Predictive value of serum levels of transforming growth factor beta 1 for the short-term effects of radiotherapy and chemotherapy in patients with esophageal cancer

Objective To investigate variation in levels of transforming growth factor beta 1(TGF-β1)before and after radiotherapy in patients with esophageal cancer in order to evaluate the predictive value of TGF-β1 for the effects of radiotherapy Methods A total of 140 patients with esophageal squamous carcinoma undergoing radical radiation therapy in the Department of Oncology from March 2015 to December 2017 were enrolled.The patients were divided into the effective(115 cases)and ineffective(25 cases)groups according to World Health Organization(WHO)criteria for the evaluation of solid tumors(2009 RECIST standard).TGF-β1 levels were measured in all patients by using enzyme-linked immunosorbent assay(ELISA).Multiple-factor analysis of the predictive value of the treatment efficacy was performed by Cox regression analysis.Results After radiotherapy,36,79,and 25 cases experienced complete response(CR),partial response(PR),and no response(NR),respectively,with a total effective rate of 82.14%.The TGF-β1 level was significantly lower in the effective group than that in the ineffective group(P<0.05)and covariance analysis revealed significantly reduced TGF-β1 level in esophageal cancer patients following radiotherapy.The multi-factor Cox regression model revealed that the predictive value of TGF-β1 for the effect of radiotherapy was largest,with a hazard ratio[HR]of 1.955(P=0.002),followed by exposure dose,with(HR=1.367;P=0.035).Conclusion Serum TGF-β1 level can serve as a predictor for the short-term effects of radiotherapy in patients with esophageal cancer.

Effects of long-term fertilization on soil gross N transformation rates and their implications

Application of fertilizer has been found to significantly affect soil N cycling. However, a comprehensive understanding of the effects of long-term fertilization on soil gross N transformation rates is still lacking. We compiled data of observations from 10 long-term fertilization experiments and conducted a meta-analysis of the effects of long-term fertilization on soil gross N transformation rates. The results showed that if chemical fertilizers of N, P and K were applied in balance, soil p H decreased very slightly. There was a significantly positive effect of long-term fertilization, either chemical or organic fertilizers or their combinations, on gross N mineralization rate compared to the control treatment（the mean effect size ranged from 1.21 to 1.25 at 95% confidence intervals（CI） with a mean of 1.23）, mainly due to the increasing soil total N content. The long-term application of organic fertilizer alone and combining organic and chemical fertilizer could increase the mineralization-immobilization turnover, thus enhance available N for plant while reduce N losses potential compared to the control treatment. However, long-term chemical fertilizer application did not significantly affect the gross NH4＋ immobilization rate, but accelerated gross nitrification rate（1.19; 95% CI： 1.08 to 1.31）. Thus, long-term chemical fertilizer alone would probably induce higher N losses potential through NO3– leaching and runoff than organic fertilizer application compared to the control treatment. Therefore, in the view of the effects of long-term fertilization on gross N transformation rates, it also supports that organic fertilizer alone or combination of organic and chemical fertilizer could not only improve crop yield, but also increase soil fertility and reduce the N losses potential.

The Grow th of the Entire Func tion Represented by the Laplace-Stieltjes Transform in the Whole Plane

In the paper, the α-order of the Laplace-Stieltjes Transform is introducedfirstly, then we get the relationship between α-order represented by the maximum modulus and α-order represented by A^*n, λn. Lastly, we obtain the relationship between type τrepresented by the maximum modulus and type τ represented by A^*n, λn.

Oscillation of Second-order Nonlinear Dynamic Equation on Time Scales

： The oscillation for a class of second order nonlinear variable delay dynamic equation on time scales with nonlinear neutral term and damping term was discussed in this article. By using the generalized Riccati technique, integral averaging technique and the time scales theory, some new sufficient conditions for oscillation of the equation are proposed. These results generalize and extend many knownresults for second order dynamic equations. Some examples are given to illustrate the main results of this article.

Exact Solution of Fractional Diffusion Model with Source Term used in Study of Concentration of Fission Product in Uranium Dioxide Particle

The exact solution of fractional diffusion model with a location-independent source term used in the study of the concentration of fission product in spherical uranium dioxide （U02） particle is built. The adsorption effect of the fission product on the surface of the U02 particle and the delayed decay effect are also considered. The solution is given in terms of Mittag-Leffler function with finite Hankel integral transformation and Laplace transformation. At last, the reduced forms of the solution under some special physical conditions, which is used in nuclear engineering, are obtained and corresponding remarks are given to provide significant exact results to the concentration analysis of nuclear fission products in nuclear reactor.

Analysis of frequency shifting in seismic signals using Gabor-Wigner transform

A hybrid time-frequency method known as Gabor-Wigner transform (GWT) is introduced in this paper for examining the time-frequency patterns of earthquake damaged buildings. GWT is developed by combining the Gabor transform (GT) and Wigner-Ville distribution (WVD). GT and WVD have been used separately on synthetic and recorded earthquake data to identify frequency shifting due to earthquake damages, but GT is prone to windowing effect and WVD involves ambiguity function. Hence to obtain better clarity and to remove the cross terms (frequency interference), GT and WVD are judiciously combined and the resultant GWT used to identify frequency shifting. Synthetic seismic response of an instrumented building and real-time earthquake data recorded on the building were investigated using GWT. It is found that GWT offers good accuracy for even slow variations in frequency, good time-frequency resolution, and localized response. Presented results confirm the efficacy of GWT when compared with GT and WVD used separately. Simulation results were quantified by the Renyi entropy measures and GWT shown to be an adequate technique in identifying localized response for structural damage detection.

Detecting Periodicity Associated with the Alpha-Helix Structure Using Fourier Transform

Alpha helix is a common type of secondary structure in the protein structure that consists of repeating helical turns. Patterns in the protein sequences that cause this repetitive pattern in the structure have long been sought. We used the discrete Fourier transform (DFT) to detect the periodicity signals correlated to the helical structure. We studied the distribution of multiple properties along the protein sequence, and found a property that showed strong periodicity correlated with the helical structure. Using a short-time Fourier transform (STFT) method, we investigated the amplitude of the periodical signals at each amino acid position. The results show that residues in the helix structure tend to display higher amplitudes than residues outside of the helices. This tendency is dramatically strengthen when sequence profiles obtained from multiple alignment were used to detect the periodicity. A simple method that predicted helices based on the amplitude yielded overall true positive rate (TPR) of 63%, 49% sensitivity, 72% specificity, and 0.22 Matthews Correlation Coefficient (MCC). The performance seemed to depend on the length of helices that the proteins had.

Short-Term Load Forecasting Using Soft Computing Techniques

Electric load forecasting is essential for developing a power supply strategy to improve the reliability of the ac power line data network and provide optimal load scheduling for developing countries where the demand is increased with high growth rate. In this paper, a short-term load forecasting realized by a generalized neuron–wavelet method is proposed. The proposed method consists of wavelet transform and soft computing technique. The wavelet transform splits up load time series into coarse and detail components to be the features for soft computing techniques using Generalized Neurons Network (GNN). The soft computing techniques forecast each component separately. The modified GNN performs better than the traditional GNN. At the end all forecasted components is summed up to produce final forecasting load.

Better Refined Adsorption Isotherm than BET Equation

During studying the heat capacity of metals and brightening more than the original Lena’s image, the temperature increasing term obtained in binomial expansion is transformed into the adsorption increasing term and thereafter we have derived the total adsorption rate equation with it. In the first layer the quantization does not occur and from 2nd layer to nth layer the quantization occurs. So as to get the total adsorption rate equation we add the quantized terms of the second to nth layers to the non-quantized term of the first layer. All terms are based on the unit surface sites. Instead of the unit surface sites, the new adsorption site term appears in the denominator of the adsorption equation. Hence the adsorption equations come out much better than BET equation. The surface area is also calculated through the integration of the adsorption isotherm equation excluding the first layer adsorption equation from the inflection point to the wanted relative pressure.

基于DWT-Informer的台区短期负荷预测

电力负荷预测是确保电力系统安全高效运行的关键任务,针对台区短期电力负荷预测这一关键问题,该文章研究了电气特性数据处理和Informer模型优化的新方法。文章通过离散小波变换(DWT)对电流数据进行降噪处理,同时使用Prophet模型提取时序特征优化输入数据;并采用Informer的稀疏自注意力机制和自注意力蒸馏,增强了模型的特征捕捉和预测速度。实例数据验证表明,经过DWT和Prophet特征提取后的模型在各项相同的指标下均优于原始模型,验证了DWT-Informer模型在数据预处理和模型优化方面均取得了显著的性能提升。

Enhanced Topic-Aware Summarization Using Statistical Graph Neural Networks

The rapid expansion of online content and big data has precipitated an urgent need for efficient summarization techniques to swiftly comprehend vast textual documents without compromising their original integrity.Current approaches in Extractive Text Summarization(ETS)leverage the modeling of inter-sentence relationships,a task of paramount importance in producing coherent summaries.This study introduces an innovative model that integrates Graph Attention Networks(GATs)with Transformer-based Bidirectional Encoder Representa-tions from Transformers(BERT)and Latent Dirichlet Allocation(LDA),further enhanced by Term Frequency-Inverse Document Frequency(TF-IDF)values,to improve sentence selection by capturing comprehensive topical information.Our approach constructs a graph with nodes representing sentences,words,and topics,thereby elevating the interconnectivity and enabling a more refined understanding of text structures.This model is stretched to Multi-Document Summarization(MDS)from Single-Document Summarization,offering significant improvements over existing models such as THGS-GMM and Topic-GraphSum,as demonstrated by empirical evaluations on benchmark news datasets like Cable News Network(CNN)/Daily Mail(DM)and Multi-News.The results consistently demonstrate superior performance,showcasing the model’s robustness in handling complex summarization tasks across single and multi-document contexts.This research not only advances the integration of BERT and LDA within a GATs but also emphasizes our model’s capacity to effectively manage global information and adapt to diverse summarization challenges.

A Probabilistic Analysis on the Harmonic Cancellation Characteristics of the Scott Transformer

The Scott transformer is widely used in electric railway systems when there are two unbalanced single-phase loads as this transformer can reduce unbalance currents. This paper investigates whether or not the Scott transformer can also reduce harmonic current. Our study shows that it can reduce the harmonic current when single-phase loads have identical harmonic characteristics. This harmonic reduction occurs through the cancellation of harmonic currents of single-phase loads in the transformer windings. Our studies also show that there is some degree of cancelation even when the loads do not have identical harmonic characteristics. The cancellation depends on load balance factor and harmonic order.

Relationship of Transforming Growth Factor-β1 and Arginase-1 Levels with Long-term Survival after Kidney Transplantation

In this study, we compared the serum levels of transforming growth factor-β1 （TGF-β1）, interleukin-10 （IL-10）, and arginase-1 in long-term survival kidney transplant recipients （LTSKTRs） with those in short-term survival kidney transplant recipients （STSKTRs）. We then evaluated the relationship between these levels and graft function. Blood samples were collected from 50 adult LTSKTRs and 20 STSKTRs （graft survival approximately 1-3 years post-transplantation）. All patients had stable kidney function. The samples were collected at our institution during the patients＇ follow-up examinations between March 2017 and September 2017. The plasma levels of TGF-β1, IL- 10, and arginase- 1 were analyzed using enzyme-linked immunosorbent assays （ELISA）. The levels of TGF-β1 and arginase-1 were significantly higher in the LTSKTRs than in the STSKTRs. The time elapsed since transplantation was positively correlated with the levels of TGF-β1 and arginase-1 in the LTSKTRs. The estimated glomerular filtration rate was positively correlated with the TGF-β1 level, and the serum creatinine level was negatively correlated with the TGF-β1 level. Higher serum levels of TGF-β1 and arginase-1 were found in LTSKTRs than in STSKTRs, and we found that TGF-β1 was positively correlated with long-term graft survival and function. Additionally, TGF-β1 and arginase-1 levels were positively correlated with the time elapsed since transplantation. On the basis of these findings, TGF-β1 and arginase- 1 may play important roles in determining long-term graft survival. Thus, we propose that TGF-β1 and arginase-1 may potentially be used as predictive markers for evaluating long-term graft survival.

A Neuro-Based Software Fault Prediction with Box-Cox Power Transformation

Software fault prediction is one of the most fundamental but significant management techniques in software dependability assessment. In this paper we concern the software fault prediction using a multilayer-perceptron neural network, where the underlying software fault count data are transformed to the Gaussian data, by means of the well-known Box-Cox power transformation. More specially, we investigate the long-term behavior of software fault counts by the neural network, and perform the multi-stage look ahead prediction of the cumulative number of software faults detected in the future software testing. In numerical examples with two actual software fault data sets, we compare our neural network approach with the existing software reliability growth models based on nonhomogeneous Poisson process, in terms of predictive performance with average relative error, and show that the data transformation employed in this paper leads to an improvement in prediction accuracy.

急性缺血性脑卒中患者血清CKLF1、claudin-5预测溶栓后出血转化的价值及与短期预后的关系

目的分析急性缺血性脑卒中(AIS)患者血清趋化素样因子1(CKLF1)、claudin-5预测静脉溶栓后出血转化(HT)的价值及与短期预后的关系。方法选取2019年7月—2021年1月武汉市中医医院神经内科收治接受静脉溶栓的AIS患者321例为AIS组,根据是否发生HT分为HT亚组63例和非HT亚组258例。患者出院后3个月根据改良Rankin量表(mRS)评分再分为预后不良亚组88例和预后良好亚组233例,另选取医院同期体检健康志愿者60例为健康对照组。采用酶联免疫吸附法检测血清CKLF1、claudin-5水平,比较各组血清CKLF1、claudin-5水平。采用多因素Logistic回归分析AIS患者静脉溶栓后HT的影响因素;Spearman相关性分析AIS患者血清CKLF1、claudin-5水平与mRS评分的相关性;受试者工作特征(ROC)曲线分析血清CKLF1、claudin-5水平对AIS患者静脉溶栓后HT的预测价值。结果与健康对照组比较,AIS组血清CKLF1、claudin-5水平升高(Z=11.953、11.069,P均<0.001)。AIS患者321例静脉溶栓后HT发生率为19.63%(63/321)。心源性栓塞、美国国立卫生研究院卒中量表(NIHSS)评分升高和血清CKLF1、claudin-5升高为AIS患者静脉溶栓后HT的独立危险因素[OR(95%CI)=1.104(1.047~1.165)、4.377(2.011~9.526)、1.195(1.106~1.292)、1.039(1.024~1.052)]。AIS患者321例静脉溶栓后3个月预后不良发生率为27.41%(88/321)。与预后良好亚组比较,预后不良亚组血清CKLF1、claudin-5水平升高(Z=7.145、7.279,P均<0.001)。AIS患者血清CKLF1、claudin-5水平与mRS评分呈正相关(rs=0.517、0.491,P均<0.001)。血清CKLF1、claudin-5水平单独及二者联合预测AIS患者静脉溶栓后HT的曲线下面积(AUC)分别为0.769、0.772、0.876,二者联合预测的AUC大于CKLF1、claudin-5单独预测(Z=2.542、3.786,P均<0.001)。结论血清CKLF1、claudin-5水平升高是AIS患者静脉溶栓后HT的独立危险因素,二者联合预测AIS患者静脉溶栓后HT的价值较高,且血清CKLF1、claudin-5水平升高与AIS患者短期预后不良有关,有望成为AIS患者静脉溶栓后HT和短期预后的辅助评估指标。

广义Dedekind函数误差项的加权平方积分均值

研究了和式 n≤xnψ(n)r的渐近公式中的误差项E(x;r)的加权平方积分均值.其中,r是正整数,ψ(n)是Dedekind函数.

基于DFT-S-OFDM传输方式的物理层双矩阵密钥加密算法

现有基于OFDM调制的物理层加密算法的安全性普遍依赖于较大的子载波个数,且不能抵抗明文密文攻击,当子载波个数比较少时,其密钥空间快速变小,安全性急剧下降,因此,很难适应资源受限终端的安全通信需求.针对这一共性问题,文中利用LTE上行链路采用的DFT-S-OFDM传输方式以及资源块划分方式,提出了一种基于双矩阵变换的物理层加密算法.该算法主要包含两个步骤:一是通过AES计数器模式控制产生两个对角密钥矩阵;二是通过密钥矩阵控制N点DFT变换前后的数据,使得密文、明文和密钥之间形成非线性方程组关系.通过这个加密过程,实现两个目的,一是利用DFT-S-OFDM传输方式的特点,实现对输入的明文和输出的密文分别加密的目的,保证算法具备抵抗明文密文攻击能力;二是实现明文、密文和密钥三者之间的非线性关系,保证算法的安全.算法设置了两种密钥工作模式,第一种为每加密N-1组明文就改变一次子密钥,第二种为每加密大于等于N组明文才改变一次子密钥.在无噪的理想情况和有噪的非理想情况下,详细分析了两种密钥工作模式的安全性.理论分析结果表明,在子载波数大于等于12的情况下,第一种密钥工作模式无论在理想条件还是有扰信道条件下,均可以抵抗穷举攻击和明文密文攻击,保证算法的安全性,第二种密钥工作模式只有在有扰信道条件下才可以抵抗穷举攻击和明文密文攻击,保证算法安全;在两种密钥工作模式下,算法均不会改变DFT-S-OFDM系统中的峰均功率比.分别仿真分析了加密前后系统的峰均比、误码率等参数的变化,仿真数据证实了理论分析所得的结论,表明算法对系统的峰均比、功率以及误码特性等固有性能影响较小,能够在子载波数比较小的情况下,很好的保证通信数据的安全性,满足资源受限终端的安全通信需求.

基于Informer神经网络的农产品物流需求预测分析——以华中地区为例

保障农产品物流稳定性即是保障民生问题的关键。对农产品物流需求的预测是合理规划农产品物流稳定性的重要保证。然而,农产品物流需求的预测实际较为复杂,预测过程中会受到各种因素影响。因此,为了保证对农产品物流需求预测的准确性,需要考虑多方面影响因素。本研究以农产品物流需求作为研究对象,利用Informer神经网络构建预测农产品物流需求的神经网络模型,以华中地区河南省、湖北省和湖南省为例,对三省的农产品物流需求进行预测。同时用长短时记忆(Long Short-Term Memory,LSTM)网络和Transformer神经网络对华中三省农产品物流进行需求预测,将三种模型预测结果进行对比。对比结果表明本研究构建的基于Informer神经网络模型预测测试误差平均百分比为3.39%,低于LSTM和Transformer神经网络模型的4.43%和4.35%。并且用该Informer神经网络模型对三省预测出的预测值与实际值结果较为接近,河南省2021年的预测值为4185.33,实际值为4048.1,误差为3.389%;湖北省2021年的预测值为2503.64,实际值2421.78,误差为3.380%;湖南省2021年的预测值,2933.31,实际值为2836.86,误差为3.340%。表明该模型对华中三省的农产品物流需求预测的结果较为准确。三省2023年的预测值高于2021年的预测值。因此,在2021年物流运输配套设施的基础上,必须保证物流运输效率,加强物流运输能力,以满足华中地区日益增长的物流需求。

与鞅相关的广义Ornstein-Uhlenbeck过程及其在金融中的应用

本文研究一个由谱负广义Ornstein-Uhlenbeck过程的恒定水平首达时的二维联合分布及其在这个首达时的原始停止。基于有关Levy过程和GOU过程的一些结果,通过使用鞅和马尔可夫链方法,给出了依据新特殊函数分布的拉普拉斯变换的显式表达式。详细研究了稳定情况下的广义Ornstein-Uhlenbeck过程,给出了计算广义Vasicek模型中的欧式最大值看涨期权价格的拉普拉斯变换公式,推广了已有结果。

Lévy过程驱动下的反射Cox-Ingersoll-Ross利率模型的平稳性（英文）

布朗运动与正态分布已被广泛应用在Cox-Ingersoll-Ross利率框架的瞬时动态利率模型中,然而,实证研究表明,利率的回报率分布比正常分布有一个更高的峰值和两个更胖的尾部.同时,当罕见的灾难性的冲击发生或在经济和金融的体制发生转变,货币市场可能会出现跳.在本文中,我们将考虑一类带Levy噪声的反射Cox-Ingersoll-Ross利率模型.此外,我们得出了这种带跳的反射扩散过程平稳分布的拉普拉斯变换.