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Simple and fast detection of small molecules is critical for health and environmental monitoring. Methods for chemical detection often use mass spectrometers or enzymes; the former relies on expensive equipment, and the latter is limited to those that can act as enzyme substrates. Affinity reagents like antibodies can target a variety of small-molecule analytes, but the detection requires the successful design of chemically conjugated targets or analogs for competitive binding assays. Here, we developed a generalizable method for the highly sensitive and specific in-solution detection of small molecules, using cannabidiol (CBD) as an example. Our sensing platform uses gold nanoparticles (AuNPs) functionalized with a pair of chemically induced dimerization (CID) nanobody binders (nanobinders), where CID triggers AuNP aggregation and sedimentation in the presence of CBD. Despite moderate binding affinities of the two nanobinders to CBD (equilibrium dissociation constants KD of ∼6 and ∼56 μM), a scheme consisting of CBD−AuNP preanalytical incubation, centrifugation, and electronic detection (ICED) was devised to demonstrate a high sensitivity (limit of detection of ∼100 picomolar) in urine and saliva, a relatively short sensing time (∼2 h), a large dynamic range (5 logs), and a sufficiently high specificity to differentiate CBD from its analog, tetrahydrocannabinol. The high sensing performance was achieved with the multivalency of AuNP sensing, the ICED scheme that increases analyte concentrations in a small assay volume, and a portable electronic detector. This sensing system is readily applicable for wide molecular diagnostic applications. 

Under low-potassium (K⁺) stress, a Ca²⁺signaling network consisting of calcineurin B-like proteins (CBLs) and CBL-interacting kinases (CIPKs) play essential roles. Specifically, the plasma membrane CBL1/9-CIPK pathway and the tonoplast CBL2/3-CIPK pathway promotes K⁺uptake and remobilization, respectively, by activating a series of K⁺channels. While the dual CBL-CIPK pathways enable plants to cope with low-K⁺stress, little is known about the early events that link external K⁺levels to the CBL-CIPK proteins. Here we show that K⁺status regulates the protein abundance and phosphorylation of the CBL-CIPK-channel modules. Further analysis revealed low K⁺-induced activation of VM-CBL2/3 happened earlier and was required for full activation of PM-CBL1/9 pathway. Moreover, we identified CIPK9/23 kinases to be responsible for phosphorylation of CBL1/9/2/3 in plant response to low-K⁺stress and the HAB1/ABI1/ABI2/PP2CA phosphatases to be responsible for CBL2/3-CIPK9 dephosphorylation upon K⁺-repletion. Further genetic analysis showed that HAB1/ABI1/ABI2/PP2CA phosphatases are negative regulators for plant growth under low-K⁺, countering the CBL-CIPK network in plant response and adaptation to low-K⁺stress.

 

Abstract Buckling, a phenomenon historically considered undesirable, has recently been harnessed to enable innovative functionalities in materials and structures. While approaches to achieve specific buckling behaviors are widely studied, tuning these behaviors in fabricated structures without altering their geometry remains a major challenge. Here, we introduce an inverse design approach to tune buckling behavior in magnetically active structures through the variation of applied magnetic stimuli. Our proposed magneto-mechanical topology optimization formulation not only generates the geometry and magnetization distribution of these structures but also informs how the external magnetic fields should be applied to control their buckling behaviors. By utilizing the proposed strategy, we discover magnetically active structures showcasing a broad spectrum of tunable buckling mechanisms, including programmable peak forces and buckling displacements, as well as controllable mechano- and magneto-induced bistability. Furthermore, we experimentally demonstrate that multiple unit designs can be assembled into architectures, resulting in tunable multistability and programmable buckling sequences under distinct applied magnetic fields. By employing a hybrid fabrication method, we manufacture and experimentally validate the generated designs and architectures, confirming their ability to exhibit precisely programmed and tunable buckling behaviors. This research contributes to the advancement of multifunctional materials and structures that harness buckling phenomena, unlocking transformative potential for various applications, including robotics, energy harvesting, and deployable and reconfigurable devices. 

We propose a method for optimizing the energy efficiency of software code running on small computing devices in the Internet of Things (IoT) that are powered exclusively by electricity harvested from ambient energy in the environment. Due to the weak and unstable nature of the energy source, it is challenging for developers to manually optimize the software code to deal with mismatch between the intermittent power supply and the computation demand. Our method overcomes the challenge by using a combination of three techniques. First, we use static program analysis to automatically identify opportunities for precomputation, i.e., computation that may be performed ahead of time as opposed to just in time. Second, we optimize the precomputation policy, i.e., a way to split and reorder steps of a computation task in the original software to match the intermittent power supply while satisfying a variety of system requirements; this is accomplished by formulating energy optimization as a constraint satisfiability problem and then solving the problem using an off-the-shelf SMT solver. Third, we use a state-of-the-art compiler platform (LLVM) to automate the program transformation to ensure that the optimized software code is correct by construction. We have evaluated our method on a large number of benchmark programs, which are C programs implementing secure communication protocols that are popular for energy-harvesting IoT devices. Our experimental results show that the method is efficient in optimizing all benchmark programs. Furthermore, the optimized programs significantly outperform the original programs in terms of energy efficiency and latency, and the overall improvement ranges from 2.3X to 36.7X. 

We propose a method for certifying the fairness of the classification result of a widely used supervised learning algorithm, the k-nearest neighbors (KNN), under the assumption that the training data may have historical bias caused by systematic mislabeling of samples from a protected minority group. To the best of our knowledge, this is the first certification method for KNN based on three variants of the fairness definition: individual fairness, ϵ -fairness, and label-flipping fairness. We first define the fairness certification problem for KNN and then propose sound approximations of the complex arithmetic computations used in the state-of-the-art KNN algorithm. This is meant to lift the computation results from the concrete domain to an abstract domain, to reduce the computational cost. We show effectiveness of this abstract interpretation based technique through experimental evaluation on six datasets widely used in the fairness research literature. We also show that the method is accurate enough to obtain fairness certifications for a large number of test inputs, despite the presence of historical bias in the datasets. 

Data poisoning aims to compromise a machine learning based software component by contaminating its training set to change its prediction results for test inputs. Existing methods for deciding data-poisoning robustness have either poor accuracy or long running time and, more importantly, they can only certify some of the truly-robust cases, but remain inconclusive when certification fails. In other words, they cannot falsify the truly-non-robust cases. To overcome this limitation, we propose a systematic testing based method, which can falsify as well as certify data-poisoning robustness for a widely used supervised-learning technique named k-nearest neighbors (KNN). Our method is faster and more accurate than the baseline enumeration method, due to a novel over-approximate analysis in the abstract domain, to quickly narrow down the search space, and systematic testing in the concrete domain, to find the actual violations. We have evaluated our method on a set of supervised-learning datasets. Our results show that the method significantly outperforms state-of-the-art techniques, and can decide data-poisoning robustness of KNN prediction results for most of the test inputs. 

While mixed integer linear programming (MILP) solvers are routinely used to solve a wide range of important science and engineering problems, it remains a challenging task for end users to write correct and efficient MILP constraints, especially for problems specified using the inherently non-linear Boolean logic operations. To overcome this challenge, we propose a syntax guided synthesis (SyGuS) method capable of generating high-quality MILP constraints from the specifications expressed using arbitrary combinations of Boolean logic operations. At the center of our method is an extensible domain specification language (DSL) whose expressiveness may be improved by adding new integer variables as decision variables, together with an iterative procedure for synthesizing linear constraints from non-linear Boolean logic operations using these integer variables. To make the synthesis method efficient, we also propose an over-approximation technique for soundly proving the correctness of the synthesized linear constraints, and an under-approximation technique for safely pruning away the incorrect constraints. We have implemented and evaluated the method on a wide range of benchmark specifications from statistics, machine learning, and data science applications. The experimental results show that the method is efficient in handling these benchmarks, and the quality of the synthesized MILP constraints is close to, or higher than, that of manually-written constraints in terms of both compactness and solving time.

 

Polarimetric imaging has a wide range of applications for uncovering features invisible to human eyes and conventional imaging sensors. Chip-integrated, fast, cost-effective, and accurate full-Stokes polarimetric imaging sensors are highly desirable in many applications, which, however, remain elusive due to fundamental material limitations. Here we present a chip-integratedMetasurface-based Full-StokesPolarimetricImaging sensor (MetaPolarIm) realized by integrating an ultrathin (~600 nm) metasurface polarization filter array (MPFA) onto a visible imaging sensor with CMOS compatible fabrication processes. The MPFA is featured with broadband dielectric-metal hybrid chiral metasurfaces and double-layer nanograting polarizers. This chip-integrated polarimetric imaging sensor enables single-shot full-Stokes imaging (speed limited by the CMOS imager) with the most compact form factor, records high measurement accuracy, dual-color operation (green and red) and a field of view up to 40 degrees. MetaPolarIm holds great promise to enable transformative applications in autonomous vision, industry inspection, space exploration, medical imaging and diagnosis.