Nephrotic syndrome associated with solid malignancies: a systematic review.

BACKGROUND: Nephrotic syndrome (NS) can occur as a paraneoplastic disorder in association with various types of carcinoma. However, paraneoplastic nephrotic syndrome (PNS) is often misdiagnosed as idiopathic nephrotic syndrome or as an adverse effect of oncology treatment, leading to delayed diagnosis and suboptimal treatment. The characteristics of NS associated with solid malignancies are not yet elucidated. We systematically summarized the clinical data for 128 cases of NS combined with solid malignancies with the aim of informing the clinical management of PNS. METHODS: We searched the PubMed database for articles published from the date of inception through to October 2023 using the following keywords: "cancer" or "malignant neoplasms" or "neoplasia" or "tumors" and "nephrotic syndrome", "nephrotic" or "syndrome, nephrotic". All data were extracted from case reports and case series, and the extraction included a method for identifying individual-level patient data. RESULTS: A literature search yielded 105 cases of PNS and 23 of NS induced by cancer therapy. The median age at diagnosis was 60 years, with a male to female ratio of 1.8:1. In patients with PNS, manifestations of NS occurred before, concomitantly with, or after diagnosis of the tumor (in 36%, 30%, and 34% of cases, respectively). Membranous nephropathy (49%) was the most prevalent renal pathology and found particularly in patients with lung, colorectal, or breast carcinoma. Regardless of whether treatment was for cancer alone or in combination with NS, the likelihood of remission was high. CONCLUSION: The pathological type of NS may be associated with specific malignancies in patients with PNS. Prompt identification of PNS coupled with suitable therapeutic intervention has a significant impact on the outcome for patients.

Two-dimensional Mo1-xB2 with ordered metal vacancies obtained for advanced thermoelectric applications based on first-principles calculations.

The study and development of high thermoelectric properties is crucial for the next generation of microelectronic and wearable electronics. Derived from the recent experimental realization of layers of transition metal molybdenum and boride, we report the theoretical realization of advanced thermoelectric properties in two-dimensional (2D) transition metal boride Mo1-xB2 (x = 0, 0.05, 0.10, 0.125, 0.15)-based defect sheets. The introduction of metal vacancies results in stronger d-p exchange interactions and hybridization between the Mo-d and B-p atoms. Meanwhile, the ordered metal vacancies enabled transition metal borides (n-type Mo0.9B2) to widen the d-bandwidth and raise the d-band center, leading to a relatively high carrier mobility of 3262 cm2 V-1 s-1 and conductivity twice that of a bug-free n-type MoB2 layer, which indicates that it presents good electronic and thermal transport properties. Furthermore, investigations of the thermoelectric performance exhibit a maximum ZT of up to 3.29, which is superior to those of currently reported 2D materials. Modulation by defect engineering suggests that 2D transition metal boride sheets with ordered metal vacancies have promising applications in microelectronics, wearable electronics and thermoelectric devices.

Outward foreign direct investment and GVC position of manufacturing industry: A perspective on China's general trade and processing trade structure.

Depending on the trading modes, the effect of Outward Foreign Direct Investment (OFDI) on the manufacturing industry's position within the global value chain (GVC) may differ considerably. This paper examines the GVC position of China's manufacturing industry from 2003 to 2018, specifically focusing on the general trade and processing trade. Drawing upon this premise, this paper analyzes the effect and mechanism by which OFDI influences the GVC position of China's manufacturing industry. The result demonstrates that: (1) China's processing trade manufacturing industry has a much lower GVC position than general trade manufacturing industry. The GVC position of China's general trade manufacturing industry rose from 2.76 to 2.90 from 2003 to 2018, while processing trade manufacturing industry remained around 1.93. (2) OFDI boosts the GVC position of general trade manufacturing industry through facilitating reverse technology spillover, inducing industry structure upgrading, and enabling export scale expansion. (3) OFDI hinders the GVC position growth of processing trade manufacturing industry. The research findings offer theoretical backing for China to develop OFDI strategies that are tailored to different trading modes within the new framework of dual circulation. These strategies aim to facilitate the transformation and advancement of the manufacturing industry, as well as the growth of the GVC position.

Charge Shielding-Oriented Design of Zinc-Based Nanoparticle Liquids for Controlled Nanofabrication.

Metal-containing nanoparticles possess nanoscale sizes, but the exploitation of their nanofeatures in nanofabrication processes remains challenging. Herein, we report the realization of a class of zinc-based nanoparticle liquids and their potential for applications in controlled nanofabrication. Utilizing the metal-core charge shielding strategy, we prepared nanoparticles that display glass-to-liquid transition behavior with glass transition temperature far below room temperature (down to -50.9 °C). Theoretical calculations suggest the outer surface of these unusual nanoparticles is almost neutral, thus leading to interparticle interactions weak enough to give them liquefaction characteristics. Such features endow them with extraordinarily high dispersibility and excellent film-forming capabilities. Twenty-two types of nanoparticles synthesized by this strategy have all shown good lithographic properties in the mid-ultraviolet, electron beam, or extreme ultraviolet light, and these nanoparticle liquids have achieved controlled top-down nanofabrication with predesigned 18 or 16 nm patterns. This proposed strategy is synthetically scalable and structurally extensible and is expected to inspire the design of entirely new forms of nanomaterials.

ISA: Ingenious Siamese Attention for object detection algorithms towards complex scenes.

The interference of complex environments on object detection tasks dramatically limits the application of object detection algorithms. Improving the detection accuracy of the object detection algorithms is able to effectively enhance the stability and reliability of the object detection algorithm-based tasks in complex environments. In order to ameliorate the detection accuracy of object detection algorithms under various complex environment transformations, this work proposes the Siamese Attention YOLO (SAYOLO) object detection algorithm based on ingenious siamese attention structure. The ingenious siamese attention structure includes three aspects: Attention Neck YOLOv4 (ANYOLOv4), siamese neural network structure and special designed network scoring module. In the Complex Mini VOC dataset, the detection accuracy of SAYOLO algorithm is 12.31%, 48.93%, 17.80%, 10.12%, 18.79% and 1.12% higher than Faster-RCNN (Resnet50), SSD (Mobilenetv2), YOLOv3, YOLOv4, YOLOv5-l and YOLOX-x, respectively. Compared with traditional object detection algorithms based on image preprocessing, the detection accuracy of SAYOLO is 4.88%, 11.51%, 1.73%, 23.27%, 18.12%, and 5.76% higher than Image-Adaptive YOLO, MSBDN-DFF + YOLOv4, Dark Channel Prior + YOLOv4, Zero-DCE + YOLOv4, MSBDN-DFF + Zero-DCE + YOLOv4, and Dark Channel Prior + Zero-DCE + YOLOv4, respectively.

Critical dimension prediction of metal oxide nanoparticle photoresists for electron beam lithography using a recurrent neural network.

The critical dimension (CD) of lithographic patterns is the most significant indicator for evaluating the imaging performance of photoresists, and its value is seriously affected by process conditions. However, the lithographic imaging system is highly nonlinear, and extensive exposure experiments are needed to obtain the desired CD. This consumes lots of time, manpower, and cost in screening for optimal process conditions. Here, we report a combined electron beam lithography (EBL) experiment and recurrent neural network (RNN) study on the CDs of metal oxide nanoparticle photoresists, and establish a CD RNN model. Leveraging the RNN model, a process condition filter is developed to screen suitable process conditions. The experimental results demonstrate that the prediction accuracy of the CD model exceeds 93%, and the photoresist patterns under the screened process conditions can satisfy the requirements of a preset CD. This work opens up a novel perspective for accurate EBL process modeling, and provides guidance for EBL experiments.

Theoretical Insights into the Solubility Polarity Switch of Metal-Organic Nanoclusters for Nanoscale Patterning.

Metal-organic nanoclusters(MOCs) are being increasingly used as prospective photoresist candidates for advanced nanoscale lithography technologies. However, insight into the irradiation-induced solubility switching process remains unclear. Hereby, the theoretical study employing density functional theory (DFT) calculations of the alkene-containing zirconium oxide MOC photoresists is reported, which is rationally synthesized accordingly, to disclose the mechanism of the nanoscale patterning driven by the switch of solubility from the acid-catalyzed or electron-triggered ligand dissociation. By evaluating the dependence of MOCs' imaging process on photoacid, lithographies of photoresists with and without photoacid generators after exposure to ultraviolet (UV), electron beam, and soft X-ray, it is revealed that photoacid is essential in UV lithography, but it demonstrates little effect on exposure dose in high-energy lithography. Furthermore, theoretical studies using DFT simulations to investigate the plausible photoacid-catalyzed, electron-triggered dissociation, and accompanying radical reaction are performed, and a mechanism is demonstrated that the nanoscale patterning of this type of MOCs is driven by the solubility switch resulting from dissociation-induced strong electrostatic interaction and low-energy barrier radical polymerization with other species. This study can give insights into the chemical mechanisms of patterning, and guide the rational design of photoresists to realize high resolution and high sensitivity.

Process optimization of line patterns in extreme ultraviolet lithography using machine learning and a simulated annealing algorithm.

Resolution, line edge/width roughness, and sensitivity (RLS) are critical indicators for evaluating the imaging performance of resists. As the technology node gradually shrinks, stricter indicator control is required for high-resolution imaging. However, current research can improve only part of the RLS indicators of resists for line patterns, and it is difficult to improve the overall imaging performance of resists in extreme ultraviolet lithography. Here, we report a lithographic process optimization system of line patterns, where RLS models are first established by adopting a machine learning method, and then these models are optimized using a simulated annealing algorithm. Finally, the process parameter combination with optimal imaging quality of line patterns can be obtained. This system can control resist RLS indicators, and it exhibits high optimization accuracy, which facilitates the reduction of process optimization time and cost and accelerates the development of the lithography process.

The effect of institutions and urbanization on environmental quality: evidence from the Belt and Road Initiative countries using dynamic panel models.

Increased globalization in urban areas raise energy consumption that leads to high carbon dioxide discharge and degrade environmental quality. Other economic activities also produce emission; however, a well-established institutional framework can overcome the issues of environmental degradation and minimize the effect of harmful factors on the environment. In this regard, this study investigates the effect of urbanization, energy consumption, and industrialization on carbon dioxide emission by taking into consideration the role of institutional quality in the Belt and Road Initiative (BRI) countries for the period of 2002 to 2019. Employing dynamic panel techniques, the results are in line with theories which show that increased urbanization, energy consumption, industrialization, and economic growth raise carbon dioxide emission and lead to environmental degradation. The study also found that international trade and political stability reduce emission; however, institutional quality as a whole positively affects carbon dioxide emission. The study also found a U-shape relationship between urbanization and carbon dioxide emission. The interaction term between institutional quality and urbanization significantly mitigates carbon dioxide emission and raise environmental sustainability. The findings of this study have considerable policy suggestions for the sample countries.

Dynamic multi-objective evolutionary optimization algorithm based on two-stage prediction strategy.

Tracking pareto-optimal set or pareto-optimal front in limited time is an important problem of dynamic multi-objective optimization evolutionary algorithms (DMOEAs). However, the current DMOEAs suffer from some deficiencies. In the early optimization process, the algorithms may suffer from random search. In the late optimization process, the knowledge which can accelerate the convergence rate is not fully utilized. To address the above issue, a DMOEA based on the two-stage prediction strategy (TSPS) is proposed. TSPS divides the optimization progress into two stages. At the first stage, multi-region knee points are selected to capture the pareto-optimal front shape, which can accelerate the convergence and maintaining good diversity at the same time. At the second stage, improved inverse modeling is applied to search the representative individuals, which can improve the diversity of the population and is beneficial to predicting the moving location of the pareto-optimal front. Experimental results on dynamic multi-objective optimization test suites show that TSPS is superior to the other six DMOEAs. In addition, the experimental results also show that the proposed method has the ability to respond quickly to environmental changes.

Artificial intelligence of digital morphology analyzers improves the efficiency of manual leukocyte differentiation of peripheral blood.

BACKGROUND AND OBJECTIVE: Morphological identification of peripheral leukocytes is a complex and time-consuming task, having especially high requirements for personnel expertise. This study is to investigate the role of artificial intelligence (AI) in assisting the manual leukocyte differentiation of peripheral blood. METHODS: A total of 102 blood samples that triggered the review rules of hematology analyzers were enrolled. The peripheral blood smears were prepared and analyzed by Mindray MC-100i digital morphology analyzers. Two hundreds leukocytes were located and their cell images were collected. Two senior technologists labeled all cells to form standard answers. Afterward, the digital morphology analyzer unitized AI to pre-classify all cells. Ten junior and intermediate technologists were selected to review the cells with the AI pre-classification, yielding the AI-assisted classifications. Then the cell images were shuffled and re-classified without AI. The accuracy, sensitivity and specificity of the leukocyte differentiation with or without AI assistance were analyzed and compared. The time required for classification by each person was recorded. RESULTS: For junior technologists, the accuracy of normal and abnormal leukocyte differentiation increased by 4.79% and 15.16% with the assistance of AI. And for intermediate technologists, the accuracy increased by 7.40% and 14.54% for normal and abnormal leukocyte differentiation, respectively. The sensitivity and specificity also significantly increased with the help of AI. In addition, the average time for each individual to classify each blood smear was shortened by 215 s with AI. CONCLUSION: AI can assist laboratory technologists in the morphological differentiation of leukocytes. In particular, it can improve the sensitivity of abnormal leukocyte differentiation and lower the risk of missing detection of abnormal WBCs.

Multi-objective evolutionary multitasking algorithm based on cross-task transfer solution matching strategy.

The superior performance of evolutionary multitasking (EMT) algorithms is largely owing to the potential synergy between tasks. Current EMT algorithms only involve a unidirectional process of transferring individuals from the source task to the target task. This method does not consider the search preference of the target task in the process of finding transferred individuals; therefore, the potential synergy between tasks is not fully utilized. Herein, we propose a bidirectional knowledge transfer method, which refers to the search preference of the target task in the process of finding transferred individuals. These transferred individuals fit the search process well for the target task. In addition, an adaptive strategy for adjusting the intensity of the knowledge transfer is proposed. This method enables the algorithm to adjust the intensity of knowledge transfer independently according to the living conditions of the individuals to be transferred to balance the convergence of the population with the computational intensity of the algorithm. The proposed algorithm is compared with comparison algorithms on 38 multi-objective multitasking optimization benchmarks. Experimental results show that the proposed algorithm is not only outperforming other comparison algorithms in more than 30 benchmarks, but also has considerable convergence efficiency.

Canny Algorithm Enabling Precise Offline Line Edge Roughness Acquisition in High-Resolution Lithography.

The line edge roughness (LER) is one of the most critical indicators of photoresist imaging performance, and its measurement using a reliable method is of great significance for lithography. However, most studies only investigate photoresist resolution and sensitivity because LER measurements require an expensive and not widely available critical dimension scanning electron microscopy (SEM) technology; thus, the imaging performance of photoresist has not been adequately evaluated. Here, we report an image processing software developed for offline calculation of LER that can analyze lithographic patterns with resolutions up to ∼15 nm. This software can effectively process all graphic files obtained from commonly used SEM machines by utilizing the adjustable double threshold. To realize the effective detection of high-resolution patterns in advanced lithography, we used SEM images generated from extreme ultraviolet and electron beam lithography to develop and validate the software's graphic recognition algorithm. This image processing software can process typical SEM images and produce reliable LER in an efficient and user-friendly manner, constituting a powerful tool for promoting the development of high-performance photoresist materials.

Exceptional Light Sensitivity by Thiol-Ene Click Lithography.

Lithographic patterning, which utilizes the solubility switch of photoresists to convert optical signals into nanostructures on the substrate, is the primary top-down approach for nanoscale fabrication. However, the low light/electron-energy conversion efficiency severely limits the throughput of lithography. Thiol-ene reaction, as a photoinitiated radical addition reaction, is widely known as click reaction in the field of chemistry due to its extremely high efficiency. Here, we introduce a click lithography strategy utilizing the rapid thiol-ene click reaction to realize ultraefficient nanofabrication. This novel approach facilitated by the implementation of ultrahigh-functionality material designs enables high-contrast patterning of metal-containing nanoclusters under an extremely low deep-ultraviolet exposure dose, e.g., 7.5 mJ cm-2, which is 10-20 times lower than the dose used in the photoacid generator-based photoresist system. Meanwhile, 45 nm dense patterns were also achieved at a low dose using electron beam lithography, revealing the great potential of this approach in high-resolution patterning. Our results demonstrated the high-sensitivity and high-resolution features of click lithography, providing inspiration for future lithography design.

Porous carbon-based metal-free monolayers towards highly stable and flexible wearable thermoelectrics and microelectronics.

In the search for high mechanical strength and flexibility, ultrahigh semiconducting speed is crucial for the next generation of microelectronic and wearable electronics. Herein, we propose two 2D graphene-like macrocyclic complex carbon-based monolayers, namely g-MC-A and g-MC-B. Both monolayers are dynamically stable according to phonon dispersion and ab initio molecular dynamics simulations. The yield stress of these two layers reaches half that of graphene, revealing remarkably high mechanical strength. Besides, both monolayers are semiconductors. The electron mobility of g-MC-A is high: up to 104 cm2 V-1 s-1, comparable to black phosphorene. Furthermore, these two monolayers exhibit excellent inherent conductivity with anisotropic characteristics. Interestingly, an extra valley is observed near the conduction band edge for both layers, further simulation predicted both metal-free monolayers will exhibit ZT > 1, implying high thermoelectric performance. Therefore, these two C-based metal-free layers have promising applications in mechanical enhancement, microelectronics, wearable electronics and thermoelectric devices.

A multi-objective multi-tasking evolutionary algorithm based inverse mapping and adaptive transformation strategy: IM-MFEA.

Multi-tasking optimization algorithm attracts much attention because the knowledge transfer between tasks enables the algorithm to process multiple related tasks simultaneously. However, negative knowledge transfer occasionally occurs, which may weaken the performance of the algorithm. To reduce the impact of negative knowledge transfer, a multi-objective multi-tasking optimization algorithm (IM-MFEA) based on inverse model mapping and an objective transformation strategy is proposed. First, correlation analysis is applied in an inverse mapping strategy to improve the accuracy of the inverse mapping model. Then, following the pattern of using the source domain solutions to assist the optimization of the target domain, the adaptive transformation strategy is used to improve the quality of the source domain solution in the objective space. These transformed solutions are reconstructed through the inverse mapping strategy. Finally, these reconstructed source domain solutions are mated with the target domain solutions to generate competitive offspring individuals for the target domain. To verify the effectiveness of the IM-MFEA, comprehensive experiments were conducted on nine multi-objective multi-factorial optimization (MFO) benchmark problems. Empirical results demonstrate that IM-MFEA is superior to other algorithms in 90% of test instances by inverted generational distance (IGD) and hypervolume (HV) value indicators.

First-Principles Study on Mechanical and Optical Behavior of Plutonium Oxide under Typical Structural Phases and Vacancy Defects.

The chemical corrosion aging of plutonium is a very important topic. It is easy to be corroded and produces oxidation products of various valence states because of its 5f electron orbit between local and non-local. On the one hand, the phase diagram of plutonium and oxygen is complex, so there is still not enough research on typical structural phases. On the other hand, most of the studies on plutonium oxide focus on PuO2 and Pu2O3 with stoichiometric ratio, while the understanding of non-stoichiometric ratio, especially for Pu2O3-x, is not deep enough. Based on this, using the DFT + U theoretical scheme of density functional theory, we have systematically studied the structural stability, lattice parameters, electronic structure, mechanical and optical properties of six typical high temperature phases of ß-Pu2O3, α-Pu2O3,γ-Pu2O3, PuO, α-PuO2,γ-PuO2. Further, the mechanical properties and optical behavior of Pu2O3-x under different oxygen vacancy concentrations are analyzed and discussed in detail. The result shows that the elasticity modulus of single crystal in mechanical properties is directly related to the oxygen/plutonium ratio and crystal system. As the number of oxygen vacancies increases, the mechanical constants continue to increase. In terms of optical properties, PuO has the best optical properties, and the light absorption rate decreases with the increase of oxygen vacancy concentration.

Directed Brain Network Analysis for Fatigue Driving Based on EEG Source Signals.

Fatigue driving is one of the major factors that leads to traffic accidents. Long-term monotonous driving can easily cause a decrease in the driver's attention and vigilance, manifesting a fatigue effect. This paper proposes a means of revealing the effects of driving fatigue on the brain's information processing abilities, from the aspect of a directed brain network based on electroencephalogram (EEG) source signals. Based on current source density (CSD) data derived from EEG signals using source analysis, a directed brain network for fatigue driving was constructed by using a directed transfer function. As driving time increased, the average clustering coefficient as well as the average path length gradually increased; meanwhile, global efficiency gradually decreased for most rhythms, suggesting that deep driving fatigue enhances the brain's local information integration abilities while weakening its global abilities. Furthermore, causal flow analysis showed electrodes with significant differences between the awake state and the driving fatigue state, which were mainly distributed in several areas of the anterior and posterior regions, especially under the theta rhythm. It was also found that the ability of the anterior regions to receive information from the posterior regions became significantly worse in the driving fatigue state. These findings may provide a theoretical basis for revealing the underlying neural mechanisms of driving fatigue.

MOEA3H: Multi-objective evolutionary algorithm based on hierarchical decision, heuristic learning and historical environment.

Generating feasible solution and selecting valuable solution are the most important issues when dealing with complicated multi-objective problems. Focusing on these issues, the mechanism of multi-objective problem is analyzed by evolutionary history and environmental information. Hierarchical decision based on rank fitness of distance correlation is proposed to guide the evolutionary operator. Heuristic learning by dynamic evolutionary is introduced to deal with static optimization problem. History information acquired from solution landscape is used to achieve a comprehensive search on feasible region. Based on these improvement, multi-objective evolutionary algorithm based on hierarchical decision, heuristic learning and historical environment (MOEA3H) is proposed. The proposed algorithm performs best on 10 and 14 of 19 test problems on IGD and Hvpervolume, respectively.

An ensemble JITL method based on multi-weighted similarity measures for cold rolling force prediction.

In the cold tandem rolling process, the product quality and yield are affected by the accuracy of rolling force prediction directly. Fix prediction model is not applicable to the multi-operating conditions rolling environment. In addition, appropriate samples can be hardly selected by a single similarity measure because of the insufficient process knowledge. In order to solve these issues, an ensemble just-in-time-learning modeling method based on multi-weighted similarity measures (MWS-EJITL) is proposed. Firstly, multi-weighted similarity measures is used to select relevant samples. Then, the local model is constructed and the output value of the query data is estimated. Finally, the ensemble learning strategy is adopted to integrate the outputs of each local model. On this basis, the cumulative similarity factor is introduced to optimize the number of samples of local modeling, and the similarity threshold is set to update the local model adaptively. The rolling force prediction experiment verify the effectiveness and accuracy of MWS-EJITL method.