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In this paper，the typical site environmental events covered by 48 stations in 7 center stations of Hebei geophysical station network are reviewed, involving deformation, geoelectricity, geomagnetic and fluid disciplines. According to the number of recorded events and the number of affecting instrument sets, typical influential factors such as HVDC transmission, equipment leakage, water (oil) well spring pumping, and reservoir water level fluctuation are selected for analysis. The process of occurrence of the event is described in detail, its rules and reasons are found, and effective solutions are proposed to provide reference for anomaly verification, analysis and prediction.

Abstract Objective Deep brain stimulation (DBS) is a promising approach for the treatment of epilepsy. However, the optimal target for DBS and underlying mechanisms are still not clear. Here, we compared the therapeutic effects of DBS on distinct septal subregions, aimed to find the precise targets of septal DBS and related mechanisms for the clinical treatment. Methods Assisted by behavioral test, electroencephalography (EEG) recording and analyzing, selectively neuronal manipulation and immunohistochemistry, we assessed the effects of DBS on the three septal subregions in kainic acid (KA)‐induced mouse seizure model. Results DBS in the medial septum (MS) not only delayed generalized seizure (GS) development, but reduced the severity; DBS in the vertical diagonal band of Broca (VDB) only reduced the severity of GS, while DBS in the horizontal diagonal band of Broca (HDB) subregion showed no anti‐seizure effect. Notably, DBS in the MS much more efficiently decreased abnormal activation of hippocampal neurons. EEG spectrum analysis indicated that DBS in the MS and VDB subregions mainly increased the basal hippocampal low‐frequency (delta and theta) rhythm. Furthermore, ablation of cholinergic neurons in the MS and VDB subregions blocked the anti‐seizure and EEG‐modulating effects of septal DBS, suggesting the seizure‐alleviating effect of DBS was dependent on local cholinergic neurons. Significance DBS in the MS and VDB, rather than HDB, attenuates hippocampal seizure by activation of cholinergic neurons‐augmented hippocampal delta/theta rhythm. This may be of great therapeutic significance for the clinical treatment of epilepsy with septal DBS. Plain Language Summary The optical target of deep brain stimulation in the septum is still not clear. This study demonstrated that stimulation in the medial septum and vertical diagonal band of Broca subregions, but not the horizontal diagonal band of Broca, could alleviate hippocampal seizure through cholinergic neurons‐augmented hippocampal delta/theta rhythm. This study may shed light on the importance of precise regulation of deep brain stimulation therapy in treating epileptic seizures.

Abstract Background The prevalence of obesity-associated insulin resistance (IR) is increasing along with the increase in obesity rates. In this study, we compared the predictive utility of four alternative indexes of IR [triglyceride glucose index (TyG index), metabolic score for insulin resistance (METS-IR), the triglyceride/high-density lipoprotein cholesterol (TG/HDL-C) ratio and homeostatic model assessment of insulin resistance (HOMA-IR)] for all-cause mortality and cardiovascular mortality in the general population based on key variables screened by the Boruta algorithm. The aim was to find the best replacement index of IR. Methods In this study, 14,653 participants were screened from the National Health and Nutrition Examination Survey (2001–2018). And TyG index, METS-IR, TG/HDL-C and HOMA-IR were calculated separately for each participant according to the given formula. The predictive values of IR replacement indexes for all-cause mortality and cardiovascular mortality in the general population were assessed. Results Over a median follow-up period of 116 months, a total of 2085 (10.23%) all-cause deaths and 549 (2.61%) cardiovascular disease (CVD) related deaths were recorded. Multivariate Cox regression and restricted cubic splines analysis showed that among the four indexes, only METS-IR was significantly associated with both all-cause and CVD mortality, and both showed non-linear associations with an approximate “U-shape”. Specifically, baseline METS-IR lower than the inflection point (41.33) was negatively associated with mortality [hazard ratio (HR) 0.972, 95% CI 0.950–0.997 for all-cause mortality]. In contrast, baseline METS-IR higher than the inflection point (41.33) was positively associated with mortality (HR 1.019, 95% CI 1.011–1.026 for all-cause mortality and HR 1.028, 95% CI 1.014–1.043 for CVD mortality). We further stratified the METS-IR and showed that significant associations between METS-IR levels and all-cause and cardiovascular mortality were predominantly present in the nonelderly population aged

Abstract Translational studies benefit from experimental designs where laboratory organisms use human-relevant behaviors. One such behavior is decision-making, however studying complex decision-making in rodents is labor-intensive and typically restricted to two levels of cost/reward. We design a fully automated, inexpensive, high-throughput framework to study decision-making across multiple levels of rewards and costs: the REward-COst in Rodent Decision-making (RECORD) system. RECORD integrates three components: 1) 3D-printed arenas, 2) custom electronic hardware, and 3) software. We validated four behavioral protocols without employing any food or water restriction, highlighting the versatility of our system. RECORD data exposes heterogeneity in decision-making both within and across individuals that is quantifiably constrained. Using oxycodone self-administration and alcohol-consumption as test cases, we reveal how analytic approaches that incorporate behavioral heterogeneity are sensitive to detecting perturbations in decision-making. RECORD is a powerful approach to studying decision-making in rodents, with features that facilitate translational studies of decision-making in psychiatric disorders.

Abstract Background Organisms frequently experience environmental stresses that occur in predictable patterns and combinations. For wild Saccharomyces cerevisiae yeast growing in natural environments, cells may experience high osmotic stress when they first enter broken fruit, followed by high ethanol levels during fermentation, and then finally high levels of oxidative stress resulting from respiration of ethanol. Yeast have adapted to these patterns by evolving sophisticated “cross protection” mechanisms, where mild ‘primary’ doses of one stress can enhance tolerance to severe doses of a different ‘secondary’ stress. For example, in many yeast strains, mild osmotic or mild ethanol stresses cross protect against severe oxidative stress, which likely reflects an anticipatory response important for high fitness in nature. Results During the course of genetic mapping studies aimed at understanding the mechanisms underlying natural variation in ethanol-induced cross protection against H2O2, we found that a key H2O2 scavenging enzyme, cytosolic catalase T (Ctt1p), was absolutely essential for cross protection in a wild oak strain. This suggested the absence of other compensatory mechanisms for acquiring H2O2 resistance in that strain background under those conditions. In this study, we found surprising heterogeneity across diverse yeast strains in whether CTT1 function was fully necessary for acquired H2O2 resistance. Some strains exhibited partial dispensability of CTT1 when ethanol and/or salt were used as mild stressors, suggesting that compensatory peroxidases may play a role in acquired stress resistance in certain genetic backgrounds. We leveraged global transcriptional responses to ethanol and salt stresses in strains with different levels of CTT1 dispensability, allowing us to identify possible regulators of these alternative peroxidases and acquired stress resistance in general. Conclusions Ultimately, this study highlights how superficially similar traits can have different underlying molecular foundations and provides a framework for understanding the diversity and regulation of stress defense mechanisms.

IntroductionIn the field of facility agriculture, the accurate identification of tomatoes at multiple stages has become a significant area of research. However, accurately identifying and localizing tomatoes in complex environments is a formidable challenge. Complex working conditions can impair the performance of conventional detection techniques, underscoring the necessity for more robust methods.MethodsTo address this issue, we propose a novel model of YOLOv8-EA for the localization and identification of tomato fruit. The model incorporates a number of significant enhancements. Firstly, the EfficientViT network replaces the original YOLOv8 backbone network, which has the effect of reducing the number of model parameters and improving the capability of the network to extract features. Secondly, some of the convolutions were integrated into the C2f module to create the C2f-Faster module, which facilitates the inference process of the model. Third, the bounding box loss function was modified to SIoU, thereby accelerating model convergence and enhancing detection accuracy. Lastly, the Auxiliary Detection Head (Aux-Head) module was incorporated to augment the network's learning capacity.ResultThe accuracy, recall, and average precision of the YOLOv8-EA model on the self-constructed dataset were 91.4%, 88.7%, and 93.9%, respectively, with a detection speed of 163.33 frames/s. In comparison to the baseline YOLOv8n network, the model weight was increased by 2.07 MB, and the accuracy, recall, and average precision were enhanced by 10.9, 11.7, and 7.2 percentage points, respectively. The accuracy, recall, and average precision increased by 10.9, 11.7, and 7.2 percentage points, respectively, while the detection speed increased by 42.1%. The detection precision for unripe, semi-ripe, and ripe tomatoes was 97.1%, 91%, and 93.7%, respectively. On the public dataset, the accuracy, recall, and average precision of YOLOv8-EA are 91%, 89.2%, and 95.1%, respectively, and the detection speed is 1.8 ms, which is 4, 4.21, and 3.9 percentage points higher than the baseline YOLOv8n network. This represents an 18.2% improvement in detection speed, which demonstrates good generalization ability.DiscussionThe reliability of YOLOv8-EA in identifying and locating multi-stage tomato fruits in complex environments demonstrates its efficacy in this regard and provides a technical foundation for the development of intelligent tomato picking devices.

Computational Fluid Dynamics (CFD) plays a crucial role in investigating new physical phenomena and exploring the principles of fluid mechanics. However, CFD numerical methods often face the challenges of long research cycles, high costs, and extensive human-computer interactions due to the growing complexity of computational tasks. To meet the burgeoning requirements of contemporary physical sciences, in recent years, the coupling of traditional scientific computing techniques with promising deep learning techniques well-known from computer science have emerged as a new research paradigm. This paradigm aims to create automated, intelligent tools for obtaining valuable insights as well as being able to categorize, predict, and make evidence-based decisions in novel ways. These tools can be used to reduce the reliance on expert experience and laborious computations inherent in existing numerical theories and methods. In this paper, we delve into the essence of science paradigms, the evolution of computing intelligence, and provide a comprehensive overview of the key applications driving the development of a new intelligence paradigm in CFD simulations. In addition, we outline a prototype platform for CFD simulations within this new paradigm. Based on this platform, three intelligent workflows are proposed, anticipating to serve as a reference source for future research and foster the emergence of innovative applications in the field of CFD.Highlights Deep learning techniques emerged as a new method to create automated, intelligent tools for CFD simulations.A review of deep learning methods for mesh pre-processing.A review of deep learning methods for numerical solving.A review of deep learning methods for post-processing visualization.A prototype platform for CFD simulations within the new paradigm.Perspectives on challenges and future directions.

Against the backdrop of near-saturation in global marine capture fisheries, current mariculture production now exceeds one-third of capture production and continues to hold significant growth potential. However, the lack of spatially detailed global offshore surface seawater mariculture (OSSM) data hampers scientific understanding and management from a spatial perspective. In this study, utilizing multi-source remote sensing time-series imagery, we achieve the world’s first spatially detailed mapping of both raft and cage mariculture types. The results show that (1) in 2020, the total global OSSM area was 20,079.97 km², comprising 85.04% raft mariculture and 14.96% cage mariculture. 95.33% of the OSSM was concentrated in Asia. (2) The interpreted national OSSM areas align well with the FAO production statistics trends. Obtaining these areas using remote sensing offers greater timeliness compared to production statistics, which is advantageous for dynamic monitoring and production estimation. (3) Through analysis of global pond aquaculture data, it was found that most coastal areas worldwide had a single-type of aquaculture. However, in parts of Asia, regions with both OSSM and pond aquaculture coexisted. The data acquired in this study, characterized by detailed spatial information, provide significant insights for constructing spatial models of mariculture, researching resource-environment effects, and informing management practices.

ABSTRACTMulti-scale landscape functions play a critical role in revealing intricate functional structures within large regions. However, previous studies on landscape functions have predominantly focused on a single macro or micro scale, impeding a holistic multi-scale understanding of the spatial distribution and heterogeneity of landscape functions. To address this gap, this study proposes a framework leveraging the power of big geodata to mine multi-scale landscape functions from parcel to entire urban agglomerations, as well as non-administrative divisions. Our study focuses on the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) in China. Firstly, we integrated multi-source big geodata to derive parcel-scale landscape functions. Subsequently, we employed the Normalized Revealed Comparative Advantage index to derive landscape functions at broader scales, including towns, counties and cities. The effectiveness of our approach is validated through in-field investigations and comparisons with established policy planning positions. The outcomes not only offer distinctive planning insights at various scales but also highlight the versatility of big geodata in extracting landscape functions across scales. This study demonstrates that big geodata is adept at uncovering multi-scale landscape functions irrespective of administrative boundaries, providing valuable insights for fostering multi-scale regional coordinated development.

This study investigates the genetic determinants of seed coat color and pattern variations in cowpea (Vigna unguiculata), employing a genome-wide association approach. Analyzing a mapping panel of 296 cowpea varieties with 110,000 single nucleotide polymorphisms (SNPs), we focused on eight unique coat patterns: (1) Red and (2) Cream seed; (3) White and (4) Brown/Tan seed coat; (5) Pink, (6) Black, (7) Browneye and (8) Red/Brown Holstein. Across six GWAS models (GLM, SRM, MLM, MLMM, FarmCPU from GAPIT3, and TASSEL5), 13 significant SNP markers were identified and led to the discovery of 23 candidate genes. Among these, four specific genes may play a direct role in determining seed coat pigment. These findings lay a foundational basis for future breeding programs aimed at creating cowpea varieties aligned with consumer preferences and market requirements.

The FHD-2B proton magnetometer at Wen’an station had large F component noise from 2020 to 2021. There is a high correlation between noise interference and temperature, which is generally characterized by high noise in winter and low noise in summer. The surrounding environment, power supply voltage, grounding resistance, power supply line leakage, probe leakage and power frequency interference of the magnetic room were checked respectively, and no interference sources were found. By studying the principle of FHD proton magnetometer, it is found that the harmonic capacitance capacity of FHD proton magnetometer changes with temperature. The capacity is small at low temperature and large at high temperature. The change of the capacity of the harmonic capacitor will affect the center frequency of the oscillation circuit f0, resulting in increased noise. Since the noise interference of the FHD proton magnetometer at Wen’an station is highly correlated with temperature, it is speculated that the noise interference may be related to the change in the capacity of the harmonic capacitance. We can adjust the inductance L of the probe to correct the influence of the circuit center frequency of the harmonic capacitance capacity transformation. The probe inductance was adjusted several times through experiments, and 34.4 mH was selected as the most appropriate probe inductance value. After the experiment, the noise value of F component decreased, the data curve became smooth, and the noise interference was solved.

Background & Aims: Hepatocellular carcinoma (HCC) is a heterogeneous cancer with varying levels of liver tumor initiating or cancer stem cells in the tumors. We aimed to investigate the expression of different liver cancer stem cell (LCSC) markers in human HCCs and identify their regulatory mechanisms in stemness-related cells. Methods: We used an unbiased, single-marker sorting approach by flow cytometry, fluorescence-activated cell sorting, and transcriptomic analyses on HCC patients’ resected specimens. Knockdown approach was used, and relevant functional assays were conducted on the identified targets of interest. Results: Flow cytometry on a total of 60 HCC resected specimens showed significant heterogeneity in the expression of LCSC markers, with CD24, CD13, and EpCAM mainly contributing to this heterogeneity. Concomitant expression of CD24, CD13, and EpCAM was detected in 32 HCC samples, and this was associated with advanced tumor stages. Transcriptomic sequencing on the HCC cells sorted for these individual markers identified epidermal growth factor receptor kinase substrate 8-like protein 3 (EPS8L3) as a common gene associated with the 3 markers and was functionally validated in HCC cells. Knocking down EPS8L3 suppressed the expression of all 3 markers. To search for the upstream regulation of EPS8L3, we found SP1 bound to EPS8L3 promoter to drive EPS8L3 expression. Furthermore, using Akt inhibitor MK2206, we showed that Akt signaling–driven SP1 drove the expression of the 3 LCSC markers. Conclusions: Our findings suggest that Akt signaling–driven SP1 promotes EPS8L3 expression, which is critical in maintaining the downstream expression of CD24, CD13, and EpCAM. The findings provide insight into potential LCSC-targeting therapeutic strategies.

Abstract As the most prominent RNA modification, N6‐methyladenosine (m6A) participates in the regulation of tumour initiation and progression. Circular RNAs (circRNAs) also play crucial roles in ubiquitous life processes. Whether circRNAs are required for m6A regulation in renal cell carcinoma (RCC) remains unclear. Meta‐analysis and bioinformatics identified that IGF2BP3 was upregulated in RCC and indicated a worse prognosis. IGF2BP3 significantly promoted RCC progression in vitro and in vivo. Mechanistically, circRARS bound to KH1–KH2 domains of IGF2BP3 to enhance m6A modification recognition. A 12‐nt sequence (GUCUUCCAGCAA) was proven to be the IGF2BP3‐binding site of circRARS. Additionally, CAPN15, CD44, HMGA2, TNRC6A and ZMIZ2 were screened to be the target genes regulated by the IGF2BP3/circRARS complex in an m6A‐dependent manner. Stabiliser proteins, including HuR, Matrin3 and pAbPC1, were recruited by circRARS, thereby increasing the mRNA stability of the forementioned five target genes. Consequently, the IGF2BP3/circRARS complex facilitated the lipid accumulation of RCC cells and promoted sunitinib resistance via target genes. circRARS synergised with IGF2BP3 to facilitate m6A recognition, thereby promoting RCC progression. Thus, IGF2BP3 could be a potential biomarker for RCC diagnosis and prognosis and a therapeutic target.

The objective of this study was to examine the effects of dietary Chinese herbal medicine (CHM) consisting of Astragalus membranaceus (Fisch.) Bunge (AMT) and Codonopsis pilosula (Franch.) Nannf (CPO) extracts on growth performance, antioxidant capacity, immune status, and intestinal health of broiler chickens. Two groups were formed, each consisting of six replicates of 12 one-day-old healthy male 817 white feather broilers. Broilers were fed either a basal diet (CON group) or a basal diet supplemented with 500 mg/kg CHM. The trial lasted 50 days. The results showed that CHM supplementation resulted in enhanced feed efficiency and antioxidant capacity in both the serum and liver, while it reduced uric acid and endotoxin levels, as well as diamine oxidase activity (p

Fluorescence-guided surgery (FGS) with tumor-targeted imaging agents, particularly those using the near-infrared wavelength, has emerged as a real-time technique to highlight the tumor location and margins during a surgical procedure. For accurate visualization of prostate cancer (PCa) boundary and lymphatic metastasis, we developed a new approach involving an efficient self-quenched near-infrared fluorescence probe, Cy-KUE-OA, with dual PCa-membrane affinity. Cy-KUE-OA specifically targeted the prostate-specific membrane antigen (PSMA), anchored into the phospholipids of the cell membrane of PCa cells and consequently showed a strong Cy7-de-quenching effect. This dual–membrane-targeting probe allowed us to detect PSMA-expressing PCa cells both in vitro and in vivo and enabled clear visualization of the tumor boundary during fluorescence-guided laparoscopic surgery in PCa mouse models. Furthermore, the high PCa preference of Cy-KUE-OA was confirmed on surgically resected patient specimens of healthy tissues, PCa, and lymph node metastases. Taken together, our results serve as a bridge between preclinical and clinical research in FGS of PCa and lay a solid foundation for further clinical research.

It has been challenging to achieve discernible mechanoluminophore devices under ambient light. Here, authors integrate top-emitting organic light-emitting device and piezoelectric generator on thin polymer substrate for the realization of flexible devices under an ambient illuminance of 3000 lux.

Cognitive impairment is the most common complication in patients with temporal lobe epilepsy with hippocampal sclerosis. There is no effective treatment for cognitive impairment. Medial septum cholinergic neurons have been reported to be a potential target for controlling epileptic seizures in temporal lobe epilepsy. However, their role in the cognitive impairment of temporal lobe epilepsy remains unclear. In this study, we found that patients with temporal lobe epilepsy with hippocampal sclerosis had a low memory quotient and severe impairment in verbal memory, but had no impairment in nonverbal memory. The cognitive impairment was slightly correlated with reduced medial septum volume and medial septum-hippocampus tracts measured by diffusion tensor imaging. In a mouse model of chronic temporal lobe epilepsy induced by kainic acid, the number of medial septum cholinergic neurons was reduced and acetylcholine release was reduced in the hippocampus. Furthermore, selective apoptosis of medial septum cholinergic neurons mimicked the cognitive deficits in epileptic mice, and activation of medial septum cholinergic neurons enhanced hippocampal acetylcholine release and restored cognitive function in both kainic acid- and kindling-induced epilepsy models. These results suggest that activation of medial septum cholinergic neurons reduces cognitive deficits in temporal lobe epilepsy by increasing acetylcholine release via projections to the hippocampus.

The systematic exploration of the application prospects of lotus stem fibers (LSFs) in natural composites is significantly important for the development of green, lightweight, functional biomass materials and products. In this study, the physical and chemical properties, micro/nano structures, micromechanical characteristics, cellulose crystallinity, and thermal stability of LSFs were systematically explored. The density, microstructure, and nanoindentation analyses revealed that the LSF is a lightweight natural fiber with a high modulus (11.2–11.7 GPa) and a good hardness (0.41–0.57 GPa). The transmission electron microscopy analysis indicated the existence of four types of sclerenchyma cells in a gradient distribution in the epidermis and exodermis of the LS. The chemical analysis, X-ray diffraction, and Fourier transform infrared spectroscopy indicated that the outer-layer LSFs have a high cellulose content (44.73%), high relative crystallinity index (49.2%) of cellulose, and small cellulose crystallite size (2.2 nm). As a result, these LSFs possess good mechanical properties.

To explore and expand the application of luffa sponge (LS) fiber, three methods were used to chemically modify the LS fibers. The nanoindentation (NI) results show that the LS fibers exhibit a high elastic modulus (12.4 GPa) and hardness (0.7 GPa). Upon treating the fibers with 5%NaOH-5%H2O2, the NI hardness of the fibers increases by 14.3%. However, when 10%NaOH-20%CH3COOH (method B) is used, the hardness of the cell walls decreases by 28.6%. In this case, the removal of amorphous substances from the LS fibers and the swelling reaction of the LS cellulose plays a significant role. Three chemical treatments can partially remove the hemicellulose in the LS fiber. 18%NaOH-1.6%CO(NH2)2 (method C) modifies the crystalline structure of the cellulose in the LS fibers. Gas chromatography-mass spectrometry results show that the LS fibers are rich in hexadecanoic acid (14.3%), as well as in substances, such as squalene, oleic acid, and behenic alcohol, which exhibit a high antibiofilm activity. Furthermore, method B increases the total relative content of squalene and squalane in the LS extract by 49.4%; while method C treatment allows LS fibers have the potential to be used to develop phase-change energy storage materials and anti-photoaging discoloration materials.

Abstract Transfer learning is designed to leverage knowledge in the source domain with labels to help build classification models in the target domain where labels are scarce or even unavailable. Previous studies have shown that high-level concepts extracted from original features are more suitable for cross-domain classification tasks, so many transfer learning methods transfer knowledge by modeling high-level concepts on the original feature space. However, there are two limitations to this method: First, learning high-level concepts directly on the original feature space will reduce the proportion of shared information contained in common features in the process of knowledge transfer bridge construction. Second, only learning multiple high-level concepts on the original feature space, the latent shared information contained in the domain-specific features cannot be targeted learned, so the latent shared information in the domain-specific features cannot be effectively used. To overcome these limitations, this paper proposes a novel method named Dual-Space Transfer Learning based on an Indirect Mutual Promotion Strategy (DSTL). The DSTL method is formalized as an optimization problem based on non-negative matrix tri-factorization. DSTL first extracts the common features between domains and constructs the common feature space. Then, the learning of the high-level concepts of the common feature space and the original feature space is integrated through an indirect promotion strategy, which can enhance the learning effect of common features and domain-specific features through the mutual help of the two feature spaces. The system test on benchmark data sets shows the superiority of the DSTL method.

Obtaining spatial distribution information on mariculture in a low-cost, fast, and efficient manner is crucial for the sustainable development and regulatory planning of coastal zones and mariculture industries. This study, based on the Segment Anything Model (SAM) and high-resolution remote sensing imagery, rapidly extracted mariculture areas in Liaoning Province, a typical northern province in China with significant mariculture activity. Additionally, it explored the actual marine ownership data to investigate the marine use status of Liaoning Province’s mariculture. The total area of mariculture we extracted in Liaoning Province is 1052.89 km2. Among this, the area of cage mariculture is 27.1 km2, while raft mariculture covers 1025.79 km2. Through field investigations, it was determined that in the western part of Liaodong Bay, cage mariculture predominantly involves sea cucumbers. In the southern end of Dalian, the raft mariculture focuses on cultivating kelp. On the other hand, around the islands in the eastern region, the primary crop in raft mariculture is scallops, showing a significant geographical differentiation pattern. In the planned mariculture areas within Liaoning Province’s waters, the proportion of actual development and utilization is 11.2%, while the proportion approved for actual mariculture is 90.2%. This indicates a suspicion that 9.8% of mariculture is possibly in violation of sea occupation rights, which could be due to the untimely updating of marine ownership data. Based on SAM, efficient and accurate extraction of cage mariculture can be achieved. However, the extraction performance for raft mariculture is challenging and remains unsatisfactory. Manual interpretation is still required for satisfactory results in this context.

Natural antioxidants are more attractive than synthetic chemical oxidants because of their non-toxic and non-harmful properties. Microalgal bioactive components such as carotenoids, polysaccharides, and phenolic compounds are gaining popularity as very effective and long-lasting natural antioxidants. Few articles currently exist that analyze microalgae from a bibliometric and visualization point of view. This study used a bibliometric method based on the Web of Science Core Collection database to analyze antioxidant research on bioactive compounds in microalgae from 1996 to 2022. According to cluster analysis, the most studied areas are the effectiveness, the antioxidant mechanism, and use of bioactive substances in microalgae, such as carotene, astaxanthin, and tocopherols, in the fields of food, cosmetics, and medicine. Using keyword co-occurrence and keyword mutation analysis, future trends are predicted to improve extraction rates and stability by altering the environment of microalgae cultures or mixing extracts with chemicals such as nanoparticles for commercial and industrial applications. These findings can help researchers identify trends and resources to build impactful investigations and expand scientific frontiers.

This study aimed to investigate the impact of Bacillus subtilis HC6 on the growth performance, immunity, antioxidant capacity, and intestinal health of broilers. A total of 180 one-day-old white feather broilers were randomly divided into two experimental groups, each comprising six replicates of fifteen chicks from 1 to 50 d of age. The groups were either fed a basal diet (CON) or the same diet supplemented with 5 × 108 cfu/kg of Bacillus subtilis HC6 (BS). Our results indicated that compared with the CON, dietary supplementation with BS increased feed efficiency during d 21–50 and d 1–50 (p < 0.05). Moreover, BS supplementation enhanced antioxidant capacity in the serum and liver, and also decreased the activity of diamine oxidase and the level of endotoxins (p < 0.05). Additionally, BS treatment increased the villi height in the jejunum and ileum, increased the ratio of villus height/crypt depth in the ileum, upregulated the expression of tight junction proteins in the jejunal mucosa, and downregulated the levels of IL-22 and IFN-γ on day 50 (p < 0.05). Principal coordinates analysis yielded clear clustering of two groups; dietary BS increased the relative abundance of Bacteroidales_unclassified (genus) and Olsenella (genus), and decreased the abundance of genera Alistipes on day 50, which identified a strong correlation with FCR, serum differential metabolites, or differential gene expression in the jejunal mucosa by spearman correlation analysis. The PICRUSt2 analysis revealed that supplementation with BS enriched the pathways related to xenobiotics biodegradation and metabolism, carbohydrate metabolism, energy metabolism, signaling molecules and interaction, the digestive system, and transport and catabolism. These results demonstrated that dietary BS increased feed efficiency, antioxidant capacity, and the mRNA expression of pro-inflammatory cytokines in the jejunal mucosa; and decreased the activity of diamine oxidase in serum, which might be attributed to the modulation of community composition and the functions of cecal microbiota in white-feathered broilers.

Structured grid-based sparse matrix-vector multiplication and Gauss–Seidel iterations are very important kernel functions in scientific and engineering computations, both of which are memory intensive and bandwidth-limited. GPDSP is a general purpose digital signal processor, which is a very significant embedded processor that has been introduced into high-performance computing. In this paper, we designed various optimization methods, which included a blocking method to improve data locality and increase memory access efficiency, a multicolor reordering method to develop Gauss–Seidel fine-grained parallelism, a data partitioning method designed for GPDSP memory structures, and a double buffering method to overlap computation and access memory on structured grid-based SpMV and Gauss–Seidel iterations for GPDSP. At last, we combined the above optimization methods to design a multicore vectorization algorithm. We tested the matrices generated with structured grids of different sizes on the GPDSP platform and obtained speedups of up to 41× and 47× compared to the unoptimized SpMV and Gauss–Seidel iterations, with maximum bandwidth efficiencies of 72% and 81%, respectively. The experiment results show that our algorithms could fully utilize the external memory bandwidth. We also implemented the commonly used mixed precision algorithm on the GPDSP and obtained speedups of 1.60× and 1.45× for the SpMV and Gauss–Seidel iterations, respectively.

Accurate underwater target detection and recognition in complex marine environments has always been a challenge. There is a lot of information in underwater target radiation noise that is important for underwater target recognition. However, the traditional underwater target radiation noise process is inefficient and inaccurate, severely limiting underwater target recognition. This paper proposed a new method for underwater target recognition based on compressed sensing multiscale entropy. For starters, compressing a signal improves its signal-to-noise ratio and broadens its linear spectrum. The multiscale sample entropy for the signal is then calculated after it has been denoised, and the most separated sample entropy is chosen by comparing the different scales of sample entropy to achieve effective underwater target radiation noise recognition. The experimental results show that the feature extraction method proposed in the paper can classify underwater target radiation noise quickly and effectively, improving recognition efficiency.

Abstract Background Aberrant alternative splicing plays critical role in aging and age-related diseases. Heterogeneous nuclear ribonucleoproteins (hnRNPs) reportedly regulate RNA splicing process. Whether and how hnRNPs contribute to age-related neurodegenerative diseases, especially Alzheimer’s disease (AD), remain elusive. Methods Immunoblotting and immunostaining were performed to determine expression patterns and cellular/subcellular localization of the long isoform of hnRNP D-like (L-DL), which is a hnRNP family member, in mouse hippocampus. Downregulation of L-DL in WT mice was achieved by AAV-mediated shRNA delivery, followed by memory-related behavioural tests. L-DL interactome was analysed by affinity-precipitation and mass spectrometry. Alternative RNA splicing was measured by RNA-seq and analyzed by bioinformatics-based approaches. Downregulation and upregulation of L-DL in APP/PS1 mice were performed using AAV-mediated transduction. Results We show that L-DL is specifically localized to nuclear speckles. L-DL levels are decreased in the hippocampus of aged mouse brains and downregulation of L-DL impairs cognition in mice. L-DL serves as a structural component to recruit other speckle proteins, and regulates cytoskeleton- and synapse-related gene expression by altering RNA splicing. Mechanistically, these splicing changes are modulated via L-DL-mediated interaction of SF3B3, a core component of U2 snRNP, and U2AF65, a U2 spliceosome protein that guides U2 snRNP’s binding to RNA. In addition, L-DL levels are decreased in APP/PS1 mouse brains. While downregulation of L-DL deteriorates memory deficits and overexpression of L-DL improves cognitive function in AD mice, by regulating the alternative splicing and expression of synaptic gene CAMKV. Conclusions Our findings define a molecular mechanism by which hnRNP L-DL regulates alternative RNA splicing, and establish a direct role for L-DL in AD-related synaptic dysfunction and memory decline.

Introduction: Immunotherapy has resulted in pathologic responses in hepatocellular carcinoma (HCC), but the benefits and molecular mechanisms of neoadjuvant immune checkpoint blockade are largely unknown. Methods: In this study, we evaluated the efficacy and safety of preoperative nivolumab (anti-PD-1) in patients with intermediate and locally advanced HCC and determined the molecular markers for predicting treatment response. Results: Between July 2020 and November 2021, 20 treatment-naive HCC patients with intermediate and locally advanced tumors received preoperative nivolumab at 3 mg/kg for 3 cycles prior to surgical resection. Nineteen patients underwent surgical resection on trial. Seven (36.8%) of the 19 patients had major pathologic tumor necrosis (≥60%) in the post-nivolumab resection specimens, with 3 having almost complete (>90%) tumor necrosis. The tumor necrosis was hemorrhagic and often accompanied by increased or dense immune cell infiltrate at the border of the tumors. None of the patients developed major adverse reactions contradicting hepatectomy. RNA-sequencing analysis on both pre-nivolumab tumor biopsies and post-nivolumab resected specimens showed that, in cases with major pathologic necrosis, the proportion of CD8 T cells in the HCC tissues predominantly increased after treatment. Moreover, to investigate noninvasive biomarker for nivolumab response, we evaluated the copy number variation (CNV) using target-panel sequencing on plasma cell-free DNA of the patients and derived a CNV-based anti-PD-1 score. The score correlated with the extent of tumor necrosis and was validated in a Korean patient cohort with anti-PD-1 treatment. Conclusion: Neoadjuvant nivolumab demonstrated promising clinical activity in intermediate and locally advanced HCC patients. We also identified useful noninvasive biomarker predicting responsiveness.

Recent studies have identified multiple RNA-binding proteins tightly associated with lipid and neuronal cholesterol metabolism and cardiovascular disorders. However, the role of heterogeneous nuclear ribonucleoprotein R (hnRNPR) in cholesterol metabolism and homeostasis, whether it has a role in regulating 3-hydroxy-3-methyl-glutaryl-coenzyme A reductase (HMGCR), is largely unknown. This research identifies hnRNPR as a repressor of HMGCR. Knockdown and overexpression of hnRNPR in cultured neuroblastoma cell (N2a) and MN1 cell lines enhances and inhibits HMGCR in vitro, respectively. hnRNPR may exert its repressive activity on HMGCR mRNA and protein levels by using its RNA recognition motif (RRM) in recognizing and modulating the stability of HMGCR transcript. Our RNA immunoprecipitation and luciferase reporter assays demonstrate a direct interaction between hnRNPR and HMGCR mRNA. We also demonstrated that hnRNR binds to the 3′ untranslated region (3′ UTR) of HMGCR and reduces its translation, while hnRNPR silencing increases HMGCR expression and cholesterol levels in MN1 and N2a cells. Overexpression of HMGCR significantly restores the decreased cholesterol levels in hnRNPR administered cells. Taken together, we identify hnRNPR as a novel post-transcriptional regulator of HMGCR expression in neuronal cholesterol homeostasis.

N6-methyladenosine (m6A) RNA modification regulates RNA metabolism, and has been implicated in immune regulation. Here, the authors show that the m6A methyltransferase, METTL3, translocates into the cytoplasm to increase viral RNA m6A modification, decreases viral ds RNA content, and thereby dampens the RIG/MDA5-induced anti-viral immunity.

In the field of underwater acoustic signal processing, the ship radiated noise contains a large amount of ship information, which is of great significance to the ship identification. The traditional method relies too much on the operator and prior knowledge, which seriously reduces the efficiency and accuracy of the ship radiated noise identification. This paper presented a novel ship radiated noise feature extraction method based on compression sensing and center frequency. Firstly, to compression sensing of the ship radiated noise, enhance its line spectrum energy. Then, the ship radiated noise is decomposed by empirical mode decomposition to obtain multiple intrinsic mode function, calculate the mutual information entropy of adjacent intrinsic mode function to determine the key parameter K of the variational mode decomposition. Finally, perform variational model of ship radiated noise based on K, extract the center frequency of maximum energy intrinsic mode function as the ship radiated noise recognition feature. Experimental results show that the proposed feature extraction method can classify ship radiated noise quickly and effectively, and reduce the dependence on operators and prior knowledge.

Abstract Background Compositional data refer to the data that lie on a simplex, which are common in many scientific domains such as genomics, geology and economics. As the components in a composition must sum to one, traditional tests based on unconstrained data become inappropriate, and new statistical methods are needed to analyze this special type of data. Results In this paper, we consider a general problem of testing for the compositional difference between K populations. Motivated by microbiome and metagenomics studies, where the data are often over-dispersed and high-dimensional, we formulate a well-posed hypothesis from a Bayesian point of view and suggest a nonparametric test based on inter-point distance to evaluate statistical significance. Unlike most existing tests for compositional data, our method does not rely on any data transformation, sparsity assumption or regularity conditions on the covariance matrix, but directly analyzes the compositions. Simulated data and two real data sets on the human microbiome are used to illustrate the promise of our method. Conclusions Our simulation studies and real data applications demonstrate that the proposed test is more sensitive to the compositional difference than the mean-based method, especially when the data are over-dispersed or zero-inflated. The proposed test is easy to implement and computationally efficient, facilitating its application to large-scale datasets.

The fast-developing computer network not only brings convenience to people but also brings security problems to people due to the appearance of various abnormal flows. However, various current detection systems for abnormal network flows have more or less flaws, such as the most common intrusion detection system (IDS). Due to the lack of self-learning capabilities of market-oriented IDS, developers and maintenance personnel have to update the virus database of the system in real time to make the system work normally. With the emergence of machine learning and data mining in recent years, new ideas and methods have emerged in the detection of abnormal network flows. In this paper, the random forest algorithm is introduced into the detection of abnormal samples, and the concept of abnormal point scale is proposed to measure the abnormal degree of the sample based on the similarity of the samples, and the abnormal samples are screened out according to this scale. Simulation experiments show that compared with the other two distance-based abnormal sample detection techniques, the random forest-based abnormal sample detection has greater advantages than the other two methods in terms of improving the accuracy of the model and reducing the computing time.

A total of 1,281 Chinese students in grades 3–6 participated in a study that examined the relationships among student-perceived supportive learning environment (PSLE), mathematical achievement, autonomous self-regulation, and creative thinking. The results demonstrated that student PSLE is positively associated with autonomous self-regulation, creative thinking, and mathematical achievement. In addition, the study also demonstrated that the influence of PSLE on students’ mathematical achievements could be mediated through autonomous self-regulation and creative thinking, respectively. The results shed light on the effectiveness of a supportive learning environment on educational and psychological outcomes in Chinese mathematical classrooms.

Status epilepticus (SE), a condition with abnormally prolonged seizures, is a severe type of epilepsy. At present, SE is not well controlled by clinical treatments. Antiepileptic drugs (AEDs) are the main therapeutic approaches, but they are effective for SE only with a narrow intervening window, and they easily induce resistance. Thus, in this review, we provide an updated summary for an insight into drug-resistant SE, hoping to add to the understanding of the mechanism of refractory SE and the development of active compounds. Firstly, we briefly outline the limitations of current drug treatments for SE by summarizing the extensive experimental literature and clinical data through a search of the PubMed database, and then summarize the common animal models of refractory SE with their advantages and disadvantages. Notably, we also briefly review some of the hypotheses about drug resistance in SE that are well accepted in the field, and furthermore, put forward future perspectives for follow-up research on SE.

Urbanization brings great enrichment to human production and life, but also has certain environmental impact on the area where the city is located. Many studies have revealed the negative effects of urbanization on the ecological environment of urban or urban agglomerations, especially in the early stage of urbanization, but there are few studies on the impact on the peripheral ecological space environment. Will the peripheral environment be better off with less human interference as people move to cities during urbanization? In order to answer this question, we took the Guangdong-Hong Kong-Macao Greater Bay Area, the most economically dynamic area in China, as an example to explore the relationship between impervious changes of urban agglomerations monitored by remote sensing in the Bay Area and ecological indicators of forest and grassland in Guangdong Province outside the Bay area. The results showed that:(1) in the past 30 years, the area of grassland outside the bay area did not change regularly, while the area of forest decreased year by year. The landscape indices of forest and grassland were gradually fragmented and discrete. Moreover, the distribution of Fraction Vegetation Coverage (FVC) of forest and grassland has changed since before urbanization. (2) Through correlation analysis, it is found that the changes in forest area and the landscape index of forest and grassland are strongly correlated with the development of urbanization in the Greater Bay Area. This shows that the process of urbanization in the Greater Bay Area will have a non-negligible impact on the peripheral environment. In the process of urban development, we should not only focus on the inner city but also consider the outer environment of the city.

Monte Carlo (MC) methods have been widely used to solve the particle transport equation due to their high accuracy and capability of processing complex geometries. History-based and event-based algorithms that are applicable to different architectures are two methods for parallelizing the MC code. There is a large work on evaluating and optimizing parallel algorithms with continuous-energy schemes. In this work, we evaluate the single-node performance of history-based and event-based algorithms for multigroup MC methods on both CPUs and GPUs with Quicksilver, a multigroup MC transport code that has already implemented the history-based algorithms. We first implement and optimize the event-based algorithm based on Quicksilver and then perform the evaluation work extensively on the Coral2 benchmark. Numerical results indicate that contrary to continuous-energy schemes, the history-based approach with multigroup schemes outperforms the event-based algorithm on both architectures in all cases. We summarize that the performance loss of the event-based algorithm is mainly due to: 1) extra operations to reorganize particles, 2) batched atomic operations, and 3) poor particle data locality. Despite the poor performance, the event-based algorithm achieves higher memory bandwidth utilization. We further discuss the impact of memory access patterns and calculation of cross sections (xs) on the performance of the GPU. Built on the analytics, and shed light on the algorithm choice and optimizations for paralleling the MC transport code on different architectures.

Intelligent connected vehicles are autonomous vehicles. With the increasing degree of automation of autonomous vehicles and the development of open applications in the future, the computing tasks of autonomous vehicles are becoming more and more complex. In practice, the computing resources of vehicles are limited and the processing of data is not always guaranteed to be completed in time. However, the timely processing and integrity of the data are very important for vehicles because it affects the path selection of the traveling vehicle and the passenger experience. Therefore, it is necessary to reduce latency in processing data and verify the integrity of data for vehicles. In this paper, a novel message authentication scheme for multiple mobile devices in intelligent connected vehicles based on edge computing is proposed. The task of processing data in the vehicle is migrated to mobile devices, and tasks are executed utilizing the computing resources of multiple mobile devices in the edge computing model. The vehicle use certificateless ring signature technology to ensure the integrity of data processed by mobile devices. A security analyses show that our proposed schemes are secure in the random oracle model and can resist two types of adversaries under certificateless public key encryption. One type of adversary may be able to replace the mobile device's public key and the other type of adversary has access to the system master key. The results of performance analysis indicate the proposed scheme has high efficiency and applicability in practical intelligent connected vehicles system.

This paper proposes a new dynamic multi-objective optimization algorithm by integrating a new fitting-based prediction (FBP) mechanism with regularity model-based multi-objective estimation of distribution algorithm (RM-MEDA) for multi-objective optimization in changing environments. The prediction-based reaction mechanism aims to generate high-quality population when changes occur, which includes three subpopulations for tracking the moving Pareto-optimal set effectively. The first subpopulation is created by a simple linear prediction model with two different stepsizes. The second subpopulation consists of some new sampling individuals generated by the fitting-based prediction strategy. The third subpopulation is created by employing a recent sampling strategy, generating some effective search individuals for improving population convergence and diversity. Experimental results on a set of benchmark functions with a variety of different dynamic characteristics and difficulties illustrate that the proposed algorithm has competitive effectiveness compared with some state-of-the-art algorithms.

Through the objective and fair evaluation of the comprehensive ability of accounting talents cultivated in colleges and universities, on the one hand, it is beneficial to provide direction for cultivating accounting applied talents in colleges and universities, and on the other hand, it can also provide reference for enterprises to select talents. Firstly, this study constructs an evaluation index system for the comprehensive ability of accounting applied talents and selects 15 indicators from three aspects of knowledge level, ability level, and moral level. Secondly, the group fuzzy analytic hierarchy process is used to determine the index weight. Finally, a fuzzy comprehensive evaluation model based on the cloud model is constructed and tested in practice. This study hopes to make an objective and fair evaluation for accounting talents by quantitatively evaluating the comprehensive ability of accounting applied talents.

This paper develops a multi-physics interface code MC-FLUENT to couple the Monte Carlo code OpenMC with the commercial computational fluid dynamics code ANSYS FLUENT. The implementations and parallel performances of block Gauss–Seidel-type and block Jacobi-type Picard iterative algorithms have been investigated. In addition, this paper introduces two adaptive load-balancing algorithms into the neutronics and thermal-hydraulics coupled simulation to reduce the time cost of computation. Considering that the different scalability of OpenMC and FLUENT limits the performance of block Gauss–Seidel algorithm, an adaptive load-balancing algorithm that can increase the number of nodes dynamically is proposed to improve its efficiency. Moreover, with the natural parallelism of block Jacobi algorithm, another adaptive load-balancing algorithm is proposed to improve its performance. A 3 x 3 PWR fuel pin model and a 1000 MWt ABR metallic benchmark core were used to compare the performances of the two algorithms and verify the effectiveness of the two adaptive load-balancing algorithms. The results show that the adaptive load-balancing algorithms proposed in this paper can greatly improve the computing efficiency of block Jacobi algorithm and improve the performance of block Gauss–Seidel algorithm when the number of nodes is large. In addition, the adaptive load-balancing algorithms are especially effective when a case demands different computational power of OpenMC and FLUENT.

In recent years, metal coordination macrocycles have obtained great interests due to the fact that they combined the rich host-guest properties of macro-cyclic hosts and the unique optical properties of the organic ligands. In this work, we constructed two porphyrin-based organoplatinum(II) metallacycles (MC1 and MC2) through coordination-driven self-assembly. 1H NMR, 31P NMR, and HRMS technologies were used to characterize the structures of MC1 and MC2. Interestingly, MC1 and MC2 can be used as catalysts for photooxidization under light irradiation with higher efficiency compared with the porphyrin ligand only. We hope that the coordination-driven self-assembly strategy can provide an efficient method to construct photo-active materials.

Chimeric antigen receptor- (CAR-) T cell therapy is one of the most recent innovative immunotherapies and is rapidly evolving. Like other technologies, CAR-T cell therapy has undergone a long development process, and persistent explorations of the actions of the intracellular signaling domain and make several improvements have led to the superior efficacy when anti-CD19 CAR-T cell treatments in B cell cancers. At present, CAR-T cell therapy is developing rapidly, and many clinical trials have been established on a global scale, which has great commercial potential. This review mainly describes the toxicity of CAR-T cell therapy and the challenges of CAR-T cells in the treatment of solid tumors, and looks forward to future development and opportunities for immunotherapy and reviews major breakthroughs in CAR-T cell therapy.

Over the last few years, cryo electron microscopy has become the most important method in structural biology. While 80% of deposited maps are from single particle analysis, electron tomography has grown to become the second most important method. In particular sub-tomogram averaging has matured as a method, delivering structures between 2 and 5 Å from complexes in cells as well as in vitro complexes. While this resolution range is not standard, novel developments point toward a promising future. Here, we provide a guide for the workflow from sample to structure to gain insight into this emerging field.

Summary: RNA binding proteins, the key regulators in gene expression at the posttranscriptional level, remain largely uncharacterized with respect to aging and relevant cognitive deterioration. Here, we report that the levels of SFRS11 are substantially decreased in the prefrontal cortex (PFC) of aged brains. Notably, mice with SFRS11 deficiency in the PFC show impaired learning and memory. We demonstrate that SFRS11 directly binds to the 3′ UTR of LRP8 mRNA, as well as to the third exon of apoE mRNA, resulting in stabilization of these mRNAs, eventually deactivating JNK signaling. Importantly, restoration of LRP8 and apoE reduces JNK signaling that is significantly enhanced in SFRS11-deficient cells. In addition, LRP8 and apoE rescue aging-like phenotypes induced by SFRS11 loss. Our findings demonstrate that age-dependent loss of SFRS11 in the PFC reduces levels of apoE and LRP8, leading to activation of the JNK pathway, ultimately influencing cognitive deficits. : Raihan et al. describe a role of SFRS11 in prefrontal cortex, which is required for higher cognitive functions. They show that SFRS11 regulates LRP8/apoE/JNK signaling, a pathway involved in cognition that declines in aging. Keywords: SFRS11, PFC, cognitive decline, LRP8, ApoE, aging

Feature screening has become a real prerequisite for the analysis of high-dimensional genomic data, as it is effective in reducing dimensionality and removing redundant features. However, existing methods for feature screening have been mostly relying on the assumptions of linear effects and independence (or weak dependence) between features, which might be inappropriate in real practice. In this paper, we consider the problem of selecting continuous features for a categorical outcome from high-dimensional data. We propose a powerful statistical procedure that consists of two steps, a nonparametric significance test based on edge count and a multiple testing procedure with dependence adjustment for false discovery rate control. The new method presents two novelties. First, the edge-count test directly targets distributional difference between groups, therefore it is sensitive to nonlinear effects. Second, we relax the independence assumption and adapt Efron's procedure to adjust for the dependence between features. The performance of the proposed procedure, in terms of statistical power and false discovery rate, is illustrated by simulated data. We apply the new method to three genomic datasets to identify genes associated with colon, cervical and prostate cancers.

Gaussian Bayesian networks have become a widely used framework to estimate directed associations between joint Gaussian variables, where the network structure encodes the decomposition of multivariate normal density into local terms. However, the resulting estimates can be inaccurate when the normality assumption is moderately or severely violated, making it unsuitable for dealing with recent genomic data such as the Cancer Genome Atlas data. In the present paper, we propose a mixture copula Bayesian network model which provides great flexibility in modeling non-Gaussian and multimodal data for causal inference. The parameters in mixture copula functions can be efficiently estimated by a routine expectation–maximization algorithm. A heuristic search algorithm based on Bayesian information criterion is developed to estimate the network structure, and prediction can be further improved by the best-scoring network out of multiple predictions from random initial values. Our method outperforms Gaussian Bayesian networks and regular copula Bayesian networks in terms of modeling flexibility and prediction accuracy, as demonstrated using a cell signaling data set. We apply the proposed methods to the Cancer Genome Atlas data to study the genetic and epigenetic pathways that underlie serous ovarian cancer.

Measuring and testing association between categorical variables is one of the long-standing problems in multivariate statistics. In this paper, I define a broad class of association measures for categorical variables based on weighted Minkowski distance. The proposed framework subsumes some important measures including Cramér’s V, distance covariance, total variation distance and a slightly modified mean variance index. In addition, I establish the strong consistency of the defined measures for testing independence in two-way contingency tables, and derive the scaled forms of unweighted measures.

With the increasing attention of energy saving and emission reduction technology, the recent application of hybrid powertrain technology affects the development of construction machinery industry. This article reviews these publications and provides comprehensive references. This article reviews the state-of-art for the hybrid wheel loader and excavator, which focuses on powertrain configuration, energy storage devices, and energy management strategies. The basis of classification and characteristic of each powertrain configuration are described. Advantages and disadvantages of batteries, supercapacitors, hydraulic accumulators, and flywheel used in hybrid construction machinery are summarized. The existing energy management strategies for hybrid construction machinery are also elaborated. The technological challenges and developing trends in the near future for hybrid construction machinery are discussed.