To evaluate the efficacy and safety of irinotecan combined with xeloda (CAPIRI regimen) in patients with metastatic colorectal cancer after failure of chemotherapy with oxaliplatin.Totally 38 patients with metastatic colorectal cancer after failure of chemotherapy with oxaliplatin were enrolled. Patients received xeloda 1 000 mg/m2 orally twice daily on day 1 to 14 and intravenous irinotecan 100 mg/m2 on day 1 and 8 every 3 weeks.The median age of 38 patients was 58.5 (27-77) years. CAPIRI regimen was used 11.0 (3.0-24.0) months after the diagnosis of metastatic colorectal cancer (CAPIRI regimen as second-line chemotherapy in 33 patients, third-line in 4 patients, and fourth-line in 1 patient). A total of 121 cycles of chemotherapy (median 3.0) were administered. Thirty-four patients were evaluable for response. The overall response rate and disease control rate were 5.9% and 61.8%, respectively. The median time to progression and overall survival were 4.5 months (95% CI, 3.4-5.6 months) and 11.0 months (95% CI, 10.2-11.8 months), respectively. All 38 patients were evaluable for safety. The most common adverse events were leukopenia (73.7%), neutropenia (65.8%), nausea and vomiting (60.5%), and diarrhea (28.9%). The occurrence rates of these grade 3-4 events were 10.5%, 13.2%, 10.5%, and 7.9%, respectively. All adverse events were tolerable.CAPIRI regimen is effective and well-tolerated in Chinese patients with metastatic colorectal cancer after failure of chemotherapy with oxaliplatin.
Representation of uncertain knowledge by using a Bayesian network requires the acquisition of a conditional probability table (CPT) for each variable. The CPT can be acquired by data mining or elicitation. When data are insufficient for supporting mining, causal modeling such as the noisy-OR aids elicitation by reducing the number of probability parameters to be acquired from human experts. Multiple causes can reinforce each other in producing the effect or can undermine the impact of each other. Most existing causal models do not consider causal interactions from the perspective of reinforcement or undermining. Our analysis shows that none can represent both interactions. Except for the RNOR, other models also limit parameters to probabilities of single-cause events. We present the first general causal model, that is, the nonimpeding noisy -AND tree, that allows encoding of both reinforcement and undermining. It supports efficient CPT acquisition by elicitating a partial ordering of causes in terms of a tree topology, plus the necessary numerical parameters. It also allows the incorporation of probabilities for multicause events.
Recently, there has been increasingly interest in hosting desktop applications in virtual machine environment and accessing them with thin-client devices. However, the interactive performance in such scenario has not been fully investigated yet. We thus measure the performance of the interactive desktop system hosted by Xen VMM. Our experimental results show that with heave workload VMs coexisting on the same physical server, one guest domain may get poor interactive performance for the existence of occasional large "latency-peak". We argue that for interactive operation, the larger variance of response latency, instead of the longer latency itself, is the most important performance problem for the virtualized approach. We enhance the credit scheduler of Xen to minimize both the height and frequency of latency peak. The results exhibited improvements in average height of latency-peak of up to 97.4% and in frequency of latencypeak of up to 94.2% for a variety of consolidation scenarios.
Objective To discuss the efficacy of Sulfontanshinone ⅡA Sodium Injection combined with prednisone for primary nephrotic syndrome. Methods 48 patients with PNS were randomized into two groups.24 patients in control group were treated with full dosage of prednisone and conventional therapy,24 patients in treatment group received full dosage of prednisone,conventional therapy and intravenous Sulfontanshinone ⅡA Sodium . Rennal function,blood fat and 24-hour urine protein were detected. Results 24-hour urine protein in patients of both groups were all decreased;total effective rate was 90.7%in treatment group and 78% in control group. As compared with control group,blood fat and renal function in treatment group were improved significantly(P 0.05).Conclusion Sulfontanshinone ⅡA Sodium Injection combined with prednisone may improve PNS.
Probabilistic safety assessment (PSA) on a specific reactor are often implemented without considering the applicability of generic reliability data, and doubt about such assessments is aroused because of the lack of plant-specific reliability data. The applicability of generic reliability data is analyzed in present paper, in order to remove the doubt in a way. Several sets of reliability data composing from different sources are researched. The following analysis evaluate a fault tree for a typical example of a reactor, using several sets of reliability data and show the differences in the results. Additionally, a comparison is made with a procedure of analysis using reliability data ranges. The results show that the probabilistic safety analysis on a specific reactor using reliability data which come from different sources is feasible. The differences are slight for most components, only a few key components should be separated in the first place and concentrated more attention on them. The superiority of plant-specific data should be advocated. In the mean time, the lack of data should not be a barrier for PSA on a specific reactor. And the analysis facing a lack of data is advised to be encouraged as an approach to improve the safety of specific reactors.
With the continuous growth of the automation level, the production process is featured by multiple stages and process parameters.There is a huge sum of diverse data on automated production.With a low value density, these data come from heterogenous sources, and respond to lots of concurrent processing demands.It is necessary to simulate and optimize the production scheduling of the automated production system.Drawing on the existing research, this paper illustrates the process of multi-stage production scheduling of automated production, and simulates the automated production line on Plant Simulation.The flow of the simulation model was illustrated, the simulation objectives were specified, and the model hypotheses were detailed.From the angle of deterministic simulation modelling, a joint optimization model was established for the multi-stage production scheduling of automated production, and the production task assignment was improved for traditional pull scheduling model to meet the demand of dynamic collaborative demand for machines.The proposed model was proved effective through simulations.
Task-specific data-fusion networks have marked considerable achievements in urban scene parsing. Among these networks, our recently proposed RoadFormer successfully extracts heterogeneous features from RGB images and surface normal maps and fuses these features through attention mechanisms, demonstrating compelling efficacy in RGB-Normal road scene parsing. However, its performance significantly deteriorates when handling other types/sources of data or performing more universal, all-category scene parsing tasks. To overcome these limitations, this study introduces RoadFormer+, an efficient, robust, and adaptable model capable of effectively fusing RGB-X data, where ``X'', represents additional types/modalities of data such as depth, thermal, surface normal, and polarization. Specifically, we propose a novel hybrid feature decoupling encoder to extract heterogeneous features and decouple them into global and local components. These decoupled features are then fused through a dual-branch multi-scale heterogeneous feature fusion block, which employs parallel Transformer attentions and convolutional neural network modules to merge multi-scale features across different scales and receptive fields. The fused features are subsequently fed into a decoder to generate the final semantic predictions. Notably, our proposed RoadFormer+ ranks first on the KITTI Road benchmark and achieves state-of-the-art performance in mean intersection over union on the Cityscapes, MFNet, FMB, and ZJU datasets. Moreover, it reduces the number of learnable parameters by 65\% compared to RoadFormer. Our source code will be publicly available at mias.group/RoadFormerPlus.
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