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Blunting the tumor's stress-sensing ability is an effective strategy for controlling tumor adaptive survival and metastasis. Here, we have designed a cyclically amplified nano-energy interference device based on lipid nanoparticles (LNP), focused on altering cellular energy metabolism. This innovative nano device efficiently targets and monitors the tumor's status while simultaneously inhibiting mitochondrial respiration, biogenesis and ribosome production. To this end, we first identified azelaic acid (AA), a binary acid capable of disrupting the mitochondrial respiratory chain. Upon encapsulation in LNP and linkage to mitochondrial-targeting molecules, this disruptive effect is further augmented. Consequently, tumors exhibit a substantial upregulation of the glycolytic pathway, intensifying their glucose demand and worsening the tumor's energy-deprived microenvironment. Then, the glucose analog, 2-Deoxy-D-glucose (2-DG), linked to the LNP, efficiently targets tumors and competitively inhibits the tumor's normal glucose uptake. The synergetic results of combining AA with 2-DG induce comprehensive energy deficiency within tumors, blocking the generation of energy-sensitive ribosomes. Ultimately, the disruption of both mitochondria and ribosomes depletes energy supply and new protein-generating capacity, weakening tumor's ability to adapt to environmental stress and thereby inhibiting growth and metastasis. Comprehensively, this nano-energy interference device, by controlling the tumor's stress-sensing ability, provides a novel therapeutic strategy for refractory tumors.

Over 170 years after the infamous Irish Potato Famine, potato late blight (PLB) caused by Phytophthora infestans remains the single most devastating disease of global potato production, causing up to 10 billion USD in yield loss and management costs. Through decades of research, growers and agronomists in the field as well as laboratory scientists have made significant progress in understanding the molecular pathogenesis process of this critical pathosystem and effective management strategies to control PLB. Yet, the need to feed an ever-increasing global population under changing climate demands continued improvement in efficient and sustainable PLB management schemes that can be implemented across a broad economic spectrum. In this review, we briefly summarize the current understanding of the molecular interaction between P. infestans and its host plants, highlight the current integrated pest management strategy to control PLB on local and continental scales, and discuss the potential of further improvement of sustainable PLB control through genetic enhancement of crop resistance and emerging crop protection technologies.

For a quantale I, the unit interval endowed with a continuous triangular norm, we introduce the canonical, op-canonical and Kleisli extensions of the conical I-semifilter monad to I-Rel. It is proved that the op-canonical extension coincides with the Kleisli extension.

Soil nailing has become a common method for improving slope stability due to its cost-effectiveness and easy installation. However, concerns have been arisen regarding the quality and integrity of soil nailing works. This study proposed a novel method for soil nail integrity assessment using an actively heated fiber-optic (AHFO) sensor. First, numerical simulations were conducted to determine the thermal conductivity profiles of soil nails with various defects, measured using AHFO. By establishing the relationship between defects and thermal conductivity profiles, the material and severity of soil nail defects can be estimated. It was found that the thermal conductivity profiles obtained by the AHFO sensor accurately correspond to defect locations, and the entrained material and severity of defects in the section can be identified for defects with a length exceeding 0.1 m. Physical model tests were performed to further validate the feasibility of the proposed quantitative soil nail integrity testing method. This paper also provides recommendations for heating parameters and testing processes to facilitate the implementation of this method by potential users. [ABSTRACT FROM AUTHOR]

Identification of reservoir types in deep carbonates has always been a great challenge due to complex logging responses caused by the heterogeneous scale and distribution of storage spaces. Traditional cross-plot analysis and empirical formula methods for identifying reservoir types using geophysical logging data have high uncertainty and low efficiency, which cannot accurately reflect the nonlinear relationship between reservoir types and logging data. Recently, the kernel Fisher discriminant analysis (KFD), a kernel-based machine learning technique, attracts attention in many fields because of its strong nonlinear processing ability. However, the overall performance of KFD model may be limited as a single kernel function cannot simultaneously extrapolate and interpolate well, especially for highly complex data cases. To address this issue, in this study, a mixed kernel Fisher discriminant analysis (MKFD) model was established and applied to identify reservoir types of the deep Sinian carbonates in central Sichuan Basin, China. The MKFD model was trained and tested with 453 datasets from 7 coring wells, utilizing GR, CAL, DEN, AC, CNL and RT logs as input variables. The particle swarm optimization (PSO) was adopted for hyper-parameter optimization of MKFD model. To evaluate the model performance, prediction results of MKFD were compared with those of basic-kernel based KFD, RF and SVM models. Subsequently, the built MKFD model was applied in a blind well test, and a variable importance analysis was conducted. The comparison and blind test results demonstrated that MKFD outperformed traditional KFD, RF and SVM in the identification of reservoir types, which provided higher accuracy and stronger generalization. The MKFD can therefore be a reliable method for identifying reservoir types of deep carbonates. [ABSTRACT FROM AUTHOR]

Long noncoding RNAs (lncRNAs) can compete with endogenous RNAs to modulate the gene expression and contribute to oncogenesis and tumor metastasis. lncRNA NKX2‐1‐AS1 (NKX2‐1 antisense RNA 1) plays a pivotal role in cancer progression and metastasis; however, the contribution of aberrant expression of NKX2‐1‐AS1 and the mechanism by which it functions as a competing endogenous RNA (ceRNA) in gastric cancer (GC) remains elusive. NKX2‐1‐AS1 expression was detected in paired tumor and nontumor tissues of 178 GC patients by quantitative reverse transcription PCR (qRT‐PCR). Using loss‐of‐function and gain‐of‐function experiments, the biological functions of NKX2‐1‐AS1 were evaluated both in vitro and in vivo. Further, to assess that NKX2‐1‐AS1 regulates angiogenic processes, tube formation and co‐culture assays were performed. RNA binding protein immunoprecipitation (RIP) assay, a dual‐luciferase reporter assay, quantitative PCR, Western blot, and fluorescence in situ hybridization (FISH) assays were performed to determine the potential molecular mechanism underlying this ceRNA. The results indicated that NKX2‐1‐AS1 expression was upregulated in GC cell lines and tumor tissues. Overexpression of NKX2‐1‐AS1 was significantly associated with tumor progression and enhanced angiogenesis. Functionally, NKX2‐1‐AS1 overexpression promoted GC cell proliferation, metastasis, invasion, and angiogenesis, while NKX2‐1‐AS1 knockdown restored these effects, both in vitro and in vivo. RIP and dual‐luciferase assays revealed that the microRNA miR‐145‐5p is a direct target of NKX2‐1‐AS1 and that NKX2‐1‐AS1 serves as a ceRNA to sponge miRNA and regulate angiogenesis in GC. Moreover, serpin family E member 1 (SERPINE1) is an explicit target for miR‐145‐5p; besides, the NKX2‐1‐AS1/miR‐145‐5p axis induces the translation of SERPINE1, thus activating the VEGFR‐2 signaling pathway to promote tumor progression and angiogenesis. NKX2‐1‐AS1 overexpression is associated with enhanced tumor cell proliferation, angiogenesis, and poor prognosis in GC. Collectively, NKX2‐1‐AS1 functions as a ceRNA to miR‐145‐5p and promotes tumor progression and angiogenesis by activating the VEGFR‐2 signaling pathway via SERPINE1.

This study investigated the behavior of a 38-m deep twin circular peanut-shaped cofferdam interconnected with a rectangular section for cut-and-cover tunnel construction using distributed fiber-optic sensors (DFOSs) based on optical frequency domain reflectometry (OFDR). The distributed sensors revealed that temperature changes on the two sides of the diaphragm wall were different upon its exposure by excavation, while the measured strains were used to evaluate the wall deflection and bending moments. The high spatial resolution achieved by DFOS measurements revealed unique aspects of the wall response, which are difficult to obtain by conventional types of instrumentation. In particular, the strains along vertical and lateral directions of the wall panels were measured, the latter of which indicated eccentric compression in the concrete panels that arises from the distinctive peanut-shaped geometry. Developments of hoop forces and circumferential bending moments in the panels at various construction stages are discussed, with particular focus on the release of such during partial demolition of a temporary cross wall to facilitate the assembly and launching of tunnel boring machines. The mechanisms of stress developments and release were simulated using three-dimensional finite-element models that, together with the field measurements, enhance the understanding of the behavior of multicell cofferdams. [ABSTRACT FROM AUTHOR]

Lipid droplets (LDs) are important organelles conserved across eukaryotes with a fascinating biogenesis and consumption cycle. Recent intensive research has focused on uncovering the cellular biology of LDs, with emphasis on their degradation. Briefly, two major pathways for LD degradation have been recognized: (1) lipolysis, in which lipid degradation is catalyzed by lipases on the LD surface, and (2) lipophagy, in which LDs are degraded by autophagy. Both of these pathways require the collective actions of several lipolytic and proteolytic enzymes, some of which have been purified and analyzed for their in vitro activities. Furthermore, several genes encoding these proteins have been cloned and characterized. In seed plants, seed germination is initiated by the hydrolysis of stored lipids in LDs to provide energy and carbon equivalents for the germinating seedling. However, little is known about the mechanism regulating the LD mobilization. In this review, we focus on recent progress toward understanding how lipids are degraded and the specific pathways that coordinate LD mobilization in plants, aiming to provide an accurate and detailed outline of the process. This will set the stage for future studies of LD dynamics and help to utilize LDs to their full potential. [ABSTRACT FROM AUTHOR]

For a quantale I , the unit interval endowed with a continuous triangular norm, we introduce the canonical, op-canonical and Kleisli extensions of the conical I -semifilter monad to I - Rel. It is proved that the op-canonical extension coincides with the Kleisli extension. [ABSTRACT FROM AUTHOR]

Abstract Background Herpes simplex virus type 1 strain 129 (H129) has represented a promising anterograde neuronal circuit tracing tool, which complements the existing retrograde tracers. However, the current H129 derived tracers are multisynaptic, neither bright enough to label the details of neurons nor capable of determining direct projection targets as monosynaptic tracer. Methods Based on the bacterial artificial chromosome of H129, we have generated a serial of recombinant viruses for neuronal circuit tracing. Among them, H129-G4 was obtained by inserting binary tandemly connected GFP cassettes into the H129 genome, and H129-ΔTK-tdT was obtained by deleting the thymidine kinase (TK) gene and adding tdTomato coding gene to the H129 genome. Then the obtained viral tracers were tested in vitro and in vivo for the tracing capacity. Results H129-G4 is capable of transmitting through multiple synapses, labeling the neurons by green florescent protein, and visualizing the morphological details of the labeled neurons. H129-ΔTK-tdT neither replicates nor spreads in neurons alone, but transmits to and labels the postsynaptic neurons with tdTomato in the presence of complementary expressed TK from a helper virus. H129-ΔTK-tdT is also capable to map the direct projectome of the specific neuron type in the given brain regions in Cre transgenic mice. In the tested brain regions where circuits are well known, the H129-ΔTK-tdT tracing patterns are consistent with the previous results. Conclusions With the assistance of the helper virus complimentarily expressing TK, H129-ΔTK-tdT replicates in the initially infected neuron, transmits anterogradely through one synapse, and labeled the postsynaptic neurons with tdTomato. The H129-ΔTK-tdT anterograde monosynaptic tracing system offers a useful tool for mapping the direct output in neuronal circuitry. H129-G4 is an anterograde multisynaptic tracer with a labeling signal strong enough to display the details of neuron morphology.

Quinoa (Chenopodium quinoa Willd.) is a dicotyledonous cereal that is rich in nutrients. This important crop has been shown to have significant tolerance to abiotic stresses such as salinization and drought. Understanding the underlying mechanism of stress response in quinoa would be a significant advantage for breeding crops with stress tolerance. Here, we treated the low-altitude quinoa cultivar CM499 with either NaCl (200 mM), Na2CO3/NaHCO3 (100 mM, pH 9.0) or PEG6000 (10%) to induce salinity, alkalinity and hypertonia, respectively, and analyzed the subsequent expression of genes and small RNAs via high-throughput sequencing. A list of known/novel genes were identified in quinoa, and the ones responding to different stresses were selected. The known/novel quinoa miRNAs were also identified, and the target genes of the stress response ones were predicted. Both the differently expressed genes and the targets of differently expressed miRNAs were found to be enriched for reactive oxygen species homeostasis, hormone signaling, cell wall synthesis, transcription factors and some other factors. Furthermore, we detected changes in reactive oxygen species accumulation, hormone (auxin and ethylene) responses and hemicellulose synthesis in quinoa seedlings treated with stresses, indicating their important roles in the response to saline, alkaline or hyperosmotic stresses in quinoa. Thus, our work provides useful information for understanding the mechanism of abiotic stress responses in quinoa, which would provide clues for improving breeding for quinoa and other crops. [ABSTRACT FROM AUTHOR]

How to fit a properly nonlinear classification model from conventional well logs to lithofacies is a key problem for machine learning methods. Kernel methods (e.g., KFD, SVM, MSVM) are effective attempts to solve this issue due to abilities of handling nonlinear features by kernel functions. Deep mining of log features indicating lithofacies still needs to be improved for kernel methods. Hence, this work employs deep neural networks to enhance the kernel principal component analysis (KPCA) method and proposes a deep kernel method (DKM) for lithofacies identification using well logs. DKM includes a feature extractor and a classifier. The feature extractor consists of a series of KPCA models arranged according to residual network structure. A gradient-free optimization method is introduced to automatically optimize parameters and structure in DKM, which can avoid complex tuning of parameters in models. To test the validation of the proposed DKM for lithofacies identification, an open-sourced dataset with seven conventional logs (GR, CAL, AC, DEN, CNL, LLD, and LLS) and lithofacies labels from the Daniudi Gas Field in China is used. There are eight lithofacies, namely clastic rocks (pebbly, coarse, medium, and fine sandstone, siltstone, mudstone), coal, and carbonate rocks. The comparisons between DKM and three commonly used kernel methods (KFD, SVM, MSVM) show that (1) DKM (85.7%) outperforms SVM (77%), KFD (79.5%), and MSVM (82.8%) in accuracy of lithofacies identification; (2) DKM is about twice faster than the multi-kernel method (MSVM) with good accuracy. The blind well test inWell D13 indicates that compared with the other three methods DKM improves about 24% in accuracy, 35% in precision, 41% in recall, and 40% in F1 score, respectively. In general, DKM is an effective method for complex lithofacies identification. This work also discussed the optimal structure and classifier for DKM. Experimental results show that (m1, m2, 0) is the optimal model structure and linear SVM is the optimal classifier. (m1, m2, 0) means there are m1 KPCAs, and then m2 residual units. A workflow to determine an optimal classifier in DKM for lithofacies identification is proposed, too. [ABSTRACT FROM AUTHOR]

Summary: Alternative splicing of pre‐mRNAs is crucial for plant growth and development. Serine/arginine‐rich (SR) proteins are a conserved family of RNA‐binding proteins that are critical for both constitutive and alternative splicing. However, how phosphorylation of SR proteins regulates gene transcription and alternative splicing during plant development is poorly understood.We found that the Arabidopsis thaliana L. SR protein‐specific kinase II family proteins (SRPKIIs) play an important role in plant development, including flowering. SRPKIIs regulate the phosphorylation status of a subset of specific SR proteins, including SR45 and SC35, which subsequently mediates their subcellular localization.A phospho‐dead SR45 mutant inhibits the assembly of the apoptosis‐and splicing‐associated protein complex and thereby upregulates the expression of FLOWERING LOCUS C (FLC) via epigenetic modification. The splicing efficiency of FLC introns was significantly increased in the shoot apex of the srpkii mutant. Transcriptomic analysis revealed that SRPKIIs regulate the alternative splicing of c. 400 genes, which largely overlap with those regulated by SR45 and SC35‐SCL family proteins.In summary, we found that Arabidopsis SRPKIIs specifically affect the phosphorylation status of a subset SR proteins and regulate the expression and alternative splicing of FLC to control flowering time. [ABSTRACT FROM AUTHOR]

Acupuncture can induce changes in the brain. However, the majority of studies to date have focused on a single acupoint at a time. In the present study, we observed activity changes in the brains of healthy volunteers before and after acupuncture at Taichong (LR3) and Taixi (KI3) using resting-state functional magnetic resonance imaging. Fifteen healthy volunteers underwent resting-state functional magnetic resonance imaging of the brain 15 minutes before acupuncture, then received acupuncture at Taichong and Taixi using the nail-pressing needle insertion method, after which the needle was retained in place for 30 minutes. Fifteen minutes after withdrawal of the needle, the volunteers underwent a further session of resting-state functional magnetic resonance imaging, which revealed that the amplitude of low-frequency fluctuation, a measure of spontaneous neuronal activity, increased mainly in the cerebral occipital lobe and middle occipital gyrus (Brodmann area 18/19), inferior occipital gyrus (Brodmann area 18) and cuneus (Brodmann area 18), but decreased mainly in the gyrus rectus of the frontal lobe (Brodmann area 11), inferior frontal gyrus (Brodmann area 44) and the center of the posterior lobe of the cerebellum. The present findings indicate that acupuncture at Taichong and Taixi specifically promote blood flow and activation in the brain areas related to vision, emotion and cognition, and inhibit brain areas related to emotion, attention, phonological and semantic processing, and memory.

Lipid droplets (LDs) are ubiquitous, dynamic organelles found in almost all organisms, including animals, protists, plants and prokaryotes. The cell biology of LDs, especially biogenesis, has attracted increasing attention in recent decades because of their important role in cellular lipid metabolism and other newly identified processes. Emerging evidence suggests that LD biogenesis is a highly coordinated and stepwise process in animals and yeasts, occurring at specific sites of the endoplasmic reticulum (ER) that are defined by both evolutionarily conserved and organism- and cell type-specific LD lipids and proteins. In plants, understanding of the mechanistic details of LD formation is elusive as many questions remain. In some ways LD biogenesis differs between plants and animals. Several homologous proteins involved in the regulation of animal LD formation in plants have been identified. We try to describe how these proteins are synthesized, transported to the ER and specifically targeted to LD, and how these proteins participate in the regulation of LD biogenesis. Here, we review current work on the molecular processes that control LD formation in plant cells and highlight the proteins that govern this process, hoping to provide useful clues for future research. [ABSTRACT FROM AUTHOR]

Typically, relationship between well logs and lithofacies is complex, which leads to low accuracy of lithofacies identification. Machine learning (ML) methods are often applied to identify lithofacies using logs labelled by rock cores. However, these methods have accuracy limits to some extent. To further improve their accuracies, practical and novel ensemble learning strategy and principles are proposed in this work, which allows geologists not familiar with ML to establish a good ML lithofacies identification model and help geologists familiar with ML further improve accuracy of lithofacies identification. The ensemble learning strategy combines ML methods as sub-classifiers to generate a comprehensive lithofacies identification model, which aims to reduce the variance errors in prediction. Each sub-classifier is trained by randomly sampled labelled data with random features. The novelty of this work lies in the ensemble principles making sub-classifiers just overfitting by algorithm parameter setting and subdataset sampling. The principles can help reduce the bias errors in the prediction. Two issues are discussed, videlicet (1) whether only a relatively simple single-classifier method can be as sub-classifiers and how to select proper ML methods as sub-classifiers; (2) whether different kinds of ML methods can be combined as sub-classifiers. If yes, how to determine a proper combination. In order to test the effectiveness of the ensemble strategy and principles for lithofacies identification, different kinds of machine learning algorithms are selected as sub-classifiers, including regular classifiers (LDA, NB, KNN, ID3 tree and CART), kernel method (SVM), and ensemble learning algorithms (RF, AdaBoost, XGBoost and LightGBM). In this work, the experiments used a published dataset of lithofacies from Daniudi gas field (DGF) in Ordes Basin, China. Based on a series of comparisons between ML algorithms and their corresponding ensemble models using the ensemble strategy and principles, conclusions are drawn: (1) not only decision tree but also other single-classifiers and ensemble-learning-classifiers can be used as subclassifiers of homogeneous ensemble learning and the ensemble can improve the accuracy of the original classifiers; (2) the ensemble principles for the introduced homogeneous and heterogeneous ensemble strategy are effective in promoting ML in lithofacies identification; (3) in practice, heterogeneous ensemble is more suitable for building a more powerful lithofacies identification model, though it is complex. [ABSTRACT FROM AUTHOR]

Very-long-chain fatty acids (VLCFAs) are precursors for the synthesis of various lipids, such as triacylglycerols, sphingolipids, cuticular waxes, and suberin monomers, which play important roles in plant growth and stress responses. However, the underlying molecular mechanism regulating VLCFAs' biosynthesis in quinoa (Chenopodium quinoa Willd.) remains unclear. In this study, we identified and functionally characterized putative 3-ketoacyl-CoA synthases (KCSs) from quinoa. Among these KCS genes, CqKCS2B.1 showed high transcript levels in the root tissues and these were rapidly induced by salt stress. CqKCS2B.1 was localized to the endoplasmic reticulum. Overexpression of CqKCS2B.1 in Arabidopsis resulted in significantly longer primary roots and more lateral roots. Ectopic expression of CqKCS2B.1 in Arabidopsis promoted the accumulation of suberin monomers. The occurrence of VLCFAs with C22–C24 chain lengths in the overexpression lines suggested that CqKCS2B.1 plays an important role in the elongation of VLCFAs from C20 to C24. The transgenic lines of overexpressed CqKCS2B.1 showed increased salt tolerance, as indicated by an increased germination rate and improved plant growth and survival under salt stress. These findings highlight the significant role of CqKCS2B.1 in VLCFAs' production, thereby regulating suberin biosynthesis and responses to salt stress. CqKCS2B.1 could be utilized as a candidate gene locus to breed superior, stress-tolerant quinoa cultivars. [ABSTRACT FROM AUTHOR]