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The Nash–Sutcliffe efficiency (NSE) is a widely used score in hydrology, but it is not common in the other environmental sciences. One of the reasons for its unpopularity is that its scientific meaning is somehow unclear in the literature. This study attempts to establish a solid foundation for the NSE from the viewpoint of signal progressing. Thus, a simulation is viewed as a received signal containing a wanted signal (observations) contaminated by an unwanted signal (noise). This view underlines an important role of the error model between simulations and observations. By assuming an additive error model, it is easy to point out that the NSE is equivalent to an important quantity in signal processing: the signal-to-noise ratio. Moreover, the NSE and the Kling–Gupta efficiency (KGE) are shown to be equivalent, at least when there are no biases, in the sense that they measure the relative magnitude of the power of noise to the power of the variation in observations. The scientific meaning of the NSE suggests a natural way to define NSE=0 as the threshold for good or bad model distinction, and this has no relation to the benchmark simulation that is equal to the observed mean. Corresponding to NSE=0, the threshold of the KGE is given by approximately 0.5. In the general cases, when the additive error model is replaced by a mixed additive–multiplicative error model, the traditional NSE is shown to be prone to contradiction in model evaluations. Therefore, an extension of the NSE is derived, which only requires one to divide the traditional noise-to-signal ratio by the multiplicative bias. This has a practical implication: if the multiplicative bias is not considered, the traditional NSE and KGE underestimate or overestimate the generalized NSE and KGE when the multiplicative bias is greater or smaller than one, respectively. In particular, the observed mean turns out to be the worst simulation from the viewpoint of the generalized NSE.

The Lower Mekong River basin (LMB) has experienced droughts in recent decades, causing detrimental economic losses and food security conundrums. This study quantified the impact of climate change on drought, and rainfed rice production in the LMB. The Soil and Water Assessment Tool (SWAT) and AquaCrop models were used to evaluate long-term drought indices and rainfed rice yields under historical and future climate conditions (1954–2099) with four climate models and two emission scenarios (RCP 4.5 and RCP8.5) from the Coupled Model Intercomparison Project Phase 5 (CMIP5). We found that rice yield might increase (24–43%) due to the elevated levels of atmospheric CO 2 concentration (+ 34.3 to + 121.9%) and increases in precipitation. Contrastingly, considerable decreases in rice yield up to 1.5 ton/ha in the Vietnam Central High Plain (VCHP) region could be expected resulting from reduced precipitation by about 34% during drought years. To avert any major food crisis, an expansion of irrigation areas could be required to compensate for the expected reduction in rice yields. We conclude that a framework combining hydrology and crop models to assess climate change impacts on food production is key to develop adaptation strategies in the future.

Computational drug repositioning seeks to identify new therapeutic uses for existing or experimental drugs. Network-based methods are effective as they integrate relationships among drugs, diseases, and target proteins/genes into prediction models. However, traditional approaches often rely on a single phenotype-based disease similarity network, limiting the diversity of disease information. In this study, we constructed three disease similarity networks—phenotypic, ontological, and molecular—using data from OMIM, Human Phenotype Ontology annotations, and gene interaction network, respectively. These were integrated into disease multiplex networks and multiplex-heterogeneous networks. We applied a tailored Random Walk with Restart (RWR) algorithm to predict novel drug-disease associations. Experimental results show that both disease multiplex and multiplex-heterogeneous networks outperform their single-layer counterparts in leave-one-out cross-validation. Using 10-fold cross-validation, our method, MHDR, outperformed the state-of-the-art methods TP-NRWRH, DDAGDL and RGLDR, demonstrating the advantage of integrating multiple disease similarity networks. We predicted novel drug-disease associations by ranking candidates, identifying 68 associations supported by shared proteins/genes, 1,064 by shared pathways, and 84 by shared protein complexes, with many validated by clinical trials, underscoring the practical impact of our approach.

The evolution of information technology has been driven by the discovery of new forms of large magnetoresistance, such as giant magnetoresistance1,2 and tunnelling magnetoresistance3,4, in magnetic multilayers. Recently, new types of this effect have been observed in much simpler bilayers consisting of ferromagnetic and non-magnetic thin films5–10. However, the magnitude of the change in resistance with magnetic field in these materials is very small, varying between 0.01 and 1%. Here, we demonstrate that non-magnetic–ferromagnetic bilayers consisting of a conducting non-magnetic InAs quantum well and an insulating ferromagnetic (Ga,Fe)Sb layer exhibit giant proximity magnetoresistance of approximately 80% at high magnetic field, and that its magnitude can be controlled by a gate. The mechanism for this large magnetoresistance is a strong magnetic proximity effect. The spin splitting in the InAs quantum well induced by the magnetic proximity effect can be varied between 0.17 meV and 3.8 meV by varying the gate voltage. In principle, this provides a mechanism to locally access Majorana fermions in InAs-based Josephson junctions11–14 and introduces a new concept of magnetic-gating spin transistors in which the non-magnetic channel current is modulated by both electrical and magnetic means.

In this study, chitosan and alginate were selected to prepare alginate/chitosan nanoparticles to load the drug lovastatin by the ionic gelation method. The synthesized nanoparticles loaded with drug were characterized by Fourier transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), laser scattering and differential scanning calorimetry (DSC) methods. The FTIR spectrum of the alginate/chitosan/lovastatin nanoparticles showed that chitosan and alginate interacted with lovastatin through hydrogen bonding and dipolar-dipolar interactions between the C-O, C=O, and OH groups in lovastatin, the C-O, NH, and OH groups in chitosan and the C-O, C=O, and OH groups in alginate. The laser scattering results and SEM images indicated that the alginate/chitosan/lovastatin nanoparticles have a spherical shape with a particle size in the range of 50–80 nm. The DSC diagrams displayed that the melting temperature of the alginate/chitosan/lovastatin nanoparticles was higher than that of chitosan and lower than that of alginate. This result means that the alginate and chitosan interact together, so that the nanoparticles have a larger crystal degree when compared with alginate and chitosan individually. Investigations of the in vitro lovastatin release from the alginate/chitosan/lovastatin nanoparticles under different conditions, including different alginate/chitosan ratios, different solution pH values and different lovastatin contents, were carried out by ultraviolet-visible spectroscopy. The rate of drug release from the nanoparticles is proportional to the increase in the solution pH and inversely proportional to the content of the loaded lovastatin. The drug release process is divided into two stages: a rapid stage over the first 10 hr, then the release becomes gradual and stable. The Korsmeyer-Peppas model is most suitable for the lovastatin release process from the alginate/chitosan/lovastatin nanoparticles in the first stage, and then the drug release complies with other models depending on solution pH in the slow release stage. In addition, the toxicity of alginate/chitosan/lovastatin (abbreviated ACL) nanoparticles was sufficiently low in mice in the acute toxicity test. The LD 50 of the drug was higher than 5000 mg/kg, while in the subchronic toxicity test with treatments of 100 mg/kg and 300 mg/kg ACL nanoparticles, there were no abnormal signs, mortality, or toxicity in general to the function or structure of the crucial organs. The results show that the ACL nanoparticles are safe in mice and that these composite nanoparticles might be useful as a new drug carrier.

A bstract We present a study at next-to-leading-order (NLO) of the process pp → W ± Z → ℓν l ℓ ′+ ℓ ′− , where ℓ, ℓ ′ = e, μ , at the Large Hadron Collider. We include the full NLO QCD corrections and the NLO electroweak (EW) corrections in the double-pole approximation. We define eight fiducial polarization coefficients directly constructed from the polar-azimuthal angular distribution of the decay leptons. These coefficients depend strongly on the kinematical cuts on the transverse momentum or rapidity of the individual leptons. Similarly, fiducial polarization fractions are also defined and they can be directly related to the fiducial coefficients. We perform a detailed analysis of the NLO QCD+EW fiducial polarization observables including theoretical uncertainties stemming from the scale variation and parton distribution function uncertainties, using the fiducial phase space defined by the ATLAS and CMS experiments. We provide results in the helicity coordinate system and in the Collins-Soper coordinate system, at a center-of-mass energy of 13 TeV. The EW corrections are found to be important in two of the angular coefficients related to the Z boson, irrespective of the kinematical cuts or the coordinate system. Meanwhile, those EW corrections are very small for the W ± bosons.

Human Papillomavirus (HPV) vaccine coverage in France is below 30%, despite proven effectiveness against HPV infections and (pre-)cancerous cervical lesions. To optimise vaccine promotion among adolescents, we used a discrete choice experiment (DCE) to identify optimal statements regarding a vaccination programme, including vaccine characteristics. Girls and boys enrolled in the last two years of five middle schools in three French regions (aged 13–15 years) participated in an in-class cross-sectional self-administered internet-based study. In ten hypothetical scenarios, participants decided for or against signing up for a school-based vaccination campaign against an unnamed disease. Scenarios included different levels of four attributes: the type of vaccine-preventable disease, communication on vaccine safety, potential for indirect protection, and information on vaccine uptake among peers. One scenario was repeated with an added mention of sexual transmission. The 1,458 participating adolescents (estimated response rate: 89.4%) theoretically accepted vaccination in 80.1% of scenarios. All attributes significantly impacted theoretical vaccine acceptance. Compared to a febrile respiratory disease, protection against cancer was motivating (odds ratio (OR) 1.29 [95%-CI 1.09–1.52]), but not against genital warts (OR 0.91 [0.78–1.06]). Compared to risk negation (“vaccine does not provoke serious side effects”), a reference to a positive benefit-risk balance despite a confirmed side effect was strongly dissuasive (OR 0.30 [0.24–0.36]), while reference to ongoing international pharmacovigilance without any scientifically confirmed effect was not significantly dissuasive (OR 0.86 [0.71–1.04]). The potential for indirect protection motivated acceptance among girls but not boys (potential for eliminating the disease compared to no indirect protection, OR 1.57 [1.25–1.96]). Compared to mentioning “insufficient coverage”, reporting that “>80% of young people in other countries got vaccinated” motivated vaccine acceptance (OR 1.94 [1.61–2.35]). The notion of sexual transmission did not influence acceptance. HPV vaccine communication to adolescents can be tailored to optimise the impact of promotion efforts.

Enhancers regulate transcription of target genes, causing a change in expression level. Thus, the aberrant activity of enhancers can lead to diseases. To date, a large number of enhancers have been identified, yet a small portion of them have been found to be associated with diseases. This raises a pressing need to develop computational methods to predict associations between diseases and enhancers. In this study, we assumed that enhancers sharing target genes could be associated with similar diseases to predict the association. Thus, we built an enhancer functional interaction network by connecting enhancers significantly sharing target genes, then developed a network diffusion method RWDisEnh, based on a random walk with restart algorithm, on networks of diseases and enhancers to globally measure the degree of the association between diseases and enhancers. RWDisEnh performed best when the disease similarities are integrated with the enhancer functional interaction network by known disease-enhancer associations in the form of a heterogeneous network of diseases and enhancers. It was also superior to another network diffusion method, i.e., PageRank with Priors, and a neighborhood-based one, i.e., MaxLink, which simply chooses the closest neighbors of known disease-associated enhancers. Finally, we showed that RWDisEnh could predict novel enhancers, which are either directly or indirectly associated with diseases. Taken together, RWDisEnh could be a potential method for predicting disease-enhancer associations.

Background Despite standard treatments including chemoradiotherapy with temozolomide (TMZ) (STUPP protocol), the prognosis of glioblastoma patients remains poor. AGuIX nanoparticles have a high radiosensitizing potential, a selective and long-lasting accumulation in tumors and a rapid renal elimination. Their therapeutic effect has been proven in vivo on several tumor models, including glioblastoma with a potential synergetic effect when combined with TMZ based chemoradiotherapy, and they are currently evaluated in 4 ongoing Phase Ib and II clinical trials in 4 indications (brain metastases, lung, pancreatic and cervix cancers) (> 100 patients received AGuIX). Thus, they could offer new perspectives for patients with newly diagnosed glioblastoma. The aim of this study is to determine the recommended dose of AGuIX as a radiosensitizer in combination with radiotherapy and TMZ during the concurrent radio-chemotherapy period for phase II (RP2D) and to estimate the efficacy of the combination. Methods NANO-GBM is a multicenter, phase I/II, randomized, open-label, non-comparative, therapeutic trial. According to a dose escalation scheme driven by a TITE-CRM design, 3 dose levels of AGuIX (50, 75 and 100 mg/kg) will be tested in phase I added to standard concomitant radio-chemotherapy. Patients with grade IV glioblastoma, not operated or partially operated, with a KPS ≥ 70% will be eligible for the study. The primary endpoints are i) for phase I, the RP2D of AGuIX, with DLT defined as any grade 3–4 NCI-CTCAE toxicity and ii) for phase II, the 6-month progression-free survival rate. The pharmacokinetics, distribution of nanoparticles, tolerance of the combination, neurological status, overall survival (median, 6-month and 12-month rates), response to treatment, and progression-free survival (median and 12-month rates) will be assessed as secondary objectives. Maximum sixty-six patients are expected to be recruited in the study from 6 sites. Discussion The use of AGuIX nanoparticles could allow to overpass the radioresistance to the reference treatment of newly diagnosed glioblastomas that have the poorest prognosis (incomplete resection or biopsy only). Trial registration Clinicaltrials.gov: NCT04881032 , registered on April 30, 2021. Identifier with the French National Agency for the Safety of Medicines and Health Products (ANSM): N°Eudra CT 2020-004552-15. Protocol: version 3, 23 May 2022.

We investigate possible effects of unparticles at the MUonE experiment by considering a general model for unparticle with broken scale invariance, characterized by the scaling dimension d and the energy scale μ at which the scale invariance is broken. Taking into account available relevant constraints on the couplings of the unparticles with the Standard Model (SM) leptons, we found that the MUonE experiment at the level of 10 ppm systematic accuracy is sensitive to such effects if 1 < d ≲ 1.4 and 1 ≤ μ ≲ 12 GeV for vector unparticles. The effects of scalar unparticles are too feeble to be detected. The vector unparticles can induce a significant shift on the best-fit value of a μ had at the MUonE, thereby providing an opportunity to detect unparticles or to obtain a new bound on the unparticle-SM couplings in the case of no anomaly.