PURPOSE To ascertain if preoperative short-term radiotherapy followed by chemotherapy is not inferior to a standard schedule of long-term chemoradiotherapy in patients with locally advanced rectal cancer. MATERIALS AND METHODS Patients with distal or middle-third, clinical primary tumor stage 3-4 and/or regional lymph node–positive rectal cancer were randomly assigned (1:1) to short-term radiotherapy (25 Gy in five fractions over 1 week) followed by four cycles of chemotherapy (total neoadjuvant therapy [TNT]) or chemoradiotherapy (50 Gy in 25 fractions over 5 weeks, concurrently with capecitabine [chemoradiotherapy; CRT]). Total mesorectal excision was undertaken 6-8 weeks after preoperative treatment, with two additional cycles of CAPOX (intravenous oxaliplatin [130 mg/m2, once a day] on day 1 and capecitabine [1,000 mg/m2, twice a day] from days 1 to 14) in the TNT group and six cycles of CAPOX in the CRT group. The primary end point was 3-year disease-free survival (DFS). RESULTS Between August 2015 and August 2018, a total of 599 patients were randomly assigned to receive TNT (n = 302) or CRT (n = 297). At a median follow-up of 35.0 months, 3-year DFS was 64.5% and 62.3% in TNT and CRT groups, respectively (hazard ratio, 0.883; one-sided 95% CI, not applicable to 1.11; P < .001 for noninferiority). There was no significant difference in metastasis-free survival or locoregional recurrence, but the TNT group had better 3-year overall survival than the CRT group (86.5% v 75.1%; P = .033). Treatment effects on DFS and overall survival were similar regardless of prognostic factors. The prevalence of acute grade III-V toxicities during preoperative treatment was 26.5% in the TNT group versus 12.6% in the CRT group ( P < .001). CONCLUSION Short-term radiotherapy with preoperative chemotherapy followed by surgery was efficacious with acceptable toxicity and could be used as an alternative to CRT for locally advanced rectal cancer.

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Convolutional Neural Networks (CNNs) are widely adopted in object recognition, speech processing and machine translation, due to their extremely high inference accuracy. However, it is challenging to compute massive computationally expensive convolutions of deep CNNs on traditional CPUs and GPUs. Emerging Nanophotonic technology has been employed for on-chip data communication, because of its CMOS compatibility, high bandwidth and low power consumption. In this paper, we propose a nanophotonic accelerator, HolyLight, to boost the CNN inference throughput in datacenters. Instead of an all-photonic design, HolyLight performs convolutions by photonic integrated circuits, and process the other operations in CNNs by CMOS circuits for high inference accuracy. We first build HolyLight-M by microdisk-based matrix-vector multipliers. We find analog-todigital converters (ADCs) seriously limit its inference throughput per Watt. We further use microdisk-based adders and shifters to architect HolyLight-A without ADCs. Compared to the stateof-the-art ReRAM-based accelerator, HolyLight-A improves the CNN inference throughput per Watt by 13× with trivial accuracy degradation.

Small-cell lung cancer (SCLC) is one of the most aggressive cancers, yet the molecular mechanisms underlying its devastating clinical outcome remain elusive. In this study, we investigated whether microRNA (miRNA) expression profiles can predict the clinical outcomes of SCLC patients. A total of 82 patients with limited SCLC, who were treated with surgical resection and adjuvant chemotherapy, were enrolled in this study. First, we surveyed the expression of 924 miRNAs from 42 SCLC patients to discover survival-relevant miRNAs and develop prognostic models, which were then validated in an independent cohort of 40 cases using quantitative real-time PCR. We found that the miR-150/miR-886-3p signature was significantly correlated with the overall survival (OS) of SCLC patients (p = 0.02) in the training set, and both miRNA expression levels were much lower in the SCLC samples than normal lung samples. The miRNA signature also proved to be a significant predictor of survival in the validation set. Patients with high-risk miRNA signatures had poor overall survival (p = 0.005) and progression-free survival (p = 0.017) compared with those with low-risk scores. These findings retained statistical significance after adjusting for age, gender and smoking status (HR: 0.26, 95%: CI 0.10–0.69, p = 0.007), which suggested it may be an independent predictor of survival. In summary, we developed a prognostic miR-150/miR-886-3p signature and validated expression in an independent dataset of resectable SCLC. These preliminary results indicated that miRNAs may serve as promising molecular prognostic markers and new therapeutic targets for SCLC.

Purpose The purpose of this paper is to design a model that can accurately forecast the supply chain sales. Design/methodology/approach This paper proposed a new model based on lightGBM and LSTM to forecast the supply chain sales. In order to verify the accuracy and efficiency of this model, three representative supply chain sales data sets are selected for experiments. Findings The experimental results show that the combined model can forecast supply chain sales with high accuracy, efficiency and interpretability. Practical implications With the rapid development of big data and AI, using big data analysis and algorithm technology to accurately forecast the long-term sales of goods will provide the database for the supply chain and key technical support for enterprises to establish supply chain solutions. This paper provides an effective method for supply chain sales forecasting, which can help enterprises to scientifically and reasonably forecast long-term commodity sales. Originality/value The proposed model not only inherits the ability of LSTM model to automatically mine high-level temporal features, but also has the advantages of lightGBM model, such as high efficiency, strong interpretability, which is suitable for industrial production environment.

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Radiotherapy plays a crucial role in combined treatment modality in local advanced rectal cancer (LARC). While neoadjuvant chemoradiotherapy responses were variable in LARC patients, so, it is important to identify genes that closely associated with short‐term and long‐term responses to radiotherapy. In this study, we profiled long noncoding RNAs (lncRNAs) and messenger RNAs (mRNAs) expression values of LARC patients with different neoadjuvant chemoradiotherapy downstaging depth score based on Agilent Arraystar Human LncRNA V3.0 Array(Agilent, CA). LncRNAs and mRNAs with aberrant expression values between the two groups of LARC patients were identified and lncRNA‐miRNA‐mRNA regulation network was also obtained through the combination of miRcode and miRTarBase database. Gene interaction network and module analysis of differential expression mRNAs contained in the lncRNA‐miRNA‐mRNA network identified five hub genes, including KRAS, PDPK1, PPP2R5C, PPP2R1B, and YES1, that should be closely associated with LARC’s response to chemoradiotherapy. Besides, Kaplan‐Meier analysis based on the Cyber Research Center (CRC) data set from The Cancer Genome Atlas indicated that aberrant expression of the five hub genes is significantly associated with CRC overall survival. In conclusion, we obtained several biomarkers that should be associated with neoadjuvant chemoradiotherapy response in LARC, which should be helpful for individual treatment and prognosis improvement.

Peridynamics is a theory of continuum mechanics expressed in forms of integral equations rather than partial differential equations. In this paper, a peridynamics code is implemented using a graphics processing unit for highly parallel computation, and numerical studies are conducted to investigate the responses of brittle and ductile material models. Stress-strain behavior with different grid sizes and horizons is studied for a brittle material model. A comparison of stresses and strains between finite element analysis (FEA) and peridynamic solutions is performed for a ductile material. By applying the proposed procedure to bridge the material model defined for peridynamic bonds and the corresponding macroscale material model for FEA, peridynamics and FEA show good agreements as regards the stresses and strains.