Tree Complex Research Articles

Maternal fluoxetine impairs synaptic transmission and plasticity in the medial prefrontal cortex and alters the structure and function of dorsal raphe nucleus neurons in offspring mice

Selective serotonin (5-HT) reuptake inhibitors (SSRIs) are commonly prescribed to women during pregnancy and breastfeeding despite posing a risk of adverse cognitive outcomes and affective disorders for the child. The consequences of SSRI-induced excess of 5-HT during development for the brain neuromodulatory 5-HT system remain largely unexplored. In this study, an SSRI - fluoxetine (FLX) - was administered to C57BL/6 J mouse dams during pregnancy and lactation to assess its effects on the offspring. We found that maternal FLX decreased field potentials, impaired long-term potentiation, facilitated long-term depression and tended to increase the density of 5-HTergic fibers in the medial prefrontal cortex (mPFC) of female but not male adolescent offspring. These effects were accompanied by deteriorated performance in the temporal order memory task and reduced sucrose preference with no change in marble burying behavior in FLX-exposed female offspring. We also found that maternal FLX reduced the axodendritic tree complexity of 5-HT dorsal raphe nucleus (DRN) neurons in female but not male offspring, with no changes in the excitability of DRN neurons of either sex. While no effects of maternal FLX on inhibitory postsynaptic currents (sIPSCs) in DRN neurons were found, we observed a significant influence of FLX exposure on kinetics of spontaneous excitatory postsynaptic currents (sEPSCs) in DRN neurons. Finally, we report that no changes in field potentials and synaptic plasticity were evident in the mPFC of the offspring after maternal exposure during pregnancy and lactation to a new antidepressant, vortioxetine. These findings show that in contrast to the mPFC, long-term consequences of maternal FLX exposure on the structure and function of DRN 5-HT neurons are mild and suggest a sex-dependent, distinct sensitivity of cortical and brainstem neurons to FLX exposure in early life. Vortioxetine appears to exert fewer side effects with regards to the mPFC when compared with FLX.

Lower skeletal muscle density and airway structure on computed tomography in asthma

BackgroundLower skeletal muscle density may reflect muscle adiposity and metabolic dysregulation that potentially impair disease control and lung function independent of high body mass index (BMI) in patients with asthma. ObjectiveTo investigate whether the lower density of pectoralis muscles (PMs) and erector spinae muscles (ESMs) on chest computed tomography was associated with airway structural changes in patients with asthma. MethodsConsecutive patients with asthma and healthy controls undergoing chest computed tomography were retrospectively analyzed. The ESM and PM density, areas of subcutaneous adipose tissue near the PM and epicardial adipose tissue, wall area percent of the airways, and airway fractal dimension (AFD) were quantified on computed tomography. ResultsThe study included 179 patients with asthma (52% women) and 88 controls (47% women). All the controls were 60 years old or younger. The PM and ESM density in female patients with asthma who were 60 years old or younger were significantly lower than those in controls after adjustment for BMI. In female patients with asthma at all ages, lower PM and ESM density (but not subcutaneous or epicardial adipose tissue area) was associated with greater wall area percent of the airways and lower AFD after adjusting for age, height, BMI, smoking status, blood eosinophil count, and oral corticosteroid use. The only association between ESM density and AFD was found in male patients with asthma. ConclusionLower skeletal muscle density may be associated with airway wall thickening and less complexity of the airway luminal tree in female patients with asthma.

Semantic Code Clone Detection Based on Community Detection

Semantic code clone detection is to find code snippets that are structurally or syntactically different, but semantically identical. It plays an important role in software reuse, code compression. Many existing studies have achieved good performance in non-semantic clone, but semantic clone is still a challenging task. Recently, several works have used tree or graph, such as Abstract Syntax Tree (AST), Control Flow Graph (CFG) or Program Dependency Graph (PDG) to extract semantic information from source codes. In order to reduce the complexity of tree and graph, some studies transform them into node sequences. However, this transformation will lose some semantic information. To address this issue, we propose a novel high-performance method that utilizes community detection to extract features of AST while preserving its semantic information. First, based on the AST of source code, we exploit community detection to split AST into different subtrees to extract the underlying semantics information of different code blocks, and use centrality analysis to quantify the semantic information as the weight of AST nodes. Then, the AST is converted into a sequence of tokens with weights, and a Siamese neural network model is used to detect the similarity of token sequences for semantic code clone detection. Finally, to evaluate our approach, we conduct experiments on two standard benchmark datasets, Google Code Jam (GCJ) and BigCloneBench (BCB). Experimental results show that our model outperforms the eight publicly available state-of-the-art methods in detecting code clones. It is five times faster than the tree-based method (ASTNN) in terms of time complexity.

SAT-based Decision Tree Learning for Large Data Sets

Decision trees of low depth are beneficial for understanding and interpreting the data they represent. Unfortunately, finding a decision tree of lowest complexity (depth or size) that correctly represents given data is NP-hard. Hence known algorithms either (i) utilize heuristics that do not minimize the depth or (ii) are exact but scale only to small or medium-sized instances. We propose a new hybrid approach to decision tree learning, combining heuristic and exact methods in a novel way. More specifically, we employ SAT encodings repeatedly to local parts of a decision tree provided by a standard heuristic, leading to an overall reduction in complexity. This allows us to scale the power of exact SAT-based methods to comparatively very large data sets. We evaluate our new approach experimentally on a range of real-world instances that contain up to several thousand samples. In almost all cases, our method successfully decreases the complexity of the initial decision tree; often, the decrease is significant.

Comparative Analysis of Deterministic and Nondeterministic Decision Trees for Decision Tables from Closed Classes.

In this paper, we consider classes of decision tables with many-valued decisions closed under operations of the removal of columns, the changing of decisions, the permutation of columns, and the duplication of columns. We study relationships among three parameters of these tables: the complexity of a decision table (if we consider the depth of the decision trees, then the complexity of a decision table is the number of columns in it), the minimum complexity of a deterministic decision tree, and the minimum complexity of a nondeterministic decision tree. We consider the rough classification of functions characterizing relationships and enumerate all possible seven types of relationships.

On parity decision trees for Fourier-sparse Boolean functions

We study parity decision trees for Boolean functions. The motivation of our study is the log-rank conjecture for XOR functions and its connection to Fourier analysis and parity decision tree complexity. Our contributions are as follows: Let \(f : \mathbb {F}_2^n \rightarrow \lbrace -1, 1\rbrace\) be a Boolean function with Fourier support 𝒮 and Fourier sparsity k .

The Root-Lesion Nematode, Pratylenchus penetrans, Affects Early Growth and Physiology of Grafted M.9, G.41, and G.935 Apple Rootstocks Similarly Under Field Microplot Conditions.

The root-lesion nematode, Pratylenchus penetrans, is a ubiquitous parasite of roots of temperate fruit trees. It affects early growth of trees replanted into former orchard sites where populations have built up and may contribute to decline complexes of older trees. Most British Columbia, Canada, apple acreage is planted with M.9 rootstock, but growers are increasingly considering Geneva-series rootstocks such as G.41 and G.935. Among these rootstocks, responses to P. penetrans, specifically, are poorly known. To compare the resistance and tolerance to P. penetrans of G.41, G.935, and M.9 rootstocks ('Ambrosia' scion), a field microplot experiment was established in spring of 2020 at the Summerland Research and Development Centre. The experimental design was a two by three factorial combination of: P. penetrans inoculation (+/-) and rootstock (G.41, G.935, and M.9), with 20 replicate microplots of each of the six treatment combinations arranged in a randomized complete block design. The P. penetrans inoculum was 5,400 nematodes per microplot (54 P. penetrans liter-1 soil), which is below commonly accepted damage thresholds. Though P. penetrans population densities were lower for the G.41 rootstock by the end of the 2021 growing season, the effects of P. penetrans were similar among rootstocks. In the establishment year (2020), P. penetrans caused significant reductions in aboveground growth. In 2021, shoot growth and root weight were reduced by P. penetrans. The nematode also reduced rates of leaf gas exchange and stem water potential. These data suggest that while G.41 and G.935 may have other horticultural benefits over M.9, they are equally susceptible to P. penetrans at the early stages of tree growth.

Vehicle detection in varied weather conditions using enhanced deep YOLO with complex wavelet

Traffic congestion is prevalent in many major and medium-sized cities throughout different countries in contemporary society. In traffic images, various multi-sized vehicles are tightly clustered together and obstructed from one another. Identifying vehicles in such instances is crucial for urban traffic surveillance, safety monitoring, and legal concerns but it also presents major challenges. The remarkable detection accuracy and efficiency of deep learning-based systems have led to their recent and extensive use in vehicle identification. There are significant advanced YOLO models with different backbone architectures and frameworks developed for vehicle detection. Yet, the performance of YOLO variants are facing the challenges of handling false detection against occluded and densely sophisticated scenarios. The proposed model is developed to address such types of limitations, for example; dynamic illumination, noisy images, and scale sensitivity to improve the vehicle detection rate in different traffic scenarios and varying weather conditions. The proposed study employs an improved YOLOv4 to identify moving vehicles in different lighting conditions including daylight, cloudy, rainy, and night. For hybridization, three techniques are utilized such as the Multiscale Retinex, Dual tree complex wavelet transform (DTCWT), and Pulse Coupled Neural Networks (PCNN). The DTCWT is employed for multiscale decomposition and to denoise the complex high frequency subband information, then the denoised subbands are reconstructed into a denoised image. The Multiscale retinex is utilized to reduce the halo artifacts on high-contrast edges and maintain the balance with dynamic range compression and color reproduction. The synchronizing pulse burst property of PCNN is used to detect the isolated noisy pixels and modify the detected noisy pixels. From the results it is worth noting that the developed model surpasses state-of-the-art methods in sunny, night, cloudy, and rainy modes. The proposed method using the DTCWT technique can detect the vehicles with mAP of 91.09% and 35FPS.

Veni, Vidi, Vici—When Is Home Video Seizure Monitoring Helpful?

Seizure detection is vital for managing epilepsy as seizures can lead to injury and even death, in addition to impacting quality of life. Prompt detection of seizures and intervention can help prevent injury and improve outcomes for individuals with epilepsy. Wearable sensors show promising results for automated detection of certain seizures, but they have limitations such as patient tolerance, impracticality for newborns, and the need for recharging. Non-contact video and audio-based technologies have become available, but a comprehensive literature review on these methods is lacking. This scoping literature review provides an overview of video and audio-based seizure detection, highlighting their potential benefits and challenges. It encompasses a thorough search and evaluation of relevant articles, summarizing methods and performances of these systems. The primary aim of this review is to examine and analyze existing research to identify patterns and gaps and establish a foundation for future advancements. We screened 7 databases using a set of standardized search criteria to minimize any potential missed articles. Four thousand four hundred eighty-seven deduplicated abstracts were screened and narrowed down to 34 studies that varied in design, algorithm methods, types of seizures detected, and performance metrics. Seizure detection sensitivity ranged from 100% to 0%, with optical flow analysis showing the highest sensitivity. The specificity of all included articles ranged from 97.7% to 60%. While limited studies reported accuracy, the highest reported was 100% using Radon Transform based technique on Dual Tree Complex Wavelet coefficients. Video and audio-based tools offer novel, noncontact approaches for detecting and monitoring seizures. Available studies are limited in sample sizes, dataset diversity, and standardized evaluation protocols, impacting the generalizability of results. Future research focusing on larger-scale investigations with diverse datasets, standardized evaluation protocols, and consistent reporting metrics is needed.

Optimal randomized algorithms of weakly-balanced multi-branching AND-OR trees

This article treats AND-OR tree computation in terms of query complexity. We are interested in the cases where assignments (inputs) or algorithms are randomized. For the former case, it is known that there is a unique randomized assignment achieving the distributional complexity of balanced trees. On the other hand, the dual problem has the opposite result; the optimal randomized algorithms for balanced trees are not unique. We extend the latter study on randomized algorithms to weakly-balanced trees, and see that the uniqueness still fails.

Improved Magnetic Resonance Image Reconstruction using Compressed Sensing and Adaptive Multi Extreme Particle Swarm Optimization Algorithm

One powerful technique that can offer a thorough examination of the body's internal structure is magnetic resonance imaging (MRI). MRI's lengthy acquisition times, however, may restrict its clinical usefulness, particularly in situations where time is of the essence. Compressed sensing (CS) has emerged as a potentially useful method for cutting down on MRI acquisition times; nevertheless, the effectiveness of CS-MRI is dependent on the selection of the sparsity-promoting algorithm and sampling scheme. This research paper presents a novel method based on adaptive multi-extreme particle swarm optimization (AMEPSO) and dual tree complex wavelet transform (DTCWT) for fast image acquisition in magnetic resonance. The method uses AMEPSO in order to maximize the sampling pattern and minimize reconstruction error, while also exploiting the sparsity of MR images in the DTCWT domain to improve directional selectivity and shift invariance. MATLAB software was used for simulation of the proposed method. In comparison with the particle swarm optimized-DTCWT (PSODTCWT) and DTCWT algorithms, respectively, the results demonstrated an improvement in the peak signal-to-noise ratio of 8.92% and 15.92% and a higher structural similarity index measure of 3.69% and 7.5%. Based on these improvements, the proposed method could potentially make high-quality, real-time MRI imaging possible, which might improve detection and treatment of medical conditions and increase the throughput of MRI machines.

Multitask Multi attention Residual Shrinkage Convolutional Neural Network Personalized Assessment Model of Tennis Teaching Based on Data Mining and Intelligent Recommendation

Tennis is highly explosive, continuous, intense sport that involves numerous continuous short-term explosive actions. This type of training is short-term, high-intensity, and focuses on competitive skills. Athletes must be physically fit and have long-term endurance to excel in technical and tactical skills during competition. In this manuscript, Multitask Multi attention Residual Shrinkage Convolutional Neural Network Personalized Assessment Model of Tennis Teaching Based on Data Mining and Intelligent Recommendation (MMARSCNN-PAM-TT-DM-IR) is proposed. The input data are collected from training levels of technical – sportive actions of 12-14 years old tennis players’ dataset. Initially the input data is preprocessed using Generalized Multi-kernel Maximum Correntropy Kalman Filter for exploring the data. The pre-processed data is given into Gradient Descent Namib Beetle Optimization (GDNBO) for choosing features from the dataset. Then, selected features are given to Dual Tree Complex Discrete Wavelet Transform to extract gray-scale statistical features such as Homogeneity, Entropy, Energy and Smoothness. Finally, the extracted feature attributes are given to MMARSCNN is used to Tennis Teaching Based on Data Mining. In general, MMARSCNN does not express some adaption of optimization strategies for determining optimal parameters to assure accurate Teaching of Tennis. Therefore, Zebra Optimization Algorithm (ZOA) is proposed to enhance weight parameter of MMARSCNN, which precisely Teaching of tennis depend on Data Mining. The proposed model is implemented, its efficacy is assessed utilizing some performance metrics such as accuracy, precision, F1-score, MSE, AUC. The proposed MMARSCNN-PAM-TT-DM-IR method provides 22.55%, 24.72% and 29.63% higher accuracy; 32.66%, 34.97% and 29.57% higher precision; 25.18%, 21.52% and 28.68% higher F1-Score is compared with existing method such as analysis characteristics of Tennis singles matches depend on 5G with data mining technology (AOC-TSM-DM), Tennis online teaching information platform depend on android mobile intelligent terminal (AMIT-TTP-DM), optimization analysis of Tennis players’ physical fitness index depend on data mining with mobile computing (OA-TP-DM-MC) respectively.

Balancing composite motion optimization using R-ERNN with plant disease

Soybean is a crop with a long cultivation history that plays a significant role on agricultural field. Early diagnosis of Soybean Mosaic Virus Disease (SMVD) is must require, because it causes a fast reduction and significant losses in soybean yields. Therefore, this paper proposes a Balancing Composite Motion optimization utilizing Recalling-Enhanced Recurrent Neural Network with Plant Disease for separating its severity into 0, 1 and 2 grades. Initially, hyper spectral image data was taken from Spec View software. Then, the spectral imageries of soybean leaves are processed to remove the redundancy in frequency bands that emerges owing to hyper spectral camera does not present any important information are filtered by Dual Tree Complex Wavelet Transform (DTCWT). In this, hyper spectral image features are extracted using Ternary pattern and discrete wavelet transform (TP-DWT) method. After completing the process of feature extraction, the feature extracted imageries are given to Recalling-Enhanced Recurrent Neural Network (RE-RNN) and it is used to classify the Soybean mosaic disease. Here, Balancing Composite Motion Optimization (BCMO) algorithm is employed for tuning the RE-RNN hyper parameters. The proposed PDI-RE-RNN-BCMO-HSI method provides 27.03%, 28.94% and 39.04% higher precision compared with existing method like grading model of soybean mosaic infection depending upon hyper spectral imaging technology (PDI-CNN-SVM-HSI), recognization of soybean varieties under hyper spectral imaging technology and one-dimensional CNN (PDI-1D-CNN-HSI) and hyper spectral imaging technology combined with multivariate models to recognize soybean disease (PDI-SNV-ELM-HSI) respectively.

The Seismicity Rate Model for the 2022 Aotearoa New Zealand National Seismic Hazard Model

ABSTRACT A seismicity rate model (SRM) has been developed as part of the 2022 Aotearoa New Zealand National Seismic Hazard Model revision. The SRM consists of many component models, each of which falls into one of two classes: (1) inversion fault model (IFM); or (2) distributed seismicity model (DSM). Here we provide an overview of the SRM and a brief description of each of the component models. The upper plate IFM forecasts the occurrence rate for hundreds of thousands of potential ruptures derived from the New Zealand Community Fault Model version 1.0 and utilizing either geologic- or geodetic-based fault-slip rates. These ruptures are typically less than a couple of hundred kilometers long, but can exceed 1500 km and extend along most of the length of the country (albeit with very low probabilities of exceedance [PoE]). We have also applied the IFM method to the two subduction zones of New Zealand and forecast earthquake magnitudes of up to ∼Mw 9.4, again with very low PoE. The DSM combines a hybrid model developed using multiple datasets with a non-Poisson uniform rate zone model for lower seismicity regions of New Zealand. Forecasts for 100 yr are derived that account for overdispersion of the rate variability when compared with Poisson. Finally, the epistemic uncertainty has been modeled via the range of models and parameters implemented in an SRM logic tree. Results are presented, which indicate the sensitivity of hazard results to the logic tree branches and that were used to reduce the overall complexity of the logic tree.

Environmental complexity and predator density mediate a stable earwig-woolly apple aphid interaction

Regulation of insect pests by predators is an integral component of biological pest control. However, predator-prey interactions often reach a stable state leading to coexistence, which can be problematic for plant protection if the persisting herbivores exceed a damage threshold. Consequently, understanding the factors mediating this stability is crucial for successful biocontrol. Here, we investigated the predator-prey interaction of an important apple pest, the woolly apple aphid (WAA) (Eriosoma lanigerum Hausm.) (Hemiptera: Aphididae), and its predator, the omnivorous common earwig (Forficula auricularia L.) (Dermaptera: Forficulidae). To this end, we introduced increasing densities of earwigs in gauze-bagged branches harbouring defined amounts of WAA and recorded predation on individual WAA colonies for almost one month. We showed that the spatial complexity of the predation environment and the earwig density determined the predation efficiency on WAAs. Earwigs could eradicate WAAs regardless of density on short apple branches without lateral twigs (first trial). However, on longer, more complex branches (second trial), stable states of WAAs and earwigs were observed. Only the highest earwig density could completely consume WAA regardless of branch complexity. We introduce a conceptual model to describe the searching capability of earwigs, which determines their efficiency in locating prey. Our model predicts that searching capability is reduced by environmental complexity; this effect is alleviated by increased earwig density, as more predators possess a higher searching capability. Conclusively, our model explains why only the highest earwig density could completely consume WAA irrespective of complexity and illustrates how predator density and environmental complexity jointly influence predation. Thus environmental characteristics not inherent to our predator-prey interaction were a decisive factor for effective predation on gauze-bagged branches. Based on these results, reducing tree complexity by pruning or augmenting earwig populations in orchards can increase biocontrol of the WAA.

Accuracy evaluation and effect factor analysis of GEDI aboveground biomass product for temperate forests in the conterminous United States

ABSTRACT The Global Ecosystem Dynamics Investigation (GEDI) is expected to revolutionize the quantification of aboveground carbon at continental scales, through its unprecedented dense vertical observations of vegetation structure. As its primary task, GEDI recently introduced GEDI L4A, the 25 m near-global footprint aboveground biomass density (AGBD) product. As a global mission with significant policy and management applications, it is urgent to conduct a comprehensive evaluation of GEDI L4A and to analyze the factors affecting the product’s performance. In this study, the accuracy of GEDI L4A is assessed using co-registered airborne Lidar surveys collected during 2018 ~ 2019 and corresponding AGBD plots at 19 sites of the National Ecological Observatory Network (NEON). The analysis included 11 forest types and spanned 17 eco-climatic domains across the conterminous United States to ensure the representativeness and comprehensiveness of the evaluation result. The interplay of nine factors affecting GEDI L4A is quantified, including the simulated waveform strategy deviation (SWSD) used in GEDI L4A, canopy characteristics (tree height, crown size, and canopy cover), canopy heterogeneity (crown size standard deviation, tree height standard deviation, and tree density), and other factors (forest type and topographic slope). Results show that compared with NEON observations, GEDI L4A generally underestimates the AGBD (Bias: −31.65 Mg/ha), with a moderate relative error exhibited in 14 of 19 sites (%RMSE ranging from 19% to 50%). For half of the forest types, the threshold of the lowest accuracy requirement of AGBD products set by GCOS was met or was close to being met. Broadleaf forests with high AGBD values had the lowest %RMSE (less than 35%), while coniferous forests with low AGBD values had the highest %RMSE (over 50%). Among the different factors considered, the SWSD contributed the most to GEDI L4A’s accuracy, with a relative importance of 56.63%, and manifested the indirect impacts of canopy heterogeneity and canopy characteristics. The relative importance of canopy heterogeneity (32.40%) was the second highest after SWSD; it was also much higher than that of canopy characteristics (3.99%). These results indicate the limitation of using only relative heights as predictors in GEDI L4A due to limited representation of horizontal structure and vertical tree complexity within a footprint. The findings in this study are a step forward in GEDI L4A’s appropriate application and provide perspectives to aid its improvement.

Evaluation of WATERMARK Channel for Underwater Communication using Dual Tree Complex Wavelet Transform based Orthogonal Frequency Division Multiplexing Model

Underwater communication is one of the important research areas which involves design and development of communication systems that can demonstrate high data rate and low Bit Error Rate (BER). In this work three different modulation schemes are compared for their performances in terms of BER and Peak to Average Power Ratio (PAPR). The realistic channel model called WATERMARK is used as a benchmark to evaluate channel performances. The mathematical model is developed in MATLAB and channel environments such as Norway Oslo fjord (NOF1), Norway Continental Shelf (NCS1), Brest Commercial Harbour (BCH1), Kauai (KAU1, KAU2) are considered for modelling different underwater channels. The data symbols are modulated using Dual Tree Complex Wavelet Transform (DTCWT) Orthogonal Frequency Division Multiplexing (OFDM) model to generate multi subcarriers and are demodulated at the receiver considering underwater channel environments. The BER results are evaluated for channel depth less than 10m and 10-50m. An improvement of 2x10-2 in terms of BER is observed when compared with Fast Fourier Transform (FFT) based OFDM model.

Combining Fuzzy Partitioning and Incremental Methods to Construct a Scalable Decision Tree on Large Datasets

The Decision tree algorithm is a very popular classifier for reasoning through recursive partitioning of the data space. To choose the best attributes for splitting, the range of each continuous attribute should be split into two or more intervals. Then partitioning criteria are calculated for each value. Fuzzy partitioning can be used to reduce sensitivity to noise and increase tree stability. Also, tree-building algorithms face memory limitations as they need to keep the entire training dataset in the main memory. In this paper, we introduced a fuzzy decision tree approach based on fuzzy sets. To avoid storing the entire training dataset in the main memory and overcome the memory limitations, the algorithm incrementally builds FDTs. Membership functions are automatically generated. The Fuzzy Information Gain (FIG) is then used as the fast split attribute selection criterion, and leaf expansion is performed only on the instances stored in it. The efficiency of this algorithm is examined in terms of accuracy and tree complexity. The results show that the proposed algorithm can overcome memory limitations and balance accuracy and complexity while reducing the complexity of the tree.

Catalpol rescues LPS-induced cognitive impairment via inhibition of NF-Κb-regulated neuroinflammation and up-regulation of TrkB-mediated BDNF secretion in mice

Ethnopharmacological relevanceSeptic-associated encephalopathy (SAE) is a key manifestation of sepsis. Nevertheless, specific treatment for SAE is still lacking. Catalpol is an active component derived from Rehmanniae Radix, and has been demonstrated to be a potential neuroprotective agent. However, its effect on SAE still needs to be fully explored. AimTo address the benefits of catalpol on post-sepsis cognitive deterioration and related mechanisms. Materials and methodsNovel object recognition test, temporal order task, histopathology, and immunochemistry were applied to address the benefits of catalpol on LPS-triggered post-sepsis cognitive decline in mice. Xuebijing injection (10 ml/kg) has been utilized as a positive control in the above animal studies. After treatment, the catalpol content in the hippocampus was determined using LC-MS/MS. Finally, the mechanisms of catalpol were further assessed in BV2 and PC12 cells in vitro using Western blot, RT-PCR, flow cytometry, molecular docking tests, thermal shift assay, transmission electron microscopy, and immunofluorescence analysis. ResultsBehavior tests showed that catalpol therapy could lessen the cognitive impairment induced by LPS damage. HE, Nissl, immunofluorescence, transmission electron microscopy, and Golgi staining further reflected that catalpol treatment could restore lymphocyte infiltration, blood-brain barrier (BBB) degradation, and the decreasing complexity of dendritic trees. According to LC-MS/MS analysis, catalpol had a 136 ng/mg concentration in the hippocampus. In vitro investigation showed that catalpol could inhibit microglia M1 polarization via blocking NF-κB phosphorylation, translocation and then reducing inflammatory cytokine release in BV2 microglia cells. Brain-derived neurotrophic factor (BDNF) release up-regulation and TrkB pathway activation were observed in the catalpol treatment group in vivo and in vitro. The effect of catalpol on enhancing BDNF expression was inhibited by the specific inhibitor of TrkB (GNF-5837) in PC12 cells. Further molecular docking tests showed that catalpol formed weak hydrophobic bonds with TrkB. Besides, thermal shift assay also reflected that catalpol incubation caused a considerable change in the melting temperature of the TrkB. ConclusionCatalpol alleviates LPS-triggered post-sepsis cognitive impairment by reversing neuroinflammation via blocking the NF-κB pathway, up-regulating neurotrophic factors via the activation of TrkB pathway, and preserving BBB integrity.

On Complexity of Deterministic and Nondeterministic Decision Trees for Conventional Decision Tables from Closed Classes.

In this paper, we consider classes of conventional decision tables closed relative to the removal of attributes (columns) and changing decisions assigned to rows. For tables from an arbitrary closed class, we study the dependence of the minimum complexity of deterministic and nondeterministic decision trees on the complexity of the set of attributes attached to columns. We also study the dependence of the minimum complexity of deterministic decision trees on the minimum complexity of nondeterministic decision trees. Note that a nondeterministic decision tree can be interpreted as a set of true decision rules that covers all rows of the table.