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ABSTRACT Background Oral mucositis affects about 20% of children undergoing high‐dose methotrexate (HDMTX) for acute lymphoblastic leukemia (ALL), despite existing management strategies. Personalized HDMTX dosing, adjusted by pharmacokinetics and leukemia risk, has reduced mucositis incidence, but variations still occur with similar 24‐h methotrexate levels. Methods This retrospective study investigated risk factors for oral mucositis under individualized methotrexate protocols. Data from patients with ≥ Grade 2 oral mucositis (CTCAE v4.0) were analyzed from the St. Jude Children's Research Hospital total 16 trial. A 1:1 case–control matching method considered age, sex, risk classification, immunophenotype, and methotrexate course. McNemar's, Bowker's symmetry, and Wilcoxon signed‐rank tests were used for statistical analyses. Risk factors for recurrent mucositis were identified in a case‐only analysis. Results The study found significant associations between methotrexate‐induced mucositis and new‐onset skin rashes (p = 0.0027), fever (p = 0.0016), neutropenic fever (p = 0.0008), lower absolute neutrophil count (p

Abstract La Palma, Canary Islands, underwent volcanic unrest which culminated in its largest historical eruption. We study this unrest along 2021 using Interferometric Synthetic Aperture Radar (InSAR) and a new improved interpretation methodology, comparing achieved results with the crustal structure. We reproduce the final phase of La Palma volcanic unrest, highligthing a shallow magma accumulation which begins about 3.5 months before the eruption in a crustal volume charactherized by low density and fractured rocks. Our modeling, together with our improved pictures of the crustal structure, allows us to explain the location and characteristics of the eruption and to detect failed eruption paths. These can be used to explain post-eruptive phenomena and hazards to the local population, such as detected gases anomalies in La Bombilla and Puerto Naos. Our results have implications for understanding volcanic activity in the Canaries and volcano monitoring elsewhere, helping to support decision-making and providing significant insights into urban and infrastructure planning in volcanic areas.

Abstract Despite the low cost and high capacity of Ni‐rich layered oxides (NRLOs), their widespread implementation in electric vehicles is hindered by capacity decay and O release. These issues originate from chemo‐mechanical heterogeneity, which is mainly related to oxygen anion redox (OAR). However, what to tune regarding OAR in NRLOs and how to tune it remains unknown. In this study, a close correlation between the OAR chemistry and Li/Ni antisite defects is revealed. Experiments and calculations show the opposite effects of aggregative and dispersive Li/Ni antisite defects on the NiO6 configuration and Ni spin state in NRLOs. The resulting broad or narrow spans for the energy bands caused by spin states lead to different OAR chemistries. By tuning the Li/Ni antisite defects to be dispersive rather than aggregative, the threshold voltage for triggering OAR is obviously elevated, and the generation of bulk‐O2‐like species and O2 release at phase transition nodes is fundamentally restrained. The OAR is regulated from irreversible to reversible, fundamentally addressing structural degradation and heterogeneity. This study reveals the interaction of the Li/Ni antisite defect/OAR chemistry/chemo‐mechanical heterogeneity and presents some insights into the design of high‐performance NRLO cathodes.

Abstract DS‐ALL has a higher rate of relapse and treatment‐related mortality. The newer immunotherapies are potentially better options. Relapsed ALL with positive MRD has a poor prognosis. Transient long‐term remission after ALL relapse due to partial chemotherapy combined severe infection is rare.

Underground coal fire is a global geological disaster that causes the loss of resources as well as environmental pollution. Xinjiang, China, is one of the regions suffering from serious underground coal fires. The accurate monitoring of underground coal fires is critical for management and extinguishment, and many remote sensing-based approaches have been developed for monitoring over large areas. Among them, the multi-temporal interferometric synthetic aperture radar (MT-InSAR) techniques have been recently employed for underground coal fires-related ground deformation monitoring. However, MT-InSAR involves a relatively high computational cost, especially when the monitoring area is large. We propose to use a more cost-efficient Stacking-InSAR technique to monitor ground deformation over underground coal fire areas in this study. Considering the effects of atmosphere on Stacking-InSAR, an ERA5 data-based estimation model is employed to mitigate the atmospheric phase of interferograms before stacking. Thus, an adaptive ERA5-Corrected Stacking-InSAR method is proposed in this study, and it is tested over the Fukang coal fire area in Xinjiang, China. Based on original and corrected interferograms, four groups of ground deformation results were obtained, and the possible coal fire areas were identified. In this paper, the ERA5 atmospheric delay products based on the estimation model along the LOS direction (D-LOS) effectively mitigate the atmospheric phase. The accuracy of ground deformation monitoring over a coal fire area has been improved by the proposed method choosing interferograms adaptively for stacking. The proposed Adaptive ERA5-Corrected Stacking-InSAR method can be used for efficient ground deformation monitoring over large coal fire areas.

Abstract La Palma island is one of the highest potential risks in the volcanic archipelago of the Canaries and therefore it is important to carry out an in-depth study to define its state of unrest. This has been accomplished through the use of satellite radar observations and an original state-of-the-art interpretation technique. Here we show the detection of the onset of volcanic unrest on La Palma island, most likely decades before a potential eruption. We study its current evolution seeing the spatial and temporal changing nature of activity at this potentially dangerous volcano at unprecedented spatial resolutions and long time scales, providing insights into the dynamic nature of the associated volcanic hazard. The geodetic techniques employed here allow tracking of the fluid migration induced by magma injection at depth and identifying the existence of dislocation sources below Cumbre Vieja volcano which could be associated with a future flank failure. Therefore they should continue being monitored using these and other techniques. The results have implications for the monitoring of steep-sided volcanoes at oceanic islands.

The grey prediction evolution algorithm based on the even grey model (GPEAe) proposed by Z.B.Hu et al. in 2019 is a competitively stochastic real-parameter optimization algorithm with characters of simple code, less parameters and strong exploration capability. To improve the algorithmic overall performance, a topological opposition-based learning strategy (TOBL) is first developed to enhance its exploitation capability in this paper. The TOBL determines offsprings by calculating the Manhattan distances between the current best individual and all the vertices of the hypercube inspired by the opposition-based learning strategy. An improved grey prediction evolutionary algorithm based on the TOBL (TOGPEAe) is then proposed. The performance of the TOGPEAe is tested on CEC2005, CEC2014 benchmark functions and a test suite composed of six engineering design problems. The experimental results of the TOGPEAe are very competitive compared with those of the original GPEAe and other state-of-the-art algorithms.

The environmental/economic dispatch (EED) problem, as one of the most important optimization problems in power systems operations, is a highly constrained, nonlinear, multiobjective optimization problem. Multiobjective evolutionary algorithms have become effective tools for solving the EED problem. To obtain higher quality Pareto solutions for EED as well as further improve the uniformity and diversity of the Pareto set, this paper proposes a novel multiobjective evolutionary algorithm, namely multiobjective grey prediction evolution algorithm (MOGPEA). The MOGPEA first develops a novel grey prediction evolution algorithm (GPEA) based on the even grey model (EGM(1,1)). Unlike other evolutionary algorithms, the GPEA considers the population series of evolutionary algorithms as a time series and uses the EGM(1,1) model to construct an exponential function as a reproduction operator for obtaining offspring. In addition, the MOGPEA adopts two learning strategies to improve the uniformity and diversity of the Pareto optimal solutions of the EED. One is a leader-updating strategy based on the maximum distance of each solution in an external archive, and the other is a leader-guiding strategy based on one solution of each external archive. To validate the effectiveness of the MOGPEA, a standard IEEE 30-bus 6-generator test system (with/without considering losses) is studied with fuel cost and emission as two conflicting objectives to be simultaneously optimized. The experimental results are compared with those obtained using a number of algorithms reported in the literature. The results reveal that the MOGPEA generates superior Pareto optimal solutions of the multiobjective EED problem. Matlab_Codes of this article can be found in https://github.com/Zhongbo-Hu/Prediction-Evolutionary-Algorithm-HOMEPAGE.

The grey prediction evolution algorithm based on the even grey model (GPEAe) is a pioneer of prediction-based evolutionary algorithms. Its offsprings are generated by a first-order inverse accumulating generation operation (1-IAGO) depending on every prediction of the even grey model. For the fact that the original values are already known for the first few predicted values in 1-IAGO, this paper firstly develops an accelerated 1-IAGO (1-AIAGO) which replaces a particular prediction with the corresponding original value. An accelerated even grey model (AEGM(1,1)) based on the 1-AIAGO is then proposed. Finally, this paper proposes a new grey prediction evolution algorithm (GPEAae) which uses the AEGM(1,1) as the reproduction operator of evolutionary algorithm to forecast the offspring. The performance of GPEAae is verified on the CEC2014 benchmark functions and the set containing nine engineering constrained design problems. The experimental results show that the GPEAae has superiority and highly competitive effectiveness when compared with the GPEAe and other state-of-the-art algorithms. The motivation of the GPEAae is using the iterative frame of evolutionary algorithms to superimpose the weak effect generated by the proposed 1-AIAGO which replaces the approximate (predicted) value with the corresponding accurate (original) value. Matlab_Codes of this article can be found in https://github.com/Zhongbo-Hu/Prediction-Evolutionary-Algorithm-HOMEPAGE.

Task offloading and resource allocation are the major elements of edge computing. A reasonable task offloading strategy and resource allocation scheme can reduce task processing time and save system energy consumption. Most of the current studies on the task migration of edge computing only consider the resource allocation between terminals and edge servers, ignoring the huge computing resources in the cloud center. In order to sufficiently utilize the cloud and edge server resources, we propose a coarse-grained task offloading strategy and intelligent resource matching scheme under Cloud-Edge collaboration. We consider the heterogeneity of mobile devices and inter-channel interference, and we establish the task offloading decision of multiple end-users as a game-theory-based task migration model with the objective of maximizing system utility. In addition, we propose an improved game-theory-based particle swarm optimization algorithm to obtain task offloading strategies. Experimental results show that the proposed scheme outperforms other schemes with respect to latency and energy consumption, and it scales well with increases in the number of mobile devices.

Ocular diseases include various anterior and posterior segment diseases. Due to the unique anatomy and physiology of the eye, efficient ocular drug delivery is a great challenge to researchers and pharmacologists. Although there are conventional noninvasive and invasive treatments, such as eye drops, injections and implants, the current treatments either suffer from low bioavailability or severe adverse ocular effects. Alternatively, the emerging nanoscience and nanotechnology are playing an important role in the development of novel strategies for ocular disease therapy. Various active molecules have been designed to associate with nanocarriers to overcome ocular barriers and intimately interact with specific ocular tissues. In this review, we highlight the recent attempts of nanotechnology-based systems for imaging and treating ocular diseases, such as corneal d iseases, glaucoma, retina diseases, and choroid diseases. Although additional work remains, the progress described herein may pave the way to new, highly effective and important ocular nanomedicines.

The collective decision optimization algorithm (CDOA) is a new stochastic population-based evolutionary algorithm which simulates the decision behavior of human. In this paper, a multiobjective collective decision optimization algorithm (MOCDOA) is first proposed to solve the environmental/economic dispatch (EED) problem. MOCDOA uses three novel learning strategies, that is, a leader-updating strategy based on the maximum distance of each solution in an external archive, a wise random perturbation strategy based on the sparse mark around a leader, and a geometric center-updating strategy based on an extreme point. The proposed three learning strategies benefit the improvement of the uniformity and the diversity of Pareto optimal solutions. Several experiments have been carried out on the IEEE 30-bus 6-unit test system and 10-unit test system to investigate the performance of MOCDOA. In terms of extreme solutions, compromise solution, and three metrics (SP, HV, and CM), MOCDOA is compared with other existing multiobjective optimization algorithms. It is demonstrated that MOCDOA can generate the well-distributed and the high-quality Pareto optimal solutions for the EED problem and has the potential to solve the multiobjective optimization problems of other power systems.

Differential SAR Interferometry (DInSAR) has proven its unprecedented ability and merits of monitoring ground deformation on a large scale with centimeter to millimeter accuracy. However, atmospheric artifacts due to spatial and temporal variations of the atmospheric state often affect the reliability and accuracy of its results. The commonly-known Atmospheric Phase Screen (APS) appears in the interferograms as ghost fringes not related to either topography or deformation. Atmospheric artifact mitigation remains one of the biggest challenges to be addressed within the DInSAR community. State-of-the-art research works have revealed that atmospheric artifacts can be partially compensated with empirical models, point-wise GPS zenith path delay, and numerical weather prediction models. In this study, we implement an accurate and realistic computing strategy using atmospheric reanalysis ERA5 data to estimate atmospheric artifacts. With this approach, the Line-of-Sight (LOS) path along the satellite trajectory and the monitored points is considered, rather than estimating it from the zenith path delay. Compared with the zenith delay-based method, the key advantage is that it can avoid errors caused by any anisotropic atmospheric phenomena. The accurate method is validated with Sentinel-1 data in three different test sites: Tenerife island (Spain), Almería (Spain), and Crete island (Greece). The effectiveness and performance of the method to remove APS from interferograms is evaluated in the three test sites showing a great improvement with respect to the zenith-based approach.

The differential evolution algorithm (DE) is one of the most powerful stochastic real-parameter optimization algorithms. The theoretical studies on DE have gradually attracted the attention of more and more researchers. However, few theoretical researches have been done to deal with the convergence conditions for DE. In this paper, a sufficient condition and a corollary for the convergence of DE to the global optima are derived by using the infinite product. A DE algorithm framework satisfying the convergence conditions is then established. It is also proved that the two common mutation operators satisfy the algorithm framework. Numerical experiments are conducted on two parts. One aims to visualize the process that five convergent DE based on the classical DE algorithms escape from a local optimal set on two low dimensional functions. The other tests the performance of a modified DE algorithm inspired of the convergent algorithm framework on the benchmarks of the CEC2005.

Germline deletion of Jak2 in mice results in embryonic lethality at E12.5 due to impaired hematopoiesis. However, the role that Jak2 might play in late gestation and postnatal life is unknown. To understand this, we utilized a conditional knockout approach that allowed for the deletion of Jak2 at various stages of prenatal and postnatal life. Specifically, Jak2 was deleted beginning at either mid/late gestation (E12.5), at postnatal day 4 (PN4), or at ∼2 months of age. Deletion of Jak2 beginning at E12.5 resulted in embryonic death characterized by a lack of hematopoiesis. Deletion beginning at PN4 was also lethal due to a lack of erythropoiesis. Deletion of Jak2 in young adults was characterized by blood cytopenias, abnormal erythrocyte morphology, decreased marrow hematopoietic potential, and splenic atrophy. However, death was observed in only 20% of the mutants. Further analysis of these mice suggested that the increased survivability was due to an incomplete deletion of Jak2 and subsequent re-population of Jak2 expressing cells, as conditional deletion in mice having one floxed Jak2 allele and one null allele resulted in a more severe phenotype and subsequent death of all animals. We found that the deletion of Jak2 in the young adults had a differential effect on hematopoietic lineages; specifically, conditional Jak2 deletion in young adults severely impaired erythropoiesis and thrombopoiesis, modestly affected granulopoiesis and monocytopoiesis, and had no effect on lymphopoiesis. Interestingly, while the hematopoietic organs of these mutant animals were severely affected by the deletion of Jak2, we found that the hearts, kidneys, lungs, and brains of these same mice were histologically normal. From this, we conclude that Jak2 plays an essential and non-redundant role in hematopoiesis during both prenatal and postnatal life and this has direct implications regarding the inhibition of Jak2 in humans.

The relationship between crystal structure and lattice thermal conductivity (LTC) is of great importance for material development. Herein, we reveal the substantial effect of cation inversion on the LTC of spinels. Although the theoretical LTC of a normal cubic-phased spinel Co2MnO4 is much higher than that of the tetragonal-phased spinel CoMn2O4, we experimentally observe a significantly lower thermal conductivity in the prepared Co2MnO4 samples than that of CoMn2O4 samples. The abnormally low thermal conductivity results from the cation inversion in the prepared Co2MnO4 samples. Further theoretical calculations indicate that the cation inversion of Co2MnO4 triggers an about 80% LTC decrease from 26.56 W m−1 K−1 to 4.14 W m−1 K−1 at 300 K. We further reveal that the LTC has a negative relation with the degree of cation inversion, which is also validated for other spinels such as NiCo2O4 and NiFe2O4. Furthermore, we found that the thermal conductivity of Co–Mn–O spinels could be decreased ulteriorly by cation occupation disorder in CoxMn3−xO4, and the lowest thermal conductivity was observed for Co1.8Mn1.2O4. The results provide an advanced understanding of the structure–performance relationship between cation occupation and LTC in spinel materials and demonstrate a potential method for LTC regulation. [ABSTRACT FROM AUTHOR]