Easy but Efficient: Facile Approach to Molecule with Theoretically Justified Donor-Acceptor Structure for Effective Photothermal Conversion and Intravenous Photothermal Therapy.

To accelerate the pace in the field of photothermal therapy (PTT), it is urged to develop easily accessible photothermal agents (PTAs) showing high photothermal conversion efficiency (PCE). As a proof-of-concept, hereby a conventional strategy is presented to prepare donor-acceptor (D-A) structured PTAs through cycloaddition-retroelectrocyclization (CA-RE) reaction, and the resultant PTAs give high PCE upon near-infrared (NIR) irradiation. By joint experimental-theoretical study, these PTAs exhibit prominent D-A structure with strong intramolecular charge transfer (ICT) characteristics and significantly twisting between D and A units which account for the high PCEs. Among them, the DMA-TCNQ exhibits the strongest absorption in NIR range as well as the highest PCE of 91.3% upon irradiation by 760-nm LED lamp (1.2 W cm-2 ). In vitro and in vivo experimental results revealed that DMA-TCNQ exhibits low dark toxicity and high phototoxicity after IR irradiation along with nude mice tumor inhibition up to 81.0% through intravenous therapy. The findings demonstrate CA-RE reaction as a convenient approach to obtain twisted D-A structured PTAs for effective PTT and probably promote the progress of cancer therapies.

[Study of Modeling Samples Selection Method Based on Near Infrared Spectrum].

For more wheat varieties classification problem, we use near infrared spectrumto do qualitative analysis. Increasing the size of modeling sample could increase information of the model, however, at the same time, it also makes information redundancy so that modeling time and storage space will increase, thus, we need to decrease the size of modeling sample though selecting them. Some information must be lost and the effects of the model must be worse if we select samples blindly. We put forward the k nearest neighbor-density sample selection based on the traditional selection methods. Experiments use the near infrared diffuse reflection spectrum of wheat seed from lots of days. First, we use preprocessing and feature extraction to deal with the wheat original spectrum, then select modeling sample by three methods that are random sampling, k nearest neighbor and k nearest neighbor-density, finally, we establish the models of BPR(Biomimetic Pattern Recognition) and BPRI(Biomimetic Pattern Recognition Improved). The experimental results show that in the model of BPR we get the best results using the selection method of k nearest neighbor-density, especially it also decreases the size of modeling sample deeply, and in the model of BPRI the results using the selection method of k nearest neighbor-density are much better than random sampling and a little better than k nearest neighbor, but in the meanwhile the size of modeling sample using the selection method of k nearest neighbor-density are much smaller than k nearest neighbor. The experimental results prove that the sample selection method of k nearest neighbor-density can not only greatly reduce the modeling sample size, and ensure the quality of the model, it has obvious effect on varieties classification problem of wheat.

[Maize Hybrid Seed Purity Identification Based on Near Infrared Reflectance (NIR) and Transmittance (NIT) Spectra].

This article explore the feasibility of using Near Infrared Reflectance (NIR) and Transmittance (NIT) Spectroscopy (908.1-1677.2 nm wavelength range) to identify maize hybrid purity, and compare the performance of NIR and NIT spectroscopy. Principle Component Analysis (PCA) and Orthogonal Linear Discriminant Analysis (OLDA) were used to reduce the dimension of spectra which have been pretreated by first derivative and vector normalization. The hybrid purity identification model of Nonghua101 and Jingyu16 were built by SVM. Models based on NIR spectra obtained correct identification rate as 100% and 90% for Nonghua101 and Jingyu16 respectively. But NIR spectra were greatly influenced by the placement of seeds, and there existed significant difference between NIR spectra of embryo and non-embryo side. Models based on NIT spectroscopy yielded correct identification rate as 98% both for Nonghua101 and Jingyu16. NIT spectra of embryo and non-embryo side were highly similar. The results indicate that it is feasible to identify maize hybrid purity based on NIR and NIT spectroscopy, and NIT spectroscopy is more suitable to analyze single seed kernel than NIR spectroscopy.