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Background Circular ribonucleic acids (circRNAs) are highly stable and conserved forms of RNAs present in all eukaryotes. They can modulate the expression of genes by sponging specific micro RNAs (miRNAs), thereby affecting various disease processes. However, their expression pattern in human breast cancer has not been elucidated. Methods In this study, differentially expressed circRNAs in breast cancer tissues and paired noncancerous tissues were analyzed using an Arraystar Human circRNA Microarray, and hsa_circ_0006220 was selected for its 27-fold downregulation in breast cancer tissues. Its expression was also verified in 50 breast cancer and paired noncancerous tissues using real-time polymerase chain reaction (RT-PCR). An analysis of the expression of hsa_circ_0006220 and the clinicopathological factors in breast cancer was conducted. A receiver operating characteristic (ROC) curve of hsa_circ_0006220 was constructed. The interaction between hsa_circ_0006220 and five possible target miRNAs was predicted, and their expression were verified when overexpressing hsa_circ_0006220 by RT-PCR. Results Hsa_circ_0006220 was found to be significantly downregulated in breast cancer tissues compared to the paired noncancerous tissues by microarray and RT-PCR. The expression of hsa_circ_0006220 was significantly inversely correlated with histological type (P=0.0028) and lymph node metastasis (P=0.0341). The area under the ROC curve (AUC) was 0.706. Five miRNAs that might be sponged by hsa_circ_0006220 were predicted. MiR-197-5p was significantly downregulated after overexpression of hsa_circ_0006220. Conclusions Our results indicated that hsa_circ_0006220 may play a role in human breast cancer and might be a potential tumor marker for breast cancer screening.

In recent years, some studies showed that inflammatory bowel disease (IBD) might be associated with an increased risk of prostate cancer (PC), whereas some other studies indicated that IBD was not associated with the increased risk of PC. In the present study, we aimed to conduct a systematic review and meta-analysis to evaluate the association of IBD and PC risk.Web of Science and PubMed were systematically searched on for link of PC risk and IBD published from January 1976 to February 2019. The pooled multivariate odd ratio (OR) or relative risk (RR) and 95% confidence intervals (CI) were obtained by the use of STATA 12.0 software.The meta-analysis indicated that IBD showed a 78% increase in PC risk (95% CI: 1.32-2.41). Sensitivity analyses showed no changes in the direction of effect after excluding any one study (supplementary figure 1). A significant heterogeneity was detected between different studies (Q test, P0.001). Moreover, Begg's test, Egger's tests and funnel plots indicated no significant publication bias between included studies [Begg's test (P=0.371); Egger's test (p=0.100)].The present meta-analysis demonstrated that IBD was associated with an increased diagnosis of PC. In addition, large scale prospective studies are essential to determine whether IBD increase the PC risk.

The paper proposes a new HCI mechanism for device-free gesture recognition on the table using acoustic signal, which can extend the gesture input and interactions beyond the tiny screen of mobile device and allow users to provide input without blocking screen view. Previous researches have either relied on additional devices (e.g., special wearable device and mouse) or required active acoustic signals which demand additional cost and be less prone to popularize, while we explore the device-free gesture recognition using passive acoustic signals. This technology is more challenging due to the lack of an effective approach to eliminate the inherent ambient noise disturbances and extract stable gesture features. We fuse both short time energy (STE) and zero-crossing rate (ZCR) to identify the effective signals from the original input, and leverage the Mel frequency cepstral coefficients (MFCC), cochlear filter cepstral coefficients (CFCC) to extract the stable features from different gestures. The unique features in support vector machine (SVM) classifier achieve a high gesture recognition accuracy from the noisy scenarios and mismatched conditions. Implementation on the Android system has realized real-time processing of the feature extraction and gesture recognition. Extensive evaluations show our algorithm has a better noise tolerant performance and the system could recognize seven common gestures (click, flip left/right, scroll up/down, zoom in/out) on smart devices with an accuracy of 93.2%.

Probiotics have been widely used in the treatment of inflammatory bowel disease (IBD) due to their special conditioning effects on the intestinal flora, but their efficacy in inducing and maintaining remission is still controversial. In the present study, we analyzed randomized controlled trials (RCTs) on the treatment of probiotics in IBD to systematically evaluate the efficacy of probiotics on the induction and maintenance of remission of IBD.PubMed, Cochrane Central Register of Controlled Trials, Embase, Web of Science, Chinese Journal Full-text Database, Chinese Biomedical Literature Database, Wanfang, and Weipu databases were searched for RCTs on the treatment of probiotics in IBD induction and/or maintenance of remission. Two researchers independently extracted literature data and cross-checked the extracted data. The Cochrane bias risk assessment tool and the Jadad score were used to evaluate the quality of the literature. RevMan 5.20 software was used for the meta-analysis.A total of ten RCTs were included, which included 777 patients with ulcerative colitis (UC) and Crohn's disease (CD). Meta-analysis showed that there was no significant difference between the clinical recurrence rate of probiotics in the CD remission phase and the control group [relative risk (RR): 0.80, 95% confidence interval (CI): 0.61-1.06, P=0.12]. The clinical recurrence rate of UC in remission phase, the efficacy of probiotics compared with control group were not significantly different (RR: 1.07, 95% CI: 0.80-1.42, P=0.65). The induction remission rate of UC during the active phase, the efficacy of probiotics is better than that of the control group (RR: 1.47, 95% CI: 1.09-1.98, P=0.01).In the present study, a systematic analysis of the efficacy of probiotics in CD and UC found that probiotics can induce remission during the active period of UC, but have no obvious therapeutic advantage in maintaining CD and UC remission.

Laboratory investigation was carried out to assess the effect of amino-alcohol-based corrosion inhibitors on concrete durability, and the inhibition mechanism in concrete was analyzed. Both admixed type and surface-applied type corrosion inhibitors were studied by chloride penetration test, saturation test, scanning electron microscope, and X-ray diffraction. The results showed that the anti-penetrability, compactness, and hydration of concrete were improved after the two types of corrosion inhibitors were applied. The use of corrosion inhibitors mitigated the penetration of chloride ions and water into concrete, which effectively delayed corrosion of the reinforcing bar. The surface-applied corrosion inhibitor showed better efficiency in the concrete specimens compared to the admixed type. Moreover, there was a better synergy between the two agents and the best performance when the two agents were applied to the concrete simultaneously.

This paper explores the possibility of extending the input and interactions beyond the small screen of the mobile device onto ad hoc adjacent surfaces, e.g., a wooden tabletop with acoustic signals. While the existing finger tracking approaches employ the active acoustic signal with a fixed frequency, our proposed system Ipanel employs the acoustic signals generated by sliding of fingers on the table for tracking. Different from active signal tracking, the frequency of the finger-table generated acoustic signals keeps changing, making accurate tracking much more challenging than the traditional approaches with fix frequency signal from the speaker. Unique features are extracted by exploiting the spatio-temporal and frequency domain properties of the generated acoustic signals. The features are transformed into images and then we employ the convolutional neural network (CNN) to recognize the finger movement on the table. Ipanel is able to support not only commonly used gesture (click, flip, scroll, zoom, etc.) recognition, but also handwriting (10 numbers and 26 alphabets) recognition at high accuracies. We implement Ipanel on smartphones, and conduct extensive real environment experiments to evaluate its performance. The results validate the robustness of Ipanel, and show that it maintains high accuracies across different users with varying input behaviours (e.g., input strength, speed and region). Further, Ipanel's performance is robust against different levels of ambient noise and varying surface materials.

Although the smartwatches have rapid development in recent years, inconvenient interaction mode limits the prevalence of smartwatches, which raises an urgent need for new interaction medium beyond the small built-in screen. For natural interaction with smart devices, full development of terminal sensing, and no dependence on hardware, we purpose to write numbers on the hand back to extend the input interface of the smartwatch. We have to address both the weakness of writing signal on the hand back and adaptability among various people. In this paper, we raise a passive acoustic sensing, where the smartwatches sample the ambient sound during writing. First, we employ the wavelet transformation to drop the interference of surrounding noise and devise the time–frequency features into images for machine learning-enabled processing. Then, we design a hybrid convolutional neural network model for number recognition, where three layers of convolution are followed by three layers of max pool. We choose the three most representative places to set up the experiment, namely, laboratory, dormitory, and canteen. The accuracy of number recognition could be over 95% when we adopt single data and be around 91% when 10 persons are incorporated. On promotion of writing speed under the limited area of hand back, we propose to write 2–3 consecutive numbers, where we could achieve the segmentation accuracy to be above 85%.

// Wanchen Wang 1, * , Chengwen Hang 1, * , Yanqing Zhang 1 , Mingshi Chen 1 , Xinyu Meng 1 , Qing Cao 1 , Nana Song 1 , Jacobi Itkow 1 , Feiyang Shen 1 and Duonan Yu 1, 2, 3, 4 1 Jiangsu Key Laboratory of Experimental & Translational Non-coding RNA Research, Yangzhou University School of Medicine, Yangzhou 225001, China 2 Institute of Comparative Medicine, Yangzhou University, Yangzhou 225001, China 3 Institute of Translational Medicine, Yangzhou University School of Medicine, Yangzhou 225001, China 4 Jiangsu Co-Innovation Center for Prevention and Control of Important Animal Infectious Disease and Zoonosis, Yangzhou 225001, China * These authors have contributed equally to this work Correspondence to: Duonan Yu, email: dnyu@yzu.edu.cn Keywords: microRNA; food safety; miR-144/451 knockout mice; erythroid cell Received: January 16, 2017 Accepted: August 26, 2017 Published: November 10, 2017 ABSTRACT One fundamental issue in public health is the safety of food products derived from plants and animals. A recent study raised a concern that microRNAs, which widely exist in everyday foods, may alter consumers’ functions. However, some studies have strongly questioned the likelihood of dietary uptake of functional microRNAs in mammals. Here we use a microRNA gene knockout animal model to show that miR-144/451 null mice can orally uptake miR-451 from a daily chow diet, and ingestion of wild type blood, that contains abundant miR-451, also enhances the level of miR-451 in the circulating blood of knockout mice. Moreover, reducing miR-451 level in miR-144/451 knockout blood by consuming food lacking miR-451 reduces the anti-oxidant capacity of miR-144/451 null red blood cells by targeting the 14-3-3ζ/Foxo3 pathway, while increasing miR-451 level via gavage-feeding of wild type blood increases the anti-oxidant capacity of miR-144/451 null red blood cells. We conclude that 1) some miRNAs in food can pass through the gastrointestinal tract into the blood to affect consumers’ function and 2) microRNA knockout animals such as miR-144/451 null mice can acquire the deleted genetic information from daily foods, which might alter the results and conclusions from the studies using such animals.

Summary Evapotranspiration (ET) is an important component of the water cycle – ET from the land surface returns approximately 60% of the global precipitation back to the atmosphere. ET also plays an important role in energy transport among the biosphere, atmosphere, and hydrosphere. Current regional to global and daily to annual ET estimation relies mainly on surface energy balance (SEB) ET models or statistical and empirical methods driven by remote sensing data and various climatological databases. These models have uncertainties due to inevitable input errors, poorly defined parameters, and inadequate model structures. The eddy covariance measurements on water, energy, and carbon fluxes at the AmeriFlux tower sites provide an opportunity to assess the ET modeling uncertainties. In this study, we focused on uncertainty analysis of the Operational Simplified Surface Energy Balance (SSEBop) model for ET estimation at multiple AmeriFlux tower sites with diverse land cover characteristics and climatic conditions. The 8-day composite 1-km MODerate resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) was used as input land surface temperature for the SSEBop algorithms. The other input data were taken from the AmeriFlux database. Results of statistical analysis indicated that the SSEBop model performed well in estimating ET with an R2 of 0.86 between estimated ET and eddy covariance measurements at 42 AmeriFlux tower sites during 2001–2007. It was encouraging to see that the best performance was observed for croplands, where R2 was 0.92 with a root mean square error of 13 mm/month. The uncertainties or random errors from input variables and parameters of the SSEBop model led to monthly ET estimates with relative errors less than 20% across multiple flux tower sites distributed across different biomes. This uncertainty of the SSEBop model lies within the error range of other SEB models, suggesting systematic error or bias of the SSEBop model is within the normal range. This finding implies that the simplified parameterization of the SSEBop model did not significantly affect the accuracy of the ET estimate while increasing the ease of model setup for operational applications. The sensitivity analysis indicated that the SSEBop model is most sensitive to input variables, land surface temperature (LST) and reference ET ( ET o ); and parameters, differential temperature ( dT ), and maximum ET scalar ( K max ), particularly during the non-growing season and in dry areas. In summary, the uncertainty assessment verifies that the SSEBop model is a reliable and robust method for large-area ET estimation. The SSEBop model estimates can be further improved by reducing errors in two input variables ( ET o and LST) and two key parameters ( K max and dT ).

Finger gesture input is emerged as an increasingly popular means of human-computer interactions. In this paper, we propose iPand, an acoustic sensing system that enables finger gesture input on the skin, which is more convenient, user-friendly and always accessible. Unlike previous works, which implement gesture input with dedicated devices, our system exploits passive acoustic sensing to identify the gestures, e.g. swipe left, swipe right, pinch and spread. The insight of our system is that specific gesture emits unique friction sound, which can be captured by the microphone embedded in wearable devices. We capture these acoustic signals and extract the features by using bandpass filters and short-time Fourier Transform. The offline convolutional neural network is adopted to recognize the gestures. iPand is implemented and evaluated using COTS smartphones and smartwatches. Experiment results show that iPand can achieve the recognition accuracy of 89%, 83% and 78% in three daily scenarios (i.e., library, lab and cafe), respectively. Particularly, our system supports multi-touch function where 2–4 fingers are enabled for more efficient and expressive gesture input, and its average accuracy for individual finger gesture reaches up to 83% within 12 gestures.

Finger gesture input is emerged as an increasingly popular means of human-computer interactions. In this demo, we propose iPand, an acoustic sensing system that enables finger gesture input on the skin, which is more convenient, user-friendly and always accessible. Unlike past works, which implement gesture input with dedicated devices, our system exploits passive acoustic sensing to identify the gestures, e.g. swipe left, swipe right, pinch and spread. The intuition underlying our system is that specific gesture emits unique friction sound, which can be captured by the microphone embedded in wearable devices. We then adopt convolutional neural network to recognize the gestures. We implement and evaluate iPand using COTS smartphones and smartwatches. Results from three daily scenarios (i.e., library, lab and cafe) of 10 volunteers show that iPand can achieve the recognition accuracy of 87%, 81% and 77% respectively.

Although smartwatch has drawn many attentions in recent years, small and inconvenient interaction mode limits the prevalence of smartwatches. Writing numbers with hands will naturally extend the input interface for smart watch. In this work, we design a passive acoustic sensing, where smart watches are collecting the ambient sound during writing. First of all, we use the wavelet transformation to mitigate the surrounding noise, and devise the time-frequency figures for AI enabled processing. After that, we apply the CNN(Convolutional Neural Network) model for number recognition, where three layers of convolution and three layers of max pool are incorporated. The number recognition accuracy rate could be above 95% when single person is well trained, and be around 92% when 7 to 9 persons are incorporated.

This paper compares contending advanced data assimilation algorithms using the same dynamical model and measurements. Assimilation experiments use the ensemble Kalman filter (EnKF), the ensemble Kalman smoother (EnKS) and the representer method involving a nonlinear model and synthetic measurements of a mesoscale eddy. Twin model experiments provide the “truth” and assimilated state. The difference between truth and assimilation state is a mispositioning of an eddy in the initial state affected by a temporal shift. The systems are constructed to represent the dynamics, error covariances and data density as similarly as possible, though because of the differing assumptions in the system derivations subtle differences do occur. The results reflect some of these differences in the tangent linear assumption made in the representer adjoint and the temporal covariance of the EnKF, which does not correct initial condition errors. These differences are assessed through the accuracy of each method as a function of measurement density. Results indicate that these methods are comparably accurate for sufficiently dense measurement networks; and each is able to correct the position of a purposefully misplaced mesoscale eddy. As measurement density is decreased, the EnKS and the representer method retain accuracy longer than the EnKF. While the representer method is more accurate than the sequential methods within the time period covered by the observations (particularly during the first part of the assimilation time), the representer method is less accurate during later times and during the forecast time period for sparse networks as the tangent linear assumption becomes less accurate. Furthermore, the representer method proves to be significantly more costly (2–4 times) than the EnKS and EnKF even with only a few outer iterations of the iterated indirect representer method.

A proposed system for real-time modeling of wildfires is described. The system involves numerical weather and fire prediction, automated data acquisition from Internet sources, and input from aerial photographs and sensors. The system will be controlled by a non-Gaussian ensemble filter capable of assimilating out-of-order data. The computational model will run on remote supercomputers, with visualization on PDAs in the field connected to the Internet via a satellite.

We report on an ongoing effort to build a Dynamic Data Driven Application System (DDDAS) for short-range forecast of wildfire behavior from real-time weather data, images, and sensor streams. The system should change the forecast when new data is received. The basic approach is to encapsulate the model code and use an ensemble Kalman filter in time-space. Several variants of the ensemble Kalman filter are presented, for out-of-sequence data assimilation, hidden model states, and highly nonlinear problems. Parallel implementation and web-based visualization are also discussed.