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Accurate assessment of N staging in patients with non-small cell lung cancer (NSCLC) is critical for the development of effective treatment plans, the optimization of therapeutic strategies, and the enhancement of patient survival rates. This study proposes a hybrid model based on 3D convolutional neural networks (CNNs) and transformers for predicting the N-staging and survival rates of NSCLC patients within the NSCLC radiogenomics and Nsclc-radiomics datasets. The model achieved accuracies of 0.805, 0.828, and 0.819 for the training, validation, and testing sets, respectively. By leveraging the strengths of CNNs in local feature extraction and the superior performance of transformers in global information modeling, the model significantly enhances predictive accuracy and efficacy. A comparative analysis with traditional CNN and transformer architectures demonstrates that the CNN-transformer hybrid model outperforms N-staging predictions. Furthermore, this study extracts the one-year survival rate as a feature and employs the Lasso–Cox model for survival predictions at various time intervals (1, 3, 5, and 7 years), with all survival prediction p-values being less than 0.05, illustrating the time-dependent nature of survival analysis. The application of time-dependent ROC curves further validates the model’s accuracy and reliability for survival predictions. Overall, this research provides innovative methodologies and new insights for the early diagnosis and prognostic evaluation of NSCLC.

Abstract Hafnium‐oxide‐based ferroelectrics have garnered considerable research interest, primarily for their robust ferroelectricity at the nanoscale and their high compatibility with complementary metal‐oxide‐semiconductors processes. However, the impact of electrodes on the ferroelectric properties of hafnium‐oxide layer, particularly that of top electrodes, is not yet fully understood even in the simplest capacitor geometry. In this study, the La0.67Sr0.33MnO3/Hf0.5Zr0.5O2 (LSMO/HZO) epitaxial heterostructure is utilized as a model system to conduct a systematic comparative study on ferroelectricity between the LSMO/HZO (H‐LS) bilayer and LSMO/HZO/LSMO (LS‐H‐LS) trilayer samples. In comparison to the H‐LS sample, the LS‐H‐LS sample exhibits a more uniform polar domain configuration and larger ferroelectric polarization. Moreover, the LS‐H‐LS sample exhibits significant improvements in leakage, endurance, and retention. These substantial enhancements in ferroelectricity are likely due to interfacial stress imposed by the LSMO capping layer and its capacity to accommodate extra oxygen vacancies. These results underscore the pivotal role of oxide‐based top electrodes in determining the ferroelectricity of hafnium‐oxide‐based heterostructures, providing crucial insights for optimizing the performance of innovative ferroelectric devices.

Capacitorless DRAM architectures based on Back-End-of-Line (BEOL)-transistors are promising for long-retention, high-density and low-power 3D DRAM solutions due to its low leakage, operational flexibility, and monolithic integration capability. Different from classical silicon-based devices, in-depth studies on the performances of nanoscale multi-gate transistors (e.g., a-InGaZnO-FET) are still barely conducted for physical description, due to the complicated multi-gating principle, finite-size effects on transport, increased variation sources and enlarged parasitic effect. Hence, high-performance multi-nanoscale (down to $\sim ~50$ nm) dual-gate a-IGZO transistors are fabricated, and a physical compact model is developed based on the surface potential for dual-gated coupling and the disordered transport with finite-size-correction. The short channel behaviors on sub-threshold swing, mobility and threshold voltage are investigated, and contact effects are validated by the transfer-line method (TLM). Regarding the specific challenge of dual-gate alignment, possible misalignment and parasitic effects on multi-device fluctuations are important of large-scale circuit design and analyzed by TCAD simulations. Besides, the bias-temperature instability (BTI) has been comprehensively investigated. In awareness of the above effects, this model bridges fabrication-based material properties and structural parameters, assisting in a threshold fluctuation-resistant operation scheme for capacitorless multi-bit memory, showing a great potential in future monolithic integration circuit design using BEOL-transistor.

Abstract Designing a broad-spectrum gas sensor capable of identifying gas components in complex environments, such as mixed atmospheres or extreme temperatures, is a significant concern for various technologies, including energy, geological science, and planetary exploration. The main challenge lies in finding materials that exhibit high chemical stability and wide working temperature range. Materials that amplify signals through non-chemical methods could open up new sensing avenues. Here, we present the discovery of a broad-spectrum gas sensor utilizing correlated two-dimensional electron gas at a delta-doped LaAlO3/SrTiO3 interface with LaFeO3. Our study reveals that a back-gating on this two-dimensional electron gas can induce a non-volatile metal to insulator transition, which consequently can activate the two-dimensional electron gas to sensitively and quantitatively probe very broad gas species, no matter whether they are polar, non-polar, or inert gases. Different gas species cause resistance change at their sublimation or boiling temperature and a well-defined phase transition angle can quantitatively determine their partial pressures. Such unique correlated two-dimensional electron gas sensor is not affected by gas mixtures and maintains a wide operating temperature range. Furthermore, its readout is a simple measurement of electric resistance change, thus providing a very low-cost and high-efficient broad-spectrum sensing technique.

When dealing with small targets in lung cancer detection, the YOLO V8 algorithm may encounter false positives and misses. To address this issue, this study proposes an enhanced YOLO V8 detection model. The model integrates a large separable kernel attention mechanism into the C2f module to expand the information retrieval range, strengthens the extraction of lung cancer features in the Backbone section, and achieves effective interaction between multi-scale features in the Neck section, thereby enhancing feature representation and robustness. Additionally, depth-wise convolution and Coordinate Attention mechanisms are embedded in the Fast Spatial Pyramid Pooling module to reduce feature loss and improve detection accuracy. This study introduces a Minimum Point Distance-based IOU loss to enhance correlation between predicted and ground truth bounding boxes, improving adaptability and accuracy in small target detection. Experimental validation demonstrates that the improved network outperforms other mainstream detection networks in terms of average precision values and surpasses other classification networks in terms of accuracy. These findings validate the outstanding performance of the enhanced model in the localization and recognition aspects of lung cancer auxiliary diagnosis.

Two-dimensional (2D) materials have gained increasing prominence not only in fundamental research but also in daily applications. However, to fully harness their potential, it is crucial to optimize their properties with an external parameter and track the electronic structure simultaneously. Magnetotransport over a wide magnetic field range is a powerful method to probe the electronic structure and, for metallic 2D materials, quantum oscillations superimposed on the transport signals encode Fermi surface parameters. In this manuscript, we utilize biaxial strain as an external tuning parameter and investigate the effects of strain on the electronic properties of two quasi-2D superconductors, MoTe2 and RbV3Sb5, by measuring their magnetoresistance in pulsed magnetic fields up to 60 T. With a careful selection of insulating substrates, we demonstrate the possibility of both the compressive and tensile biaxial strains imposed on MoTe2 and RbV3Sb5, respectively. For both systems, the applied strain has led to superconducting critical temperature enhancement compared to their free-standing counterparts, proving the effectiveness of this biaxial strain method at cryogenic temperatures. Clear quantum oscillations in the magnetoresistance—the Shubnikov–de Haas (SdH) effect—are obtained in both samples. In strained MoTe2, the magnetoresistance exhibits a nearly quadratic dependence on the magnetic field and remains non-saturating even at the highest field, whereas in strained RbV3Sb5, two SdH frequencies showed a substantial enhancement in effective mass values, hinting at a possible enhancement of charge fluctuations. Our results demonstrate that combining biaxial strain and pulsed magnetic field paves the way for studying 2D materials under unprecedented conditions.

Abstract The continuous increase of electric vehicles is being facilitating the large-scale distributed charging-pile deployment. It is crucial to guarantee normal operation of charging piles, resulting in the importance of diagnosing charging-pile faults. The existing fault-diagnosis approaches were based on physical fault data like mechanical log data and sensor data streams. However, there are other types of fault data, which cannot be used for diagnosis by these existing approaches. This paper aims to fill this gap and consider 8 types of fault data for diagnosing, at least including physical installation error fault, charging-pile mechanical fault, charging-pile program fault, user personal fault, signal fault (offline), pile compatibility fault, charging platform fault, and other faults. We aim to find out how to combine existing feature-extraction and machine learning techniques to make the better diagnosis by conducting experiments on realistic dataset. 4 word embedding models are investigated for feature extraction of fault data, including N-gram, GloVe, Word2vec, and BERT. Moreover, we classify the word embedding results using 10 machine learning classifiers, including Random Forest (RF), Support Vector Machine, K-Nearest Neighbor, Multilayer Perceptron, Recurrent Neural Network, AdaBoost, Gradient Boosted Decision Tree, Decision Tree, Extra Tree, and VOTE. Compared with original fault record dataset, we utilize paraphrasing-based data augmentation method to improve the classification accuracy up to 10.40%. Our extensive experiment results reveal that RF classifier combining the GloVe embedding model achieves the best accuracy with acceptable training time. In addition, we discuss the interpretability of RF and GloVe.

With the popularity of mobile terminals and social media increasing, misinformation about science has increased in China. To understand the nature of the popularity of scientific rumors, we analyzed 206 typical cases released by four authoritative platforms in China from 2010 to 2020. Content analysis revealed that the majority of scientific rumors are related to health and safety (76.8%), use a visual format (61.2%), are published on social media (62.2%), and provide more than three narrative elements (78.2%). In addition, rumors from unidentified netizens’ claims or homemade experiments are the most common (35.9%), followed by highly credible sources, such as expert assertions (20.9%) or scientific research results (19.4%). A further qualitative comparative analysis indicated that a combination of details and fear-mongering are significant conditions that make rumors receive significant attention. Visual presentation also plays an important role, while state media and the presence of scientific terminology have a weak effect.

Background The coronavirus disease 2019 (COVID-19) pandemic resulted in radical changes in many aspects of life. To deal with this, each country has implemented continuous health measures from the beginning of the outbreak. Discovering how governmental actions impacted public behaviour during the outbreak stage is the purpose of this study. Methods This study uses a hybrid large-scale data visualisation method to analyse public behaviour (epidemic concerns, self- protection, and mobility trends), using the data provided by multiple authorities. Meanwhile, a content analysis method is used to qualitatively code the health measures of three countries with severe early epidemic outbreaks from different continents, namely China, Italy, and the United States. Eight dimensions are coded to rate the mobility restrictions implemented in the above countries. Results (1) Governmental measures did not immediately persuade the public to change their behaviours during the COVID-19 epidemic. Instead, the public behaviour proceeded in a three-phase rule, which is typically witnessed in an epidemic outbreak, namely the wait-and-see phase, the surge phase and the slow-release phase. (2) The strictness of the mobility restrictions of the three countries can be ranked as follows: Hubei Province in China (with an average score of 8.5 out of 10), Lombardy in Italy (7.125), and New York State in the United States (5.375). Strict mobility restrictions are more likely to cause a surge of population outflow from the epidemic area in the short term, whereas the effect of mobility restrictions is positively related to the stringency of policies in the long term. Conclusion The public showed generally lawful behaviour during regional epidemic outbreaks and blockades. Meanwhile public behaviour was deeply affected by the actions of local governments, rather than the global pandemic situation. The contextual differences between the various countries are important factors that influence the effects of the different governments’ health measures.

Genome-wide association meta-analysis (GWAMA) by the Cortisol Network (CORNET) consortium identified genetic variants spanning the SERPINA6/SERPINA1 locus on chromosome 14 associated with morning plasma cortisol, cardiovascular disease (CVD), and SERPINA6 mRNA expression encoding corticosteroid-binding globulin (CBG) in the liver. These and other findings indicate that higher plasma cortisol levels are causally associated with CVD; however, the mechanisms by which variations in CBG lead to CVD are undetermined. Using genomic and transcriptomic data from The Stockholm Tartu Atherosclerosis Reverse Networks Engineering Task (STARNET) study, we identified plasma cortisol-linked single-nucleotide polymorphisms (SNPs) that are trans-associated with genes from seven different vascular and metabolic tissues, finding the highest representation of trans-genes in the liver, subcutaneous fat, and visceral abdominal fat, [false discovery rate (FDR) = 15%]. We identified a subset of cortisol-associated trans-genes that are putatively regulated by the glucocorticoid receptor (GR), the primary transcription factor activated by cortisol. Using causal inference, we identified GR-regulated trans-genes that are responsible for the regulation of tissue-specific gene networks. Cis-expression Quantitative Trait Loci (eQTLs) were used as genetic instruments for identification of pairwise causal relationships from which gene networks could be reconstructed. Gene networks were identified in the liver, subcutaneous fat, and visceral abdominal fat, including a high confidence gene network specific to subcutaneous adipose (FDR = 10%) under the regulation of the interferon regulatory transcription factor, IRF2. These data identify a plausible pathway through which variation in the liver CBG production perturbs cortisol-regulated gene networks in peripheral tissues and thereby promote CVD.

Accurate organ segmentation is a fundamental step in disease-assisting diagnostic systems, and the precise segmentation of lung is crucial for subsequent lesion detection. Prior to this, lung segmentation algorithms had typically segmented the entire lung tissue. However, the trachea is also essential for diagnosing lung diseases. Challenges in lung parenchyma segmentation include the limited robustness of U-Net in acquiring contextual information and the small size of the trachea being mixed up with lung, making it difficult to identify and reconstruct the lungs. To overcome these difficulties, this paper proposes three improvements to U-Net: multiple concatenation modules to enhance the network’s ability to capture context, multi-scale residual learning modules to improve the model’s multi-scale learning capabilities, and an enhanced gated attention mechanism to enhance the fusion of various hierarchical features. The experimental results demonstrate that our model has achieved a significant improvement in trachea segmentation compared to existing models.

With the continuous development of electric vehicles (EV), large-scale distributed charging piles have been deployed in the wild. Therefore, it is extremely essential to evaluate the risk state of EV charging piles efficiently and effectively. This paper aims to measure the capability of supervised and semi-supervised machine learning techniques in assessing the risk state of EV charging piles. We investigate 8 algorithms, including Support Vector Machine (SVM), Random Forest (RF), Adaptive Boosting (AdaBoost), Gradient Boosting Decision Tree (GBDT), Self-Training based on SVM (ST-SVM), Self-Training based on RF (ST-RF), Self-Training based on AdaBoost (ST-AdaBoost) and Self-Training based on GBDT (ST-GBDT). We first collect data on normal and abnormal termination of charging services from an actual Internet of Vehicles platform. The dataset consists of 17,773 recordings and 7 features generated from the records, which are used for classification. According to the statistical times of 7 features, 20% of recordings are labeled by knowledgable experts into three classes: low-risk, medium-risk and high-risk. Experimental results indicate that ST-AdaBoost and ST-GBDT show more excellent overall classification performance, compared with the other traditional supervised methods. We also apply ST-GBDT to predict the risk state of the unclassified piles and produce the statistic of piles from different manufacturers.

Background: Type 1 diabetes mellitus (T1DM) is one of the most common endocrine and metabolic diseases in children. Pancreatic β cells are thought to be critical cells involved in the progression of T1DM, and their injury would directly lead to impaired insulin secretion.Purpose: To investigate the protective effects of allicin on pancreatic β cell injury and elucidate the underlying mechanism.Methods: The streptozotocin (STZ)-induced mouse T1DM model in vivo and STZ-induced pancreatic β cell Min6 model in vitro were used to explore the effects of allicin on T1DM. The experiments include fasting blood glucose test, oral glucose tolerance detection, HE staining, immunohistochemistry, immunofluorescence, TUNEL staining, western blot, real-time quantitative PCR (RT-qPCR), and flow cytometry.Results: Allicin could significantly decrease blood glucose level, improve islet structure and insulin expression, and inhibit apoptosis to reduce STZ-induced pancreatic β cell injury and loss through activating AMPK/mTOR mediated autophagy pathway.Conclusion: Allicin treatment significantly reduced STZ-induced T1DM progression, suggesting that allicin may be a potential therapy option for T1DM patients.

AV _3 Sb _5 (A = Cs, K, Rb) is a recently discovered superconducting system ( $T_\mathrm{c}\sim0.9$ –2.5 K) in which the vanadium atoms adopt the kagome structure. Intriguingly, these systems enter a charge-density-wave (CDW) phase ( $T_\mathrm{CDW}\sim80$ –100 K), and further evidence shows that the time-reversal symmetry is broken in the CDW phase. Concurrently, the anomalous Hall effect (AHE) has been observed in KV _3 Sb _5 and CsV _3 Sb _5 inside the novel CDW phase. Here, we report a comprehensive study of a high-quality RbV _3 Sb _5 single crystal with magnetotransport measurements. Our data demonstrate the emergence of the AHE in RbV _3 Sb _5 when the CDW state develops. The magnitude of the anomalous Hall resistivity in the low temperature limit is comparable to the reported values in KV _3 Sb _5 and CsV _3 Sb _5 . The magnetoresistance channel further reveals a rich spectrum of quantum oscillation frequencies, many of which have not been reported before. In particular, a large quantum oscillation frequency (2235 T), which occupies ∼56% of the Brillouin zone area, was recorded. For the quantum oscillation frequencies with sufficient signal-to-noise ratio, we further perform field angle-dependent measurements, and our data indicate two-dimensional Fermi surfaces in RbV _3 Sb _5 . Our results provide indispensable information for understanding the AHE and band structure in kagome metal AV _3 Sb _5 .

For the first time, a core drain current model based on surface potential without any implicit functions is developed for beta-phase gallium oxide (β-Ga2O3) power metal-oxide-semiconductor field-effect transistors (MOSFETs). The surface potential solution is analytically deduced by solving the Poisson equation with appropriate simplification assumptions in accumulation, partial-depletion, and full-depletion modes. Then, the drain current expression is analytically derived from the Pao–Sah integral as a function of the mobile charge density obtained from the surface potential at the source and drain terminals. In addition, nonlinear resistors in the source/drain access region are considered. It continuously predicts the characteristics of β-Ga2O3 power MOSFETs in all operation modes, including accumulation mode, partial-depletion mode, and full-depletion mode. Furthermore, the validity of the model is verified by comparing the results of the model with the numerical simulations carried out with the technology computer-aided design (TCAD) tool ATLAS Device Simulator from Silvaco. Good agreement between the proposed model and TCAD simulations is shown for β-Ga2O3 power MOSFETs with different intrinsic channel lengths, channel doping concentrations, and channel thicknesses. Ultimately, the Gummel symmetry test and the harmonic balance simulation test are performed to validate the model’s robustness and convergence.

Normalisr removes technical bias in single-cell RNA-seq and detects gene differential and coexpression accurately and efficiently. It also infers gene regulatory and co-expression networks from conventional and CRISPR screen single-cell RNA-seq datasets.

Metformin is a first-line drug for type 2 diabetes, and its anticancer effects have also been widely studied in recent years. The Sonic hedgehog (Shh) signaling pathway is involved in the initiation and progression of medulloblastoma. In order to develop a new treatment strategy for medulloblastoma (MB), this study investigated the inhibitory effect of metformin on MB and the underlying mechanism of metformin on the Shh signaling pathway. The effect of metformin on proliferation was evaluated by the cell counting kit-8 (CCK-8) test and colony formation experiment. The effect of metformin on metastasis was assessed by the scratch-wound assay and transwell invasion assay. Cell cycle and apoptosis were evaluated by flow cytometry, and the associated proteins were examined by western blotting. The mRNA and protein expression levels related to the Shh pathway were measured by quantitative PCR, western blotting, and immunofluorescence staining. The xenograft murine model was carried out to evaluate the anticancer effect of metformin on medulloblastoma in vivo. Metformin inhibited proliferation and metastasis of the Shh subgroup MB cell line, and the inhibitory effect on proliferation was related to apoptosis and the block of the cell cycle at the G0/G1 phase. Animal experiments showed that metformin inhibits medulloblastoma growth in vivo. Moreover, metformin decreased mRNA and protein expression levels of the Shh pathway, and this effect was reversed by the AMP-activated protein kinase (AMPK) siRNA. Furthermore, the pro-apoptotic and cell cycle arrest effects of metformin on Daoy cells could be reversed by the Shh pathway activators. Our findings demonstrated that metformin could inhibit medulloblastoma progression in vitro and in vivo, and this effect was associated with AMPK-mediated inhibition of the Shh signaling pathway in vitro studies.

Abstract Oxygen vacancy in oxide ferroelectrics can be strongly coupled to the polar order via local strain and electric fields, thus holding the capability of producing and stabilizing exotic polarization patterns. However, despite intense theoretical studies, an explicit microscopic picture to correlate the polarization pattern and the distribution of oxygen vacancies remains absent in experiments. Here we show that in a high-quality, uniaxial ferroelectric system, i.e., compressively strained BaTiO3 ultrathin films (below 10 nm), nanoscale polarization structures can be created by intentionally introducing oxygen vacancies in the film while maintaining structure integrity (namely no extended lattice defects). Using scanning transmission electron microscopy, we reveal that the nanodomain is composed of swirling electric dipoles in the vicinity of clustered oxygen vacancies. This finding opens a new path toward the creation and understanding of the long-sought topological polar objects such as vortices and skyrmions.

The visual object tracking technology of remote sensing images has important applications in areas with high safety performance such as national defense, homeland security, and intelligent transportation in smart cities. However, previous research demonstrates that adversarial examples pose a significant threat to remote sensing imagery. This article first explores the impact of adversarial examples in the field of visual object tracking in remote sensing imagery. We design a classification- and regression-based loss function for the popular Siamese RPN series of visual object tracking models and use the PGD gradient-based attack method to generate adversarial examples. Additionally, we consider the temporal consistency of video frames and design an adversarial examples attack method based on momentum continuation. We evaluate our method on the remote sensing visual object tracking datasets SatSOT and VISO and the traditional datasets OTB100 and UAV123. The experimental results show that our approach can effectively reduce the performance of the tracker.

PROTACs have emerged as promising therapeutic agents but their cellular uptake is often inefficient. Here, the authors show that reversible covalent warhead chemistry improves PROTAC intracellular accumulation and target engagement, and develop a dual inhibitor/degrader of Bruton’s tyrosine kinase

The squirrel search algorithm (SSA) is a recently proposed nature-inspired algorithm based on the dynamic foraging and gliding behavior of squirrels. Because of its simplicity and stability, the squirrel algorithm has attracted increasing research interest. However, the lack of exploration ability of the SSA may lead to premature convergence to the local optimum. To overcome this disadvantage, an improved SSA with reproductive behavior (RSSA) is proposed to solve the numerical optimization problem. First, the reproductive behavior of the invasive weed algorithm (IWO) is introduced to the conventional SSA to generate offspring individuals, and these offspring individuals are scattered into the search space by Gaussian distribution to complete the location update. This method makes it possible for individuals with poor fitness to enter the next generation search, improving the exploration ability of the SSA. Second, an adaptive step strategy is proposed to adaptively adjust the search step of squirrels according to the distance between each squirrel and other family members. This strategy effectively balances the exploration and exploitation of the algorithm. Finally, the performance of the proposed RSSA algorithm is evaluated using Wilcoxon's test on unimodal, multimodal, fixed-dimensional multimodal and CEC 2014 benchmark functions. Experimental results and statistical tests show that RSSA has better performance in terms of convergence, accuracy, and search capability compared with other state-of-the-art algorithms.

BackgroundThe mixed density hematoma (MDH) has a high recurrence rate in chronic subdural hematoma (CSDH). This study adopted rigid neuroendoscopy assisted hematoma resection to evacuate CSDH and investigated its efficacy as compared with the traditional burr-hole craniostomy (BHC) in CSDH with mixed density.MethodsA retrospective cohort study was conducted at two centers between January 2015 and December 2020. The data of 124 patients who underwent BHC for CSDH with mixed density were collected and analyzed. A total of 41 patients underwent rigid neuroendoscopy assisted hematoma resection (neuroendoscopy group) and 83 patients were treated by the traditional BHC (control group). Follow-ups were conducted 6 months after the surgery.ResultsThere was no significant difference in the baseline characteristics and preoperative CT features between the two groups (p > 0.05). The neuroendoscopy group had a lower recurrence rate than the control group (p = 0.043). Besides the neuroendoscopy group had a higher rate of hematoma evacuation (p < 0.001), less pneumocephalus volume (p < 0.001), shorter hospital stay (p < 0.001) and better Markwalder score (p < 0.001) than the control group within 24–48 h after operation. However, there was no significant difference between the two groups in the incidence of pneumocephalus, Markwalder score (at discharge and 6 months after surgery) and mortality. Moreover, the operation time was longer in the neuroendoscopy group (p < 0.001).ConclusionsWhen compared with the traditional BHC, rigid neuroendoscopy assisted hematoma resection can better reduce the recurrence rate of CSDH with mixed density. Also, it surpassed the results obtained from BHC in reducing the volume of pneumocephalus, improving hematoma evacuation rate, promoting short-term neurological recovery, and shortening hospital stays.

The continued scaling-down of nanoscale semiconductor devices has made it very challenging to obtain analytic surface potential solutions from complex equations in physics, which is the fundamental purpose of the MOSFET compact model. In this work, we proposed a general framework to automatically derive analytical solutions for surface potential in MOSFET, by leveraging the universal approximation power of deep neural networks. Our framework incorporated a physical-relation-neural-network (PRNN) to learn side-by-side from a general-purpose numerical simulator in handling complex equations of mathematical physics, and then instilled the “knowledge’’ from the simulation data into the neural network, so as to generate an accurate closed-form mapping between device parameters and surface potential. Inherently, the surface potential was able to reflect the numerical solution of a two-dimensional (2D) Poisson equation, surpassing the limits of traditional 1D Poisson equation solutions, thus better illustrating the physical characteristics of scaling devices. We obtained promising results in inferring the analytic surface potential of MOSFET, and in applying the derived potential function to the building of 130 nm MOSFET compact models and circuit simulation. Such an efficient framework with accurate prediction of device performances demonstrates its potential in device optimization and circuit design.

Abstract The neurokinin‐1 receptor (NK‐1R) antagonists are approved as treatment for chemotherapy‐associated nausea and vomiting in cancer patients. The emerging role of the substance P‐NK‐1R system in oncogenesis raises the possibility of repurposing well‐tolerated NK‐1R antagonists for cancer treatment. This study reports that human colorectal cancer (CRC) patients with high NK‐1R expression have poor survival, and NK‐1R antagonists SR140333 and aprepitant induce apoptotic cell death in CRC cells and inhibit CRC xenograft growth. This cytotoxicity induced by treatment with NK‐1R antagonists is mediated by induction of endoplasmic reticulum (ER) stress. ER stress triggers calcium release, resulting in the suppression of prosurvival extracellular signal‐regulated kinase (ERK)‐c‐Myc signaling. Along with ER calcium release, one ER stress pathway mediated by protein kinase RNA‐like ER kinase (PERK) is specifically activated, leading to increased expression of proapoptotic C/EBP‐homologous protein (CHOP). Moreover, NK‐1R antagonists enhance the efficacy of chemotherapy by increasing the sensitivity and overcoming resistance to 5‐fluorouracil in CRC cells through the induction of sustained ER stress and the consequent suppression of ERK‐c‐Myc signaling both in vitro and in vivo. Collectively, the findings provide novel mechanistic insights into the efficacy of NK‐1R antagonists either as a single agent or in combination with chemotherapy for cancer treatment.

Effective control of magnetic anisotropy is important for developing spintronic devices. In this work, we performed a case study of stacking periods (N)-mediated reorientation of lateral magnetic anisotropy in ultrathin La0.67Ca0.33MnO3/SrRuO3 superlattices. As N increases from 1 to 15, the magnetic easy-axis switches from the orthorhombic [010] to [100]-axis. The maximum anisotropy constant of the superlattice (SL) (N = 15) reaches −1.83 × 105 erg/cm3. X-ray absorption spectroscopy and x-ray linear dichroism further suggest that the observed changes in lateral magnetic anisotropy are driven by in-plane orbital polarization. For SLs with small N, anisotropic strain-induced orbital polarization along the b-axis can result in the [010]-oriented magnetic easy axis. For SLs with large N, the dimension crossover from 2-dimension to 3-dimension could enhance the hybridization of Ru t2g and Mn dx2−y2 orbitals, which can compete with the strain effect and switch the magnetic easy axis to [100]. Our results suggest a potential strategy for engineering magnetic anisotropy through the cooperation of strain engineering and interfacial orbital engineering.

Image encryption is the most direct and effective technical means for protecting the security of image information. Based on the space filling property of the Hilbert curve and the infinite property of the H-geometric fractal, a new image encryption technique is proposed, which combines the pseudo-randomness of a hyperchaotic system and the sensitivity to initial values. First, the hash value of a plaintext image is calculated using the secure hash algorithm 3 (SHA-3) as the initial value of the piece-wise linear chaotic map (PWLCM) and Rossler chaotic systems, which associates the key with the plaintext. In addition, the chaotic sequences that are generated by the chaotic systems are used to scramble the global pixel positions and the pixel values of the images, thereby disturbing the distribution of the pixel positions and the pixel values. Second, the Hilbert curve and H-fractal are alternately used to scramble the local pixel positions and diffuse the pixel values twice. Finally, the ciphertext feedback is used to further enhance the confusion and diffusion characteristics of the algorithm in order to achieve higher security. The experimental results and security analysis show that the encryption technique has enough key space to resist exhaustive attacks and can effectively resist statistical attacks, differential attacks, noise attacks, and cropping attacks. It can be used for military, judicial, and other privacy-related digital images secure storage and network security transmissions.

Resistance to radiotherapy is the main reason causing treatment failure in locally advanced rectal cancer. MicroRNAs (miRNAs) have been well demonstrated to regulate cancer development and progression. However, how miRNAs regulate radiotherapy resistance in colorectal cancer remains unknown. Herein, we established two human colorectal cancer cell lines resistant to radiotherapy, named HCT116-R and RKO-R, using the strategy of fractionated irradiation. The radioresistant phenotypical changes of the two cell lines were validated by cell viability assay, colony formation assay and apoptosis assay. The miRNA expression profilings of HCT116-R and RKO-R were determined using RNA-seq analyses, and further confirmed by quantitative real-time PCR. Multiple miRNAs, including miR-423-5p, miR-7-5p, miR-522-3p, miR-3184-3p, and miR-3529-3p, were identified with altered expression in both of the radiotherapy-resistant cells, compared to the parental cells. The downregulation of miR-423-5p was further validated in the rectal cancer tissues from radiotherapy-resistant patients. Silencing of miR-423-5p in parental HCT116 and RKO cells decreased the sensitivity to radiation treatment, and inhibited the radiation-induced apoptosis. In consistence, overexpression of miR-423-5p in HCT116-R and RKO-R cells partially rescued their sensitivity to radiotherapy, and promoted the radiation-induced apoptosis. Bcl-xL (Bcl-2-like protein 1) was predicted to be a potential target gene for miR-423-5p, and miR-423-5p/Bcl-xL axis could be a critical mediator of radiosensitivity in colorectal cancer cells. The current finding not only revealed a novel role of miR-423-5p in regulating the radiosensitivity in colorectal cancer, but also suggested miR-423-5p as a molecular candidate for combination therapy with radiation to treat colorectal cancer.

This study investigated the characteristics and development of peer support networks in an effort to unravel the role of friendship in this developmental process. The relationships between friendship networks and peer support networks were explored, and the influence of dyadic and triadic friendships on the development of peer support relationships was examined. Two waves of data were collected among a sample of adolescents in six Chinese junior high schools (n = 913 students from 28 classrooms; mean age = 14.13 years; 50.49% boys), and classroom friendship networks and peer support networks were analyzed. The results showed that peer support networks were sparse, hierarchical, and sex-segregated. Furthermore, peer support networks and friendship networks partially overlapped. Friends tended to have similar support-seeking and support-providing ties. Longitudinal multiplex social network analysis revealed that peer support networks changed moderately over time, and friendships played various roles in the development of peer support networks. Dyadic friendships improved the formation of peer support ties. A mutual friend improved the formation of support relationships between two students when the mutual friend chose the two students as friends, but a mutual friend also hindered or had no effects on the formation of support relationships in other cases. The implications for educators to improve peer support networks are presented, and directions for future research are discussed.

Epidermal growth factor receptor (EGFR) mutations are common in non-small cell lung cancers, but rare in small cell lung cancers (SCLCs). In previous reports, some SCLC patients with EGFR mutations could benefit from EGFR tyrosine kinase inhibitors (TKIs). In this study, we reported a case in which an SCLC patient with EGFR exon 19 deletion (19-Del) mutation did not benefit from EGFR-TKIs. Interestingly, the standard treatment strategies for SCLC also failed to control tumor progression. Moreover, we screened 43 SCLC patients in China and found that the frequency of EGFR mutations in Chinese SCLC patients was about 4.65% by next-generation sequencing (NGS). Collectively, this case illustrated a rare subtype of SCLCs which harbored EGFR mutations and was intrinsically resistant to standard treatments and EGFR-TKIs. We also tried to explore the mechanisms underlying drug resistance. The literature concerning SCLCs with EGFR mutations is reviewed.

In this paper, an image encryption algorithm based on a hyperchaotic system and variable-step Josephus problem is proposed. Based on an in-depth analysis of the classic Josephus problem, a new variable-step Josephus problem that combines the pseudorandom sequence with the Josephus problem is proposed. Firstly, the hash value of the plaintext image is calculated, which is converted to the initial value of the chaotic system. Secondly, the chaotic system is iterated to generate four pseudorandom sequences X, Y, Z, and W. The sequences X, Y, and Z are input as parameters into the variable-step Josephus function to scramble the positions of the rows, pixel bits, and columns of the image, respectively. Finally, the elements of the sequence W and the image pixels are used to perform the addition operation. According to the experiments, the information entropy of the encrypted image with size 256∗256 reaches 7.997 and the adjacent correlations in three directions are within ±0.01. The experimental results show that image encryption algorithm proposed in this paper has plaintext sensitivity and can resist the common attacks.

The properties of complex oxides such as strontium titanate are strongly affected by the presence and distribution of oxygen vacancies. Here, the authors demonstrate that a scanning probe microscope tip can be used to manipulate vacancies by the flexoelectric effect.

Studying the impact of genetic variation on gene regulatory networks is essential to understand the biological mechanisms by which genetic variation causes variation in phenotypes. Bayesian networks provide an elegant statistical approach for multi-trait genetic mapping and modelling causal trait relationships. However, inferring Bayesian gene networks from high-dimensional genetics and genomics data is challenging, because the number of possible networks scales super-exponentially with the number of nodes, and the computational cost of conventional Bayesian network inference methods quickly becomes prohibitive. We propose an alternative method to infer high-quality Bayesian gene networks that easily scales to thousands of genes. Our method first reconstructs a node ordering by conducting pairwise causal inference tests between genes, which then allows to infer a Bayesian network via a series of independent variable selection problems, one for each gene. We demonstrate using simulated and real systems genetics data that this results in a Bayesian network with equal, and sometimes better, likelihood than the conventional methods, while having a significantly higher overlap with groundtruth networks and being orders of magnitude faster. Moreover our method allows for a unified false discovery rate control across genes and individual edges, and thus a rigorous and easily interpretable way for tuning the sparsity level of the inferred network. Bayesian network inference using pairwise node ordering is a highly efficient approach for reconstructing gene regulatory networks when prior information for the inclusion of edges exists or can be inferred from the available data.

Wisdom of the crowd, the collective intelligence from responses of multiple human or machine individuals to the same questions, can be more accurate than each individual and improve social decision-making and prediction accuracy. Crowd wisdom estimates each individual’s error level and minimizes the overall error in the crowd consensus. However, with problem-specific models mostly concerning binary (yes/no) predictions, crowd wisdom remains overlooked in biomedical disciplines. Here we show, in real-world examples of transcription factor target prediction and skin cancer diagnosis, and with simulated data, that the crowd wisdom problem is analogous to one-dimensional unsupervised dimension reduction in machine learning. This provides a natural class of generalized, accurate and mature crowd wisdom solutions, such as PCA and Isomap, that can handle binary and also continuous responses, like confidence levels. They even outperform supervised-learning-based collective intelligence that is calibrated on historical performance of individuals, e.g. random forest. This study unifies crowd wisdom and unsupervised dimension reduction, and extends its applications to continuous data. As the scales of data acquisition and processing rapidly increase, especially in high-throughput sequencing and imaging, crowd wisdom can provide accurate predictions by combining multiple datasets and/or analytical methods.

Traditional encryption algorithms are inefficient when applied to image encryption because image data have the characteristics of large data sizes and strong correlations between adjacent pixels. The shortcomings of the traditional Data encryption standard (DES) encryption algorithm when applied to image encryption are analyzed, and a new image encryption algorithm based on the traditional DES encryption algorithm model, chaotic systems, DNA computing, and select cipher-text output is proposed. Select cipher-text output selects cipher image with the biggest entropy, and it can increase the randomness of cipher image and reduce the risk of encryption system being broken down. This algorithm overcomes the shortcomings of high computational complexity and inconvenient key management that the traditional text encryption algorithm has when applied to image encryption. The experimental results show that the security of this algorithm is verified by analyzing the information entropy, image correlation of adjacent pixels and other indexes. At the same time, this algorithm passes the noise attack test and the occlusion attack test, so it can resist common attacks.

On 25 June 2021, the State Council issued the new "Outline of the National Action Scheme for Scientific Literacy for All Chinese Citizens (2020-2035) (Outline of Scientific Literacy)." In order to provide reference for its implementation, this study analyzes the achievements and obstacles in the implementation of the old "Outline of Scientific Literacy (2006-2010-2020)" based on the results of all previous surveys on civic scientific literacy (CSL) in China and from the perspective of science education. The results showed a continued steady growth in CSL, from 1.6 in 2005 to 10.56% in 2020. Specifically, male, urban, and younger adults were more likely to qualify as possessing CSL. Moreover, education level was found to be positively related to CSL. The study also found that in China, the effectiveness of formal science education has been hampered by the long-term division of the arts and sciences, examination-oriented education, the urban-rural gap, and the aging population. In terms of informal education, 37.2% of Chinese citizens visited science museums in 2020, and the Internet plays an increasing important role. Nowadays, Chinese science popularization lacks interaction, with limited opportunities for public engagement. There are deficiencies in both the country's formal and informal science education, meaning that there is still much room for improvement in the promotion of CSL in China.

Assessing the effects of future land use and land cover change (LULC) on ecological processes and functions is crucial for improving regional sustainability in arid and semiarid areas. Taking the Agro-Pastoral Transitional Zone of Northern China (APTZNC) as an example, four IPCC Special Report on Emissions Scenarios scenarios (Scenario of economic emphasis on a regional scale (A1B), Scenario of economic emphasis on a global scale (A2), Scenario of environmental protection on a regional scale (B1), Scenario of environmental protection on a global scale (B2)) were adopted in the study to analyze the influence of the future land use and land cover change on the net primary production (NPP), soil organic matter (SOM), soil total nitrogen (TN), and soil erosion (ERO) using the model of Terrestrial Ecosystem Simulator-Land use/land cover model (TES-LUC) linking ecological processes and land-use change dynamics. The results were analyzed from the perspectives of LULC components, LULC conversions, and landscape patterns under the four scenarios. The main results include the following: (1) Environmentally oriented scenarios (A1B and B1) experienced the conservation of forest and grassland; economically oriented scenarios (A2 and B2) were characterized by significant loss of natural land covers and expansion of agricultural and urban land uses. (2) The NPP and soil nutrients are the highest while the ERO is the lowest in the woodland; the trend in cultivated land is opposite to that in woodland; the grassland ecosystem function is relatively stable and could make an important contribution to effectively mitigate global climate change. (3) The general trend in NPP, SOM, and TN under the four scenarios is B1 > A1B > baseline (2010) > B2 > A2, and that in ERO is A2 > B2 > baseline (2010) > A1B > B1. (4) Trade-offs between ecosystem functions and the ecological effects of LULC can be evaluated and formulated into decision-making.

Mapping gene expression as a quantitative trait using whole genome-sequencing and transcriptome analysis allows to discover the functional consequences of genetic variation. We developed a novel method and ultra-fast software Findr for higly accurate causal inference between gene expression traits using cis-regulatory DNA variations as causal anchors, which improves current methods by taking into consideration hidden confounders and weak regulations. Findr outperformed existing methods on the DREAM5 Systems Genetics challenge and on the prediction of microRNA and transcription factor targets in human lymphoblastoid cells, while being nearly a million times faster. Findr is publicly available at https://github.com/lingfeiwang/findr.

Verticillium wilt is a soil-borne disease, and severely limits the development of cotton production. To investigate the role of endophytic fungi on Verticillium wilt, CEF-818 (Penicillium simplicissimum), CEF-714 (Leptosphaeria sp.), CEF-642 (Talaromyces flavus.) and CEF-193 (Acremonium sp.) isolated from cotton roots were used to assess their effects against cotton wilt disease caused by a defoliating V. dahliae strain Vd080. In the greenhouse, all treatments significantly reduced disease incidence and disease index, with the control efficacy ranging from 26% (CEF-642) to 67% (CEF-818) at 25 days (d) after inoculation. In the disease nursery, compared to controls (with disease incidence of 33.8% and disease index of 31), CEF-818, CEF-193, CEF-714 and CEF-642 provided a protection effect of 69.5%, 69.2%, 54.6% and 45.7%, respectively. Especially, CEF-818 and CEF-714 still provided well protection against Verticillium wilt with 46.9% and 56.6% or 14.3% and 33.7% at the first peak of the disease in heavily infected field, respectively (in early July). These results indicated that these endophytes not only delayed but also reduced wilt symptoms on cotton. In the harvest, the available cotton bolls of plant treated with CEF-818 and CEF-714 increased to 13.1, and 12.2, respectively. And the seed cotton yield significantly increased after seed bacterization with CEF-818 (3442.04 kg/ha) compared to untreated control (3207.51 kg/ha) by 7.3%. Furtherly, CEF-818 and CET-714 treatment increased transcript levels for PAL, PPO, POD, which leads to the increase of cotton defense reactions. Our results indicate that seed treatment of cotton plants with CEF-818 and CET-714 can help in the biocontrol of V. dahliae and improve seed cotton yield in cotton fields. This study provided a better understanding of cotton-endophyte interactions which will aid in developing effective biocontrol agents for Verticillium wilt of cotton in futhre.