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A new tripodal tris(hydroxycoumarin) based Schiff base, HCTN was synthesized and characterized by FT-IR, 1H NMR, 13C NMR and ESI-HRMS. The probe, HCTN exhibits cyan emission in DMSO/HEPES buffer (9:1, v/v) which selectively detects Cu2+ ion via turn-off fluorescence. The quenching of the fluorescence was due to the binding of the probe, HCTN towards paramagnetic Cu2+ ion resulting in chelation enhanced quenching effect (CHEQ). From the spectroscopic results, the limit of detection of Cu2+ ion was obtained as very low as 0.40 × 10-9 M. The complexation of the metal ion, Cu2+ towards the probe HCTN was confirmed by the ESI-HRMS and Job's plot analysis which supports 1:1 binding stochiometric ratio. In order to validate the affinity of Cu2+ ion towards histidine, the HCTN+Cu2+ system was utilized for the detection of histidine via turn-on mode by the metal displacement approach. The detection limit of His was found to be 7.31 × 10-10 M. In addition to the above, the probe was utilized for various detection applications such as paper strips, cotton swabs, logic gates and thin film applications. The probe, HCTN extends its application to the confocal bioimaging to sense the Cu2+ and Histidine intracellularly.
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Predictive real-time control (RTC) strategies are usually more effective than reactive strategies for the intelligent management of urban stormwater storage systems. However, it remains a challenge to ensure the practicality of RTC strategies that use accessible, non-idealized predictive information while improving their efficiency for successive rainfall events instead of specific phases. This study developed a predictive fuzzy logic and rule-based control (PFL-RBC) approach to address the continuous control of individual storage systems. This approach incorporates total rainfall depth forecast information with an intra-storm fuzzy logic system to optimize peak flow control and several rule-based strategies for pre-storm water detention, reuse, and release control. Computational experiments were conducted using a storage tank case study to test the proposed approach under various rainfall conditions and storage sizes. The results showed that PFL-RBC outperformed static rule-based control in infrequent design storms and realistic continuous rainfall events, reducing flood peaks and volumes by 55 %â¼87 % and 7 %â¼20 %, respectively, and significantly increasing water detention time and reuse volume. Meanwhile, PFL-RBC required less predictive information to achieve a 6 %â¼15 % advantage in peak flow control compared to optimized model predictive control. More importantly, PFL-RBC was reliable in the face of input uncertainty, with <25 % performance loss for water quantity control when the realistic forecast error ranged from -50 % to +50 %. These findings suggest that the proposed approach has great potential to enhance the efficiency and practicality of stormwater storage operations.
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In the realm of healthcare, an imperative necessity for all, institutions are increasingly recognizing the advantages of adopting lean strategies to enhance performance. Lean implementation in healthcare can lead to significant improvements in efficiency, patient care, and overall institutional performance. This paper aims to assess the readiness levels for implementing lean practices in healthcare institutes in Bangladesh, employing a fuzzy logic approach. The construction of a conceptual model is grounded in literature review and expert opinions, incorporating critical enablers, criteria, and attributes identified from extensive research. Factors measured include leadership commitment, workforce capability, operational processes, technological infrastructure, and organizational culture, each pivotal in determining readiness for lean implementation. The fuzzy logic approach is particularly useful in this context due to its ability to handle uncertainty and imprecision, which are common in complex environments like healthcare. This methodology not only provides a clear picture of current capabilities but also highlights specific areas that need enhancement, paving the way for more targeted and effective lean interventions. Data sourced from consultations with experts in three prominent hospitals in Bangladesh forms the basis of the analysis, enabling a detailed examination of readiness levels. The model's application of fuzzy logic facilitates a comprehensive assessment, revealing 12 critical attributes across the hospitals that require attention. Interestingly, the evaluation identifies varying levels of readiness, with two hospitals demonstrating moderate readiness and one showing a lower level. This conceptual approach has significant potential to assist top management in healthcare companies by providing a structured framework to prioritize crucial areas for improvement. By accurately assessing readiness levels and pinpointing weaker aspects before implementing lean strategies, this study aims to transform the healthcare industry. Ultimately, its implementation has the potential to enhance organizational performance and elevate standards in patient care, contributing to improved healthcare delivery in Bangladesh and beyond.
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The use of electronic load controllers (ELCrs) is widely adopted in pico hydropower systems to maintain output power supplied to the consumer load, regardless of changes in consumer demand. This is due to the absence of moving mechanical parts, affordability, prevention of the hammer effect in pipes, and being more efficient than the governor systems. However, implementing existing ELCrs in a pico hydropower system can pose challenges related to power quality, efficiency, or costs. In this paper, a fuzzy PI-based single-switch bidirectional AC chopper electronic load controller (FP-SSBAC ELCr) is proposed. This configuration reduces the number of insulated gate bipolar transistors (IGBTs) from two, typically found in the conventional bidirectional AC choppers, to one per phase, resulting in cost reduction. A hybrid controller, comprising fuzzy and PI controllers, is designed to quickly maintain a constant output voltage and frequency when consumer load abruptly changes. The gains of the PI controller are updated by the fuzzy logic controller based on the voltage error and its derivative. The proposed model is simulated in MATLAB/Simulink and validated experimentally under sudden changes in consumer load. The results achieved with the FP-SSBAC ELCr demonstrate improved dynamic performance without overshoot compared to PI-based ELCrs. The highest recorded voltage and current total harmonic distortions (THDs) are 2.8 % and 2.1 %, respectively, meeting the IEEE 519 standard. Therefore, the proposed model has the potential to enhance performance and efficiency and can be implemented cost-effectively in pico hydropower systems.
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In this study, the absorption and fluorescence characteristics of sulfa drugs, specifically Sulfadiazine (SDZ), Sulfamerazine (SMZ), and Sulfamethazine (STZ), were examined across a pH range of 1-14. The absorption and fluorescence spectra of these sulfa drugs showed significant changes depending on the pH value. Analysis of their pH-dependent absorption and fluorescence properties indicated that these sulfa drugs could used to design molecular logic gates. The absorption and fluorescence behaviors at various wavelength maxima were utilized to design IMPLICATION and Improved-INHIBIT (I-INHIBIT) molecular logic gates.
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The Signal is an end-to-end encrypted communication protocol composed of a double ratchet (DR) protocol and an extended triple Diffie-Hellman (X3DH) protocol. Its complex ratchet structure and the characteristics of protocol composition make it challenging to realize formal analysis. A formal analysis method based on logic of events theory (LoET) is proposed to conduct a security analysis of the Signal protocol. The method includes inference rules with key relation and key chain as the core to realize the formal analysis of ratchet structure, and the inference relation between sub-protocols is established by putting forward the composition theorem. The proposed method achieves a formal analysis of Signal, revealing that it does not satisfy a strong authentication property during the X3DH phase. The results show that the LoET-based method can be effectively applied in the formal analysis of Signal protocols, thus promoting the application and development of these protocols with ratchet structure and composition properties.
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Wireless sensor networks (WSNs) are usually composed of tens or hundreds of nodes powered by batteries that need efficient resource management to achieve the WSN's goals. One of the techniques used to manage WSN resources is clustering, where nodes are grouped into clusters around a cluster head (CH), which must be chosen carefully. In this article, a new centralized clustering algorithm is presented based on a Type-1 fuzzy logic controller that infers the probability of each node becoming a CH. The main novelty presented is that the fuzzy logic controller employs three different knowledge bases (KBs) during the lifetime of the WSN. The first KB is used from the beginning to the instant when the first node depletes its battery, the second KB is then applied from that moment to the instant when half of the nodes are dead, and the last KB is loaded from that point until the last node runs out of power. These three KBs are obtained from the original KB designed by the authors after an optimization process. It is based on a particle swarm optimization algorithm that maximizes the lifetime of the WSN in the three periods by adjusting each rule in the KBs through the assignment of a weight value ranging from 0 to 1. This optimization process is used to obtain better results in complex systems where the number of variables or rules could make them unaffordable. The results of the presented optimized approach significantly improved upon those from other authors with similar methods. Finally, the paper presents an analysis of why some rule weights change more than others, in order to design more suitable controllers in the future.
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Covalent organic framework (COF) film with electrofluorochromic (EFC) and electrochromic (EC) properties has been synthesized by using triphenylamine-based monomers. The film exhibited a high maximum fluorescence contrast of 151 when subjected to a drive voltage of 0.75 V vs the Ag/AgCl electrode, causing the fluorescence to be quenched, which resulted in the EFC process's "fluorescence off" state. The switching times for the fluorescence on and off states were 0.51 and 7.79 s, respectively. Over the same voltage range, the COF film also displayed EC properties, achieving a contrast of 50.23% and a coloration efficiency of 297.4 cm2 C-1 at 532 nm, with switching times of 18.6 s for coloration and 0.7 s for bleaching. Notably, the quenched fluorescence of the COF film could be restored by adding dopamine as a reductant. This phenomenon enabled the implementation of a NAND logic gate using the applied potential as a physical input and dopamine addition as a chemical input. This study demonstrates the successful development of COF films with bifunctional EFC and EC properties, showcasing their potential for use in constructing advanced optoelectronic devices.
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Optoelectronic logic devices (OELDs) provide a cure for many visually impaired individuals. However, traditional OELDs have limitations, such as excessive channel resistance and complex structure, leading to high supply voltage and decreased efficiency of signal transmission. We report ultralow-voltage OELDs by seriating two 2D MoTe2 transistors with sub-10 nm channel lengths. The short channel length and atomically flat interface result in a low-resistance light-sensing unit that can operate with a low supply voltage and function well in weak-light conditions. The devices achieve an on state without light signal input and an off state with light signal input at an ultralow supply voltage of 50 mV, lower than the retinal bearing voltage of 70 mV. Additionally, MoTe2's excellent optoelectronic properties allow the device to perceive light from visible to near-infrared wavelengths with high sensitivity to weak light signals. The specific perception of visible light intensity is 0.03 mW·mm-2, and the near-infrared light intensity is 0.1 mW mm-2. The device also has a response time of 8 ms, meeting human needs. Our findings provide a promising solution for developing low-voltage artificial retinas.
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BACKGROUND: Sedentary behaviour and physical inactivity are independent risk factors for sarcopenia for long-term care facility residents. Understanding the components, mechanisms and context of interventions that target change in these risk factors can help optimize sarcopenia management approaches. This study aimed to identify, appraise and synthesize the interventions targeting sedentary behaviour and physical inactivity, construct a Theory of Change logic model, inform complex sarcopenia intervention development and identify areas for improvement. METHODS: Eight electronic databases, including Embase and Web of Science, were searched for eligible interventional studies from inception until February 2024. Narrative synthesis was used. The Theory of Change was applied to develop a logic model presenting the synthesized results. A Cochrane risk of bias assessment tool was used for quality appraisal. RESULTS: The study included 21 articles involving 1014 participants, with mean ages ranging from 72.5 to 90.4 years. The proportion of female participants ranged from 8.0% to 100.0%. The applied sarcopenia diagnosis criteria varied, including those of the Asian Working Group for Sarcopenia and the European Working Group on Sarcopenia in Older People. The overall risk of bias in the included studies was moderate. Interventions primarily targeted physical inactivity, with resistance training being the most common intervention type. The reporting of intervention adherence was insufficient (only 11 out of 21 included studies provided adherence reports), and adherence overall and by intervention type was not possible to discern due to inconsistent criteria for high adherence across these studies. Four categories of intervention input were identified: educational resources; exercise equipment and accessories; monitoring and tailoring tools; and motivational strategies. Intervention activities fell into five categories: determining the intervention plan; educating; tailoring; organizing, supervising, assisting and motivating; and monitoring. While sarcopenia-related indicators were commonly used as desired outcomes, intermediate outcomes (i.e., sedentary time and physical activity level) and other long-term outcomes (i.e., economic outcomes) were less considered. Contextual factors affecting intervention use included participant characteristics (i.e., medical condition and education level) and intervention provider characteristics (i.e., trustworthiness). CONCLUSIONS: The findings led to the development of a novel logic model detailing essential components for interventions aimed at managing sarcopenia in long-term care facilities, with a focus on addressing sedentary behaviour and physical inactivity. Future sarcopenia interventions in long-term care facilities should fully attend to sedentary behaviour, enhance adherence to interventions through improved education, monitoring, tailoring and motivation and establish an agreed standard set of outcome measures.
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Bacteriorhodopsin (bR) of purple membrane (PM) has increasing technical interests, particularly in photonic devices and bioelectronics. The present work has concerned with monitoring the temperature dependence of passive electric responses in-plane and out-of-plane of the membranes. Based on thermal properties observed orthogonally here for PM, a high-temperature intermediate of bR has been suggested to populate at around 60 °C, which may be ascribed to a molten globule-like state. This intermediate has been found to be enclosed between two reversible thermal transitions for PM. Large-scale turnover in the energy of activation, for these two thermal transitions, occurs steeply at such state at 60 °C, above which does bR reverse the sign of dielectric anisotropy (i.e. crossover) provided the operating frequency should be above the crossover frequency, at which the reversal occurs. No such crossover was found to occur below the crossover frequency, even above the crossover temperature (i.e. 60 °C). Likewise, no such crossover was found to occur below the crossover temperature, even above the crossover frequency. Relying on this reasoning, a logic gate operation may be declared implicating bR for bioelectronics and sense technological relevance. In addition, the results specify "dual frequency" as well as "dual temperature" characteristics to bacteriorhodopsin.
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In addition to designing certain excitation modulated logic systems, we have created the first-ever genuine molecular 1-bit magnitude comparator, using acid and base guided varied absorption responses at separate channels. Designed and manufactured Bifunctional Bis(indolyl)methane Derivative (1) demonstrates distinct optical responses (in UV-visible and fluorescence mode) to a range of chemical stimuli (acid, base, Hg2+, Cu2+, EDTA, GSH, etc.) in aqueous medium. Intriguingly, the compound's excitation-modulated fluorescence responses appeared to change at different detection channels depending on whether the aforementioned analytes were present or not. We have proposed not only an excitation driven logic system with switchable molecular IMPLICATION and XNOR logic gates, but also a molecular 1-bit magnitude comparator in our proposal. A second excitation driven logic system with switchable molecular COMPLEMENT and NOR logic gates was also designed with two different optical channels and used Hg(II) & Cu(II) as chemical inputs.
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BACKGROUND: Depression, anxiety, and stress disorders have significant and widespread impacts worldwide, affecting millions of individuals and their communities. According to the World Health Organization, depression impacts the daily lives of more than 300 million people, making it one of the most important diseases globally. Treatment for these mental disorders (MD) typically involves medication and psychotherapies, but also incorporates technological resources like Artificial Intelligence (AI) to indicate personalized therapies and care. While various AI approaches have been applied in the context of MD in the literature, they often focus solely on aiding diagnosis. OBJECTIVE: This research proposes an AI approach for mapping symptoms and assisting in the personalized care of depression, anxiety, and stress. METHODS: Symptom mapping utilizes data mining (DM) techniques to generate rules representing knowledge extracted from data of 242 patients collected using the Depression, Anxiety, and Stress Scale (DASS-21). This knowledge elucidates how symptoms impact the severity degrees of considered MDs. Subsequently, the generated rules are employed to construct a Fuzzy Inference System (FIS) for inferring the severities of MDs based on patient symptoms and personal data. RESULTS AND CONCLUSIONS: The results achieved in the DM (accuracy ≥92.98 %, sensibility ≥86.02 %, specificity ≥97.32 %, and kappa statistic ≥87.98 %), indicating consistent patterns, along with the results produced by the FIS, demonstrate the potential of the proposed approach to assist health professionals in rapidly predicting symptoms of depression, anxiety, and stress, thereby facilitating outpatient screening and emergency care. Furthermore, it can improve the association of symptoms, referral to specialized care, therapeutic proposals, and even investigations of other diseases unrelated to MD.
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In livestock, temperature, humidity, and Temperature-Humidity Index (THI) affect the welfare, yields, health and viability of animals. This study aimed to develop optimal temperature, humidity, and THI thresholds for dairy farms in temperate climate regions using a fuzzy logic model. THI values were calculated using three different literature-derived equations, considering different temperature and humidity situations in dairy farms. The Mamdani-type fuzzy logic method was utilized to formulate linguistic expressions for temperature, humidity, and THI values. According to the THI thresholds, the areas below the Receiver Operating Characteristic (ROC) were found to be significant (p < 0.001) in all fuzzy algorithms. The study found 100% harmony with the THI thresholds of 66 and 72 for cattle in temperate climates, but only 73.6% harmony with the threshold of 74 for cattle adapted to tropical climate. Briefly, in temperate dairy farms, the fuzzy logic revealed that the optimal temperature, humidity and THI values should be between 14-18.5°C, 65-70% and 52.5-64.5, respectively. However, further research is required to understand the impact of thresholds determined by fuzzy logic on dairy cow production and welfare.
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This research introduces a hardware implementation of DC-DC boost converter designed to elevate the DC voltage generated by renewable sources while effectively regulating it against line and load fluctuations for inverter application. The main objective is to boost the DC link voltage to the level of Vmax in the output AC voltage obtained from inverter circuits. This enables the inverters for transformer-less power conversion from DC to AC to reduce magnetic losses, size and weight of the inverter circuits used in the utility application. The proposed converter's topology and switching sequences play a crucial role in enhancing overall performance. Utilizing a Zero Current Switching (ZCS) technique, the converter efficiently recovers stored energy from the magnetics. The proposed converter attained the output voltage of 350 V at its current of 1A from the input voltage of 20 V at its current of 19 A. The ZCS technique and the topology of the converter enhances the efficiency to 92 %. The study employs traditional Proportional-Integral (PI) and Proportional-Integral-Derivative (PID) controllers for effective voltage regulation, analysing time domain specifications. Additionally, a Fuzzy logic controller is introduced as an alternative to PID controllers to compare their performance metrics, evaluating the optimization of the converter's transient and steady-state behaviours. The proposed converter is designed, simulated and their performance metrics are analysed using MATLAB for both with and without controllers. The step-time characteristics of the proposed converter with load resistance of RL = 500 Ω and an input voltage of Vi = 20 V has been determined and analysed. The PID system attained a rise time of 88.781 ms, an overshoot value of 9.341 %, and a steady-state error of 0.00043. The fuzzy system achieved a low-rise time of 10.624 ms, a low overshoot of 0.55 %, and a steady-state error of 0.0584. The hardware prototype of the proposed converter is implemented with a FPGA based PID and Fuzzy logic controllers for providing better voltage regulation and to improve the performance metrics of the converter. The simulation and experimental findings are contrasted, examined, and confirmed to ensure improved consistency in performance measures.
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According to the World Health Organisation and European Commission definitions, substances shall be considered as having endocrine disrupting properties if they show adverse effects, have endocrine activity and the adverse effects are a consequence of the endocrine activity (using a weight-of-evidence approach based on biological plausibility), unless the adverse effects are not relevant to humans or non-target organisms at the (sub)population level. To date, there is no decision logic on how to establish endocrine disruption via the thyroid modality in non-mammalian vertebrates. This paper describes an evidence-based decision logic compliant with the integrated approach to testing and assessment (IATA) concept, to identify thyroid-mediated effect patterns in aquatic vertebrates using amphibians as relevant models for thyroid disruption assessment. The decision logic includes existing test guidelines and methods and proposes detailed considerations on how to select relevant assays and interpret the findings. If the mammalian dataset used as the starting point indicates no thyroid concern, the Xenopus Eleutheroembryonic Thyroid Assay allows checking out thyroid-mediated activity in non-mammalian vertebrates, whereas the Amphibian Metamorphosis Assay or its extended, fixed termination stage variant inform on both thyroid-mediated activity and potentially population-relevant adversity. In evaluating findings, the response patterns of all assay endpoints are considered, including the direction of changes. Thyroid-mediated effect patterns identified at the individual level in the amphibian tests are followed by mode-of-action and population relevance assessments. Finally, all data are considered in an overarching weight-of-evidence evaluation. The logic has been designed generically and can be adapted, e.g. to accommodate fish tests once available for thyroid disruption assessments. It also ensures that all scientifically relevant information is considered, and that animal testing is minimised. The proposed decision logic can be included in regulatory assessments to facilitate the conclusion on whether substances meet the endocrine disruptor definition for the thyroid modality in non-mammalian vertebrates.
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At present, as the problem of water shortage and pollution is growing serious, it is particularly important to understand the recycling and treatment of wastewater. Artificial intelligence (AI) technology is characterized by reliable mapping of nonlinear behaviors between input and output of experimental data, and thus single/integrated AI model algorithms for predicting different pollutants or water quality parameters have become a popular method for simulating the process of wastewater treatment. Many AI models have successfully predicted the removal effects of pollutants in different wastewater treatment processes. Therefore, this paper reviews the applications of artificial intelligence technologies such as artificial neural networks (ANN), adaptive network-based fuzzy inference system (ANFIS) and support vector machine (SVM). Meanwhile, this review mainly introduces the effectiveness and limitations of artificial intelligence technology in predicting different pollutants (dyes, heavy metal ions, antibiotics, etc.) and different water quality parameters such as biochemical oxygen demand (BOD), chemical oxygen demand (COD), total nitrogen (TN) and total phosphorus (TP) in wastewater treatment process, involving single AI model and integrated AI model. Finally, the problems that need further research together with challenges ahead in the application of artificial intelligence models in the field of environment are discussed and presented.
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A one-step logical analysis of multiple targets remains challenging. Herein, we report a one-pot and intelligent DNA logical analysis platform for the diagnosis of avian influenza virus (AIV) biomarkers based on chemiluminescence resonance energy transfer (CRET) and logic operations. On the surface of Lum/PEI/CaCO3 microparticles, the excited state of luminol underwent CRET with fluorescein isothiocyanate or rhodamine B isothiocyanate, producing three well-separated light emissions at 425, 530, and 590 nm, respectively. Taking advantage of the close distance between fluorophores aligned by the catalytic hairpin assembly reaction, the CRET efficiency was greatly enhanced (53.1%). H1N1, H7N9, and H5N1 were detected with limits of detection values as low as 15, 34, and 58 pM, respectively. Three-input logic circuits were simultaneously conducted on the surface of Lum/PEI/CaCO3 microparticles, enabling the rapid and accurate discrimination of multiple AIV biomarkers in one solution. In terms of peak positions and the normalized value of the total peak intensity, three biomarkers can be simultaneously discriminated without any other complex operations. In summary, the CRET-based multiple analytical assay was developed as an intelligent biosensor for identifying AIV biomarkers, having promising application prospects in the field of multiple analysis and precise disease diagnosis.
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Adoptive cell therapy using chimeric antigen receptor (CAR) T cells has proven to be lifesaving for many cancer patients. However, its therapeutic efficacy has been limited in solid tumors. One key factor for this is cancer-associated fibroblasts (CAFs) that modulate the tumor microenvironment (TME) to inhibit T cell infiltration and induce "T cell dysfunction." Additionally, the sparsity of tumor-specific antigens (TSA) and expression of CAR-directed tumor-associated antigens (TAA) on normal tissues often results in "on-target off-tumor" cytotoxicity, raising safety concerns. Using TALEN-mediated gene editing, we present here an innovative CAR T cell engineering strategy to overcome these challenges. Our allogeneic "Smart CAR T cells" are designed to express a constitutive CAR, targeting FAP+ CAFs in solid tumors. Additionally, a second CAR targeting a TAA such as mesothelin is specifically integrated at a TCR signaling-inducible locus like PDCD1. FAPCAR-mediated CAF targeting induces expression of the mesothelin CAR, establishing an IF/THEN-gated circuit sensitive to dual antigen sensing. Using this approach, we observe enhanced anti-tumor cytotoxicity, while limiting "on-target off-tumor" toxicity. Our study thus demonstrates TALEN-mediated gene editing capabilities for design of allogeneic IF/THEN-gated dual CAR T cells that efficiently target immunotherapy-recalcitrant solid tumors while mitigating potential safety risks, encouraging clinical development of this strategy.
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Creating high-quality contacts between high-melting-point metals and delicate two-dimensional (2D) semiconductors poses a critical challenge to polarity control due to inevitable chemical disorder and Fermi-level pinning observed in the contact regions. Here, we report a van der Waals (vdW) integration strategy to precisely tailor the WSe2 polarity by meticulously modulating metal contact compositions. Controlling the low-melting-point bismuth (Bi) thickness effectively modulates the Bi/Au dominant contact with WSe2. This facilitates the precise polarity transformation between n-type, ambipolar, and p-type, with exceptional field-effect mobilities of 200 cm2 V-1 s-1 for electrons and 136 cm2 V-1 s-1 for holes. Within this vdW geometry, we further demonstrate the fundamental electrical components such as diodes and complementary inverters with enhanced rectification ratios and voltage gains. Our results showcase an effective and compatible with mass manufacturing method for precise polarity modulation of 2D semiconductors, providing a promising pathway toward large-scale high-performance 2D electronics and integrated circuits.