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Background: Increasing studies have demonstrated the biological function of RNA N6-methyladenosine (m6A) modifications in tumorigenesis. However, the potential role of m6A modifications in the tumor immune microenvironment (TIME) of hepatocellular carcinoma (HCC) remains unclear. Methods: Herein, 23 m6A regulators were fetched and introduced into consensus clustering to identify distinct m6A modification patterns and develop m6A-based molecular signatures. Then, a principal component analysis algorithm was employed to construct an m6A-based scoring system to further quantify m6A modification patterns in individual tumors. Immunophenoscore (IPS) was used to estimate the immunotherapeutic response of patients. Results: Three different m6A modification patterns with distinct prognoses and biological signatures were identified among 611 HCC samples. The TIME characteristics of these three patterns were consistent with three known immune profiles: immune-oasis, immune-excluded, and immune-inflamed phenotypes. Identifying m6A modification patterns within individual tumors based on the m6Ascore, developed under the m6A-related signature genes, contributed to elaborating biological processes, clinical outcomes, immune cell infiltration, immunotherapeutic effects, and genetic variations. The low-m6Ascore subtype, characterized by immunosuppression, suggested an immune-suppressed phenotype and a low probability of benefiting from immunotherapy. Finally, the potential function of PRDM4 in HCC was explored. Conclusion: This study comprehensively elucidated the indispensable role of m6A modification patterns in the complexity of TIME. The quantitative identification of m6A modification patterns in individual tumors will contribute to optimizing precision immunotherapy. [ABSTRACT FROM AUTHOR]

Deep learning models have been widely used in many supervised learning applications. However, these models suffer from overfitting due to various types of uncertainty with deteriorating performance when facing data biases, class imbalance, or noise propagation. The Information-Set Deep learning (ISDL) architectures with four variants are developed by integrating information set theory and deep learning principles to address the critical problem of the absence of robust deep learning models. There is a description of the ISDL architectures, learning algorithms, and analytic workflows. The performance of the ISDL models and standard architectures is evaluated using a noise-corrupted benchmark dataset. The experimental results show that the ISDL models can efficiently handle noise-dominated uncertainty and outperform peer architectures. [ABSTRACT FROM AUTHOR]

Quantum process tomography is a fundamental and critical benchmarking and certification tool that is capable of fully characterizing an unknown quantum process. Standard quantum process tomography suffers from an exponentially scaling number of measurements and complicated data post-processing due to the curse of dimensionality. On the other hand, non-unitary operators are more realistic cases. In this work, we put forward a variational quantum process tomography method based on the supervised quantum machine learning framework. It approximates the unknown non-unitary quantum process utilizing a relatively shallow depth parametric quantum circuit and fewer input states. Numerically, we verified our method by reconstructing the non-unitary quantum mappings up to eight qubits in two cases: the weighted sum of the randomly generated quantum circuits and the imaginary time evolution of the Heisenberg XXZ spin chain Hamiltonian. Results show that those quantum processes could be reconstructed with high fidelities (>99%) and shallow depth parametric quantum circuits ( d ≤ 8 ), while the number of input states required is at least two orders of magnitude less than the demands of the standard quantum process tomography. Our work shows the potential of the variational quantum process tomography method in characterizing non-unitary operators. [ABSTRACT FROM AUTHOR]

Pancreatic ductal adenocarcinoma (PDAC), as an intractable malignancy, still causes an extremely high mortality worldwide. The ubiquitin-specific protease (USP) family constitutes the major part of deubiquitinating enzymes (DUBs) which has been reported to be involved in initiation and progression of various malignancies via the function of deubiquitination. However, the biological function and clinical values of USPs in PDAC have not been comprehensively elucidated. In this study, Gene Expression Profiling Interactive Analysis (GEPIA), Gene Expression Omnibus (GEO) datasets, UALCAN database, and the Human Protein Atlas (HPA) online tool were used to analyze the expression level and the relationship between USP expression and clinicopathological features in PDAC. Survival module of HPA and Kaplan-Meier plotter (KMP) databases was recruited to explore the prognostic value of USPs. Tumor Immune Estimation Resource (TIMER) online tool and KMP databases were utilized to elucidate tumor immune infiltration and immune-related survival of USPs. CBioPortal online tool was used to identify the gene mutation level of USPs in PDAC. Both cBioPortal and LinkedOmics were used to confirm the potential biological functions of USPs in PDAC. Our study showed that USP10, USP14, USP18, USP32, USP33, and USP39 (termed as six-USPs) expressions were significantly elevated in tumor tissues. The high expression of the four USPs (USP10, USP14, USP18, and USP39) indicated a poor prognosis. A significant relationship was indicated between the expression of six-USPs and clinicopathological features. Also, the expression of six-USPs was related to promoter methylation level. Moreover, more than 40% genetic alterations and mutations were discovered in six-USPs. Furthermore, the six-USP expression was correlated with immune infiltration and immune-related prognosis. The functional analysis found that the six-USPs were involved in various biological processes and signaling pathways, such as nucleocytoplasmic transport, choline metabolism in cancer, cell cycle, ErbB signaling pathway, RIG-I-like receptor signaling pathway, TGF-β signaling pathway, and TNF signaling pathway. In conclusion, the results showed that six-USPs are potential prognostic biomarkers and can be recruited as possible therapeutic targets of PDAC. [ABSTRACT FROM AUTHOR]

Introduction: Cesarean section scar defects (CSDs) are one of the long‐term complications following cesarean section. They can be detected by transvaginal sonography, hysterosalpingography, sonohysterography and magnetic resonance imaging (MRI). Hysteroscopy is frequently used in evaluating endometrial disease. However, the description of CSDs by hysteroscopy is very limited. Only a few papers about hysteroscopy evaluation have been published. This is an exploratory study to compare hysteroscopic findings with myometrial thickness and post‐surgical outcomes. Material and methods: From February 2019 to December 2020, 143 women with CSDs were enrolled in the observational study. All women suffered from abnormal uterine bleeding and were evaluated in a standardized way with hysteroscopy before vaginal surgery. Dome‐shaped CSDs could be clearly observed in all patients under hysteroscopy. We recorded the pictures of each patient under hysteroscopy and classified them. All patients underwent outpatient review at 3 and 6 months after surgery to obtain menstrual information and CSD scar size by MRI or transvaginal sonography. Results: Pale mucosae in the defect were meager endometrial lining covering the surface of muscle layer, cyst lesions were some cyst lesions in the defect, increased local vascularization was a vascular tree with branching and irregular vascular distribution in defect, polypoid lesions were polypoid lesions in the defect, and serrated niches were two niches at the anterior uterine isthmus. The features of the CSDs observed under hysteroscopy were identified as five phenotypes: pale mucosae (90/143, 62.9%), cyst lesions (23/143, 16.1%), polypoid lesions (19/143, 13.3%), increased local vascularization (27/143, 18.9%) and serrated niches (7/143, 4.9%). The most common finding in scar defects under hysteroscopy was pale mucosae in the CSD. The results suggest that patients with increased local vascularization and serrated niches have a high risk of thinner residual myometrium before vaginal repair (p < 0.05). However, there was no significant difference in menstrual duration or in the outcome of vaginal repair for CSDs between these five phenotypes (p > 0.05). Conclusions: Patients with the abnormal blood vessel or serration phenotypes of defects under hysteroscopy may have a thinner residual myometrium. The phenotypes of hysteroscopic findings of CSDs have no correlation with the outcome of repair. [ABSTRACT FROM AUTHOR]

We developed a minimum-cost circulation framework for solving the global data association problem, which plays a key role in the tracking-by-detection paradigm of multi-object tracking (MOT). The global data association problem was extensively studied under the minimum-cost flow framework, which is theoretically attractive as being flexible and globally solvable. However, the high computational burden has been a long-standing obstacle to its wide adoption in practice. While enjoying the same theoretical advantages and maintaining the same optimal solution as the minimum-cost flow framework, our new framework has a better theoretical complexity bound and leads to orders of practical efficiency improvement. This new framework is motivated by the observation that minimum-cost flow only partially models the data association problem and it must be accompanied by an additional and time-consuming searching scheme to determine the optimal object number. By employing a minimum-cost circulation framework, we eliminate the searching step and naturally integrate the number of objects into the optimization problem. By exploring the special property of the associated graph, that is, an overwhelming majority of the vertices are with unit capacity, we designed an implementation of the framework and proved it has the best theoretical computational complexity so far for the global data association problem. We evaluated our method with 40 experiments on five MOT benchmark datasets. Our method was always the most efficient in every single experiment and averagely 53 to 1,192 times faster than the three state-of-the-art methods. When our method served as a sub-module for global data association methods utilizing higher-order constraints, similar running time improvement was attained. We further illustrated through several case studies how the improved computational efficiency enables more sophisticated tracking models and yields better tracking accuracy. We made the source code publicly available on GitHub with both Python and MATLAB interfaces. [ABSTRACT FROM AUTHOR]

Forests play a key role in global ecosystem conservation and environmental maintenance. Forestry, which includes plant active components, germplasm resources and vegetation dynamics, has received significant attention. Compared with conventional techniques, near-infrared spectroscopy (NIRS) provides rapid, noninvasive, economically efficient and real time results. In this review, we assess the historical and contemporary applications of qualitative and quantitative analysis using NIRS in forestry, including the discrimination of tree species, seeds, woods, and insect species, and the determination of properties of woods, plant tissues, soils, litters and others. In addition, we consider the disadvantages and future application trend of NIRS in forestry. [ABSTRACT FROM AUTHOR]

It is very difficult to characterize the transport properties of porous media due to the disorder of pores distribution in porous media. In this paper, we study heat conduction through porous media with randomly fractures which are considered as tree-like networks. An expression for effective thermal conductivity (ETC) of saturated dual-porosity media is derived based on the fractal characteristics of pores diameter and fractures size. It is shown that the ETC is a function of structure parameters of the fractal dimension ( D g ), the porosity ( ϕ g ), the tortuous fractal dimension ( D t ) and the maximum diameter ( λ max ) for the porous matrix; the fractal dimension ( D f ), the porosity ( ϕ f ), the maximum diameter ( d 0 max ), the diameter ratio (β), the length ratio (α) and the branching levels (n) for the fracture networks. The dependence of structure parameters on the ETC is studied in detail. The results of our model are compared with the available experiments, which show good agreement. The proposed model for the ETC does not contain empirical parameters. The model is useful for predicting the heat conduction of materials. [ABSTRACT FROM AUTHOR]

Pancreatic cancer is one of the main causes of tumor-related deaths worldwide because of its low morbidity but extremely high mortality, and is therefore colloquially known as the "king of cancer." Sudden onset and lack of early diagnostic biomarkers directly contribute to the extremely high mortality rate of pancreatic cancer patients, and also make it indistinguishable from benign pancreatic diseases and precancerous pancreatic lesions. Additionally, the lack of effective prognostic biomarkers makes it difficult for clinicians to formulate precise follow-up strategies based on the postoperative characteristics of the patients, which results in missed early diagnosis of recurrent pancreatic cancer. Long non-coding RNAs (lncRNAs) can influence cell proliferation, invasion/migration, apoptosis, and even chemoresistance via regulation of various signaling pathways, leading to pro- or anti-cancer outcomes. Given the versatile effects of lncRNAs on tumor progression, using a single lncRNA or combination of several lncRNAs may be an effective method for tumor diagnosis and prognostic predictions. This review will give a comprehensive overview of the most recent research related to lncRNAs in pancreatic cancer progression, as targeted therapies, and as biomarkers for the diagnosis and prognosis of pancreatic cancer. [ABSTRACT FROM AUTHOR]

Cancer remains one of the major culprits causing disease-related deaths and leads to a high morbidity and similar mortality. Insidious onset, difficult early detection and a lack of broad-spectrum and effective multi-cancer therapeutic targets have limited the prolongation of cancer patients' survival for decades. Therefore, a versatile therapeutic target which is involved in various cancer-related signaling pathways and different cancers may be more effective for cancer targeted therapy. USP4, one of the DUBs members which participates in deubiquitination, an inverse process of ubiquitination, can regulate various classical cancer-related signaling pathways, and thereby plays a vital role in some pathological and physiological processes including tumor initiation and progression. Recently, USP4 has been found to exert versatile influences on cells proliferation, migration and invasion, also apoptosis of various tumors. Moreover, USP4 can also act as a prognostic biomarker in several cancers. This review will give a comprehensive introduction of USP4 about its regulatory mechanisms, related signaling pathways, pathophysiological functions and the roles in various cancers which may help us better understand its biological functions and improve future studies to construct suitable USP4-targeted cancer therapy system. [ABSTRACT FROM AUTHOR]

The compressive, splitting tensile and immersion strength, drying shrinkage and microstructure of the cementing material made with waste silty clay soil from waterway regulation engineering were investigated in this paper. Cement (11.0–24.0 wt.%), fly ash (4.6–9.2 wt.%) and gypsum (1.9–3.8 wt.%) at different proportions were used in the mixtures. Results show that the specimen addition of appropriate cement, fly ash and gypsum presents excellent mechanical properties and water resistance and low drying shrinkage. The microstructure observation indicates that there are quite a lot of hydrate products in matrix which verifies the formation of cementing compounds. The results obtained suggest that there is potential to manufacture cementing material with silty clay soil in substitution of ordinary concrete to use in hydraulic engineering. [ABSTRACT FROM AUTHOR]

Motivation Liquid chromatography–mass spectrometry (LC-MS) is a standard method for proteomics and metabolomics analysis of biological samples. Unfortunately, it suffers from various changes in the retention times (RT) of the same compound in different samples, and these must be subsequently corrected (aligned) during data processing. Classic alignment methods such as in the popular XCMS package often assume a single time-warping function for each sample. Thus, the potentially varying RT drift for compounds with different masses in a sample is neglected in these methods. Moreover, the systematic change in RT drift across run order is often not considered by alignment algorithms. Therefore, these methods cannot effectively correct all misalignments. For a large-scale experiment involving many samples, the existence of misalignment becomes inevitable and concerning. Results Here, we describe an integrated reference-free profile alignment method, neighbor-wise compound-specific Graphical Time Warping (ncGTW), that can detect misaligned features and align profiles by leveraging expected RT drift structures and compound-specific warping functions. Specifically, ncGTW uses individualized warping functions for different compounds and assigns constraint edges on warping functions of neighboring samples. Validated with both realistic synthetic data and internal quality control samples, ncGTW applied to two large-scale metabolomics LC-MS datasets identifies many misaligned features and successfully realigns them. These features would otherwise be discarded or uncorrected using existing methods. The ncGTW software tool is developed currently as a plug-in to detect and realign misaligned features present in standard XCMS output. Availability and implementation An R package of ncGTW is freely available at Bioconductor and https://github.com/ChiungTingWu/ncGTW. A detailed user's manual and a vignette are provided within the package. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Background: The aim of this study was to evaluate the effect of vaginal repair in patients with cesarean section diverticulum (CSD) who had one or two previous cesarean sections (CSs).Methods: From January 2012 to December 2014, 248 women with CSD underwent vaginal repair surgery in Shanghai First Maternity and Infant Hospital. These included 193 women with one previous cesarean section and 55 women with two previous cesarean sections. Excision and suture of CSD was performed through a vaginal approach. The duration of menstruation, the length, width and depth of the CSD and thickness of the remaining muscular layer (TRM) were evaluated before and after surgery by transvaginal three-dimensional (3D) color Doppler ultrasound.Results: A total of 221 (89.11%) women were followed-up for more than 3 months, and 168 (67.74%) women were followed-up for more than 6 months. There were significant differences in the average duration of menstruation (7.77 ± 2.05 and 8.02 ± 2.06 days VS 13.99 ± 3.71 days), the average size of CSD (5.54*9.19*5.60 and 5.75*9.04*6.18 mm VS 7.99*12.43*6.62 mm) and the TRM (7.61 ± 2.52 and 7.60 ± 3.00 mm VS 2.51 ± 1.02 mm) after surgery compared with those figures before surgery. The results of this study reveal that vaginal repair could shorten the duration of menstruation and improve anatomical defects (P < 0.05). Moreover, there was no significant difference in the effect of clinical repair between women with one or two previous cesarean sections (P > 0.05).Conclusion: In CSD patients, the clinical effectiveness of vaginal repair was equivalent between women with one or two previous cesarean sections. [ABSTRACT FROM AUTHOR]

Motivation Synapses are essential to neural signal transmission. Therefore, quantification of synapses and related neurites from images is vital to gain insights into the underlying pathways of brain functionality and diseases. Despite the wide availability of synaptic punctum imaging data, several issues are impeding satisfactory quantification of these structures by current tools. First, the antibodies used for labeling synapses are not perfectly specific to synapses. These antibodies may exist in neurites or other cell compartments. Second, the brightness of different neurites and synaptic puncta is heterogeneous due to the variation of antibody concentration and synapse-intrinsic differences. Third, images often have low signal to noise ratio due to constraints of experiment facilities and availability of sensitive antibodies. These issues make the detection of synapses challenging and necessitates developing a new tool to easily and accurately quantify synapses. Results We present an automatic probability-principled synapse detection algorithm and integrate it into our synapse quantification tool SynQuant. Derived from the theory of order statistics, our method controls the false discovery rate and improves the power of detecting synapses. SynQuant is unsupervised, works for both 2D and 3D data, and can handle multiple staining channels. Through extensive experiments on one synthetic and three real datasets with ground truth annotation or manually labeling, SynQuant was demonstrated to outperform peer specialized unsupervised synapse detection tools as well as generic spot detection methods. Availability and implementation Java source code, Fiji plug-in, and test data are available at https://github.com/yu-lab-vt/SynQuant. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

An improved whale optimization algorithm (IWOA) is presented to estimate the surface duct. First, the iteration and selection mechanism in whale optimization algorithm (WOA) are modified to avoid local optimum. Then, orthogonal crossover is embedded into WOA to improve its exploration ability. Finally, the IWOA is applied to benchmark functions and the optimization problem of surface duct to test the performance of IWOA, and the results are compared with those of genetic algorithm (GA), WOA and variant WOA. The comparison results show that IWOA has best accuracy and stability for almost all the benchmark functions and the inversion of the surface duct. [ABSTRACT FROM AUTHOR]

As one of the fundamental problems in document analysis, scene character recognition has attracted considerable interests in recent years. But the problem is still considered to be extremely challenging due to many uncontrollable factors including glyph transformation, blur, noisy background, uneven illumination, etc. In this paper, we propose a novel methodology for boosting scene character recognition by learning canonical forms of glyphs, based on the fact that characters appearing in scene images are all derived from their corresponding canonical forms. Our key observation is that more discriminative features can be learned by solving specially designed generative tasks compared to traditional classification-based feature learning frameworks. Specifically, we design a GAN-based model to make the learned deep feature of a given scene character capable of reconstructing corresponding glyphs in a number of standard font styles. In this manner, we obtain deep features for scene characters that are more discriminative in recognition and less sensitive against the above-mentioned factors. Our experiments conducted on several publicly available databases demonstrate the superiority of our method compared to the state of the art. [ABSTRACT FROM AUTHOR]

Cancer is a principal cause of human morbidity and mortality, with gastrointestinal malignancies, in particular, resulting in a marked number of tumor-associated mortalities. The progression of gastrointestinal malignancy is regulated by a variety of aberrantly expressed proteins, a number of which facilitate tumor progression, whereas, others function as tumor suppressors. The detection of such proteins not only contributes to the early diagnosis of cancer, they may additionally serve as potential therapeutic targets. In normal tissues, numerous proteins encoded by proto-oncoproteins are degraded by ubiquitylation enzymes, consisting of F-box/WD-40 repeat-containing protein 7 (Fbw7) and other proteins, thus avoiding tumorigenesis and maintaining homeostasis. In tumor tissues, the downregulation of Fbw7, caused by various factors, leads to disorders in ubiquitinase synthesis, which may induce tumor progression and chemoresistance, particularly in gastrointestinal malignancy. Therefore, an in-depth study of the regulatory mechanisms involved in disorders of Fbw7 expression and the role of Fbw7 in chemoresistance of gastrointestinal tumors may suggest improvements for early diagnostic screening and targeted therapy. [ABSTRACT FROM AUTHOR]

Mycobacterium tuberculosis (Mtb) is the pathogen responsible for tuberculosis, a leading cause of illness and death worldwide. Growing evidence suggests that the proinflammatory cytokine IL‐32 plays a major role in host defences against pathogens such as Mtb. IL‐32 exists in six alternatively spliced isoforms, but antituberculosis effects have been reported only for some of them. In this study, we examined the effect of all six IL‐32 isoforms on Mtb replication in the murine macrophage cell line RAW 264.7. Compared with cells transfected with the other isoforms, IL‐32ε‐transfected cells exhibited the strongest antituberculosis effect and the highest rate of Mtb‐induced apoptosis. Of note, this apoptosis pathway was independent of caspase‐3 activation. Instead, N‐Myc interactor (NMI), an inhibitor of Wnt signalling, was a key player in IL‐32ε‐mediated apoptosis by inhibiting Wnt/β‐catenin signalling and thereby activating c‐Myc‐mediated apoptosis. Moreover, we identified two cis‐acting elements that are binding sites for the transcriptional regulators paired box 6 (PAX6) and transcription factor CP2 (TFCP2) in the promoter of NMI and these elements proved essential for IL‐32ε‐induced upregulation of Nmi expression. Furthermore, IL‐32ε‐mediated activation of the mitogen‐activated protein kinase p38 also contributed to NMI upregulation. In conclusion, our results demonstrate that Mtb infection‐induced IL‐32ε‐mediated apoptosis in macrophages plays a key role in host defences against Mtb. In this study we demonstrate that IL‐32ε exhibits antibacterial and pro‐apoptotic effects in RAW264.7 cells infected with Mycobacterium tuberculosis (Mtb). IL‐32ε leads to activation of p38 MAPK, which in turn activates Pax6 and CP2, resulting in upregulation of N‐Myc interactor (NMI) expression. Finally, IL‐32ε represses Wnt/β‐catenin signalling, which results in c‐Myc‐induced apoptosis. Our results contribute to understanding the role of IL‐32 in control of Mtb. [ABSTRACT FROM AUTHOR]

With great mechanical properties and corrosion resistance, amorphous alloy fiber (AAF) is a highly anticipated material in the fiber-reinforced concrete (FRC) field. In this study, the mechanical properties of AAFRC such as compressive strength, tensile strength, and flexural strength were examined. The comparison and analysis between AAFRC and steel fiber-reinforced concrete (SFRC) were also carried out. The results show that adding fibers significantly improves the concrete strength and toughness index. Compared with plain concrete, the compressive strength, splitting tensile strength, and flexural strength of AAFRC increase by 8.21–16.72%, 10.4–32.8%, and 18.12–45.21%, respectively. Meanwhile, the addition of AAF with a greater tensile strength and larger unit volume quantity improves the splitting tensile strength and flexural strength of concrete more noticeably than that of SF. Adding AAF improves the ductility of concrete more significantly in comparison to the SF. AAFRC shows great interfacial bonding performance as well. A prediction equation for the strength of AAFRC was proposed, which verified good accuracy calibrated based on the test results. [ABSTRACT FROM AUTHOR]

In reversal learning tasks, the behavior of humans and animals is often assumed to be uniform within single experimental sessions to facilitate data analysis and model fitting. However, behavior of agents can display substantial variability in single experimental sessions, as they execute different blocks of trials with different transition dynamics. Here, we observed that in a deterministic reversal learning task, mice display noisy and sub-optimal choice transitions even at the expert stages of learning. We investigated two sources of the sub-optimality in the behavior. First, we found that mice exhibit a high lapse rate during task execution, as they reverted to unrewarded directions after choice transitions. Second, we unexpectedly found that a majority of mice did not execute a uniform strategy, but rather mixed between several behavioral modes with different transition dynamics. We quantified the use of such mixtures with a state-space model, block Hidden Markov Model (block HMM), to dissociate the mixtures of dynamic choice transitions in individual blocks of trials. Additionally, we found that blockHMM transition modes in rodent behavior can be accounted for by two different types of behavioral algorithms, model-free or inference-based learning, that might be used to solve the task. Combining these approaches, we found that mice used a mixture of both exploratory, model-free strategies and deterministic, inference-based behavior in the task, explaining their overall noisy choice sequences. Together, our combined computational approach highlights intrinsic sources of noise in rodent reversal learning behavior and provides a richer description of behavior than conventional techniques, while uncovering the hidden states that underlie the block-by-block transitions. Author summary: Humans and animals can use diverse decision-making strategies to maximize rewards in uncertain environments, but previous studies have not investigated the use of multiple strategies that involve distinct latent switching dynamics in reward-guided behavior. Here, using a reversal learning task, we showed that mice displayed a much more variable behavior than would be expected from a uniform strategy, suggesting that they mix between multiple behavioral modes in the task. We develop a computational method to dissociate these learning modes from behavioral data, addressing the challenges faced by current analytical methods when agents mix between different strategies. We found that the use of multiple strategies is a key feature of rodent behavior even in the expert stages of learning, and applied our tools to quantify the highly diverse strategies used by individual mice in the task. We further mapped these behavioral modes to two types of underlying algorithms, model-free Q-learning and inference-based behavior. These rich descriptions of underlying latent states form the basis of detecting abnormal patterns of behavior in reward-guided decision-making. [ABSTRACT FROM AUTHOR]

We develop a robust optimization model for designing a three-echelon supply chain network that consists of manufacturers, distribution centers, and retailers under both demand uncertainty and supply disruptions. The market demands are assumed to be random variables with known distribution and the supply disruptions caused by some of the facilities faults or connection links interruptions are formulated by several scenarios with unknown occurrence probabilities. In particular, we assume the probabilities that the disruption scenarios happen belong to the two predefined uncertainty sets, named box and ellipsoid uncertainty sets, respectively. Through mathematical deductions, the proposed robust SCN design models can be transformed into the tractable linear program for box uncertainty and into second-order cone program for ellipsoid uncertainty. We further offer propositions with proof to show the equivalence of the transformed problems with the original ones. The applications of the proposed models together with solution approaches are investigated in a real case to design a tea supply chain network and validate their effectiveness. Numerical results obtained from model implementation and sensitivity analysis arrive at important practical insights. [ABSTRACT FROM AUTHOR]

Eucalypt trees are a key feature of many Australian ecosystems; however, it can be difficult to identify the various species based on their leaves alone. The rapid, non-destructive analytical technique of near-infrared spectroscopy (NIRS) has shown promise for discrimination of similar species in other flora types; thus this study investigated the prospect of using NIRS to differentiate between the leaves of six eucalypt species from two genera (Corymbia and Eucalyptus). The results showed good results for the discrimination of genera using PLS-DA (98 % accuracy) and SIMCA (72–90 % accuracy). Moving window analysis showed that the region between 1300 and 1500 nm – which includes CH and OH bonds – had the largest influence on model accuracy. Discrimination of the six species was more challenging, with PLS-DA showing accuracies of 83–97 % for cross-validation. However, SIMCA showed more acceptable results, with accuracies of 100 % for cross-validation and 79–90 % for a dependent test set. These results suggest that NIRS combined with appropriate chemometric methods may be suitable for the rapid discrimination of leaves from different eucalypt genera and possibly different species. [ABSTRACT FROM AUTHOR]

Gibbs free energy was applied to analyze the carbothermal reduction process of TiO. The phase stable diagram of Ti-C-CO was drawn, and it can be found that TiO will be reduced to Ti-O Magnéli phase, TiO (3 < n < 10) or TiO first and then further reduced to TiC. However, in the present study, it was found that TiO can be reduced to TiC directly when the temperature was lower than 800 K and lg( p/ p) < −1.1. The reduction route was proved by the results of non-isothermal TG-DTA combined XRD. The isothermal analysis of carbothermal reduction process of TiO via TG-DTA under the temperature of 1363, 1413, 1463 and 1513 K was performed. The whole reduction process was divided into two sections: Initially it was rate controlled by an interfacial reaction, and later was rate controlled by three-dimensional diffusion. The modified mathematical expressions were deduced. They fit the experimental curves well. Their activation energy of steps was 241.1 and 323.4 kJ mol, respectively. [ABSTRACT FROM AUTHOR]

This paper presents a new broadband ocean bottom seismograph (OBS) developed by the SUSTech OBS lab for passive-source seafloor seismic observations. This instrument, called Pankun, has several key features that set it apart from traditional OBS instruments. In addition to the seismometer-separated scheme, these features include a unique shielding structure to minimize current-induced noise, a compact gimbal for accurate leveling, and low power consumption for extended operation on the seafloor. The design and testing of Pankun's primary components are thoroughly described in this paper. The instrument has been successfully tested in the South China Sea, demonstrating its ability to record high-quality seismic data. The anti-current shielding structure of Pankun OBS has the potential to improve low-frequency signals, particularly on the horizontal components, in seafloor seismic data. [ABSTRACT FROM AUTHOR]

As per research, causing cancer cells to necroptosis might be used as a therapy to combat cancer drug susceptibility. Long non-coding RNA (lncRNA) modulates the necroptosis process in Skin Cutaneous Melanoma (SKCM), even though the precise mechanism by which it does so has yet been unknown. RNA sequencing and clinical evidence of SKCM patients were accessed from The Cancer Genome Atlas database, and normal skin tissue sequencing data was available from the Genotype-Tissue Expression database. Person correlation analysis, differential screening, and univariate Cox regression were successively utilized to identify necroptosis-related hub lncRNAs. Following this, we adopt the least absolute shrinkage and selection operator regression analysis to construct a risk model. The model was evaluated on various clinical characteristics using many integrated approaches to ensure it generated accurate predictions. Through risk score comparisons and consistent cluster analysis, SKCM patients were sorted either high-risk or low-risk subgroups as well as distinct clusters. Finally, the effect of immune microenvironment, m7G methylation, and viable anti-cancer drugs in risk groups and potential clusters was evaluated in further detail. Included USP30-AS1, LINC01711, LINC00520, NRIR, BASP1-AS1, and LINC02178, the 6 necroptosis-related hub lncRNAs were utilized to construct a novel prediction model with excellent accuracy and sensitivity, which was not influenced by confounding clinical factors. Immune-related, necroptosis, and apoptosis pathways were enhanced in the model structure, as shown by Gene Set Enrichment Analysis findings. TME score, immune factors, immune checkpoint-related genes, m7G methylation-related genes, and anti-cancer drug sensitivity differed significantly between the high-risk and low-risk groups. Cluster 2 was identified as a hot tumor with a better immune response and therapeutic effect. Our study may provide potential biomarkers for predicting prognosis in SKCM and provide personalized clinical therapy for patients based on hot and cold tumor classification. [ABSTRACT FROM AUTHOR]

Variable speed limit (VSL) is well-known for the ability to eliminate freeway wide moving jam (WMJ). Different VSL maneuvers have been proposed and proved to be useful theoretically or empirically. However, the coordination of VSL still needs to be analyzed in order to cater for different traffic conditions encountered on the roadway. In this paper, we execute a study on freeway VSL method based on the three-phase traffic theory and Cellular Automata (CA) based simulations. Results indicate that VSL is a promising method in solving freeway WMJs, but in some cases, it induces new congestions due to improper configurations and the stochastic feature of traffic flow. Therefore, in this paper, we propose an optimal coordination model of VSL including five main principles. The purpose of our model lies on two aspects, to ensure the elimination of WMJ and to avoid the emergence of new traffic jams. By analyzing the traffic dynamics, we first present several theoretical principles of VSL configuration with respect to the WMJ elimination. Second, we study the relation between VSL effectiveness and the traffic uncertainty through CA model-based simulations and some empirical principles are raised. [ABSTRACT FROM AUTHOR]

One frequently observed congested traffic flow pattern is wide moving jam (WMJ), in which the average vehicle speed is very low and the density is very high. In some recent studies, variable speed limits (VSL) were proposed as effective measures to eliminate or abate the influence of jam waves. However, in most of these studies, the stochastic features of driving behaviors and the resulting uncertainty of traffic flow dynamics were not fully considered. In this paper, we use cellular automaton (CA) model-based simulations to test the performances of different VSL control strategies and apply the three-phase traffic theory to further analyze the obtained results. Based on the simulation results, we got two novel findings. Firstly, we observed seven, instead of the previously assumed six, states of traffic flow in the evolution process of WMJ, when VSL were applied. Secondly and more importantly, we found that inappropriate speed limit may induce new WMJ and exaggerate congestions in two ways: one way corresponds to an F → J transition and the other corresponds to an F → S → J transition. Based on these findings, the appropriate lower bound of VSL was finally discussed in this paper. [ABSTRACT FROM AUTHOR]

Ubiquitin-specific protease 4 (USP4), has been reported to participate in the progression of various cancers due to its role in post-translational modulation. However, the prognostic significance and mechanism of USP4 in pancreatic cancer (PC) have not been well elucidated before. In the present study, we found that USP4 expression was higher in PC tissues than that in adjacent normal tissues and PC patients with high level of USP4 expression have a poor prognosis via immunohistochemistry and bioinformatics analyses. In vitro study showed that knockdown of USP4 inhibited PC cells proliferation, migration and invasion. Mechanistically, USP4 can activate nuclear factor kappa-B signaling pathway via stabilizing TNF receptor associated factor 6 at its protein level to promote the ability of proliferation, migration and invasion of PC cells. The results of this study revealed that USP4 plays a tumor-promoting role in PC and can be used as a prognostic indicator and therapeutic target for patients with resected PC. • USP4 expression was significantly up-regulated in PC tissues than that in adjacent normal tissues. • High USP4 expression significantly correlated with low PC histological grade and unfavorable prognosis of PC patients. • USP4 promoted proliferation, migration and invasion of PC cells. • USP4 promoted the malignant biological behaviors of PC through activating NF-κB signaling pathway by stabilizing TRAF6. [ABSTRACT FROM AUTHOR]

• Low-freq compensation algo improves the geophone's response, extending its natural freq from 4.5 Hz to < 0.2 Hz. • The improved short-period seismometer boast a compact design, affordability, and robust construction. • The bandwidth extension circuit has extremely low power consumption. • The improved short-period seismometer is well-suited for use in dense array observations. Short-period geophones are widely used instruments in dense seismic array investigations. However, inherent limitations in capturing low-frequency signals restrict their versatility. This study implements a low-frequency compensation algorithm via a state-variable filtering circuit to enhance the frequency response of a traditional short-period geophone. Through simulation experiments and vibration testing, we validated the performance of the proposed bandwidth-extension (BE) system. Additionally, time–frequency earthquake signal analysis compared outcomes between our BE seismometers, benchmark short-period seismometers, and high-fidelity broadband seismometers across three seismic events with epicentral distances up to 5,186 km. Results demonstrate the BE system successfully extends the natural frequency cutoff from 4.5 Hz to below 0.2 Hz while maintaining strong consistency with broadband counterparts. Moreover, negligible power demands (10 mW) facilitate embedding the BE circuit directly within acquisition hardware. Given these advantages, the BE method shows promise for upgrading future sensors of seismic networks to record low-frequency content with minimal costs. [ABSTRACT FROM AUTHOR]

Background: The aim of this study was to develop and validate an individualized score based on preoperative parameters to predict patient outcomes after vaginal repair of cesarean section diverticulum.Methods: This is a retrospective cohort study (Canadian Task Force classification II-2). Patients were enrolled between Jun 11, 2012, to May 27, 2016. Multivariable logistic regression analyses were used to construct the predictive model. Then, we generated a nomogram to assess the individualized risk of poor prognosis after operation. This prediction model included information from 167 eligible patients diagnosed with cesarean section diverticulum who underwent vaginal repair. Class-A healing group was defined as CSD patients who had menstruation duration of no more than 7 days and a thickness of the remaining muscular layer of no less than 5.8 mm after vaginal repair according to conferences. Others were included in the non-class-A healing group. A final nomogram was computed using a multivariable logistic regression model.Results: The factors contained in the individualized prediction nomogram included the depth/ the thickness of the remaining muscular layer ratio, number of menstruation days before surgery, White blood cell and fibrinogen. This model demonstrated adequate discrimination and calibration (C-index = 0.718). There was a significant difference in the number of postmenstrual spotting days (12.98 ± 3.86 VS 14.46 ± 2.86, P = 0.022) and depth/ the thickness of the remaining muscular layer ratio (2.81 ± 1.54 VS 4.00 ± 3.09, P = 0.001) between two groups. Decision curve analysis showed that this nomogram was clinically useful.Conclusions: This cesarean section diverticulum score can predict the outcomes of cesarean section diverticulum and can be useful for counseling patients who are making treatment decisions. [ABSTRACT FROM AUTHOR]

Cellular senescence is a persistently hypoproliferative state with diverse stressors in a specific aging microenvironment. Senescent cells have a double‐edged sword effect: they can be physiologically beneficial for tissue repair, organ growth, and body homeostasis, and they can be pathologically harmful in age‐related diseases. Among the hallmarks of senescence, the SASP, especially SASP‐related extracellular vesicle (EV) signalling, plays the leading role in aging transmission via paracrine and endocrine mechanisms. EVs are successful in intercellular and interorgan communication in the aging microenvironment and age‐related diseases. They have detrimental effects on downstream targets at the levels of immunity, inflammation, gene expression, and metabolism. Furthermore, EVs obtained from different donors are also promising materials and tools for antiaging treatments and are used for regeneration and rejuvenation in cell‐free systems. Here, we describe the characteristics of cellular senescence and the aging microenvironment, concentrating on the production and function of EVs in age‐related diseases, and provide new ideas for antiaging therapy with EVs. [ABSTRACT FROM AUTHOR]

(1) Background: Excessive nitrogen (N) fertilizer application in tea plantations leads to challenges such as soil acidification and nitrogen loss, impending the sustainable development of the plantation system. Yet, there is a lack of research on blended fertilization strategies, and limited data regarding N loss when substituting with organic fertilizer. (2) Methods: A year-long field monitoring experiment was conducted to evaluate the effects of substituting compound fertilizer with organic fertilizer, specifically with respect to runoff N loss and uptake of chemical fertilizer N by tea trees. (3) Results: The annual runoff N loss ranged from 0.16 to 0.57 kg·hm−2 and accounted for a mere 0.22–0.48% of N from fertilizer applications. Substitution with organic fertilizer reduced runoff N loss by 21–53% and improved the tea tree utilization efficiency of chemical fertilizer N from 16% to 27%. A 50% organic fertilizer substitution (based on the amount of N) promoted a net soil N mineralization rate, creating an ammonium-rich environment favored by tea trees. (4) Conclusions: The positive effects of partially substituting N fertilizer with organic fertilizer in tea plantation systems on both N utilization efficiency and N loss were confirmed. If conditions permit, the study team would aim to expand the temporal scope of the study, and to investigate the impact of organic fertilizer substitution on N loss under various precipitation intensities. [ABSTRACT FROM AUTHOR]

Simple Summary: This study proposes an innovative approach to automatically identify invasive carcinoma regions in breast cancer immunohistochemistry whole-slide images, which is crucial for fully automated immunohistochemistry quantification. The proposed method leverages a neural network that combines multi-scale morphological features with boundary features, enabling precise segmentation of invasive carcinoma regions without the need for additional staining slides. The model demonstrated an impressive intersection over union score on the test set, and a fully automated Ki-67 scoring system based on the model's predictions exhibited high consistency with the scores given by experienced pathologists. The proposed method brings the breast cancer fully immunohistochemistry quantitative scoring system one step closer to clinical application. Aims: The automation of quantitative evaluation for breast immunohistochemistry (IHC) plays a crucial role in reducing the workload of pathologists and enhancing the objectivity of diagnoses. However, current methods face challenges in achieving fully automated immunohistochemistry quantification due to the complexity of segmenting the tumor area into distinct ductal carcinoma in situ (DCIS) and invasive carcinoma (IC) regions. Moreover, the quantitative analysis of immunohistochemistry requires a specific focus on invasive carcinoma regions. Methods and Results: In this study, we propose an innovative approach to automatically identify invasive carcinoma regions in breast cancer immunohistochemistry whole-slide images (WSIs). Our method leverages a neural network that combines multi-scale morphological features with boundary features, enabling precise segmentation of invasive carcinoma regions without the need for additional H&E and P63 staining slides. In addition, we introduced an advanced semi-supervised learning algorithm, allowing efficient training of the model using unlabeled data. To evaluate the effectiveness of our approach, we constructed a dataset consisting of 618 IHC-stained WSIs from 170 cases, including four types of staining (ER, PR, HER2, and Ki-67). Notably, the model demonstrated an impressive intersection over union (IoU) score exceeding 80% on the test set. Furthermore, to ascertain the practical utility of our model in IHC quantitative evaluation, we constructed a fully automated Ki-67 scoring system based on the model's predictions. Comparative experiments convincingly demonstrated that our system exhibited high consistency with the scores given by experienced pathologists. Conclusions: Our developed model excels in accurately distinguishing between DCIS and invasive carcinoma regions in breast cancer immunohistochemistry WSIs. This method paves the way for a clinically available, fully automated immunohistochemistry quantitative scoring system. [ABSTRACT FROM AUTHOR]

We analyze a political competition model of redistributive policies. We provide an equilibrium existence result and a full characterization of the net transfers to the different income groups. We also derive several testable predictions about the way in which the net group transfers and the after-tax Gini coefficient vary with the main parameters of the model. In accordance with the theory, the empirical evidence from a sample of developed and developing democracies supports a highly statistically significant association between: (i) the net group transfer and the gap between the population and the group mean initial income, and (ii) the net group transfer (and resp., the Gini coefficient) and power sharing disproportionality. In addition, the data also provide some empirical evidence confirming a significant relationship between the net transfers to the poor (and resp., the Gini) and the concern of the political parties with income inequality. [ABSTRACT FROM AUTHOR]

Paraboea nanxiensis Lei Cai & Gui L. Zhang, a new species of Gesneriaceae from karst area around the Sino,Vietnamese border, is described here. The new species is morphologically similar to P. nutans D. Fang & D. H. Qin in the shape and indumentum characters of leaf blade, color of flower, length of the inflorescence and glabrous pistil, but it can be easily distinguished by the shape of the corolla, the color of flower interior, the shape and indumentum of calyx lobes, and capsule morphology. The detailed descriptions, color photographs, distribution and ecology, as well as its morphological relationship with similar species are also provided. In recent years, many new taxa or newly record species have been found in limestone areas along the Sino Vietnam border, it is necessary to strengthen the investigation of plant diversity for better understanding the high biodiversity in this area. [ABSTRACT FROM AUTHOR]

Anoikis is a unique form of apoptosis associated with vascularization and distant metastasis in cancer. Eliminating anoikis resistance in tumor cells could be a promising target for improving the prognosis of terminal cancer patients. However, current studies have not elaborated on the prognosis effect of anoikis-related long non-coding RNAs (lncRNAs) in cutaneous melanoma. Pre-processed data, including RNA sequences and clinical information, were retrieved from TCGA and GTEx databases. After a series of statistical analyses, anoikis-related lncRNAs with prognostic significance were identified, and a unique risk signature was constructed. Risk scores were further analyzed in relation to the tumor microenvironment, tumor immune dysfunction and exclusion, immune checkpoint genes, and RNA methylation genes. The indicators were also used to predict the potentially sensitive anti-cancer drugs. An anoikis-related lncRNAs risk signature consisting of LINC01711, POLH-AS1, MIR205HG, and LINC02416 was successfully established in cutaneous melanoma. Overall survival and progression-free survival of patients were strongly linked with the risk score, independently of other clinical factors. The low-risk group exhibited a more beneficial immunological profile, was less affected by RNA methylation, and was more sensitive to the majority of anti-cancer drugs, all of which indicated a better prognostic outcome. The 4 hub lncRNAs may be fundamental to studying the mechanism of anoikis in cutaneous melanoma and provide personalized therapy for salvaging drug resistance. [ABSTRACT FROM AUTHOR]

To enhance the surface insulation properties of SiO2/epoxy resin composites, the SiO2 filler is co-modified with a chemical method and dielectric barrier discharge plasma in this work. The effects on the micro-structures, electrical parameters and surface insulation properties of the materials are studied. The results show that chemical modification using the silane coupling agent (KH550) can effectively introduce organo-functional groups into SiO2 filler. On the other hand, plasma modification shows little effect on the organo-functional group but significantly increases the dispersity of the nanoparticles, therefore reducing filler conglobation in epoxy resin composite. The composite samples with SiO2 doping concentration of 1 wt.%, 2 wt.%, 3 wt.%, 5 wt.% and 7 wt.% are prepared and characterized. It is found that the synergy of chemical and plasma methods could significantly improve the surface insulation of composite samples. Through doping 2 wt.% of the co-modified SiO2 filler, the direct current flashover voltage of the composites in dry air at atmospheric pressure can be increased to 1.53 times of the pure epoxy. The enhanced surface insulation properties are explained by the trap effect and the change of electrical parameters through the co-modification process. [ABSTRACT FROM AUTHOR]

Given the idea that financial institutions can have a significant impact on environmental performance, green credit, which essentially involves capital allocation under environmental awareness, is worth to be explored and implemented by banking sectors worldwide. This paper explores the impact of environmental awareness on firms' loan rates, with a focus on borrowers who have a record of environmental penalties. Using a comprehensive loan dataset from one of China's "Big Five" banks between 2010 and 2013, we employ the volatility of particulate matter 2.5 (PM 2.5) as a proxy to measure environmental awareness. Our findings suggest that the volatility of air pollution has a positive effect on the floating ratio of the loan interest rate for firms with a history of environmental penalties. Furthermore, we find that this mechanism is particularly pronounced for private-owned enterprises (POEs), new loan customers, firms with low crediting ratings, and small-sized enterprises. We also confirm the role of environmental awareness in firms' loan costs by demonstrating that the volatility of air pollution is sensitive to variations in insolation duration, environmental attention, and PM2.5 monitoring stations. We suggest that the volatility of air pollution is a more meaningful indicator than the degree of air pollution, and that people's voluntary environmental awareness can transfer the environmental risk of firms to their internal operating costs. The significance of this study lies in its potential to aid policymakers and investors in advancing sustainable finance practices, as well as stimulating individual engagement with environmental awareness and prompting financial institutions to give greater importance to these issues. • Environmental awareness increases non-compliant firms' loan costs. • Environmental awareness can transfer the environmental risk of firms to their operating costs. • The mechanism more strongly impacts POEs and small-size firms. • Sensitivity of environmental awareness to insolation, attention, and PM 2.5 monitoring. • The volatility of air pollution is a more meaningful predictor than the degree of air pollution. [ABSTRACT FROM AUTHOR]

Background: CD133 is considered a marker for cancer stem cells (CSCs) in several types of tumours, including hepatocellular carcinoma (HCC). Chimeric antigen receptor-specific T (CAR-T) cells targeting CD133-positive CSCs have emerged as a tool for the clinical treatment of HCC, but immunogenicity, the high cost of clinical-grade recombinant viral vectors and potential insertional mutagenesis limit their clinical application. Methods: CD133-specific CAR-T cells secreting PD-1 blocking scFv (CD133 CAR-T and PD-1 s cells) were constructed using a sleeping beauty transposon system from minicircle technology, and the antitumour efficacy of CD133 CAR-T and PD-1 s cells was analysed in vitro and in vivo. Results: A univariate analysis showed that CD133 expression in male patients at the late stage (II and III) was significantly associated with worse progression-free survival (PFS) (P = 0.0057) and overall survival (OS) (P = 0.015), and a multivariate analysis showed a trend toward worse OS (P = 0.041). Male patients with advanced HCC exhibited an approximately 20-fold higher PD-L1 combined positive score (CPS) compared with those with HCC at an early stage. We successfully generated CD133 CAR-T and PD-1 s cells that could secrete PD-1 blocking scFv based on a sleeping beauty system involving minicircle vectors. CD133 CAR-T and PD-1 s cells exhibited significant antitumour activity against HCC in vitro and in xenograft mouse models. Thus, CD133 CAR-T and PD-1 s cells may be a therapeutically tractable strategy for targeting CD133-positive CSCs in male patients with advanced HCC. Conclusions: Our study provides a nonviral strategy for constructing CAR-T cells that could also secrete checkpoint blockade inhibitors based on a Sleeping Beauty system from minicircle vectors and revealed a potential benefit of this strategy for male patients with advanced HCC and high CD133 expression (median immunohistochemistry score > 2.284). [ABSTRACT FROM AUTHOR]

Highlights • Heat-treated mortar exhibits lower mass loss than that of untreated mortar. • The drying shrinkage of untreated and heat-treated mortar decreases with the increase of the replacement amount of GGBS. • There is an optimal content of about 40% GGBS to restrain drying shrinkage. • The main effect of heat curing treatment on mortar incorporating lower content GGBS is the structure of C-S-H gel. • The hydration degree and pore structure have a more significant influence for mortar incorporating higher content GGBS. Abstract The effect of heat curing treatment that samples cured from room temperature to an elevated temperature of 60 °C for 24 h on the mass loss, drying shrinkage and microstructure of mortar incorporating ground granulated blast-furnace slag (GGBS) as cement replacement at 0%, 20%, 40% and 60% by weight was experimentally investigated in this study. The results show that the heat-treated mortar exhibits lower mass loss compared to the untreated mortar. In addition, when the content of GGBS is 0% or 20%, the heat-treated mortar has a lower drying shrinkage than that of untreated mortar. However, when the content of GGBS is 40% or 60%, the result is the opposite. Moreover, for the heat-treated and untreated mortar, there is an optimal replacement content of about 40% GGBS to restrain drying shrinkage. For the mortar incorporating 0% or 20% GGBS, infrared spectrum (IR) and nuclear magnetic resonance (NMR) analysis reveal that the heat curing treatment mainly leads to a higher degree of silicon polymerization in C-S-H gel and consequently affects the drying shrinkage. For the mortar incorporating 40% or 60% GGBS, scanning electron microscopy (SEM), chemical bound water and mercury intrusion porosimetry (MIP) analysis show that heat curing treatment mainly increases the amount of C-S-H gel and mesopore (<50 nm) of mortar and consequently affects the drying shrinkage. [ABSTRACT FROM AUTHOR]

Background: Inflammation is known to have an intricate relationship with tumorigenesis and tumor progression while it is also closely related to tumor immune microenvironment. Whereas the role of inflammation‐related genes (IRGs) in lung squamous carcinoma (LUSC) is barely understood. Herein, we recognized IRGs associated with overall survival (OS), built an IRGs signature for risk stratification and explored the impact of IRGs on immune infiltration landscape of LUSC patients. Methods: The RNA‐sequencing and clinicopathological data of LUSC patients were downloaded from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database, which were defined as training and validation cohorts. Cox regression and least absolute shrinkage and selection operator analyses were performed to build an IRG signature. CIBERSORT, microenvironment cell populations‐counter and tumor immune dysfunction and rejection (TIDE) algorithm were used to perform immune infiltration analysis. Results: A two‐IRG signature consisting of KLF6 and SGMS2 was identified according to the training set, which could categorize patients into two different risk groups with distinct OS. Patients in the low‐risk group had more anti‐tumor immune cells infiltrated while patient with high‐risk had lower TIDE score and higher levels of immune checkpoint molecules expressed. The IRG signature was further identified as an independent prognostic factor of OS. Subsequently, a prognostic nomogram including IRG signature, age, and cancer stage was constructed for predicting individualized OS, whose concordance index values were 0.610 (95% CI: 0.568–0.651) in the training set and 0.652 (95% CI: 0.580–0.724) in validation set. Time‐dependent receiver operator characteristic curves revealed that the nomogram had higher prediction accuracy compared with the traditional tumor stage alone. Conclusion: The IRG signature was a predictor for patients with LUSC and might serve as a potential indicator of the efficacy of immunotherapy. The nomogram based on the IRG signature showed a relatively good predictive performance in survival. [ABSTRACT FROM AUTHOR]

Understanding the interactions among climate change, carbon emission allowance trading, crude oil and renewable energy stock markets, especially the role of climate change in this system is of great significance for policy makers, energy producers/consumers and relevant investors. The present paper aims to quantify the time-varying connectedness effects among the four factors by using the TVP-VAR based extensions of both time- and frequency-domain connectedness index measurements proposed by Antonakakis et al. (2020) and Ellington and Barunik (2021) [8,48]. The empirical results suggest that, firstly, the average total connectedness among climate change, carbon emission allowance trading, crude oil and renewable energy stock markets is not so strong for the heterogenous fundamentals underlying them. Nevertheless, the time-varying total connectedness fluctuates fiercely through May 2005 to September 2021, varying from about 8% to 30% and rocket to very high levels during the global subprime mortgage crisis and the COVID-19 pandemic. Furthermore, the total connectedness mainly centers on the short-term frequency, i.e., 1–3 months. Secondly, climate change is generally the leading information contributor among the four factors, although not particularly strong, and its leading role also performs mainly on the short-term frequency (1–3 months). Thirdly, renewable energy stock market and crude oil market show tight interactions between them and they are the two major bridges of information exchanges across various time frequencies (horizons) in this system. Finally, we confirm the evidence that the primary net connectedness contributor and receiver switch frequently across different time frequencies, implying that it is extremely essential for policy makers, energy producers/consumers and investors to make time-horizon-specific regulatory, production/purchasing or investment decisions when facing the uncertain effects of climate change on the interactions among carbon emission allowance, crude oil and renewable energy stock markets. [ABSTRACT FROM AUTHOR]

Metal rolls in a non-ferrous-metal manufacturing workshop manifest the characteristics of symmetry, multiple scales and mutual covering, which poses great challenges for metal roll detection. To solve this problem, firstly, an efficient attention mechanism algorithm named ECLAM (efficient capture location attendant model) is proposed for capturing spatial position features efficiently, to obtain complete location information for metal rolls in a complex environment. ECLAM can improve the ability to extract the spatial features of backbone networks and reduce the influence of the non-critical background. In addition, in order to give feature maps a larger receptive field and improve the weight of location information in multi-scale feature maps, a nonlinear feature fusion module named LFFM (location feature fusion module) is used to fuse two adjacent feature images. Finally, a multi-scale object detection network named L-MSNet (location-based multi-scale object detection network) based on the combination of ECLAM and LFFM is proposed and used to accurately detect multi-scale metal rolls. In the experiments, multi-scale metal roll images are collected from an actual non-ferrous-metal manufacturing workshop. On this basis, a pixel-level image dataset is constructed. Comparative experiments show that, compared with other object detection methods, L-MSNet can detect multi-scale metal rolls more accurately. The average accuracy is improved by 2% to 5%, and the average accuracy of small and medium-sized objects is also significantly improved by 3% to 6%. [ABSTRACT FROM AUTHOR]

Novel amended sands enveloped in bio-film (B-NAS) were employed to treat the ammonium-nitrogen (NH4+-N) in raw water. The backwashing conditions, treatment efficiency, and morphological characteristics of the B-NAS were investigated via bio-filtration experiments. The bare quartz sands (BQS) and NAS exhibited biomass levels of 24.32 nmol-P g-sand-1and 83.71 nmol-P g-sand−1, respectively. B-NAS was most effective when air alone (at a strength ofqair,I= 7 L s−1m−2fortair,I= 6 min), a combination of air–water (at strengths ofqair,II= 7 L s−1m−2andqwater,II= 5 L s−1m−2fortair-water,II= 6 min), and water alone (at a strength ofqwater,III= 7 L s−1m−2fortwater,III= 4 min) were employed alternately for flushing. The proposed B-NAS with nano-pores surface effectively (1) resisted the shock produced by high NH4+-N concentrations (4 mg L−1), (2) exhibited removal rate of 96%, and (3) a high capturing capacity indicated by a high head loss (126.5 cm) for 72 hr. [ABSTRACT FROM PUBLISHER]

Past research suggests that people with different thinking styles show different cognitive processes. Accordingly, we test how thinking style and advice jointly affect investment decision. We conduct three experiments with 530 participants. In Study 1, coin, card and stamp investors who had high levels of holistic thinking and made decisions on their own obtained the lowest returns. In Study 2, participants who used analytic thinking to make decisions on their own in the Balloon Analog Risk Task (BART) earned the most. In Study 3, Westerners who made decisions on their own using analytic thinking had the highest incomes, while Easterners using holistic thinking and listening to others also had positive returns. The results support the framing effect in investment decisions, and the two simulation paradigms are presented for future studies and to confirm the impacts of thinking styles. [ABSTRACT FROM AUTHOR]

Missing values are a major issue in quantitative proteomics analysis. While many methods have been developed for imputing missing values in high-throughput proteomics data, a comparative assessment of imputation accuracy remains inconclusive, mainly because mechanisms contributing to true missing values are complex and existing evaluation methodologies are imperfect. Moreover, few studies have provided an outlook of future methodological development. We first re-evaluate the performance of eight representative methods targeting three typical missing mechanisms. These methods are compared on both simulated and masked missing values embedded within real proteomics datasets, and performance is evaluated using three quantitative measures. We then introduce fused regularization matrix factorization, a low-rank global matrix factorization framework, capable of integrating local similarity derived from additional data types. We also explore a biologically-inspired latent variable modeling strategy—convex analysis of mixtures—for missing value imputation and present preliminary experimental results. While some winners emerged from our comparative assessment, the evaluation is intrinsically imperfect because performance is evaluated indirectly on artificial missing or masked values not authentic missing values. Nevertheless, we show that our fused regularization matrix factorization provides a novel incorporation of external and local information, and the exploratory implementation of convex analysis of mixtures presents a biologically plausible new approach. [ABSTRACT FROM AUTHOR]

For the airborne pod strapdown inertial navigation system, it is necessary to use the host aircraft's inertial navigation system for the transfer alignment as quickly and accurately as possible in the flight process of the aircraft. The purpose of this paper is to propose an adaptive transfer alignment method based on the observability analysis for the strapdown inertial navigation system, which is able to meet the practical need of maintaining the navigation accuracy of the airborne pod. The observability of each state variable is obtained by observability analysis of system state variables. According to the weight of the observability, a transfer alignment filter algorithm based on adaptive adjustment factor is constructed to reduce the influence of weak observability state variables on the whole filter, which can improve the estimation accuracy of transfer alignment. Simulations and experiment tests of the airborne pod and the master strapdown inertial navigation systems show that the adaptive transfer alignment method based on the observability analysis can overcome the shortage of the weak observability state variables, so as to improve the alignment and the navigation performance in practical applications, thus improving the adaptability of the airborne pod. [ABSTRACT FROM AUTHOR]

From unstable crystals to relatively stable monohydrate crystals, many researchers have been working on S-nitrosocaptopril for more than two decades. S-nitrosocaptopril monohydrate (Cap-NO·H2O) is a novel crystal form of S-nitrosocaptopril (Cap-NO), and is not only a nitric oxide (NO) donor, but also an angiotensin-converting enzyme inhibitor (ACEI). Yet, a method for long-term storage has never been reported. In order to determine the optimal storage conditions, Plackett–Burman (PB) design was performed to confirm the critical factors. Response surface methodology (RSM) was employed to determine the optimal Cap-NO·H2O storage condition, based on the rough interval determined by the path of steepest ascent experiment. The optimized storage condition was denoted as nitrogen purity of 97%, temperature of −10 °C and 1.20 g deoxidizer. In this case, a final preservation rate of 97.91 ± 0.59% could be obtained. In specific storage conditions, Cap-NO·H2O was found to be stable for at least 6 months in individual PE package, procreating a potentially applicable avenue. [ABSTRACT FROM AUTHOR]

The neurotrophin receptor p75NTR conveys multiple signals via its intracellular death domain. However, how the death domain is activated and interacts with downstream adaptors remains unclear. Here, we report two crystal structures of the p75NTR death domain in the form of a non-covalent asymmetric dimer and a Cys379-Cys379 disulfide bond linked symmetric dimer, respectively. These two dimer arrangements have not previously been observed in other death domain-containing proteins. Further analysis shows that both the Cys379-Cys379 disulfide linked and non-covalent full-length p75NTR dimers are present on the cell surface. These observations suggest that various oligomers may exist simultaneously on the cell surface, and that p75NTR activation and signalling may be modulated by neurotrophins or other factors via inducing a shift of the equilibrium between different oligomeric states. [ABSTRACT FROM AUTHOR]