Coupling intention and actions of vehicle-pedestrian interaction: A virtual reality experiment study.

The interactions between vehicles and pedestrians are complex due to their interdependence and coupling. Understanding these interactions is crucial for the development of autonomous vehicles, as it enables accurate prediction of pedestrian crossing intentions, more reasonable decision-making, and human-like motion planning at unsignalized intersections. Previous studies have devoted considerable effort to analyzing vehicle and pedestrian behavior and developing models to forecast pedestrian crossing intentions. However, these studies have two limitations. First, they mainly focus on investigating variables that explain pedestrian crossing behavior rather than predicting pedestrian crossing intentions. Moreover, some factors such as age, sensation seeking and social value orientation, used to establish decision-making models in these studies are not easily accessible in real-world scenarios. In this paper, we explored the critical factors influencing the decision-making processes of human drivers and pedestrians respectively by using virtual reality technology. To do this, we considered available kinematic variables and analyzed the internal relationship between motion parameters and pedestrian behavior. The analysis results indicate that longitudinal distance and vehicle acceleration are the most influential factors in pedestrian decision-making, while pedestrian speed and longitudinal distance also play a crucial role in determining whether the vehicle yields or not. Furthermore, a mathematical relationship between a pedestrian's intention and kinematic variables is established for the first time, which can help dynamically assess when pedestrians desire to cross. Finally, the results obtained in driver-yielding behavior analysis provide valuable insights for autonomous vehicle decision-making and motion planning.

Extended Quinolizinium-Fused Corannulene Derivatives: Synthesis and Properties.

Reported here is the design and synthesis of a novel class of extended quinolizinium-fused corannulene derivatives with curved geometry. These intriguing molecules were synthesized through a rationally designed synthetic strategy, utilizing double Skraup-Doebner-Von Miller quinoline synthesis and a rhodium-catalyzed C-H activation/annulation (CHAA) as the key steps. Single-crystal X-ray analysis revealed a bowl depth of 1.28-1.50 Å and a unique "windmill-like" shape packing of 12a(2PF6-) due to the curvature and incorporation of two aminium ions. All of the newly reported curved salts exhibit green to orange fluorescence with enhanced quantum yields (Φf = 9-13%) and improved dispersibility compared to the pristine corannulene (Φf = 1%). The reduced optical energy gap and lower energy frontier orbital found by doping extended corannulene systems with nitrogen cations was investigated by UV-vis, fluorescence, and theoretical calculations. Electrochemical measurements reveal a greater electron-accepting behavior compared with that of their pyridine analogues. The successful synthesis, isolation, and evaluation of these curved salts provide a fresh perspective and opportunity for the design of cationic nitrogen-doped curved aromatic hydrocarbon-based materials.

Elimination of antibiotic-resistant bacteria and resistance genes by earthworms during vermifiltration treatment of excess sludge.

Vermifiltration (VF) and a conventional biofilter (BF, no earthworm) were investigated by metagenomics to evaluate the removal rates of antibiotic-resistant bacteria (ARB), antibiotic resistance genes (ARGs), and class 1 integron-integrase (intI1), as well as the impact mechanism in combination with the microbial community. According to the findings of qPCR and metagenomics, the VF facilitated greater removal rates of ARGs (78.83% ± 17.37%) and ARB (48.23% ± 2.69%) than the BF (56.33% ± 14.93%, 20.21% ± 6.27%). Compared to the control, the higher biological activity of the VF induced an increase of over 60% in the inhibitory effect of earthworm coelomic fluid on ARB. The removal rates of ARGs by earthworm guts also reached over 22%. In addition, earthworms enhanced the decomposition of refractory organics, toxic, and harmful organics, which led to a lower selective pressure on ARGs and ARB. It provides a strategy for reducing resistant pollution in sewage treatment plants and recognizing the harmless stability of sludge.

Evaluation of Osteogenic Potential for Rat Adipose-Derived Stem Cells under Xeno-Free Environment.

This study aimed to develop a novel culture method for rat adipose-derived stem cells (rADSC) and evaluate their osteogenic potential. The rADSC cultured in xeno-free culture medium (XF-rADSCs) or conventional culture medium containing fetal bovine serum (FBS-rADSCs) were combined with micropieces of xeno-free recombinant collagen peptide to form 3-dimensional aggregates (XF-rADSC-CellSaic or FBS-rADSC-CellSaic). Both FBS-rADSC and XF-ADSC in CellSaic exhibited multilineage differentiation potential. Compared to FBS-rADSC-CellSaic, XF-rADSC-CellSaic accelerated and promoted osteogenic differentiation in vitro. When transplanted into rat mandibular congenital bone defects, the osteogenically differentiated XF-rADSC-CellSaic induced regeneration of bone tissue with a highly maturated structure compared to FBS-rADSC-CellSaic. In conclusion, XF-rADSC-CellSaic is a feasible 3-dimensional platform for efficient bone formation.

The Impact of LiDAR Configuration on Goal-Based Navigation within a Deep Reinforcement Learning Framework.

Over the years, deep reinforcement learning (DRL) has shown great potential in mapless autonomous robot navigation and path planning. These DRL methods rely on robots equipped with different light detection and range (LiDAR) sensors with a wide field of view (FOV) configuration to perceive their environment. These types of LiDAR sensors are expensive and are not suitable for small-scale applications. In this paper, we address the performance effect of the LiDAR sensor configuration in DRL models. Our focus is on avoiding static obstacles ahead. We propose a novel approach that determines an initial FOV by calculating an angle of view using the sensor's width and the minimum safe distance required between the robot and the obstacle. The beams returned within the FOV, the robot's velocities, the robot's orientation to the goal point, and the distance to the goal point are used as the input state to generate new velocity values as the output action of the DRL. The cost function of collision avoidance and path planning is defined as the reward of the DRL model. To verify the performance of the proposed method, we adjusted the proposed FOV by ±10° giving a narrower and wider FOV. These new FOVs are trained to obtain collision avoidance and path planning DRL models to validate the proposed method. Our experimental setup shows that the LiDAR configuration with the computed angle of view as its FOV performs best with a success rate of 98% and a lower time complexity of 0.25 m/s. Additionally, using a Husky Robot, we demonstrate the model's good performance and applicability in the real world.

CircNOLC1 Promotes Colorectal Cancer Liver Metastasis by Interacting with AZGP1 and Sponging miR-212-5p to Regulate Reprogramming of the Oxidative Pentose Phosphate Pathway.

Liver metastasis is a common cause of death in progressive colorectal cancer patients, but the molecular mechanisms remain unclear. Here, it is reported that a conserved and oxidative pentose phosphate pathway-associated circular RNA, circNOLC1, plays a crucial role in colorectal cancer liver metastasis. It is found that circNOLC1 silencing reduces the oxidative pentose phosphate pathway-related intermediate metabolites and elevates NADP+ /NADPH ratio and intracellular ROS levels, thereby attenuating colorectal cancer cell proliferation, migration, and liver metastasis. circNOLC1 interacting with AZGP1 to activate mTOR/SREBP1 signaling, or sponging miR-212-5p to upregulate c-Met expression, both of which can further induce G6PD to activate oxidative pentose phosphate pathway in colorectal cancer liver metastasis. Moreover, circNOLC1 is regulated by the transcription factor YY1 and specifically stabilized HuR induces its parental gene mRNA expression. The associations between circNOLC1 and these signaling molecules are validated in primary CRC and corresponding liver metastasis tissues. These findings reveal that circNOLC1 interacting with AZGP1 and circNOLC1/miR-212-5p/c-Met axis plays a key role in oxidative pentose phosphate pathway-mediated colorectal cancer liver metastasis, which may provide a novel target for precision medicine of colorectal cancer.

High expression of NOLC1 as an independent prognostic factor for survival in patients with colorectal cancer.

BACKGROUND: As a phosphorylated protein, NOLC1 is mainly located in the nucleus and is highly expressed in a variety of tumors, participating in the regulation of cell proliferation and aging. This study further investigated the role of NOLC1 in colorectal cancer tumors, aiming to provide sufficient scientific evidence for the clinical treatment of colorectal cancer. METHODS: We used TCGA, GEO, TNMplot, GEPIA, and other databases to explore the expression level of NOLC1 in colorectal cancer patients, as well as the correlation between the clinical characteristics of colorectal cancer patients and their expression, and conducted the prognostic analysis. Immunohistofluorescence (IHF) staining verified the analytical results. Subsequently, KEGG and GO enrichment analysis was used to identify the potential molecular mechanism of NOLC1 promoting the occurrence and development of colorectal cancer. The influence of NOLC1 expression on the immune microenvironment of colorectal cancer patients was further investigated using the TIMER database. GDSC database analysis was used to screen out possible anti-colorectal cancer drugs against NOLC1. Finally, we demonstrated the effect of NOLC1 on the activity and migration of colorectal cancer cells by Edu Cell proliferation assay and Wound Healing assay in vitro. RESULTS: Our results suggest that NOLC1 is overexpressed in colorectal cancer, and that overexpression of NOLC1 is associated with relevant clinical features. NOLC1, as an independent risk factor affecting the prognosis of colorectal cancer patients, can lead to a poor prognosis of colorectal cancer. In addition, NOLC1 may be associated with MCM10, HELLS, NOC3L, and other genes through participating in Wnt signaling pathways and jointly regulate the occurrence and development of colorectal cancer under the influence of the tumor microenvironment and many other influencing factors. Related to NOLC1: Selumetinib, Imatinib, and targeted drugs such as Lapatinib have potential value in the clinical application of colorectal cancer. NOLC1 enhances the proliferation and migration of colorectal cancer cells. CONCLUSIONS: High expression of NOLC1 as an independent prognostic factor for survival in patients with colorectal cancer. NOLC1 enhances the proliferation and migration of colorectal cancer cells. Further studies and clinical trials are needed to confirm the role of NOLC1 in the development and progression of colorectal cancer.

Regulating highly photoelectrochemical activity of Zr-based mixed-linker metal-organic frameworks toward sensitive electrogenerated chemiluminescence sensing of α-glucosidase.

The conductivity and emission efficiency of metal-organic frameworks (MOFs) remain challenging factors that limit their electrogenerated chemiluminescence (ECL) sensing applications. Herein, we report a facile approach to address these challenges by integrating an electroactive linker (H2-TCPP) with an ECL active electrogenerated chemiluminescence linker (H4-TBAPy) to construct a highly photoelectrochemical active mixed-linker MOFs (ML-MOFs). ECL results revealed a remarkable 15.4-fold enhancement for the top-performing ML-MOFs (M6-MOFs), surpassing the single linker MOFs. In addition, M6-MOFs also exhibit a remarkable 73-fold enhancement in ECL efficiency compared to commercial Ru (bpy)32+. This improvement should be attributed to the synergistic effects resulting from the combination of two linkers. Furthermore, M6-MOFs are found to be served as a model ECLphore for sensitive and selective detection of α-glucosidase for the first time with good potential practicability in human serum samples. This work represents a promising direction to guide for designing good conductivity and high ECL efficiency MOFs in terms of linker functionalization and thus bandgap modulation for advancing their ECL sensing applications.

Exploitation of nanocrystal suspension as an effective oral formulation for oxfendazole.

An oxfendazole (OFZ) nanocrystal suspension was prepared by acid-base neutralization and crystallization combined with ultrasonic dispersion to overcome the challenge of its poor oral bioavailability. The nanosuspensions were screened and optimized by single-factor experiments and an orthogonal design using size and appearance as indices. The morphology (differential scanning calorimetry (DSC), X-ray powder diffraction (XRPD)) properties and pharmacokinetics of the best formulation were further developed. The results showed that the best cosolvent and stabilizer were malic acid and hydrogenated castor oil polyoxyethylene ether (HEL-40), respectively. Scanning electron microscopy demonstrated that the oxfendazole nanocrystals are irregular sheets with relative uniformity. The prepared nanocrystals have an average particle diameter of 431 ± 18 nm, a polydispersity index (PDI) of 0.376 ± 0.128, a zeta potential of 2.30 ± 0.44 mV, and a sedimentation coefficient of 0.993. The equilibrium solubility of nanocrystals in different solvents was significantly improved by 2.02-109.99-fold compared to OFZ crude. In 0.5% SDS-PBS (pH 2) and 0.5% SDS-PBS (pH 8) solution, oxfendazole nanocrystals were completely released within 5 min, while the OFZ crude only released 60.26% and 28.31%, respectively. The pharmacokinetics showed that the Cmax, Tmax, and AUC0-∞ of OFZ nanosuspension and OFZ granules in rats after oral dosage at 50 mg/kg were 4.23 and 13.63 µg/mL, 2.04 and 1.67 h, and 111.36 and 295.80 µg*h/mL, respectively. The relative bioavailability of the oxfendazole nanosuspension was 265.61% compared to the OFZ granules. These results showed that the nanosuspension might be a promising oral formulation for the hardly soluble OFZ.

Separation of mono- and di-valent ions from seawater reverse osmosis brine using selective electrodialysis.

As water scarcity has become a serious global issue, seawater reverse osmosis (SWRO) is considered as a promising technique to expand traditional water supplies. However, the reject brine from SWRO systems is still a major environmental concern. In this research, the monovalent selective electrodialysis (S-ED) was used to separate and recover one of the primary components, i.e., sodium chloride, from the SWRO brine, thereby avoiding the direct discharge of the brine and achieving the brine valorization. The permselectivity of selective ion-exchange membranes (IEMs) was elucidated by comparing with the standard IEMs in structure-property via membrane characterization techniques. Results indicated that the permselectivity of Selemion CSO membrane was attributed to the positive-charged layer with a low sulfonate/ammonium ratio of 1.28. Whereas the permselectivity of Selemion ASV membrane resulted from the highly cross-linked layer, according to the similar content of the fixed quaternary amines and the shift of the C­N absorption peak. In addition, the effects of the current density and temperature on the membrane performance were studied, including permselectivity ([Formula: see text] and [Formula: see text]), Na+ recovery, and specific energy consumption (ESEC). Finally, the NaCl-rich brine with the total dissolved solid (TDS) value of 167.5 ± 3.3 g/L was obtained using SWRO brine with the initial TDS of 76.8 g/L. The Na+/Mg2+ mass ratio of the concentrate was 222.4, compared with the initial value of 9.7 in SWRO brine.

FNA++: Fast Network Adaptation via Parameter Remapping and Architecture Search.

Deep neural networks achieve remarkable performance in many computer vision tasks. Most state-of-the-art (SOTA) semantic segmentation and object detection approaches reuse neural network architectures designed for image classification as the backbone, commonly pre-trained on ImageNet. However, performance gains can be achieved by designing network architectures specifically for detection and segmentation, as shown by recent neural architecture search (NAS) research for detection and segmentation. One major challenge though is that ImageNet pre-training of the search space representation (a.k.a. super network) or the searched networks incurs huge computational cost. In this paper, we propose a Fast Network Adaptation (FNA++) method, which can adapt both the architecture and parameters of a seed network (e.g., an ImageNet pre-trained network) to become a network with different depths, widths, or kernel sizes via a parameter remapping technique, making it possible to use NAS for segmentation and detection tasks a lot more efficiently. In our experiments, we apply FNA++ on MobileNetV2 to obtain new networks for semantic segmentation, object detection, and human pose estimation that clearly outperform existing networks designed both manually and by NAS. We also implement FNA++ on ResNets and NAS networks, which demonstrates a great generalization ability. The total computation cost of FNA++ is significantly less than SOTA segmentation and detection NAS approaches: 1737× less than DPC, 6.8× less than Auto-DeepLab, and 8.0× less than DetNAS. A series of ablation studies are performed to demonstrate the effectiveness, and detailed analysis is provided for more insights into the working mechanism. Codes are available at https://github.com/JaminFong/FNA.

Toward Accurate Predictions of Atomic Properties via Quantum Mechanics Descriptors Augmented Graph Convolutional Neural Network: Application of This Novel Approach in NMR Chemical Shifts Predictions.

In this study, an augmented Graph Convolutional Network (GCN) with quantum mechanics (QM) descriptors was reported for its accurate predictions of NMR chemical shifts with respect to experimental values. The prediction errors of 13C/1H NMR chemical shifts can be as small as 2.14/0.11 ppm. There are two crucial characteristics for this modified GCN: in one aspect, such a novel neural network could efficiently extract the overall molecule structure information; in another aspect, it could accurately solve the chemical environment of the target atom. As there exists an imperfect linear regression between the experimental NMR chemical shifts (δ) and the density functional theory (DFT) calculated isotropic shielding constants (σ), the inclusion of QM descriptors within GCN can largely improve its performance. Moreover, few-shot learning also becomes feasible with these descriptors. The success of this novel GCN in chemical shifts predictions also indicates its potential applicability for other computational studies.

Accurate predictions of aqueous solubility of drug molecules via the multilevel graph convolutional network (MGCN) and SchNet architectures.

Deep learning based methods have been widely applied to predict various kinds of molecular properties in the pharmaceutical industry with increasingly more success. In this study, we propose two novel models for aqueous solubility predictions, based on the Multilevel Graph Convolutional Network (MGCN) and SchNet architectures, respectively. The advantage of the MGCN lies in the fact that it could extract the graph features of the target molecules directly from the (3D) structural information; therefore, it doesn't need to rely on a lot of intra-molecular descriptors to learn the features, which are of significance for accurate predictions of the molecular properties. The SchNet performs well in modelling the interatomic interactions inside a molecule, and such a deep learning architecture is also capable of extracting structural information and further predicting the related properties. The actual accuracy of these two novel approaches was systematically benchmarked with four different independent datasets. We found that both the MGCN and SchNet models performed well for aqueous solubility predictions. In the future, we believe such promising predictive models will be applicable to enhancing the efficiency of the screening, crystallization and delivery of drug molecules, essentially as a useful tool to promote the development of molecular pharmaceutics.

Injection of minocycline into the periaqueductal gray attenuates morphine withdrawal signs.

This study investigated the effects of minocycline microinjections, into the midbrain periaqueductal gray (PAG), on morphine withdrawal and the expression of pannexin-1 (panx1), phosphorylated mammalian target of rapamycin (p-mTOR), protein kinase A (PKA), and cAMP response element-binding protein (CREB). Rats were injected with morphine, intraperitoneally, at increasing doses, twice per day, to establish animal models of morphine exposure. Minocycline was administered into the PAG before the first intraperitoneal (i.p.) injection of morphine each day, on days 1-4. On the last day of the experiment, all rats were injected with naloxone, and morphine withdrawal was observed, and then changes in the expression levels of ionized calcium-binding adaptor molecule 1 (Iba1) and its downstream factors, panx1, p-mTOR, PKA, and CREB were evaluated by western blot and immunohistochemistry analyses. Morphine withdrawal increased microglial activation, whereas minocycline could inhibit microglial activation and withdrawal and the downregulation of panx1, p-mTOR, PKA, and CREB expression, reducing the effects of morphine withdrawal.

lncRNA STEAP3-AS1 Modulates Cell Cycle Progression via Affecting CDKN1C Expression through STEAP3 in Colon Cancer.

Previous studies have reported that long noncoding RNAs (lncRNAs) have acted as new players during tumorigenesis. Metallothionein also plays an important role in tumor progression. It is mainly considered to be involved in the process of cell proliferation, oxidative stress, and multidrug resistance. However, the potential involvement of metallothionein-related lncRNAs in colon cancer remains poorly understood. In our study, we found that MT1M affected the expression of lncRNA STEAP3-AS1. STEAP3-AS1 is located in physical contiguity with STEAP3 and notably increased in colon cancer tissues and cell lines. STEAP3-AS1 expression was negatively associated with the expression of STEAP3. High levels of STEPA3-AS1 were associated with poor overall survival in colon cancer patients. In in vitro assays, STEAP3-AS1 knockdown could inhibit colon cancer cell proliferation and migration and arrest colon cancer cells at the G0-G1 phase. In tumorigenicity assays, STEAP3-AS1 knockdown could strongly inhibit tumor growth. Mechanistic investigations demonstrated that STEAP3-AS1 downregulation could increase the expression of cyclin-dependent kinase inhibitor 1C (CDKN1C) by STEAP3 upregulation. Overall, we identify the underlying role of MT1M-related lncRNA STEAP3-AS1 in colon cancer progression, which provides a novel strategy for colon cancer therapy.

Molecular dynamics simulations of solvent effects on the crystal morphology of lithium carbonate.

The attachment energy (AE) model was employed to investigate the growth morphology of Li2CO3 under vacuum and water solvent conditions by molecular dynamics simulations. The attachment energy calculation predicted the growth morphology in vacuum dominated by the (1 1 -1), (0 0 2) and (1 1 0) crystal faces. A modified attachment energy model, accounting for the surface chemistry and the corresponding topography of the habit crystal plane, was established to predict the morphological importance of crystal faces in a water solvent. Moreover, radial distribution function (RDF) and diffusion coefficient analyses were performed to explore the adsorption and diffusion behaviors of solvent molecules on the Li2CO3 crystal faces. The calculated results showed that with the solvent effects, the (0 0 2) and (1 1 0) faces were of great morphological importance, while the (1 1 -1) face disappeared gradually. These finally resulted in a cuboid-like Li2CO3 crystal. The growth morphology and the corresponding X-ray powder diffraction pattern derived from the modified AE model were in accordance with the results observed in experiments. The related model provides an important basis for the further investigation of the effects of impurities.

Nanoparticles for antiparasitic drug delivery.

As an emerging novel drug carrier, nanoparticles provide a promising way for effective treatment of parasitic diseases by overcoming the shortcomings of low bioavailability, poor cellular permeability, nonspecific distribution and rapid elimination of antiparasitic drugs from the body. In recent years, some kinds of ideal nanocarriers have been developed for antiparasitic drug delivery. In this review, the progress of the enhanced antiparasitic effects of different nanoparticles payload and their influencing factors were firstly summarized. Secondly, the transport and disposition process in the body were reviewed. Finally, the challenges and prospects of nanoparticles for antiparasitic drug delivery were proposed. This review will help scholars to understand the development trend of nanoparticles in the treatment of parasitic diseases and explore strategies in the development of more efficient nanocarriers to overcome the difficulty in the treatment of parasite infections in the future.

Photonic nonlinearities via quantum Zeno blockade.

Realizing optical-nonlinear effects at a single-photon level is a highly desirable but also extremely challenging task, because of both fundamental and practical difficulties. We present an avenue to surmounting these difficulties by exploiting quantum Zeno blockade in nonlinear optical systems. Considering specifically a lithium-niobate microresonator, we find that a deterministic phase gate can be realized between single photons with near-unity fidelity. Supported by established techniques for fabricating and operating such devices, our approach can provide an enabling tool for all-optical applications in both classical and quantum domains.