Long-term efficacy of concurrent chemoradiation therapy on patients with nasopharyngeal carcinoma

The objective of the study is to analyze and evaluate the long-term efficacy of concurrent chemoradiation therapy and the impact of other related factors to the prognosis of patients with nasopharyngeal carcinoma. A total of 897 cases of patients with nasopharyngeal carcinoma who were treated in our hospital in the period from December 2007 to February 2010 were divided into two groups according to the different treatment methods. The first group consists of 769 cases of patients treated with radical radiation therapy alone (radiotherapy group), and the second group consists of 128 patients treated with a combination of radical radiotherapy and chemotherapy (chemoradiotherapy group). While both groups of patients were subjected to the same modes of radiotherapy, an additional 2-3 cycles of concurrent chemotherapy were conducted for the second group of patients (using DDP + 5-Fu + BLM or DDP + 5-Fu). The overall 5-year survival rate of all patients was 51.23%, the 5-year survival rate among patients in the radiotherapy group was 48.46% and the 5-year survival rate of the chemoradiotherapy group was 56.80%. These results were found to be statistically significant (P <0.05). A multivariate stepwise regression analysis indicated that different N staging of nasopharyngeal carcinoma had a very significant effect (P <0.0001) for the patients’ survival. In addition, the different T staging also significantly affected (P <0.05) the patient's survival. The type of treatment (radiotherapy or chemoradiotherapy) administered can also have a significant impact on the prognosis, whereby the chemoradiotherapy group patients’ survival rate was significantly higher than the radiotherapy group (P <0.001). The implementation of concurrent chemoradiotherapy show long-term beneficial effects for naso-pharyngeal carcinoma patients. In particular, chemotherapy has a greater impact towards the survival of N2 and N3 stage nasopharyngeal carcinoma patients.Abstract: The objective of the study is to analyze and evaluate the long-term efficacy of concurrent chemoradiation therapy and the impact of other related factors to the prognosis of patients with nasopharyngeal carcinoma. A total of 897 cases of patients with nasopharyngeal carcinoma who were treated in our hospital in the period from December 2007 to February 2010 were divided into two groups according to the different treatment methods. The first group consists of 769 cases of patients treated with radical radiation therapy alone (radiotherapy group), and the second group consists of 128 patients treated with a combination of radical radiotherapy and chemotherapy (chemoradiotherapy group). While both groups of patients were subjected to the same modes of radiotherapy, an additional 2-3 cycles of concurrent chemotherapy were conducted for the second group of patients (using DDP + 5-Fu + BLM or DDP + 5-Fu). The overall 5-year survival rate of all patients was 51.23%, the 5-year survival rate among patients in the radiotherapy group was 48.46% and the 5-year survival rate of the chemoradiotherapy group was 56.80%. These results were found to be statistically significant (P <0.05). A multivariate stepwise regression analysis indicated that different N staging of nasopharyngeal carcinoma had a very significant effect (P <0.0001) for the patients’ survival. In addition, the different T staging also significantly affected (P <0.05) the patient's survival. The type of treatment (radiotherapy or chemoradiotherapy) administered can also have a significant impact on the prognosis, whereby the chemoradiotherapy group patients’ survival rate was significantly higher than the radiotherapy group (P <0.001). The implementation of concurrent chemoradiotherapy show long-term beneficial effects for naso-pharyngeal carcinoma patients. In particular, chemotherapy has a greater impact towards the survival of N2 and N3 stage nasopharyngeal carcinoma patients

Weighted endpoint estimates for commutators of multilinear fractional integral operators

summary:Let $m$ be a positive integer, $0<\alpha <mn$, $\vec {b}=(b_{1},\cdots ,b_{m})\in {\rm BMO}^m$. We give sufficient conditions on weights for the commutators of multilinear fractional integral operators \Cal {I}^{\vec {b}}_{\alpha } to satisfy a weighted endpoint inequality which extends the result in D. Cruz-Uribe, A. Fiorenza: Weighted endpoint estimates for commutators of fractional integrals, Czech. Math. J. 57 (2007), 153–160. We also give a weighted strong type inequality which improves the result in X. Chen, Q. Xue: Weighted estimates for a class of multilinear fractional type operators, J. Math. Anal. Appl., 362, (2010), 355–373

Present Situation and Progress of Pancreatic Cancer Radiotherapy

Purpose: The research purpose is to effectively improve treatment effect of postoperative pancreatic cancer.Approaches: Adjuvant radiotherapy and chemotherapy; Palliative radiotherapy is adpoted for patients whose pancreatic cancer cannot be removed through excision. Comprehensive treatment approach is adopted where gemcitabine is added during radiotherapy. Result: The treatment effect of palliative operation is a little bit better than the traditional operative treatment, adoption of comprehensive treatment is obviously superior to the aforesaid two treatment approaches.Conculsion: Comprehensive treatment effectively improves survival rate of patients, and the survival time is greatlyextended.Operation is generally adopted for pancreatic cancer treatment, but the effect is unsatisfactory. The averagelifetime of patients after palliative operation treatment is only 5 to 76 months, and the survival rate after five years only reaches 8% to 15%, while the curative effect of radical resection is a little bit better, but the survival rate after five years also only reaches 12% to 24%, and the average lifetime is about 10 to 19 months. In recent years, with gradual progress of medical treatment level, clinical cases of adopting radiotherapy for pancreatic cancer are increasing, but its main effect is palliative or auxiliary. This article refers to plenty of documentation, makes thorough study of present situation and progress of pancreatic cancer radiotherapy, put forward its own suggestions and opinions, and have certainreference value

When Source-Free Domain Adaptation Meets Label Propagation

Source-free domain adaptation, where only a pre-trained source model is used to adapt to the target distribution, is a more general approach to achieving domain adaptation. However, it can be challenging to capture the inherent structure of the target features accurately due to the lack of supervised information on the target domain. To tackle this problem, we propose a novel approach called Adaptive Local Transfer (ALT) that tries to achieve efficient feature clustering from the perspective of label propagation. ALT divides the target data into inner and outlier samples based on the adaptive threshold of the learning state, and applies a customized learning strategy to best fits the data property. Specifically, inner samples are utilized for learning intra-class structure thanks to their relatively well-clustered properties. The low-density outlier samples are regularized by input consistency to achieve high accuracy with respect to the ground truth labels. In this way, local clustering can be prevented from forming spurious clusters while effectively propagating label information among subpopulations. Empirical evidence demonstrates that ALT outperforms the state of the arts on three public benchmarks: Office-31, Office-Home, and VisDA

Learning Local to Global Feature Aggregation for Speech Emotion Recognition

Transformer has emerged in speech emotion recognition (SER) at present. However, its equal patch division not only damages frequency information but also ignores local emotion correlations across frames, which are key cues to represent emotion. To handle the issue, we propose a Local to Global Feature Aggregation learning (LGFA) for SER, which can aggregate longterm emotion correlations at different scales both inside frames and segments with entire frequency information to enhance the emotion discrimination of utterance-level speech features. For this purpose, we nest a Frame Transformer inside a Segment Transformer. Firstly, Frame Transformer is designed to excavate local emotion correlations between frames for frame embeddings. Then, the frame embeddings and their corresponding segment features are aggregated as different-level complements to be fed into Segment Transformer for learning utterance-level global emotion features. Experimental results show that the performance of LGFA is superior to the state-of-the-art methods.Comment: This paper has been accepted on INTERSPEECH 202

Constraints on millicharged dark matter and axion-like particles from timing of radio waves

We derive novel constraints on millicharged dark matter and ultralight axion-like particles using pulsar timing and fast radio burst observations. Millicharged dark matter affects the dispersion measure of the time of arrival of radio pulses in a way analogous to free electrons. Light pseudo-scalar dark matter, on the other hand, causes the polarization angle of radio signals to oscillate. We show that current and future data can set strong constraints in both cases. For dark matter particles of charge $\epsilon e$, these constraints are ${\epsilon}/{m_{\rm milli}} \lesssim 10^{-8}{\rm eV}^{-1}$, for masses $m_{\rm milli}\gtrsim 10^{-6}\,$eV. For axion-like particles, the analysis of signals from pulsars yields constraints in the axial coupling of the order of $g/m_a\lesssim 10^{-13} {\rm GeV}^{-1}/(10^{-22}{\rm eV})$. Both bounds scale as $(\rho/\rho_{\rm dm})^{1/2}$ if the energy density $\rho$ of the components is a fraction of the total dark matter energy density $\rho_{\rm dm}$. We do a detailed study of both effects using data from two samples of pulsars in the galaxy and in globular clusters, as well as data from FRB 121102 and PSR J0437$-$4715. We show that in both cases actual pulsar data constrain a new region of the parameter space for these models, and will improve with future pulsar-timing observations.Comment: 6 pages, 2 figures; v3: to appear on PR

Layer-Adapted Implicit Distribution Alignment Networks for Cross-Corpus Speech Emotion Recognition

In this paper, we propose a new unsupervised domain adaptation (DA) method called layer-adapted implicit distribution alignment networks (LIDAN) to address the challenge of cross-corpus speech emotion recognition (SER). LIDAN extends our previous ICASSP work, deep implicit distribution alignment networks (DIDAN), whose key contribution lies in the introduction of a novel regularization term called implicit distribution alignment (IDA). This term allows DIDAN trained on source (training) speech samples to remain applicable to predicting emotion labels for target (testing) speech samples, regardless of corpus variance in cross-corpus SER. To further enhance this method, we extend IDA to layer-adapted IDA (LIDA), resulting in LIDAN. This layer-adpated extention consists of three modified IDA terms that consider emotion labels at different levels of granularity. These terms are strategically arranged within different fully connected layers in LIDAN, aligning with the increasing emotion-discriminative abilities with respect to the layer depth. This arrangement enables LIDAN to more effectively learn emotion-discriminative and corpus-invariant features for SER across various corpora compared to DIDAN. It is also worthy to mention that unlike most existing methods that rely on estimating statistical moments to describe pre-assumed explicit distributions, both IDA and LIDA take a different approach. They utilize an idea of target sample reconstruction to directly bridge the feature distribution gap without making assumptions about their distribution type. As a result, DIDAN and LIDAN can be viewed as implicit cross-corpus SER methods. To evaluate LIDAN, we conducted extensive cross-corpus SER experiments on EmoDB, eNTERFACE, and CASIA corpora. The experimental results demonstrate that LIDAN surpasses recent state-of-the-art explicit unsupervised DA methods in tackling cross-corpus SER tasks

Multi-node Acceleration for Large-scale GCNs

Limited by the memory capacity and compute power, singe-node graph convolutional neural network (GCN) accelerators cannot complete the execution of GCNs within a reasonable amount of time, due to the explosive size of graphs nowadays. Thus, large-scale GCNs call for a multi-node acceleration system (MultiAccSys) like TPU-Pod for large-scale neural networks. In this work, we aim to scale up single-node GCN accelerators to accelerate GCNs on large-scale graphs. We first identify the communication pattern and challenges of multi-node acceleration for GCNs on large-scale graphs. We observe that (1) coarse-grained communication patterns exist in the execution of GCNs in MultiAccSys, which introduces massive amount of redundant network transmissions and off-chip memory accesses; (2) overall, the acceleration of GCNs in MultiAccSys is bandwidth-bound and latency-tolerant. Guided by these two observations, we then propose MultiGCN, the first MultiAccSys for large-scale GCNs that trades network latency for network bandwidth. Specifically, by leveraging the network latency tolerance, we first propose a topology-aware multicast mechanism with a one put per multicast message-passing model to reduce transmissions and alleviate network bandwidth requirements. Second, we introduce a scatter-based round execution mechanism which cooperates with the multicast mechanism and reduces redundant off-chip memory accesses. Compared to the baseline MultiAccSys, MultiGCN achieves 4~12x speedup using only 28%~68% energy, while reducing 32% transmissions and 73% off-chip memory accesses on average. It not only achieves 2.5~8x speedup over the state-of-the-art multi-GPU solution, but also scales to large-scale graphs as opposed to single-node GCN accelerators.Comment: To appear in T