Miracle-dz/UniFAR-FTUs huggingface dataset:
Randomized trial of cyclophosphamide versus methotrexate for induction of remission in early systemic antineutrophil cytoplasmic antibody-associated vasculitis.

OBJECTIVE Standard therapy for antineutrophil cytoplasmic antibody-associated systemic vasculitis (AASV) with cyclophosphamide (CYC) and prednisolone is limited by toxicity. This unblinded, prospective, randomized, controlled trial was undertaken to determine whether methotrexate (MTX) could replace CYC in the early treatment of AASV. METHODS Patients with newly diagnosed AASV, with serum creatinine levels <150 mumoles/liter, and without critical organ manifestations of disease were randomized to receive either standard oral CYC, 2 mg/kg/day or oral MTX, 20-25 mg/week; both groups received the same prednisolone regimen. All drug treatments were gradually tapered and withdrawn by 12 months. Followup continued to 18 months. The primary end point was the remission rate at 6 months (noninferiority testing). RESULTS One hundred patients were recruited from 26 European centers; 51 patients were randomized to the MTX group and 49 to the CYC group. At 6 months, the remission rate in patients treated with MTX (89.8%) was not inferior to that in patients treated with CYC (93.5%) (P = 0.041). In the MTX group, remission was delayed among patients with more extensive disease (P = 0.04) or pulmonary involvement (P = 0.03). Relapse rates at 18 months were 69.5% in the MTX group and 46.5% in the CYC group; the median time from remission to relapse was 13 months and 15 months, respectively (P = 0.023, log rank test). Two patients from each group died. Adverse events (mean 0.87 episodes/patient) included leukopenia, which was less frequent in the MTX versus the CYC group (P = 0.012), and liver dysfunction, which was more frequent in the MTX group (P = 0.036). CONCLUSION MTX can replace CYC for initial treatment of early AASV. The MTX regimen used in the present study was less effective for induction of remission in patients with extensive disease and pulmonary involvement and was associated with more relapses than the CYC regimen after termination of treatment. The high relapse rates in both treatment arms support the practice of continuation of immunosuppressive treatment beyond 12 months.
Heartbeat detection with Doppler sensor using adaptive scale factor selection on learning

Heart rate variability gives information about health and mental condition. Noncontact detection of heartbeat using Doppler sensor has been researched in many studies. There is a major issue which is how to reduce the influence of body movement. A conventional algorithm uses the continuous wavelet transform. To extract heartbeat, a constant scale factor is selected during a learning phase which is then used to detect heartbeat during a test phase. However, to select the scale factor, the authors do not consider the difference of heart rate between learning and test. Thus, the root mean square error (RMSE) of R-R interval which represents the peak-to-peak of heartbeat is deteriorated. In this paper, we propose a method to improve the RMSE of R-R interval compared with the conventional one. During learning, we search for a scale factor interval corresponding to the heart rate obtained with the Doppler sensor. To take the difference of heart rate between learning and test into consideration, we extend the scale factor interval depending on the action during test. After we select a certain scale factor from some scale factors in the extended interval, we detect heart rate during test by counting the peaks of wavelet coefficients of the selected scale factor. Through experiments, when a subject is sitting still or doing a typing game, we show that the RMSE of R-R interval is improved by about 60 msec and 65 msec, respectively, compared with the conventional method.
A robust image reconstruction based on convex combination of criteria

In this paper we propose a novel regularization method for robust image reconstruction against noise, based on convex combination of the least squares and least absolute deviations. Unlike conventional regularization methods with an assumption of Guaussian noise, the proposed regularization method can deal with Gaussian noise and non-Gaussian noise. To overcome difficulty of the non-smooth objective function, we develop an efficient sub-gradient algorithm. Computed examples with an application to MR images show that the proposed subgradient algorithm can give better reconstruction quality than the conventional reconstruction regularization algorithms in various noise.
A common analysis framework for smart distribution networks applied to survivability analysis of distribution automation

Smart distribution networks shall improve the efficiency and reliability of power distribution by intelligently managing the available power and requested load. Such intelligent power networks pose challenges for information and communication technology (ICT). Their design requires a holistic assessment of traditional power system topology and ICT architecture. Existing analysis approaches focus on analyzing the power networks components separately. For example, communication simulation provides failure data for communication links, while power analysis makes predictions about the stability of the traditional power grid. However, these insights are not combined to provide a basis for design decisions for future smart distribution networks. In this paper, we describe a common model-driven analysis framework for smart distribution networks based on the Common Information Model (CIM). This framework provides scalable analysis of large smart distribution networks by supporting analyses on different levels of abstraction. Furthermore, we apply our framework to holistic survivability analysis. We map the CIM on a survivability model to enable assessing design options with respect to the achieved survivability improvement. We demonstrate our approach by applying the mapping transformation in a case study based on a real distribution circuit. We conclude by evaluating the survivability impact of three investment options.
Network Resource Allocation for Users With Multiple Connections: Fairness and Stability

This paper studies network resource allocation between users that manage multiple connections, possibly through different routes, where each connection is subject to congestion control. We formulate a user-centric Network Utility Maximization problem that takes into account the aggregate rate a user obtains from all connections, and we propose decentralized means to achieve this fairness objective. In a first proposal, cooperative users control their number of active connections based on congestion prices from the transport layer to emulate suitable primal-dual dynamics in the aggregate rate; we show this control achieves asymptotic convergence to the optimal user-centric allocation. For the case of noncooperative users, we show that network stability and user-centric fairness can be enforced by a utility-based admission control implemented at the network edge. We also study stability and fairness issues when routing of incoming connections is enabled at the edge router. We obtain in this case a characterization of the stability region of loads that can be served with routing alone and a generalization of our admission control policy to ensure user-centric fairness when the stability condition is not met. The proposed algorithms are implemented at the packet level in ns2 and demonstrated through simulation.
Circulating endothelial progenitor cells in adults with sickle cell disease

We appreciated the quality of the project by Anjum et al.,[1] concerning circulating endothelial progenitor cells (EPCs) in patients with sickle cell disease. An increasing body of data indicates that intravascular hemolysis contributes to a state of vascular dysfunction in sickle cell disease, especially involving diminished nitric oxide (NO) bioavailability. This vascular dysfunction is detected on venous occlusion plethysmography blood flow physiology studies as a relative blunting of NOinduced vasodilation[2-4] and this state of NO resistance has also been observed in sickle cell mouse models. [5,6] This vascular dysfunction may play a role in the development of pulmonary hypertension in sickle cell disease. [7]
A New Classification System for the Actions of IRS Chemicals Traditionally Used For Malaria Control

Knowledge of how mosquitoes respond to insecticides is of paramount importance in understanding how an insecticide functions to prevent disease transmission. A suite of laboratory assays was used to quantitatively characterize mosquito responses to toxic, contact irritant, and non-contact spatial repellent actions of standard insecticides. Highly replicated tests of these compounds over a range of concentrations proved that all were toxic, some were contact irritants, and even fewer were non-contact repellents. Of many chemicals tested, three were selected for testing in experimental huts to confirm that chemical actions documented in laboratory tests are also expressed in the field. The laboratory tests showed the primary action of DDT is repellent, alphacypermethrin is irritant, and dieldrin is only toxic. These tests were followed with hut studies in Thailand against marked-released populations. DDT exhibited a highly protective level of repellency that kept mosquitoes outside of huts. Alphacypermethrin did not keep mosquitoes out, but its strong irritant action caused them to prematurely exit the treated house. Dieldrin was highly toxic but showed no irritant or repellent action. Based on the combination of laboratory and confirmatory field data, we propose a new paradigm for classifying chemicals used for vector control according to how the chemicals actually function to prevent disease transmission inside houses. The new classification scheme will characterize chemicals on the basis of spatial repellent, contact irritant and toxic actions.
Advanced contouring error compensation in high performance motion control systems

Because of nonlinearities, uncertainties and inherent performance limitations of the mechatronic system, there is a trade-off between tracking error minimization and robust stabilization. This implies that the tracking error is unavoidable in practice. Fortunately, in the path following motion control system, it is the geometric error that measures the quality of the product. Since the tracking error gives an upper bound on the geometric error, by the principle of uncertainty, more preference can be given to the geometric error dynamics to improve the quality of the product. For generic parametric tool path, although the geometric error is hard to be computed numerically, it can be estimated by a projection operator on the tracking error vector. Then the tasks of contouring error compensation are twofold: to internally stabilize the feedback system, and to minimize the estimated contouring error. A novel multiple-loop structure is proposed in this paper. By using this structure, the internal stability of the feedback system is guaranteed, if the norm of the compensator is less than a function value on the infinity norm of the tracking error dynamics. Experiments were done on milling machines to verify the feasibility of the proposed structure. Low order compensators were designed and compared with each other. In the experiments, the estimated contouring error was reduced by more than 20%, while the tracking error did not change too much.
Short fatigue crack growth in plain and notched specimens of an 8090 Al-Li alloy

— Fatigue tests on notched and plain specimens were carried out under a combined acoustic and optical microscope. Crack initiation in plain specimens occurs invariably at inclusions which act as sites of stress concentration. Short crack propagation behaviour shows the usual pattern of accelerations and retardations associated with microstructural variations. Growth can only be sustained if the applied stress is sufficiently high to overcome the microstructural barriers. For the notched specimens the extent of the notch tip and crack tip plastic zone sizes control crack propagation. A short crack will continue to propagate only if its own plastic zone can sustain growth as the crack tip extends beyond the notch zone, otherwise it becomes non-propagating. Notch plastic zones are calculated using both approximate and accurate solutions. Some microstructural effects are discussed.
Compact Printed Parasitic Arrays for WLAN Applications

Antenna arrays enable to achieve high gains and to select the main lobe direction; on the other hand, these arrays require a complicated feeding network. In this letter, we show that whenever a radiating structure with a planar geometry is preferable and only a moderate gain (<; 6 dB) is required, a microstrip array composed of an active monopole and two parasitic elements is a very simple, yet effective design. The phases of the currents flowing through the active and passive elements determine the main lobe direction and can be adjusted during the design procedure by inserting planar meanders. The active element can be fabricated on the front of a standard FR4 substrate, whereas the ground plane and the parasitic elements are located on the back of the same substrate. We present the design and the experimental characterization of both broadside and endfire arrays working in the wireless local area network (WLAN) band around 2.4 GHz. The design is extremely simple and neat since a balun section is not necessary, therefore the proposed antennas occupy a circuit board area smaller than 0.6 λ×0.5 λ.
Energy-efficient Virtual Machine Provision Algorithms for Cloud Systems

Power consumption is one of the most critical problems in data centers. One effective way to reduce power consumption is to consolidate the hosting workloads and shut down physical machines which become idle after consolidation. Server consolidation is a NP-hard problem. In this paper, we propose two new algorithms, Dynamic Round-Robin (DRR) and Hybrid, which combines DRR and First-Fit, for energy aware virtual machine scheduling and consolidation. We also propose an accurate power model to estimate the power consumption resulted from each algorithm. Strategies we proposed are compared with GREEDY, ROUNDROBIN and POWERSAVE scheduling strategies implemented in the Eucalyptus Cloud system. Our experiment results show that our DRR and Hybrid algorithms reduce power consumption by 56.4% and 55.9% respectively, compared with the ROUNDROBIN scheduling strategy in Eucalyptus. DDR and Hybrid also result in 3% less power consumption on average, compared with the POWERSAVE scheduling strategy in Eucalyptus.
USING ALTERNATIVE MULTI-VARIATE DATA ANALYSIS METHODS FOR PREDICTING PHYSICAL-CHEMICAL PROPERTIES OF ORGANIC COMPOUNDS

In drug design, the knowledge of physical properties of organic compounds is vital. The special field of ADMET (Administration, Distribution, Metabolism, Excretion, Toxicity) involves the determination of properties governing the effect of a drug on the human body, such as aqueous solubility, intestinal absorption and permeability. Assessing these properties experimentally, however, is often laborious and time-consuming. This master’s thesis investigates the applicability of three neural network methods as computer aided means for predicting physical properties from molecular structure. The examined methods include fuzzy ARTMAP, Adaptive Network-based Fuzzy Inference System (ANFIS) and Support Vector Machines (SVM). Furthermore, the possibility of constructing a so-called consensus model, in which the results of the above mentioned models are weighted together in order to receive improved predictions, is investigated. The results from this study show that the SVM technique produced the most accurate and stable predictions on a majority of the data sets. Both fuzzy ARTMAP and ANFIS experienced difficulties in generalising to unseen data, as a consequence of over-training. This was in particular valid for the smaller data sets (< 30 compounds). Nevertheless, some satisfactory results were obtained. Fuzzy ARTMAP made several excellent test set predictions, even though the cross-validation results were genuinely poor for the small data sets. ANFIS turned out to be less appropriate for biological applications, since the number of adjustable network parameters grows exponentially with the number of molecular descriptors, owing to the network’s complex architecture. For practical use, the number of descriptors was restricted to at most ten. Finally, utilising a simple consensus model was found to be an excellent method for improving the predictions. ANVÄNDNING AV ALTERNATIVA DATAANALYSMETODER FÖR PREDIKTION AV FYSIKALISKA OCH KEMISKA EGENSKAPER HOS ORGANISKA FÖRENINGAR
Preliminary Study of Biological Activated Carbon Treatment for Removing MTBE from Groundwater

Methyl tert-butyl ether (MTBE) is a common additive for producing a clean burning fuel with a higher Octane number and less emissions. Batch adsorptive capacity, continuous flow breakthrough and biological activated carbon (BAC) treatment experiments were conducted to compare the adsorptive capacities of four activated carbons for MTBE, to obtain the adsorptive capacity indicators useful for predicting the carbon's MTBE capacity and its utilization in actual treatment, and to establish an effective start-up procedure to enable an activated carbon adsorber to function as the desired BAC system for long term treatment without needing carbon replacement. Although the coconut carbon YK's adsorptive capacity for MTBE was much larger, its column loading was only 20% higher than the coal carbon Coal because of its slower intraparticle diffusion rate due to the lack of larger micropores; the YK loading increased >60% when the adsorber flowrate was reduced by 50%. Two inoculation methods were utilized to seed the MTBE degrading bacteria present in the spent activated carbon (Spent) obtained from a remediation site; circulation of the supernatant of the Spent-water mixture to seed the adsorber failed, while the three Spent-filled adsorbers (65, 40 and 100%; the balance was YK) became BAC systems after the start-up period of 5-7 weeks during which the MTBE concentration of the feed increased from 0 to 30 mg/L. Each Spent-filled adsorber removed >50% of MTBE present in the feed (up to 40 mg/L); the more the Spent filled, the faster the BAC function was established.
Process Conflict Conflict in Small Groups : The Meaning and Consequences of

Through three studies of interacting small groups, we aimed to better understand the meaning and consequences of process conflict. Study 1 was an exploratory analysis of qualitative data that helped us to identify the unique dimensions of process conflict to more clearly distinguish it from task and relationship conflict. Study 2 used a broader sampling of participants to (a) demonstrate why process conflict has been difficult to discriminate from task conflict in many conflict scales, and (b) develop a two-factor Process Conflict Scale that effectively distinguishes process from task conflict. Study 3 used this new scale to examine the relationship between process conflict and group viability (group performance, satisfaction, and effective group process). The results showed that process conflict negatively affects group performance, member satisfaction, and group coordination.
Clinical Practice Guideline: The Diagnosis, Management, and Prevention of Bronchiolitis

This guideline is a revision of the clinical practice guideline, “Diagnosis and Management of Bronchiolitis,” published by the American Academy of Pediatrics in 2006. The guideline applies to children from 1 through 23 months of age. Other exclusions are noted. Each key action statement indicates level of evidence, benefit-harm relationship, and level of recommendation. Key action statements are as follows:
A Multiplier-less Implementation of Two-Dimensional Circular-Support Wavelet Transform on FPGA

: In this paper, a two-dimensional (2-D) circular-support wavelet transform (2-D CSWT) is presented. 2-D CSWT is a new geometrical image transform, which can efficiently represent images using 2-D circular spectral split schemes (circularly-decomposed frequency subspaces). 2-D all-pass functions and lattice structure are used to produce 1-level circular symmetric 2-D discrete wavelet transform with approximate linear phase 2-D filters. The classical one-dimensional (1-D) analysis Haar filter bank branches H 0 (z) and H 1 (z) which work as low-pass and high-pass filters, respectively are transformed into their 2-D counterparts H 0 (z 1 ,z 2 ) and H 1 (z 1 ,z 2 ) by applying a circular-support version of the digital spectral transformation (DST). The designed 2-D wavelet filter bank is realized in a separable architecture. The proposed architecture is simulated using Matlab program to measure the deflection ratio (DR) of the high frequency coefficient to evaluate its performance and compare it with the performance of the classical 2-D wavelet architecture. The correlation factor between the input and reconstructed images is also calculated for both architectures. The FPGA (Spartan-3E) Kit is used to implement the resulting architecture in a multiplier-less manner and to calculate the die area and the critical path or maximum frequency of operation. The achieved multiplier-less implementation takes a very small area from FPGA Kit (the die area in 3-level wavelet decomposition takes 300 slices with 7% occupation ratio only at a maximum frequency of 198.447 MHz).
Simulation Research on the Stock Control Strategy of VMI Combined Revenue Sharing

A mixed strategy which combines VMI and revenue sharing is put forward in this paper due to the problem of supply chain's hierarchy stock control strategy. A hierarchy and multi-cycle simulation model is constructed by multi-agent as well. The stock strategy of(S, Q), VMI, and VMI which is combined with revenue-sharing is carried out by discrete event simulation software EM-Plant. As the result, the stock strategy of VMI combined with revenue sharing not only reduces bullwhip effect and average holding stock cost but also distributes the profit of supply chain members reasonable and coordinates the supply chain well.
Characterization of the Genome of the Dairy Lactobacillus helveticus Bacteriophage ΦAQ113

ABSTRACT The complete genomic sequence of the dairy Lactobacillus helveticus bacteriophage ΦAQ113 was determined. Phage ΦAQ113 is a Myoviridae bacteriophage with an isometric capsid and a contractile tail. The final assembled consensus sequence revealed a linear, circularly permuted, double-stranded DNA genome with a size of 36,566 bp and a G+C content of 37%. Fifty-six open reading frames (ORFs) were predicted, and a putative function was assigned to approximately 90% of them. The ΦAQ113 genome shows functionally related genes clustered together in a genome structure composed of modules for DNA replication/regulation, DNA packaging, head and tail morphogenesis, cell lysis, and lysogeny. The identification of genes involved in the establishment of lysogeny indicates that it may have originated as a temperate phage, even if it was isolated from natural cheese whey starters as a virulent phage, because it is able to propagate in a sensitive host strain. Additionally, we discovered that the ΦAQ113 phage genome is closely related to Lactobacillus gasseri phage KC5a and Lactobacillus johnsonii phage Lj771 genomes. The phylogenetic similarities between L. helveticus phage ΦAQ113 and two phages that belong to gut species confirm a possible common ancestral origin and support the increasing consideration of L. helveticus as a health-promoting organism.