LLukas22/scidocs huggingface dataset:
Effect of azathioprine or mesalazine therapy on incidence of re-hospitalization in sub-occlusive ileocecal Crohn’s disease patients

BACKGROUND
Although the cost of Crohn's disease (CD) treatment differs considerably, hospitalization and surgery costs account for most of the total treatment cost. Decreasing hospitalization and surgery rates are pivotal issues in reducing health-care costs. MATERIAL/METHODS We evaluated the effect of azathioprine (AZA) compared with mesalazine on incidence of re-hospitalizations due to all causes and for CD-related surgeries. In this controlled, randomized study, 72 subjects with sub-occlusive ileocecal CD were randomized for AZA (2-3 mg/kg per day) or mesalazine (3.2 g per day) therapy during a 3-year period. The primary end point was the re-hospitalization proportion due to all causes, as well as for surgical procedures during this period evaluated between the groups.


RESULTS
On an intention-to-treat basis, the proportion of patients re-hospitalized within 36 months due to all causes was lower in patients treated with AZA compared to those on mesalazine (0.39 vs. 0.83, respectively; p=0.035). The AZA group had also significantly lower proportions of re-hospitalization for surgical intervention (0.25 vs. 0.56, respectively; p=0.011). The number of admissions (0.70 vs. 1.41, p=0.001) and the length of re-hospitalization (3.8 vs. 7.7 days; p=0.002) were both lower in AZA patients.


CONCLUSIONS
Patients with sub-occlusive ileocecal CD treated with AZA had lower re-hospitalization rates due to all causes and for surgical management of CD compared to those treated with mesalazine during a 3-year period. The long-term use of AZA in ileocecal CD patients recovering from a sub-occlusion episode can save healthcare costs.
Behavioral models of frequency pulling in oscillators

Based on the state equations of an RLC circuit and hard-limiting characteristics of a transconductor a method is introduced to study and simulate the pulling effect in differential LC oscillators. This method includes phase and amplitude disturbances all at once. The model is validated by measurement results and an example is given to illustrate the application of this method in verifying the real circuits.
Anti-ulcer activity of Centella asiatica leaf extract against ethanol-induced gastric mucosal injury in rats

The present study was performed to evaluate the anti-ulcerogenic activity of ethanol extract of Centella asiatica against ethanol-induced gastric mucosal injury in rats. Five groups of adult Sprague Dawley rats were orally pre-treated respectively with carboxymethyl cellulose (CMC) solution (ulcer control group), Omeprazole 20 mg/kg (reference group), and 100, 200 and 400 mg/kg C. asiatica leaf extract in CMC solution (experimental groups), one hour before oral administration of absolute ethanol to generate gastric mucosal injury. Rats were sacrificed and the ulcer areas of the gastric walls were determined. Grossly, the ulcer control group exhibited severe mucosal injury, whereas pre-treatment with C. asiatica leaf extract exhibited significant protection of gastric mucosal injury. Histological studies revealed that ulcer control group exhibited severe damage of gastric mucosa, along with edema and leucocytes infiltration of submucosal layer compared to rats pre-treated with C. asiatica leaf extract which showed gastric mucosal protection, reduction or absence of edema and leucocytes infiltration of submucosal layer. Acute toxicity study did not manifest any toxicological signs in rats. The present finding suggests that C. asiatica leaf extract promotes ulcer protection as ascertained grossly and histologically compared to the ulcer control group.
Learning Analytics: A Survey

Learning analytics is a research topic that is gaining increasing popularity in recent time. It analyzes the learning data available in order to make aware or improvise the process itself and/or the outcome such as student performance. In this survey paper, we look at the recent research work that has been conducted around learning analytics, framework and integrated models, and application of various models and data mining techniques to identify students at risk and to predict student performance. Keywords— Learning Analytics, Student Performance, Student Retention, Academic analytics, Course success.
Liraglutide Versus Exenatide Once Weekly: Persistence, Adherence, and Early Discontinuation.

PURPOSE
This study examines real-world, evidence-based comparisons of persistence and adherence to daily versus weekly glucagon-like peptide 1 (GLP-1) receptor agonists for the treatment of type 2 diabetes (T2D).


METHODS
This retrospective observational study used U.S. insurance claims data to compare persistence and adherence to GLP-1 receptor agonists in patients with T2D initiating once weekly (QW) exenatide or daily liraglutide over a 6-month follow-up period. Eligible patients had ≥2 diagnoses of T2D, were 18 years of age or older, initiated a new prescription of either the index drug between February 1, 2012 (market availability launch date of exenatide QW) and March 31, 2013, and had ≥6 months continuous eligibility in the pre- and postindex periods. A 1:1 propensity score match was used to account for selection bias. Outcome measures included persistence as measured by the percentage of patients who continued to take the index drug over an index period of 182 days with an allowable gap of 60 days and adherence as measured by the proportion of days covered (PDC). The percentage of patients achieving PDC ≥0.8 and ≥0.9 was also calculated.


FINDINGS
There were no significant differences between baseline characteristics after propensity score matching. Each matched cohort included 12,306 patients. The overall persistence observed with liraglutide was 66% compared with 63% for exenatide QW. The mean (SD) PDC adherence during the 6-month follow-up period was 0.694 (0.309) for the exenatide QW cohort and 0.689 (0.286) for the liraglutide cohort. The PDC threshold of ≥0.8 during the 6-month follow-up period was met by 6309 (51%) and 5820 (47%) patients in the exenatide QW and liraglutide cohorts, respectively. For the exenatide QW cohort, 76% of patients treated previously with BID exenatide continued treatment in the 6-month follow-up period compared with 59% who were not previously treated with exenatide BID. For the liraglutide cohort, 77% of previous exenatide BID patients continued treatment versus 63% of patients who were not previously treated with exenatide BID.


IMPLICATIONS
These results reveal slight differences in persistence and adherence rates in patients receiving exenatide QW versus patients receiving liraglutide daily that vary by outcome and previous incretin-based therapy used. Differences may be due to dosing device differences for exenatide QW and liraglutide, which, in the case of liraglutide, allows the opportunity for daily self-titration dosing. Implications of these findings for clinical practice are that persistence is determined by the broader context of treatment and medications being used and should be considered when prescribing GLP-1 receptor agonists.
White-matter structure in the right hemisphere predicts Mandarin Chinese learning success

Second language learning becomes increasingly difficult with age, but some adults learn more successfully than others. We examined whether inter-subject variability in the microstructure of white matter pathways, as measured by diffusion tensor imaging (DTI), would predict native English speakers' outcomes in learning Mandarin Chinese. Twenty-one adults were scanned before participating in an intensive 4-week Mandarin course. At the end of the Mandarin course, participants completed a final exam that assessed their skills in both spoken and written Mandarin. Individual participants' white-matter tracts were reconstructed from their native DTI data and related to final-exam performance. Superior language learning was correlated with DTI measures in the right hemisphere, but not in the left hemisphere. In particular, greater initial fractional anisotropy (FA) in both the right superior longitudinal fasciculus (parietal bundle) and the right inferior longitudinal fasciculus was associated with more successful Mandarin learning. The relation between white-matter structure in the right hemisphere of native English speakers and successful initial language learning may reflect the tonal and visuo-spatial properties, respectively, of spoken and written Mandarin Chinese. © 2014 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http:// creativecommons.org/licenses/by-nc-nd/3.0/). of Technology, 43 Vassar St., 46-4033, Cambridge, MA 02139, USA. ier Ltd. This is an open access article under the CC BY-NC-ND license (http:// Z. Qi et al. / Journal of Neurolinguistics 33 (2015) 14e28 15
Experimental study of humic acid adsorption onto bacteria and Al-oxide mineral surfaces

Abstract Humic acid adsorption onto bacterial and mineral surfaces was measured as a function of pH and humic:surface ratio. The bacterial species used was the aerobic gram-positive species Bacillus subtilis , and Al mineral surface site interactions were isolated by using corundum (Al 2 O 3 ). Adsorption of humic acid onto both surfaces is strongly pH-dependent, increasing with decreasing pH over the pH range 2.5–11.5. Humic acid adsorption also increases with increasing concentration of the sorbing surface. Because adsorption of humic acid onto the bacterial surface is strongest under low-pH conditions when both the humic acid (with three proton-active functional group types: L1, L2, and L3) and the bacterial surface are uncharged, the interaction is dominantly hydrophobic. We model the adsorption with a site-specific surface complexation approach based on the reaction: (1) X–H(L3) 0 +R–COOH 0 ⇔R–COOH–H(L3)–X 0 , where X and R represent the humic acid molecule and bacteria, respectively, to which the L3 and carboxyl functional groups are attached. Conversely, humic acid adsorption onto the mineral surface is strongest when the humic acid is negatively charged and the mineral surface is positively charged, with the adsorption decreasing as the concentration of positively charged mineral surface sites decreases with increasing pH. This type of adsorption is dominantly electrostatic, and can successfully be modeled also with a site-specific approach using the reaction: (2) X–(L1) −1 +>Al(OH 2 ) + ⇔>Al(OH 2 )–(L1)–X 0 , where >Al represents a crystallographically bound surface Al atom. We test the surface complexation approach by comparing observed humic acid adsorption in systems containing both bacteria and mineral surfaces to quantitative estimates of adsorption in these systems. The estimates are conducted independent of the experiments, based on the results from the simplified systems. The model predictions of humic acid adsorption in mixed humic–bacteria–mineral systems are reasonably accurate in describing the observed adsorption behavior in these mixed systems, indicating that the surface complexation approach can be successfully used to quantify the distribution of humic acid in realistic subsurface environments.
A Mixed Classification-Regression Framework for 3D Pose Estimation from 2D Images

In Tables 2 and 3, we present the results of our eight models and three baselines compared to current state-of-the-art under the two metric of MedErr and Acc π 6 respectively. As we mentioned in the paper, we run all experiments three times and report the mean and standard deviation (in brackets) across these three trials. We also show figures of images where we obtain the least pose estimation error and the most pose estimation error for every object category using one run of model MG+. As can be seen from Figs. [1-12], we make the most error under three conditions: (i) when the objects are really blurry (very small in pixel size in the original image), (ii) the shape of the object is uncommon (possibly very few examples seen during training) and (iii) the pose of a test image is very different from common poses observed during training. The first condition is best observed in the bad cases for categories aeroplane and car where almost all the images shown are very blurry. The second condition is best observed in categories boat and chair where the bad cases contain uncommon boats and chairs. The third condition is best observed in categories bottle and tvmonitor where the bad images are in very different poses compared to the best images. We also present the performance of our models MG and MG+ across different object categories of the Pascal3D+ dataset during ablation experiments in Tables 4-11. These are detailed tables for the results shown in Tables 3 and 4 of the main paper and an overview of the experiments is shown below.
An integration of urban spatial data with energy simulation to produce X3D city models: the case of Landkreis Ludwigsburg

In this paper, we describe a chain of steps to produce X3D city models based on transformation and integration of urban 3D CityGML models with DTM tiles, orthophotos and energy simulation outcome of energy demand and photovoltaic potential generated by the SimStadt platform.
Physical activity and osteoporotic hip fracture risk in men.

BACKGROUND
Physical activity has been related to reduced risk of osteoporotic hip fractures, but the evidence among men is weak.


OBJECTIVE
To determine the association between baseline leisure physical activity and future risk of osteoporotic hip fracture in men.


METHODS
At baseline in 1975 our prospective study cohort included 3,262 men who were 44 years or older and did not have chronic disease restricting their ability to exercise. At baseline, physical activity was assessed by a questionnaire. Hip fractures were followed for 21 years, or from the age of 50 years for subjects who were initially younger than 50 years.


RESULTS
The hazard ratio of osteoporotic hip fracture, adjusted for other possible predictors (height, body mass index, baseline diseases, smoking, use of alcohol, work-related physical activity, and occupational group), in men participating in vigorous physical activity compared with men not participating was 0.38 (95% confidence interval, 0.16-0.91) (P = .03).


CONCLUSION
These results provide further evidence that there is an inverse association between baseline physical activity and future hip fracture risk among men.
A non-inferiority trial of an attenuated combination strategy (‘COBRA-light’) compared to the original COBRA strategy: clinical results after 26 weeks

BACKGROUND
Early, intensive treatment of rheumatoid arthritis (RA) with the combination of (initially high dose) prednisolone, methotrexate and sulfasalazine (COBRA therapy) considerably lowers disease activity and suppresses radiological progression, but is infrequently prescribed in daily practice. Attenuating the COBRA regimen might lessen concerns about side effects, but the efficacy of such strategies is unknown.


OBJECTIVE
To compare the 'COBRA-light' strategy with only two drugs, comprising a lower dose of prednisolone (starting at 30 mg/day, tapered to 7.5 mg/day in 9 weeks) and methotrexate (escalated to 25 mg/week in 9 weeks) to COBRA therapy (prednisolone 60 mg/day, tapered to 7.5 mg/day in 6 weeks, methotrexate 7.5 mg/week and sulfasalazine 2 g/day).


METHOD
An open, randomised controlled, non-inferiority trial in 164 patients with early active RA, all treated according to a treat to target strategy.


RESULTS
At baseline patients had moderately active disease: mean (SD) 44-joint disease activity score (DAS44) 4.13 (0.81) for COBRA and 3.95 (0.9) for COBRA-light. After 6 months, DAS44 significantly decreased in both groups (-2.50 (1.21) for COBRA and -2.18 (1.10) for COBRA-light). The adjusted difference in DAS44 improvement between the groups, 0.21 (95% CI -0.11 to 0.53), was smaller than the predefined clinically relevant difference of 0.5. Minimal disease activity (DAS44 <1.6) was reached in almost half of patients in both groups (49% and 41% in COBRA and COBRA-light, respectively).


CONCLUSIONS
At 6 months COBRA-light therapy is most likely non-inferior to COBRA therapy.


CLINICAL TRIAL REGISTRATION NUMBER
55552928.
Log-determinant relaxation for approximate inference in discrete Markov random fields

Graphical models are well suited to capture the complex and non-Gaussian statistical dependencies that arise in many real-world signals. A fundamental problem common to any signal processing application of a graphical model is that of computing approximate marginal probabilities over subsets of nodes. This paper proposes a novel method, applicable to discrete-valued Markov random fields (MRFs) on arbitrary graphs, for approximately solving this marginalization problem . The foundation of our method is a reformulation of the marginalization problem as the solution of a low-dimensional convex optimization problem over the marginal polytope. Exactly solving this problem for general graphs is intractable; for binary Markov random fields, we describe how to relax it by using a Gaussian bound on the discrete entropy and a semidefinite outer bound on the marginal polytope. This combination leads to a log-determinant maximization problem that can be solved efficiently by interior point methods, thereby providing approximations to the exact marginals. We show how a slightly weakened log-determinant relaxation can be solved even more efficiently by a dual reformulation. When applied to denoising problems in a coupled mixture-of-Gaussian model defined on a binary MRF with cycles, we find that the performance of this log-determinant relaxation is comparable or superior to the widely used sum-product algorithm over a range of experimental conditions.
Characteristics of network traffic flow anomalies

One of the primary tasks of network administrators is monitoring routers and switches for anomalous traffic behavior such as outages, configuration changes, flash crowds and abuse. Recognizing and identifying anomalous behavior is often based on ad hoc methods developed from years of experience in managing networks. A variety of commercial and open source tools have been developed to assist in this process, however these require policies and/or or thresholds to be defined by the user in order to trigger alerts. The better the description of the anomalous behavior, the more effective these tools become. In this extended abstract we describe a project focused on precise characterization of anomalous network traffic behavior. The first step in our project is to gather passive measurements of network traffic at the IP flow level. IP flow level data as defined in [1] is a unidirectional series of IP packets of a given protocol traveling between a source and a destination IP/port pair within a certain period of time. While flow level data is certainly not as precise as passive measurements of packet level data, we demonstrate that it is sufficient for exposing many different types of aberrant network traffic behavior in close to real time. It also has the benefit of generating much smaller data sets than packet level measurements which can become a significant issue in large, heavily used networks. We use the FlowScan [2] open source software to gather and analyze network flow data. FlowScan takes Netflow [3] feeds from Cisco or other Lightweight Flow Accounting Protocol (LFAP) enabled routers, processes the data and then it in an efficient data structure. FlowScan also has a graphical interface which is currently the principal mean s for anomaly identification by network managers. FlowScan is currently deployed at the border router at the University of Wisconsin Madison as well as over 100 other sites nation wide.
Design and evolution of modular neural network architectures

To investigate the relations between structure and function in both artificial and natural neural networks, we present a series of simulations and analyses with modular neural networks. We suggest a number of design principles in the form of explicit ways in which neural modules can cooperate in recognition tasks. These results may supplement recent accounts of the relation between structure and function in the brain. The networks used consist out of several modules, standard subnetworks that serve as higher-order units with a distinct structure and function. The simulations rely on a particular network module called CALM (Murre, Phaf, and Wolters, 1989, 1992). This module, developed mainly for unsupervised categorization and learning, is able to adjust its local learning dynamics. The way in which modules are interconnected is an important determinant of the learning and categorization behaviour of the network as a whole. Based on arguments derived from neuroscience, psychology, computational learning theory, and hardware implementation, a framework for the design of such modular networks is laid-out. A number of small-scale simulation studies shows how intermodule connectivity patterns implement ’neural assemblies’ (Hebb, 1949) that induce a particular category structure in the network. Learning and categorization improves as the induced categories are more compatible with the structure of the task domain. In addition to structural compatibility, two other principles of design are proposed that underlie information processing in interactive activation networks:replication and recurrence. Because a general theory for relating network architectures to specific neural functions does not exist, we extend the biological metaphor of neural networks, by applying ge etic algorithms(a biocomputing method for search and optimization based on natural selection and evolution) to search for optimal modular network architectures for learning a visual categorization task. The best performing network architectures seemed to have reproduced some of the overall characteristics of the natural visual system, such as the organization of coarse and fine processing of stimuli in separate pathways. A potentially important result is that a genetically defined initial architecture cannot only enhance learning and recognition performance, but it can also induce a system to better generalizeits learned behaviour to instances never encountered before. This may explain why for many vital learning tasks in organisms only a minimal exposure to relevant stimuli is necessary. 1 Leiden Connectionist Group, Unit of Experimental and Theoretical Psychology, P.O. Box 9555, 2300 RB Leiden, The Netherlands. E-mail: Happel@rulfsw.leidenuniv.nl. 2 The second author is presently at the MRC Applied Psychology Unit, 15 Chaucer Road, Cambridge CB2 2EF, United Kingdom. E-mail: Jaap.Murre@mrc-apu.cam.ac.uk. This work is sponsored in part by the Dutch Foundation for Neural Networks and by the Medical Research Council. Happel and Murre The design and evolution of modular neural networks2
Generating images with recurrent adversarial networks

Gatys et al. (2015) showed that optimizing pixels to match features in a convolutional network with respect reference image features is a way to render images of high visual quality. We show that unrolling this gradient-based optimization yields a recurrent computation that creates images by incrementally adding onto a visual “canvas”. We propose a recurrent generative model inspired by this view, and show that it can be trained using adversarial training to generate very good image samples. We also propose a way to quantitatively compare adversarial networks by having the generators and discriminators of these networks compete against each other.