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Abstract Background Inositol‐requiring enzyme 1 (IRE1) is an endoplasmic reticulum (ER)‐resident transmembrane protein that senses ER stress and mediates an essential arm of the unfolded protein response (UPR). IRE1 reduces ER stress by upregulating the expression of multiple ER chaperones through activation of X‐box‐binding protein 1 (XBP1). Emerging lines of evidence have revealed that IRE1‐XBP1 axis serves as a multipurpose signal transducer during oncogenic transformation and cancer development. In this study, we explore how IRE1‐XBP1 signaling promotes chemoresistance in lung cancer. Methods The expression patterns of UPR components and MRP1 were examined by Western blot. qRT‐PCR was employed to determine RNA expression. The promoter activity was determined by luciferase reporter assay. Chemoresistant cancer cells were analyzed by viability, apoptosis. CUT & Tag (Cleavage under targets and tagmentation)‐qPCR analysis was used for analysis of DNA‐protein interaction. Results Here we show that activation of IRE1α‐XBP1 pathway leads to an increase in MDR‐related protein 1 (MRP1) expression, which facilitates drug extrusion and confers resistance to cytotoxic chemotherapy. At the molecular level, XBP1‐induced c‐Myc is necessary for SREBP1 expression, and SREBP1 binds to the MRP1 promoter to directly regulate its transcription. Conclusions We conclude that IRE1α‐XBP1 had important role in chemoresistance and appears to be a novel prognostic marker for lung cancer.

Abstract Indoor photovoltaics (IPVs) have attracted increasing attention for sustainably powering Internet of Things (IoT) electronics. Sb2S3 is a promising IPV candidate material with a bandgap of ~1.75 eV, which is near the optimal value for indoor energy harvesting. However, the performance of Sb2S3 solar cells is limited by nonradiative recombination, which is dependent on the quality of the absorber films. Additive engineering is an effective strategy to fine tune the properties of solution-processed films. This work shows that the addition of monoethanolamine (MEA) into the precursor solution allows the nucleation and growth of Sb2S3 films to be controlled, enabling the deposition of high-quality Sb2S3 absorbers with reduced grain boundary density, optimized band positions, and increased carrier concentration. Complemented with computations, it is revealed that the incorporation of MEA leads to a more efficient and energetically favorable deposition for enhanced heterogeneous nucleation on the substrate, which increases the grain size and accelerates the deposition rate of Sb2S3 films. Due to suppressed carrier recombination and improved charge-carrier transport in Sb2S3 absorber films, the MEA-modulated Sb2S3 solar cell yields a power conversion efficiency (PCE) of 7.22% under AM1.5 G illumination, and an IPV PCE of 17.55% under 1000 lux white light emitting diode (WLED) illumination, which is the highest yet reported for Sb2S3 IPVs. Furthermore, we construct high performance large-area Sb2S3 IPV minimodules to power IoT wireless sensors, and realize the long-term continuous recording of environmental parameters under WLED illumination in an office. This work highlights the great prospect of Sb2S3 photovoltaics for indoor energy harvesting.

Abstract The assembly of polymers at liquid-liquid interfaces offers a promising strategy for fabricating two-dimensional polymer films. However, a significant challenge arises when the polymers lack inherent interfacial traction. In response, we introduce an approach termed chaperone solvent-assisted assembly. This approach utilizes a target polymer, X, along with three solvents: α, β, and γ. α and β are poor solvents for X and immiscible with each other, while γ is a good solvent for X and miscible with both α and β, thus serving as the chaperone solvent. The cross-interface diffusion of γ induces the assembly of interfacially nonactive X at the α-β interface, and this mechanism is verified through systematic in situ and ex situ studies. We show that chaperone solvent-assisted assembly is versatile and reliable for the interfacial assembly of polymers, including those that are interfacially nonactive. Several practical applications based on chaperone solvent-assisted assembly are also demonstrated.

Protocatechuic acid (PCA), a major active component found in Eucommia ulmoides Oliver, is involved in regulating oxidative stress and inflammation. Heat stress poses a significant threat to the poultry industry. In this study, we investigated the protective effect of PCA on intestinal homeostasis under heat stress. Our results indicated that PCA improved the growth performance of broilers during heat stress. Furthermore, PCA mitigated jejunal damage and attenuated the upregulation of inflammatory mediators, including TNF-α, IL-6, and IL-1β, triggered by heat stress. Concurrently, it restored the activity of superoxide dismutase (SOD) and total antioxidant capacity (T-AOC). Furthermore, PCA maintained the jejunum function by increasing the levels of ZO-1, Claudin-1, and Occludin. Mechanistically, PCA inhibited the activation of TLR4/p38 MAPK and NF-κB pathways, thereby regulating the imbalance in oxidative stress and inflammatory responses caused by heat stress.

Amidst the global COVID-19 pandemic, the urgent need for timely and precise patient prognosis assessment underscores the significance of leveraging machine learning techniques. In this study, we present a novel predictive model centered on routine clinical laboratory test data to swiftly forecast patient survival outcomes upon admission. Our model integrates feature selection algorithms and binary classification algorithms, optimizing algorithmic selection through meticulous parameter control. Notably, we developed an algorithm coupling Lasso and SVM methodologies, achieving a remarkable area under the ROC curve of 0.9277 with the use of merely 8 clinical laboratory parameters collected upon admission. Our primary contribution lies in the utilization of straightforward laboratory parameters for prognostication, circumventing data processing intricacies, and furnishing clinicians with an expeditious and precise prognostic assessment tool.

To investigate the recovery effect of natural self-healing of freeze-thaw damage of cementitious material-poor hydraulic concrete, we carried out a natural self-healing test for hydraulic concrete damaged by up to 200 rapid freeze-thaw cycles. The self-healing effect of macro-mechanical properties of hydraulic concrete damaged by freeze-thaw cycles was quantitatively characterized by the recovery rate of the specimens which underwent different numbers of freeze-thaw cycles then continued the standard curing to 28 days. Next, the microscopic properties of damaged hydraulic concrete after self-healing were further investigated by combining XRD, SEM and nitrogen adsorption tests. Finally, a prediction model of the recovery rate of macro-mechanical properties was established by using the microscopic pore test results. The results showed that, when the hydraulic concrete undergoes the damage of different freeze-thaw cycles and continues to standard curing for 28 days, the concrete specimens manifest natural self-healing to some extent even if damaged by up to 200 freeze-thaw cycles.

Dampness-heat syndrome diarrhea (DHSD) is a common clinical disease with a high prevalence but still has no satisfactory therapeutic medicine, so the search for a safe and effective drug candidate is ongoing. This study aims to explore the efficacy and mechanisms of Lianweng granules (LWG) in the treatment of DHSD and to identify the blood transport components of LWG. We assessed the efficacy of LWG in DHSD by various in vivo metrics such as body weight, disease activity index (DAI), histopathologic examination, intestinal barrier function, levels of inflammatory, apoptotic biomarkers, and oxidative stress. We identified the blood components of LWG using ultra-high performance liquid chromatography-mass spectrometry/mass spectrometry (UHPLC-MS/MS), and the resolved key components were used to explore the relevant targets. We next predicted the potential mechanisms of LWG in treating DHSD using network pharmacology and molecular docking based on the relevant targets. Finally, the mechanisms were validated in vivo using RT-qPCR, Western blotting, ELISA, and immunofluorescence and evaluated in vitro using Cell Counting Kit-8 (CCK-8), small interfering RNA, cellular enthusiasm transfer assay (CETSA), and drug affinity response target stability (DARTS). Ninety-one pharmacodynamic components of LWG enter the bloodstream and exert possible therapeutic effects. In vivo, LWG treatment improved body weight, reduced colonic injury and DAI scores, lowered inflammation, oxidative stress, and apoptosis markers, and partially restored intestinal barrier function in DHSD mice. Guided by network pharmacology and molecular docking, it is suggested that LWG may exert therapeutic effects by inhibiting IL-6/STAT3/PI3K/AKT signaling. LWG significantly decreased the expression of IL-6, p-STAT3, p-PI3K, p-AKT, and other proteins. These findings were supported by in vitro experiments, where CETSA, DARTS, and siRNA evidenced LWG’s targeting of STAT3. LWG targeted STAT3 to inhibit inflammation, oxidative stress, and apoptosis in the colon, thereby restoring the intestinal barrier function to some extent and exerting a therapeutic effect on DHSD.

A key step in the study of tree pathology is the identification of an appropriate method for inoculating pathogens of diseases in branches and trunks. Pathogens of diseases in branches and trunks are commonly inoculated through punching, burning, and toothpick inoculation. However, there is a lack of comparative analyses of the inoculation outcomes of these three methods. In this work, six-year-old P. alba var. pyramidalis were inoculated with V. sordida using punching, burning, and toothpick techniques to investigate the differences in the effectiveness of these inoculation methods. Results reveal that the incidence rate was 93.55% in the toothpick inoculation group, significantly higher than the 80.65% in the burning inoculation group (chi-square, n = 90, p = 0.007), while punching inoculation exhibited significant pathological responses in the early stages, with spontaneous healing in the later stage. Additionally, toothpick inoculation was more efficient in inducing Valsa canker when inoculating the pathogen at the bottom of the tree, with lower intra- and inter-row spacing (stand density) providing better outcomes than higher intra- and inter-row spacing. The results of this study demonstrate that toothpick inoculation is an optimal option for studying the artificial inoculation of V. sordida in six-year-old P. alba var. pyramidalis, providing technical support for research on poplar diseases and offering a theoretical basis for the inoculation of other diseases in the branch and trunk.

Alcoholic liver disease (ALD) is a liver injury caused by chronic or binge alcohol consumption. Jiubanban (JBB) is a 10-edible-herb- formula. The current study investigated the protective effects and mechanisms of JBB against chronic alcoholic liver injury. The results showed that JBB significantly alleviated chronic alcoholic liver injury caused by chronic-binge alcohol consumption. JBB accelerated ethanol metabolism and had a protective effect on both acute and chronic alcoholic liver injury. By modulating the NFκB, KEAP1, and HIF1 pathways, JBB was shown to reduce the production of cytokines (IL-6 and TNFα), enhanced the activity of anti-oxidative enzymes (SOD and GSH-px), reduced associated products (ROS and MDA), reversing inflammation, oxidative stress, and apoptosis. The study findings suggest that this functional herbal food combination may be a risk-free therapeutic strategy for treating ALD.

Recently, a novel poplar mosaic disease caused by bean common mosaic virus (BCMV) was investigated in Populus alba var. pyramidalis in China. Symptom characteristics, physiological performance of the host, histopathology, genome sequences and vectors, and gene regulation at the transcriptional and posttranscriptional levels were analyzed and RT−qPCR (quantitative reverse transcription PCR) validation of expression was performed in our experiments. In this work, the mechanisms by which the BCMV pathogen impacts physiological performance and the molecular mechanisms of the poplar response to viral infection were reported. The results showed that BCMV infection decreased the chlorophyll content, inhibited the net photosynthesis rate (Pn) and stomatal conductance (Gs), and significantly changed chlorophyll fluorescence parameters in diseased leaves. Transcriptome analysis revealed that the expression of the majority of DEGs (differentially expressed genes) involved in the flavonoid biosynthesis pathway was promoted, but the expression of all or almost all DEGs associated with photosynthesis-antenna proteins and the photosynthesis pathway was inhibited in poplar leaves, suggesting that BCMV infection increased the accumulation of flavonoids but decreased photosynthesis in hosts. Gene set enrichment analysis (GSEA) illustrated that viral infection promoted the expression of genes involved in the defense response or plant-pathogen interaction. MicroRNA-seq analysis illustrated that 10 miRNA families were upregulated while 6 families were downregulated in diseased poplar leaves; moreover, miR156, the largest family with the most miRNA members and target genes, was only differentially upregulated in long-period disease (LD) poplar leaves. Integrated transcriptome and miRNA-seq analyses revealed 29 and 145 candidate miRNA−target gene pairs; however, only 17 and 76 pairs, accounting for 2.2% and 3.2% of all DEGs, were authentically negatively regulated in short-period disease (SD) and LD leaves, respectively. Interestingly, 4 miR156/SPL (squamosa promoter-binding-like protein) miRNA−target gene pairs were identified in LD leaves: the miR156 molecules were upregulated, but SPL genes were downregulated. In conclusion, BCMV infection significantly changed transcriptional and posttranscriptional gene expression in poplar leaves, inhibited photosynthesis, increased the accumulation of flavonoids, induced systematic mosaic symptoms, and decreased physiological performance in diseased poplar leaves. This study elucidated the fine-tuned regulation of poplar gene expression by BCMV; moreover, the results also suggested that miR156/SPL modules played important roles in the virus response and development of viral systematic symptoms in plant virus disease.

Abstract Ecogeographic rules that describe quantitative relationships between morphologies and climate might help us predict how morphometrics of animals was shaped by local temperature or humidity. Although the ecogeographic rules had been widely tested in animals of Europe and North America, they had not been fully validated for species in regions that are less studied. Here, we investigate the morphometric variation of a widely distributed East Asian passerine, the vinous‐throated parrotbill (Sinosuthora webbiana), to test whether its morphological variation conforms to the prediction of Bergmann's rule, Allen's rules, and Gloger's rule. We at first described the climatic niche of S. webbiana from occurrence records (n = 7838) and specimen records (n = 290). The results of analysis of covariance (ANCOVA) suggested that the plumage coloration of these parrotbills was darker in wetter/warmer environments following Gloger's rule. However, their appendage size (culmen length, beak volume, tarsi length) was larger in colder environments, the opposite of the predictions of Allen's rule. Similarly, their body size (wing length) was larger in warmer environments, the opposite of the predictions of Bergmann's rule. Such disconformity to both Bergmann's rule and Allen's rule suggests that the evolution of morphological variations is likely governed by multiple selection forces rather than dominated by thermoregulation. Our results suggest that these ecogeographic rules should be validated prior to forecasting biological responses to climate change especially for species in less‐studied regions.

Abstract This work investigates the unified coding and identification of smart grid IoT devices, as more and more IoT devices in smart grid need to be managed and controlled. We combine blockchain technology with 5G MEC to realize the connection of massive power IoT devices at the edge of 5G network. Due to blockchain’s distributed storage and credibility, it is used to identify and register IoT devices in smart grid, ensuring the reliability and accuracy of smart grid IoT devices management. In this paper, we propose a hybrid blockchain mechanism based on 5G MEC smart grid, where both public blockchain and private blockchain are deployed on the MEC gateway/server. To facilitate the data searching and extracting, we endeavor to build a blockchain explorer indexed by IoT device identifier. After that, we study the typical consensus algorithms in the blockchain such as PoW, PoS, DPoS, PDFT, and discuss their feasibility in the hybrid blockchain. Finally, we analyzed and compared the performance of different consensus algorithms from the perspective of average computing time and average time to agreement.

Electrocardiogram (ECG) is a method used by physicians to detect cardiac disease. Requirements for batch processing and accurate recognition of clinical data have led to the applications of deep-learning methods for feature extraction, classification, and denoising of ECGs; however, deep learning requires large amounts of data and multi-feature integration of datasets, with most available methods used for ECGs incapable of extracting global features or resulting in unstable, low quality training. To address these deficiencies, we proposed a novel generative adversarial architecture called RPSeqGAN using a training process reliant upon a sequence generative adversarial network (SeqGAN) algorithm that adopts the policy gradient (PG) in reinforcement learning. Based on clinical records collected from the MIT-BIH arrhythmia database, we compared our proposed model with three deep generative models to evaluate its stability by observing the variance of their loss curves. Additionally, we generated ECGs with five periods and evaluated them according to six metrics suitable for time series. The results indicate that the proposed model showed the highest stability and data quality.

The segmentation of structures in electron microscopy (EM) images is very important for neurobiological research. The low resolution neuronal EM images contain noise and generally few features are available for segmentation, therefore application of the conventional approaches to identify the neuron structure from EM images is not successful. We therefore present a multi-scale fused structure boundary detection algorithm in this study. In the algorithm, we generate an EM image Gaussian pyramid first, then at each level of the pyramid, we utilize Laplacian of Gaussian function (LoG) to attain structure boundary, we finally assemble the detected boundaries by using fusion algorithm to attain a combined neuron structure image. Since the obtained neuron structures usually have gaps, we put forward a reinforcement learning-based boundary amendment method to connect the gaps in the detected boundaries. We use a SARSA (λ)-based curve traveling and amendment approach derived from reinforcement learning to repair the incomplete curves. Using this algorithm, a moving point starts from one end of the incomplete curve and walks through the image where the decisions are supervised by the approximated curve model, with the aim of minimizing the connection cost until the gap is closed. Our approach provided stable and efficient structure segmentation. The test results using 30 EM images from ISBI 2012 indicated that both of our approaches, i.e., with or without boundary amendment, performed better than six conventional boundary detection approaches. In particular, after amendment, the Rand error and warping error, which are the most important performance measurements during structure segmentation, were reduced to very low values. The comparison with the benchmark method of ISBI 2012 and the recent developed methods also indicates that our method performs better for the accurate identification of substructures in EM images and therefore useful for the identification of imaging features related to brain diseases.

Traffic problems often occur due to the traffic demands by the outnumbered vehicles on road. Maximizing traffic flow and minimizing the average waiting time are the goals of intelligent traffic control. Each junction wants to get larger traffic flow. During the course, junctions form a policy of coordination as well as constraints for adjacent junctions to maximize their own interests. A good traffic signal timing policy is helpful to solve the problem. However, as there are so many factors that can affect the traffic control model, it is difficult to find the optimal solution. The disability of traffic light controllers to learn from past experiences caused them to be unable to adaptively fit dynamic changes of traffic flow. Considering dynamic characteristics of the actual traffic environment, reinforcement learning algorithm based traffic control approach can be applied to get optimal scheduling policy. The proposed Sarsa(λ)-based real-time traffic control optimization model can maintain the traffic signal timing policy more effectively. The Sarsa(λ)-based model gains traffic cost of the vehicle, which considers delay time, the number of waiting vehicles, and the integrated saturation from its experiences to learn and determine the optimal actions. The experiment results show an inspiring improvement in traffic control, indicating the proposed model is capable of facilitating real-time dynamic traffic control.

Reinforcement learning (RL) is one kind of interactive learning methods. Its main characteristics are “trial and error” and “related reward.” A hierarchical reinforcement learning method based on action subrewards is proposed to solve the problem of “curse of dimensionality,” which means that the states space will grow exponentially in the number of features and low convergence speed. The method can reduce state spaces greatly and choose actions with favorable purpose and efficiency so as to optimize reward function and enhance convergence speed. Apply it to the online learning in Tetris game, and the experiment result shows that the convergence speed of this algorithm can be enhanced evidently based on the new method which combines hierarchical reinforcement learning algorithm and action subrewards. The “curse of dimensionality” problem is also solved to a certain extent with hierarchical method. All the performance with different parameters is compared and analyzed as well.

Unregistered biological words recognition is the process of identification of terms that is out of vocabulary. Although many approaches have been developed, the performance approaches are not satisfactory. As the identification process can be viewed as a Markov process, we put forward a Q-learning with transfer learning algorithm to detect unregistered biological words from texts. With the Q-learning, the recognizer can attain the optimal solution of identification during the interaction with the texts and contexts. During the processing, a transfer learning approach is utilized to fully take advantage of the knowledge gained in a source task to speed up learning in a different but related target task. A mapping, required by many transfer learning, which relates features from the source task to the target task, is carried on automatically under the reinforcement learning framework. We examined the performance of three approaches with GENIA corpus and JNLPBA04 data. The proposed approach improved performance in both experiments. The precision, recall rate, and F score results of our approach surpassed those of conventional unregistered word recognizer as well as those of Q-learning approach without transfer learning.

The recommendation algorithm based on bipartite network is superior to traditional methods on accuracy and diversity, which proves that considering the network topology of recommendation systems could help us to improve recommendation results. However, existing algorithms mainly focus on the overall topology structure and those local characteristics could also play an important role in collaborative recommend processing. Therefore, on account of data characteristics and application requirements of collaborative recommend systems, we proposed a link community partitioning algorithm based on the label propagation and a collaborative recommendation algorithm based on the bipartite community. Then we designed numerical experiments to verify the algorithm validity under benchmark and real database.

Using aqueous two-phase systems (ATPSs) for three-dimensional (3D) printed complex structures has attracted considerable attention in the field of biomedicine. In this study, we present an unusual approach to constructing reconfigurable 3D printed structures within an aqueous environment. Inspired by biological systems, we introduce both specific and nonspecific interactions to anchor functionalized nanoparticles to the water-water interface, thereby imparting adaptive dual locks of structural integrity and permeability to the 3D printed liquid structures. Using state-of-the-art in situ liquid-liquid interfacial atomic force microscopy imaging, we successfully demonstrate various morphologies of interfacial films formed at the ATPS interface. In addition, by incorporating d-glucose or sodium alginate into the systems, the dual locks can be easily manipulated. Our study paves a pathway for 3D printing multiresponsive all-aqueous systems with controllable structures and permeability, showing promising implications for the development of smart drug delivery systems and in vivo reactions. [ABSTRACT FROM AUTHOR]