Safety and efficacy of iodine-125 seed strand for intraluminal brachytherapy on ureteral carcinoma

ObjectiveOur aim is to evaluate the safety and efficacy of iodine-125 seed strand for intraluminal brachytherapy on ureteral carcinoma.MethodsFrom November 2014 to November 2021, 22 patients with ureteral cancer not suitable for surgical resection were enrolled. Iodine-125 seed strand was inserted under c-arm CT and fluoroscopic guidance. The technical success rate, complications, disease control rate, and survival time were evaluated. Hydronephrosis Girignon grade and ureteral cancer sizes before and after treatment were compared.ResultsA total of 46 seed strands were successfully inserted and replaced, with a technical success rate of 100% and median procedure time of 62 min. No procedure-related death, ureteral perforation, infection, or severe bleeding occurred. Minor complications were observed in eight (36.4%) patients, and migration of seed strand was the most common complication. Six months after seed strand brachytherapy, one complete response, three partial responses, and five stable diseases were evaluated, and the disease control rate was 64.3%. The Girignon grade of hydronephrosis was significantly improved 1 to 3 months after seed strand insertion. Disease control rates were 94.4, 62.5, and 64.3% at 1-, 3-, and 6-month follow-up. Twenty patients were successfully followed up, with a mean follow-up of 18.0 ± 14.5 months. The median overall survival and progress-free survival were 24.7 and 13.0 months, respectively.ConclusionIodine-125 seed strand is safe and effective for intraluminal brachytherapy and can be used as an alternative to patients with ureteral carcinoma who are not suitable for surgical resection or systemic combined therapy

Identification and expression analysis of EDR1-like genes in tobacco (Nicotiana tabacum) in response to Golovinomyces orontii

ENHANCED DISEASE RESISTANCE1 (EDR1) encodes a Raf-like mitogen-activated protein kinase, and it acts as a negative regulator of disease resistance and ethylene-induced senescence. Mutations in the EDR1 gene can enhance resistance to powdery mildew both in monocotyledonous and dicotyledonous plants. However, little is known about EDR1-like gene members from a genome-wide perspective in plants. In this study, the tobacco (Nicotiana tabacum) EDR1-like gene family was first systematically analyzed. We identified 19 EDR1-like genes in tobacco, and compared them to those from Arabidopsis, tomato and rice. Phylogenetic analyses divided the EDR1-like gene family into six clades, among them monocot and dicot plants were respectively divided into two sub-clades. NtEDR1-1A and NtEDR1-1B were classified into clade I in which the other members have been reported to negatively regulate plant resistance to powdery mildew. The expression patterns of tobacco EDR1-like genes were analyzed after plants were challenged by Golovinomyces orontii, and showed that several other EDR1-like genes were induced after infection, as well as NtEDR1-1A and NtEDR1-1B. Expression analysis showed that NtEDR1-13 and NtEDR1-16 had exclusively abundant expression patterns in roots and leaves, respectively, and the remaining NtEDR1-like members were actively expressed in most of the tissue/organ samples investigated. Our findings will contribute to further study of the physiological functions of EDR1-like genes in tobacco

A Meeting Detector to Provide Context to a SIP Proxy

As sensing technology develops, it plays an important role in context-aware systems. Using context information improves the user experience of ubiquitous computing. One use of sensed information is to detect a meeting in progress in an office or a conference room. In our system, sensors gather context information from an office environment and act as a presence user agent to update a presence server with context changes. These context changes can be utilized by context-aware services. The presence messaging uses the SIP for Instant Messaging and Presence Leveraging Extensions (SIMPLE) protocol and the presence information is described in eXtensible Makeup Language (XML) format. In this thesis we present a context-sensing component that recognizes meetings in a typical office environment. A context-aware system is able to use this occupancy information to infer that the room is empty, an individual is alone in the room, or a meeting is taking place in the meeting room. Context-aware services might utilize this environmental information to automatically forward a user's incoming calls to their voice mail server. This and other example applications were developed to show the usefulness of this context information.Så som sensor tekniken utvecklas, spelar de en viktig roll i kontextmedvetna system. Genom att använda kontextuell information förbättras användarupplevelsen av 'ubiquitous computing'. Ett användningsområde för sensorinsamlad information är att upptäcka ett möte som pågår i ett kontor eller konferenslokal. I vårt system samlar sensorer information från en kontorsmiljö och uppdaterar en närvaroserver med kontextuella förändringar. Dessa förändringar kan sedan utnyttjas av kontextmedvetna tjänster. För att förmedla den närvarostatusen använder närvaroservern SIP och ’Presence Leveraging Extensions’ (SIMPLE) protokoll. Närvaro information levereras i 'eXtensible Makeup Language' (XML) format. I denna avhandling presenterar vi en kontextsensorkomponent som känner av möten i en typisk kontorsmiljö. Ett kontextmedvetet system kan använda denna komponent för att dra slutsatsen att lokalen är tom, en person är ensam i lokalen, eller ett möte äger rum i lokalen. Kontextmedvetna tjänster kan utnyttja denna information för att automatiskt vidarebefordra en användares inkommande samtal till deras röstbrevlåda. Detta och andra exempel, har utvecklats för att visa nyttan av denna kontextuella information

A Meeting Detector to Provide Context to a SIP Proxy

As sensing technology develops, it plays an important role in context-aware systems. Using context information improves the user experience of ubiquitous computing. One use of sensed information is to detect a meeting in progress in an office or a conference room. In our system, sensors gather context information from an office environment and act as a presence user agent to update a presence server with context changes. These context changes can be utilized by context-aware services. The presence messaging uses the SIP for Instant Messaging and Presence Leveraging Extensions (SIMPLE) protocol and the presence information is described in eXtensible Makeup Language (XML) format. In this thesis we present a context-sensing component that recognizes meetings in a typical office environment. A context-aware system is able to use this occupancy information to infer that the room is empty, an individual is alone in the room, or a meeting is taking place in the meeting room. Context-aware services might utilize this environmental information to automatically forward a user's incoming calls to their voice mail server. This and other example applications were developed to show the usefulness of this context information.Så som sensor tekniken utvecklas, spelar de en viktig roll i kontextmedvetna system. Genom att använda kontextuell information förbättras användarupplevelsen av 'ubiquitous computing'. Ett användningsområde för sensorinsamlad information är att upptäcka ett möte som pågår i ett kontor eller konferenslokal. I vårt system samlar sensorer information från en kontorsmiljö och uppdaterar en närvaroserver med kontextuella förändringar. Dessa förändringar kan sedan utnyttjas av kontextmedvetna tjänster. För att förmedla den närvarostatusen använder närvaroservern SIP och ’Presence Leveraging Extensions’ (SIMPLE) protokoll. Närvaro information levereras i 'eXtensible Makeup Language' (XML) format. I denna avhandling presenterar vi en kontextsensorkomponent som känner av möten i en typisk kontorsmiljö. Ett kontextmedvetet system kan använda denna komponent för att dra slutsatsen att lokalen är tom, en person är ensam i lokalen, eller ett möte äger rum i lokalen. Kontextmedvetna tjänster kan utnyttja denna information för att automatiskt vidarebefordra en användares inkommande samtal till deras röstbrevlåda. Detta och andra exempel, har utvecklats för att visa nyttan av denna kontextuella information

DBMF: A Novel Method for Tree Species Fusion Classification Based on Multi-Source Images

Multi-source data remote sensing provides innovative technical support for tree species recognition. Tree species recognition is relatively poor despite noteworthy advancements in image fusion methods because the features from multi-source data for each pixel in the same region cannot be deeply exploited. In the present paper, a novel deep learning approach for hyperspectral imagery is proposed to improve accuracy for the classification of tree species. The proposed method, named the double branch multi-source fusion (DBMF) method, could more deeply determine the relationship between multi-source data and provide more effective information. The DBMF method does this by fusing spectral features extracted from a hyperspectral image (HSI) captured by the HJ-1A satellite and spatial features extracted from a multispectral image (MSI) captured by the Sentinel-2 satellite. The network has two branches in the spatial branch to avoid the risk of information loss, of which, sandglass blocks are embedded into a convolutional neural network (CNN) to extract the corresponding spatial neighborhood features from the MSI. Simultaneously, to make the useful spectral feature transfer more effective in the spectral branch, we employed bidirectional long short-term memory (Bi-LSTM) with a triple attention mechanism to extract the spectral features of each pixel in the HSI with low resolution. The feature information is fused to classify the tree species after the addition of a fusion activation function, which could allow the network to obtain more interactive information. Finally, the fusion strategy allows for the prediction of the full classification map of three study areas. Experimental results on a multi-source dataset show that DBMF has a significant advantage over other state-of-the-art frameworks

DBMF: A Novel Method for Tree Species Fusion Classification Based on Multi-Source Images

Multi-source data remote sensing provides innovative technical support for tree species recognition. Tree species recognition is relatively poor despite noteworthy advancements in image fusion methods because the features from multi-source data for each pixel in the same region cannot be deeply exploited. In the present paper, a novel deep learning approach for hyperspectral imagery is proposed to improve accuracy for the classification of tree species. The proposed method, named the double branch multi-source fusion (DBMF) method, could more deeply determine the relationship between multi-source data and provide more effective information. The DBMF method does this by fusing spectral features extracted from a hyperspectral image (HSI) captured by the HJ-1A satellite and spatial features extracted from a multispectral image (MSI) captured by the Sentinel-2 satellite. The network has two branches in the spatial branch to avoid the risk of information loss, of which, sandglass blocks are embedded into a convolutional neural network (CNN) to extract the corresponding spatial neighborhood features from the MSI. Simultaneously, to make the useful spectral feature transfer more effective in the spectral branch, we employed bidirectional long short-term memory (Bi-LSTM) with a triple attention mechanism to extract the spectral features of each pixel in the HSI with low resolution. The feature information is fused to classify the tree species after the addition of a fusion activation function, which could allow the network to obtain more interactive information. Finally, the fusion strategy allows for the prediction of the full classification map of three study areas. Experimental results on a multi-source dataset show that DBMF has a significant advantage over other state-of-the-art frameworks

Smish: A Novel Activation Function for Deep Learning Methods

Activation functions are crucial in deep learning networks, given that the nonlinear ability of activation functions endows deep neural networks with real artificial intelligence. Nonlinear nonmonotonic activation functions, such as rectified linear units, Tan hyperbolic (tanh), Sigmoid, Swish, Mish, and Logish, perform well in deep learning models; however, only a few of them are widely used in mostly all applications due to their existing inconsistencies. Inspired by the MB-C-BSIF method, this study proposes Smish, a novel nonlinear activation function, expressed as f(x)=x·tanh[ln(1+sigmoid(x))], which could overcome other activation functions with good properties. Logarithmic operations are first used to reduce the range of sigmoid(x). The value is then calculated using the tanh operator. Inputs are ultimately used to multiply the previous value, thus exhibiting negative output regularization. Experiments show that Smish tends to operate more efficiently than Logish, Mish, and other activation functions on EfficientNet models with open datasets. Moreover, we evaluated the performance of Smish in various deep learning models and the parameters of its function f(x)=αx·tanh[ln(1+sigmoid(βx))], and where α = 1 and β = 1, Smish was found to exhibit the highest accuracy. The experimental results show that with Smish, the EfficientNetB3 network exhibits a Top-1 accuracy of 84.1% on the CIFAR-10 dataset; the EfficientNetB5 network has a Top-1 accuracy of 99.89% on the MNIST dataset; and the EfficientnetB7 network has a Top-1 accuracy of 91.14% on the SVHN dataset. These values are superior to those obtained using other state-of-the-art activation functions, which shows that Smish is more suitable for complex deep learning models

Study on karyotype, C-banding and rDNA physical location in Nicotiana alata

The karyotype, C-banding, and chromosomal location of 45S and 5S rDNA of Nicotiana alata were studied by methods of wall digestion and hypotonic treatment, BSG, and double color fluorescent in situ hybridization, respectively. Karyotype analysis of root tip cells of four N. alata accessions showed that the average karyotype formula was 2n = 2x = 12m + 6st, the variation and average value of arm ratio was 1.14~4.22 and 2.17, respectively. The rate of the longest and minimum chromosome was 2.16, and the karyotype classification was 2B of Stebbins system (1971). Results of C-banding study showed that chromosome C-banding of N. alata was abundant with 25 bands being displayed, including 7 centromeric bands, 15 terminal bands, and 3 intercalary bands. Results of FISH study showed, in metaphase chromosomes of root tip cell of N. alata, there were three pairs of 45S rDNA loci, locating on IS, IIIS, and VL, respectively (L and S represented chromosome long and short arm respectively, and the Roman number indicated chromosomes serial number; the same hereinafter), and two pairs of 5S rDNA loci, locating on IL and IVS, respectively. Pair I of homologous chromosome had loci of both 45S and 5S rDNA. These results could provide reference for studies on phylogenesis of genus Nicotiana, and were of great significance to genetic breeding and germplasm innovation in tobacco

Application of near-infrared spectroscopy and CNN-TCN for the identification of foreign fibers in cotton layers

Foreign fibers in cotton layers have a particular impact on the quality of the cotton. Traditional image processing methods are ineffective in detecting foreign fibers in cotton layers, which are time-consuming and costly. In order to identify foreign fibers effectively, a classification and identification method for foreign fibers in cotton layers was proposed based on NIR spectroscopy and CNN-TCN. In this study, near-infrared spectroscopy ranging from 780 nm to 2360 nm was used to identify the type of foreign fibers. Savitzky-Golay smoothing was used to preprocess spectroscopy data, and LightGBM-ANOVA was used to determine optimal wavelengths. Preprocessed spectral data extracted spectral features through the 1D convolutional neural network(1D-CNN). Then Temporal convolutional neural network (TCN), Long-Short Term Memory (LSTM), Gated Recurrent Unit (GRU), Recurrent Neural Network (RNN), and 1D-CNN were used to establish classification models. Compared with other time series models, CNN-TCN methods obtained better performances with the classification accuracy of over 99% in the test set and the shorter training time. The overall results illustrated that near-infrared spectral combined with the CNN-TCN method was efficient and accurate for identifying foreign fibers in the cotton layer

Numerical Noise Transfer Analysis of a Flexible Supported Gearbox Based on Impedance Model and Noise Transfer Function

To investigate the gearbox radiation noise properties under various rotational speeds, a noise prediction method based on impedance model and noise transfer function (NTF) is proposed. One only needs to extract the NTF of the housing once rapid gearbox noise prediction under different working conditions is realized. Taking a flexible supported gearbox as a research object, the external excitation of the housing (the bearing excitation load and isolator excitation load) is calculated through a gear-housing-foundation-coupled impedance model, and the noise transfer function is simulated through the vibroacoustic-coupled boundary element model; then the radiation noise is obtained. Based on this model, the noise transfer analysis of the housing is carried out, different excitation components and NTF components are compared, and the contributions of different excitation components to noise are compared. Results show that the radiation noise of gearbox is mainly excited by the high-speed bearing, while the low-speed bearing and isolator have little influence on noise. At low speed, vertical force, axial force, and moment excitation of bearings all contribute to the radiation noise while at high speed, the gearbox radiation noise is mainly generated by vertical excitation force of bearings