Controlling phase transfer of molybdenum carbides by various metals for highly efficient hydrogen production

The α phase Mo carbide has been widely investigated recently for its high activity in hydrogen production from water gas shift (WGS) reaction. However, high loading of noble metals as well as high economic and environmental cost derived from high-temperature ammonification and carbonization process will lead to high cost of hydrogen production. Thus, the efficient controlling of phase transfer is promising. Herein, metals (Au, Pt, Rh, Cu) with a wide range of loadings were impregnated on flame spray pyrolysis (FSP) made MoO_(3) to produce Mo carbides by one-step carbonization. A breakthrough high metal-normalized hydrogen production rate of 213 mmol H2·gmetal^(-1)·s^(-1) was achieved on 0.025 wt% Rh/MoCx, which was much higher than Pt and Au based Mo carbides ever reported. The addition of trace Rh induced direct MoO_(3) transformation to high purity α-MoC_(1-x) in one-step carbonization instead of two-steps ammonification and carbonization process. In comparison to Rh, the addition of Pt, Au and Cu tend to transfer MoO_(3) into β-Mo2C at the same conditions. Besides, the one with 2 wt% Rh exhibited high stability in WGS reaction even at high temperature (300 ℃) due to its inhibition on carbides oxidation induced by H2O. We demonstrate that it is feasible to control phase transfer of Mo carbides even by trace amount of metals to simplify the production process of catalysts. The catalytic performance improved by Rh in aspects of both activity and stability provides a guide for producing more stable Mo carbides catalysts.

Sensitive system calls based packed malware variants detection using principal component initialized MultiLayers neural networks

Malware detection has become mission sensitive as its threats spread from computer systems to Internet of things systems.Modern malware variants are generally equipped with sophisticated packers,which allow them bypass modern machine learning based detection systems.To detect packed malware variants,unpacking techniques and dynamic malware analysis are the two choices.However,unpacking techniques cannot always be useful since there exist some packers such as private packers which are hard to unpack.Although dynamic malware analysis can obtain the running behaviours of executables,the unpacking behaviours of packers add noisy information to the real behaviours of executables,which has a bad affect on accuracy.To overcome these challenges,in this paper,we propose a new method which first extracts a series of system calls which is sensitive to malicious behaviours,then use principal component analysis to extract features of these sensitive system calls,and finally adopt multi-layers neural networks to classify the features of malware variants and legitimate ones.Theoretical analysis and real-life experimental results show that our packed malware variants detection technique is comparable with the the state-of-art methods in terms of accuracy.Our approach can achieve more than 95.6\%of detection accuracy and 0.048 s of classification time cost.

盐介质环境对苯胺聚合、产物结构及导电性能的影响

本文研究了苯胺在室温下,以H2O2/HCl为氧化体系的聚合过程,深入探讨了不同盐(NaCl、CuCl2、NiCl2、FeSO4、CuSO4)介质环境对苯胺聚合速率及产率的影响,并对产物的结构及性能进行了分析表征.结果发现,NaCl对苯胺聚合具有很好的催化作用,过渡金属Cu^2+和Fe2+对苯胺聚合速率提高更显著;在室温下,CuCl2可使苯胺聚合产率达到95%以上,在盐介质影响下,苯胺聚合产物从纤维状转变为短棒状,或以珊瑚状纳米结构及球形粒子出现;Na^+、Cu^2+和Ni^2+介质环境有利于聚苯胺的掺杂,使其具有一定的导电性,Fe^2+介质中产生的芬顿反应降低了苯胺聚合产物的掺杂程度,所制聚苯胺为无定型结构,且主要为还原态,不导电.

Guidelines for the diagnosis and treatment of pancreatic cancer in China (2021)

The incidence of pancreatic cancer has been rising worldwide,and its clinical diagnosis and treatment remain a great challenge.To present the update and improvements in the clinical diagnosis and treatment of pancreatic cancer in recent years,Chinese Pancreatic Association,the Chinese Society of Surgery,Chinese Medical Association revised the Guidelines for the Diagnosis and Treatment of Pancreatic Cancer in China(2014)after reviewing evidence-based and problem-oriented literature published during 2015-2021,mainly focusing on highlight issues regarding diagnosis and surgical treatment of pancreatic cancer,conversion strategies for locally advanced pancreatic cancer,treatment of pancreatic cancer with oligo metastasis,adjuvant and neoadjuvant therapy,standardized processing of surgical specimens and evaluation of surgical margin status,systemic treatment for unresectable pancreatic cancer,genetic testing,as well as postoperative follow up of patients with pancreatic cancer.Forty recommendation items were finally proposed based on the above issues,and the quality of evidence and strength of recommendations were graded using the Grades of Recommendation,Assessment,Development,and Evaluation system.This guideline aims to standardize the clinical diagnosis and therapy,especially surgical treatment of pancreatic cancer in China,and further improve the prognosis of patients with pancreatic cancer.

Discovery of C19-9 as a novel non-RGD inhibitor of αvβ3 to overcome enzalutamide resistance in castration-resistant prostate cancer

Dear Editor,The integrinαvβ3 receptor is a promising target for anticancer therapy.1,2 However,there are no effective marketed treatments targetingαvβ3.One possible limitation of Arginine-Glycine-Aspartic(RGD)-mimeticαvβ3 antagonists has been shown to cause partial agonism,which could induce major conformational changes that trigger paradoxical cell adhesion and angiogenesis.

Enhanced YOLOv5 network-based object detection(BALFilter Reader)promotes PERFECT filter-enabled liquid biopsy of lung cancer from bronchoalveolar lavage fluid(BALF)

Liquid biopsy of cancers,detecting tumor-related information from liquid samples,has attracted wide attentions as an emerging technology.Our previously reported large-area PERFECT(Precise-Efficient-Robust-Flexible-Easy-ControllableThin)filter has demonstrated competitive sensitivity in recovering rare tumor cells from clinical samples.However,it is time-consuming and easily biased to manually inspect rare target cells among numerous background cells distributed in a large area(Φ≥13 mm).This puts forward an urgent demand for rapid and bias-free inspection.Hereby,this paper implemented deep learning-based object detection for the inspection of rare tumor cells from large-field images of PERFECT filters with hematoxylin-eosin(HE)-stained cells recovered from bronchoalveolar lavage fluid(BALF).CenterNet,EfficientDet,and YOLOv5 were trained and validated with 240 and 60 image blocks containing tumor and/or background cells,respectively.YOLOv5 was selected as the basic network given the highest mAP@0.5 of 92.1%,compared to those of CenterNet and EfficientDet at 85.2%and 91.6%,respectively.Then,tricks including CIoU loss,image flip,mosaic,HSV augmentation and TTA were applied to enhance the performance of the YOLOv5 network,improving mAP@0.5 to 96.2%.This enhanced YOLOv5 network-based object detection,named as BALFilter Reader,was tested and cross-validated on 24 clinical cases.The overall diagnosis performance(~2 min)with sensitivity@66.7%±16.7%,specificity@100.0%±0.0%and accuracy@75.0%±12.5%was superior to that from two experienced pathologists(10–30 min)with sensitivity@61.1%,specificity@16.7%and accuracy@50.0%,with the histopathological result as the gold standard.The AUC of the BALFilter Reader is 0.84±0.08.Moreover,a customized Web was developed for a user-friendly interface and the promotion of wide applications.The current results revealed that the developed BALFilter Reader is a rapid,bias-free and easily accessible AI-enabled tool to promote the transplantation of the BALFilter technique.This work can easily expand to other cytopathological diagnoses and improve the application value of micro/nanotechnology-based liquid biopsy in the era of intelligent pathology.