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Semi-Supervised Few-Shot Learning (SSFSL) aims to train a classifier that can adapt to new tasks using limited labeled data and a fixed amount of unlabeled data. Various sophisticated methods have been proposed to tackle the challenges associated with this problem. In this paper, we present a simple but quite effective approach to predict accurate negative pseudo-labels of unlabeled data from an indirect learning perspective. We leverage these pseudo-labels to augment the support set, which is typically limited in few-shot tasks, e.g., 1-shot classification. In such label-constrained scenarios, our approach can offer highly accurate negative pseudo-labels. By iteratively excluding negative pseudo-labels one by one, we ultimately derive a positive pseudo-label for each unlabeled sample in our approach. The integration of negative and positive pseudo-labels complements the limited support set, resulting in significant accuracy improvements for SSFSL. Our approach can be implemented in just few lines of code by only using off-the-shelf operations, yet it outperforms state-of-the-art methods on four benchmark datasets. Furthermore, our approach exhibits good adaptability and generalization capabilities when used as a plug-and-play counterpart alongside existing SSFSL methods and when extended to generalized linear models.

Our work focuses on tackling the problem of fine-grained recognition with incomplete multi-modal data, which is overlooked by previous work in the literature. It is desirable to not only capture fine-grained patterns of objects but also alleviate the challenges of missing modalities for such a practical problem. In this paper, we propose to leverage a meta-learning strategy to learn model abilities of both fast modal adaptation and more importantly missing modality completion across a variety of incomplete multi-modality learning tasks. Based on that, we develop a meta-completion method, termed as MECOM, to perform multimodal fusion and explicit missing modality completion by our proposals of cross-modal attention and decoupling reconstruction. To further improve fine-grained recognition accuracy, an additional partial stream (as a counterpart of the main stream of MECOM, i.e., holistic) and the part-level features (corresponding to fine-grained objects' parts) selection are designed, which are tailored for fine-grained nature to capture discriminative but subtle part-level patterns. Comprehensive experiments from quantitative and qualitative aspects, as well as various ablation studies, on two fine-grained multimodal datasets and one generic multimodal dataset show our superiority over competing methods. Our code is open-source and available at https://github.com/SEU-VIPGroup/MECOM.

Our work focuses on tackling large-scale fine-grained image retrieval as ranking the images depicting the concept of interests (i.e., the same sub-category labels) highest based on the fine-grained details in the query. It is desirable to alleviate the challenges of both fine-grained nature of small inter-class variations with large intra-class variations and explosive growth of fine-grained data for such a practical task. In this paper, we propose attribute-aware hashing networks with self-consistency for generating attribute-aware hash codes to not only make the retrieval process efficient, but also establish explicit correspondences between hash codes and visual attributes. Specifically, based on the captured visual representations by attention, we develop an encoder-decoder structure network of a reconstruction task to unsupervisedly distill high-level attribute-specific vectors from the appearance-specific visual representations without attribute annotations. Our models are also equipped with a feature decorrelation constraint upon these attribute vectors to strengthen their representative abilities. Then, driven by preserving original entities' similarity, the required hash codes can be generated from these attribute-specific vectors and thus become attribute-aware. Furthermore, to combat simplicity bias in deep hashing, we consider the model design from the perspective of the self-consistency principle and propose to further enhance models' self-consistency by equipping an additional image reconstruction path. Comprehensive quantitative experiments under diverse empirical settings on six fine-grained retrieval datasets and two generic retrieval datasets show the superiority of our models over competing methods. Moreover, qualitative results demonstrate that not only the obtained hash codes can strongly correspond to certain kinds of crucial properties of fine-grained objects, but also our self-consistency designs can effectively overcome simplicity bias in fine-grained hashing.

This study demonstrates the implementation of the Hamming code using DNA-based nanostructures for error detection and correction in communication systems. The designed DNA nanostructures conduct logical operations to compute check codes and identify and correct erroneous data based on fluorescence signals. The execution of intricate DNA logic operations requires individuals with specialized training. By interpretation of the fluorescence signals generated by the DNA nanostructures, binary language can be extracted, effectively protecting data security. The findings highlight the potential of DNA as a versatile platform for reliable data transmission.

The task of multi-label image recognition is to predict a set of object labels that present in an image. As objects normally co-occur in an image, it is desirable to model the label dependencies to improve the recognition performance. To capture and explore such important information, we propose graph convolutional networks (GCNs) based models for multi-label image recognition, where directed graphs are constructed over classes and information is propagated between classes to learn inter-dependent class-level representations. Following this idea, we design two particular models that approach multi-label classification from different views. In our first model, the prior knowledge about the class dependencies is integrated into classifier learning. Specifically, we propose Classifier Learning GCN (C-GCN) to map class-level semantic representations (e.g., word embeddings) into classifiers that maintain the inter-class topology. In our second model, we decompose the visual representation of an image into a set of label-aware features and propose prediction learning GCN (P-GCN) to encode such features into inter-dependent image-level prediction scores. Furthermore, we also present an effective correlation matrix construction approach to capture inter-class relationships and consequently guide information propagation among classes. Empirical results on generic multi-label image recognition demonstrate that both of the proposed models can obviously outperform other existing state-of-the-arts. Moreover, the proposed methods also show advantages in some other multi-label classification related applications.

Object detection requires plentiful data annotated with bounding boxes for model training. However, in many applications, it is difficult or even impossible to acquire a large set of labeled examples for the target task due to the privacy concern or lack of reliable annotators. On the other hand, due to the high-quality image search engines, such as Flickr and Google, it is relatively easy to obtain resource-rich unlabeled datasets, whose categories are a superset of those of target data. In this article, to improve the target model with cost-effective supervision from source data, we propose a partial transfer learning approach QBox to actively query labels for bounding boxes of source images. Specifically, we design two criteria, i.e., informativeness and transferability, to measure the potential utility of a bounding box for improving the target model. Based on these criteria, QBox actively queries the labels of the most useful boxes from the source domain and, thus, requires fewer training examples to save the labeling cost. Furthermore, the proposed query strategy allows annotators to simply labeling a specific region, instead of the whole image, and, thus, significantly reduces the labeling difficulty. Extensive experiments are performed on various partial transfer benchmarks and a real COVID-19 detection task. The results validate that QBox improves the detection accuracy with lower labeling cost compared to state-of-the-art query strategies for object detection.

Fine-grained image analysis (FGIA) is a longstanding and fundamental problem in computer vision and pattern recognition, and underpins a diverse set of real-world applications. The task of FGIA targets analyzing visual objects from subordinate categories, e.g., species of birds or models of cars. The small inter-class and large intra-class variation inherent to fine-grained image analysis makes it a challenging problem. Capitalizing on advances in deep learning, in recent years we have witnessed remarkable progress in deep learning powered FGIA. In this paper we present a systematic survey of these advances, where we attempt to re-define and broaden the field of FGIA by consolidating two fundamental fine-grained research areas - fine-grained image recognition and fine-grained image retrieval. In addition, we also review other key issues of FGIA, such as publicly available benchmark datasets and related domain-specific applications. We conclude by highlighting several research directions and open problems which need further exploration from the community.

The fluorescent properties of conjugated microporous polyphenylene (CMPs) were tuned through a wide range by inclusion of small amount of comonomer as chromophore in the network. The multi-color CMPs were used for explosives sensing and demonstrated broad sensitivity (ranging from -0.01888 μM -1 to -0.00467 μM -1 ) and LODs (ranging from 31.0 nM to 125.3 nM) against thirteen explosive compounds including nitroaromatics (NACs), nitramines (NAMs) and nitrogen-rich heterocycles (NRHCs). The CMPs were also developed as a sensor array for discrimination of thirteen explosives, specifically including NT, p-DNB, DNT, TNT, TNP, TNR, RDX, HMX, CL-20, FOX-7, NTO, DABT and DHT. By using classical statistical method "Linear Discriminant Analysis (LDA)", the thirteen explosives at a fixed concentration were completely discriminated and unknown test samples were indentied with 88% classification accuracy. Moreover, explosives in different concentrations and the mixtures of explosives were also successfully classified. Compared with LDA, Machine Learning algorithms have significant advantages in analyzing the array-based sensing data. Different Machine Learning models for pattern recognition have also been implemented and discussed here and much higher accuracy (96% for "neural network") can be achieved in predicting unknown test samples after training., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

In this article, we present a novel lightweight path for deep residual neural networks. The proposed method integrates a simple plug-and-play module, i.e., a convolutional encoder-decoder (ED), as an augmented path to the original residual building block. Due to the abstract design and ability of the encoding stage, the decoder part tends to generate feature maps where highly semantically relevant responses are activated, while irrelevant responses are restrained. By a simple elementwise addition operation, the learned representations derived from the identity shortcut and original transformation branch are enhanced by our ED path. Furthermore, we exploit lightweight counterparts by removing a portion of channels in the original transformation branch. Fortunately, our lightweight processing does not cause an obvious performance drop but brings a computational economy. By conducting comprehensive experiments on ImageNet, MS-COCO, CUB200-2011, and CIFAR, we demonstrate the consistent accuracy gain obtained by our ED path for various residual architectures, with comparable or even lower model complexity. Concretely, it decreases the top-1 error of ResNet-50 and ResNet-101 by 1.22% and 0.91% on the task of ImageNet classification and increases the mmAP of Faster R-CNN with ResNet-101 by 2.5% on the MS-COCO object detection task. The code is available at https://github.com/Megvii-Nanjing/ED-Net.

The practical task of Automatic Check-Out (ACO) is to accurately predict the presence and count of each product in an arbitrary product combination. Beyond the large-scale and the fine-grained nature of product categories as its main challenges, products are always continuously updated in realistic check-out scenarios, which is also required to be solved in an ACO system. Previous work in this research line almost depends on the supervisions of labor-intensive bounding boxes of products by performing a detection paradigm. While, in this paper, we propose a Self-Supervised Multi-Category Counting (S2MC2) network to leverage the point-level supervisions of products in check-out images to both lower the labeling cost and be able to return ACO predictions in a class incremental setting. Specifically, as a backbone, our S2MC2 is built upon a counting module in a class-agnostic counting fashion. Also, it consists of several crucial components including an attention module for capturing fine-grained patterns and a domain adaptation module for reducing the domain gap between single product images as training and check-out images as test. Furthermore, a self-supervised approach is utilized in S2MC2 to initialize the parameters of its backbone for better performance. By conducting comprehensive experiments on the large-scale automatic check-out dataset RPC, we demonstrate that our proposed S2MC2 achieves superior accuracy in both traditional and incremental settings of ACO tasks over the competing baselines.

Circular RNAs (circRNAs) are stable covalently closed non-coding RNAs (ncRNAs). Many studies indicate that circRNAs are involved in the pathological and physiological processes of liver cancer. However, the functions of circRNAs in liver cancer immunity are less known. In this review, we summarized the functions of circRNAs in liver cancer, including proliferative, metastasis and apoptosis, liver cancer stemness, cell cycle, immune evasion, glycolysis, angiogenesis, drug resistance/sensitizer, and senescence. Immune escape is considered to be one of the hallmarks of cancer development, and circRNA participates in the immune escape of liver cancer cells by regulating natural killer (NK) cell function. CircRNAs may provide new ideas for immunotherapy in liver cancer., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Tang, Jiang, Jiang, Ye, Huang, Li, Qin, Zhou, Pan and Zheng.)

Background: Due to tumor heterogeneity, the diagnosis, treatment, and prognosis of patients with lung squamous cell carcinoma (LUSC) are difficult. DNA methylation is an important regulator of gene expression, which may help the diagnosis and therapy of patients with LUSC., Methods: In this study, we collected the clinical information of LUSC patients in the Cancer Genome Atlas (TCGA) database and the relevant methylated sequences of the University of California Santa Cruz (UCSC) database to construct methylated subtypes and performed prognostic analysis., Results: Nine hundred sixty-five potential independent prognosis methylation sites were finally identified and the genes were identified. Based on consensus clustering analysis, seven subtypes were identified by using 965 CpG sites and corresponding survival curves were plotted. The prognostic analysis model was constructed according to the methylation sites' information of the subtype with the best prognosis. Internal and external verifications were used to evaluate the prediction model., Conclusions: Models based on differences in DNA methylation levels may help to classify the molecular subtypes of LUSC patients, and provide more individualized treatment recommendations and prognostic assessments for different clinical subtypes. GNAS, FZD2, FZD10 are the core three genes that may be related to the prognosis of LUSC patients.

Landmark/pose estimation in single monocular images has received much effort in computer vision due to its important applications. It remains a challenging task when input images come with severe occlusions caused by, e.g., adverse camera views. Under such circumstances, biologically implausible pose predictions may be produced. In contrast, human vision is able to predict poses by exploiting geometric constraints of landmark point inter-connectivity. To address the problem, by incorporating priors about the structure of pose components, we propose a novel structure-aware fully convolutional network to implicitly take such priors into account during training of the deep network. Explicit learning of such constraints is typically challenging. Instead, inspired by how human identifies implausible poses, we design discriminators to distinguish the real poses from the fake ones (such as biologically implausible ones). If the pose generator G generates results that the discriminator fails to distinguish from real ones, the network successfully learns the priors. Training of the network follows the strategy of conditional Generative Adversarial Networks (GANs). The effectiveness of the proposed network is evaluated on three pose-related tasks: 2D human pose estimation, 2D facial landmark estimation and 3D human pose estimation. The proposed approach significantly outperforms several state-of-the-art methods and almost always generates plausible pose predictions, demonstrating the usefulness of implicit learning of structures using GANs.

Humans are capable of learning a new fine-grained concept with very little supervision, e.g., few exemplary images for a species of bird, yet our best deep learning systems need hundreds or thousands of labeled examples. In this paper, we try to reduce this gap by studying the fine-grained image recognition problem in a challenging few-shot learning setting, termed few-shot fine-grained recognition (FSFG). The task of FSFG requires the learning systems to build classifiers for the novel fine-grained categories from few examples (only one or less than five). To solve this problem, we propose an end-to-end trainable deep network, which is inspired by the state-of-the-art fine-grained recognition model and is tailored for the FSFG task. Specifically, our network consists of a bilinear feature learning module and a classifier mapping module: while the former encodes the discriminative information of an exemplar image into a feature vector, the latter maps the intermediate feature into the decision boundary of the novel category. The key novelty of our model is a "piecewise mappings" function in the classifier mapping module, which generates the decision boundary via learning a set of more attainable sub-classifiers in a more parameter-economic way. We learn the exemplar-to-classifier mapping based on an auxiliary dataset in a meta-learning fashion, which is expected to be able to generalize to novel categories. By conducting comprehensive experiments on three fine-grained datasets, we demonstrate that the proposed method achieves superior performance over the competing baselines.

Deep convolutional neural network models pre-trained for the ImageNet classification task have been successfully adopted to tasks in other domains, such as texture description and object proposal generation, but these tasks require annotations for images in the new domain. In this paper, we focus on a novel and challenging task in the pure unsupervised setting: fine-grained image retrieval. Even with image labels, fine-grained images are difficult to classify, letting alone the unsupervised retrieval task. We propose the selective convolutional descriptor aggregation (SCDA) method. The SCDA first localizes the main object in fine-grained images, a step that discards the noisy background and keeps useful deep descriptors. The selected descriptors are then aggregated and the dimensionality is reduced into a short feature vector using the best practices we found. The SCDA is unsupervised, using no image label or bounding box annotation. Experiments on six fine-grained data sets confirm the effectiveness of the SCDA for fine-grained image retrieval. Besides, visualization of the SCDA features shows that they correspond to visual attributes (even subtle ones), which might explain SCDA's high-mean average precision in fine-grained retrieval. Moreover, on general image retrieval data sets, the SCDA achieves comparable retrieval results with the state-of-the-art general image retrieval approaches.

Multi-instance learning (MIL) has been widely applied to diverse applications involving complicated data objects, such as images and genes. However, most existing MIL algorithms can only handle small- or moderate-sized data. In order to deal with large-scale MIL problems, we propose MIL based on the vector of locally aggregated descriptors representation (miVLAD) and MIL based on the Fisher vector representation (miFV), two efficient and scalable MIL algorithms. They map the original MIL bags into new vector representations using their corresponding mapping functions. The new feature representations keep essential bag-level information, and at the same time lead to excellent MIL performances even when linear classifiers are used. Thanks to the low computational cost in the mapping step and the scalability of linear classifiers, miVLAD and miFV can handle large-scale MIL data efficiently and effectively. Experiments show that miVLAD and miFV not only achieve comparable accuracy rates with the state-of-the-art MIL algorithms, but also have hundreds of times faster speed. Moreover, we can regard the new miVLAD and miFV representations as multiview data, which improves the accuracy rates in most cases. In addition, our algorithms perform well even when they are used without parameter tuning (i.e., adopting the default parameters), which is convenient for practical MIL applications.

A rapid UV-Vis spectrophotometric method was proposed to determine the concentration of DMP in aqueous solutions. The linear concentration range of DMP solution at the range of 250～400 nm is 0.5～70 mmol·L(-1). At 275 nm, the linear fitting equation is A=0.030 7c+0.133 0 with a correlation of 0.980 9. The detection limitation is 9.46×10-5 mmol·L-1, the RSD (n=6) of the method were at the range of 0.100%～0.612%. The recovery ratio for salt solutions sample is 95%～104%. Temperature, pH, and coexisting K(+), Na(+), Mg(2+), Cl(-), Br(-), I(-), SO(2-)(4) ions do not affect the detection. The coexisting CO(2-)(3) and HCO(-)(3) ions can be eliminated with acidification. The results showed that the proposed method is simple in pretreatment process and has high accuracy and precision. It is a quick measurement method of DMP concentration in water solution, and can be used to measure DMP concentration in reverse flotation tail liquid and reverse flotation material pulp.

In this paper, we propose a fine-grained image categorization system with easy deployment. We do not use any object/part annotation (weakly supervised) in the training or in the testing stage, but only class labels for training images. Fine-grained image categorization aims to classify objects with only subtle distinctions (e.g., two breeds of dogs that look alike). Most existing works heavily rely on object/part detectors to build the correspondence between object parts, which require accurate object or object part annotations at least for training images. The need for expensive object annotations prevents the wide usage of these methods. Instead, we propose to generate multi-scale part proposals from object proposals, select useful part proposals, and use them to compute a global image representation for categorization. This is specially designed for the weakly supervised fine-grained categorization task, because useful parts have been shown to play a critical role in existing annotation-dependent works, but accurate part detectors are hard to acquire. With the proposed image representation, we can further detect and visualize the key (most discriminative) parts in objects of different classes. In the experiments, the proposed weakly supervised method achieves comparable or better accuracy than the state-of-the-art weakly supervised methods and most existing annotation-dependent methods on three challenging datasets. Its success suggests that it is not always necessary to learn expensive object/part detectors in fine-grained image categorization.

The high similarity of MO and EO made it difficult to measure concentration of MOD and EOD. In this paper, dual wavelength spectrophotometry was used to determinate the concentration of MOD and EOD, which was proved to be fast and accrate. 429.00 nm and 469.50 nm were selected as detemination wavelengthes at pH 12. And the influences of Na+ and Cl- on determination of MOD and EOD were surveyed. The standard equations were A429 = 0.003 47 + 0.061 cm + 0.056 46 C(E) and A469.50 = 0.002 8 + 0.074 37 cm + 0.083 94 c(E) . Recovery of standard additions of MOD and EOD were larger than 95%, and relative standard deviation of standard equations were less than 2%. The addition of NaCl has little effect on recovery of standard additions and relative standard deviation of this method.

Aim: To assess whether differential expression of caspase-3 in paired metastatic lymph nodes (LNs) is prognostic of survival in patients with resectable esophageal squamous cell carcinoma (ESCC)., Methods: Capases-3 expression was evaluated immunohistochemically in 122 pairs of primary ESCCs and regional metastatic LNs assembled on tissue microarrays. The impact of caspase-3 expression on survival outcomes was analyzed by the Kaplan-Meier method and Cox proportional hazards regression model., Results: The level of caspase-3 expression was significantly higher in LN metastases than in primary tumors (P < 0.001). Caspase-3 expression in the primary tumors was associated with longer median survival (23 mo vs 21 mo, P = 0.033), whereas higher expression in paired metastatic LNs was associated with shorter median survival (20 mo vs 22 mo, P = 0.043). Multivariate analysis showed that both were independent prognostic factors., Conclusion: Caspase-3 expression in metastatic LNs may be a potential independent predictor of poorer overall survival in patients with resected ESCC and LN metastasis. Protein expression in metastatic tumors may be a biomarker prognostic of survival.

Background: The STK15 gene located on chromosome 20q13.2 encodes a centrosome-associated kinase critical for regulated chromosome segregation and cytokinesis. Recent studies have demonstrated STK15 to be significantly associated with many tumors, with aberrant expression obseved in many human malignancies. The purpose of this study was to investigate expression of STK15 in esophageal squamous cell carcinomas (ESCCs) in a Mongolian population., Methods: Two non-synonymous single nucleotide polymorphisms in the coding region of STK15, rs2273535 (Phe31Ile) and rs1047972 (Val57Ile) were assessed in 380 ESCC patients and 380 healthy controls. We also detected STK15 mRNA expression in 39 esophageal squamous cell carcinomas and corresponding adjacent tissues by real time PCR., Results: rs2273535 showed a significant association with ESCC in our Mongolian population (rs227353, P allele=0.0447, OR (95%CI)=1.259 (1.005~1.578)). Real time PCR analysis of ESCC tissues showed that expression of STK15 mRNA in cancer tissues was higher than in normal tissues (p=0.013)., Conclusions: Our study showed that functional SNPs in the STK15 gene are associated with ESCC in a Mongolian population and up-regulation of STK15 mRNAoccurs in ESCC tumors compared adjacent normal tissues. STK15 may thus have an important role in the prognosis of ESCC and be a potential therapeutic target.

The effect of flotation agent octadecyl amine (ODA) on the complex titration of both magnesium and calcium ions was studied with two groups of comparative experiments: (1) Before titration, the suspension was not filtered. In this case, ODA had a great effect on the complex titration of both magnesium and calcium ions. The titration end-point of magnesium ions was difficult to be determined. Although the titration end-point of calcium ions could be determined, there was an obvious experimental error compared with the blank solution without ODA. These results were confirmed by the UV-Visible spectrum analyses of the related solutions. (2) Before titration, the suspension was filtered. In this case, the influence of ODA on the complex titration of both magnesium and calcium ions could be removed. UV-Visible spectrum studies showed that, in this case, both the spectra and time scanning curves of the tested solutions were similar to those of the blank solutions.

Flame atomic absorption spectrometry (FAAS) was applied to the determination of rubidium and cesium in chloride type oilfield water by considering the interferences of the coexistent K+, Na+, Ca2+, and Mg2+ ions, Standard curve method and standard addition method were compared in the determination of rubidium and cesium in the simulated oilfield water and the real oilfield water from the Nanyishan region in Qaidam Basin. Although rubidium and cesium have similar physical-chemical properties, they present different characters during their analyses using the FAAS technique. When the standard addition method was used for the determination of rubidium and cesium in the simulated oilfield water, the results of rubidium were very poor, whereas the results of cesium were satisfactory. When the standard curve method was used for the determination of rubidium and cesium in the simulated oilfield water, the results of both rubidium and cesium were satisfactory within the linear ranges of the standard curves. For the real oilfield water, standard addition method is also only applicable for the determination of cesium with a recovery ranging from 90% to 110%. While standard curve method is applicable for the determination of both rubidium and cesium with a recovery ranging from 90% to 110%.

Flame atomic absorption spectrometry was applied to the determination of micro amount of lithium in the oil field water of certain area. In order to determine which method is more appropriate for the determination of lithium content in the oil field water, standard curve method and standard addition method were compared. The effects of dilution, coexistent ions, and deionizers on the determination were studied. For the determination of lithium content in the same diluted oil field water samples, there exist obvious differences between the results obtained from standard addition method and standard curve method. Standard addition method gives results with a larger error, whereas standard curve method gives more accurate results. It is difficult to eliminate the interferences when the standard addition method is used. The standard curve method is found to be more suitable for the determination of micro amount of lithium in the oil field water for its accuracy, simplicity, and feasibility. When the standard curve method is used, both the determined lithium concentration and the recovery change with the dilution extent of the oil field water. In order to get an accurate result, the oil field water sample should be diluted to 1/200 or less. In this case, the recovery by standard addition method ranges from 94.3% to 96.9%. When sodium phosphate or sodium chloride is used as the deionizer, the recovery by standard addition method ranges from 94.6% to 98.6%, or from 94.2% to 96.3%. In the determination of lithium content in oil field water, there are larger experimental errors without the addition of any deionizer. When the concentration of coexistent ions is within an allowed range, the addition of sodium phosphate as a deionizer can eliminate the interferences of the coexistent ions with the determination of the lithium content. If sodium chloride is used as a deionizer, a more accurate result can be obtained when the sodium content in the samples is near the sodium content in the standard solutions. In general, under suitable experimental conditions, sodium chloride can be used as the deionizer for the determination of lithium content in the oil field water.

Puerarin (1) is a major effective ingredient extracted from the traditional Chinese medicine Ge-gen (Radix Puerariae, RP). Recently, puerarin has been used to treat patients with coronary artery diseases (CAD). However, the mechanisms of puerarin on CAD are still not very clear. In this study, we investigated the role of puerarin on serum nitric oxide (NO) concentration, myocardial endothelial nitric oxide synthase (eNOS) gene expression, the protein expression of eNOS and inducible nitric oxide synthase (iNOS), as well as the level of protein kinase B (Akt/PKB) phosphorylation in rats with myocardial infarction. We found that puerarin (120 mg/kg/day, i.p.) could increase serum nitrite concentration in rat with myocardial ischemia (MI). It also induced the gene expression or activation of eNOS, protein expression of eNOS, and the Akt/PKB phosphorylation. From these results, we suggested that puerarin could increase serum nitric oxide level of rat with myocardial infarction, which should be one of the mechanisms of the therapeutic effect of puerarin on CAD. The increased expression of eNOS and the Akt/PKB pathway may be the underlying mechanism by which puerarin stimulates NO production.

Background & Objective: The prognosis of esophageal cancer is not only affected by TNM stage but also by the level of serum vascular endothelial growth factor (S-VEGF). This study was to investigate the effect of concurrent chemoradiotherapy on S-VEGF in esophageal squamous cell carcinoma (ESCC), and to explore the correlation of S-VEGF to the prognosis of ESCC., Methods: Serum samples were obtained from ESCC patients, treated with concurrent chemoradiotherapy in Cancer Center of Sun Yat-sen University from Dec. 2002 to May 2004, before treatment and 1 month after treatment. The serum samples from sex- and age-matched healthy donors were used as controls. Two courses of chemotherapy, comprised of cisplatin and 5-fluorouracil, were given during radiotherapy at 4-week intervals. S-VEGF level was measured by ELISA. The changes of S-VEGF level before and after treatment were observed, and its correlation to progress-freely survival rate of ESCC patients was analyzed., Results: S-VEGF level was significantly higher in ESCC patients before and 1 month after treatment than in healthy controls [(516.27+/-67.89) ng/L and (347.19+/-35.42) ng/L vs. (294.20+/-23.40) ng/L, P<0.01, P=0.002]; concurrent chemoradiotherapy significantly reduced S-VEGF level (P<0.01). S-VEGF level before treatment was significantly lower in the patients achieved complete remission than in those achieved partial remission or had progressive disease [(345.82+/-76.29) ng/L vs. (669.37+/-99.04) ng/L, P =0.020]. The 1-year progress-freely survival rate was 0 in the patients with S-VEGF level of > 516.27 ng/L before treatment and >347.19 ng/L after treatment, 17% in the patients with S-VEGF level of > 516.27 ng/L and <347.19 ng/L, respectively, 57% in the patients with S-VEGF level of < 516.27 ng/L and >347.19 ng/L, respectively, and 72% in the patients with S-VEGF level of < 516.27 ng/L and <347.19 ng/L, respectively (P= 0.005)., Conclusions: S-VEGF level is higher in ESCC patients than in healthy control. Concurrent chemoradiotherapy could reduce S-VEGF level in ESCC. The changes of S-VEGF level before and after treatment may provide prognostic information for ESCC patients.

Objective: To investigate the mechanism of erectile dysfunction (ED) in rat models with chronic renal failure (CRF)., Methods: Chronic renal failure was induced by adult male Sprague-Dawley rats, which were subjected to an excisional 5/6 nephrectomy. The rats in NCRF group and CRF group were divided into three groups randomly. Injected with apomorphine(APO, 80 microg/kg), penile erections of three groups were observed and noted by the 2nd week, 4th week respectively and 6th week from the 14th day of 5/6 nephrectomy and experimental models of ED with CRF were selected; NOS activity was examined and the microstructures of penile were observed under optical microscope with computer configuration image analysis system in selected rat models., Results: Compared with the controls, the areas of smooth muscle and NOS activity in the penile cavernous tissue of ED rats with CRF decreased significantly (P < 0.01 or P < 0.05) , and collagen fibers slightly increased, and these alterations had close correlations with the duration of CRF. The, Conclusions: Penile erection is seriously affected in rats with CRF. The decreases of areas of blood sinus had no obvious changes. the number of smooth muscles and NOS activity might be the most important factors.

Objective: To analyze treatment and prognostic factors of 74 patients with dermatofibro-sarcoma protuberans (DFSP)., Methods: From August 1990 to November 1999, 74 patients with DFSP confirmed pathologically were treated. There were 52 males and 22 females with a median age of 37 years (range 4 to 80 years) on diagnosis. Seventeen patients were treated by extensive excision and 2 by limited excision. Fifty-two patients had surgical resection alone (S), and 22 postoperative radiotherapy (S + R) of 50-70 Gy. The multivariate parameters were analyzed using Cox model. Kaplan-Meier and Log-Rank test were used to evaluate the results of the recurrence-free survival., Results: The rate of recurrence was 28.4% for all patients. The 5-year recurrence-free survival rate (RFSR) was 66.6% and the 10-year RFSR was 52.5%. The 5-year and 10-year in the S group were 58.4% and 41.2%, compared with 90.0% and 83.3% in the S + R group (P < 0.05). The 5-year and 10-year RFSR in the pathologically positive margin group were 57.5% and 41.4% respectively, compared with the 75.0% and 56.6% in the pathologically negative group (P < 0.05). Multivariate analysis suggested radiotherapy and negative pathological margins were favorable prognostic factors., Conclusion: Post-operation radiotherapy and pathological margin are the independent prognostic factors.

Background & Objective: Transcatheter arterial chemoembolization (TACE) is the routine treatment for unresectable primary liver cancer, but 3-year survival rate of patients received TACE alone is only about 20%. This research was to evaluate efficacy of external radiotherapy (RT) combined with TACE on unresectable primary live cancer., Methods: From Jun. 1994 to Apr. 2002, 114 patients with unresectable primary liver cancer were non-randomized to receive TACE plus RT (54 patients), or TACE alone (60 patients) as control. For TACE, after skiagram confirmed catheterization, suspension of 300 mg of carboplatin, 50-60 mg of epirubicin, 14-20 mg of mitomycin, and 10-30 ml of iodized oil was perfused into hepatic arteries, 1-2 mm of Gelfoam particles was given to embolize hepatic arteries according to blood supply conditions of tumors, this process was repeated every 4-8 weeks. Either group was treated with 1-4 sessions of TACE. In TACE+RT group, patients received radiation on tumor and generous margin 21-28 days after TACE. The radiation dose was 46-60 Gy in daily 2 Gy fractions., Results: In TACE+RT group, response rate (AFP titer decrease of >50%) was 61.1%, and 1-, 2-, 3-year survival rates of TACE+RT group were significantly higher than those of TACE group (66.5% vs. 53.9%, 48.4% vs. 37.2%, and 37.4% vs. 17.8%, P<0.05). Three-year survival rate correlated with tumor size, liver function grade, and portal vein embolus., Conclusion: TACE combined with RT may prolong survival time of patients with unresectable primary live cancer.

With the development of multidisciplinary treatment for cancer, great changes have taken place in the therapeutic strategy of breast cancer. However, radiotherapy as a method of local management, still plays an important role in the combined treatment of breast cancer. The recurrence in the chest wall ranks the first, accounting for 44-69% of the total local-regional relapse, therefore, the chest wall is commonly regarded as the most important target of radiotherapy after mastectomy. The traditional irradiation techniques cannot reach an ideal dose distribution due to the irregular shape of the chest wall. Electron arc therapy, by using the electron characteristics of dose distribution, combining the shape of thorax and the depth of target volume, make the dose distribution of target volume more reasonable, decreases the dose of heart or lung and has more clinical benefit than traditional techniques. On the other hand, it had been demonstrated by some clinical research that irradiation with electron arc could provide a high rate of local control and generally acceptable acute and long-term toxicity, comparing with the traditional irradiation techniques.