramatically among donors-a critical factor often overlooked in clinical practice. Rapid, benchtop, and cost-effective methods for evaluating stored red blood cells (RBCs) at the site of transfusion are lacking, with concerns persisting about the association between metabolic signatures of stored RBC quality and transfusion outcomes. Recent studies utilizing metabolomics approaches to evaluate stored erythrocytes find that donor biology (e.g., genetics, age, lifestyle factors) underlies the heterogeneity associated with blood storage and transfusion. The appreciation of donor-intrinsic factors provides opportunities for precision transfusion medicine approaches for the evaluation of storage quality and prediction of transfusion efficacy. Here we propose a new platform, the Surface Acoustic Wave Hemolysis Assay (SAW-HA), for on-site evaluation of stored RBCs utilizing SAW Hemolysis Temperature (SAWHT) as a marker for RBC quality. We report SAWHT as a mechanism-dependent reproducible methodology for evaluating stored human RBCs up to 42 days. Our results define unique signatures for SAW hemolysis and metabolic profiles in RBCs from two of the six donors in which high body mass index (BMI) and RBC triglycerides associated with increased susceptibility to hemolysis. Metabolic age of the stored RBCs - a recently appreciated predictor of post-transfusion efficacy-reveal that RBCs from the two low SAWHT units were characterized by disrupted redox control, deficient tryptophan metabolism, and high BMI. Together, these findings indicate the potential of the SAW-HA as a point-of-care analysis for transfusion medicine.

Sickle cell disease (SCD) remains a critical global health issue, with high child mortality in low-resource regions. Early screening and diagnosis is essential for improving health outcomes, but conventional screening methods are unsuitable for widespread use due to the high costs of laboratory equipment. There is an urgent need for portable, cost-effective, and user-friendly point-of-care tools that can quickly assess blood health. Here, we explore two new biomarkers enabled by acoustic probing for rapid SCD screening: cell membrane stability from measuring red blood cell (RBC) lysis temperature in whole blood, and plasma protein concentration from measuring relative protein precipitation in blood plasma. Both biomarkers effectively differentiate healthy HbAA samples from pre-/no transfusion HbSS samples with high accuracy. Additionally, the RBC lysis biomarker can distinguish post-transfusion exchange HbSS samples with a lower percentage of sickled cells, indicating the potential to initially screen for milder forms of SCD as well as sickle cell trait.

Genetic engineering of cells has a range of applications in treating incurable diseases. Plasmid DNA is a popular choice of nucleic acid for cell engineering due to its low cost and stability. However, plasmid DNA must survive the protective mechanisms present in the cell's cytoplasm to enter the nucleus for translation. Many of the existing methods for nucleic acid delivery, such as chemical-based and virus-based delivery, suffer from drawbacks induced by the nucleic acid carrier itself. Mechanical methods present an alternative to nucleic acid carriers by physically producing openings in the cell to deliver cargos. However, in most systems, the cell membrane openings are too small to deliver large cargos, or the poration process leads to low cell viability. In this study, we present a microfluidic device with integrated high aspect ratio nanostructures that repeatably rupture the cell membrane and nuclear envelope. These sharp-tipped nanolancets penetrate the cell deep enough to allow direct delivery of cargos into the nucleus, but still allow for cell recovery after treatment. We show the device's ability to deliver cargo to a variety of cell types while maintaining high viability. Then, we demonstrate the rapid onset of plasmid DNA expression that results from direct nuclear delivery of naked DNA, showing expression speeds comparable to microinjection, but with significantly greater throughput. We envision the use of this device as a tool to quickly produce high quantities of genetically engineered cells to treat a myriad of diseases.

Previous papers have outlined nowcasting methods to track the current state of earthquake hazard using only observed seismic catalogs. The basis for one of these methods, the "counting method", is the Gutenberg-Richter (GR) magnitude-frequency relation. The GR relation states that for every large earthquake of magnitude greater than MT , there are on average NGR small earthquakes of magnitude MS. In this paper we use this basic relation, combined with the Receiver Operating Characteristic (ROC) formalism from machine learning, to compute the probability of a large earthquake. The probability is conditioned on the number of small earthquakes n(t) that have occurred since the last large earthquake. We work in natural time, which is defined as the count of small earthquakes between large earthquakes. We do not need to assume a probability model, which is a major advantage. Instead, the probability is computed as the Positive Predictive Value (PPV) associated with the ROC curve. We find that the PPV following the last large earthquake initially decreases as more small earthquakes occur, indicating the property of temporal clustering of large earthquakes as is observed. As the number of small earthquakes continues to accumulate, the PPV subsequently begins to increase. Eventually a point is reached beyond which the rate of increase becomes much larger and more dramatic. Here we describe and illustrate the method by applying it to a local region around Los Angeles, California, following the January 17, 1994 magnitude M6.7 Northridge earthquake.

Africa is the world region that is most affected by malaria. Among the therapies used, injectable quinine is considered to be one of the effective antimalarial drug, however non-quality assured antimalarials clearly have a strong market penetration across Africa. To overcome this problem, it becomes more and more necessary to set up quantitative and qualitative analysis system for antimalarial quality control. The objective of the present investigation is an attempt to use customized multispectral microscope equipped with UV-Visible lasers for injectable quinine quality assurance control routinely. For that, we have established the calibration curve of quinine solution concentration as a function of area under light intensity histogram crossing the solution. From this calibration curve, we can check the conformity of any injectable quinine according to the pharmacopoeia involved. The proposed technique is a promising alternative for drug quality control routinely.