Analisis Penggunaan Google Dan Pengaruhnya Terhadap Kinerja (Studi Pada Mahasiswa S-1 Angkatan 2013-2014 Program Studi Administrasi Bisnis Fakultas Ilmu Administrasi Universitas Brawijaya)

The goal of this study to analyzes influence esay to use Google, usefulness Google, and use Google towards bachelor degree performence. The sample in this study is university student bachelor degree on 2013-2014 level at Administration Business Department, Administration Scients Faculty in Brawijaya University. They are 164 university students using questionnaire as the instrument of this research. The result showed that easy of use Google has positif significant towards usefulness Google, esay of use Google has positif significance towards use Google, usefulness Google has positif significant towards use Google, use Google has positif significant toward university students\u27 performance. Further showed that use Google that used by university student. The used of Goggle is considered easy and useful, in implementation. This research showed a high assessment is easy and usefull Google on its use towards university student\u27s performence

Variability in genes regulating vitamin D metabolism is associated with vitamin D levels in type 2 diabetes

Mortality rate is increased in type 2 diabetes (T2D). Low vitamin D levels are associated with increased mortality risk in T2D. In the general population, genetic variants affecting vitamin D metabolism (DHCR7 rs12785878, CYP2R1 rs10741657, GC rs4588) have been associated with serum vitamin D. We studied the association of these variants with serum vitamin D in 2163 patients with T2D from the "Sapienza University Mortality and Morbidity Event Rate (SUMMER) study in diabetes". Measurements of serum vitamin D were centralised. Genotypes were obtained by Eco™ Real-Time PCR. Data were adjusted for gender, age, BMI, HbA1c, T2D therapy and sampling season. DHCR7 rs12785878 (p = 1 x 10-4) and GC rs4588 (p = 1 x 10-6) but not CYP2R1 rs10741657 (p = 0.31) were significantly associated with vitamin D levels. One unit of a weighted genotype risk score (GRS) was strongly associated with vitamin D levels (p = 1.1 x 10-11) and insufficiency (<30 ng/ml) (OR, 95%CI = 1.28, 1.16-1.41, p = 1.1 x 10-7). In conclusion, DHCR7 rs12785878 and GC rs4588, but not CYP2R1 rs10741657, are significantly associated with vitamin D levels. When the 3 variants were considered together as GRS, a strong association with vitamin D levels and vitamin D insufficiency was observed, thus providing robust evidence that genes involved in vitamin D metabolism modulate serum vitamin D in T2D

Network references for rare diseases: state of the art for the paroxysmal nocturnal hemoglobinuria

recently, healthcare network models have been proposed to improve general awareness of rare diseases for patients and specific knowledge about diagnosis, treatment, and management for healthcare services. Paroxysmal nocturnal hemoglobinuria (PNH) is a rare haematological disease that still has no framing in an official network. OBJECTIVES: to describe the use of network models in diagnosis, treatment, and management of PNH patients both in Italy and abroad and its impact on patients and healthcare service. DISEGN: literature search was performed using the keywords "Hemoglobinuria", "Network", "PHN", and "Screening" in both MedLine and EMBASE. Search was restricted to the articles published in the last 5 years and written in English, French or Italian language. RESULTS: from the total 251 articles of the initial search, only 21 were finally included in our review. None of the included study explicitly described a network model. In general, we were able to identify two different kind of networks implicitly described in the studies: laboratory networks for diagnostic harmonization or screening of the population at risk of PNH (10/21 studies) and PNH registry as network of clinical information to be use for better understanding of the natural history of the disease and to assess therapeutic effectiveness (11/21 studies). CONCLUSIONS: few network approaches in PNH diagnosis, treatment, and management are described in literature. Despite the scarce application of the networks, our review highlights the positive impact that networks have in both patients and healthcare services

A new mutant genetic resource for tomato crop improvement by TILLING technology

<p>Abstract</p> <p>Background</p> <p>In the last decade, the availability of gene sequences of many plant species, including tomato, has encouraged the development of strategies that do not rely on genetic transformation techniques (GMOs) for imparting desired traits in crops. One of these new emerging technology is TILLING (Targeting Induced Local Lesions In Genomes), a reverse genetics tool, which is proving to be very valuable in creating new traits in different crop species.</p> <p>Results</p> <p>To apply TILLING to tomato, a new mutant collection was generated in the genetic background of the processing tomato cultivar Red Setter by treating seeds with two different ethylemethane sulfonate doses (0.7% and 1%). An associated phenotype database, LycoTILL, was developed and a TILLING platform was also established. The interactive and evolving database is available online to the community for phenotypic alteration inquiries. To validate the Red Setter TILLING platform, induced point mutations were searched in 7 tomato genes with the mismatch-specific ENDO1 nuclease. In total 9.5 kb of tomato genome were screened and 66 nucleotide substitutions were identified. The overall mutation density was estimated and it resulted to be 1/322 kb and 1/574 kb for the 1% EMS and 0.7% EMS treatment respectively.</p> <p>Conclusions</p> <p>The mutation density estimated in our collection and its comparison with other TILLING populations demonstrate that the Red Setter genetic resource is suitable for use in high-throughput mutation discovery. The Red Setter TILLING platform is open to the research community and is publicly available via web for requesting mutation screening services.</p

Architecture and performance of the KM3NeT front-end firmware

The KM3NeT infrastructure consists of two deep-sea neutrino telescopes being deployed in the Mediterranean Sea. The telescopes will detect extraterrestrial and atmospheric neutrinos by means of the incident photons induced by the passage of relativistic charged particles through the seawater as a consequence of a neutrino interaction. The telescopes are configured in a three-dimensional grid of digital optical modules, each hosting 31 photomultipliers. The photomultiplier signals produced by the incident Cherenkov photons are converted into digital information consisting of the integrated pulse duration and the time at which it surpasses a chosen threshold. The digitization is done by means of time to digital converters (TDCs) embedded in the field programmable gate array of the central logic board. Subsequently, a state machine formats the acquired data for its transmission to shore. We present the architecture and performance of the front-end firmware consisting of the TDCs and the state machine

Reconnaissance of 2016 Central Italy Earthquake Sequence

The Central Italy earthquake sequence nominally began on 24 August 2016 with a M6.1 event on a normal fault that produced devastating effects in the town of Amatrice and several nearby villages and hamlets. A major international response was undertaken to record the effects of this disaster, including surface faulting, ground motions, landslides, and damage patterns to structures. This work targeted the development of high-value case histories useful to future research. Subsequent events in October 2016 exacerbated the damage in previously affected areas and caused damage to new areas in the north, particularly the relatively large town of Norcia. Additional reconnaissance after a M6.5 event on 30 October 2016 documented and mapped several large landslide features and increased damage states for structures in villages and hamlets throughout the region. This paper provides an overview of the reconnaissance activities undertaken to document and map these and other effects, and highlights valuable lessons learned regarding faulting and ground motions, engineering effects, and emergency response to this disaster

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino de tector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower-or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Combined Forward-Backward Asymmetry Measurements in Top-Antitop Quark Production at the Tevatron

The CDF and D0 experiments at the Fermilab Tevatron have measured the asymmetry between yields of forward- and backward-produced top and antitop quarks based on their rapidity difference and the asymmetry between their decay leptons. These measurements use the full data sets collected in proton-antiproton collisions at a center-of-mass energy of $\sqrt s =1.96$ TeV. We report the results of combinations of the inclusive asymmetries and their differential dependencies on relevant kinematic quantities. The combined inclusive asymmetry is $A_{\mathrm{FB}}^{t\bar{t}} = 0.128 \pm 0.025$. The combined inclusive and differential asymmetries are consistent with recent standard model predictions

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements