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Molecular analysis of circulating tumor cells (CTCs) at single-cell resolution offers great promise for cancer diagnostics and therapeutics from simple liquid biopsy. Recent development of massively parallel single-cell RNA-sequencing (scRNA-seq) provides a powerful method to resolve the cellular heterogeneity from gene expression and pathway regulation analysis. However, the scarcity of CTCs and the massive contamination of blood cells limit the utility of currently available technologies. Here, we present Hydro-Seq, a scalable hydrodynamic scRNA-seq barcoding technique, for high-throughput CTC analysis. High cell-capture efficiency and contamination removal capability of Hydro-Seq enables successful scRNA-seq of 666 CTCs from 21 breast cancer patient samples at high throughput. We identify breast cancer drug targets for hormone and targeted therapies and tracked individual cells that express markers of cancer stem cells (CSCs) as well as of epithelial/mesenchymal cell state transitions. Transcriptome analysis of these cells provides insights into monitoring target therapeutics and processes underlying tumor metastasis. Transcriptome analysis of circulating tumor cells (CTCs) provides insights into monitoring target therapeutics and underlying tumor metastasis. Here the authors present Hydro-Seq, a contamination-free high-throughput hydrodynamic scRNA-seq barcoding technique for rare CTCs.

The authors derive a condition for how natural selection chooses between two competing strategies on any graph for weak selection, elucidating which population structures promote certain behaviours, such as cooperation. Evolution, the great game Evolution is a game that anyone can play. The traits that evolve in a population depend on how the players interact. Students are familiar with toy populations in which every member of the population can interact equally with any other, but as W. S. Gilbert wrote, “When everyone is somebody, then no one's anybody”. In the real world, the numbers and identities of the players can change, and realistic simulations of evolution have proven exceedingly hard to create. Recent models have worked only in special cases in which all individuals have the same number of neighbours. Benjamin Allen and colleagues have now devised a model that works for any number of neighbours, providing that natural selection is weak. They simulate how small changes in population structure can affect evolutionary outcomes, and that cooperation flourishes most in populations with strong ties between pairs of individuals. Evolution occurs in populations of reproducing individuals. The structure of a population can affect which traits evolve 1 , 2 . Understanding evolutionary game dynamics in structured populations remains difficult. Mathematical results are known for special structures in which all individuals have the same number of neighbours 3 , 4 , 5 , 6 , 7 , 8 . The general case, in which the number of neighbours can vary, has remained open. For arbitrary selection intensity, the problem is in a computational complexity class that suggests there is no efficient algorithm 9 . Whether a simple solution for weak selection exists has remained unanswered. Here we provide a solution for weak selection that applies to any graph or network. Our method relies on calculating the coalescence times 10 , 11 of random walks 12 . We evaluate large numbers of diverse population structures for their propensity to favour cooperation. We study how small changes in population structure—graph surgery—affect evolutionary outcomes. We find that cooperation flourishes most in societies that are based on strong pairwise ties.

Post-acute care (PAC) is an interdisciplinary healthcare service used to assist in returning patients to their community after they receive acute medical services. To establish a fully developed PAC service model, the National Health Insurance Administration in Taiwan attempts to recruit professional medical service institutions (consisting of medical centers and regional and district hospitals) in a trial program for improving the rehabilitation quality of patients with cerebrovascular diseases (CVDs) and to reduce the re-hospitalization rate. The evaluation and selection of adequate medical service institutions are critical in the program for enhancing the effectiveness of hospital-based PAC in acute stroke management. However, because of the complexity of the national health insurance system and the healthcare system in Taiwan, the determination of pilot hospitals is a highly complicated and ambiguous multiple criteria decision analysis (MCDA) problem. Focusing on the requirement of generating a set of pilot hospitals for the PAC program and tackling imprecise and uncertain information associated with a complex medical circumstance, the purpose of this paper is to develop a novel compromising decision-making method based on the interval-valued Pythagorean fuzzy (IVPF) set theory and the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) methodology to address the multiple criteria selection problem of pilot hospitals in assisting the establishment of a hospital-based PAC model. Considering the powerfulness of IVPF sets when handling vagueness and complex uncertainty in practical problems, this paper proposes a useful IVPF VIKOR method that is significantly different from the existing VIKOR methodology. The proposed methodology represents a comprehensive integration of high-order uncertainties within a decision environment into the basic VIKOR structure that is designed to lead to both better description and better applicability of MCDA. This paper presents some novel concepts such as remoteness indices and remoteness-based multiple criteria ranking indices and investigates their desirable properties in detail. With the theoretical support of these new concepts, this paper establishes useful remoteness-based group utility indices, individual regret indices, and compromise indices for the assessment of acceptable advantage and acceptable stability. Unlike the current VIKOR-based ranking process, this paper provides a systematic ranking procedure that is capable of improving the efficiency of determining ultimate compromise solutions. Overall, the present study provides several significant contributions, such as structuring the selection problem for hospital-based PAC, extending the IVPF theory and VIKOR to the medical and healthcare fields, simplifying the manipulation procedure in handling IVPF information, constructing valuable concepts of remoteness-based indices, and developing an effective IVPF VIKOR ranking procedure. A real-world example of selecting pilot hospitals in the PAC program for CVDs is provided to illustrate the application of the proposed method and to demonstrate its practicality and effectiveness. Furthermore, other valuable MCDA applications with a comparative analysis are conducted to validate the advantages of the proposed method in a variety of fields.

Light-induced halide segregation limits the bandgap tunability of mixed-halide perovskites for tandem photovoltaics. Here we report that light-induced halide segregation is strain-activated in MAPb(I 1−x Br x ) 3 with Br concentration below approximately 50%, while it is intrinsic for Br concentration over approximately 50%. Free-standing single crystals of CH 3 NH 3 Pb(I 0.65 Br 0.35 ) 3 (35%Br) do not show halide segregation until uniaxial pressure is applied. Besides, 35%Br single crystals grown on lattice-mismatched substrates (e.g. single-crystal CaF 2 ) show inhomogeneous segregation due to heterogenous strain distribution. Through scanning probe microscopy, the above findings are successfully translated to polycrystalline thin films. For 35%Br thin films, halide segregation selectively occurs at grain boundaries due to localized strain at the boundaries; yet for 65%Br films, halide segregation occurs in the whole layer. We close by demonstrating that only the strain-activated halide segregation (35%Br/45%Br thin films) could be suppressed if the strain is properly released via additives (e.g. KI) or ideal substrates (e.g. SiO 2 ). Mixed-halide perovskites are of interest for photovoltaic devices, but light-induced halide segregation obstructs bandgap tuning and is not fully understood. Here the authors study the effects of strain and iodide/bromide ratio on light-induced halide segregation in mixed-halide perovskites.

All-solid-state batteries using Si as the anode have shown promising performance without continual solid-electrolyte interface (SEI) growth. However, the first cycle irreversible capacity loss yields low initial Coulombic efficiency (ICE) of Si, limiting the energy density. To address this, we adopt a prelithiation strategy to increase ICE and conductivity of all-solid-state Si cells. A significant increase in ICE is observed for Li 1 Si anode paired with a lithium cobalt oxide (LCO) cathode. Additionally, a comparison with lithium nickel manganese cobalt oxide (NCM) reveals that performance improvements with Si prelithiation is only applicable for full cells dominated by high anode irreversibility. With this prelithiation strategy, 15% improvement in capacity retention is achieved after 1000 cycles compared to a pure Si. With Li 1 Si, a high areal capacity of up to 10 mAh cm –2 is attained using a dry-processed LCO cathode film, suggesting that the prelithiation method may be suitable for high-loading next-generation all-solid-state batteries. All-solid-state batteries with silicon anodes have high capacities but low initial coulombic efficiencies (ICEs) because of first cycle irreversible capacity loss. Here, the authors report a prelithiation strategy to improve ICEs and reversibility.

Summary Plant protoplasts are useful for assessing the efficiency of clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR‐associated protein 9 (Cas9) mutagenesis. We improved the process of protoplast isolation and transfection of several plant species. We also developed a method to isolate and regenerate single mutagenized Nicotianna tabacum protoplasts into mature plants. Following transfection of protoplasts with constructs encoding Cas9 and sgRNAs, target gene DNA could be amplified for further analysis to determine mutagenesis efficiency. We investigated N. tabacum protoplasts and derived regenerated plants for targeted mutagenesis of the phytoene desaturase (NtPDS) gene. Genotyping of albino regenerants indicated that all four NtPDS alleles were mutated in amphidiploid tobacco, and no Cas9 DNA could be detected in most regenerated plants.

Ever since the introduction of Pythagorean fuzzy (PF) sets, many scholars have focused on solving multicriteria decision-making (MCDM) problems with PF information. The technique for order preference by similarity to ideal solutions (TOPSIS) is a well-known and effective method for MCDM problems. The objective of this study is to extend the TOPSIS to tackle MCDM problems under the PF environment. In this study, we develop a novel distance measure that considers the length, the angle, and the greater space, which reflect the properties of PF sets. Then, we apply the proposed distance measure in PF-TOPSIS to calculate the distances from the PF positive ideal solution and the PF negative ideal solution. Finally, we take the evaluation of emerging technology commercialization as an MCDM problem to illustrate the proposed approaches, and we then compare these approaches to demonstrate the scalar type PF-TOPSIS is the most feasible and effective approach in practice.

This study aims to extend classic VIKOR technique for multicriteria decision-making (MCDM) problems within Pythagorean fuzzy (PF) scenario. First, judgments from decision makers (DMs) are expressed by PF sets that can describe more uncertain and ambiguous information than available fuzzy sets. Second, PF point operators are applied to denote the risk preference of the DM who may express an attitude toward an emerging science and technology. Third, a new generalized distance measurement formula considering all the characteristics of PF sets is proposed, and some attractive properties of distance measure, which outperforms available distance measures, are proved. Fourth, the novel generalized distance measure is employed to relative distance to identify the optimum and worst PF values and then employed in L p -metric VIKOR formula to accurately gain the group utility, individual regret, and compromise index. The novel PF-VIKOR algorithm considering DM’s risk preference and a novel distance measure is described in detail, and a blockchain technology solution selection problem is utilized to validate the feasibility of our technique. Then, the sensitivity analysis is implemented to test stability of our PF-VIKOR technique when the parameters in risk preferences and generalized distance measure are adjusted. Fifth, the comparison among various PF-MCDM techniques is performed to validate superiority and practicability of our presented technique.

Time-to-event endpoints for patient-reported outcomes, such as time to deterioration of symptoms or function, are frequently used in cancer clinical trials. Although time-to-deterioration endpoints might seem familiar to cancer researchers for being similar to survival or disease-progression endpoints, there are unique considerations associated with their use. The complexity of time-to-deterioration endpoints should be weighed against the information that they add to the tumour, survival, and safety data used to inform the risks and benefits of an investigational drug. Here we use the estimand framework to show how analytical decisions answer different clinical questions of interest, some of which might be uninformative. Challenges including the consideration of intercurrent events, the difficulty in maintaining adequate completion rates, and considerable patient and trial burden from long-term, serial, patient-reported outcome measurements render time to deterioration a problematic approach for widespread use. For trials in which a comparative benefit in symptoms or function is an objective, an analysis at pre-specified relevant timepoints could be a better approach.

Long-term home-based sleep monitoring requires wearable sensing devices that strictly balance signal precision with power constraints. This study presents the design and implementation of a low-noise, low-power wearable single-channel electroencephalography (EEG) system for automatic sleep staging. The hardware architecture integrates a TI ADS1298 analog front-end with an STM32F4 microcontroller, utilizing differential sampling and hardware-based filtering to effectively suppress power-line interference and baseline drift. System-level testing demonstrates an average power consumption of approximately 150.85 mW, enabling over 24.6 h of continuous operation on a 1000 mAh battery, which meets the requirements for overnight monitoring. To achieve accurate staging without draining the wearable’s battery, we adopted and deployed a lightweight deep learning model, SleePyCo, on the cloud backend. This architecture was specifically optimized for our edge–cloud collaborative execution, which combines contrastive representation learning with temporal dependency modeling. Validation on the ISRUC dataset yielded an overall accuracy of 79.3% ± 3.0%, with a notable F1-score of 88.3% for Deep Sleep (N3). Furthermore, practical field trials involving 10 healthy subjects verified the system’s engineering stability, achieving a valid data rate exceeding 97% and a Bluetooth packet loss rate of only 0.8%. These results confirm that the proposed hardware–software co-designed system provides a robust, energy-efficient IoMT sensing solution for daily sleep health management.