Search

Abstract The cell membrane transport capacity and surface targets of multiple myeloma (MM) cells heavily influence chemotherapy and immunotherapy. Here, it is found that caveolin‐1 (CAV1), a primary component of membrane lipid rafts and caveolae, is highly expressed in MM cells and is associated with MM progression and drug resistance. CAV1 knockdown decreases MM cell adhesion to stromal cells and attenuates cell adhesion‐mediated drug resistance to bortezomib. CAV1 inhibition in MM cells enhances natural killer cell‐mediated cytotoxicity through increasing CXCL10, SLAMF7, and CD112. CAV1 suppression reduces mitochondrial membrane potential, increases reactive oxygen species, and inhibits autophagosome‐lysosome fusion, resulting in the disruption of redox homeostasis. Additionally, CAV1 knockdown enhances glutamine addiction by increasing ASCT2 and LAT1 and dysregulates glutathione metabolism. As a result of CAV1 inhibition, MM cells are more sensitive to starvation, glutamine depletion, and glutamine transporter inhibition, and grow more slowly in vivo in a mouse model treated with bortezomib. The observation that CAV1 inhibition modulated by 6‐mercaptopurine, daidzin, and statins enhances the efficacy of bortezomib in vitro and in vivo highlights the translational significance of these FDA‐approved drugs in improving MM outcomes. These data demonstrate that CAV1 serves as a potent therapeutic target for enhancing chemotherapy and immunotherapy for MM.

This research presents and confirms an intermediary model, deeply anchored in self-determination theory, to dissect the influence of Chinese high school students’ core psychological needs and intrinsic drive on the nexus between educators’ autonomous backing and students’ proficiency in feedback literacy, highlighting the mediating roles of these elements. A survey of 704 Chinese senior high school students, including 319 males and 385 females, employed the Feedback Literacy Scale, Basic Psychological Needs Scale, Intrinsic Motivation Scale, and Perceived Teacher Autonomy Support Scale. The study’s discoveries illuminate that educators’ autonomous support not only directly amplifies students’ feedback literacy but also has an indirect impact through the intermediation of basic psychological needs and intrinsic motivation, along with their interconnected dynamics. This inquiry not only deepens our grasp of the mechanisms interlinking teacher support with feedback literacy but also critically evaluates the findings to proffer targeted recommendations, thereby enhancing our comprehension of the underlying processes and guiding educational practices and student development.

In the realm of classroom assessment, the shift towards “assessment as learning” underscores the pivotal role of students in developing assessment literacy. This study aims to bridge the gap in the literature by examining the understudied area of students' assessment literacy. The author introduces a novel chain mediation effect structure model to investigate the interactions between teacher autonomy support, self-efficacy, critical reflection, and students' assessment literacy. The sample comprises 704 high school students from central China, with an even gender distribution (321 boys and 383 girls), which provides a robust dataset for analysis. By utilizing the Student Evaluative Scale, Critical Reflection Scale, and Self-Efficacy Scale, the study reveals a significant and positive correlation between teacher autonomy support and the level of assessment literacy among students. Furthermore, the findings indicate that self-efficacy and critical reflection act as mediators in the relationship between teacher autonomy support and students' assessment literacy. This suggests that educators who foster an environment of autonomy, empowering students to take ownership of their learning, can significantly enhance students' belief in their own abilities (self-efficacy) and their capacity for reflective thinking, thereby improving students’ assessment literacy. The study concludes with concrete suggestions for classroom practice and identifies avenues for future research to further refine our understanding of the complex interplay between teacher support, student self-efficacy, critical reflection, and assessment literacy.

Early screening for Mild Cognitive Impairment (MCI) is crucial in delaying cognitive deterioration and treating dementia. Conventional neuropsychological tests, commonly used for MCI detection, often lack ecological validity due to their simplistic and quiet testing environments. To address this gap, our study developed an immersive VR supermarket cognitive assessment program (IVRSCAP), simulating daily cognitive activities to enhance the ecological validity of MCI detection. This program involved elderly participants from Chengdu Second People’s Hospital and various communities, comprising both MCI patients (N=301) and healthy elderly individuals (N=1027). They engaged in the VR supermarket cognitive test, generating complex datasets including User Behavior Data, Tested Cognitive Dimension Game Data, Trajectory Data, and Regional Data. To analyze this data, we introduced an adaptive ensemble learning method for imbalanced samples. Our study’s primary contribution is demonstrating the superior performance of this algorithm in classifying MCI and healthy groups based on their performance in IVRSCAP. Comparative analysis confirmed its efficacy over traditional imbalanced sample processing methods and classic ensemble learning voting algorithms, significantly outperforming in metrics such as recall, F1-score, AUC, and G-mean. Our findings advocate the combined use of IVRSCAP and our algorithm as a technologically advanced, ecologically valid approach for enhancing early MCI detection strategies. This aligns with our broader aim of integrating realistic simulations with advanced computational techniques to improve diagnostic accuracy and treatment efficacy in cognitive health assessments.

The preparation of new solder joint materials for chip packaging by designing a variety of brazing material mixes has become one of the future research directions for solder joints. They added low melting point elements to traditional solder materials to prepare low-temperature hybrid solder to meet the low-temperature packaging trend. To improve the performance of low-temperature solder, adding some alloying elements can refine the grain, change the IMC morphology and size, and improve the joint strength to a certain extent. The design of the hybrid solder joint system needs to be based on different metal pads. There are various failure modes of interconnected solder joints on different metal pads. Therefore, the interfacial response, microstructure, and mechanical properties between different hybrid solder and different metal pads need to be studied to optimize the design of the hybrid solder joint system. This paper investigated hybrid low-temperature Sn–3Ag-0.5Cu/Sn–58Bi solder joints on Au/Ni/Cu (ENIG) and Au/Pd/Ni/Cu (ENEPIG) substrates. The addition of Pd elements can change the IMC morphology of hybrid solder joints and affect the fracture failure mode of the joints under shear, with a higher average shear strength at different process parameters than ENIG welded joints. Finally, the presence form of Pd elements in the solder joints was observed under TEM and the fracture mechanism was analyzed. The results showed that Pd atoms on ENEPIG pads can replace some Cu atoms to form (Cu, Pd)6Sn5 intermetallic compounds, distorting the lattice stripe to produce distortions. And compared to ENIG pads, the IMC morphology is pin-rod-like and finer, with more complex grain boundary paths between the grains of the hybrid solder.

Unforeseen additional costs are major sources of cost overruns for the UK's highway projects. General contractors normally allocate cost contingencies in their tender prices to cover these costs, primarily based on the judgements of their cost estimators. However, cost contingencies allocated to the same risk by different cost estimators can vary significantly. Although objective factors influencing the estimation of cost contingencies have been extensively investigated, subjective factors remain underexplored. Therefore, this paper aims to address this gap by investigating the subjective factors influencing cost estimators' judgements through a qualitative study. Twelve semi-structured interviews were conducted with experienced cost estimators working for general contractors in the UK's construction industry. A follow-up focus group study was also conducted with seven experienced cost estimators in the UK's construction industry to validate the interview results. The findings reveal that there are thirteen influencing factors affecting cost estimators' judgements on cost contingencies in highway construction projects. Seven of them, namely cost estimators' ability to effectively communicate with stakeholders, self-reflection on previous practice, open-mindedness to project uniqueness, previous project experiences, risk attitude, assumption of the controllability of risks, and perceived quality of tender documents were identified as critical ones. They were grouped into two categories, including expertise-related factors and personal factors. The findings contribute to the lack of research on the subjective factors influencing cost estimators' judgement-making process in highway construction projects in developed countries. The results also provide new practical insights for general contractors to develop more focused strategies and training programs to enhance the reliability of cost estimators' judgements on cost contingencies, which can effectively reduce the probability of cost overruns.

Target detection for aerial images has been the focus of research. However, there are several difficulties in aerial image detection, such as complex backgrounds, high resolution, and a large number of small targets in UAV (Unmanned Aerial Vehicles) aerial images. In order to achieve high-precision detection of ground targets, a lightweight aerial image target detection algorithm LATD-YOLO (Lightweight Aerial Image Target Detector) based on YOLOv7 is proposed in this paper. Firstly, a new network structure dedicated to small target detection is proposed, and the feature extraction network and feature fusion network architectures are both lightweight. The fusion relationship between shallow and deep features is reconstructed. Then, the ELAN-OD module is proposed to reduce the model computation and strengthen the feature extraction ability of the network. In addition, the hybrid attention mechanism is added to the structure of the feature extraction network, where the effective information is extracted to enhance the learning ability. Finally, a new anchor frame position metric is introduced to improve the model’s ability to handle small targets. The experimental results show that LATD-YOLO can effectively improve the detection effect. The detection accuracy is improved by 359.6% on the ITCVD and 3.6% on the VisDrone2019 datasets respectively; the volume of the model is decreased by 67.74%; the amount of parameters is reduced by 67.91%; the amount of computation is decreased by 30.92%. Therefore, LATD-YOLO can achieve excellent detection performance in high-precision and lightweight computation scenarios.

With the increase in wind power generation, wind turbine blades require regular inspections to ensure they continue to operate safely. You only look once (YOLO) is one of the most widely used object detection algorithms and is easy to deploy into drone devices. To enhance the real-time detection of small target defects in wind turbine blades, this paper proposes an improved attention and feature balanced YOLO (AFB-YOLO) algorithm based on YOLOv5s. Specifically, AFB-YOLO improves the feature pyramid network by using weighted feature fusion and cross-scale connections. The improved feature pyramid network solves the problem that most previous feature pyramid networks treat all input features equally, and obtains more feature information. Furthermore, the coordinate attention (CA) module is introduced into the network to augment the representations of the objects of interest. Finally, the paper redesigned the loss function through efficient intersection over union (EIoU) loss to make the model obtain a better localization effect. The experimental results on the imagery of wind turbine blade defects indicate that our method shows significant gains in performance. The mean average precision (mAP50) of AFB-YOLO is 83.7% and the detection accuracy is improved by 4.0% compared to the original YOLOv5s model. The experiments in this paper demonstrate that AFB-YOLO is more effective and robust than state-of-the-art detectors.

Abstract A compact high‐sensitivity probe for sensing up tangential E‐field is proposed and measured in this paper. The probe consists of an electric dipole with patches used to induce the tangential E‐field, a resonator made up of open circuit stubs (capacitor) and short circuit stubs (inductor), for achieving a specific resonance, and a compact coupled balun for transforming the differential‐mode voltage into the single‐output voltage and impedance matching. The probe is fabricated on five‐layer dielectric lamina. Compared with the referenced broadband probe, the measured |S12| of the probe is enhanced by 18.81 dB. The proposed probe in this paper is validated with simulation and measurement.

This paper addresses the current imbalance in construction social procurement research toward Western countries with neo-liberal models of public governance. It does this by exploring the potential value of construction social procurement in the Chinese centralized unitary state and socialist market system. Findings from a survey of one hundred and sixty-four professionals from the Chinese construction industry are reported. They highlight the institutional foundations into which social procurement could be implemented and the significant untapped social value that could be created. However, they also show that for social procurement to achieve its full potential in the Chinese construction industry, such policies need to be underpinned by meaningful industry consultation, effective education and clearly mandated targets that create a market for social value. This paper contributes to the global advancement of social procurement research in construction by providing new insights into the implementation of social procurement beyond the narrow confines of the Western political and governmental orthodoxies where such research has hitherto been focused.

Allogeneic amnion tissues have been widely used in tissue repair and regeneration, especially a remarkable trend of clinical uses in chronic wound repair. The virus inactivation procedures are necessary and required to be verified for the clinical use and approval of biological products. Cobalt-60 (Co-60) or electron-beam (e-beam) is the common procedure for virus and bacterial reduction, but the excessive dose of irradiation was reported to be harmful to biological products. Herein, we present a riboflavin (RB)-ultraviolet light (UV) method for virus inactivation of amnion and chorion tissues. We used the standard in vitro limiting dilution assay to test the viral reduction capacity of the RB-UV method on amnion or chorion tissues loaded with four types of model viruses. We found RB-UV was a very effective procedure for inactivating viruses of amnion and chorion tissues, which could be used as a complementary method to Co-60 irradiation. In addition, we also screened the washing solutions and drying methods for the retention of growth factors.

The inertia response and primary frequency regulation capability of synchronous grids are declining owing to the increasing penetration of inverter-based resources. The fast frequency response (FFR) of inverter-based resources is an important mitigation option for maintaining grid security under the conditions of low inertia and insufficient primary frequency response capability. However, the understanding and technical characteristics of the FFR of inverter-based resources are still unclear. Aiming at solving the aforementioned problems, this paper proposes a definition for FFR based on the impact mechanism of FFR on system frequency. The performance requirements of FFR are clarified. Then, the effects of FFR on system frequency characteristics are further analyzed based on steady-state frequency deviation, the initial rate of change of frequency, and the maximum transient frequency deviation. Finally, the system requirements for FFR and its application effects are verified by simulating an actual bulk power grid, providing technical support for subsequent engineering application.

Prestress in amorphous solids bears the memory of their formation and plays a profound role in their mechanical properties. Here we develop a set of mathematical tools to investigate mechanical response of prestressed systems, using stress rather than strain as the fundamental variable. This theory allows microscopic prestress to vary for the same bond or contact configuration and is particularly convenient for nonconservative systems, such as granular packings and jammed suspensions, where there is no well-defined reference state, invalidating conventional elasticity. Using prestressed nonconservative triangular lattices and a computational model of amorphous solids, we show that drastically different mechanical responses can show up in amorphous materials at the same density, due to nonconservative interactions which evolve over time, or different preparation protocols. In both cases, the information is encoded in the prestress of the network and not visible at all from the configurations of the network in the case of nonconservative interactions.

Emotion recognition has become increasingly prominent in the medical field and human-computer interaction. When people’s emotions change under external stimuli, various physiological signals of the human body will fluctuate. Electroencephalography (EEG) is closely related to brain activity, making it possible to judge the subject’s emotional changes through EEG signals. Meanwhile, machine learning algorithms, which are good at digging out data features from a statistical perspective and making judgments, have developed by leaps and bounds. Therefore, using machine learning to extract feature vectors related to emotional states from EEG signals and constructing a classifier to separate emotions into discrete states to realize emotion recognition has a broad development prospect. This paper introduces the acquisition, preprocessing, feature extraction, and classification of EEG signals in sequence following the progress of EEG-based machine learning algorithms for emotion recognition. And it may help beginners who will use EEG-based machine learning algorithms for emotion recognition to understand the development status of this field. The journals we selected are all retrieved from the Web of Science retrieval platform. And the publication dates of most of the selected articles are concentrated in 2016–2021.

Introduction: Bilateral spastic cerebral palsy (BSCP) is the most common subtype of cerebral palsy (CP), which is characterized by various motor and cognitive impairments, as well as emotional instability. However, the neural basis of these problems and how repetitive transcranial magnetic stimulation (rTMS) can make potential impacts on the disrupted structural brain network in BSCP remain unclear. This study was aimed to explore the topological characteristics of the structural brain network in BSCP following the treatment of rTMS.Methods: Fourteen children with BSCP underwent 4 weeks of TMS and 15 matched healthy children (HC) were enrolled. Diffusion tensor imaging (DTI) data were acquired from children with bilateral spastic cerebral palsy before treatment (CP1), children with bilateral spastic cerebral palsy following treatment (CP2) and HC. The graph theory analysis was applied to construct the structural brain network. Then nodal clustering coefficient (Ci) and shortest path length (Li) were measured and compared among groups.Results: Brain regions with significant group differences in Ci were located in the left precental gyrus, middle frontal gyrus, calcarine fissure, cuneus, lingual gyrus, postcentral gyrus, inferior parietal gyri, angular gyrus, precuneus, paracentral lobule and the right inferior frontal gyrus (triangular part), insula, posterior cingulate gyrus, precuneus, paracentral lobule, pallidum. In addition, significant differences were detected in the Li of the left precental gyrus, lingual gyrus, superior occipital gyrus, middle occipital gyrus, superior parietal gyrus, precuneus and the right median cingulate gyrus, posterior cingulate gyrus, hippocampus, putamen, thalamus. Post hoc t-test revealed that the CP2 group exhibited increased Ci in the right inferior frontal gyrus, pallidum and decreased Li in the right putamen, thalamus when compared with the CP1 group.Conclusion: Significant differences of node-level metrics were found in various brain regions of BSCP, which indicated a disruption in structural brain connectivity in BSCP. The alterations of the structural brain network provided a basis for understanding of the pathophysiological mechanisms of motor and cognitive impairments in BSCP. Moreover, the right inferior frontal gyrus, putamen, thalamus could potentially be biomarkers for predicting the efficacy of TMS.

The University-Enterprise-Cooperation (UEC) has been adopted by many universities globally to effectively utilize the high-quality resources of the construction industry, to improve the general competence, problem-solving skills and innovation capability of graduates of an engineering discipline. There are a limited amount of papers published in international journals on UEC, compared to a significant volume of Chinese published literature. However, based on the best knowledge of the authors, no existing literature has conducted a quantitative statistical analysis on UEC research in Chinese and international journals. In order to fill this knowledge gap, 300 high-quality papers on UEC were derived through the Chinese CNKI database from 2010-2019. The general development trend of UEC research in the past 30 years was also mapped and analyzed. The most popular topics under the UEC theme and the most frequently adopted research methods within the UEC field were also identified. It was found that since the first UEC publication in 1990, the number of UEC publications experienced a steady increase during the last decade, aiding by strong promotion from the government and active practice within universities and from educators in China. Course development (52.94%) and practice resource development (24.89%) were two of the most popular research topics in the UEC field. The elaboration research method was adopted by more than half (55.94%) of UEC papers, followed by case study (21.25%). The findings of this study are valuable for researchers and practitioners globally, to better understand the development status of UEC research in China and embark on future research in this field.

Moving beds have been regarded as a promising alternative for the heat recovery from the gravity-driven granular flow, which includes counter-current moving bed (CCMB) and gravity-driven moving bed (GDMB). However, the heat transfer characteristics still deserve investigating to optimise the direct heat transfer (CCMB) and the indirect transfer (GDMB), particularly for the appropriate particle size in different moving beds. In this paper, the simple analytical models were established to compare CCMB and GDMB in different particle sizes, which was interwoven with the effects of solid granular density, thermal conductivity, the inlet temperature. It was found that, as the technology significantly affected by the solid-to-gas ratio of mass flow rate, CCMB enables the efficient heat recovery for the particles larger than 3 – 4 mm. For the other fine solid granular, GDMB becomes a good candidate once there is enough heat transfer area, fins in the airside and particles mixing in the granular side. It has more advantages in the quick flow with the compact design. The study can provide useful suggestions for improvement and optimisation in the future.

Comparative questions in Chinese, as a special and complex form of question answering (QA), have their own unique sentence structure, existing methods cannot solve them well. Inspired by cognitive studies on how humans solve complex problems, we propose a hybrid framework which combines Logic Programming and attention based Bi-LSTM. This framework is decomposed into three consecutive components: 1) identify comparative questions, 2) extract comparative elements from the identified comparative questions, and 3) answer factoid questions containing the extracted comparative elements. Specifically, for the former two components, Logic Programming is adopted to filter out non-comparative questions and extract comparative elements. For the latter one, a bidirectional long and short term memory (Bi-LSTM) model with attention mechanism is utilized. Experimental results on Chinese geographical question datasets show that our proposed hybrid framework achieves outstanding performance for practical use.

Renal fibrosis represents a final common outcome of many renal diseases and has attracted a great deal of attention. To better understand whether lncRNAs could be a player in this process or be a biomarker for renal fibrosis diagnosis, we compared transcriptome sequencing data on renal tissues and urine respectively between UUO (unilateral ureteral obstruction) and shamed (Sham) rat model. Numerous genes including lncRNAs with significant changes in their expression were identified. 24 lncRNAs were up-regulated and 79 lncRNAs were down-regulated in the renal tissues of the UUO rats. 625 lncRNAs were up-regulated and 177 lncRNAs were down-regulated in urines of the UUO rats. Among the lncRNAs upregulated in renal tissue of UUO rats, 19 lncRNAs were predicted containing several conserved Smad3 binding motifs in the promoter. Among them, lncRNAs with putative promoter containing more than 4 conserved Smad3 binding motifs were demonstrated to be induced by TGF-β significantly in normal rat renal tubular epithelial NRK-52E cells. We further confirmed that lncRNA TCONS_00088786 and TCONS_01496394 were regulated by TGF-β stimulation and also can influence the expression of some fibrosis-related genes through a feedback loop. Based on transcriptome sequencing data, bioinformatics analysis and qRT-PCR detection, we also demonstrated lncRNA in urine are detectable and might be a novel biomarker of renal fibrosis. These results provide new information for the involvement of lncRNAs in renal fibrosis, indicating that they may serve as candidate biomarkers or therapeutic targets in the future.

The miniaturization of spectrometer can broaden the application area of spectrometry, which has huge academic and industrial value. Among various miniaturization approaches, filter-based miniaturization is a promising implementation by utilizing broadband filters with distinct transmission functions. Mathematically, filter-based spectral reconstruction can be modeled as solving a system of linear equations. In this paper, we propose an algorithm of spectral reconstruction based on sparse optimization and dictionary learning. To verify the feasibility of the reconstruction algorithm, we design and implement a simple prototype of a filter-based miniature spectrometer. The experimental results demonstrate that sparse optimization is well applicable to spectral reconstruction whether the spectra are directly sparse or not. As for the non-directly sparse spectra, their sparsity can be enhanced by dictionary learning. In conclusion, the proposed approach has a bright application prospect in fabricating a practical miniature spectrometer.