The optimal perioperative chemotherapeutic regimen for locally advanced gastric cancer remains undefined. We evaluated the efficacy and safety of perioperative and postoperative S-1 and oxaliplatin ...(SOX) compared with postoperative capecitabine and oxaliplatin (CapOx) in patients with locally advanced gastric cancer undergoing D2 gastrectomy.
We did this open-label, phase 3, superiority and non-inferiority, randomised trial at 27 hospitals in China. We recruited antitumour treatment-naive patients aged 18 years or older with historically confirmed cT4a N+ M0 or cT4b Nany M0 gastric or gastro-oesophageal junction adenocarcinoma, with Karnofsky performance score of 70 or more. Patients undergoing D2 gastrectomy were randomly assigned (1:1:1) via an interactive web response system, stratified by participating centres and Lauren classification, to receive adjuvant CapOx (eight postoperative cycles of intravenous oxaliplatin 130 mg/m2 on day one of each 21 day cycle plus oral capecitabine 1000 mg/m2 twice a day), adjuvant SOX (eight postoperative cycles of intravenous oxaliplatin 130 mg/m2 on day one of each 21 day cycle plus oral S-1 40–60 mg twice a day), or perioperative SOX (intravenous oxaliplatin 130 mg/m2 on day one of each 21 day plus oral S-1 40–60 mg twice a day for three cycles preoperatively and five cycles postoperatively followed by three cycles of S-1 monotherapy). The primary endpoint, assessed in the modified intention-to-treat population, 3-year disease-free survival to assess the superiority of perioperative-SOX compared with adjuvant-SOX and the non-inferiority (hazard ratio non-inferiority margin of 1·33) of adjuvant-SOX compared with adjuvant-CapOx. Safety analysis were done in patients who received at least one dose of the assigned treatment. This study is registered with ClinicalTrials.gov, NCT01534546.
Between Aug 15, 2012, and Feb 28, 2017, 1094 patients were screened and 1022 (93%) were included in the modified intention-to-treat population, of whom 345 (34%) patients were assigned to the adjuvant-CapOx, 340 (33%) patients to the adjuvant-SOX group, and 337 (33%) patients to the perioperative-SOX group. 3-year disease-free survival was 51·1% (95% CI 45·5–56·3) in the adjuvant-CapOx group, 56·5% (51·0–61·7) in the adjuvant-SOX group, and 59·4% (53·8–64·6) in the perioperative-SOX group. The hazard ratio (HR) was 0·77 (95% CI 0·61–0·97; Wald p=0·028) for the perioperative-SOX group compared with the adjuvant-CapOx group and 0·86 (0·68–1·07; Wald p=0·17) for the adjuvant-SOX group compared with the adjuvant-CapOx group. The most common grade 3–4 adverse events was neutropenia (32 12% of 258 patients in the adjuvant-CapOx group, 21 8% of 249 patients in the adjuvant-SOX group, and 30 10% of 310 patients in the perioperative-SOX group). Serious adverse events were reported in seven (3%) of 258 patients in adjuvant-CapOx group, two of which were related to treatment; eight (3%) of 249 patients in adjuvant-SOX group, two of which were related to treatment; and seven (2%) of 310 patients in perioperative-SOX group, four of which were related to treatment. No treatment-related deaths were reported.
Perioperative-SOX showed a clinically meaningful improvement compared with adjuvant-CapOx in patients with locally advanced gastric cancer who had D2 gastrectomy; adjuvant-SOX was non-inferior to adjuvant-CapOx in these patients. Perioperative-SOX could be considered a new treatment option for patients with locally advanced gastric cancer.
National Key Research and Development Program of China, Beijing Scholars Program 2018–2024, Peking University Clinical Scientist Program, Taiho, Sanofi-Aventis, and Hengrui Pharmaceutical.
For the Chinese translation of the abstract see Supplementary Materials section.
Finding top-
k
elephant flows in high-speed networks is one of the most fundamental network measurement tasks. It is more challenging than per-flow size estimation since the IDs and sizes of top-
k
...flows must be tracked simultaneously. Most existing studies only record the IDs of a small number of elephant flows to fit their estimators in the extremely limited high-speed on-chip memory. However, these solutions need too many memory accesses when a packet arrives to track the elephant flows with high accuracy, which limits their practicability. Therefore, this paper proposes Jigsaw-Sketch, a new algorithm to find the top-
k
elephant flows with much fewer memory accesses while achieving high memory efficiency and accuracy. In this design, we propose a novel two-stage jigsaw storage scheme, which can capture the candidate top-
k
flows from massive network steams efficiently, and further find the top-
k
elephant flows with high memory efficiency and only a few memory accesses for each packet. Extensive experimental results based on real network traces show that Jigsaw-Sketch improves the packet processing throughput by at least 86%, while achieving smaller memory footprints and higher accuracy compared to the SOTA.
With the proliferation of mobile devices, mobile crowd sensing (MCS) has emerged as a new data collection paradigm, which allows the crowd to act as sensors and contribute their observations about ...entities. Unfortunately, users with varied skills and motivations may provide conflicting information for the same entity. Existing work solves this problem by estimating user reliability and inferring the correct observations (i.e., truths). However, these methods assume that users' expertise degrees are dependent on the truths, but ignore the finer clusters that exist even in the entities with the same truths. To capture users' fine-grained reliability on different entity clusters, we propose a novel Bayesian co-clustering truth discovery model for the task of observation aggregation. This model enables us to produce a more precise estimation while taking into account the entity clusters and the user clusters. Experiments on four real-world datasets reveal that our method outperforms the state-of-the-art approaches in terms of accuracy and F1-score.
Finding top-<inline-formula> <tex-math notation="LaTeX">k </tex-math></inline-formula> elephant flows in high-speed network is a crucial task for network traffic measurement. Since modern routers do ...not hold enough on-chip memory to precisely track all flow sizes, identifying elephant flows becomes more challenging than ever. Existing solutions mostly use compact data structures to fit in limited on-chip memory. However, they have to use large-size counters, i.e. , 20-bit counter, to prevent overflows when recording flow sizes. This wastes significant on-chip memory since flow size distribution is highly skewed. Most counters only require 8 bits to track mouse flows rather than 20 bits. In this letter, we propose ActiveKeeper, an efficient top-<inline-formula> <tex-math notation="LaTeX">k </tex-math></inline-formula> finding solution that uses two-mode active counter to record flow sizes, reducing memory usage while ensuring high accuracy. We also introduce dynamically exponential decay to increase the precision in expelling the small flows while holding the large ones. Experimental results based on real-world Internet traces show that our method outperforms the state-of-the-art by achieving high accuracy and higher precision with small on-chip memory usage.
Intelligent vehicular cyber-physical systems can perform automatic traffic measurement, which provides critical information for transportation engineering. However, one of the biggest challenges in ...traffic measurement is to protect the vehicles' location and trajectory privacy, which may be revealed from the recorded traffic data. Prior studies in traffic measurement only offer heuristic privacy protection but lack a precisely defined privacy model. This article proposes an efficient traffic estimator with differential privacy protection. In our design, each road-side unit communicates with the passing vehicles and records their presence in a privacy-preserving data structure. By performing probabilistic analysis on the anonymized records, the proposed method can precisely estimate the number of common vehicles passed by multiple given locations during the given measurement period. Through theoretical analysis, we prove that the proposed method can protect the trajectory privacy of the vehicles with <inline-formula><tex-math notation="LaTeX">\epsilon</tex-math></inline-formula>-differential privacy even when the point privacy has been leaked. We also evaluated our traffic estimator based on a real-world transportation traffic dataset. The evaluation results demonstrate that the proposed estimator can achieve high estimation accuracy and high-level privacy protection through controllable tradeoffs.
Per-flow traffic measurement in the high-speed network plays an important role in many practical applications. Due to the limited on-chip memory and the mismatch between off-chip memory speed and ...line rate, sampling-based methods select and forward a part of flow traffic to off-chip memory, which complements sketch-based solutions in estimation accuracy and online query support. However, most current work uses the same sampling probability for all flows, leading to the waste in storage and communication resources. In practice, different flows often require different sampling rates to meet the same accuracy constraint. This paper presents self-adaptive sampling, a framework to sample each flow with a probability adapted to flow size/spread. Then we propose three algorithms, SAS-LC, SAS-LOG, and SAS-HYB. SAS-LC and SAS-LOG are geared towards per-flow spread estimation and per-flow size estimation by using different compression functions. SAS-HYB combines the advantages of SAS-LC and SAS-LOG, showing higher efficiency when both small flows and large flows are interested. We implement our estimators in hardware using NetFPGA. Experimental results based on real Internet traces show that, compared to the state-of-the-art in per-flow spread estimation, SAS-LC can save around 10% on-chip space and reduce up to 40% communication cost for large flows. In per-flow size estimation, SAS-LOG can save 40% on-chip space and reduce up to 96% communication costs for large flows. Moreover, SAS-HYB's on-chip memory usage will not be larger than SAS-LC or SAS-LOG and can save up to 19% on-chip space than SAS-LOG when both small flows and large flows are interested.
Per-flow spread measurement in high-speed networks has many practical applications. It is a more difficult problem than the traditional per-flow size measurement. Most prior work is based on ...sketches, focusing on reducing their space requirements in order to fit in on-chip cache memory. This design allows the measurement to be performed at the line rate, but it suffers from expensive computation for spread queries (unsuitable for online operations) and large errors in spread estimation for small flows. This paper complements the prior art with a new spread estimator design based on an on-chip/off-chip model. By storing traffic statistics in off-chip memory, our new design faces a key technical challenge to design an efficient online module of non-duplicate sampling that cuts down the off-chip memory access. We first propose a two-stage solution for non-duplicate sampling, which is efficient but cannot handle well a sampling probability that is either too small or too big. We then address this limitation through a three-stage solution that is more space-efficient. Our analysis shows that the proposed spread estimator is highly configurable for a variety of probabilistic performance guarantees. We implement our spread estimator in hardware using FPGA. The experiment results based on real Internet traffic traces show that our estimator produces spread estimation with much better accuracy than the prior art, reducing the mean relative (absolute) error by about one order of magnitude. Moreover, it increases the query throughput by around three orders of magnitude, making it suitable for supporting online queries in real time.
Data stream processing plays a critical role in providing fundamental statistics for various applications, such as anomaly detection. Still, the unbalanced distribution of data streams severely ...affects the performance of related algorithms, which motivates the recent studies on filter structure design to enhance the existing algorithms for a more precise estimation result. However, these filters are mainly designed for frequency-based filtration, while none of them can conduct universal filtration; apparently, frequency is not the only targeted metric in practical processing tasks, metrics like cardinality and persistence are of equal importance. To cope with the issue, we propose a novel filter framework to implement universal, lightweight, and accurate filtration. The filter framework is called Coupon Filter due to the interpretation of its flow-level filtration as a coupon collection process. We prove the filtration efficiency of our filter design and formally analyze its recording process. We deploy our filter on the three typical metrics (frequency, cardinality, and persistence) to illustrate its advantages. The experimental results on real Internet traces demonstrate the effectiveness of our filter in enhancing existing stream processing approaches in terms of accuracy and throughput. All source codes are available at Github https://github.com/duyang92/coupon-filter-paper.
Flow label collection plays an important role in high-speed network traffic measurement. However, most existing measurement studies assume the flow labels are known a priori. Only a few methods are ...proposed to collect flow labels from network packet streams, which still have limitations like expensive communication cost and high missing rates. This letter addresses the limitations of the prior art by proposing an efficient flow label collector. It bounds the duplications of large flows and discards the small flows that do not matter, achieving lower missing rates at low communication cost. The experimental results based on real network traces show that, compared to the state-of-the-art methods, it can reduce about 90% flow label missing rate for medium flows or save more than 75% communication cost.
Blockchain came to prominence as the distributed ledger underneath Bitcoin, which protects the transaction histories in a fully-connected, peer-to-peer network. The blockchain mining process requires ...high computing power to solve a Proof-of-Work (PoW) puzzle, which is hard to implement on users' mobile devices. So these miners may leverage the edge/cloud service providers (ESPs/CSP) to calculate the PoW puzzle. The existing edge-assisted blockchain networks assumed that all ESPs have a uniform propagation delay, which is unrealistic. In this article, we consider a more practical scene where ESPs locate in diverse positions of the blockchain network, which causes different propagation delays when supporting the computation of the PoW puzzle. Additionally, these ESPs connect to a remote CSP for resource scheduling when the computing tasks exceed their maximum capacity. The blockchain mining process generally involves complicated competition and games among CSP, ESPs, and miners. Each service provider focuses on how to determine his resource price so that he can maximize his utility. According to the set resource price, each miner concentrates on scheduling his resource requests for each ESP to maximize individual personal utility, which depends on ESPs' resource price and propagation delays. We first model such a resource pricing and scheduling problem as a three-stage multi-leader multi-follower Stackelberg game and aim at finding the Stackelberg equilibrium. Then, we analyze the subgame optimization problem in each stage and propose an iterative algorithm based on backward induction to achieve the Nash equilibrium of the Stackelberg game. Finally, extensive simulations are conducted to verify the significant performance of the proposed solution.
