Airport Development and Regional Economic Growth in China

Air transport has experienced phenomenal growth in China over the last 30 years, but studies on China’s airport development are few. This paper aims to fill in this literature gap by focusing on the determinants of airport development in the Chinese regions using the most up-to-day and comprehensive data on China’s airports and their related economic and geographical variables. The empirical results based on an augmented production function indicate that airport development is positively related with economic growth, industrial structure, population density, and openness, but negatively related with ground transportation. The growth of airport transportation in the eastern region is slower than in the inland areas, implying a more significant substitution effect of air transport on ground transport in the less densely populated areas, irrespective of economic activities. The results have useful policy implications as any regional transportation development plan has to simultaneously consider the competitive and supplementary effects of both air and land transports in a specific location.Airport Development, Regional Economic Growth, China

Combination therapy with mTOR and PI3 kinase inhibitors is broadly synergistic in a wide variety of endometrial cancer cells

Dysregulation of mammalian target of rapamycin (mTOR) signaling has been found in many human tumors, including endometrial cancer, and mTOR inhibitors have been utilized in clinical trials as targeted therapies with only limited success. Herein we identify a viable treatment alternative that overcomes temsirolimus-induced AKT phosphorylation in endometrial cancer. Our data suggest temsirolimus and BEZ235 inhibit different components of the AKT/mTOR signaling pathway to accomplish synergistic pathway inhibition, which is necessary for therapeutic efficacy to abrogate the increased signaling through AKT that occurs with mTOR inhibition alon

Two-family outbreak of botulism associated with the consumption of smoked ribs in Sichuan Province, China

SummaryBackgroundOn September 22, 2013, two patients from Sichuan Province, China presented with symptoms of food-borne botulism, a rare but fatal illness caused by the consumption of foods containing Clostridium botulinum neurotoxins.MethodsInvestigators reviewed the medical charts and food consumption histories, and interviewed patients and family members. Food samples and clinical specimens were tested for botulinum toxin and neurotoxin-producing Clostridium species by standard methods.ResultsThe first two index cases presented with cranial neuropathies and flaccid paralysis, and required mechanical ventilation. There were 12 confirmed outbreak-associated cases. Botulinum toxin type A was identified in the smoked ribs, and all of the patients had consumed the smoked ribs from the same local restaurant. The smoked ribs contained no added salt, sugar, or preservative. Botulinum toxin production likely resulted from the cold-smoking preparation method and inappropriate refrigeration.ConclusionsSmoked ribs produced by a local restaurant, contaminated with type A botulism, was the contributor to this outbreak. The supervision of food safety should be strengthened to prevent future outbreaks in China

Bridging the Gap between Pre-Training and Fine-Tuning for End-to-End Speech Translation

End-to-end speech translation, a hot topic in recent years, aims to translate a segment of audio into a specific language with an end-to-end model. Conventional approaches employ multi-task learning and pre-training methods for this task, but they suffer from the huge gap between pre-training and fine-tuning. To address these issues, we propose a Tandem Connectionist Encoding Network (TCEN) which bridges the gap by reusing all subnets in fine-tuning, keeping the roles of subnets consistent, and pre-training the attention module. Furthermore, we propose two simple but effective methods to guarantee the speech encoder outputs and the MT encoder inputs are consistent in terms of semantic representation and sequence length. Experimental results show that our model outperforms baselines 2.2 BLEU on a large benchmark dataset.Comment: AAAI202

Male Clients of Male Sex Workers in China: An Ignored High-Risk Population.

BackgroundThere is a high prevalence of HIV/syphilis among male sex workers, but no formal study has ever been conducted focusing on male clients of male sex workers (MCM). A detailed investigation was thus called for, to determine the burden and sociobehavioral determinants of HIV and syphilis among these MCM in China.MethodsAs part of a multicenter cross-sectional study, using respondent-driven and snowball sampling, 2958 consenting adult men who have sex with men (MSM) were recruited, interviewed, and tested for HIV and syphilis between 2008 and 2009. The distributions of sociodemographic characteristics, risk behaviors, and HIV/syphilis prevalence were determined and compared between MCM and other MSM.ResultsAmong recruited MSM, 5.0% (n = 148) were MCM. HIV prevalences for MCM and other MSM were 7.4% and 7.7%, whereas 18.9% and 14.0% were positive for syphilis, respectively. Condomless anal intercourse (CAI) was reported by 59.5% of MCM and 48.2% of MSM. Multiple logistic regression revealed that compared with other MSM, MCM were more likely to have less education [for ≤ elementary level, adjusted odds ratio (aOR) = 3.13, 95% confidence interval (95% CI): 1.42 to 6.90], higher income (for >500 US Dollars per month, aOR = 2.97, 95% CI: 1.53 to 5.77), more often found partners at parks/restrooms (aOR = 4.01, 95% CI: 2.34 to 6.85), reported CAI (aOR = 1.49, 95% CI: 1.05 to 2.10), reported a larger sexual network (for ≥ 10, aOR = 2.70, 95% CI: 1.44 to 5.07), and higher odds of syphilis (aOR = 1.54, 95% CI: 1.00 to 2.38).ConclusionsThe greater frequency of risk behaviors and high prevalence of HIV and syphilis indicated that HIV/syphilis prevention programs in China need to pay special attention to MCM as a distinct subgroup, which was completely ignored until date

GADY: Unsupervised Anomaly Detection on Dynamic Graphs

Anomaly detection on dynamic graphs refers to detecting entities whose behaviors obviously deviate from the norms observed within graphs and their temporal information. This field has drawn increasing attention due to its application in finance, network security, social networks, and more. However, existing methods face two challenges: dynamic structure constructing challenge - difficulties in capturing graph structure with complex time information and negative sampling challenge - unable to construct excellent negative samples for unsupervised learning. To address these challenges, we propose Unsupervised Generative Anomaly Detection on Dynamic Graphs (GADY). To tackle the first challenge, we propose a continuous dynamic graph model to capture the fine-grained information, which breaks the limit of existing discrete methods. Specifically, we employ a message-passing framework combined with positional features to get edge embeddings, which are decoded to identify anomalies. For the second challenge, we pioneer the use of Generative Adversarial Networks to generate negative interactions. Moreover, we design a loss function to alter the training goal of the generator while ensuring the diversity and quality of generated samples. Extensive experiments demonstrate that our proposed GADY significantly outperforms the previous state-of-the-art method on three real-world datasets. Supplementary experiments further validate the effectiveness of our model design and the necessity of each module

Knockdown of MTDH increases drug sensitivity to HDAC inhibitor and TRAIL combination treatment in endometrial cancer cells

Understanding the molecular mechanisms of chemoresistance is vital to design therapies to restore chemosensitivity. MTDH mediates drug resistance by regulating expression of genes required for the control of apoptosis and cell cycle. These findings indicate that sensitivity to chemotherapy agents and combination treatment with HDAC inhibitor and TRAIL can be restored by manipulating MTDH, and hence depletion of MTDH is a potentially novel avenue for effective cancer therapy

BigDataBench: a Big Data Benchmark Suite from Internet Services

As architecture, systems, and data management communities pay greater attention to innovative big data systems and architectures, the pressure of benchmarking and evaluating these systems rises. Considering the broad use of big data systems, big data benchmarks must include diversity of data and workloads. Most of the state-of-the-art big data benchmarking efforts target evaluating specific types of applications or system software stacks, and hence they are not qualified for serving the purposes mentioned above. This paper presents our joint research efforts on this issue with several industrial partners. Our big data benchmark suite BigDataBench not only covers broad application scenarios, but also includes diverse and representative data sets. BigDataBench is publicly available from http://prof.ict.ac.cn/BigDataBench . Also, we comprehensively characterize 19 big data workloads included in BigDataBench with varying data inputs. On a typical state-of-practice processor, Intel Xeon E5645, we have the following observations: First, in comparison with the traditional benchmarks: including PARSEC, HPCC, and SPECCPU, big data applications have very low operation intensity; Second, the volume of data input has non-negligible impact on micro-architecture characteristics, which may impose challenges for simulation-based big data architecture research; Last but not least, corroborating the observations in CloudSuite and DCBench (which use smaller data inputs), we find that the numbers of L1 instruction cache misses per 1000 instructions of the big data applications are higher than in the traditional benchmarks; also, we find that L3 caches are effective for the big data applications, corroborating the observation in DCBench.Comment: 12 pages, 6 figures, The 20th IEEE International Symposium On High Performance Computer Architecture (HPCA-2014), February 15-19, 2014, Orlando, Florida, US

Characteristics and properties of a polysaccharide isolated from Wolfiporia cocos as potential dietary supplement for IBS

IntroductionAs low FODMAP (Fermentable oligosaccharides, disaccharides, monosaccharides and polyols) diet therapy is recommended for most of Irritable Bowel Syndrome (IBS) patients, the consequent insufficient of dietary fibers (DFs) intake exert an adverse impact on intestinal health. It is necessary to find suitable DFs for IBS patients.MethodsThis study extracted a water-insoluble polysaccharide from Wolfiporia cocos (WIP) by alkali-extraction and acid-precipitation method. Its molecular weight was detected by high performance gel permeation chromatography (HPGPC) analysis. The structure of WIP was analyzed by Fourier transform infrared (FT-IR) spectrum, Nuclear Magnetic Resonance (NMR) spectra and X-ray diffraction (XRD). The properties related to stability, digestion, viscosity, osmotic activity, adsorption and fermentation were investigated, aimed to explore the feasibility of WIP as a new DF supplement for patients with IBS. In addition, 16S rRNA sequencing analysis was conducted to explore its effects on IBS-related gut microbiota.Results and DiscussionThe results showed that WIP had a single homogeneous composition and the molecular weight was 8.1 × 103 Da. WIP was indicated as a kind of pyranose form with β anomeric configuration and the main chain of WIP was 1,3-β-glucan with amorphous structure. In addition to good thermal stability, WIP also has low bioavailability and can reach the colon mostly without being digested. Moreover, the low viscosity and osmotic activity, the high water- swelling and water/oil-holding capacity, fructose adsorption capacity and poor fermentation performance of WIP demonstrated that it is suitable for IBS patients. It is worth noting that WIP regulates IBS associated gut microbiota effectively, such as the abundance of Lachnospiraceae and Prevotella. These findings provide a theoretical basis for the development of WIP as a dietary supplement for IBS patients with low FODMAP diet therapy.GRAPHICAL ABSTRAC

Open-TransMind: A New Baseline and Benchmark for 1st Foundation Model Challenge of Intelligent Transportation

With the continuous improvement of computing power and deep learning algorithms in recent years, the foundation model has grown in popularity. Because of its powerful capabilities and excellent performance, this technology is being adopted and applied by an increasing number of industries. In the intelligent transportation industry, artificial intelligence faces the following typical challenges: few shots, poor generalization, and a lack of multi-modal techniques. Foundation model technology can significantly alleviate the aforementioned issues. To address these, we designed the 1st Foundation Model Challenge, with the goal of increasing the popularity of foundation model technology in traffic scenarios and promoting the rapid development of the intelligent transportation industry. The challenge is divided into two tracks: all-in-one and cross-modal image retrieval. Furthermore, we provide a new baseline and benchmark for the two tracks, called Open-TransMind. According to our knowledge, Open-TransMind is the first open-source transportation foundation model with multi-task and multi-modal capabilities. Simultaneously, Open-TransMind can achieve state-of-the-art performance on detection, classification, and segmentation datasets of traffic scenarios. Our source code is available at https://github.com/Traffic-X/Open-TransMind