Miracle-dz/UniFAR-FTUs huggingface dataset:
Highly Pathogenic Avian Influenza Virus Infection of Mallards with Homo- and Heterosubtypic Immunity Induced by Low Pathogenic Avian Influenza Viruses

The potential role of wild birds as carriers of highly pathogenic avian influenza virus (HPAIV) subtype H5N1 is still a matter of debate. Consecutive or simultaneous infections with different subtypes of influenza viruses of low pathogenicity (LPAIV) are very common in wild duck populations. To better understand the epidemiology and pathogenesis of HPAIV H5N1 infections in natural ecosystems, we investigated the influence of prior infection of mallards with homo- (H5N2) and heterosubtypic (H4N6) LPAIV on exposure to HPAIV H5N1. In mallards with homosubtypic immunity induced by LPAIV infection, clinical disease was absent and shedding of HPAIV from respiratory and intestinal tracts was grossly reduced compared to the heterosubtypic and control groups (mean GEC/100 µl at 3 dpi: 3.0×102 vs. 2.3×104 vs. 8.7×104; p<0.05). Heterosubtypic immunity induced by an H4N6 infection mediated a similar but less pronounced effect. We conclude that the epidemiology of HPAIV H5N1 in mallards and probably other aquatic wild bird species is massively influenced by interfering immunity induced by prior homo- and heterosubtypic LPAIV infections.
Astrolabe: A robust and scalable technology for distributed system monitoring, management, and data mining

Scalable management and self-organizational capabilities areemerging as central requirements for a generation of large-scale,highly dynamic, distributed applications. We have developed anentirely new distributed information management system calledAstrolabe. Astrolabe collects large-scale system state, permittingrapid updates and providing on-the-fly attribute aggregation. Thislatter capability permits an application to locate a resource, andalso offers a scalable way to track system state as it evolves overtime. The combination of features makes it possible to solve a widevariety of management and self-configuration problems. This paperdescribes the design of the system with a focus upon itsscalability. After describing the Astrolabe service, we presentexamples of the use of Astrolabe for locating resources,publish-subscribe, and distributed synchronization in largesystems. Astrolabe is implemented using a peer-to-peer protocol,and uses a restricted form of mobile code based on the SQL querylanguage for aggregation. This protocol gives rise to a novelconsistency model. Astrolabe addresses several securityconsiderations using a built-in PKI. The scalability of the systemis evaluated using both simulation and experiments; these confirmthat Astrolabe could scale to thousands and perhaps millions ofnodes, with information propagation delays in the tens of seconds.
Character-Level Dependencies in Chinese: Usefulness and Learning

We investigate the possibility of exploiting character-based dependency for Chinese information processing. As Chinese text is made up of character sequences rather than word sequences, word in Chinese is not so natural a concept as in English, nor is word easy to be defined without argument for such a language. Therefore we propose a character-level dependency scheme to represent primary linguistic relationships within a Chinese sentence. The usefulness of character dependencies are verified through two specialized dependency parsing tasks. The first is to handle trivial character dependencies that are equally transformed from traditional word boundaries. The second furthermore considers the case that annotated internal character dependencies inside a word are involved. Both of these results from character-level dependency parsing are positive. This study provides an alternative way to formularize basic character-and word-level representation for Chinese.
A Maximum Entropy Approach to Identifying Sentence Boundaries

We present a trainable model for identifying sentence boundaries in raw text. Given a corpus annotated with sentence boundaries, our model learns to classify each occurrence of. , ? , and ! as either a valid or invalid sentence boundary. The training procedure requires no hand-crafted rules, lexica, part-of-speech tags, or domain-specific information. The model can therefore be trained easily on any genre of English, and should be trainable on any other Romanalphabet language. Performance is comparable to or better than the performance of similar systems, but we emphasize the simplicity of retraining for new domains.
Sum-rate maximization for filter-forward relay network using virtual WMMSE algorithm

Filter-and-forward (FF) relay protocols can combat the impact of frequency-selective fading. This paper studies filter design for FF multicast multi-relay networks. We formulate the sum-rate maximization problem with individual relay power constraints. Different from the sum-rate maximization problems of general channel setups, the studied sum-rate problem cannot be directly tackled using the popular WMMSE technique. Based on the special structure of the sum-rate maximization problem at hand, we introduce virtual parallel flat-fading channel and establish an equivalent problem of the sum-rate maximization problem. The equivalent problem can be addressed using block coordinate descent (BCD) algorithm, which leads to virtual WMMSE algorithm. We show that the virtual WMMSE algorithm can monotonically converge to a stationary point of the sum-rate maximization problem. Simulations demonstrate the effectiveness of the proposed virtual WMMSE algorithm.
Diagnosis of Pathologically Early HCC with EOB-MRI: Experiences and Current Consensus

Objective: After much debate, the International Consensus Group for Hepatocellular Neoplasia (ICGHN) has recently arrived at a conclusion regarding the pathological criteria for early hepatocellular carcinoma (HCC). They have stated that stromal invasion should be recognized as the most important pathological finding for precisely diagnosing and differentiating early HCC from dysplastic nodules (DN). Methods: We conducted a review of the imaging findings from multi-imaging modalities of early HCC cases diagnosed according to the pathological criteria of the ICGHN. The multi-imaging modalities included gadoxetic acid (Gd-EOB-DTPA) enhanced magnetic resonance imaging (MRI), abbreviated as EOB-MRI, contrast-enhanced CT (CE-CT), CT during arterioportography (CTAP), and CT during hepatic arteriography (CTHA). It has been shown that EOB-MRI is the only imaging modality that has sufficient resolution for the detection and classification of early HCC. Results: The most significant imaging feature for diagnosing early HCC was hypointensity on hepatobiliary-phase (HP) images of EOB-MRI; all of the cases of early HCC that were detected on HP images of EOB-MRI showed hypointensity, while all of the images of DN showed isointensity or hyperintensity compared with the liver parenchyma. The results of the diagnostic performance analysis showed that EOB-MRI had excellent sensitivity (97%) for detecting early HCC and outstanding specificity (100%) for distinguishing early HCC from DN. Conclusions: Considering the results from imaging-pathologic correlations and follow-up studies indicating that many early-stage hepatocellular nodules showing hypointensity on HP images of EOB-MRI tend to develop hypervascularization during a relatively short follow-up period, it is beginning to be accepted that such nodules may be treated as early HCC. However, hepatologists and radiologists should also recognize that some cases of early HCC may show isointensity or hyperintensity on HP images of EOB-MRI, making it impossible to differentiate early HCC from DN, although the low prevalence of such nodules may be unlikely to affect the generally accepted follow-up protocols for cirrhotic patients. Our results and other recent reports have indicated that signal-intensity patterns on HP images of EOB-MRI for DN and early HCC directly correlate with the degree of expression of the organic anion transporting polypeptide (OATP) 1B3 in the nodules. Thus, the diagnostic performance of pathological analyses for early HCC cases may be dramatically improved, nearly up to that of EOB-MRI, by incorporating an OATP1B3 staining method.
The importance of general intelligence

A flow exchanging process for diverting a fluid flowing on the periphery of a conduit towards the center thereof and simultaneously diverting a second fluid flowing in the center of the conduit to the periphery thereof, utilizes open tapered pipe having a plurality of openings on its walls and protruding lips around each of the openings for diverting the peripheral flow towards the center of the conduit. The center flow is diverted to the periphery through the wall openings. The process is particularly adapted to use in the spinning of cellulose films from two qualities of viscose.
Enoxaparin Versus Dabigatran or Rivaroxaban for Thromboprophylaxis After Hip or Knee Arthroplasty: Results of Separate Pooled Analyses of Phase III Multicenter Randomized Trials

Background—Dabigatran and rivaroxaban are novel oral anticoagulants approved for prevention of venous thromboembolism after hip or knee arthroplasty. However, information assessing clinically important efficacy and bleeding outcomes of these 2 new agents versus low-molecular-weight heparin (enoxaparin) is lacking. Methods and Results—We separately pooled efficacy and safety data from 6 phase III randomized trials (18 405 participants) comparing equivalent durations of treatment with enoxaparin (40 mg once daily [od] or 30 mg twice daily) versus dabigatran (220 mg od) or versus rivaroxaban (10 mg od) after hip or knee arthroplasty. Odds ratios (OR) for individual outcomes were calculated for each trial and were pooled using the Mantel-Haenszel method. Compared with dabigatran, enoxaparin had a similar risk of symptomatic venous thromboembolism plus all-cause mortality (0.9% versus 1.1%; OR, 0.76; 95% confidence interval [CI], 0.44 to 1.31; I2=76%) and bleeding (5.0% versus 5.6%; OR, 0.90; 95% CI, 0.71 to 1.15; I2=0%). Compared with rivaroxaban, enoxaparin had a 2-fold higher risk of symptomatic venous thromboembolism plus all-cause mortality (1.2% versus 0.6%; OR, 2.04; 95% CI, 1.32 to 3.17; P<0.001; number needed to treat, 167; I2=0%) but demonstrated a significant lower risk of bleeding (2.5% versus 3.1%; OR, 0.79; 95% CI, 0.62 to 0.99; P=0.049; number needed to harm, 167; I2=0%). Conclusions—In patients undergoing hip or knee arthroplasty, enoxaparin and dabigatran showed similar rates of efficacy and bleeding. Enoxaparin was less effective than rivaroxaban but had a lower risk of bleeding. These results may have important implications for the choice of prophylactic agent in major joint arthroplasty.
A research of DC motor dual close-loop PID speed-tuning system on the basis of ABC Algorithm

This thesis, subject to the obstacles exist throughout the process of tuning and optimization of the prevailing DC Double close-loop PID controller parameters, explains to realize the tuning and the optimization of PID controller parameters by introducing the Artificial Bee Colony Algorithm on the basis of Nectar Collecting Behavior to the DC Double close-loop tuning system. The comparison and the analysis, processed by step response between the consequences of the Artificial Bee Colony Algorithm and Engineering Methods, indicate that the improved Artificial Bee Colony Algorithm can help us obtain a better dynamic performance index, and a faster following behaviors and robustness, due to which a practical and more comprehensive method to optimizing PID parameters is discovered;.
MiR-25 Regulates Wwp2 and Fbxw7 and Promotes Reprogramming of Mouse Fibroblast Cells to iPSCs

Background miRNAs are a class of small non-coding RNAs that regulate gene expression and have critical functions in various biological processes. Hundreds of miRNAs have been identified in mammalian genomes but only a small number of them have been functionally characterized. Recent studies also demonstrate that some miRNAs have important roles in reprogramming somatic cells to induced pluripotent stem cells (iPSCs). Methods We screened 52 miRNAs cloned in a piggybac (PB) vector for their roles in reprogramming of mouse embryonic fibroblast cells to iPSCs. To identify targets of miRNAs, we made Dgcr8-deficient embryonic stem (ES) cells and introduced miRNA mimics to these cells, which lack miRNA biogenesis. The direct target genes of miRNA were identified through global gene expression analysis and target validation. Results and conclusion We found that over-expressing miR-25 or introducing miR-25 mimics enhanced production of iPSCs. We identified a number of miR-25 candidate gene targets. Of particular interest were two ubiquitin ligases, Wwp2 and Fbxw7, which have been proposed to regulate Oct4, c-Myc and Klf5, respectively. Our findings thus highlight the complex interplay between miRNAs and transcription factors involved in reprogramming, stem cell self-renewal and maintenance of pluripotency.
Effects of NXY‐059 in experimental stroke: an individual animal meta‐analysis

Background and purpose: Disodium 2,4‐disulphophenyl‐N‐tert‐butylnitrone (NXY‐059) was neuroprotective in experimental stroke models but ineffective in a large clinical trial. This first‐ever individual animal meta‐analysis was used to assess the preclinical studies.
Uplink Component Carrier Selection for LTE-Advanced Systems with Carrier Aggregation

Carrier aggregation (CA) is one of the key features for LTE-Advanced. By means of CA, two or more component carriers (CCs) can be aggregated to form a much wider transmission bandwidth up to 100 MHz in order to meet the IMT-Advanced requirements. With CA, it is possible to schedule a user equipment (UE) on multiple component carriers simultaneously. From radio resource management (RRM) perspective, CC selection plays an important role in optimizing the system performance with CA. In this paper, we investigate the uplink resource allocation for LTE-Advanced Systems, i.e., how to assign CCs to different users, and how to do the power scaling for each user. A pathloss threshold based CC selection algorithm is proposed to distinguish between power-limited and non-power-limited LTE-Advanced UEs, and assign only one CC to power-limited LTE-Advanced UEs, while assign multiple CCs to non-power-limited LTE-Advanced UEs. Simulation results show that the derived pathloss threshold works effectively and efficiently compared to other thresholds, and the proposed CC selection algorithm can achieve much better performance in terms of cell edge, average, and cell center user throughput compared to blindly assigning all LTE-Advanced UEs on all CCs.
Multi-modal bike sensing for automatic geo-annotation geo-annotation of road/terrain type by participatory bike-sensing

This paper presents a novel road/terrain classification system based on the analysis of volunteered geographic information gathered by bikers. By ubiquitous collection of multi-sensor bike data, consisting of visual images, accelerometer information and GPS coordinates of the bikers' smartphone, the proposed system is able to distinguish between 6 different road/terrain types. In order to perform this classification task, the system employs a random decision forest (RDF), fed with a set of discriminative image and accelerometer features. For every instance of road (5 seconds), we extract these features and map the RDF result onto the GPS data of the users' smartphone. Finally, based on all the collected instances, we can annotate geographic maps with the road/terrain types and create a visualization of the route. The accuracy of the novel multi-modal bike sensing system for the 6-class road/terrain classification task is 92%. This result outperforms both the visual and accelerometer only classification, showing that the combination of both sensors is a win-win. For the 2-class on-road/off-road classification an accuracy of 97% is achieved, almost six percent above the state-of-the-art in this domain. Since these are the individual scores (measured on a single user/bike segment), the collaborative accuracy is expected to even further improve these results.
Shifting From Legacy Systems to a Data Mart and Computer Assisted Information Resources Navigation Framework

Computer Assisted Information Resources Navigation (CAIRN) was specified, in the past, as a framework that allows the end-users to import and store full text and multimedia documents and then retrieve information using Natural Language or field based queries. Our CAIRN system is a general tool that has focused on medical information covering the needs of physicians. Today, concepts related to Data Mining and Data Marts have to be incorporated into such a framework. In this paper a CAIRN-DAMM (Computer Assisted Medical Information Resources Navigation & Diagnosis Aid Based On Data Marts & Data Mining) environment is proposed and discussed. This integrated environment offers: document management, multimedia documents retrieval, a Diagnosis–aid subsystem and a Data Mart subsystem that permits the integration of legacy system’s data. The diagnosis is based on the International Classification of Diseases and Diagnoses, 9th revision (ICD-9). The document collection stored in the CAIRN-DAMM system consists of data imported from the Hospital Information System (HIS), laboratory tests extracted from the Laboratory Information System (LIS), patient discharge letters, ultrasound, CT and MRI images, statistical information, bibliography, etc. There are also methods permitting us to propose, evaluate and organize in a systematic way uncontrolled terms and to propose relationships between these terms and ICD9 codes. Finally, our experience from the use of the tool for creating a Data Mart at the ARETEION University Hospital is presented. Experimental results and a number of interesting observations are also
Metapopulation extinction risk is increased by environmental stochasticity and assemblage complexity

Extinction risk is a key area of investigation for contemporary ecologists and conservation biologists. Practical conservation efforts for vulnerable species can be considerably enhanced by thoroughly understanding the ecological processes that interact to determine species persistence or extinction. Theory has highlighted the importance of both extrinsic environmental factors and intrinsic demographic processes. In laboratory microcosms, single-species single-habitat patch experimental designs have been widely used to validate the theoretical prediction that environmental heterogeneity can increase extinction risk. Here, we develop on this theme by testing the effects of fluctuating resource levels in experimental multispecies metapopulations. We compare a three-species host–parasitoid assemblage that exhibits apparent competition to the individual pairwise, host–parasitoid interactions. Existing theory is broadly supported for two-species assemblages: environmental stochasticity reduces trophic interaction persistence time, while metapopulation structure increases persistence time. However, with increasing assemblage complexity, the effects of trophic interactions mask environmental impacts and persistence time is further reduced, regardless of resource renewal regime. We relate our findings to recent theory, highlighting the importance of taking into account both intrinsic and extrinsic factors, over a range of spatial scales, in order to understand resource–consumer dynamics.
A low-cost local cloud monitoring system

In this paper, a local cloud monitoring station equipped with a fisheye lens camera is proposed and installed at Prince of Songkla University (PSU), Phuket. The station retrieves cloud images in conjunction with several meteorological sensors. The methodology and cloud classifier algorithm is proposed. The performance test using images captured from our cloud monitoring station shows that the cloud classification accuracy in practice is as high as 98.58%. The installed cloud monitoring system can report live cloud conditions and display them on a mobile application. Our complete system is inexpensive and suitable for local use of weather monitoring and alerting.
Duty Time of Rabbit Jaw Muscles Varies with the Number of Activity Bursts

The relative duration of muscle activity during a specified period (duty time) varies depending on activity level and time of the day. Since both the number and the length of activity bursts contribute to the duty time, it was hypothesized that these variables would show intra-day variations similar to those of the duty time. To test this, we determined duty times, burst numbers, and burst lengths per hour, in relation to multiple activity levels, in a 24-hour period of concurrent radio-telemetric long-term electromyograms of various rabbit jaw muscles. The marked intra-day variation of the burst number resembled that of the duty time in all muscles, and was in contrast to the relatively invariable mean burst length. Furthermore, the duty times were more highly correlated with the number than with the length of bursts at all activity levels. Thus, the variation of the duty time in rabbit jaw muscles is caused mainly by changes in burst numbers.
A Weighted Slope One Algorithm Based on Cluster Filling and Time Weight

In order to solve defects of the Slope One algorithm that the effect of recommending is not well because of without considering the time weight, and has the problem of data sparsity and poor real-time performance. A weighted slope one algorithm based on cluster filling and time weight (WSOBCFT) was proposed in this paper. To reduce the time of generating the nearest neighbor, the rating matrix of time weight was clustered by Canopy-K-means algorithm at first. Then every class was filled to improve the density of the matrix and reduce data sparsity. Finally, considering the similarity weight and time weight, item's rating was predicted in the set of nearest neighbor. The experimental results illustrate that compared with traditional recommendation algorithm, the proposed algorithm has higher accuracy and real-time performance.
Focused Crawling by Learning HMM from User's Topic-specific Browsing

A focused crawler is designed to traverse the Web to gather documents on a specific topic. It is not an easy task to predict which links lead to good pages. In this paper, we present a new approach for prediction of the important links to relevant pages based on a learned user model. In particular, we first collect pages that a user visits during a learning session, where the user browses the Web and specifically marks which pages she is interested in. We then examine the semantic content of these pages to construct a concept graph, which is used to learn the dominant content and link structure leading to target pages using a Hidden Markov Model (HMM). Experiments show that with learned HMM from a user's browsing, the crawling performs better than Best-First strategy.