Results are presented of a search for a "natural" supersymmetry scenario with gauge mediated symmetry breaking. It is assumed that only the supersymmetric partners of the top quark (the top squark) and the Higgs boson (Higgsino) are accessible. Events are examined in which there are two photons forming a Higgs boson candidate, and at least two b-quark jets. In 19.7 fb-1 of proton-proton collision data at s=8 TeV, recorded in the CMS experiment, no evidence of a signal is found and lower limits at the 95% confidence level are set, excluding the top squark mass below 360 to 410 GeV, depending on the Higgsino mass.
The cocoa pod borer (CPB) Conopomorpha cramerella (Snellen) (Lepidoptera: Gracillaridae) is one of the major constraints for cocoa production in South East Asia. In addition to cultural and chemical control methods, autocidal control tactics such as the Sterile Insect Technique (SIT) could be an efficient addition to the currently control strategy, however SIT implementation will depend on the population genetics of the targeted pest. The aim of the present work was to search for suitable microsatellite loci in the genome of CPB that is partially sequenced. Twelve microsatellites were initially selected and used to analyze moths collected from Indonesia, Malaysia, and the Philippines. A quality control verification process was carried out and seven microsatellites found to be suitable and efficient to distinguish differences between CPB populations from different locations. The selected microsatellites were also tested against a closely related species, i.e. the lychee fruit borer Conopomorpha sinensis (LFB) from Vietnam and eight loci were found to be suitable. The availability of these novel microsatellite loci will provide useful tools for the analysis of the population genetics and gene flow of these pests, to select suitable CPB strains to implement the SIT.
Traditionally, patient travel history has been used to distinguish imported from autochthonous malaria cases, but the dormant liver stages of Plasmodium vivax confound this approach. Molecular tools offer an alternative method to identify, and map imported cases. Using machine learning approaches incorporating hierarchical fixation index and decision tree analyses applied to 799 P. vivax genomes from 21 countries, we identified 33-SNP, 50-SNP and 55-SNP barcodes (GEO33, GEO50 and GEO55), with high capacity to predict the infection's country of origin. The Matthews correlation coefficient (MCC) for an existing, commonly applied 38-SNP barcode (BR38) exceeded 0.80 in 62% countries. The GEO panels outperformed BR38, with median MCCs > 0.80 in 90% countries at GEO33, and 95% at GEO50 and GEO55. An online, open-access, likelihood-based classifier framework was established to support data analysis (vivaxGEN-geo). The SNP selection and classifier methods can be readily amended for other use cases to support malaria control programs.
Results are presented from a search for new decaying massive particles whose presence is inferred from an imbalance in transverse momentum and which are produced in association with a single top quark that decays into a bottom quark and two light quarks. The measurement is performed using 19.7 fb^{-1} of data from proton-proton collisions at a center-of-mass energy of 8 TeV, collected with the CMS detector at the CERN LHC. No deviations from the standard model predictions are observed and lower limits are set on the masses of new invisible bosons. In particular, scalar and vector particles, with masses below 330 and 650 GeV, respectively, are excluded at 95% confidence level, thereby substantially extending a previous limit published by the CDF Collaboration.
The first evidence for the Higgs boson decay to a Z boson and a photon is presented, with a statistical significance of 3.4 standard deviations. The result is derived from a combined analysis of the searches performed by the ATLAS and CMS Collaborations with proton-proton collision datasets collected at the CERN Large Hadron Collider (LHC) from 2015 to 2018. These correspond to integrated luminosities of around 140 fb^{-1} for each experiment, at a center-of-mass energy of 13 TeV. The measured signal yield is 2.2±0.7 times the standard model prediction, and agrees with the theoretical expectation within 1.9 standard deviations.
The purely electroweak (EW) cross section for the production of two jets in association with a Z boson, in proton-proton collisions at [Formula: see text], is measured using data recorded by the CMS experiment at the CERN LHC, corresponding to an integrated luminosity of 19.7[Formula: see text]. The electroweak cross section for the [Formula: see text] final state (with [Formula: see text] or [Formula: see text] and j representing the quarks produced in the hard interaction) in the kinematic region defined by [Formula: see text][Formula: see text], [Formula: see text][Formula: see text], transverse momentum [Formula: see text][Formula: see text], and pseudorapidity [Formula: see text], is found to be [Formula: see text], in agreement with the standard model prediction. The associated jet activity of the selected events is studied, in particular in a signal-enriched region of phase space, and the measurements are found to be in agreement with QCD predictions.
A search for invisible decays of Higgs bosons is performed using the vector boson fusion and associated ZH production modes. In the ZH mode, the Z boson is required to decay to a pair of charged leptons or a [Formula: see text] quark pair. The searches use the 8 [Formula: see text] pp collision dataset collected by the CMS detector at the LHC, corresponding to an integrated luminosity of up to 19.7 [Formula: see text]. Certain channels include data from 7 [Formula: see text] collisions corresponding to an integrated luminosity of 4.9 [Formula: see text]. The searches are sensitive to non-standard-model invisible decays of the recently observed Higgs boson, as well as additional Higgs bosons with similar production modes and large invisible branching fractions. In all channels, the observed data are consistent with the expected standard model backgrounds. Limits are set on the production cross section times invisible branching fraction, as a function of the Higgs boson mass, for the vector boson fusion and ZH production modes. By combining all channels, and assuming standard model Higgs boson cross sections and acceptances, the observed (expected) upper limit on the invisible branching fraction at [Formula: see text] [Formula: see text] is found to be 0.58 (0.44) at 95 % confidence level. We interpret this limit in terms of a Higgs-portal model of dark matter interactions.
Observation of the diphoton decay mode of the recently discovered Higgs boson and measurement of some of its properties are reported. The analysis uses the entire dataset collected by the CMS experiment in proton-proton collisions during the 2011 and 2012 LHC running periods. The data samples correspond to integrated luminosities of 5.1[Formula: see text]at [Formula: see text] and 19.7[Formula: see text]at 8[Formula: see text] . A clear signal is observed in the diphoton channel at a mass close to 125[Formula: see text] with a local significance of [Formula: see text], where a significance of [Formula: see text] is expected for the standard model Higgs boson. The mass is measured to be [Formula: see text] , and the best-fit signal strength relative to the standard model prediction is [Formula: see text][Formula: see text][Formula: see text]. Additional measurements include the signal strength modifiers associated with different production mechanisms, and hypothesis tests between spin-0 and spin-2 models.
Epstein-Barr virus (EBV) causes infectious mononucleosis and is associated with epithelial-cell cancers and B cell lymphomas. An effective EBV vaccine is not available. We found that antibodies to the EBV glycoprotein gH/gL complex were the principal components in human plasma that neutralized infection of epithelial cells and that antibodies to gH/gL and gp42 contributed to B cell neutralization. Immunization of mice and nonhuman primates with nanoparticle vaccines that displayed components of the viral-fusion machinery EBV gH/gL or gH/gL/gp42 elicited antibodies that potently neutralized both epithelial-cell and B cell infection. Immune serum from nonhuman primates inhibited EBV-glycoprotein-mediated fusion of epithelial cells and B cells and targeted an epitope critical for virus-cell fusion. Therefore, unlike the leading EBV gp350 vaccine candidate, which only protects B cells from infection, these EBV nanoparticle vaccines elicit antibodies that inhibit the virus-fusion apparatus and provide cell-type-independent protection from virus infection.
Results are presented from a search for particle dark matter (DM), extra dimensions, and unparticles using events containing a jet and an imbalance in transverse momentum. The data were collected by the CMS detector in proton-proton collisions at the LHC and correspond to an integrated luminosity of 19.7[Formula: see text]at a centre-of-mass energy of 8[Formula: see text]. The number of observed events is found to be consistent with the standard model prediction. Limits are placed on the DM-nucleon scattering cross section as a function of the DM particle mass for spin-dependent and spin-independent interactions. Limits are also placed on the scale parameter [Formula: see text] in the Arkani-Hamed, Dimopoulos, and Dvali (ADD) model of large extra dimensions, and on the unparticle model parameter [Formula: see text]. The constraints on ADD models and unparticles are the most stringent limits in this channel and those on the DM-nucleon scattering cross section are an improvement over previous collider results.
This paper presents a measurement of the inclusive 3-jet production differential cross section at a proton-proton centre-of-mass energy of 7 TeV using data corresponding to an integrated luminosity of 5[Formula: see text]collected with the CMS detector. The analysis is based on the three jets with the highest transverse momenta. The cross section is measured as a function of the invariant mass of the three jets in a range of 445-3270 GeV and in two bins of the maximum rapidity of the jets up to a value of 2. A comparison between the measurement and the prediction from perturbative QCD at next-to-leading order is performed. Within uncertainties, data and theory are in agreement. The sensitivity of the observable to the strong coupling constant [Formula: see text] is studied. A fit to all data points with 3-jet masses larger than 664 GeV gives a value of the strong coupling constant of [Formula: see text].
A measurement of the cross section for the production of top quark-antiquark pairs ([Formula: see text]) in association with a vector boson V (W or Z) in proton-proton collisions at [Formula: see text][Formula: see text] is presented. The results are based on a dataset corresponding to an integrated luminosity of 19.5 fb[Formula: see text] recorded with the CMS detector at the LHC. The measurement is performed in three leptonic (e and [Formula: see text]) channels: a same-sign dilepton analysis targeting [Formula: see text] events, and trilepton and four-lepton analyses designed for [Formula: see text] events. In the same-sign dilepton channel, the [Formula: see text] cross section is measured as [Formula: see text], corresponding to a significance of 1.6 standard deviations over the background-only hypothesis. Combining the trilepton and four-lepton channels, a direct measurement of the [Formula: see text] cross section, [Formula: see text], is obtained with a significance of 3.1 standard deviations. The measured cross sections are compatible with standard model predictions within their experimental uncertainties. The inclusive [Formula: see text] process is observed with a significance of 3.7 standard deviations from the combination of all three leptonic channels.
Groundwater resources constitute the main source of clean fresh water for domestic use and it is essential for food production in the agricultural sector. Groundwater has a vital role for water supply in the Campanian Plain in Italy and hence a future sustainability of the resource is essential for the region. In the current paper novel data mining algorithms including Gaussian Process (GP) were used in a large groundwater quality database to predict nitrate (contaminant) and strontium (potential future increasing) concentrations in groundwater. The results were compared with M5P, random forest (RF) and random tree (RT) algorithms as a benchmark to test the robustness of the modeling process. The dataset includes 246 groundwater quality samples originating from different wells, municipals and agricultural. It was divided for the modeling process into two subgroups by using the 10-fold cross validation technique including 173 samples for model building (training dataset) and 73 samples for model validation (testing dataset). Different water quality variables including T, pH, EC, HCO3-, F-, Cl-, SO42-, Na+, K+, Mg2+, and Ca2+ have been used as an input to the models. At first stage, different input combinations have been constructed based on correlation coefficient and thus the optimal combination was chosen for the modeling phase. Different quantitative criteria alongside with visual comparison approach have been used for evaluating the modeling capability. Results revealed that to obtain reliable results also variables with low correlation should be considered as an input to the models together with those variables showing high correlation coefficients. According to the model evaluation criteria, GP algorithm outperforms all the other models in predicting both nitrate and strontium concentrations followed by RF, M5P and RT, respectively. Result also revealed that model's structure together with the accuracy and structure of the data can have a relevant impact on the model's results.
Diabetes mellitus in Asia accounts for more than half of the global prevalence. There is a high prevalence of cardiovascular disease (CVD) in the region among people with type 2 diabetes mellitus (T2DM) and it is often associated with multiple risk factors including hypertension, renal disease and obesity. The early onset of T2DM and the eventual long disease duration portends an increasing proportion of the population to premature CVD. In addition to lowering blood glucose, sodium-glucose co-transporter-2 (SGLT-2) inhibitors exert favourable effects on multiple risk factors (including blood pressure, body weight and renal function) and provide an opportunity to reduce the risk of CVD in patients with T2DM. In this article, we consolidated the existing literature on SGLT-2 inhibitor use in Asian patients with T2DM and established contemporary guidance for clinicians. We extensively reviewed recommendations from international and regional guidelines, published data from clinical trials in the Asian population (dapagliflozin, canagliflozin, empagliflozin, ipragliflozin, luseogliflozin and tofogliflozin), CVD outcomes trials (EMPAREG-OUTCOME, CANVAS and DECLARE-TIMI 58) and real-world evidence studies (CVD-REAL, EASEL, CVD-REAL 2 and OBSERVE-4D). A series of clinical recommendations on the use of SGLT-2 inhibitors in Asian patients with T2DM was deliberated among experts with multiple rounds of review and voting. Based on the available evidence, we conclude that SGLT-2 inhibitors represent an evidence-based therapeutic option for the primary prevention of heart failure hospitalization and secondary prevention of CVD in patients with T2DM, and should be considered early on in the treatment algorithm for patients with multiple risk factors, or those with established CVD.
We used AdaBoost (AB), alternating decision tree (ADTree), and their combination as an ensemble model (AB-ADTree) to spatially predict landslides in the Cameron Highlands, Malaysia. The models were trained with a database of 152 landslides compiled using Synthetic Aperture Radar Interferometry, Google Earth images, and field surveys, and 17 conditioning factors (slope, aspect, elevation, distance to road, distance to river, proximity to fault, road density, river density, normalized difference vegetation index, rainfall, land cover, lithology, soil types, curvature, profile curvature, stream power index, and topographic wetness index). We carried out the validation process using the area under the receiver operating characteristic curve (AUC) and several parametric and non-parametric performance metrics, including positive predictive value, negative predictive value, sensitivity, specificity, accuracy, root mean square error, and the Friedman and Wilcoxon sign rank tests. The AB model (AUC = 0.96) performed better than the ensemble AB-ADTree model (AUC = 0.94) and successfully outperformed the ADTree model (AUC = 0.59) in predicting landslide susceptibility. Our findings provide insights into the development of more efficient and accurate landslide predictive models that can be used by decision makers and land-use managers to mitigate landslide hazards.
The declining water level in Lake Urmia has become a significant issue for Iranian policy and decision makers. This lake has been experiencing an abrupt decrease in water level and is at real risk of becoming a complete saline land. Because of its position, assessment of changes in the Lake Urmia is essential. This study aims to evaluate changes in the water level of Lake Urmia using the space-borne remote sensing and GIS techniques. Therefore, multispectral Landsat 7 ETM+ images for the years 2000, 2010, and 2017 were acquired. In addition, precipitation and temperature data for 31 years between 1986 and 2017 were collected for further analysis. Results indicate that the increased temperature (by 19%), decreased rainfall of about 62%, and excessive damming in the Urmia Basin along with mismanagement of water resources are the key factors in the declining water level of Lake Urmia. Furthermore, the current research predicts the potential environmental crisis as the result of the lake shrinking and suggests a few possible alternatives. The insights provided by this study can be beneficial for environmentalists and related organizations working on this and similar topics.
Heavy metals in water and wastewater are taken into account as one of the most hazardous environmental issues that significantly impact human health. The use of biochar systems with different materials helped significantly remove heavy metals in the water, especially wastewater treatment systems. Nevertheless, heavy metal's sorption efficiency on the biochar systems is highly dependent on the biochar characteristics, metal sources, and environmental conditions. Therefore, this study implicates the feasibility of biochar systems in the heavy metal sorption in water/wastewater and the use of artificial intelligence (AI) models in investigating efficiency sorption of heavy metal on biochar. Accordingly, this work investigated and proposed 20 artificial intelligent models for forecasting the sorption efficiency of heavy metal onto biochar based on five machine learning algorithms and bagging technique (BA). Accordingly, support vector machine (SVM), random forest (RF), artificial neural network (ANN), M5Tree, and Gaussian process (GP) algorithms were used as the key algorithms for the aim of this study. Subsequently, the individual models were bagged with each other to generate new ensemble models. Finally, 20 intelligent models were developed and evaluated, including SVM, RF, M5Tree, GP, ANN, BA-SVM, BA-RF, BA-M5Tree, BA-GP, BA-ANN, SVM-RF, SVM-M5Tree, SVM-GP, SVM-ANN, RF-M5Tree, RF-GP, RF-ANN, M5Tree-GP, M5Tree-ANN, GP-ANN. Of those, the hybrid models (i.e., BA-SVM, BA-RF, BA-M5Tree, BA-GP, BA-ANN, SVM-RF, SVM-M5Tree, SVM-GP, SVM-ANN, RF-M5Tree, RF-GP, RF-ANN, M5Tree-GP, M5Tree-ANN, GP-ANN) are introduced as the novelty of this study for estimating the heavy metal's sorption efficiency on the biochar systems. Also, the biochar characteristics, metal sources, and environmental conditions were comprehensively assessed and used, and they are considered as a novelty of the study as well. For this aim, a dataset of sorption efficiency of heavy metal was collected and processed with 353 experimental tests. Various performance indexes were applied to evaluate the models, such as RMSE, R2, MAE, color intensity, Taylor diagram, box and whiskers plots. This study's findings revealed that AI models could predict heavy metal's sorption efficiency onto biochar with high reliability, and the efficiency of the ensemble models is higher than those of individual models. The results also reported that the SVM-ANN ensemble model is the most superior model among 20 developed models. The predictive model proposed that heavy metal's efficiency sorption on biochar can be accurately forecasted and early warning for the water pollution by heavy metal.
BACKGROUND: After a decade of silence, an outbreak of the contagious and Asian endemic disease, goat pox re-emerged in North Vietnam affecting more than 1800 heads with a mortality rate of 6.5%. The inevitable impact of goat pox on hide quality, breeding, chevon and milk production has resulted in a significant economic losses to the developing goat industry of Vietnam. In the act of establishing an effective control of this devastating disease, tracing the source of re-emergence via a phylogenetic study was carried out to reveal their genetic relatedness. Either skin scab or papule from the six affected provinces were collected, cultured into Vero cells followed by restricted enzyme digestion of targeted P32 gene DNA encoding. The P32 gene was then cloned and transformed into E.coli competent cells for further sequencing.
RESULTS: The isolated sequence is deposited into GenBank under Accession No. MN317561/VNUAGTP1. The phylogenetic tree revealed high similarity of nucleotide and amino acid sequences to references goat pox strains accounting for 99.6 and 99.3, respectively. The Vietnamese strain is clustered together with currently circulating goat pox virus in China, India and Pakistan which suggested the origin of South China.
CONCLUSIONS: This Vietnam isolate is clustered together with other Asian goat pox strains indicating the dissemination of a common goat pox virus within this continent.
Laboratories from 14 countries (with different levels of expertise in radionuclide measurements and 210Pb dating) participated in an interlaboratory comparison exercise (ILC) related to the application of 210Pb sediment dating technique within the framework of the IAEA Coordinated Research Project. The laboratories were provided with samples from a composite sediment core and were required to provide massic activities of several radionuclides and an age versus depth model from the obtained results, using the most suitable 210Pb dating model. Massic concentrations of Zn and Cu were also determined to be used for chronology validation. The ILC results indicated good analytical performances while the dating results didn't demonstrate the same degree of competence in part due to the different experience in dating of the participant laboratories. The ILC exercise enabled evaluation of the difficulties faced by laboratories implementing 210Pb dating methods and identified some limitations in providing reliable chronologies.
In this research, the novel metaheuristic algorithm Harris hawks optimization (HHO) is applied to landslide susceptibility analysis in Western Iran. To this end, the HHO is synthesized with an artificial neural network (ANN) to optimize its performance. A spatial database comprising 208 historical landslides, as well as 14 landslide conditioning factors-elevation, slope aspect, plan curvature, profile curvature, soil type, lithology, distance to the river, distance to the road, distance to the fault, land cover, slope degree, stream power index (SPI), topographic wetness index (TWI), and rainfall-is prepared to develop the ANN and HHO-ANN predictive tools. Mean square error and mean absolute error criteria are defined to measure the performance error of the models, and area under the receiving operating characteristic curve (AUROC) is used to evaluate the accuracy of the generated susceptibility maps. The findings showed that the HHO algorithm effectively improved the performance of ANN in both recognizing (AUROCANN = 0.731 and AUROCHHO-ANN = 0.777) and predicting (AUROCANN = 0.720 and AUROCHHO-ANN = 0.773) the landslide pattern.