bluuebunny/crossref_metadata_2025 huggingface dataset:
Musical experience partially counteracts temporal speech processing deficits in putative mild cognitive impairment
Authors: Price Caitlin N. , Bidelman Gavin M.
Published: June 2022

Mild cognitive impairment (MCI) commonly results in more rapid cognitive and behavioral declines than typical aging. Individuals with MCI can exhibit impaired receptive speech abilities that may reflect neurophysiological changes in auditory‐sensory processing prior to usual cognitive deficits. Benefits from current interventions targeting communication difficulties in MCI are limited. Yet, neuroplasticity associated with musical experience has been implicated in improving neural representations of speech and offsetting age‐related declines in perception. Here, we asked whether these experience‐dependent effects of musical experience might extend to aberrant aging and offer some degree of cognitive protection against MCI. During a vowel categorization task, we recorded single‐channel electroencephalograms (EEGs) in older adults with putative MCI to evaluate speech encoding across subcortical and cortical levels of the auditory system. Critically, listeners varied in their duration of formal musical experience (0–21 years). Musical experience sharpened temporal precision in auditory cortical responses, suggesting that musical experience produces more efficient processing of acoustic features by counteracting age‐related neural delays. Additionally, robustness of brainstem responses predicted the severity of cognitive decline, suggesting that early speech representations are sensitive to preclinical stages of cognitive impairment. Our results extend prior studies by demonstrating positive benefits of musical experience in older adults with emergent cognitive impairments.
Elevational trends of tree fine root traits in species‐rich tropical Andean forests
Authors: Pierick Kerstin , Link Roman M. , Leuschner Christoph , Homeier Jürgen
Published: June 2022

With increasing elevation, trees in tropical montane forests have to invest larger fractions of their resources into their fine roots in order to compensate for increasingly unfavorable soil conditions. It is unclear how elevation and related edaphic changes influence the variability in tree fine root traits and belowground functional diversity. We measured six fine root traits related to resource acquisition on absorptive fine roots of 288 trees from 145 species along an elevational gradient from 1000 m to 3000 m a.s.l. in tropical montane forests of the Ecuadorian Andes. We analyzed trait relationships with elevation and soil nutrient availability, and tested whether root functional diversity varied along these gradients. Fine roots at higher elevations and at more nutrient‐poor sites were thicker, had higher tissue densities, and lower specific root length and nutrient concentrations than at lower elevations. These trends were diluted by the coexistence of tree species with a broad range of different root traits within communities particularly towards lower elevations, where root functional diversity was significantly higher. We conclude that nutrient limitation and potentially further adverse conditions at higher elevations are strong environmental filters that lead to trait convergence towards a conservative resource use strategy, whereas different trait syndromes are equally successful at lower elevations.
Spectrum of paired‐like homeobox 2b immunoexpression in pediatric brain tumors with embryonal morphology
Authors: Alturkustani Murad , Walker Adam D. , Tran Ivy , Snuderl Matija , Cotter Jennifer A.
Published: June 2022

Paired‐like homeobox 2b (PHOX2B) is an established immunomarker for peripheral neuroblastoma and autonomic nervous system cells. We aimed to evaluate the utility of PHOX2B immunostaining in central nervous system (CNS) tumors with embryonal morphology. Fifty‐one tumors were stained with PHOX2B and submitted for whole slide image analysis: 35 CNS tumors with embryonal morphology (31 CNS embryonal tumors and four gliomas); and 16 peripheral neuroblastomas were included for comparison. Diffuse nuclear immunopositivity was observed in all (16/16) neuroblastomas (primary and metastatic). Among CNS embryonal tumors, focal immunoreactivity for PHOX2B was observed in most (5/7) embryonal tumors with multilayered rosettes (ETMR) and a single high‐grade neuroepithelial tumor (HGNET) with PLAGL2 amplification; the remaining 27 CNS tumors were essentially immunonegative (<0.05% positive). Among ETMR, PHOX2B expression was observed in a small overall proportion (0.04%–4.94%) of neoplastic cells but focally reached up to 39% in 1 mm ‘hot spot’ areas. In the PLAGL2‐amplified case, 0.09% of the total neoplastic population was immunoreactive, with 0.53% in the ‘hot spot’ area. Care should be taken in interpreting PHOX2B immunopositivity in a differential diagnosis that includes metastatic neuroblastoma and CNS tumors; focal or patchy expression should not be considered definitively diagnostic of metastatic peripheral neuroblastoma.
Sincerity in bulk
Authors: Paterson Grace
Published: June 2022

This paper is concerned with situations in which a speaker issues many speech acts at the same time. A common example is the publication of a large text such as a book containing many distinct assertions. It is argued that these cases present a challenge for speech act theory related to how we are to understand sincerity. With reference to the well known paradox of the preface, it is argued that sincerity of such bulk speech cannot be understood as a simple conjunction of the sincerity of the encoded acts. A proposal is given according to which sincere bulk speech requires the speaker explicitly and as precisely as possible mark any subsets of her communication about which she has doubts.
Simulated RBC antibody identification training panels created using SARS‐CoV‐2 kodecytes and immune plasma
Authors: Perry Holly , Henry Stephen M.
Published: June 2022

Background

Training is essential to develop and maintain skills required to be a competent serologist, yet samples required to achieve this are often difficult to obtain. We evaluated the feasibility of SARS‐CoV‐2 peptide modified RBCs (1144‐kodecytes) to develop simulated antibody screening and identification panels of reagent RBCs suitable for practical training, recognition, and grading of serologic reactions.

Study Design and Methods

RBCs from a single donor were modified into kodecytes using Kode Technology function‐spacer‐lipid constructs bearing a short SARS‐CoV‐2 peptide. Kodecytes and unmodified cells were then arranged in patterns representative of RBC antibody profiles as simulated antibody screening and identification reagent cell panels (SASID), and then tested against immune donor plasma samples containing SARS‐CoV‐2 antibodies. Manual tube and two different gel card serologic platforms were evaluated by routine techniques. SASID exemplars were created for antibodies including D, Cw, f (ce), Jka (strong, weak, dosing), mixtures of D + E, Jka + K, Fya + E, high and low frequency antibodies and a warm IgG autoantibody.

Results

Kodecytes (positive reactions) and unmodified cells (negative) when arranged and tested in appropriate patterns in SASID panels were able to mimic IgG antibody reactions, and were capable of measuring both accuracy and precision in reaction grading.

Conclusions

Kodecytes can be used to rapidly create in‐house simulated yet realistic antibody screening and identification panels suitable for large scale training in the recognition and grading of serologic reactions.
Coastal freshwater wetlands squeezed between migrating salt marshes and working lands
Authors: Bernhardt Emily
Published: June 2022

Creative solutions are needed to sustain the diversity of coastal wetland ecosystems as sea levels rise.
A Cytoplasmic NAD(P)H-Dependent Polysulfide Reductase with Thiosulfate Reductase Activity from the Hyperthermophilic Bacterium Thermotoga maritima
Authors: Liang Jiyu , Huang Haiyan , Wang Yubo , Li Lexin , Yi Jihong , Wang Shuning
Published: June 2022

The reduction of S
0
and thiosulfate is essential in the sulfur cycle of geothermal environments, in which thermophiles play an important role. Despite previous research on some sulfur reductases of thermophilic archaea, the mechanism of sulfur reduction in thermophilic bacteria is still not clearly understood.
Integrating Multiple Omics Identifies
Phaeoacremonium rubrigenum
Acting as
Aquilaria sinensis
Marker Fungus to Promote Agarwood Sesquiterpene Accumulation by Inducing Plant Host Phosphorylation
Authors: Liu Juan , Li Tianxiao , Chen Tong , Gao Jiaqi , Zhang Xiang , Jiang Chao , Yang Jian , Zhou Junhui , Wang Tielin , Chi Xiulian , Cheng Meng , Huang Luqi
Published: June 2022

Agarwood is an expensive resinous portion derived from
Aquilaria
plants and has been widely used as medicine, incense, and perfume. The factors involved in steady agarwood accumulation remain elusive.
Cannabis use and risks of respiratory and all-cause morbidity and mortality: a population-based, data-linkage, cohort study
Authors: Vozoris Nicholas T , Zhu Jingqin , Ryan Clodagh M , Chow Chung-Wai , To Teresa
Published: June 2022

Introduction

Although cannabis is frequently used worldwide, its impact on respiratory health is characterised by controversy.

Objective

To evaluate the association between cannabis use and respiratory-related emergency room (ER) visits and hospitalisations.

Methods

A retrospective, population-based, cohort study was carried out, linking health survey and health administrative data for residents of Ontario, Canada, aged 12–65 years, between January 2009 and December 2015. Individuals self-reporting cannabis use within the past year were matched to control individuals (people who reported never using cannabis, or used cannabis only once, and more than 12 months ago) in upwards of a 1:3 ratio on 31 different variables, using propensity score matching methods. Respiratory-related and all-cause ER visits or hospitalisations, and all-cause mortality, were evaluated up to 12 months following the index date.

Results

We identified 35 114 individuals who had either used cannabis in the past year or were controls, of whom 6425 (18.3%) used cannabis in the past year. From this group, 4807 (74.8%) were propensity-score matched to 10 395 control individuals. In the propensity score matched cohort, there was no significant difference in odds of respiratory-related ER visit or hospitalisation between cannabis users and the control group (OR 0.91, 95% CI 0.77 to 1.09). Compared with control individuals, cannabis users had significantly increased odds of all-cause ER visit or hospitalisation (OR 1.22, 95% CI 1.13 to 1.31) and there was no significant difference with respect to all-cause mortality (OR 0.99, 95% CI 0.49 to 2.02).

Conclusions

Although no significant association was observed between cannabis use and respiratory-related ER visits or hospitalisations, the risk of an equally important morbidity outcome, all-cause ER visit or hospitalisation, was significantly greater among cannabis users than among control individuals. Therefore, cannabis use is associated with increased risk for serious adverse health events and its recreational consumption is not benign.
A life cycle model to assess the abundance of black scabbardfish, a widely distributed fish with cryptic migrations
Authors: Figueiredo Ivone , Natário Isabel , Lorance Pascal , Carvalho M. Lucília
Published: June 2022

The spatio-temporal dynamics of the black scabbardfish ( Aphanopus carbo Lowe, 1839) abundance in the northeast Atlantic was modeled using two linked Bayesian state-space models fitted to fishery-dependent data from trawlers operating to the west and north off the British Isles and longliners off the west coast of Portugal. The stage-structured life cycle models included species vital processes and fishing, and are linked by the migration flow between the two areas. Although data on spawner abundance and recruitment are missing, the hierarchical nature of state-space models allows a convenient representation of black scabbardfish dynamics using reliable data from the two studied areas, which correspond to two of the three main fishing grounds for the species. The approach presented is comparable to the few models developed for other species, such as European eel, where spawning and recruitment occur at restricted and distant regions. This approach is likely to remain the only option for black scabbardfish stock assessment and fisheries monitoring, as it is unlikely that data about the unobserved spawning and early life stages will become available in the near future.
Brane cosmology with variable tension
Authors: Belinchón José Antonio , Dib Sami
Published: June 2022

We study brane-world models and demonstrate that such models do not admit self-similar solutions through the matter collineation approach. By introducing the hypothesis of variable brane tension, λ, we outline the new effective field equation (EFE) in the most simple case (symmetric embedding) under the assumption that the fundamental constants in five dimensions (5D) are constants. In this case, we find the exact form that each physical quantity may take in order that the EFE become invariant under scale transformations. By taking into account such assumptions, we find that in four dimensions the gravitational constant κ2 ∼  λ, while the cosmological constant Λ ∼  λ2 is always decreasing. These results are quite general and valid for any homogeneous self-similar metric. Nevetheless, the study of the EFE under scale symmetries suggests that ρ ∼  λ (as a functional relationship). This allows us to get a growing κ2 but, in this case, the fundamental constants in 5D must vary as well. We outline a toy model allowing such a possibility.
ACDSE: A Design Space Exploration Method for CNN Accelerator based on Adaptive Compression Mechanism
Authors: Feng Kaijie , Fan Xiaoya , An Jianfeng , Li Chuxi , Di Kaiyue , Li Jiangfei
Published: June 2022

Customized accelerators for
Convolutional Neural Network (CNN)
can achieve better energy efficiency than general computing platforms. However, the design of a high-performance accelerator should take into account a variety of parameters and physical constraints. The increasing parameters and tighter constraints gradually complicate the design space, which poses new challenges to the capacity and efficiency of design space exploration methods. In this paper, we provide a novel design space exploration method named ACDSE for optimizing the design process of CNN accelerators. ACDSE implements the adaptive compression mechanism to dynamically adjust the search range and prune low-value design points according to the exploration states. As a result, it can focus on valuable subspace while also improving exploration capacity and efficiency. Additionally, we implement ACDSE to address the problem of CNN accelerator latency optimization. The experiment indicates that, compared to former DSE methods, ACDSE can reduce latency and increase efficiency by 1.39x-5.07x and 2.07x-43.87x, respectively, under the most stringent constraint conditions, demonstrating its superior adaptability to the complicated design space.
Proactive Privacy-preserving Learning for Cross-modal Retrieval
Authors: Zhang Peng-Fei , Bai Guangdong , Yin Hongzhi , Huang Zi
Published: June 2022

Deep cross-modal retrieval techniques have recently achieved remarkable performance, which also poses severe threats to data privacy potentially. Nowadays, enormous user-generated contents that convey personal information are released and shared on the Internet. One may abuse a retrieval system to pinpoint sensitive information of a particular Internet user, causing privacy leakage. In this article, we propose a data-centric Proactive Privacy-preserving Cross-modal Learning algorithm that fulfills the protection purpose by employing a generator to transform original data into adversarial data with quasi-imperceptible perturbations before releasing them. When the data source is infiltrated, the inside adversarial data can confuse retrieval models under the attacker’s control to make erroneous predictions. We consider the protection under a realistic and challenging setting where the prior knowledge of malicious models is agnostic. To handle this, a surrogate retrieval model is instead introduced, acting as the target to fool. The whole network is trained under a game-theoretical framework, where the generator and the retrieval model persistently evolve to fight against each other. To facilitate the optimization, a Gradient Reversal Layer module is inserted between two models, enabling a one-step learning fashion. Extensive experiments on widely used realistic datasets prove the effectiveness of the proposed method.
Path Planning of Unmanned Helicopter in Complex Environment Based on Heuristic Deep Q-Network
Authors: Yao Jiangyi , Li Xiongwei , Zhang Yang , Ji Jingyu , Wang Yanchao , Liu Yicen
Published: June 2022

Unmanned helicopters (UH) can evade radar detection by flying at ultralow altitudes, so as to conduct raids on targets. Path planning is one of the key technologies to realize UH’s autonomous completion of raid missions. Since the probability of UH being detected by radar varies with height, how to accurately identify the radar coverage area to avoid crossing has become a difficult problem in UH path planning. Aiming at this problem, a heuristic deep Q-network (H-DQN) algorithm is proposed. First, as part of the comprehensive reward function, a heuristic reward function is designed. The function can generate dynamic rewards in real time according to the environmental information, so as to guide the UH to move closer to the target and at the same time promote the convergence of the algorithm. Second, in order to smooth the flight path, a smoothing reward function is proposed. This function can evaluate the pros and cons of UH’s actions, so as to prompt UH to choose a smoother path for flight. Finally, the heuristic reward function, the smooth reward function, the collision penalty, and the completion reward are weighted and summed to obtain the heuristic comprehensive reward function. Simulation experiments show that the H-DQN algorithm can help UH to effectively avoid the radar coverage area and successfully complete the raid mission.
Opinion Dynamics Model with Bounded Confidence and the Sleeper Effect
Authors: Wei Jing , Jia Yuguang , Zhu Hengmin , Hong Xiaojuan , Huang Weidong
Published: June 2022

The evolution of opinions is a complex mechanism. The evolution of individual opinions is not only influenced by own and others but also by psychological effects. Therefore, based on the classical bounded confidence model and the sleeper effect, a new opinion evolution model is proposed in this paper. In this new opinion evolution model, we increase the opinions on every step and take into account the “discount opinions.” Since mental states for human are not easy to measure, we assumed three different initial networks and carried out simulation experiments. To verify the rationality on our model, we compared the effects with and without the sleeper effect, different thresholds, and discounting opinion ratio on opinion aggregation and convergence. Finally, we found the sleeper effect can differently affect the convergence of opinions in different opinion environments.
Analysis of Engineering and Geological Conditions of International Submarine Optical Fiber Cable Routing in the East China Sea Section
Authors: Zhang Ti-qiang , Fan Zi-xian , Ning Nan , Tang Jian-zhong , Kou Dai-xin
Published: June 2022

The East China Sea section of the Chinese international submarine optical fiber cable generally starts from the Shanghai coast, crosses the East China Sea shelf and continental slope to the east, and intersects with the international optical fiber cable system in Okinawa Trough. In this paper, the regional tectonic, seismic, and other environmental conditions of the East China Sea section of the international submarine optical cable are briefly described based on the previous data. Additionally, the geological engineering conditions of the international optical cable routing in the East China Sea area will be analyzed based on the field investigation data such as multibeam, side-scan sonar, shallow stratigraphic profile, sediment sampling, and cone penetration test (CPT). It is found that the main geological factors in the East China Sea shelf area are submarine scouring, sandwave, shallow gas, and hard seabed; in the land slope area are steep slope, submarine valley, rock, landslide, and shallow gas (pockmark); and in the Okinawa Trough area are gully, sea mound/seamount, rock, shallow gas, fault, and hard strata outcrop. This paper briefly analyzes the impact of each geological disaster factor on the submarine cable project and proposes solutions for providing essential information and some scientific basis for designing, constructing, operating, and maintaining international submarine optical fiber cables in the East China Sea area.
A Cloud-Based Machine Learning Approach to Reduce Noise in ECG Arrhythmias for Smart Healthcare Services
Authors: Jain Paras , Alsanie Walaa F. , Gago Dulio Oseda , Altamirano Gilder Cieza , Sandoval Núñez Rafaél Artidoro , Rizwan Ali , Asakipaam Simon Atuah
Published: June 2022

ECG (electrocardiogram) identifies and traces targets and is commonly employed in cardiac disease detection. It is necessary for monitoring precise target trajectories. Estimations of ECG are nonlinear as the parameters TDEs (time delays) and Doppler shifts are computed on receipt of echoes where EKFs (extended Kalman filters) and electrocardiogram have not been examined for computations. ECG, certain times, results in poor accuracies and low SNRs (signal-to-noise ratios), especially while encountering complicated environments. This work proposes to track online filter performances while using optimization techniques to enhance outcomes with the removal of noise in the signal. The use of cost functions can assist state corrections while lowering costs. A new parameter is optimized using IMCEHOs (Improved Mutation Chaotic Elephant Herding Optimizations) by linearly approximating system nonlinearity where multi-iterative function (Optimized Iterative UKFs) predicts a target’s unknown parameters. To obtain optimal solutions theoretically, multi-iterative function takes less iteration, resulting in shorter execution times. The proposed multi-iterative function provides numerical approximations, which are derivative-free implementations. Signals are updated in the cloud environment; the updates are received by the patients from home. The simulation evaluation results with estimators show better performances in terms of reduced NMSEs (normalized mean square errors), RMSEs (root mean squared errors), SNRs, variances, and better accuracies than current approaches. Machine learning algorithms have been used to predict the stages of heart disease, which is updated to the patient in the cloud environment. The proposed work has a 91.0% accuracy rate with an error rate of 0.05% by reducing noise levels.
Study on Analysis of Several Molecular Identification Methods for Ciliates of Colpodea (Protista, Ciliophora)
Authors: Song Yumeng , Hao Tingting , Li Bailin , Zheng Weibin , Liu Lihui , Wang Li , Chen Ying , Pan Xuming
Published: June 2022

The application of molecular techniques to accurately identify protozoan species can correct previous misidentifications based on traditional morphological identification. Colpodea ciliates have many toxicological and cytological applications, but their subtle morphological differences and small body size hinder species delineation. Herein, we used Cox I and β-tubulin genes, alongside fluorescence in situ hybridization (FISH), to evaluate each method in delineating Colpodea species. For this analysis, Colpoda harbinensis n. sp., C. reniformis, two populations of C. inflata, Colpoda compare grandis, and five populations of Paracolpoda steinii, from the soil in northeastern China, were used. We determined that (1) the Cox I gene was more suitable than the β-tubulin gene as a molecular marker for defining intra- and interspecific level relationships of Colpoda. (2) FISH probes designed for Colpoda sp., C. inflata, Colpoda compare grandis, and Paracolpoda steinii, provided rapid interspecific differentiation of Colpodea species. (3) Colpoda harbinensis n. sp. was established and mainly characterized by its size in vivo (approximately

80
×
60
 
μ
m

), a reniform body in outline, one macronucleus, its spherical shape, a sometimes nonexistent micronucleus, 11–15 somatic kineties, and five or six postoral kineties. In conclusion, combining oligonucleotide probes, DNA barcoding, and morphology for the first time, we have greatly improved the delineation of Colpodea and confirmed that Cox I gene was a promising DNA barcoding marker for species of Colpodea, and FISH could provide useful morphological information as complementing traditional techniques such as silver carbonate.
Innovation Path of Industrialization of Ethnic Minority Culture and Art Resources Based on DM Technology
Authors: Meng Qingyi , Li Hongbo
Published: June 2022

In ethnic minority areas, the rich cultural color has strong attraction and market competitiveness. While making full use of it to promote the local economic development of ethnic minorities, it promotes the inheritance and protection of ethnic culture and the cultural exchange and integration among multiethnic groups. In this case, a new data mining (DM) technology came into being. DM is a new data processing technology for developing information resources. The object of DM is not only a database but also a file system, or any other data collection organized together. Based on DM technology, this paper studies the industrialization innovation of ethnic minority cultural and artistic resources, which carries out DM on the basis of rational utilization of ethnic minority cultural resources, boldly innovates, uses new scientific and technological achievements and new artistic expression means, establishes the people-oriented service concept of cultural and artistic resources, and takes the individual needs of the public and the scientific analysis results of big data as the basis to provide a satisfactory and ideal cultural and artistic feast for the public.
Use of Artificial Neural Networks and Response Surface Methodology for Evaluating the Reliability Index of Steel Wind Towers
Authors: Inzunza-Aragón Indira , Ruiz Sonia E. , Cruz-Reyes Laura
Published: June 2022

The estimation of structural reliability is a process that requires a large number of computational hours when statistical data are not available since it is necessary to perform a large amount of analysis or numerical simulations to estimate parameters related to the reliability. A methodology is proposed for estimating the structural reliability index, as well as the demand and structural capacity factors inherent to the structure, given the fundamental vibration period and the height of the structure, by using artificial neural networks (ANN) and, alternatively, the response surface method (RSM). Both approaches are applied to steel wind turbine towers. For the cases studied, ANN allow evaluating the reliability index and both the demand and structural capacity factors with greater accuracy than when using RSM.
Function Mechanism of Intellectual Property Capability on Relay Innovation Based on CWLBGSO-DAG-Bootstrap SEM: Mediating Effect of Knowledge Matching and Moderating Effect of Relationship Learning
Authors: Liu Qiang , Ding Taimin , Li Yafeng , Gao Ziwei , Guo Yu
Published: June 2022

Taking unicorn enterprises as the research objects, this study constructs the conceptual model of function mechanism of intellectual property capability on relay innovation performance and introduces knowledge matching as the mediating variable and relationship learning as the moderating variable. Hybrid methods composed of fusion of rough set and binary firefly algorithm based on community weak connection mechanism (CWLBGSO), directed acyclic graph (DAG), and Bootstrap structural equation modeling (Bootstrap SEM) are used to carry out empirical analysis of the conceptual model of function mechanism. The empirical analysis results show that strong intellectual property capability has a positive role in promoting the relay innovation performance of unicorn enterprises, knowledge matching plays the positive mediating role between intellectual property capability and relay innovation, relationship learning plays the moderating role of the relationships between intellectual property capability and relay innovation, relationship learning plays the moderating role of the relationships between intellectual property capability and knowledge matching, and relationship learning plays the moderating role of the relationships between knowledge matching and relay innovation.
Woody Species Composition, Structure, and Carbon Stock of Coffee-Based Agroforestry System along an Elevation Gradient in the Moist Mid-Highlands of Southern Ethiopia
Authors: Tesfay Fikrey , Moges Yitebitu , Asfaw Zebene
Published: June 2022

There is a limited effort in Ethiopia to study scientifically the ecological features of traditional coffee-based agroforestry systems. This study was initiated to determine the structure, composition, and carbon stock of woody species along an elevation gradient of a traditional coffee-based agroforestry system in Yirgacheffe district, southern Ethiopia. Woody plants’ inventory was conducted in thirty-eight sampling quadrats (20

∗

20 m) along five elevation contours and eight transects. Thirty-eight soil samples were taken from randomly selected subplots at 0–30 cm soil depths. In this study 32, woody plant species representing 23 families were recorded. Species richness ranged from 13–17 along the elevation gradient. Woody plant diversity indices appear to have a slight variation with increasing elevation gradient. Shade tree and coffee shrub density, DBH, and height showed significant variations along the elevation gradient. Total aboveground woody biomass carbon stock along elevation gradient ranged from 11.07 to 27.48 Mg·ha−1. Soil organic carbon stock was slightly different across elevation gradients with a mean range of 83.91 to 89.29 Mg·ha−1. These indicate that the agroforestry system has significant potential of storing and enhancing ecosystem carbon stocks across all the elevation gradients. The findings generally show that agroforestry systems in the study area are diverse, structurally complex with significant carbon storage in the soil and woody biomass.
Integrated Single-Cell RNA-Sequencing Analysis of Gastric Cancer Identifies FABP1 as a Novel Prognostic Biomarker
Authors: Yang Fan , Gan Lianfang , Pan Junhua , Chen Yaying , Zhang Hong , Huang Ling
Published: June 2022

Gastric cancer (GC) is usually diagnosed in an advanced stage at the first visit due to the atypical clinical symptoms. The low surgical resection rate and chemotherapy sensitivity result in dismal survival. Therefore, it is urgent to develop novel biomarkers with high sensitivity and specificity to accurately assess the prognosis of GC patients. In the present study, 3385 differentially expressed genes (DEGs) were obtained from the single-cell RNA sequencing data of GC specimens. Using the unsupervised dimensionality reduction, we further found 3 subsets of cells including gastric cells, plasmacytoid dendritic cells, and memory T cells. Based on the cell clustering, we explored the key regulatory genes for GC progression by pseudo-time analysis and functional enrichment analysis. According to the results, the significant differentially expressed fatty acid-binding protein 1 (FABP1) verified by pseudo-time analysis was identified as the hub gene of GC progression. FABP1 was shown to be closely related to the long-term survival and the age at diagnosis of patients with GC in analysis based on the TCGA (The Cancer Genome Atlas) database. To further verify the role of FABP1 in GC, we performed immunohistochemical (IHC) analysis using the GC tissue microarray and found that the expression level of FABP1 was higher in GC tissues than in the adjacent tissues. Moreover, GC patients with higher expression of FABP1 had a worse clinical outcome. In summary, our study revealed that FABP1 is a potential effective biomarker for the prognosis of GC, and high expression of FABP1 predicts unsatisfactory survival.
Evaluation and Analysis of Intelligent Logistics Distribution Using the Expectation-Maximization Algorithm Calculation Model
Authors: Wang Zhifeng
Published: June 2022

The purpose of this article is to solve the problem that the accuracy of logistics distribution path planning is affected by the lack of data in the process of traditional logistics distribution planning and management. This exploration innovatively applies an effective data addition algorithm expectation-maximization (EM) algorithm to the intelligent logistics distribution system to improve logistics distribution’s overall efficiency and management quality. First, the concept of intelligent logistics and the composition and main functions of the intelligent logistics system are introduced. Then, the core idea of the EM algorithm and its applications in intelligent logistics are described. The logistics distribution of a chain company is taken as an example. Finally, the advantages and disadvantages of the intelligent logistics system based on the EM algorithm are compared with those of the traditional intelligent logistics systems based on variable neighborhood search (VNS), Tabu search (TS), and ant colony optimization (ACO). The performance test results show that the EM algorithm’s optimal solution times are 7 times. Its convergence speed is slightly lower than that of the ACO, but there is no obvious difference. The intelligent logistics distribution system based on the EM algorithm has faster order processing speed and higher efficiency in the actual case application. The average processing time of each order is 1.78 min, which is 0.237 min less than that of VNS and only 0.022 min more than that of ACO. It reveals that the intelligent logistics distribution system based on the EM algorithm is more efficient. The study provides a new idea for the efficient distribution of enterprise logistics.
A Convex Relaxation Approach for Learning the Robust Koopman Operator
Authors: Su Yong , Jin Jiawei , Peng Weilong , Tang Keke , Khan Asad , An Simin , Xing Meng
Published: June 2022

Although data-driven models, especially deep learning, have achieved astonishing results on many prediction tasks for nonlinear sequences, challenges still remain in finding an appropriate way to embed prior knowledge of physical dynamics in these models. In this work, we introduce a convex relaxation approach for learning robust Koopman operators of nonlinear dynamical systems, which are intended to construct approximate space spanned by eigenfunctions of the Koopman operator. Different from the classical dynamic mode decomposition, we use the layer weights of neural networks as eigenfunctions of the Koopman operator, providing intrinsic coordinates that globally linearize the dynamics. We find that the approximation of space can be regarded as an orthogonal Procrustes problem on the Stiefel manifold, which is highly sensitive to noise. The key contribution of this paper is to demonstrate that strict orthogonal constraint can be replaced by its convex relaxation, and the performance of the model can be improved without increasing the complexity when dealing with both clean and noisy data. After that, the overall model can be optimized via backpropagation in an end-to-end manner. The comparisons of the proposed method against several state-of-the-art competitors are shown on nonlinear oscillators and the lid-driven cavity flow.
Modeling Various Survival Distributions using a Nonparametric Hypothesis Testing Based on Laplace Transform Approach with Some Real Applications
Authors: Gadallah A. M. , Mohammed B. I. , Al-Babtain Abdulhakim A. , Khosa Saima Khan , Kilai Mutua , Yusuf M. , Bakr M. E.
Published: June 2022

We hope to assess the different processing methodologies or the effectiveness of the devices or systems applied in this nonparametric statistical test by observing the failure behavior of the recorded survival data. The proposed second-order approach of new better than used (NBU 2) will be employed, which requires that the test data behaves either like NBU 2 property or exponentially. If the survival data is NBU 2, the proposed treatment method is likely to be beneficial. If the data are exponential, on the other hand, the recommended treatment method has no positive or negative influence on patients, as shown in the application section. To establish the validity of the test, we calculated the power of the proposed test and efficiency on both complete and censored data, compared the results to those of existing tests, and then applied the test to a range of real-world data.
Route Optimization of Agricultural Product Distribution Based on Agricultural Iot and Neural Network from the Perspective of Fabric Blockchain
Authors: Zuo Xin , Cui Zhongwei , Lin Hong , Wang Dong
Published: June 2022

With the fast growth of AI and Internet of Things (IoT) technology, many agricultural product sales businesses and logistics sectors have started to concentrate on agricultural product distribution information operations. The requirements for the delivery service time are very high due to the features of perishable and highly easy dehydration of fresh agricultural goods. To preserve the freshness and quality of agricultural goods, the logistics and distribution process must be completed as rapidly as possible via appropriate low temperature control and the use of IoT technology. IoT technology will surely bring about the intelligent operation in the circulation of agricultural products. With the decentralized management of the Fabric blockchain, the investment and maintenance costs of the agricultural IoT will be reduced, which will help to improve the intelligence and scale of the agricultural IoT. Aiming at the specific problems, the path optimization problem in the process of agricultural product distribution is brought out. This paper completes the following work: (1) the traditional agricultural product distribution process is roughly described, and the shortcomings and problems of the traditional mode are explored and studied. On this basis, the agricultural product circulation mode under the IoT and neural network technology is introduced. (2) The TSP problem is defined, then some algorithms commonly used to solve the TSP problem are introduced, and then the theory and method of the SOM neural network and the basic principle of the ORC_SOM algorithm are introduced in detail. (3) Through a large number of experiments, the results prove the validity of the algorithm in this paper and the rationality of the theory.
Application of Human Posture Recognition Based on the Convolutional Neural Network in Physical Training Guidance
Authors: Wang Qingyu
Published: June 2022

The application of sports game video analysis in athlete training and competition analysis feedback has attracted extensive attention, but the traditional sports human body posture estimation method has a large error between the athlete’s human body posture estimation results and the actual results in the complex environment and the athlete’s body parts are blocked. Therefore, this study proposes a convolutional neural network for athlete pose estimation in sports game video. Based on the improved model, multiscale model, and large perception model, a superimposed hourglass network is constructed, and the gradient disappearance problem of the convolutional neural network is solved using intermediate supervision. The experimental results show that the athlete pose estimation model based on the convolutional neural network can improve the accuracy of athlete pose estimation and reduce the negative impact of occlusion environment on athlete pose estimation to a certain extent. In addition, compared with other athletes’ standing posture estimation methods, the model has competitive advantages and high accuracy under widely used standard conditions.
Analysis of Energy Transmission and Deformation Characteristics of Coal-Rock Combined Bodies
Authors: Tuo Wang , Fuzhou Qi , Jucai Chang
Published: June 2022

A coal-rock system is a common combination form in coal mines. In order to explore the energy interchange law of a coal-rock combined body and the interaction relationship between the two bodies, loading tests of coal-rock combined bodies with different height ratios were carried out. The loading path of rock in coal-rock combined bodies was demonstrated by means of a single loading and unloading test of the same-sized rock sample. Furthermore, a method to calculate rock energy was proposed based on the area of the loading and unloading curve. The experimental results show that the greater the surrounding rock pressure is, the smaller increase rate of lateral and volumetric strain in the postpeak stage will be when the same height ratio is present. An increase in the surrounding rock pressure causes an increase in the total strain energy density of small-sized rock samples. However, the total strain energy density is always greater than the elastic strain energy density. And the elastic and dissipated strain energy densities also increase, along with the energy dissipation with unloading. When the proportion of coal bodies increases, the energy accumulation also shows an increasing trend. When the height of the coal is greater than half the height of the complete specimen, the coal energy proportion is greater than 60%. After reaching the yield load, the energy in the coal body is dissipated in forms such as plastic deformation, internal damage, block friction, radiation energy, and kinetic energy. Therefore, the energy released is, in part, reflected in the rock body.
Comparative Analysis on Alignment-Based and Pretrained Feature Representations for the Identification of DNA-Binding Proteins
Authors: Chen Die , Zhang Hua , Chen Zeqi , Xie Bo , Wang Ye
Published: June 2022

The interaction between DNA and protein is vital for the development of a living body. Previous numerous studies on in silico identification of DNA-binding proteins (DBPs) usually include features extracted from the alignment-based (pseudo) position-specific scoring matrix (PSSM), leading to limited application due to its time-consuming generation. Few researchers have paid attention to the application of pretrained language models at the scale of evolution to the identification of DBPs. To this end, we present comprehensive insights into a comparison study on alignment-based PSSM and pretrained evolutionary scale modeling (ESM) representations in the field of DBP classification. The comparison is conducted by extracting information from PSSM and ESM representations using four unified averaging operations and by performing various feature selection (FS) methods. Experimental results demonstrate that the pretrained ESM representation outperforms the PSSM-derived features in a fair comparison perspective. The pretrained feature presentation deserves wide application to the area of in silico DBP identification as well as other function annotation issues. Finally, it is also confirmed that an ensemble scheme by aggregating various trained FS models can significantly improve the classification performance of DBPs.
Clinical Usage of Different Doses of Cis-Atracurium in Intracranial Aneurysm Surgery and Its Effect on Motor-Evoked Potentials
Authors: Qiao Zhongyuan , Fan Rong
Published: June 2022

The objective of this work was to investigate the effect of different doses of cis-atracurium on patients undergoing general anesthesia induction (GAI) during intracranial aneurysm surgery (ICAS). In this work, 90 patients who underwent ICAS under the elective motor-evoked potential (MEP) monitoring in the First Affiliated Hospital of Northwest University (Xi’an No. 1 Hospital) from January 2021 to May 2022 were enrolled as the research objects. Randomly, they were rolled into a S1 group (30 cases, 2 times 95% effective dose (ED95) cis-atracurium), a S2 group (30 cases, 3 times ED95 cis-atracurium), and a S3 group (30 cases, 4 times ED95 cis-atracurium). The endotracheal intubation conditions, the train-of-four (TOF) rate (TOFR), body movement, and spontaneous breathing were compared among the three groups of patients. The results showed that the MEP inhibition time of the patients in the S3 group was much longer than that of the S1 and S2 groups, but it showed no significant difference between the S1 group and S2 group (

P
>
0.05

). The good rates of endotracheal intubation conditions in the S2 group (100%) and S3 group (100%) were obviously higher than the rate in the S1 group (43.33%). The TOFRs of patients in S2 and S3 groups at time t2 and t3 were lower obviously to that at time t0, while the TOFRs of patients in S3 group at time t2 and t3 were still lower in contrast to the S2 group (

P
<
0.05

). The mean arterial pressure (MAP) and heart rate (HR) of patients in all groups were lower at t1, t2, and t3 than at t0 (

P
<
0.05

), while the differences among different groups were not remarkable (

P
>
0.05

). Finally, using 3 times ED95 cis-atracurium for GAI could reduce the risk of intraoperative body movement and spontaneous breathing, as well as the residual degree of muscle relaxation, in patients with ICAS, without affecting MEP monitoring, improving endotracheal intubation conditions, and increasing safety during open neurosurgery operations.