bluuebunny/crossref_metadata_2025_split huggingface dataset:
New update to the guidelines on testing predictive biomarkers in non-small-cell lung cancer: a National Consensus of the Spanish Society of Pathology and the Spanish Society of Medical Oncology
Authors: Isla Dolores , Lozano Maria D. , Paz-Ares Luis , Salas Clara , de Castro Javier , Conde Esther , Felip Enriqueta , Gómez-Román Javier , Garrido Pilar , Enguita Ana Belén 
Published: December 2022

AbstractNon-small cell lung cancer (NSCLC) presents the greatest number of identified therapeutic targets, some of which have therapeutic utility. Currently, detecting EGFR, BRAF, KRAS and MET mutations, ALK, ROS1, NTRK and RET translocations, and PD-L1 expression in these patients is considered essential. The use of next-generation sequencing facilitates precise molecular diagnosis and allows the detection of other emerging mutations, such as the HER2 mutation and predictive biomarkers for immunotherapy responses. In this consensus, a group of experts in the diagnosis and treatment of NSCLC selected by the Spanish Society of Pathology and the Spanish Society of Medical Oncology have evaluated currently available information and propose a series of recommendations to optimize the detection and use of biomarkers in daily clinical practice.
WDR62-deficiency Causes Autism-like Behaviors Independent of Microcephaly in Mice
Authors: Xu Dan , Zhi Yiqiang , Liu Xinyi , Guan Le , Yu Jurui , Zhang Dan , Zhang Weiya , Wang Yaqing , Tao Wucheng , Xu Zhiheng 
Published: December 2022

AbstractBrain size abnormality is correlated with an increased frequency of autism spectrum disorder (ASD) in offspring. Genetic analysis indicates that heterozygous mutations of the WD repeat domain 62 (WDR62) are associated with ASD. However, biological evidence is still lacking. Our study showed that Wdr62 knockout (KO) led to reduced brain size with impaired learning and memory, as well as ASD-like behaviors in mice. Interestingly, Wdr62 Nex-cKO mice (depletion of WDR62 in differentiated neurons) had a largely normal brain size but with aberrant social interactions and repetitive behaviors. WDR62 regulated dendritic spinogenesis and excitatory synaptic transmission in cortical pyramidal neurons. Finally, we revealed that retinoic acid gavages significantly alleviated ASD-like behaviors in mice with WDR62 haploinsufficiency, probably by complementing the expression of ASD and synapse-related genes. Our findings provide a new perspective on the relationship between the microcephaly gene WDR62 and ASD etiology that will benefit clinical diagnosis and intervention of ASD.
An investigation of self-regulated learning in a novel MOOC platform
Authors: Onah Daniel F. O. , Pang Elaine L. L. , Sinclair Jane E. 
Published: December 2022

AbstractDespite the proliferation of massive open online courses (MOOCs) and the impressive levels of enrolment they attract, many participants do not complete these courses. High drop-out has been identified as one of the major problems with existing MOOC formats. Our work addresses two factors relating to non-completion. Firstly, MOOCs require a high degree of self-regulated learning (SRL) skills but most do not adequately develop such skills, thus making them inaccessible in practice to many. Related to this is the inflexibility and passivity of many current MOOC formats, preventing individuals from setting their own learning objectives and directing their own learning. This paper presents preliminary findings from an investigation into MOOC learners’ SRL skills and the relationship to how participants learn. Following a design science methodology, we have developed a novel MOOC platform to support learner choice and to assist participants in defining learning goals and developing individual study paths. This paper describes the architecture of the system and presents findings from a pilot MOOC developed on the platform. Our results indicate that there is a high demand for more flexible, self-directed learning but that MOOC learners exhibit deficiencies in specific SRL skills including help seeking and task strategies. The contextualised nature of SRL skills means that even learners with a strong background of formal education may not deploy the best strategies for MOOC learning. This work is of significance to MOOC development in general as it highlights the need for targeted strategies to encourage SRL in MOOC platforms and innovation.
Increases in External Sensory Observing Cross-Sectionally Mediate the Repair of Positive Affect Following Mindfulness-Based Cognitive Therapy in Individuals with Residual Depression Symptoms
Authors: Dunn Barnaby D. , Wiedemann Hanna , Kock Merle , Peeters Frenk , Wichers Marieke , Hayes Rachel , Kuyken Willem , Geschwind Nicole 
Published: December 2022

Abstract
Objectives

Mindfulness-Based Cognitive Therapy for Depression (MBCT-D) has been shown to be effective at repairing positive affect deficits in depressed individuals, but the mechanism of action underpinning these changes has not been empirically examined. To address this issue, secondary analyses of two randomised controlled trials in individuals with residual depression were conducted.

Method
Study 1 was a cross-sectional mediation analysis of a trial reporting superior effects of MBCT-D to a waitlist control in bolstering momentary positive affect in individuals with residual depression symptoms (n = 130). Study 2 replicated this analysis in the subset of individuals with residual depression symptoms (n = 117) from a second, larger trial comparing MBCT-D to maintenance antidepressants (M-ADM) to prevent depressive relapse that also included a positive affect outcome.

Results
In Study 1, an increase in external sensory observing uniquely mediated the superiority of MBCT-D over the control in repairing momentary positive affect. Replicating these findings, in Study 2, MBCT-D was superior to M-ADM at repairing positive affect and this was cross-sectionally mediated by changes in external sensory observing.

Conclusions
These findings suggest that one way in which MBCT-D enhances positive affect in individuals with residual depression may be by training the capacity to attend to external sensory experience.
A resimulation framework for event loss tables based on clustering
Authors: Funke Benedikt , Roering Harmen 
Published: December 2022

AbstractCatastrophe loss modeling has enormous relevance for various insurance companies due to the huge loss potential. In practice, geophysical-meteorological models are widely used to model these risks. These models are based on the simulation of meteorological and physical parameters that cause natural events and evaluate the corresponding effects on the insured exposure of a certain company. Due to their complexity, these models are often operated by external providers—at least seen from the perspective of a variety of insurance companies. The outputs of these models can be made available, for example, in the form of event loss tables, which contain different statistical characteristics of the simulated events and their caused losses relative to the exposure. The integration of these outputs into the internal risk model framework is fundamental for a consistent treatment of risks within the companies. The main subject of this work is the formulation of a performant resimulation algorithm of given event loss tables, which can be used for this integration task. The newly stated algorithm is based on cluster analysis techniques and represents a time-efficient way to perform sensitivities and scenario analyses.
Effective removal of hexavalent chromium from water by sustainable nano-scaled waste avocado seeds: adsorption isotherm, thermodynamics, kinetics, and error function
Authors: Mahmoud Mohamed E. , El-Said Ghada F. , Ibrahim Ghada A. A. , Elnashar Almaza A. S. 
Published: December 2022

Abstract
In the current investigation, an efficient low-cost nano-scaled material was prepared from waste avocado seeds (NASs) and employed as a sustainable nanosorbent for removal of hexavalent chromium ions. The adsorption and surface characteristics with respect to the availability of functional groups, basic component elements, and structural properties of NASs were achieved by different techniques. Analysis of NASs referred to the presence of carbon and oxygen contents providing 97.21%. The NASs particles were homogenously identified and ranged between 18.68 and 19.05 nm. The impact of various affecting adsorption parameters on hexavalent chromium ions removal was elaborated and optimized at pH 2.0, NASs dose 10 mg, contact time 30 min, and temperature 25 °C by the batch adsorption assay. Fifteen linear and nonlinear modeling tests were applied to evaluate the adsorption isotherms and arranged in the following fitting order: Tempkin < BET < Dubinin-Radushkevich < Generalized < Halsey ˂ Flory–Huggins < Freundlich < Harkins–Jura ˂ Langmuir for linear formulas and Weber-van Vliet < Langmuir < Generalized < Radke–Prausnitz (II) < Radke–Prausnitz (III) < Fritz-Schlunder IV < Baudu < Multilayer < Fritz-Schlunder V < Toth for nonlinear formulas with 2 to 5 parameters. Linear and nonlinear pseudo-second-order models, intraparticle diffusion, and Elovich models were also scrutinized, and both pore diffusion and film were similarly involved in the rate-limiting step. The values of ΔS° (0.21 kJ/mol), ΔH° (− 41.02 kJ/mol) and ΔG° (− 28.30 to − 19.97 kJ/mol) at high concentration of hexavalent chromium (50 mg L−1) and a temperature range of 20–60 °C reflected the disordering, spontaneously exothermic adsorption process. The activation energy (Ea) was also confirmed to include the diffusion process at the activation energy in the rate-limiting step. The potential application of NASs in hexavalent chromium removal from wastewater, seawater, and tap water as real aqueous matrices referred to excellent percentage removal values in the range from 98.44 to 98.86%. Based on the outlined results, one can deduce that excellent performance and efficiency for removal of high hexavalent chromium concentration at high temperature, low nanosorbent mass, and low pH from different aqueous matrices were accomplished by the evaluated NAS nanosorbent. Accordingly, the investigated NAS nanosorbent is recommended to apply in high stress industrial and wastewater treatment plants.
Multi-feature contrastive learning for unpaired image-to-image translation
Authors: Gou Yao , Li Min , Song Yu , He Yujie , Wang Litao 
Published: December 2022

AbstractUnpaired image-to-image translation for the generation field has made much progress recently. However, these methods suffer from mode collapse because of the overfitting of the discriminator. To this end, we propose a straightforward method to construct a contrastive loss using the feature information of the discriminator output layer, which is named multi-feature contrastive learning (MCL). Our proposed method enhances the performance of the discriminator and solves the problem of model collapse by further leveraging contrastive learning. We perform extensive experiments on several open challenge datasets. Our method achieves state-of-the-art results compared with current methods. Finally, a series of ablation studies proved that our approach has better stability. In addition, our proposed method is also practical for single image translation tasks. Code is available at https://github.com/gouayao/MCL.
A hybrid differential evolution algorithm for a location-inventory problem in a closed-loop supply chain with product recovery
Authors: Guo Hao , Liu Gang , Zhang Ying , Zhang Chunnan , Xiong Chuanhui , Li Wenli 
Published: December 2022

AbstractProduct recovery is an important business because of its great economic, social, and environmental benefits in practice. In this paper, a location-inventory problem (LIP) in a closed-loop supply chain (CLSC) is investigated to optimize facility location and inventory control decisions by considering product recovery. The objective is to optimize facility location and inventory control decisions to minimize the total cost of business operations in a closed-loop supply chain system. We formulate this problem as a mixed-integer nonlinear programming model and design a modified hybrid differential evolution algorithm (MHDE) to solve it efficiently. Finally, numerical results are presented to validate the performance of the new algorithm. The results show that MHDE is more efficient and effective than Lingo and other algorithms for the research problem under study. Managerial insights are also derived for business managers to improve their supply chain performance.
Analysis of Hamming and Hausdorff 3D distance measures for complex pythagorean fuzzy sets and their applications in pattern recognition and medical diagnosis
Authors: Wu Dong-Lun , Zhu Zhiyi , Ullah Kifayat , Liu Lantian , Wu Xinxing , Zhang Xu 
Published: December 2022

AbstractSimilarity measures are very effective and meaningful tool used for evaluating the closeness between any two attributes which are very important and valuable to manage awkward and complex information in real-life problems. Therefore, for better handing of fuzzy information in real life, Ullah et al. (Complex Intell Syst 6(1): 15–27, 2020) recently introduced the concept of complex Pythagorean fuzzy set (CPyFS) and also described valuable and dominant measures, called various types of distance measures (DisMs) based on CPyFSs. The theory of CPyFS is the essential modification of Pythagorean fuzzy set to handle awkward and complicated in real-life problems. Keeping the advantages of the CPyFS, in this paper, we first construct an example to illustrate that a DisM proposed by Ullah et al. does not satisfy the axiomatic definition of complex Pythagorean fuzzy DisM. Then, combining the 3D Hamming distance with the Hausdorff distance, we propose a new DisM for CPyFSs, which is proved to satisfy the axiomatic definition of complex Pythagorean fuzzy DisM. Moreover, similarly to some DisMs for intuitionistic fuzzy sets, we present some other new complex Pythagorean fuzzy DisMs. Finally, we apply our proposed DisMs to a building material recognition problem and a medical diagnosis problem to illustrate the effectiveness of our DisMs. Finally, we aim to compare the proposed work with some existing measures is to enhance the worth of the derived measures.
Resonance algorithm: an intuitive algorithm to find all shortest paths between two nodes
Authors: Liu Yu , Lin Qiguang , Hong Binbin , Peng Yunru , Hjerpe Daniel , Liu Xiaofeng 
Published: December 2022

AbstractThe shortest path problem (SPP) is a classic problem and appears in a wide range of applications. Although a variety of algorithms already exist, new advances are still being made, mainly tuned for particular scenarios to have better performances. As a result, they become more and more technically complex and sophisticated. In this paper, we developed an intuitive and nature-inspired algorithm to compute all possible shortest paths between two nodes in a graph: Resonance Algorithm (RA). It can handle any undirected, directed, or mixed graphs, irrespective of loops, unweighted or positively weighted edges, and can be implemented in a fully decentralized manner. Although the original motivation for RA is not the speed per se, in certain scenarios (when sophisticated matrix operations can be employed, and when the map is very large and all possible shortest paths are demanded), it out-competes Dijkstra’s algorithm, which suggests that in those scenarios, RA could also be practically useful.
Can financial stress be anticipated and explained? Uncovering the hidden pattern using EEMD-LSTM, EEMD-prophet, and XAI methodologies
Authors: Ghosh Indranil , Dragan Pamucar 
Published: December 2022

AbstractGlobal financial stress is a critical variable that reflects the ongoing state of several key macroeconomic indicators and financial markets. Predictive analytics of financial stress, nevertheless, has seen very little focus in literature as of now. Futuristic movements of stress in markets can be anticipated if the same can be predicted with a satisfactory level of precision. The current research resorts to two granular hybrid predictive frameworks to discover the inherent pattern of financial stress across several critical variables and geography. The predictive structure utilizes the Ensemble Empirical Mode Decomposition (EEMD) for granular time series decomposition. The Long Short-Term Memory Network (LSTM) and Facebook’s Prophet algorithms are invoked on top of the decomposed components to scrupulously investigate the predictability of final stress variables regulated by the Office of Financial Research (OFR). A rigorous feature screening using the Boruta methodology has been utilized too. The findings of predictive exercises reveal that financial stress across assets and continents can be predicted accurately in short and long-run horizons even at the time of steep financial distress during the COVID-19 pandemic. The frameworks appear to be statistically significant at the expense of model interpretation. To resolve the issue, dedicated Explainable Artificial Intelligence (XAI) methods have been used to interpret the same. The immediate past information of financial stress indicators largely explains patterns in the long run, while short-run fluctuations can be tracked by closely monitoring several technical indicators.
Mitigation of laser plasma parametric instabilities with broadband lasers
Authors: Zhao Yao , Weng Su-Ming , Ma Hang-Hang , Bai Xiao-Jun , Sheng Zheng-Ming 
Published: December 2022

AbstractLaser plasma instabilities (LPIs) cause laser energy loss, asymmetric and insufficient compression, and target preheating, thus are assumed to be among the major concerns of inertial confinement fusion research. Mitigation of LPIs can enhance the laser–target coupling efficiency and optimize the target compression dynamics, which is critical for the realization of robust and high-efficiency fusion ignition. Broadband lasers with polychromatic components or random phases have been investigated for decades as an effective alternative to mitigate LPIs. Here, we present a brief overview on the progress of broadband LPIs, including the models of broadband lasers, the involved physics, the conditions for effective suppression of LPIs, and some schemes to produce broadband lasers.
Kinetic Alfven wave (KAW) eigenmode in magnetosphere magnetic reconnection
Authors: Dai Lei , Wang Chi 
Published: December 2022

AbstractOne prominent signature of collisionless magnetic reconnection in the magnetosphere is the Hall effect. In the ion-scale region of reconnection, ions cannot follow the magnetic field line whereas electrons are still magnetized. The difference in the motions of electrons and ions leads to charge separation, the resulting transverse Hall electric field, the field-aligned streaming of electrons to preserve charge neutrality, and the corresponding Hall magnetic field. Such physics has long been invoked in the discovery of kinetic Alfven waves (KAW) when the perpendicular wavelength becomes comparable to the ion scale. In this review, we present recent progresses in theoretical modeling, numerical simulations and in situ observations that analyze Hall effects in terms of KAW eigenmode. This review will also cover analysis of the impact of KAW eigenmode on the ion acceleration and energy conversion in reconnection. We emphasize that the KAW perspective can provide a unified description of various phenomena in magnetic reconnection, including Hall magnetic fields, Hall electric fields, the field-aligned current, the electric field parallel to B, the non-gyrotropy velocity distribution of ions, and a close connection between Hall fields and a fast reconnection rate ($$\sim$$
∼
0.1). The quantitative description of these aspects is necessary for a Petschek-type model in the era of collisionless reconnection.
RETRACTED ARTICLE: Immersive animation scene design in animation language under virtual reality
Authors: Xu Chao 
Published: December 2022

AbstractThe study aims to explore a new form of immersive animation scene design and improve the application effect of virtual reality (VR) in immersive animation scene design. First, the research status of VR and the related concepts of animation scene design are analyzed, and the basic characteristics of visual effects in immersive VR animation scenes are summarized. Second, three elements to create immersion in a VR animation scene are color, light and shadow, and digital information technology. Finally, the application of VR technology to an animation scene design is analyzed, the problem of vertigo caused by VR animation scenes is deeply discussed, the vertigo test process in an immersive VR scene is proposed, and an experiment is designed to verify it. The results show that in the design of an immersive VR scene, the relative moving speed of the forward straight line should be maintained less than 6.5 times of the audience’s perceived speed, and the optimal relative moving speed of the scene should be maintained below 5 times of the audience’s perceived speed when they moving backward. In the design of a rotating animation scene, the long-term high-speed rotation should be avoided. If there must be a rotating scene, the rotation speed should be as low as 50°/s. The research provides some guidance for the application of VR to immersive animation scene design.

This study discusses immersive animation scene design from dizziness prospective. It is novel.

This study introduces relative motion perception illusion and use displacement velocity and relative displacement to analyze VR vertigo.

Len motion is divided into forward and reverse linear, horizontal and vertical rotation and camera center rotation to study vertigo source.

The dizziness evaluation model is designed and combined with the virtual indoor scene developed to achieve a visual immersion experience.
Effects of ultrasonic waves on seedling growth, biochemical constituents, genetic stability of fenugreek (Trigonella foenum-graecum) under salinity stress
Authors: El-Sattar Amira Mohamed Abd , Tawfik Eman 
Published: December 2022

AbstractFenugreek is a globally important legume that is widely cultivated for its therapeutic benefits in most parts of the world. Seeds on the other hand have a poor germination and growth rate when exposed to salinity. The effect of ultrasonic exposure period on germination and early seedling behaviors of fenugreek seeds under salt stress was investigated in a laboratory experiment. During germination and early seedling stages, all tests were conducted at 40 kHz in a water bath ultrasonic device with two durations (10 and 20 min) under salinity stress using different concentrations of NaCl (0, 1000, 3000, and 5000 mg/l). The results revealed a substantial decrease in germination percentage, all growth criteria, with increasing NaCl concentration and a significant increase in biomass produced by the Fenugreek (total soluble protein, total soluble carbohydrate, and proline), all of which are thought to be mechanisms for salinity resistance. Ultrasonication of fenugreek seeds for 10 and 20 min has a significant impact on seed germination, early seedling development and biochemical constituents under normal and stress conditions. The genetic stability of fenugreek DNA content was affected by these different treatments. This variation was estimated by RAPD-PCR molecular marker, and resulted in a total polymorphism percentage of 49.72% from all the primers. All these different treatments caused variation in the physiological responses and DNA content. This variation enhanced with more ultrasonic and salt treatments. Hence, these stresses can be used for enhancing the variable metabolic processes in fenugreek plant and stimulate its medicinal properties.