Nano-SAR Modeling for Predicting the Cytotoxicity of Metal Oxide Nanoparticles to PaCa2.

Nowadays, the impact of engineered nanoparticles (NPs) on human health and environment has aroused widespread attention. It is essential to assess and predict the biological activity, toxicity, and physicochemical properties of NPs. Computation-based methods have been developed to be efficient alternatives for understanding the negative effects of nanoparticles on the environment and human health. Here, a classification-based structure-activity relationship model for nanoparticles (nano-SAR) was developed to predict the cellular uptake of 109 functionalized magneto-fluorescent nanoparticles to pancreatic cancer cells (PaCa2). The norm index descriptors were employed for describing the structure characteristics of the involved nanoparticles. The Random forest algorithm (RF), combining with the Recursive Feature Elimination (RFE) was employed to develop the nano-SAR model. The resulted model showed satisfactory statistical performance, with the accuracy (ACC) of the test set and the training set of 0.950 and 0.966, respectively, demonstrating that the model had satisfactory classification effect. The model was rigorously verified and further extensively compared with models in the literature. The proposed model could be reasonably expected to predict the cellular uptakes of nanoparticles and provide some guidance for the design and manufacture of safer nanomaterials.

Ultrasmall, Amorphous V2O3 Intimately Anchored on a Carbon Nanofiber Aerogel Toward High-Rate Zinc-Ion Batteries.

When considered as a cathode candidate for aqueous Zn-ion batteries, V2O3 faces several problems, such as inherently unsuitable structure, fast structural degradation, and sluggish charge transport kinetics. In this paper, we report the synthesis of a V2O3 intimately coupled carbon aerogel by a controllable ion impregnation and solid-state reaction strategy using bacterial cellulose and ammonium metavanadate as raw materials. In this newly designed structure, the carbonized carbon fiber network provides fast ion and electron transport channels. More importantly, the cellulose aerogel functions as a dispersing and supporting skeleton to realize the particle size reduction, uniform distribution, and amorphous features of V2O3. These advantages work together to realize adequate electrochemical activation during the initial charging process and shorter transport distance and faster transport kinetics of Zn2+. The batteries based on the V2O3/CNF aerogel exhibit a high-rate performance and an excellent cycling stability. At a current density of 20 A g-1, the V2O3/CNF aerogel delivers a specific capacity of 159.8 mAh g-1, and it demonstrates an exceptionally long life span over 2000 cycles at 12 A g-1. Furthermore, the electrodes with active material loadings as high as 10 mg cm-2 still deliver appreciable specific capacities of 257 mAh g-1 at 0.1 A g-1.

Hierarchical carbon hollow nanospheres coupled with ultra-small molybdenum carbide as sulfiphilic sulfur hosts for lithium-sulfur batteries.

Lithium-sulfur (Li-S) batteries are an attractive candidate to replace the current state-of-the-art lithium-ion batteries due to their promising theoretical capacity of 1675 mA h g-1 and energy density of 2500 W h kg-1. However, the lithium polysulfide (LiPS) shuttle effect and the slow sulfur redox kinetics seriously decrease the utilization of sulfur and deteriorate battery performance. Here, hierarchical carbon hollow nanospheres containing intimately coupled molybdenum carbide nanocrystals were synthesized as a sulfiphilic sulfur host. The sufficient interior void space accommodates the sulfur and physically confines LiPSs, while the in situ introduced molybdenum carbide nanoparticles can chemically immobilize LiPSs and catalytically accelerate their redox transformations. As a result, the Li-S batteries with this synergistic effect achieve an excellent rate capability of 566 mA h g-1 at 2C and a long cycle stability over 300 cycles at 1C.

Tracking Beyond Detection: Learning a Global Response Map for End-to-End Multi-Object Tracking.

Most of the existing Multi-Object Tracking (MOT) approaches follow the Tracking-by-Detection and Data Association paradigm, in which objects are firstly detected and then associated in the tracking process. In recent years, deep neural network has been utilized to obtain more discriminative appearance features for cross-frame association, and noticeable performance improvement has been reported. On the other hand, the Tracking-by-Detection framework is yet not completely end-to-end, which leads to huge computation and limited performance especially in the inference (tracking) process. To address this problem, we present an effective end-to-end deep learning framework which can directly take image-sequence/video as input and output the located and tracked objects of learned types. Specifically, a novel global response network is learned to project multiple objects in the image-sequence/video into a continuous response map, and the trajectory of each tracked object can then be easily picked out. The overall process is similar to how a detector inputs an image and outputs the bounding boxes of each detected object. Experimental results based on the MOT16 and MOT17 benchmarks show that our proposed on-line tracker achieves state-of-the-art performance on several tracking metrics.

The cytotoxicity of nanomaterials: Modeling multiple human cells uptake of functionalized magneto-fluorescent nanoparticles via nano-QSAR.

The vast majority of nanomaterials have attracted an upsurge of interest since their discovery and considerable researches are being carried out about their adverse outcomes for human health and the environment. In this study, two regression-based quantitative structure-activity relationship models for nanoparticles (nano-QSAR) were established to predict the cellular uptakes of 109 functionalized magneto-fluorescent nanoparticles to pancreatic cancer cells (PaCa2) and human umbilical vein endothelial cells (HUVEC) lines, respectively. The improved SMILES-based optimal descriptors encoded with certain easily available physicochemical properties were proposed to describe the molecular structure characteristics of the involved nanoparticles, and the Monte Carlo method was used for calculating the improved SMILES-based optimal descriptors. Both developed nano-QSAR models for cellular uptake prediction provided satisfactory statistical results, with the squared correlation coefficient (R2) being 0.852 and 0.905 for training sets, and 0.822 and 0.885 for test sets, respectively. Both models were rigorously validated and further extensively compared to literature models. Predominant physicochemical features responsible for cellular uptake were identified by model interpretation. The proposed models could be reasonably expected to provide guidance for synthesizing or choosing safer, more suitable surface modifiers of desired properties prior to their biomedical applications.