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Existing studies have demonstrated significant sex differences in the neural mechanisms of daily life and neuropsychiatric disorders. The hierarchical organization of the functional brain network is a critical feature for assessing these neural mechanisms. But the sex differences in hierarchical organization have not been fully investigated. Here, we explore whether the hierarchical structure of the brain network differs between females and males using resting-state fMRI data. We measure the hierarchical entropy and the maximum modularity of each individual, and identify a significant negative correlation between the complexity of hierarchy and modularity in brain networks. At the mean level, females show higher modularity, whereas males exhibit a more complex hierarchy. At the consensus level, we use a co-classification matrix to perform a detailed investigation of the differences in the hierarchical organization between sexes and observe that the female group and the male group exhibit different interaction patterns of brain regions in the dorsal attention network (DAN) and visual network (VIN). Our findings suggest that the brains of females and males employ different network topologies to carry out brain functions. In addition, the negative correlation between hierarchy and modularity implies a need to balance the complexity in the hierarchical organization of the brain network, which sheds light on future studies of brain functions. [ABSTRACT FROM AUTHOR]

In modern healthcare, providers increasingly use cloud services to store and share electronic medical records. However, traditional cloud hosting, which depends on intermediaries, poses risks to privacy and security, including inadequate control over access, data auditing, and tracking data origins. Additionally, current schemes face significant limitations such as scalability concerns, high computational overhead, practical implementation challenges, and issues with interoperability and data standardization. Unauthorized data access by cloud providers further exacerbates these concerns. Blockchain technology, known for its secure and decentralized nature, offers a solution by enabling secure data auditing in sharing systems. This research integrates blockchain into healthcare for efficient record management. We proposed a blockchain-based method for secure EHR management and integrated Ciphertext-Policy Attribute-Based Encryption (CP-ABE) for fine-grained access control. The proposed algorithm combines blockchain and smart contracts with a cloud-based healthcare Service Management System (SMS) to ensure secure and accessible EHRs. Smart contracts automate key management, encryption, and decryption processes, enhancing data security and integrity. The blockchain ledger authenticates data transactions, while the cloud provides scalability. The SMS manages access requests, enhancing resource allocation and response times. A dual authentication system confirms patient keys before granting data access, with failed attempts leading to access revocation and incident logging. Our analyses show that this algorithm significantly improves the security and efficiency of health data exchanges. By combining blockchain's decentralized structure with the cloud's scalability, this approach significantly improves EHR security protocols in modern healthcare setting. [ABSTRACT FROM AUTHOR]

Vitamin D deficiency affects nearly half the population, with many requiring or opting for supplements with vitamin D3 (VD3), the precursor of vitamin D (1α,25‐dihydroxyVD3). 25‐HydroxyVD3, the circulating form of vitamin D, is a more effective supplement than VD3 but its synthesis is complex. We report here the engineering of cytochrome P450BM3 (CYP102A1) for the selective oxidation of VD3 to 25‐hydroxyVD3. Long‐range effects of the substrate‐channel mutation Glu435Ile promoted binding of the VD3 side chain close to the heme, enhancing VD3 oxidation activity that reached 6.62 g of 25‐hydroxyVD3 isolated from a 1‐litre scale reaction (69.1 % yield; space‐time‐yield 331 mg/L/h). [ABSTRACT FROM AUTHOR]

The effect of colloidal nanoparticles on the phase changes of the amphiphilic AB linear diblock, A1A2B, and A2B heteroarm star copolymers confined between two polymer brush substrates was investigated by using a real-space self-consistent field theory. By changing the concentrations of nanoparticles and polymer brushes, the phase structure of the amphiphilic AB copolymer transforms from lamellar to core-shell hexagonal phase to cylinder phase. The pattern of A2B heteroarm star copolymer changes from core-shell hexagonal phases to lamellar phases and the layer decreases when increasing the density of the polymer brushes. The results showed that the phase behavior of the system is strongly influenced by the polymer brush architecture and the colloidal nanoparticle numbers. The colloidal nanoparticles and the soft confined surface of polymer brushes make amphiphilic AB copolymers easier to form ordered structures. The dispersion of the nanoparticles was also investigated in detail. The soft surfaces of polymer brushes and the conformation of the block copolymers work together to force the nanoparticles to disperse evenly. It will give helpful guidance for making some new functional materials by nano etching technology, nano photoresist, and nanoprinting. [ABSTRACT FROM AUTHOR]

Existing Decision-Making Trial and Evaluation Laboratory (DEMATEL) methods are mostly suitable for simple systems with fewer factors, and lack effective integration of expert knowledge and experience from large-scale group populations, resulting in a potential compromise of the quality of the initial direct relation (IDR) matrix. To make DEMATEL better suited for the identification of critical factors in complex systems, this paper proposes a hierarchical DEMATEL method for large-scale group decision-making. Considering the limitations of expert knowledge and experience, a method based on expert consistency network for constructing the expert weight matrix is designed. The expert consistency network is constructed for different elements, and the weights of experts in different elements are determined using the clustering coefficient. Following the principles of the classic DEMATEL method, the steps for identifying key elements in complex systems using the large-scale group-hierarchical DEMATEL method are summarized. To objectively test the effectiveness and superiority of the decision algorithm, the robustness of the algorithm is analyzed in an interference environment. Finally, the superiority of the proposed method and algorithm is verified through a case study, which demonstrating that the proposed decision-making method is suitable for group decision-making in complex systems, with high algorithm stability and low algorithm deviation. [ABSTRACT FROM AUTHOR]

Acoustic wave scattering and fluid perturbations produced by interactions between incident acoustic wave and confined orifice flow were investigated by a combined numerical solution containing nonlinear flow simulation and linearized acoustic simulation. In flow simulations, the mainstream Reynolds number was fixed at 10,000, which relates to the cooling pipe system of lithography. Turbulent flow fields corresponding to different orifice geometries were solved by an opensource finite volume solver OpenFOAM with Reynolds-averaged Navier–Stokes turbulence model. The turbulence database could efficiently improve the accuracy of subsequent linearized acoustic simulations as the viscosity dissipations were considered. In acoustic simulations, the linearized Navier-Stokes equations were solved by a finite element solver with transformation into frequency domain. The incident acoustic waves with varying frequencies from 500 Hz to 4000 Hz were arranged first at the inlet and then the outlet surfaces, enabling a two-port analysis on the transmission and reflection coefficients of acoustic waves. The numerical setup and the two-port model were well validated by results in literature. Generally, acoustic waves tend to gradually dissipate as their frequencies increase or the opening ratio of the ducted orifice decreases. However, the nonlinear variation in the transmission and reflection coefficients against the frequency variation of the incident acoustic waves could be investigated by increasing the thickness ratio. The acoustically induced fluid perturbations that were characterized by the Q-criterion could form a ring shape vortex structure in the vicinity of the orifice edge and then develop into disk-like packets. When the circumferential shape of the orifice was changed to a square, the attenuation of the incident acoustic waves corresponded to the intensity of the three-dimensionality of the acoustic-induced vortex structures, which indicated a greater energy transfer from the acoustic waves to the fluid perturbations. [ABSTRACT FROM AUTHOR]

Spacetime (4D) occupancy forecasting explicitly models the future development of self-driving vehicles (SDVs) themselves and their surrounding environment, which is highly beneficial for their downstream tasks. Currently, using point cloud forecasting as a proxy to self-supervisedly optimize the 4D occupancy forecasting framework has significant application prospects due to its independence from labeled data. However, existing methods have the following shortcomings: (1) Insufficient mining of spatiotemporal information in the scene. (2) Neglecting the extraction of regional deep features. (3) Inadequate learning ability for spatiotemporal predictive learning. Based on this, we propose SuPrNet, constructing a "Super Proxy" to address the aforementioned issues. Specifically, SuPrNet first embeds the spatial and temporal information of the current frame into point-wise features through a 4D hybrid representation combined with multi-planar and grid features (4DPG). Then, a Hierarchical Encoder–Decoder Module (HED) with sparsity consideration is used to structure the point cloud and extract deep features. Thanks to the structured feature map representation, SuPrNet proceeds to use a spatiotemporal predictive learning module (STL) to implicitly predict future feature maps. Finally, we decode these feature maps to a specific size as the forecasting of future occupancy. The 4DPG, HED, and STL in the model are designed to address the aforementioned issues, and experiments on Nuscene and KITTI-Odometry show that our method achieves the best 4D occupancy forecasting and point cloud forecasting accuracy, surpassing previous state-of-the-art models by approximately 30%. Additionally, we have expanded and validated the application prospects of SuPrNet through various downstream tasks. Our project is available at https://github.com/AlanLiangC/4DPCF. • Propose SuPrNet, which combines 4D scene priors, deep features, and dynamic priors. • SuPrNet achieves SOTA on the Nuscenes and KITTI-Odometry datasets. • SuPrNet can be used for LiDAR NVS and temporal data generation tasks. • Ablation experiments show that SuPrNet is a product of framework innovation. [ABSTRACT FROM AUTHOR]

This study is inspired by the extraordinary suppression performance of the intrinsic vortex-induced vibration (VIV) of a harbor seal vibrissa, which is renowned for its excellent detection characteristics in sensing upstream flow information. A physical understanding of the suppression mechanism of vibrissal cylinder oscillation is mandatory for the design of a sensor detection system. In this study, the suppression performances are studied for a vibrissal cylinder at a reduced velocity U r = U/ (f nw D) of 5, with the corresponding Reynolds number of Re = 3,750, according to the swimming velocity and real geometrical scale for a harbor seal vibrissa. In addition, a verified fluid and structure interaction (FSI) solver implemented with an improved dynamic mesh strategy is adopted. Different transverse spring-mounted oscillation responses are obtained by alternating the angle of attack (AOA). It is found that the attachment and secondary separation of vortices in the near wake significantly influence the shedding pattern and can lead to the distinct suppression of oscillating responses. A pair of attached vortices are observed for the vibrissal cylinder at AOA = 0°, where the oscillating response is almost fully suppressed. Forced vibration is applied to address the role of attached vortices in the suppression mechanism. In contrast, when the suppression mechanism diminishes at AOA = 90°, the vortex-shedding pattern is characterized by unstable discrete secondary vortices. These secondary vortices re-separate from the primary vortices, indicating high stability of the primary vortices in the near wake, which represents the primary mechanism deteriorating the suppression of vortex-shedding. Furthermore, the interaction of attaching and secondary discrete vortices results in a partially suppressed oscillating response for the harbor seal vibrissa at AOA = 45°. Of note, these two mechanisms cause the shedding mode to switch between the "2S" and "P + S" modes while partially suppressing the oscillation response. [ABSTRACT FROM AUTHOR]

The core issue of learning-to-rank (LTR) for document retrieval lies in finding an optimal ranking policy to meet the search intent of the user. The majority of proposed LTR approaches treat the ranking as a static process, employing a fixed ranking policy to immediately assign scores to documents. By contrast, ranking is not a static but an interactive process where the user continues interacting with the document retrieval system through information exchange such as search intent (e.g., rating or clicking for the retrieved items). We model the interactive ranking process (IRP), and propose an Attention-Based Interactive LTR model (AIRank) to constitute an intent-aware flexible ranking policy to gratify the user’s need. To enhance the ranking quality, the inherent relations among documents are procured by the self-attention method to contribute to an enriched user intent representation. Furthermore, we mend the policy gradient learning method to train the AIRank in the IRP. Experiments demonstrate the effectiveness of AIRank compared to the state-of-the-art methods in terms of normalized discounted cumulative gain and expected reciprocal rank. [ABSTRACT FROM AUTHOR]

Leveraging global state information to enhance policy optimization is a common approach in multi-agent reinforcement learning (MARL). Even with the supplement of state information, the agents still suffer from insufficient exploration in the training stage. Moreover, training with batch-sampled examples from the replay buffer will induce the policy overfitting problem, i.e., multi-agent proximal policy optimization (MAPPO) may not perform as good as independent PPO (IPPO) even with additional information in the centralized critic. In this paper, we propose a novel noise-injection method to regularize the policies of agents and mitigate the overfitting issue. We analyze the cause of policy overfitting in actor–critic MARL, and design two specific patterns of noise injection applied to the advantage function with random Gaussian noise to stabilize the training and enhance the performance. The experimental results on the Matrix Game and StarCraft II show the higher training efficiency and superior performance of our method, and the ablation studies indicate our method will keep higher entropy of agents' policies during training, which leads to more exploration. [ABSTRACT FROM AUTHOR]

Xanthatin (XT) is a sesquiterpene lactone isolated from the Chinese herb Xanthium, which belongs to the Asteraceae family. In this study, we developed an inflammation model via stimulating macrophage cell line (RAW 264.7 cells) with lipopolysaccharide (LPS), which was applied to assess the anti-inflammatory effect and probable mechanisms of xanthatin. When compared with the only LPS-induced group, cells that were pretreated with xanthatin were found to decrease the amount of nitric oxide (NO), reactive oxygen species (ROS) and associated pro-inflammatory factors (TNF-α, IL-1β and IL-6), and downregulate the mRNA expression of iNOS, COX-2, TNF-α, IL-1β, and IL-6. Interestingly, phosphorylated levels of related proteins (STAT3, ERK1/2, SAPK/JNK, IκBα, p65) were notably increased only with the LPS-activated cells, while the expression of these could be reverted by pre-treatment with xanthatin in a dose-dependent way. Meanwhile, xanthatin was also found to block NF-κB p65 from translocating into the nucleus and activating inflammatory gene transcription. Collectively, these results demonstrated that xanthatin suppresses the inflammatory effects through downregulating the nuclear factor kappa-B (NF-κB), mitogen-activated protein kinase (MAPK) and signal transducer and activator of transcription (STATs) signaling pathways. Taken together, xanthatin possesses the potential to act as a good anti-inflammatory medication candidate. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel deep architecture by combining multiple sensors for 3D object detection, named MSL3D. While recently LiDAR-Camera methods introduce additional semantic cues, working with fewer false detections, there is still a performance gap compared LiDAR-only methods. We argue that this gap is caused for two reasons: 1) the 3D spherical receptive fields of the set abstraction of the point clouds are not aligned with the 2D pixel-level receptive fields of the image. 2) the premature introduction of image information makes it is difficult to apply data augmentation both LiDAR and image synchronously. For the first problem, we extend 3D set abstraction to a 2D set abstraction that can transform the 2D image features to the 3D sphere to unify the receptive field of multi-modal data. For the second problem, we design a novel two-stage 3D detection framework that employs the LiDAR-only backbone in the first stage to estimate high-recall and high-quality proposals and then integrates the image and point clouds information for box refinement and confidence prediction. Besides, we add two auxiliary networks to effectively learn image features and point cloud features when using different multi-modal data augmentation strategies synchronously. Moreover, we design a consistency-structure generator using stereo images to determine whether any of a point in the 3D space belongs to the contour of the object, thereby supplementing the sparse point cloud information. Extensive experiments on the popular KITTI 3D objects detection dataset show that our proposed MSL3D achieves better performance comparing with other LiDAR-Only or LiDAR-Camera fusion approaches. [ABSTRACT FROM AUTHOR]

Excessive inflammation leads to periodontitis, which inhibits the osteogenic differentiation of human dental pulp stem cells (hDPSCs), irreversibly injured and difficultly repaired for the important dental pulp. Hence, it is necessary to study the functional gene to enhance the osteogenic differentiation of hDPSCs. Previous found that SNHG7 expression was increased in the osteogenic differentiation of hDPSCs. However, the regulatory functions of SNHG7 on osteogenic differentiation of hDPSCs in the inflammatory microenvironment still remains unknown. In this study, hDPSCs treatment with 50 ng/mL TNF-α to mimic the inflammatory microenvironment, then cultured in osteoblast differentiation medium for 14 days. SNHG7, miR-6512–3p, BSP, DSPP, DMP-1, RUNX2 and OPN in hDPSCs were detect by RT-qPCR. We found that SNHG7 expression was reduced during the osteogenic differentiation of hDPSCs after different concentrations TNF-α treatment. SNHG7 overexpression improved the TNF-α-induced suppression of calcium deposition, ALP activity, and the expression of BSP, DSPP, DMP-1, RUNX2 and OPN. Furthermore, SNHG7 can sponge with miR-6512–3p. miR-6512–3p expression was increased during the osteogenic differentiation of hDPSCs after different concentrations TNF-α treatment while inhibited after SNHG7 overexpression. knockdown of miR-6512–3p improved the TNF-α-induced suppression of calcium deposition, ALP activity, and the expression of BSP, DSPP, DMP-1, RUNX2 and OPN. Finally, miR-6512–3p overexpression reversed the effect of SNHG7 on the osteo/dentinogenic differentiation of TNF-α-treated hDPSCs. In conclusions, SNHG7 improves the osteogenic differentiation of hDPSCs by inhibiting miR-6512–3p expression under 50 ng/mL TNF-α-induced inflammatory environment, which provided potential targets for the treatment of periodontitis. • ng/mL TNF-α treatment inhibited the hDPSCs osteo/dentinogenic differentiation. • SNHG7 expression was reduced in hDPSCs after 50 ng/mL TNF-α treatment. • SNHG7 improved the osteo/dentinogenic differentiation of TNF-α-induced hDPSCs. • SNHG7 inhibited miR-6512–3p expression in TNF-α-induced hDPSCs. [ABSTRACT FROM AUTHOR]

The currently predominant token-to-token attention mechanism has demonstrated its ability to capture word dependencies in neural machine translation. This mechanism treats a sequence as bag-of-words tokens and compute the similarity between tokens without considering their intrinsic interactions. In this paper, we argue that this attention mechanism may miss opportunity of take advantage of the state information through multiple time steps. Thus, we propose a Gated State Network which manipulates the state information flow with sequential characteristics. We also incorporate a Focal Adaptive Attention Network which utilizes a Gaussian distribution to concentrate the attention distribution to a predicted focal position and its neighborhood. Experimental results on WMT'14 English–German and WMT'17 Chinese–English translation tasks demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Background: Secreted frizzled-related protein 2 (SFRP2) in circulating tumor DNA (ctDNA) is related to gastric cancer (GC) proliferation. However, whether methylated SFRP2 in ctDNA serves as the biomarker in GC patients remains unknown. Aims: To investigate the relationship between methylated SFRP2 and the clinical outcomes of GC patients. Methods: One hundred and forty-eight GC patients receiving systemic chemotherapy were collected during 2015–2017. Aberrant SFRP2 methylation was detected before and after chemotherapy by digital PCR-based technologies. Results: Baseline SFRP2 methylation positively correlated with lymph node status (P < 0.001), distant metastasis (P < 0.001) and TNM stage (P = 0.005). The top 50% methylated SFRP2 had shorter progression-free survival (PFS) and overall survival (OS) than those with bottom 50% in stage III GC patients (median PFS, 11.0 vs. NR months; HR 13.05; 95% CI 3.05–55.95; median OS 17.0 vs. NR months; HR 7.80; 95% CI 1.81–33.60) and stage IV GC patients (median PFS, 4.0 vs. 7.0 months; HR 2.74; 95% CI 1.58–4.78; median OS 12.0 vs. 16.0 months; HR 3.14; 95% CI 1.67–5.92). Besides, the increased methylated SFPR2 from baseline to post-treatment was related to the worse PFS and OS among stage IV patients (median PFS, 5.0 vs. 7.0 months; HR 2.17; 95% CI 1.25–3.76; median OS 12.0 vs. 15.5 months; HR 3.51; 95% CI 1.94–6.35), but not to stage III patients. Conclusions: SFRP2 methylation and dynamic change are associated with GC prognosis. Our findings provide potential biomarker detection approach in ctDNA for prognosis prediction and dynamic monitoring among GC patients. [ABSTRACT FROM AUTHOR]

Discovering drug–target (protein) interactions (DTIs) is of great significance for researching and developing novel drugs, having a tremendous advantage to pharmaceutical industries and patients. However, the prediction of DTIs using wet-lab experimental methods is generally expensive and time-consuming. Therefore, different machine learning-based methods have been developed for this purpose, but there are still substantial unknown interactions needed to discover. Furthermore, data imbalance and feature dimensionality problems are a critical challenge in drug-target datasets, which can decrease the classifier performances that have not been significantly addressed yet. This paper proposed a novel drug–target interaction prediction method called PreDTIs. First, the feature vectors of the protein sequence are extracted by the pseudo-position-specific scoring matrix (PsePSSM), dipeptide composition (DC) and pseudo amino acid composition (PseAAC); and the drug is encoded with MACCS substructure fingerings. Besides, we propose a FastUS algorithm to handle the class imbalance problem and also develop a MoIFS algorithm to remove the irrelevant and redundant features for getting the best optimal features. Finally, balanced and optimal features are provided to the LightGBM Classifier to identify DTIs, and the 5-fold CV validation test method was applied to evaluate the prediction ability of the proposed method. Prediction results indicate that the proposed model PreDTIs is significantly superior to other existing methods in predicting DTIs, and our model could be used to discover new drugs for unknown disorders or infections, such as for the coronavirus disease 2019 using existing drugs compounds and severe acute respiratory syndrome coronavirus 2 protein sequences. [ABSTRACT FROM AUTHOR]

Objectives. This study is aimed at exploring the relationship of the viral load of coronavirus disease 2019 (COVID-19) with lymphocyte count, neutrophil count, and C-reactive protein (CRP) and investigating the dynamic change of patients' viral load during the conversion from mild COVID-19 to severe COVID-19, so as to clarify the correlation between the viral load and progression of COVID-19. Methods. This paper included 38 COVID-19 patients admitted to the First Hospital of Jiaxing from January 28, 2020, to March 6, 2020, and they were clinically classified according to the Guidelines on the Novel Coronavirus-Infected Pneumonia Diagnosis and Treatment. According to the instructions of the Nucleic Acid Detection Kit for the 2019 novel coronavirus (SARS-CoV-2), respiratory tract specimens (throat swabs) were collected from patients for nucleic acid testing. Patients' lymphocyte count and neutrophil count were determined by blood routine examination, and CRP was measured by biochemical test. Results. The results of our study suggested that the cycle threshold (Ct) value of Nucleocapsid protein (N) gene examined by nucleic acid test was markedly positively correlated with lymphocyte count (p = 0.0445 , R 2 = 0.1203), but negatively correlated with neutrophil count (p = 0.0446 , R 2 = 0.1167) and CRP (p = 0.0393 , R 2 = 0.1261), which indicated that patients with a higher viral load tended to have lower lymphocyte count but higher neutrophil count and CRP. Additionally, we detected the dynamic change of Ct value in patients who developed into a severe case, finding that viral load of 3 patients increased before disease progression, whereas this phenomenon was not found in 2 patients with underlying diseases. Conclusion. The results of this study demonstrated that viral load of SARS-CoV-2 is significantly negatively correlated with lymphocyte count, but markedly positively correlated with neutrophil count and CRP. The rise of viral load is very likely to be the key factor leading to the overloading of the body's immune response and resulting in the disease progression into severe disease. [ABSTRACT FROM AUTHOR]

With only global image-level annotations, weakly supervised learning of deep convolutional neural networks has shown enough capacity in classification and localization but lack of ability to present the detection explicitly. In this work, we propose a novel spatial division network, which is applied to detect bounding boxes only with weak supervision. The essence of our model is two innovative differentiable modules, determination network and parameterized division, which perform the spatial division in feature maps of classification networks. After training, the learned parameters of the spatial division would correspond to a set of predicted bounding box coordinates. To demonstrate the effectiveness of our model for multi-label classification and weakly supervised detection, we conduct extensive experiments on the multi-MNIST dataset. Experimental results show our spatial division networks can (1) help improve the accuracy of multi-label classification, (2) implement in an end-to-end way only with the image-level annotations, and (3) output accurate bounding box coordinate, thereby achieving multi-digits detection. [ABSTRACT FROM AUTHOR]

This paper examines factors of payment decision as well as the role each factor plays in casual configurations leading to high payment intention under systematic and heuristic information processing routes. Based on heuristic-systematic model (HSM), we propose a configurational analytic framework to investigate complex casual relationships between influencing factors and payment decision. In line with this approach, we use fuzzy-set qualitative comparative analysis (fsQCA) to analyze data crawled from Zhihu.com. The number of previous consultations is a necessary element in all five equivalent configurations which lead to high intention in payment decision. The heuristic processing route plays a core role while the systematic processing route plays a peripheral role in payment decision-making process. Research is limited in that moderating effect of professional fields has not been considered in the framework. Configurations in results can assist managers of knowledge communities and paid Q&A service providers in the management of information elements to motivate more payment decision. This paper is one of the few studies to apply HSM theory and fsQCA method with respect to the payment decision in paid Q&A. [ABSTRACT FROM AUTHOR]

Prior work has shown that causal structure can be uniquely identified from observational data when these follow a structural equation model whose error terms have equal variance. We show that this fact is implied by an ordering among conditional variances. We demonstrate that ordering estimates of these variances yields a simple yet state-of-the-art method for causal structure learning that is readily extendable to high-dimensional problems. [ABSTRACT FROM AUTHOR]

In the realm of flexible optoelectronic devices, intrinsic flexible fully π‐conjugated polymers (FπCPs) assume a pivotal role owing to their concurrent intrinsic viscoelasticity and robust optoelectronic properties. As flexible devices are destined to encounter diverse stress environments, the selection of functional materials based on mechanical requirements becomes imperative. Here, VPERD assessment system is introduced to scrutinize the mechanical properties of side‐chain plasticizing FπCPs. The acronym VPERD encapsulates five key aspects of mechanical characteristics, namely viscidity (V), plasticity (P), elasticity (E), rigidity (R), and ductility (D). The mechanical characteristics of FπCPs are tuned through the control of flexible chain density (FCD). Enhancing FCD induces superior chain softness, favorable solubility, and heightened viscoplasticity. Through precise regulation of FCD and thoughtful structural design, freestanding FπCPs nano‐films exhibit a remarkable maximum fracture strain of ≈50%. Deep‐blue flexible polymer light‐emitting diodes (PLEDs) reveal that maintaining FCD at ≈50% achieves a balance between mechanical and optoelectronic properties. The stretched films after strain fatigue both show that mild strain is conducive to enhancing the efficiency of FπCPs films and devices. According to VPERD assessment system, the prerequisite for achieving intrinsically flexible FπCPs lies in controlling the degree of side‐chain internal plasticization through the tuning of FCD. [ABSTRACT FROM AUTHOR]

A target-triggered strand displacement-assisted target recycling based on carbon dots-based fluorescent probe and mesoporous silica nanoparticles@polydopamine (MSNs@PDA) was established to detect miRNA. The surface of MSNs rich in mesopores was coated with a layer of PDA, which can adsorb and quench the fluorescence of single-stranded Fuel DNA with fluorescent carbon dots (CDs) modified at the end through fluorescence resonance energy transfer (FRET). After adding double-stranded DNA-gold nanoparticles (dsDNA-AuNPs) and target let-7a, it will trigger two toehold-mediated strand displacement reactions (TSDR), leading to the recovery of fluorescence and the recycling of target let-7a (excitation wavelength: 380 nm; emission wavelength: 458 nm). The recovery value of fluorescence is proportional to the logarithm of the target microRNA let-7a concentration, thus realizing the sensitivity amplification detection of disease markers. The MSNs@PDA@Fuel DNA-CDs/dsDNA-AuNPs nanoplatform based on the strategy of "on–off–on" and TSDR cyclic amplification may hold great potential as an effective and safe nanoprobe for accurate fluorescence imaging of diseases related to miRNA with low abundances. [ABSTRACT FROM AUTHOR]

Major depressive disorder (MDD) is a serious and heterogeneous psychiatric disorder that needs accurate diagnosis. Resting-state functional MRI (rsfMRI), which captures multiple perspectives on brain structure, function, and connectivity, is increasingly applied in the diagnosis and pathological research of MDD. Different machine learning algorithms are then developed to exploit the rich information in rsfMRI and discriminate MDD patients from normal controls. Despite recent advances reported, the MDD discrimination accuracy has room for further improvement. The generalizability and interpretability of the discrimination method are not sufficiently addressed either. Here, we propose a machine learning method (MFMC) for MDD discrimination by concatenating multiple features and stacking multiple classifiers. MFMC is tested on the REST-meta-MDD data set that contains 2428 subjects collected from 25 different sites. MFMC yields 96.9% MDD discrimination accuracy, demonstrating a significant improvement over existing methods. In addition, the generalizability of MFMC is validated by the good performance when the training and testing subjects are from independent sites. The use of XGBoost as the meta classifier allows us to probe the decision process of MFMC. We identify 13 feature values related to 9 brain regions including the posterior cingulate gyrus, superior frontal gyrus orbital part, and angular gyrus, which contribute most to the classification and also demonstrate significant differences at the group level. The use of these 13 feature values alone can reach 87% of MFMC's full performance when taking all feature values. These features may serve as clinically useful diagnostic and prognostic biomarkers for MDD in the future. • A new MDD discrimination method MFMC is proposed. • MFMC is tested on REST-meta-MDD including over 2400 subjects. • MFMC outperforms existing methods with significant improvements. • The good generalizability of MFMC is verified. • MFMC identifies 13 features related with 9 brain regions important for MDD. [ABSTRACT FROM AUTHOR]

Computer vision and natural language processing have been some of the long-standing challenges in artificial intelligence. In this paper, we explore a generative automatic image annotation model, which utilizes recent advances on both fronts. Our approach makes use of a deep-convolutional neural network to detect image regions, which later will be fed to recurrent neural network that is trained to maximize the likely-hood of the target sentence description of the given image. During our experimentation we found that better accuracy and training was achieved when the image representation from our detection model is coupled with the input word embedding, we also found out most of the information from the last layer of detection model vanishes when it is fed as thought vector for our LSTM decoder. This is mainly because the information within the last fully connected layer of the YOLO model represents the class probabilities for the detected objects and their bounding box and this information is not rich enough. We trained our model on coco benchmark for 60 h on 64,000 training and 12,800-validation dataset achieving 23% accuracy. We also realized a significant training speed drop when we changed the number of hidden units in the LSTM layer from 1470 to 4096. [ABSTRACT FROM AUTHOR]

Originating from model-free reinforcement learning (RL), many modern multi-agent reinforcement learning (MARL) algorithms are usually armed with the paradigm of Centralized Training with Decentralized Execution (CTDE) to mitigate the non-stationary problem and make the training process stable. However, these methods still suffer from sample inefficiency and slow training convergence as in the single-agent reinforcement learning setting. Many common methods aiming to tackle these problems utilize the experience buffer and parallel training mechanism to speed up the training process, which would cost more computing resources and may still underuse the sampled experiences. In this paper, we propose R egularization - A dapted A nderson A cceleration (RA3) for model-free, off-policy MARL algorithms. Under the CTDE paradigm, this specific RA3 approach treats the joint action-value function update as a fixed-point iteration task and speeds up the training process with the same amount of sampled experiences as in the baseline algorithms. Furthermore, our RA3 employs an adaptive regularization strategy related to Bellman residuals to stabilize the update process and enhance the training performance. Experimental results demonstrate that the improved learning speed and superior performance of our proposed method are significantly improved on the predator–prey game and the challenging StarCraft II micromanagement benchmark tasks. [ABSTRACT FROM AUTHOR]

Abstract Non-small cell lung cancer (NSCLC) patients harboring EGFR mutation who initially respond to EGFR-TKI will gradually develop acquired resistance. There is still a challenge to treat EGFR-TKI resistant NSCLC patients. Polyphyllin I (PP I), a steroidal saponin isolated from Paris polyphylla., has been exhibited antitumor activities against various carcinomas. However, its mechanism in treating EGFR-TKI resistant NSCLC has not been well elucidated. In this study, we found that PP I suppressed the cell viability and induced apoptosis of gefitinib-resistant NSCLC cells and xenograft models. These therapeutic efficacies were associated with down-regulated level of MALAT1, leading to inactivation of STAT3 signaling pathway. The cell viability inhibition and apoptosis inducing in gefitinib-resistant NSCLC triggered by PP I were abolished by MALAT1 overexpression, while the cell viability inhibition and apoptosis inducing triggered by PP I were potentiated by MALAT1 knockdown. These findings suggest that, in vitro and in vivo, PP I inhibits the viability and induces apoptosis of gefitinib-resistant NSCLC by down-regulating MALAT1 and inactivating STAT3 signaling pathway. Thus, PPI could serve a promising therapeutic agent for the treatment of gefitinib-resistant NSCLC. Highlights • There is still a challenge to treat EGFR-TKI resistant NSCLC. • PP I suppressed the cell viability and induced apoptosis of drug-resistant NSCLC. • PP I might down-regulate MALAT1 expression and inactivate STAT3 signaling pathway. • PP I could serve a promising therapeutic agent for gefitinib-resistant NSCLC. [ABSTRACT FROM AUTHOR]

Multiple drug resistance is the main reason for chemotherapeutic failure in lung cancer patients with complex molecular mechanisms. LncRNA-MALAT1 plays functional roles in the progression of carcinomas and development of drug resistance. We aimed to identify the role of MALAT1 in DDP-resistant non-small cell lung cancer as well as potential mechanisms. Human lung cancer cell line A549 and the DDP-resistant cell line A549/DDP were used. Cell transfection was performed to establish A549/MALAT1 and A549/DDP/shMALAT1 cells. The qRT-PCR analysis was performed to detect lncRNA-MALAT1 level. Cell viability, colony formation assay, apoptosis analysis, western blot analysis, immunohistochemistry, and animal study were carried out. MALAT1 was upregulated in DDP-resistant A549 cell line. MALAT1 decreased DDP sensitivity in vitro and in vivo by upregulating MRP1 and MDR1 via STAT3 activation. Overexpression of MALAT1 contributed to the DDP resistance and might confer a potently poor prognosis. [ABSTRACT FROM AUTHOR]

In a 3-D scanning task, a robot-sensor system controls a robotic arm to move a laser sensor. In order to align the coordinate system of the robotic arm and laser sensor, prior calibration is required to derive the transformation between both coordinate systems. This paper proposes a new calibration method in three steps: manual data collection, sensing data calculation, and transformation solution. First, at least four data are required to be collected by the user. The sensing data are then calculated from the collected data and adopted to provide the desired transformation. The proposed algorithm has two features: arbitrary placement of planar disk and noise tolerant. Using a planar disk, the algorithm will automatically derive the angular relationship between the disk and the sensor plane, enabling arbitrary orientation placement. Noise tolerant is guaranteed by fitting ellipses during the sensing data calculation and using a single set of sensing data in transformation solution. Experiments and comparisons are given to demonstrate the efficiency of the proposed calibration algorithm.

Note to Practitioners—This paper was motivated by the problem of calibrating a laser sensor and a positioning device (robot arm, CMM, etc.) in a robust and fast manner. Specifically, the calibration is to derive the transformation by aligning the sensors coordinate system to the positioning devices coordinate system. The proposed calibration procedure consists of two parts: manual data collection and automatic transformation calculation. During manual data collection, users only need to select four different data; whereby each data contains of two positions with the same orientation. Then, the desired transformation will be derived automatically. The calibration is designed in an efficient and robust way whereby: 1) data collection is done using a simple planar disk placed in arbitrary orientations; 2) minimum human interaction required; 3) tolerant to noise in the sensor data; and 4) easy implementation by following a proven and standard protocol. [ABSTRACT FROM PUBLISHER]

Graph clustering has received significant attention in recent years due to the breakthrough of graph neural networks (GNNs). However, GNNs frequently assume strong data homophily, which is not true in many real-world applications. Furthermore, practical graphs are typically noisy and sparse, which inevitably degrades the clustering performance. To this end, we propose a novel Contrastive Graph Clustering (CGC) method with adaptive filter framework. We first design an adaptive filter that can automatically learn a suitable filter for different data, mining holistic information beyond low-frequency components and encoding topology structure information into features. Afterward, we learn a refined graph based on a graph-level contrastive mechanism, which further boosts graph discriminability. Extensive experiments show that the proposed CGC method achieves significant improvement over state-of-the-art methods on several benchmark datasets. In particular, our simple method, which does not employ neural networks, outperforms many deep learning approaches. • High-frequency information is considered for graph clustering. • Contrastive regularizer is applied to refine graph structure. • Homophilic and heterophilic graphs are evaluated. [ABSTRACT FROM AUTHOR]

Graph neural networks (GNNs) have drawn increasing attention in recent years and achieved remarkable performance in many graph-based tasks, especially in semi-supervised learning on graphs. However, most existing GNNs are based on the message-passing paradigm to iteratively aggregate neighborhood information in a single topology space. Despite their success, the expressive power of GNNs is limited by some drawbacks, such as inflexibility of message source expansion, negligence of node-level message output discrepancy, and restriction of single message space. To address these drawbacks, we present a novel message-passing paradigm, based on the properties of multi-step message source, node-specific message output, and multi-space message interaction. To verify its validity, we instantiate the new message-passing paradigm as a Dual-Perception Graph Neural Network (DPGNN), which applies a node-to-step attention mechanism to aggregate node-specific multi-step neighborhood information adaptively. Our proposed DPGNN can capture the structural neighborhood information and the feature-related information simultaneously for graph representation learning. Experimental results on six benchmark datasets with different topological structures demonstrate that our method outperforms the latest state-of-the-art models, which proves the superiority and versatility of our method. To our knowledge, we are the first to consider node-specific message passing in the GNNs. [ABSTRACT FROM AUTHOR]

MALAT1 has been implicated in tumor progression. But the mechanism and role underlying MALAT1 in non-small cell lung cancer (NSCLC) cell resistance to gemcitabine (GEM) remain rarely understood. Through bioinformatics analysis, we predicted MALAT1/miR-27a-5p/PBOV1 regulatory axis and constructed GEM resistant A549/GEM cell line, and A549 was the parent cell line. qRT-PCR was utilized to assess MALAT1, miR-27a-5p and PBOV1 expression in A549 and A549/GEM cells. MTT method and colony formation assay were utilized to measure cell viability and cell proliferation. Flow cytometry was conducted to assess cell cycle and cell apoptosis. Wound healing and Transwell assays were conducted to measure cell migratory and invasive potentials. Dual-luciferase reporter gene assay and RNA immunoprecipitation were utilized to identify the targeted relationship between MALAT1 and miR-27a-5p, and the former assay was also utilized to determine the targeted relationship between miR-27a-5p and PBOV1. The impacts of MALAT1/miR-27a-5p/PBOV1 on tumor growth and GEM resistance of NSCLC cells in vivo were validated by using the tumor xenograft model. MALAT1 was observed to be highly expressed in tissues and cells of GEM resistant patients. Forced level of MALAT1 could markedly enhance A549 cell resistance to GEM, but this impact could be weakened by silencing MALAT1. MALAT1 downregulated miR-27a-5p level. PBOV1 was the target of miR-27a-5p and could significantly enhance GEM resistance of NSCLC cell. MALAT1 facilitated tumor growth in vivo via targeting miR-27a-5p/PBOV1 and enhanced resistance of NSCLC cells to GEM. MALAT1/miR-27a-5p/PBOV1 axis was implicated in NSCLC cell resistance to GEM. We deepened our understanding about how MALAT1 enhanced NSCLC cell resistance to GEM and provided development of therapeutic strategy for NSCLC with a possible target. • LncRNA MALAT1 was associated with gemcitabine resistance in NSCLC cells. • LncRNA MALAT1 targeted miR-27a-5p in NSCLC. • PBOV1 was the direct target of miR-27a-5p in NSCLC. • LncRNA MALAT1 enhanced non-small cell lung cancer cell resistance to gemcitabine by directly affecting miR-27a-5p/PBOV1 axis. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the intra-examination agreement between multi-echo gradient echo (MEGE) and confounder-corrected chemical shift-encoded (CSE) sequences for liver T2*/R2* estimations in a wide range of T2*/R2* and proton density fat fraction (PDFF) values. Exploratorily, to search for the T2*/R2* value where the agreement line breaks and examine differences between regions of low and high agreement. Methods: Consecutive patients at risk for liver iron overload who underwent MEGE and CSE sequences within the same exam at 1.5 T were retrospectively selected. Regions of interest were drawn in the right and one in the left liver lobes on post-processed images for R2*(sec−1) and PDFF (%) estimation. Agreement between MEGE-R2* and CSE-R2* was evaluated using intra-class correlation coefficient (ICC) and Bland–Altman analysis. 95% confidence intervals (CI) were computed. Segment-and-regression analysis was performed to find the point where the agreement between sequences is interrupted. Regions of low and high agreement were examined using tree-based partitioning analyses. Results: 49 patients were included. Mean MEGE-R2* was 94.2 s−1 (range: 31.0–737.1) and mean CSE-R2* 87.7 (29.7–748.1). Mean CSE-PDFF was 9.12% (0.1–43.3). Agreement was strong for R2* estimations (ICC: 0.992,95%CI 0.987,0.996), but the relation was nonlinear and possibly heteroskedastic. Lower agreement occurred when MEGE-R2* > 235 s−1, with MEGE-R2* values consistently lower than CSE-R2*. Higher agreement was observed when PDFF < 14%. Conclusion: MEGE-R2* and CSE-R2* strongly agree, though at higher iron content, MEGE-R2* is consistently lower than CSE-R2*. In this preliminary dataset, a breaking point for agreement was found at R2* > 235. Lower agreement was observed in patients with moderate to severe liver steatosis. [ABSTRACT FROM AUTHOR]

• Silencing TRIM66 hindered cell malignant phenotype of non-small cell lung cancer; • TRIM66 interacted with MMP9 in non-small cell lung cancer; • MMP9 activated TGF-β/SMAD pathway; • TRIM66/MMP9/TGF-β/SMAD axis promoted non-small cell lung cancer progression. To investigate regulatory function and underlying mechanism of TRIM66 in non-small cell lung cancer (NSCLC). TRIM66 and MMP9 expression in NSCLC cells and tissues was assayed via qRT-PCR and western blot. CCK-8, colony formation, Transwell and flow cytometry assays were conducted to measure cell functional alternations in NSCLC. Western blot was employed to measure expression as well as phosphorylation levels of epithelial-mesenchymal transition-(EMT) and TGF-β/SMAD pathways-related proteins. Co-immunoprecipitation (Co-IP) assay was done to probe interaction between TRIM66 and MMP9. Xenograft in vivo experiment and tumor metastasis model in nude mice were utilized to investigate effects of TRIM66 on tumor growth of NSCLC. TRIM66 and MMP9 were conspicuously highly expressed in NSCLC cells and tissues. High TRIM66 level was markedly correlated with metastasis. Silencing TRIM66 prominently repressed the proliferation, migration and invasion of transfected cells, while inducing cell apoptosis. Whereas forced expression of TRIM66 exerted the opposite effect. The aberrant expression of TRIM66 modulated EMT pathway. TRIM66 also regulated MMP9 expression, and the interaction between them was validated by Co-IP assay. Overexpression of MMP9 could activate TGF-β/SMAD pathway. Rescue experiments manifested that si-MMP9 or SB431542 could partially reverse phenotypes induced by TRIM66. In vivo experiments revealed that silencing TRIM66 could hamper NSCLC tumor growth and metastasis. TRIM66 and MMP9 were up-regulated in NSCLC. TRIM66 facilitated the malignant progression of NSCLC through modulating MMP9-mediated TGF-β/SMAD pathway. [ABSTRACT FROM AUTHOR]

Abstract: Liquid renovation is perhaps the best method to reduce contamination in immersion lithography, but it brings lens distortion because of the high density and viscosity of the immersion liquids compared to air. In this paper, considering wafer scanning, the three-dimensional computational liquid dynamics model and finite-element model are developed to investigate the lens distortion for immersion liquid renovation. After analyzing the influence of wafer scanning directions, the empirical formulas of the lens distortion for three-dimensional model are established to describe the effect of the parameters of lens and polymer which are used to mount lens and the parameters of immersion liquid flow on lens distortion. [Copyright &y& Elsevier]

Abstract: This paper considers the problem of computing the Bézier representation for a triangular sub-patch on a triangular Bézier surface. The triangular sub-patch is defined as a composition of the triangular surface and a domain surface that is also a triangular Bézier patch. Based on de Casteljau recursions and shifting operators, previous methods express the control points of the triangular sub-patch as linear combinations of the construction points that are constructed from the control points of the triangular Bézier surface. The construction points contain too many redundancies. This paper derives a simple explicit formula that computes the composite triangular sub-patch in terms of the blossoming points that correspond to distinct construction points and then an efficient algorithm is presented to calculate the control points of the sub-patch. [Copyright &y& Elsevier]

Abstract: Immersion lithography seeks to extend the resolution of optical lithography by filling the gap between the final optical element and the wafer with a liquid characterized by a high index of refraction. The semiconductor industry demands high throughput, leading to relatively large wafer scanning velocities and accelerations. For higher scanning velocities, an issue that has been identified is the deposition of the immersion liquid while confining a relatively small amount of liquid to the under-lens region. Liquid loss occurs at the receding contact line that forms when a substrate is withdrawn from a liquid, which potentially leads to defects on printed patterns. There has been substantial prior work relative to understanding and building semi-empirical correlations and numerical models to investigate this behavior of the receding three-phase contact line. In the current work, a new liquid injection and collection model with analytic solutions is presented and compared with experimental results, in which the critical velocity for liquid loss is mainly a function of the vacuum degree, the injection flow rate, the properties of the immersion liquid. This correlation allows the critical velocity to be predicted with a given gap height between wafer and lens using only a measurement of the injection speed and knowledge of the fluid properties. Experimentally, glycerin–water mixtures of varying viscosities and different injection flow rates were tested, with variable outlet vacuum degree and inlet speed, showing a mean average error within 12%. This correlation represents a useful tool that can serve to approximately guide the development of fluid control for immersion systems as well as to evaluate alternative immersion fluid candidates to minimize liquid deposition while maximizing throughput. [Copyright &y& Elsevier]

Abstract: In this paper, a rational Bézier surface is proposed as a uniform approach to modeling all three types of molecular surfaces (MS): the van der Waals surface (vdWS), solvent accessible surface (SAS) and solvent excluded surface (SES). Each molecular surface can be divided into molecular patches, which can be defined by their boundary arcs. The solution consists of three steps: topology modeling, boundary modeling and surface modeling. Firstly, using a weighted -shape, topology modeling creates two networks to describe the neighboring relationship of the molecular atoms. Secondly, boundary modeling derives all boundary arcs from the networks. Thirdly, surface modeling constructs all three types of molecular surfaces patch-by-patch, based on the networks and the boundary arcs. For an SES, the singularity is specially treated to avoid self-intersections. Instead of approximation, this proposed solution can produce precise shapes of molecular surfaces. Since rational Bézier representation is much simpler than a trimmed non-uniform rational B-spline surface (NURBS), computational load can be significantly saved when dealing with molecular surfaces. It is also possible to utilize the hardware acceleration for tessellation and rendering of a rational Bézier surface. CAGD kernel modelers typically use NURBSs as a uniform representation to handle different types of free-form surface. This research indicates that rational Bézier representation, more specifically, a bi-cubic or 2×4 rational Bézier surface, is sufficient for kernel modeling of molecular surfaces and related applications. [Copyright &y& Elsevier]

Objective: This study aimed to construct a nomogram to effectively predict the overall survival (OS) of patients with early-stage non-small-cell lung cancer (NSCLC).Methods: For the training and internal validation cohorts, a total of 26,941 patients with stage I and II NSCLC were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. A nomogram was constructed based on the risk factors affecting prognosis using a Cox proportional hazards regression model. And 505 patients were recruited from Jiaxing First Hospital for external validation. The discrimination and calibration of the nomogram were evaluated by C-index and calibration curves.Results: A Nomogram was created after identifying independent prognostic factors using univariate and multifactorial factor analysis. The C-index of this nomogram was 0.726 (95% CI, 0.718-0.735) and 0.721 (95% CI, 0.709-0.734) in the training cohort and the internal validation cohort, respectively, and 0.758 (95% CI, 0.691-0.825) in the external validation cohort, which indicates that the model has good discrimination. Calibration curves for 1-, 3-, and 5-year OS probabilities showed good agreement between predicted and actual survival. In addition, DCA analysis showed that the net benefit of the new model was significantly higher than that of the TNM staging system.Conclusion: We developed and validated a survival prediction model for patients with non-small cell lung cancer in the early stages. This new nomogram is superior to the traditional TNM staging system and can guide clinicians to make the best clinical decisions. [ABSTRACT FROM AUTHOR]

The prediction of novel drug-target interactions (DTIs) has intrinsic significance in drug discovery research. Wet-lab experiments of DTIs are laborious and expensive; computational methods can help minimize the complexity of identifying unknown DTIs and accelerate the drug repositioning process. Nowadays, the number of drug-target features and their interactions regularly increases, disabling traditional computational methods' prediction and analyzing ability. Therefore, developing a new robust model to derive the reduced features for effective prediction is important. Further, accurate interactions also depend on the negative drug-target pairs, and it is worthwhile to build a technique to generate perfect negative pairs. To this end, we propose a new multi-label approach, called idti-MLKdr, by introducing multi-kernel learning (MKL) based SVM for DTIs prediction with various dimensionality reduction techniques. First, we have extracted the drug-target features from chemical structures and protein sequences, applying different feature extraction methods. A new technique has been developed to construct the negative drug-target pairs based on drug-drug (or protein-protein) similarity scores. Then, three-dimensionality reduction techniques have been applied to the extracted drug-target features. Finally, we trained a multi kernel-based learner together with the reduced features and combined their prediction scores to show the final results. In this experiment, we considered auROC as an evaluation metric. The proposed method has been compared with the various existing methods under five-fold cross-validation, and the experimental results indicated that idti-MLKdr attains the best auROC for predicting DTIs. We believe that improved prediction performance will motivate the researchers for further drug development. [Display omitted] • Computational model idti-MLKdr is proposed for predicting Drug-Target Interactions. • FP2, AAC, DC and TC descriptors are used for extracting Drug-target features. • CluMS technique is proposed for resolving class imbalance problem, and three DR techniques are utilized to manage the high dimensionality. • Multi-kernel Learning (MKL) based SVM algorithm is used as a classifier. • Achieves the best prediction performance and can effectively predict the potential DTIs. [ABSTRACT FROM AUTHOR]

• A new deep learning framework is proposed in this paper, which can leverage the object label inter-dependent for weakly supervised learning. • We creatively introduce a novel initialization method of label embeddings for inter-relationships learning. • Ablation studies of our model are provided in detail. • Our models can be applied to object classification and weakly supervised pointwise object localization. In computer vision, the research community has been looking to how to benefit from weakly supervised learning that utilizes easily obtained image-level labels to train neural network models. The existing deep convolutional neural networks for weakly supervised learning, however, generally do not fully exploit the label dependencies in an image. To make full use of this information, in this paper, we propose a new framework for weakly supervised learning of deep convolutional neural networks, introducing graph convolutional networks to capture the semantic label co-occurrence in an image. Moreover, we propose a novel initialization method for label embedding in graph convolutional networks, which enables a smoother optimization for interrelationships learning. Extensive experiments and comparisons on four public benchmark datasets (PASCAL VOC 2007, PASCAL VOC 2012, Microsoft COCO, and NUS-WIDE) show the superior performance of our approach in both image classification and weakly supervised pointwise object localization. These results lead us to conclude that the label dependencies in the input image can provide valuable evidence for learning strongly localized features. [ABSTRACT FROM AUTHOR]

Drug-target interactions (DTIs) play a key role in drug development and discovery processes. Wet lab prediction of DTIs is time-consuming, expensive, and tedious. Fortunately, computational approaches can identify new interactions (drug-target pairs) and accelerate the process of drug repurposing. However, a vast number of interactions remain undiscovered; therefore, we proposed a deep learning-based method (deepACTION) for predicting potential or unknown DTIs. Here, each drug chemical structure and protein sequence are transformed according to structural and sequence information using different descriptors to represent their features correctly. There have been some challenges, such as the high dimensionality and class imbalance of data during the prediction process. To address these problems, we developed the MMIB technique to balance the majority and minority instances in the dataset and utilized a LASSO model to handle the high dimensionality of the data. In addition, we trained the convolutional neural network algorithm with balanced and reduced features for accurate prediction of DTIs. In this study, the AUC is considered a primary evaluation metric for comparing the performance of the deep ACTION model with that of existing methods by a 5-fold cross-validation test. Our experiential dataset obtained from the DrugBank database and our deepACTION model achieved an AUC of 0.9836 for this dataset. The experimental results ensured that the model can predict significant numbers of new DTIs and provide complete information to motivate scientists to develop drugs. Image 1 • Computational model deepACTION is proposed for predicting Drug-Target Interactions (DTIs). • Different types of drug-protein features are generated by using Rcpi (package) and PROFEAT server. • MMIB technique is developed for resolving class imbalance, and LASSO is utilized to manage the high dimensionality. • Convolutional Neural Networks (CNNs) algorithm is used as a classifier. • Achieves the best prediction performance and can effectively predict the potential DTIs. [ABSTRACT FROM AUTHOR]

Accurate identification of drug-target interaction (DTI) is a crucial and challenging task in the drug discovery process, having enormous benefit to the patients and pharmaceutical company. The traditional wet-lab experiments of DTI is expensive, time-consuming, and labor-intensive. Therefore, many computational techniques have been established for this purpose; although a huge number of interactions are still undiscovered. Here, we present pdti-EssB, a new computational model for identification of DTI using protein sequence and drug molecular structure. More specifically, each drug molecule is transformed as the molecular substructure fingerprint. For a protein sequence, different descriptors are utilized to represent its evolutionary, sequence, and structural information. Besides, our proposed method uses data balancing techniques to handle the imbalance problem and applies a novel feature eliminator to extract the best optimal features for accurate prediction. In this paper, four classes of DTI benchmark datasets are used to construct a predictive model with XGBoost. Here, the auROC is utilized as an evaluation metric to compare the performance of pdti-EssB method with recent methods, applying five-fold cross-validation. Finally, the experimental results indicate that our proposed method is able to outperform other approaches in predicting DTI, and introduces new drug-target interaction samples based on prediction probability scores. pdti-EssB webserver is available online at http://pdtiessb-uestc.com/ Image 1 • Computational model pdti-EssB is proposed for predicting Drug-Target Interactions (DTIs). • MSF, PSSM-Bigram, PseAAC and SPIDER2 are used for extracting Drug-target features. • CUS and EnsRFS techniques are proposed for resolving class imbalance problem and high dimensionality of data. • Boosting algorithm is utilized as a classifier. • Achieves the best prediction performance and can effectively predict the potential DTIs. [ABSTRACT FROM AUTHOR]

AbstractPolyetheretherketone (PEEK) implants have emerged as a clinically favored alternative to titanium alloy implants for cranial bone substitutes due to their excellent mechanical properties and biocompatibility. However, the biological inertness of PEEK has hindered its clinical application. To address this issue, we developed a dual-functional surface modification method aimed at enhancing both osteogenesis and antibacterial activity, which was achieved through the sustained release of chondroitin sulfate (CS) and levofloxacin (LVFX) from a biomimetic polydopamine (PDA) coating on the PEEK surface. CS was introduced to promote cell adhesion and osteogenic differentiation. Meanwhile, incorporation of antibiotic LVFX was essential to prevent infections, which are a critical concern in bone defect repairing. To our delight, experiment results demonstrated that the SPKD/CS-LVFX specimen exhibited enhanced hydrophilicity and sustained drug release profiles. Furthermore, in vitro experiments showed that cell growth and adhesion, cell viability, and osteogenic differentiation of mouse calvaria-derived osteoblast precursor (MC3T3-E1) cells were significantly improved on the SPKD/CS-LVFX coating. Antibacterial assays also confirmed that the SPKD/CS-LVFX specimen effectively inhibited the growth of Escherichia coli and Staphylococcus aureus, attributable to the antibiotic LVFX released from the PDA coating. To sum up, this dual-functional PEEK implant showed a promising potential for clinical application in bone defects repairing, providing excellent osteogenic and antibacterial properties through a synergistic approach. [ABSTRACT FROM AUTHOR]

As a Gram‐negative anaerobic bacterium, Akkermansia muciniphila (AKK) participates in the immune response in many cancers. Our study focused on the factors and molecular mechanisms of AKK affecting immune escape in lung adenocarcinoma (LUAD). We cultured AKK bacteria, prepared AKK outer membrane protein Amuc_1100 and constructed a subcutaneous graft tumour mouse model. A549, NCI‐H1395 cells and mice were respectively treated with inactivated AKK, Amuc_1100, Ruxolitinib (JAK inhibitor) and RO8191 (JAK activator). CD8+ T cells that penetrated the membrane were counted in the Transwell assay. The toxicity of CD8+ T cells was evaluated by lactate dehydrogenase assay. Western blot was applied to determine JAK/STAT‐related protein and PD‐L1 expression, whilst CCL5, granzyme B and INF‐γ expression were assessed through enzyme‐linked immunosorbent assay (ELISA). The proportion of tumour‐infiltrating CD8+ T cells and the levels of granzyme B and INF‐γ were determined by flow cytometry. AKK markedly accelerated A549 and NCI‐H1395 recruiting CD8+ T cells and enhanced CD8+ T cell toxicity. Amuc_1100 purified from AKK exerted the same promoting effects. Besides, Amuc_1100 dramatically suppressed PD‐L1, p‐STAT and p‐JAK expression and enhanced CCL5, granzyme B and INF‐γ expression. Treatment with Ruxolitinib accelerated A549 and NCI‐H1395 cells recruiting CD8+ T cells, enhanced CD8+ T cell toxicity, CCL5, granzyme B and INF‐γ expression, and inhibited PD‐L1 expression. In contrast, the RO8191 treatment slowed down the changes induced by Amuc_1100. Animal experiments showed that Amuc_1100 was found to increase the number of tumour‐infiltrating CD8+ T cells, increase the levels of granzyme B and INF‐γ and significantly inhibit the expression of PD‐L1, p‐STAT and p‐JAK, which exerted an antitumour effect in vivo. In conclusion, through inhibiting the JAK/STAT signalling pathway, AKK outer membrane protein facilitated the recruitment of CD8+ T cells in LUAD and suppressed the immune escape of cells. [ABSTRACT FROM AUTHOR]

Conjugated polymers with p‐orbital overlap show intramolecular‐conformation‐dependent photophysical and electrical properties. Compared to the non‐planar segments, the planar conformation segments have a relatively long effective conjugated length and rigid backbone structure, which are beneficial for the enhancement of optoelectronic properties. Herein, the effect of film thickness on the formation capacity of planar (β) conformations in polyfluorene (poly[4‐(octyloxy)‐9,9‐diphenylfluorene‐2,7‐diyl]‐co‐[5‐(octyloxy)‐9,9‐diphenylfluorene‐2,7‐diyl], PODPF) for the fabrication of stable deep‐blue polymer light‐emitting diodes (PLEDs) is explored. Thickness‐tunable films with thicknesses ranging from 15 to 270 nm by controlling the concentration of precursor solution are prepared. The main observations are as follows. The PODPF chains in thin films are less likely to adopt the β conformations associated with the loose interchain packing in nano‐aggregates than those in thick films. Red‐shifted and well‐resolved emission spectra are observed for the annealed film with a high thickness, indicating the greater presence of β‐conformation segments. More interestingly, interchain aggregation is a necessary but not sufficient condition for the formation of β conformations. Finally, PLEDs based on β‐conformation segments have outstanding brightness, low turn‐on voltage, and stable deep‐blue electroluminescent behavior, confirming the unique advantage of planar conformation for optoelectronic applications. [ABSTRACT FROM AUTHOR]

In regression problems where there is no known true underlying model, conformal prediction methods enable prediction intervals to be constructed without any assumptions on the distribution of the underlying data, except that the training and test data are assumed to be exchangeable. However, these methods bear a heavy computational cost—and, to be carried out exactly, the regression algorithm would need to be fitted infinitely many times. In practice, the conformal prediction method is run by simply considering only a finite grid of finely spaced values for the response variable. This paper develops discretized conformal prediction algorithms that are guaranteed to cover the target value with the desired probability and that offer a trade‐off between computational cost and prediction accuracy. Copyright © 2018 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Afatinib, a second-generation irreversible epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI), has been approved as EGFR, HER2, HER3 and HER4 inhibitor for non-small cell lung cancer (NSCLC) treatment. However, acquired resistance to afatinib has been found in most EGFR mutant NSCLC patients. Bladder cancer associated transcript 1 (BLACAT1) is a novel long non-coding RNAs (lncRNA) that play a functional role as an oncogenic lncRNA and is associated with chemoresistance. We aimed to identify the role of BLACAT1 in afatinib-resistant NSCLC and underlying mechanisms. Afatinib-resistant NSCLC cells were established. MTT assay, colony formation assay, apoptosis analysis, qRT-PCR and western blot analysis, immunohistochemistry, and in vivo study were carried out. BLACAT1 was up-regulated in afatinib-resistant NSCLC cells. Knockdown of BLACAT1 reversed the resistance of afatinib to NSCLC cells by modulating STAT3 signalling. The results provided a potential strategy for afatinib-resistant NSCLC by targeting BLACAT1. [ABSTRACT FROM AUTHOR]

To evaluate the value of nanopore targeted sequencing in diagnosing pneumonia pathogens. This large-scale multicentre prospective study performed in 8 hospitals across China from April to October 2022. Hospitalised patients with a diagnosis of pneumonia at admission were included. Complete clinical data were collected, and bronchoalveolar lavage fluid were obtained from each patient. These samples underwent simultaneous testing using conventional microbial testing, metagenomic next-generation sequencing, and nanopore targeted sequencing. A total of 218 patients were included. Among the 168 cases of pulmonary infection, 246 strains of pathogens were confirmed. Nanopore targeted sequencing outperformed conventional microbial testing, identifying more pathogens with a sensitivity increase of 47.9% (77.2% vs. 29.3%). Metagenomic next-generation sequencing had a sensitivity of 82.9%. Total of 70.1% patients had consistent results in both metagenomic next-generation sequencing and nanopore targeted sequencing. Nanopore targeted sequencing exhibited significantly higher sensitivity in detecting Pneumocystis jiroveci, cytomegalovirus, Mycobacterium tuberculosis, Nontuberculous mycobacteria, Streptococcus pneumoniae, and Mycoplasma pneumoniae compared to conventional microbial testing. However, metagenomic next-generation sequencing demonstrated higher sensitivity than nanopore targeted sequencing for Aspergillus (88.5% vs. 53.8%). Regarding the detection of co-infections, nanopore targeted sequencing displayed significantly higher sensitivity than conventional microbial testing (76.7% vs. 28.7%) and was on par with metagenomic next-generation sequencing (76.7% vs. 82.9%). Nanopore targeted sequencing performs equally well as metagenomic next-generation sequencing in bronchoalveolar lavage fluid for pathogen diagnosis in pneumonia, both methods showing higher sensitivity than conventional microbial testing. Nanopore targeted sequencing can be considered a reliable method for diagnosing pathogens in pneumonia. [ABSTRACT FROM AUTHOR]