Miracle-dz/UniFAR-FTUs huggingface dataset:
Serum HMGB1 Serves as a Novel Laboratory Indicator Reflecting Disease Activity and Treatment Response in Ankylosing Spondylitis Patients

Objective. High mobility group box 1 (HMGB1) is a late inflammatory factor participating in the pathogenesis of various autoimmune and inflammatory diseases. In the current study, we analyzed the association between serum levels of HMGB1 and clinical features of AS patients before and during treatment. Methods. Serum HMGB1 was detected in 147 AS patients and 61 healthy controls using ELISA. We evaluated the association between HMGB1 and extra-articular manifestations as well as disease severity indices. Among these AS patients, 41 patients received close follow-up at 1, 3, and 6 months after treatment. This group comprised 25 patients treated with anti-TNF-α biologics and 16 patients receiving oral NSAIDs plus sulfasalazine. Results. The serum HMGB1 of AS patients was significantly higher than in healthy controls and positively correlated with BASDAI, BASFI, ASDAS-ESR, ASDAS-CRP, ESR, and CRP, but not with HLA-B27, anterior uveitis, and recurrent diarrhea. There was no significant difference between patients with radiographic damage of hip joints and those without. We observed that serum HMGB1 paralleled disease activity after treatment. Conclusion. Serum level of HMGB1 is higher in AS patients, and to some extent, HMGB1 can reflect the activity of AS and be used as a laboratory indicator to reflect the therapeutic response.
Surface photometry and structure of high redshift disk galaxies in the HDF-S NICMOS field

Aims. We report on a photometric study of a sample of 22 disk galaxies in the Hubble Deep Field South NICMOS parallel field. The redshift range of the galaxies is $z=0.5{-}2.6$. Methods. We use deep NICMOS J and H band and STIS open mode images, taken as part of the HDF-S project, to construct rest-frame B -profiles and $(U-V)$ color profiles of the galaxies. Before fitting isophotes, images are deconvolved with PSF. Derived surface brightness profiles are approximated by Sersic luminosity distribution. Results. Significantly large population of disks cannot be represented by an exponential disk, but this can be well done by Sersic law, if $n n does not vary significantly with redshift. Galactic sizes decrease with redshift as $r_{\rm e}(z)/r_{\rm e}(0) = 1-0.26z$. The rest frame $(U-V)$ color shows a clear decrease at $z\approx2$, concordantly with the understanding of more intense star formation at earlier epochs. Color gradients $\Delta(U-V)/\Delta r$ are small and roughly constant at $z 2$, dominantly positive gradients appear, possibly indicating centrally concentrated star-formation. On the basis of $(U-V)$ color and chemical evolution models, the disks observed at $z\sim 2.5$ have formed between $z=3.5{-}7$. Scale radii r e of the galaxies correlate with the scale surface brightnesses $\mu_{\rm e}$ for the sample. None of the studied parameters shows clear dependence on absolute B luminosity for the galaxies.
Self-organization of Very Large Document Collections Statistical Models of Documents Construction of the Document Map

Text mining systems are developed to aid the users in satisfying their information needs, which may vary from searching answers to well-specified questions to learning more of a scientific discipline. The major tasks of web mining are searching, browsing, and visualization. Searching is best suited for answering specific questions of a well-informed user. Browsing and visualization, on the other hand, are beneficial especially when the information need is more general, or the topic area is new to the user [6]. The SOM, applied to organizing very large document collections, can aid in all the three tasks. In the vast majority of SOM applications, the input data constitute high-dimensional real feature vectors. In the SOMs that form similarity graphs of text documents, the feature sets that describe collections of words in the documents should be expressible as real vectors, too. The feature sets can simply be weighted histograms of the words, but usually some dimensionality reduction of the histograms is carried out. The primitive vector space model In the basic vector space model [1] the stored documents are represented as real vectors in which each component corresponds to the frequency of occurrence of a particular word in the document: the model or document vector can be viewed as a weighted word histogram. For the weighting of a word according to its importance one can use the Shannon entropy over document classes, or the inverse of the number of the documents in which the word occurs (" inverse document frequency "). The main problem of the vector space model is the large vocabulary in any sizable collection of free-text documents, which means a vast dimensionality of the model vectors. Latent semantic indexing (LSI) In an attempt to reduce the dimensionality of the document vectors without essentially losing information contained in the full vocabulary, one often first forms a matrix in which each column corresponds to the word histogram of a document, and there is one column for each document. After that the space spanned by the column vectors is decomposed 117 118 Self-organization of very large document collections into an ordered set of factors by a matrix-computation method called the singular-value decomposition (SVD). The decomposition has the property that the last factors have minimal influence on the matrix. When they are omitted the document vector formed from the histogram of the remaining factors has then a much smaller dimensionality while as much …
Phenology affects floral herbivory Russell and Louda pg . 1 Phenological Synchrony Affects Interaction Strength of an Exotic Weevil with Platte Thistle , a Native Host Plant

Phenological synchrony of a consumer population with its resource populations is expected to affect interaction intensity. We quantified phenological variation and synchrony of populations of an invasive Eurasian flower head weevil, Rhinocyllus conicus, that consumes florets, ovules, and seeds of developing flower heads of a native North American thistle, Cirsium canescens, in Sand Hills prairie in Nebraska USA. Variation in timing of adult activity among weevil populations was larger than variation in timing of flower head development among C. canescens populations, and it drove the observed variation in the phenological synchrony between weevil and host plant populations. Furthermore, the degree of phenological synchrony between populations was significant in explaining variation in weevil egg load on the newly acquired hostplant. Because population growth of C. canescens is limited by pre-dispersal seed losses to floral herbivores, variation in the synchrony of herbivore and plant flowering will affect the density of the plant population. These results provide strong quantitative support for the hypothesis that the synchrony of insect activity with plant resources can determine the magnitude of impact of floral herbivores on their host plant populations.
Improved Siderotic Nodule Detection in Cirrhosis with Susceptibility-Weighted Magnetic Resonance Imaging: A Prospective Study

Background Hepatic cirrhosis is a common pathway of progressive liver destruction from multiple causes. Iron uptake can occur within the hepatic parenchyma or within the various nodules that form in a cirrhotic liver, termed siderotic nodules. Siderotic nodule formation has been shown to correlate with inflammatory activity, and while the relationship between siderotic nodule formation and malignancy remains unclear, iron distribution within hepatic nodules has known implications for the detection of hepatocellular carcinoma. We aimed to evaluate the role of abdominal susceptibility-weighted imaging in the detection of siderotic nodules in cirrhotic patients. Methodology/Principal Findings Forty-six (46) cirrhotic patients with at least one siderotic nodule detected on previous imaging underwent both computed tomography and magnetic resonance imaging (T1-, T2-, T2*-, and susceptibility-weighted imaging) at 3.0 Tesla. Imaging data was independently analyzed by two radiologists. Siderotic nodule count was determined for each modality and imaging sequence. For each magnetic resonance imaging technique, siderotic nodule conspicuity was assessed on a 3 point scale (1 = weak, 2 = moderate, 3 = strong). More nodules were detected by susceptibility weighted imaging (n = 2935) than any other technique, and significantly more than by T2* weighted imaging (n = 1696, p<0.0001). Lesion conspicuity was also highest with susceptibility-weighted imaging, with all nodules found to be moderate (n = 6) or strong (n = 40); a statistically significant difference (p<0.001). Conclusions Susceptibility-weighted imaging had the greatest lesion conspicuity and detected the highest number of siderotic nodules suggesting it is the most sensitive imaging technique to detect siderotic nodules in cirrhotic patients.
A Parallel Algorithm for Spectrum-based Short Read Error Correction

Correcting sequence errors in high-throughput DNA sequencing by taking advantage of redundant sampling and low error rates is often an important first step in applications of this technology. Consequently, a number of error correction methods have been developed in the recent years. Due to an order of magnitude throughput gain per year, some of these technologies are now generating upwards of a billion reads per run. In this paper, we present an algorithm for parallel zing error correction methods that are based on frequency spectrum of kmers observed in input reads. Based on this, we present a parallelization of Reptile, a recently introduced error correction method that employs frequency spectrum of two different lengths, one for identifying correction possibilities and another for providing contextual information. Our method is well suited for distributed memory parallel computers and clusters. Experimental results indicate the method achieves near linear speedup and provides the ability to scale to larger data sets than previously demonstrated.
Separation of fNIRS Signals into Functional and Systemic Components Based on Differences in Hemodynamic Modalities

In conventional functional near-infrared spectroscopy (fNIRS), systemic physiological fluctuations evoked by a body's motion and psychophysiological changes often contaminate fNIRS signals. We propose a novel method for separating functional and systemic signals based on their hemodynamic differences. Considering their physiological origins, we assumed a negative and positive linear relationship between oxy- and deoxyhemoglobin changes of functional and systemic signals, respectively. Their coefficients are determined by an empirical procedure. The proposed method was compared to conventional and multi-distance NIRS. The results were as follows: (1) Nonfunctional tasks evoked substantial oxyhemoglobin changes, and comparatively smaller deoxyhemoglobin changes, in the same direction by conventional NIRS. The systemic components estimated by the proposed method were similar to the above finding. The estimated functional components were very small. (2) During finger-tapping tasks, laterality in the functional component was more distinctive using our proposed method than that by conventional fNIRS. The systemic component indicated task-evoked changes, regardless of the finger used to perform the task. (3) For all tasks, the functional components were highly coincident with signals estimated by multi-distance NIRS. These results strongly suggest that the functional component obtained by the proposed method originates in the cerebral cortical layer. We believe that the proposed method could improve the reliability of fNIRS measurements without any modification in commercially available instruments.
Academic discourses and the twenty first century context : empowering all students with comprehension and critical engagement

Current economic, social, educational, and philosophical conditions of university learning in the 21st century are unique, resulting in a disjuncture between traditional academic discourse and the culture, literacy and the learning modes of current students. Driven by the knowledge economy and technology, governments have encouraged the massification and globalisation of university education and continue to promote access to university for non-traditional students. In regional, post-Dawkins universities of Australia, the student demographic is increasingly diverse, spanning age groups from 18-75, cultures, socio economic and educational backgrounds and English language proficiency. Evidence suggests that high levels of abstract and technical language exclude many students from academic discourse communities denying them an opportunity to succeed, affect performance and increase drop-out rates. In neglecting to provide specific strategies for ensuring all students have access to meanings in discourses, current theories for higher learning inadvertently fail to recognise that ‘knowledge’ is constructed through language. Further, university pedagogies continue to assume high levels of literacy previously associated with traditional university students. This thesis integrates learning theory and systemic functional language analysis to provide an understanding of, (1) how we use language to express abstract technical knowledge in the humanities, social science and science, and (2) what learning theory tells us about how we might best recruit students into academic discourse communities. To help establish the most effective ways of presenting discourse to first year students, it also compares three versions of discourse presenting the same knowledge and students’ views on how easy the discourse was to comprehend. Contemporary university language and learning theory, the linguistic features of academic discourses, and students’ responses to these are examined. This analysis informs a proposal for an inclusive pedagogy, effective in the apprenticing of students to become successful participants in academia, and the new knowledge world in general. These strategies build on the “Reading to Learn” scaffolding literacy approach of Rose (2007) by incorporating more explicitly, stages that acknowledge the importance of schema, concept mapping and experiential learning and our understanding of proclivities of the net generation.
Lights, Camembert, Action! The Role of Human Orbitofrontal Cortex in Encoding Stimuli, Rewards, and Choices

Abstract: This review outlines some of the main conclusions about the contributions of the orbitofrontal cortex to reward learning and decision making arising from functional neuroimaging studies in humans. It will be argued that human orbitofrontal cortex is involved in a number of distinct functions: signaling the affective value of stimuli as they are perceived, encoding expectations of future reward, and updating these expectations, either by making use of prediction error signals generated in the midbrain, or by using knowledge of the rules or structure of the decision problem. It will also be suggested that this region contributes to the decision making process itself, by encoding signals that inform an individual about what action to take next. Evidence for functional specialization within orbitofrontal cortex in terms of valence will also be evaluated, and the possible contributions of the orbitofrontal cortex in representing the values of actions as well as that of stimuli will be discussed. Finally, some of the outstanding questions for future neuroimaging research of orbitofrontal cortex function will be highlighted.
Siamese Recurrent Architectures for Learning Sentence Similarity

We present a siamese adaptation of the Long Short-Term Memory (LSTM) network for labeled data comprised of pairs of variable-length sequences. Our model is applied to assess semantic similarity between sentences, where we exceed state of the art, outperforming carefully handcrafted features and recently proposed neural network systems of greater complexity. For these applications, we provide word-embedding vectors supplemented with synonymic information to the LSTMs, which use a fixed size vector to encode the underlying meaning expressed in a sentence (irrespective of the particular wording/syntax). By restricting subsequent operations to rely on a simple Manhattan metric, we compel the sentence representations learned by our model to form a highly structured space whose geometry reflects complex semantic relationships. Our results are the latest in a line of findings that showcase LSTMs as powerful language models capable of tasks requiring intricate understanding.
Epidemics in Small-World Communication Networks

The two main characteristics of small-world networks are strong local clustering, and small diameter. These two characteristics are desirable properties in communication networks since typical communication patterns show large amounts of local communication and a small amount of non-local communication that must be completed quickly. In this paper, we study variants of broadcasting that resemble the spread of computer viruses in networks. We also study the related problem of stopping an epidemic.
Treatment of high risk prostate cancer with combined radiotherapy and hormonal treatment- results and identification of factors influencing outcome.

PURPOSE The aim of this work was to prospectively analyze the outcome of combined hormonal treatment and radical radiotherapy in high risk non metastatic prostate cancer patients (T1=4, N0-1, M0). METHODS Between April 2003 and December 2007 196 patients with high risk prostate cancer were treated with curative intent. The treatment consisted of 2-month neoadjuvant hormonal treatment (LHRH analog), radical radiotherapy (68-78 Gy, conformal technique) and an optional 2-year adjuvant hormonal treatment. RESULTS The median follow up time was 59 months. Fiveyear overall survival was 86% and 5-year biochemical disease free survival (DFS) 70%. Factors found to be statistically significant relative to outcomes were Gleason score (p=0.017), initial PSA value (p=0.039) and adjuvant hormonal treatment (p=0.035). There was no significant association between radiotherapy dose or volume and biochemical DFS (bDFS). Late genitourinary and gastrointestinal toxicity was acceptable. CONCLUSION Treatment combining hormonal therapy and radical radiotherapy can be recommended for this subgroup of prostate cancer patients. Adjuvant hormonal treatment should also be used.
Large-Scale Meteorology and Deep Convection during TRMM KWAJEX*

Abstract An overview of the large-scale behavior of the atmosphere during the Tropical Rainfall Measuring Mission (TRMM) Kwajalein Experiment (KWAJEX) is presented. Sounding and ground radar data collected during KWAJEX, and several routinely available datasets including the Geostationary Meteorological Satellite (GMS), NOAA outgoing longwave radiation (OLR), the Special Sensor Microwave Imager (SSM/I), and ECMWF operational analyses are used. One focus is on the dynamical characterization of synoptic-scale systems in the western/central tropical Pacific during KWAJEX, particularly those that produced the largest rainfall at Kwajalein. Another is the local relationships observed on daily time scales among various thermodynamic variables and areal average rain rate. These relationships provide evidence regarding the degree and kind of local thermodynamic control of convection. Although convection in the Marshall Islands and surrounding regions often appears chaotic when viewed in satellite imagery, the lar.
Bengali text summarization by sentence extraction

Text summarization is a process to produce an abstract or a summary by selecting significant portion of the information from one or more texts. In an automatic text summarization process, a text is given to the computer and the computer returns a shorter less redundant extract or abstract of the original text(s). Many techniques have been developed for summarizing English text(s). But, a very few attempts have been made for Bengali text summarization. This paper presents a method for Bengali text summarization which extracts important sentences from a Bengali document to produce a summary.
A Unified Point-of-Interest Recommendation Framework in Location-Based Social Networks

Location-based social networks (LBSNs), such as Gowalla, Facebook, Foursquare, Brightkite, and so on, have attracted millions of users to share their social friendship and their locations via check-ins in the past few years. Plenty of valuable information is accumulated based on the check-in behaviors, which makes it possible to learn users’ moving patterns as well as their preferences. In LBSNs, point-of-interest (POI) recommendation is one of the most significant tasks because it can help targeted users explore their surroundings as well as help third-party developers provide personalized services. Matrix factorization is a promising method for this task because it can capture users’ preferences to locations and is widely adopted in traditional recommender systems such as movie recommendation. However, the sparsity of the check-in data makes it difficult to capture users’ preferences accurately. Geographical influence can help alleviate this problem and have a large impact on the final recommendation result. By studying users’ moving patterns, we find that users tend to check in around several centers and different users have different numbers of centers. Based on this, we propose a Multi-center Gaussian Model (MGM) to capture this pattern via modeling the probability of a user’s check-in on a location. Moreover, users are usually more interested in the top 20 or even top 10 recommended POIs, which makes personalized ranking important in this task. From previous work, directly optimizing for pairwise ranking like Bayesian Personalized Ranking (BPR) achieves better performance in the top-k recommendation than directly using matrix matrix factorization that aims to minimize the point-wise rating error. To consider users’ preferences, geographical influence and personalized ranking, we propose a unified POI recommendation framework, which unifies all of them together. Specifically, we first fuse MGM with matrix factorization methods and further with BPR using two different approaches. We conduct experiments on Gowalla and Foursquare datasets, which are two large-scale real-world LBSN datasets publicly available online. The results on both datasets show that our unified POI recommendation framework can produce better performance.
H1N1 novel influenza A in pregnant and immunocompromised patients

Objective:To describe the increased risk of severe disease and the appropriate management of patients at high risk such as pregnant women and immunosuppressed patients who acquire novel influenza A (H1N1). Design:Review of the literature regarding influenza A in these patient groups, and review of published and unpublished data with regard to novel influenza A (H1N1). Main Results:Pregnant women are at increased risk for severe pneumonitis and respiratory failure from influenza infection, particularly during pandemics, including the current pandemic. Fetal morbidity is significant, usually resulting from maternal fever and severe hypoxemia. Early antiviral therapy using oseltamivir may be beneficial, and intensive care unit support should target adequate oxygenation at all times. Immunosuppressed patients are at increased risk for influenza, as well as at risk for more severe or prolonged infection. Patients after hematopoietic stem cell transplantation, after lung transplantation, and those receiving chemotherapy for leukemia are at highest risk, whereas the risk for human immunodeficiency virus-infected individuals appears relatively low. Treatment with antiviral therapy may be beneficial, even after the usual cut-off of 48 hrs after symptom onset. Conclusions:Optimal management of these patients is preventive by influenza vaccination, but the neuraminidase inhibitor antiviral agents provide effective treatment.