Van der Waals polarity-engineered 3D integration of 2D complementary logic.

Vertical three-dimensional integration of two-dimensional (2D) semiconductors holds great promise, as it offers the possibility to scale up logic layers in the z axis1-3. Indeed, vertical complementary field-effect transistors (CFETs) built with such mixed-dimensional heterostructures4,5, as well as hetero-2D layers with different carrier types6-8, have been demonstrated recently. However, so far, the lack of a controllable doping scheme (especially p-doped WSe2 (refs. 9-17) and MoS2 (refs. 11,18-28)) in 2D semiconductors, preferably in a stable and non-destructive manner, has greatly impeded the bottom-up scaling of complementary logic circuitries. Here we show that, by bringing transition metal dichalcogenides, such as MoS2, atop a van der Waals (vdW) antiferromagnetic insulator chromium oxychloride (CrOCl), the carrier polarity in MoS2 can be readily reconfigured from n- to p-type via strong vdW interfacial coupling. The consequential band alignment yields transistors with room-temperature hole mobilities up to approximately 425 cm2 V-1 s-1, on/off ratios reaching 106 and air-stable performance for over one year. Based on this approach, vertically constructed complementary logic, including inverters with 6 vdW layers, NANDs with 14 vdW layers and SRAMs with 14 vdW layers, are further demonstrated. Our findings of polarity-engineered p- and n-type 2D semiconductor channels with and without vdW intercalation are robust and universal to various materials and thus may throw light on future three-dimensional vertically integrated circuits based on 2D logic gates.

Leveraging multi-omics data to empower quantitative systems pharmacology in immuno-oncology.

Understanding the intricate interactions of cancer cells with the tumor microenvironment (TME) is a pre-requisite for the optimization of immunotherapy. Mechanistic models such as quantitative systems pharmacology (QSP) provide insights into the TME dynamics and predict the efficacy of immunotherapy in virtual patient populations/digital twins but require vast amounts of multimodal data for parameterization. Large-scale datasets characterizing the TME are available due to recent advances in bioinformatics for multi-omics data. Here, we discuss the perspectives of leveraging omics-derived bioinformatics estimates to inform QSP models and circumvent the challenges of model calibration and validation in immuno-oncology.

Virtual clinical trials via a QSP immuno-oncology model to simulate the response to a conditionally activated PD-L1 targeting antibody in NSCLC.

Recently, immunotherapies for antitumoral response have adopted conditionally activated molecules with the objective of reducing systemic toxicity. Amongst these are conditionally activated antibodies, such as PROBODY® activatable therapeutics (Pb-Tx), engineered to be proteolytically activated by proteases found locally in the tumor microenvironment (TME). These PROBODY® therapeutics molecules have shown potential as PD-L1 checkpoint inhibitors in several cancer types, including both effectiveness and locality of action of the molecule as shown by several clinical trials and imaging studies. Here, we perform an exploratory study using our recently published quantitative systems pharmacology model, previously validated for triple-negative breast cancer (TNBC), to computationally predict the effectiveness and targeting specificity of a PROBODY® therapeutics drug compared to the non-modified antibody. We begin with the analysis of anti-PD-L1 immunotherapy in non-small cell lung cancer (NSCLC). As a first contribution, we have improved previous virtual patient selection methods using the omics data provided by the iAtlas database portal compared to methods previously published in literature. Furthermore, our results suggest that masking an antibody maintains its efficacy while improving the localization of active therapeutic in the TME. Additionally, we generalize the model by evaluating the dependence of the response to the tumor mutational burden, independently of cancer type, as well as to other key biomarkers, such as CD8/Treg Tcell and M1/M2 macrophage ratio. While our results are obtained from simulations on NSCLC, our findings are generalizable to other cancer types and suggest that an effective and highly selective conditionally activated PROBODY® therapeutics molecule is a feasible option.

Factors Affecting Workers' Mental Stress in Handover Activities During Human-Robot Collaboration.

OBJECTIVE: This study investigated the effects of different approach directions, movement speeds, and trajectories of a co-robot's end-effector on workers' mental stress during handover tasks. BACKGROUND: Human-robot collaboration (HRC) is gaining attention in industry and academia. Understanding robot-related factors causing mental stress is crucial for designing collaborative tasks that minimize workers' stress. METHODS: Mental stress in HRC tasks was measured subjectively through self-reports and objectively through galvanic skin response (GSR) and electromyography (EMG). Robot-related factors including approach direction, movement speed, and trajectory were analyzed. RESULTS: Movement speed and approach direction had significant effects on subjective ratings, EMG, and GSR. High-speed and approaching from one side consistently resulted in higher fear, lower comfort, and predictability, as well as increased EMG and GSR signals, indicating higher mental stress. Movement trajectory affected GSR, with the sudden stop condition eliciting a stronger response compared to the constrained trajectory. Interaction effects between speed and approach direction were observed for "surprise" and "predictability" subjective ratings. At high speed, approach direction did not significantly differ, but at low speeds, approaching from the side was found to be more surprising and unpredictable compared to approaching from the front. CONCLUSION: The mental stress of workers during HRC is lower when the robot's end effector (1) approaches a worker within the worker's field of view, (2) approaches at a lower speed, or (3) follows a constrained trajectory. APPLICATION: The outcome of this study can serve as a guide to design HRC tasks with a low level of workers' mental stress.

Lithiophilic Covalent Organic Framework as Anode Coating for High-Performance Lithium Metal Batteries.

The growth of disorganized lithium dendrites and weak solid electrolyte interphase greatly impede the practical application of lithium metal batteries. Herein, we designed and synthesized a new kind of stable polyimide covalent organic frameworks (COFs), which have a high density of well-aligned lithiophilic quinoxaline and phthalimide units anchored within the uniform one-dimensional channels. The COFs can serve as an artificial solid electrolyte interphase on lithium metal anode, effectively guiding the uniform deposition of lithium ions and inhibiting the growth of lithium dendrites. The unsymmetrical Li||COF-Cu battery exhibits a Coulombic efficiency of 99 % at a current density of 0.5 mA cm-2 , which can be well retained up to 400 cycles. Meanwhile, the Li-COF||LFP full cell shows a Coulombic efficiency over 99 % at a charge of 0.3 C. And its capacity can be well maintained up to 91 % even after 150 cycles. Therefore, the significant electrochemical cycling stability illustrates the feasibility of employing COFs in solving the disordered deposition of lithium ions in lithium metal batteries.

Pickering Emulsions and Viscoelastic Solutions Constructed by a Rosin-Based CO2-Responsive Surfactant.

Designing stimulus-switch viscoelastic solutions and Pickering emulsions with reversible CO2-responsive behavior remains a challenge. A rosin-based CO2-responsive surfactant, N-cetyl-maleimidepimaric acid N,N-dimethylenediamide (C16MPAN), was synthesized and used to prepare CO2-triggered viscoelastic solutions and Pickering emulsions. This surfactant exhibited excellent CO2-responsive performance in water and formed a viscoelastic solution. This viscoelastic system was investigated by dynamic light scattering (DLS), rheology, and cryogenic transmission electron microscopy (Cory-TEM). The shear viscosity of the system increased by 3-4 orders of magnitude after bubbling with CO2 and a large number of elongated, flexible, tubular wormlike micelles were observed. Further, Pickering emulsions were prepared by C16MPAN+ synergistically with cellulose nanocrystals (CNCs), whose stability and switchability were investigated via adsorption isotherm, droplet size, contact angle, and macroscopic photographs. C16MPAN+ was adsorbed with CNCs to form mechanical barriers at the oil-water interface, making the emulsion stable for at least three months, and desorption from CNCs enabled emulsion breaking. The cycle could be switched reversibly multiple times and the particle size distribution of emulsion was basically the same. This work enriches the application of biomass resources in intelligent responsive materials.

Simulations of tumor growth and response to immunotherapy by coupling a spatial agent-based model with a whole-patient quantitative systems pharmacology model.

Quantitative systems pharmacology (QSP) models and spatial agent-based models (ABM) are powerful and efficient approaches for the analysis of biological systems and for clinical applications. Although QSP models are becoming essential in discovering predictive biomarkers and developing combination therapies through in silico virtual trials, they are inadequate to capture the spatial heterogeneity and randomness that characterize complex biological systems, and specifically the tumor microenvironment. Here, we extend our recently developed spatial QSP (spQSP) model to analyze tumor growth dynamics and its response to immunotherapy at different spatio-temporal scales. In the model, the tumor spatial dynamics is governed by the ABM, coupled to the QSP model, which includes the following compartments: central (blood system), tumor, tumor-draining lymph node, and peripheral (the rest of the organs and tissues). A dynamic recruitment of T cells and myeloid-derived suppressor cells (MDSC) from the QSP central compartment has been implemented as a function of the spatial distribution of cancer cells. The proposed QSP-ABM coupling methodology enables the spQSP model to perform as a coarse-grained model at the whole-tumor scale and as an agent-based model at the regions of interest (ROIs) scale. Thus, we exploit the spQSP model potential to characterize tumor growth, identify T cell hotspots, and perform qualitative and quantitative descriptions of cell density profiles at the invasive front of the tumor. Additionally, we analyze the effects of immunotherapy at both whole-tumor and ROI scales under different tumor growth and immune response conditions. A digital pathology computational analysis of triple-negative breast cancer specimens is used as a guide for modeling the immuno-architecture of the invasive front.

Quasi-pointwise two-step phase-shifting profilometry with the fringe parameters estimated statistically.

Fringe projection profilometry is a popularly used three-dimensional measurement technique in which phase-measuring algorithms based on two-step phase shifting are usually used because of their best tradeoff between measurement resolution and speed. Most two-step phase-shifting algorithms involve neighboring or other spatial operations, thus having degraded accuracies at edges and discontinuities of the measured object surface. Pointwise two-step algorithms enable overcoming this issue. With them, however, the offsets of the dynamic ranges of the projector and camera are usually improperly overlooked or inaccurately estimated, thus inducing errors in their measurement results. For solving this problem, this paper suggests a quasi-pointwise two-step phase-shifting algorithm for fringe projection profilometry. This algorithm models the captured fringe patterns practically by taking the offsets of the dynamic ranges of the projector and camera into account, and estimates the fringe parameters from the statistics of fringe intensities. As a result, we can calculate fringe phases in a pointwise way from two fringe patterns having a phase difference of π/2 radians. The simulation and experimental results demonstrate that the proposed method has a relatively low level of errors in measuring object surfaces having isolated regions and discontinuities.

Identification and validation of HOXD3 and UNC5C as molecular signatures in keloid based on weighted gene co-expression network analysis.

BACKGROUND: Keloid is a benign proliferative disease characterized by excessive deposition of extracellular matrix collagen during skin wound healing. The mechanisms of keloid formation have not been fully elucidated, and the current treatment methods are not effective for all keloid patients. Therefore, there is an urgent need to find more effective therapies, and our research focused on identifying characteristic molecular signatures of keloid to explore potential therapeutic targets. METHODS: Gene expression profiles of keloid and control group samples were retrieved from the GEO database. Taking the GSE113619 dataset as the training set, the dataset collected skin tissues from non-lesion sites of healthy and keloid-prone individuals, denoted as Day0. The second sampling was performed 42 days later at the original sampling site of control and keloid groups, denoted as Day42.The 'limma' package and Venn diagram identified differentially expressed genes (DEGs) specific to keloid day42 versus day0 samples. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome pathway functional enrichment, and annotation of the characteristic genes were conducted on the Metascape website. Ingenuity canonical pathways, disease & function enrichment analysis and gene interaction network were performed and predicted in Ingenuity Pathway Analysis (IPA) software. Key module genes related to keloid were filtered out by Weighted Gene Co-expression Network Analysis (WGCNA). We utilized the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm to screen the characteristic genes in keloid by the 'glmnet' package. The area under the curve (AUC) of receiver operating characteristic (ROC) was utilized to determine the effectiveness of potential signatures in discriminating keloid samples from normal samples and performed by using the 'pROC' package. The enrich scores of 24 immune cells in each sample were calculated by the single-sample gene set enrichment analysis (ssGSEA) algorithm， and then the Gene Set Variation Analysis (GSVA) was performed. Finally, RNA from 4 normal and 6 keloid samples was extracted, and RT-qPCR and Western Blot validated the expression of characteristic genes. RESULTS: A total of 640 DEGs specific to keloid day42 versus day0 samples were detected. 69 key module genes were uncovered and implicated in 'NCAM signaling for neurite out-growth', 'oncogenic MAPK signaling', 'transmission across chemical synapses' pathways, and the mitotic cell cycle-related processes. Five characteristic genes (MTUS1, UNC5C, CEP57, NAA35, and HOXD3) of keloid were identified by LASSO, and among which UNC5C and HOXD3 were validated by ROC plot in external dataset, RT-qPCR and Western Blot in validation samples. The result of ssGSEA indicated that the infiltration of neutrophils showed a relatively higher abundance and natural killer cells with relatively low enrichment in the keloid group compared to the control group. UNC5C was correlated with more immune cells compared with other characteristic genes. CONCLUSION: In this study, characteristic genes associated with keloid were identified by bioinformatic approaches and verified in clinical validation samples, providing potential targets for the diagnosis and treatment of keloid.

Magnetic Phase Transitions and Magnetoelastic Coupling in a Two-Dimensional Stripy Antiferromagnet.

Materials with a quasi-one-dimensional stripy magnetic order often exhibit low crystal and magnetic symmetries, thus allowing the presence of various energy coupling terms and giving rise to macroscopic interplay between spin, charge, and phonon. In this work, we performed optical, electrical and magnetic characterizations combined with first-principles calculations on a van der Waals antiferromagnetic insulator chromium oxychloride (CrOCl). We detected the subtle phase transition behaviors of exfoliated CrOCl under varying temperature and magnetic field and clarified its controversial spin structures. We found that the antiferromagnetism and its air stability persist down to few-layer samples, making it a promising candidate for future 2D spintronic devices. Additionally, we verified the magnetoelastic coupling effect in CrOCl, allowing for the potential manipulation of the magnetic states via electric field or strain. These virtues of CrOCl provide us with an ideal platform for fundamental research on spin-charge, spin-phonon coupling, and spin-interactions.

Improving Workers' Musculoskeletal Health During Human-Robot Collaboration Through Reinforcement Learning.

OBJECTIVE: This study aims to improve workers' postures and thus reduce the risk of musculoskeletal disorders in human-robot collaboration by developing a novel model-free reinforcement learning method. BACKGROUND: Human-robot collaboration has been a flourishing work configuration in recent years. Yet, it could lead to work-related musculoskeletal disorders if the collaborative tasks result in awkward postures for workers. METHODS: The proposed approach follows two steps: first, a 3D human skeleton reconstruction method was adopted to calculate workers' continuous awkward posture (CAP) score; second, an online gradient-based reinforcement learning algorithm was designed to dynamically improve workers' CAP score by adjusting the positions and orientations of the robot end effector. RESULTS: In an empirical experiment, the proposed approach can significantly improve the CAP scores of the participants during a human-robot collaboration task when compared with the scenarios where robot and participants worked together at a fixed position or at the individual elbow height. The questionnaire outcomes also showed that the working posture resulted from the proposed approach was preferred by the participants. CONCLUSION: The proposed model-free reinforcement learning method can learn the optimal worker postures without the need for specific biomechanical models. The data-driven nature of this method can make it adaptive to provide personalized optimal work posture. APPLICATION: The proposed method can be applied to improve the occupational safety in robot-implemented factories. Specifically, the personalized robot working positions and orientations can proactively reduce exposure to awkward postures that increase the risk of musculoskeletal disorders. The algorithm can also reactively protect workers by reducing the workload in specific joints.

Valley manipulation in monolayer transition metal dichalcogenides and their hybrid systems: status and challenges.

Recently, the emerging conceptual valley-related devices have attracted much attention due to the progress on generating, controlling, and detecting the valley degree of freedom in the transition metal dichalcogenide (TMD) monolayers. In general, it is known that achieving valley degree of freedom with long valley lifetime is crucial in the implementation of valleytronic devices. Here, we provide a brief introduction of the basic understandings of valley degree of freedom. We as well review the recent experimental advancement in the modulation of valley degree of freedom. The strategies include optical/magnetic/electric field tuning, moiré patterns, plasmonic metasurface, defects and strain engineering. In addition, we summarize the corresponding mechanisms, which can help to obtain large degree of polarization and long valley lifetimes in monolayer TMDs. Based on these methods, two-dimensional valley-optoelectronic systems based on TMD heterostructures can be constructed, providing opportunities for such as the new paradigm in data processing and transmission. Challenges and perspectives on the development of valleytronics are highlighted as well.

Comprehensive analysis identifies IFI16 as a novel signature associated with overall survival and immune infiltration of skin cutaneous melanoma.

BACKGROUND: Skin cutaneous melanoma (SKCM) is the most common skin tumor with high mortality. The unfavorable outcome of SKCM urges the discovery of prognostic biomarkers for accurate therapy. The present study aimed to explore novel prognosis-related signatures of SKCM and determine the significance of immune cell infiltration in this pathology. METHODS: Four gene expression profiles (GSE130244, GSE3189, GSE7553 and GSE46517) of SKCM and normal skin samples were retrieved from the GEO database. Differentially expressed genes (DEGs) were then screened, and the feature genes were identified by the LASSO regression and Boruta algorithm. Survival analysis was performed to filter the potential prognostic signature, and GEPIA was used for preliminary validation. The area under the receiver operating characteristic curve (AUC) was obtained to evaluate discriminatory ability. The Gene Set Variation Analysis (GSVA) was performed, and the composition of the immune cell infiltration in SKCM was estimated using CIBERSORT. At last, paraffin-embedded specimens of primary SKCM and normal skin tissues were collected, and the signature was validated by fluorescence in situ hybridization (FISH) and immunohistochemistry (IHC). RESULTS: Totally 823 DEGs and 16 feature genes were screened. IFI16 was identified as the signature associated with overall survival of SKCM with a great discriminatory ability (AUC > 0.9 for all datasets). GSVA noticed that IFI16 might be involved in apoptosis and ultraviolet response in SKCM, and immune cell infiltration of IFI16 was evaluated. At last, FISH and IHC both validated the differential expression of IFI16 in SKCM. CONCLUSIONS: In conclusion, our comprehensive analysis identified IFI16 as a signature associated with overall survival and immune infiltration of SKCM, which may play a critical role in the occurrence and development of SKCM.

From virtual patients to digital twins in immuno-oncology: lessons learned from mechanistic quantitative systems pharmacology modeling.

Virtual patients and digital patients/twins are two similar concepts gaining increasing attention in health care with goals to accelerate drug development and improve patients' survival, but with their own limitations. Although methods have been proposed to generate virtual patient populations using mechanistic models, there are limited number of applications in immuno-oncology research. Furthermore, due to the stricter requirements of digital twins, they are often generated in a study-specific manner with models customized to particular clinical settings (e.g., treatment, cancer, and data types). Here, we discuss the challenges for virtual patient generation in immuno-oncology with our most recent experiences, initiatives to develop digital twins, and how research on these two concepts can inform each other.

From virtual patients to digital twins in immuno-oncology: lessons learned from mechanistic quantitative systems pharmacology modeling.

Virtual patients and digital patients/twins are two similar concepts gaining increasing attention in health care with goals to accelerate drug development and improve patients' survival, but with their own limitations. Although methods have been proposed to generate virtual patient populations using mechanistic models, there are limited number of applications in immuno-oncology research. Furthermore, due to the stricter requirements of digital twins, they are often generated in a study-specific manner with models customized to particular clinical settings (e.g., treatment, cancer, and data types). Here, we discuss the challenges for virtual patient generation in immuno-oncology with our most recent experiences, initiatives to develop digital twins, and how research on these two concepts can inform each other.

Quantitative systems pharmacology modeling of macrophage-targeted therapy combined with PD-L1 inhibition in advanced NSCLC.

Immune checkpoint inhibitors remained the standard-of-care treatment for advanced non-small cell lung cancer (NSCLC) for the past decade. In unselected patients, anti-PD-(L)1 monotherapy achieved an overall response rate of about 20%. In this analysis, we developed a pharmacokinetic and pharmacodynamic module for our previously calibrated quantitative systems pharmacology model (QSP) to simulate the effectiveness of macrophage-targeted therapies in combination with PD-L1 inhibition in advanced NSCLC. By conducting in silico clinical trials, the model confirmed that anti-CD47 treatment is not an optimal option of second- and later-line treatment for advanced NSCLC resistant to PD-(L)1 blockade. Furthermore, the model predicted that inhibition of macrophage recruitment, such as using CCR2 inhibitors, can potentially improve tumor size reduction when combined with anti-PD-(L)1 therapy, especially in patients who are likely to respond to anti-PD-(L)1 monotherapy and those with a high level of tumor-associated macrophages. Here, we demonstrate the application of the QSP platform on predicting the effectiveness of novel drug combinations involving immune checkpoint inhibitors based on preclinical or early-stage clinical trial data.

Quercetin encapsulation and release using rapid CO2-responsive rosin-based surfactants in Pickering emulsions.

Emulsion-based delivery systems are extensively employed for encapsulating functional active ingredients, protecting them from degradation, and enhancing bioavailability and release efficiency. Here, a CO2-responsive surfactant synthesized from rosin displays rapid responsiveness to CO2 at room temperature, transitioning reversibly switches between active and inactive states multiple times. The dual tertiary amines on the rosin rigid structure contributes to its CO2 sensitivity. When in its active cationic form, in conjunction with silica nanoparticles, it exhibits desired Pickering emulsification performance across various oil phases. In the Pickering emulsion loaded with quercetin, the encapsulation efficiency and loading efficiency reached 80.50% and 0.69%, respectively, with stability lasting at least 30 days. The system provides robust protection for quercetin against external factors, such as UV and heat, revealing sustained release effects. This study investigated the potential of using rosin-based CO2-responsive surfactants alongside nanoparticles to design stable Pickering emulsion systems for active substance encapsulation and sustained release.

Exploring the impact of human-robot interaction on workers' mental stress in collaborative assembly tasks.

Advances in robotics have contributed to the prevalence of human-robot collaboration (HRC). However, working and interacting with collaborative robots in close proximity can be psychologically stressful. Therefore, understanding the impacts of human-robot interaction (HRI) on mental stress is crucial for enhancing workplace well-being. To this end, this study investigated how the HRI factors - presence, complexity, and modality - affect the psychological stress of workers. We employed both the NASA-Task Load Index for subjective assessment and physiological metrics including galvanic skin responses, electromyography, and heart rate for objective evaluation. An experimental setup was implemented in which human operators worked together with a collaborative robot on Lego assembly tasks, using different interaction paradigms including pressing buttons, showing hand gestures, and giving verbal commands. The results revealed that the introduction of interactions during HRC helped reduce mental stress and that complex interactions resulted in higher mental stress than simple interactions. Meanwhile, using hand gestures led to significantly higher mental stress than pressing buttons and verbal commands. The findings provided practical insights for mitigating mental stress in the workplace and promoting wellness in the era of HRC.

Eliciting the antitumor immune response with a conditionally activated PD-L1 targeting antibody analyzed with a quantitative systems pharmacology model.

Conditionally activated molecules, such as Probody therapeutics (PbTx), have recently been investigated to improve antitumoral response while reducing systemic toxicity. PbTx are engineered to be proteolytically activated by proteases that are preferentially active locally in the tumor microenvironment (TME). Here, we perform an exploratory study using our recently published quantitative systems pharmacology model, previously validated for other drugs, to evaluate the effectiveness and targeting specificity of an anti-PD-L1 PbTx compared to the non-modified antibody. We have informed the model using the PbTx dynamics and pharmacokinetics published in the literature for anti-PD-L1 in patients with triple-negative breast cancer (TNBC). Our results suggest masking of the antibody slightly decreases its efficacy, while increasing the localization of active therapeutic component in the TME. We also perform a parameter optimization for the PbTx design and drug dosing regimens to maximize the response rate. Although our results are specific to the case of TNBC, our findings are generalizable to any conditionally activated PbTx molecule in solid tumors and suggest that design of a highly effective and selective PbTx is feasible.