Generalizable Strategy Research Articles

Heuristic satisficing inferential decision making in human and robot active perception

Inferential decision-making algorithms typically assume that an underlying probabilistic model of decision alternatives and outcomes may be learned a priori or online. Furthermore, when applied to robots in real-world settings they often perform unsatisfactorily or fail to accomplish the necessary tasks because this assumption is violated and/or because they experience unanticipated external pressures and constraints. Cognitive studies presented in this and other papers show that humans cope with complex and unknown settings by modulating between near-optimal and satisficing solutions, including heuristics, by leveraging information value of available environmental cues that are possibly redundant. Using the benchmark inferential decision problem known as “treasure hunt”, this paper develops a general approach for investigating and modeling active perception solutions under pressure. By simulating treasure hunt problems in virtual worlds, our approach learns generalizable strategies from high performers that, when applied to robots, allow them to modulate between optimal and heuristic solutions on the basis of external pressures and probabilistic models, if and when available. The result is a suite of active perception algorithms for camera-equipped robots that outperform treasure-hunt solutions obtained via cell decomposition, information roadmap, and information potential algorithms, in both high-fidelity numerical simulations and physical experiments. The effectiveness of the new active perception strategies is demonstrated under a broad range of unanticipated conditions that cause existing algorithms to fail to complete the search for treasures, such as unmodelled time constraints, resource constraints, and adverse weather (fog).

Single-chain nanobody inhibition of Notch and avidity enhancement utilizing the β-pore forming toxin Aerolysin.

Notch plays critical roles in developmental processes and disease pathogenesis, which has led to numerous efforts to modulate its function with small molecules and antibodies. Here we present a nanobody inhibitor of Notch signaling, derived from a synthetic phage-display library targeting the notch Negative Regulatory Region (NRR). The nanobody inhibits Notch signaling in a luciferase reporter assay and in Notch-dependent hematopoietic progenitor cell differentiation assay, despite a modest 19uM affinity for Notch. We addressed the low affinity by fusion to a membrane-associating domain derived from the β-Pore forming toxin Aerolysin, resulting in a significantly improved IC50 for Notch inhibition. The nanobody-aerolysin fusion inhibits proliferation of T-ALL cell lines with similar efficacy to other Notch pathway inhibitors. Overall, this study reports the development of a Notch inhibitory antibody, and demonstrates a proof-of-concept for a generalizable strategy to increase the efficacy and potency of low-affinity antibody binders.

Studying Chinese immigrants’ spatial distribution in the Raleigh–Durham area by linking survey and commercial data using romanized names

Abstract Many population surveys do not provide information on respondents’ residential addresses, instead offering coarse geographies like zip code or higher aggregations. However, fine resolution geography can be beneficial for characterizing neighbourhoods, especially for relatively rare populations such as immigrants. One way to obtain such information is to link survey records to records in auxiliary databases that include residential addresses by matching on variables common to both files. We present an approach based on probabilistic record linkage that enables matching survey participants in the Chinese Immigrants in Raleigh–Durham Study to records from InfoUSA, an information provider of residential records. The two files use different Chinese name romanization practices, which we address through a novel and generalizable strategy for constructing records’ pairwise comparison vectors for romanized names. Using a fully Bayesian record linkage model, we characterize the geospatial distribution of Chinese immigrants in the Raleigh–Durham area of North Carolina.

Supramolecular Guest Exchange in Cucurbit[7]uril for Bioorthogonal Fluorogenic Imaging across the Visible Spectrum.

Fluorogenic probes that unmask fluorescence signals in response to bioorthogonal reactions are a powerful new addition to biological imaging. They can significantly reduce background fluorescence and minimize nonspecific signals, potentially enabling real-time, high-contrast imaging without the need to wash out excess fluorophores. While diverse classes of highly refined synthetic fluorophores are now readily available, integrating them into a bioorthogonal fluorogenic scheme still requires extensive design efforts and customized structural alterations to optimize quenching mechanisms for each specific fluorophore scaffold. Herein, we present a highly generalizable strategy that can produce an efficient bioorthogonal fluorogenic response from essentially any readily available fluorophore without further structural alterations. We designed this strategy based on the macrocyclic cucurbit[7]uril (CB7) host, where a fluorogenic response is achieved by programming a guest exchange reaction within the macrocyclic cavity. We employed this strategy to rapidly create fluorogenic probes across the visible spectrum from diverse fluorophore scaffolds, which enabled no-wash imaging in live cells and tissues with minimal background signal. Finally, we demonstrated that this strategy can be combined with metabolic labeling for fluorogenic detection of metabolically tagged mycobacteria under no-wash conditions and paired with covalently clickable probes for high-contrast super-resolution and multiplexed imaging in cells and tissues.

N4-Hydroxycytidine/molnupiravir inhibits RNA virus-induced encephalitis by producing less fit mutated viruses.

A diverse group of RNA viruses have the ability to gain access to the central nervous system (CNS) and cause severe neurological disease. Current treatment for people with this type of infection is generally limited to supportive care. To address the need for reliable antivirals, we utilized a strategy of lethal mutagenesis to limit virus replication. We evaluated ribavirin (RBV), favipiravir (FAV) and N4-hydroxycytidine (NHC) against La Crosse virus (LACV), which is one of the most common causes of pediatric arboviral encephalitis cases in North America and serves as a model for viral CNS invasion during acute infection. NHC was approximately 3 to 170 times more potent than RBV or FAV in neuronal cells. Oral administration of molnupiravir (MOV), the prodrug of NHC, decreased neurological disease development (assessed as limb paralysis, ataxia and weakness, repeated seizures, or death) by 31% (4 mice survived out of 13) when treatment was started on the day of infection. MOV also reduced disease by 23% when virus was administered intranasally (IN). NHC and MOV produced less fit viruses by incorporating predominantly G to A or C to U mutations. Furthermore, NHC also inhibited virus production of two other orthobunyaviruses, Jamestown Canyon virus and Cache Valley virus. Collectively, these studies indicate that NHC/MOV has therapeutic potential to inhibit viral replication and subsequent neurological disease caused by orthobunyaviruses and potentially as a generalizable strategy for treating acute viral encephalitis.

Seasonality in mood disorders: Probing association of accelerometer-derived physical activity with daylength and solar insolation

Mood disorders are the leading cause of disability worldwide. Up to 30 percent of individuals with major depressive disorder (MDD) and bipolar disorder (BD) display a seasonal pattern of onset, a phenomenon now recognized in the official diagnostic manuals (DSM-5 and ICD-11). Very little is known about the influence of day length (photoperiod) and sunlight intensity (solar insolation) on seasonal patterns in MDD and BD. Here we report a quantitative approach to examine the relationship between sunlight measures and objectively measured motor activity patterns to understand environmental factors driving seasonality in MDD and BD. Our generalized linear model (GLM) assessment of the Depresjon dataset, which includes short-term (up to two weeks) motor activity recordings of 23 unipolar and bipolar depressed patients and 32 healthy controls recruited to the study at the University of Bergen Norway (60.4° N latitude, 5.3° E longitude), revealed significant association of accelerometer-derived daytime physical activity with participant’s depressed state (p<0.001), photoperiod (p<0.001), and solar insolation (p<0.001). Our study presents a generalizable strategy to decipher the complex interplay between sunlight, physical activity, and depressed state using open-source digital tools. The ability to identify mood disturbances, particularly in seasonally susceptible individuals, using passive digital biomarker data offers great promise in informing next-generation predictive, personalized diagnostics in mental health.

A Kinetic Scout Approach Accelerates Targeted Protein Degrader Development.

Bifunctional molecules such as targeted protein degraders induce proximity to promote gain-of-function pharmacology. These powerful approaches have gained broad traction across academia and the pharmaceutical industry, leading to an intensive focus on strategies that can accelerate their identification and optimization. We and others have previously used chemical proteomics to map degradable target space, and these datasets have been used to develop and train multiparameter models to extend degradability predictions across the proteome. In this study, we now turn our attention to develop generalizable chemistry strategies to accelerate the development of new bifunctional degraders. We implement lysine-targeted reversible-covalent chemistry to rationally tune the binding kinetics at the protein-of-interest across a set of 25 targets. We define an unbiased workflow consisting of global proteomics analysis, IP/MS of ternary complexes and the E-STUB assay, to mechanistically characterize the effects of ligand residence time on targeted protein degradation and formulate hypotheses about the rate-limiting step of degradation for each target. Our key finding is that target residence time is a major determinant of degrader activity, and this can be rapidly and rationally tuned through the synthesis of a minimal number of analogues to accelerate early degrader discovery and optimization efforts.

Targeted imaging of lysosomal zinc ions with a tetrahedral DNA framework fluorescent reporter.

Abnormal levels of zinc ions within endo-lysosomes have been implicated in the progression of Alzheimer's disease (AD), yet the detection of low-concentration zinc ions at the organelle level remains challenging. Here we report the design of an endo-lysosome-targeted fluorescent reporter, Znluorly, for imaging endogenous zinc ions. Znluorly is constructed from an amphiphilic DNA framework (DNF) with programmable size and shape, which can encapsulate zinc-responsive fluorophores within its hydrophobic nanocavity. We find that the tetrahedral DNFs of 20 bp in the edge length are effectively located within endo-lysosomes, which can detect zinc ions with a detection limit of ∼31.9nM (a sensitivity that is ∼2.5 times that of the free fluorophore). Given the organelle-targeting ability and high zinc sensitivity of Znluorly, we employ it to detect endogenous endo-lysosomal zinc ions in neuron cells. We monitor the dynamics of zinc levels in AD model cells and zebrafish, corroborating the positive correlation between zinc levels and AD hallmarks including Aβ aggregates and learning/memory impairments. Our study provides a generalizable strategy for organelle-specific theranostic applications.

Characterizing teacher support of debugging with physical computing: Debugging pedagogies in practice

Objectives . Physical computing systems are increasingly being integrated into secondary school science and STEM instruction, yet little is known about how teachers, especially those with little background and experience in computing, help students during the inevitable debugging moments that arise. In this paper, we describe a framework, comprising two dimensions, for characterizing how teachers support students as they debug a physical computing system called the Data Sensor Hub (DaSH). The DaSH enables students to program sensors to measure, analyze, and visualize data as they engage in science inquiry activities. Participants . Five secondary school teachers implemented an inquiry-oriented instructional unit designed to introduce students to working with the DaSH as a tool for scientific inquiry. Study Method . Findings drew on video analysis of the teachers’ classroom implementations of the unit. A review of the data corpus led to the selection of 23 moments where the teachers supported an individual or small groups of students engaged in debugging. These moments were analyzed using a grounded perspective based on Interaction Analysis to characterize the teachers' varied interactional approaches. Findings . Our analysis revealed how teachers’ moves during debugging moments fell along two dimensions. The first dimension characterizes teachers’ positioning during the debugging interactions, ranging from a positioning for teacher understanding to a positioning for student understanding of the bug. The second dimension characterizes the inquiry orientation of the teachers’ questions and guidance, ranging from focusing on the debugging process to focusing on the product – or fixing the bug. Further, teachers’ moves often fell along different points on these dimensions in any given interaction given nuances in the instructional context. Conclusions . The framework offers a first step towards characterizing teachers’ debugging pedagogy as they support students during debugging moments. It also calls attention to how teachers do not necessarily need to be programming experts to effectively help students learn independent and generalizable debugging strategies. Further, it illustrates the variety of expertise that teachers can bring to debugging moments to support students learning to debug. Finally, the framework provides implications for the design of professional learning and supports for teachers as they increasingly are asked to support students in computing – and debugging – activities across a range of disciplines.

Making target sites in large structured RNAs accessible to RNA-cleaving DNAzymes through hybridization with synthetic DNA oligonucleotides.

The 10-23 DNAzyme is one of the most active DNA-based enzymes, and in theory, can be designed to target any purine-pyrimidine junction within an RNA sequence for cleavage. However, purine-pyrimidine junctions within a large, structured RNA (lsRNA) molecule of biological origin are not always accessible to 10-23, negating its general utility as an RNA-cutting molecular scissor. Herein, we report a generalizable strategy that allows 10-23 to access any purine-pyrimidine junction within an lsRNA. Using three large SARS-CoV-2 mRNA sequences of 566, 584 and 831 nucleotides in length as model systems, we show that the use of antisense DNA oligonucleotides (ASOs) that target the upstream and downstream regions flanking the cleavage site can restore the activity (kobs) of previously poorly active 10-23 DNAzyme systems by up to 2000-fold. We corroborated these findings mechanistically using in-line probing to demonstrate that ASOs reduced 10-23 DNAzyme target site structure within the lsRNA substrates. This approach represents a simple, efficient, cost-effective, and generalizable way to improve the accessibility of 10-23 to a chosen target site within an lsRNA molecule, especially where direct access to the genomic RNA target is necessary.

Towards artificial intelligence-based disease prediction algorithms that comprehensively leverage and continuously learn from real-world clinical tabular data systems.

This manuscript presents a proof-of-concept for a generalizable strategy, the full algorithm, designed to estimate disease risk using real-world clinical tabular data systems, such as electronic health records (EHR) or claims databases. By integrating classic statistical methods and modern artificial intelligence techniques, this strategy automates the production of a disease prediction model that comprehensively reflects the dynamics contained within the underlying data system. Specifically, the full algorithm parses through every facet of the data (e.g., encounters, diagnoses, procedures, medications, labs, chief complaints, flowsheets, vital signs, demographics, etc.), selects which factors to retain as predictor variables by evaluating the data empirically against statistical criteria, structures and formats the retained data into time-series, trains a neural network-based prediction model, then subsequently applies this model to current patients to generate risk estimates. A distinguishing feature of the proposed strategy is that it produces a self-adaptive prediction system, capable of evolving the prediction mechanism in response to changes within the data: as newly collected data expand/modify the dataset organically, the prediction mechanism automatically evolves to reflect these changes. Moreover, the full algorithm operates without the need for a-priori data curation and aims to harness all informative risk and protective factors within the real-world data. This stands in contrast to traditional approaches, which often rely on highly curated datasets and domain expertise to build static prediction models based solely on well-known risk factors. As a proof-of-concept, we codified the full algorithm and tasked it with estimating 12-month risk of initial stroke or myocardial infarction using our hospital's real-world EHR. A 66-month pseudo-prospective validation was conducted using records from 558,105 patients spanning April 2015 to September 2023, totalling 3,424,060 patient-months. Area under the receiver operating characteristic curve (AUROC) values ranged from .830 to .909, with an improving trend over time. Odds ratios describing model precision for patients 1-100 and 101-200 (when ranked by estimated risk) ranged from 15.3 to 48.1 and 7.2 to 45.0, respectively, with both groups showing improving trends over time. Findings suggest the feasibility of developing high-performing disease risk calculators in the proposed manner.

Transparent and high-porosity aluminum alkoxide network-forming glasses

Metal-organic network-forming glasses are an emerging type of material capable of combining the modular design and high porosity of metal-organic frameworks and the high processability and optical transparency of glasses. However, a generalizable strategy for achieving both high porosity and high glass-forming ability in modularly designed metal-organic networks has yet to be developed. Herein, we develop a series of aluminum alkoxide glasses and monoliths by linking aluminum-oxo clusters with alcohol linkers. A bulky monodentate alcohol modulator is introduced during synthesis and act as both network plasticizer and pore template, which can be removed by the subsequent solvent exchange to give gas accessible pores. Glasses synthesized with the modulator template exhibit well-defined glass transitions in their as-synthesized form and high surface areas up to 500 m2/g after activation, making them among the most porous glassy materials. The aluminum alkoxide glasses also have optical transparency and fluorescent properties, and their structures are elucidated by pair-distribution functions, spectroscopic and compositional analysis. These findings could significantly expand the library of microporous metal-organic network-forming glasses and enable their future applications.

Bioprinting with superelastic and fatigue-resistant bioinks for large-sized tissue delivery

3D bioprinting technology is advancing rapidly to construct multi-scale preformed architectures that satisfy the demands of tissue regeneration. However, challenges remain in accurately delivering large-sized preformed architectures to the defect sites without being damaged by the mechanical environment in vivo. In this study, we proposed a versatile bioprinting strategy to fabricate large-sized architectures with remarkable injection capacity for geometry-independent minimally invasive tissue delivery. We design a novel hydrogel by mixing gelatin methacryloyl with poly(ethylene glycol) diacrylate (PEGDA) and photocrosslinking the mixture under a white light source. The hydrogel forms a reinforced crosslinking network, exhibiting superelasticity and fatigue resistance. We infer that the flexible chains of high-molecular-weight PEGDA and interconnected macropores of hydrogel contribute to the reinforcement mechanisms. With a decoupled bioprinting strategy, the bioink can be 3D printed into large-sized architectures with different geometries and tunable Poisson’s ratio. The printing architectures displayed excellent deformation and shape recovery capacity by compressing to less than 1% of the original size and fully recovering after injection. The injection capacity is geometry-independent, indicating the intrinsic properties of the hydrogel and allowing higher freedom for the structural design and geometry size of the architectures. The bioprinting process is compatible with genome-edited hepatic cells with high cell-bioprinting suitability, demonstrating high cell viability, a minimally altered transcriptomic profile after bioprinting, and a biocompatible microenvironment that supports cell survival and hepatic function maintenance. The cell-laden architectures express uncompromised injection capacity with unaffected cell viability after injection. This study presents a generalizable strategy for preparing and bioprinting with superelastic and fatigue-resistant bioinks into customized cell-laden architectures to facilitate minimally invasive large-sized tissue delivery.

Chemically Tagging Cargo for Specific Packaging inside and on the Surface of Virus-like Particles.

Virus-like particles (VLPs) have untapped potential for packaging and delivery of macromolecular cargo. To be a broadly useful platform, there needs to be a strategy for attaching macromolecules to the inside or the outside of the VLP with minimal modification of the platform or cargo. Here, we repurpose antiviral compounds that bind to hepatitis B virus (HBV) capsids to create a chemical tag to noncovalently attach cargo to the VLP. Our tag consists of a capsid assembly modulator, HAP13, connected to a linker terminating in maleimide. Our cargo is a green fluorescent protein (GFP) with a single addressable cysteine, a feature that can be engineered in many proteins. The HAP-GFP construct maintained HAP's intrinsic ability to bind HBV capsids and accelerate assembly. We investigated the capacity of HAP-GFP to coassemble with HBV capsid protein and bind to preassembled capsids. HAP-GFP binding was concentration-dependent, sensitive to capsid stability, and dependent on linker length. Long linkers had the greatest activity to bind capsids, while short linkers impeded assembly and damaged intact capsids. In coassembly reactions, >20 HAP-GFP molecules were presented on the outside and inside of the capsid, concentrating the cargo by more than 100-fold compared to bulk solution. We also tested an HAP-GFP with a cleavable linker so that external GFP molecules could be removed, resulting in exclusive internal packaging. These results demonstrate a generalizable strategy for attaching cargo to a VLP, supporting development of HBV as a modular VLP platform.

Enzyme Engineering to Produce SWCNT Nanosensors as a Generalizable Platform for Biochemical Imaging and Sensing

Single-walled carbon nanotubes (SWCNT) stand out as attractive candidates for near-infrared (nIR) fluorescent imaging and chemical sensing. However, to render SWCNT chemically-selective, rational design of the surface corona is required to achieve sufficient sensitivity and selectivity to the target analyte. Recently, we reported on a facile sonication-based generation of glucose oxidase (GOx)-SWCNT conjugate for glucose detection [1]. Notably, we found that the analyte response mechanism does not involve catalytic oxidation of glucose, which motivated us to develop nanosensors using catalytically inactive GOx for enhanced reversibility and biocompatibility. In this study, we study the generalizability of our approach in producing SWCNT-based nanosensors based on 1- facile direct probe tip sonication, and 2- with engineered enzymes, showing the potential applicability of the sonication-based approach as a generalizable strategy for generating rationally designed nanosensors capable of detecting and imaging various analytically-relevant biomolecules.In this work, to test the generalizability of generating enzyme-SWCNT nanosensors, we generated conjugates by facile direct sonication of SWCNT with following enzymes: GOx, choline oxidase (ChOx), horseradish peroxidase (HRP), acetylcholineesterase, tyrosinase, cholesterol oxidase (CholOx), lactate oxidase, alcohol oxidase, xanthine oxidase, and galactose oxidase. Remarkably, six out of nine enzymes yielded stable suspensions of enzyme-SWCNT conjugates following probe-tip sonication, all of which successfully worked as nanosensors to detect their corresponding analytes. These nanosensors, in particular HRP-SWCNT and CholOx-SWCNT, exhibited excellent responses to their targets, with a maximum ΔF/F 0 of up to 300 % and 100 %, respectively. H2O2 is known to quench SWCNT fluorescence, and H2O2 nanosensors previously developed with SWCNT exhibited a negative fluorescence modulation as the sensor output with some challenges towards nanosensor selectivity. We highlight that our approach enables HRP-SWCNT nanosensor generation, which provides a strong ΔF/F 0 = 300 % and instantaneous turn-on response towards H2O2 within 2 s. Next, we sought to show that enzyme inactivation can produce SWCNT-based nanosensors that detect analytes without analyte consumption. To do so, we use ChOx as a model system to show that recombinant enzymes can be used as a generalizable approach to generating catalytically-inactive nanosensors. As expected, nanosensors prepared with mutant-ChOx exhibited responses comparable to those prepared with native ChOx, but without consuming the analyte or producing toxic byproducts such as H2O2. Thus, sonication-based physisorption of engineered enzymes to SWCNTs holds the potential to facilitate rapid nanosensor generation capable of detecting various biologically relevant molecules with reversibility and biocompatibility, thereby motivating their use in in vivo applications.[1] Nishitani, S., Tran, T., Puglise, A., Yang, S. & Landry, M. Engineered Glucose Oxidase-Carbon Nanotube Conjugates for Tissue-Translatable Glucose Nanosensors. Angew. Chem. Int. Ed Engl. e202311476 (2023)

Polyaptamer-Driven Crystallization of Alendronate for Synergistic Osteoporosis Treatment through Osteoclastic Inhibition and Osteogenic Promotion.

Osteoclastic inhibition using antiresorptive bisphosphonates and osteogenic promotion using antisclerostin agents represent two distinct osteoporosis treatments in clinical practice, each individual treatment suffers from unsatisfactory therapeutic efficacy due to its indirect intervention in osteoclasis and promotion of osteogenesis simultaneously. Although this issue is anticipated to be resolved by drug synergism, a tempting carrier-free dual-medication nanoassembly remains elusive. Herein, we prepare such a nanoassembly made of antiresorptive alendronate (ALN) crystal and antisclerostin polyaptamer (Apt) via a nucleic acid-driven crystallization method. This nanoparticle can protect Apt from rapid nuclease degradation, avoid the high cytotoxicity of free ALN, and effectively concentrate in the cancellous bone by virtue of the bone-binding ability of DNA and ALN. More importantly, the acid microenvironment of cancellous bone triggers the disassociation of nanoparticles for sustained drug release, from which ALN inhibits the osteoclast-mediated bone resorption while Apt promotes osteogenic differentiation. Our work represents a pioneering demonstration of nucleic acid-driven crystallization of a bisphosphonate into a tempting carrier-free dual-medication nanoassembly. This inaugural advancement augments the antiosteoporosis efficacy through direct inhibition of osteoclasis and promotion of osteogenesis simultaneously and establishes a paradigm for profound understanding of the underlying synergistic antiosteoporosis mechanism of antiresorptive and antisclerostin components. It is envisioned that this study provides a highly generalizable strategy applicable to the tailoring of a diverse array of DNA-inorganic nanocomposites for targeted regulation of intricate pathological niches.

A whole-cell platform for discovering synthetic cell adhesion molecules in bacteria

Developing programmable bacterial cell-cell adhesion is of significant interest due to its versatile applications. Current methods that rely on presenting cell adhesion molecules (CAMs) on bacterial surfaces are limited by the lack of a generalizable strategy to identify such molecules targeting bacterial membrane proteins in their natural states. Here, we introduce a whole-cell screening platform designed to discover CAMs targeting bacterial membrane proteins within a synthetic bacteria-displayed nanobody library. Leveraging the potency of the bacterial type IV secretion system—a contact-dependent DNA delivery nanomachine—we have established a positive feedback mechanism to selectively enrich for bacteria displaying nanobodies that target antigen-expressing cells. Our platform successfully identified functional CAMs capable of recognizing three distinct outer membrane proteins (TraN, OmpA, OmpC), demonstrating its efficacy in CAM discovery. This approach holds promise for engineering bacterial cell-cell adhesion, such as directing the antibacterial activity of programmed inhibitor cells toward target bacteria in mixed populations.

Revolutionizing palliative care: Electrocautery-enhanced lumen-apposing metal stents in endoscopic-ultrasound-guided biliary drainage for malignant obstructions.

Patients with malignant biliary obstruction, following endoscopic retrograde cholangiopancreatography (ERCP) failure could be referred for endoscopic-ultrasound-guided biliary drainage through electrocautery-enhanced (ECE) lumen-apposing metal stent (LAMS) placement. However, the efficacy and safety of ECE-LAMS in this scenario have remained debatable due to minimal scientific evidence. The current confirmed 91.0% clinical success, 96.7% technical success, 7.3% reintervention rate, and 17.5% adverse events, following the treatment of malignant biliary obstruction with ECE-LAMS delivery. Finally, ECE-LAMS proved to be a generalizable strategy for managing biliary obstruction for patients who were excluded from ERCP.

A synchronous-twisting method to realize radial scalability in fibrous energy storage devices.

For wearable electronics, radial scalability is one of the key research areas for fibrous energy storage devices to be commercialized, but this field has been shelved for years due to the lack of effective methods and configuration arrangements. Here, the team presents a generalizable strategy to realize radial scalability by applying a synchronous-twisting method (STM) for synthesizing a coaxial-extensible configuration (CEC). As examples, aqueous fiber-shaped Zn-MnO2 batteries and MoS2-MnO2 supercapacitors with a diameter of ~500 μm and a length of 100 cm were made. Because of the radial scalability, uniform current distribution, and stable binding force in CEC, the devices not only have high energy densities (~316 Wh liter-1 for Zn-MnO2 batteries and ~107 Wh liter-1 for MoS2-MnO2 supercapacitors) but also maintain a stable operational state in textiles when external bending and tensile forces were applied. The fabricating method together with the radial scalability of the devices provides a reference for future fiber-shaped energy storage devices.

Structural and genetic diversity in the secreted mucins MUC5AC and MUC5B

The secreted mucins MUC5AC and MUC5B are large glycoproteins that play critical defensive roles in pathogen entrapment and mucociliary clearance. Their respective genes contain polymorphic and degenerate protein-coding variable number tandem repeats (VNTRs) that make the loci difficult to investigate with short reads. We characterize the structural diversity of MUC5AC and MUC5B by long-read sequencing and assembly of 206 human and 20 nonhuman primate (NHP) haplotypes. We find that human MUC5B is largely invariant (5,761-5,762 amino acids [aa]); however, seven haplotypes have expanded VNTRs (6,291-7,019 aa). In contrast, 30 allelic variants of MUC5AC encode 16 distinct proteins (5,249-6,325 aa) with cysteine-rich domain and VNTR copy-number variation. We group MUC5AC alleles into three phylogenetic clades: H1 (46%, ∼5,654 aa), H2 (33%, ∼5,742 aa), and H3 (7%, ∼6,325 aa). The two most common human MUC5AC variants are smaller than NHP gene models, suggesting a reduction in protein length during recent human evolution. Linkage disequilibrium and Tajima's D analyses reveal that East Asians carry exceptionally large blocks with an excess of rare variation (p<0.05) at MUC5AC. To validate this result, we use Locityper for genotyping MUC5AC haplogroups in 2,600 unrelated samples from the 1000 Genomes Project. We observe a signature of positive selection in H1 among East Asians and a depletion of the likely ancestral haplogroup (H3). In Europeans, H3 alleles show an excess of common variation and deviate from Hardy-Weinberg equilibrium (p<0.05), consistent with heterozygote advantage and balancing selection. This study provides a generalizable strategy to characterize complex protein-coding VNTRs for improved disease associations.