bluuebunny/crossref_metadata_2025 huggingface dataset:
Transcriptome Comparison Reveals the Difference in Liver Fat Metabolism between Different Sheep Breeds
Authors: Li Taotao , Jin Meilin , Fei Xiaojuan , Yuan Zehu , Wang Yuqin , Quan Kai , Wang Tingpu , Yang Junxiang , He Maochang , Wei Caihong
Published: June 2022

Hu sheep and Tibetan sheep are two commonly raised local sheep breeds in China, and they have different morphological characteristics, such as tail type and adaptability to extreme environments. A fat tail in sheep is the main adipose depot in sheep, whereas the liver is an important organ for fat metabolism, with the uptake, esterification, oxidation, and secretion of fatty acids (FAs). Meanwhile, adaptations to high-altitude and arid environments also affect liver metabolism. Therefore, in this study, RNA-sequencing (RNA-seq) technology was used to characterize the difference in liver fat metabolism between Hu sheep and Tibetan sheep. We identified 1179 differentially expressed genes (DEGs) (Q-value < 0.05) between the two sheep breeds, including 25 fat-metabolism-related genes. Through Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis, 16 pathways were significantly enriched (Q-value < 0.05), such as the proteasome, glutamatergic synapse, and oxidative phosphorylation pathways. In particular, one of these pathways was enriched to be associated with fat metabolism, namely the thermogenesis pathway, to which fat-metabolism-related genes such as ACSL1, ACSL4, ACSL5, CPT1A, CPT1C, SLC25A20, and FGF21 were enriched. Then, the expression levels of ACSL1, CPT1A, and FGF21 were verified in mRNA and protein levels via qRT-PCR and Western blot analysis between the two sheep breeds. The results showed that the mRNA and protein expression levels of these three genes were higher in the livers of Tibetan sheep than those of Hu sheep. The above genes are mainly related to FAs oxidation, involved in regulating the oxidation of liver FAs. So, this study suggested that Tibetan sheep liver has a greater FAs oxidation level than Hu sheep liver. In addition, the significant enrichment of fat-metabolism-related genes in the thermogenesis pathway appears to be related to plateau-adaptive thermogenesis in Tibetan sheep, which may indicate that liver- and fat-metabolism-related genes have an impact on adaptive thermogenesis.
Zearalenone and Its Metabolites in Blood Serum, Urine, and Milk of Dairy Cows
Authors: Falkauskas Rimvydas , Bakutis Bronius , Jovaišienė Jurgita , Vaičiulienė Gintarė , Gerulis Gediminas , Kerzienė Sigita , Jacevičienė Ingrida , Jacevičius Eugenijus , Baliukonienė Violeta
Published: June 2022

After oral contamination, zearalenone (ZEN) is rapidly absorbed in organisms and can be detected in biological fluids. In this study, we investigated the metabolites of ZEN in the biological fluids of cows (blood, urine, milk). The study was divided into three stages: preparation (the first stage), investigation (the second stage), and final stage (the third stage). Samples of biological fluids were taken 7, 21, and 35 days after the beginning of the study. At the first stage and at the second stage, the cows were fed a total mixed ration (TMR) with naturally contaminated mycotoxin-zearalenone (500 ± 75 µg/kg). In the third stage, the cows were fed a TMR without mycotoxins. This study established that at the second stage, the alpha-zearalenol concentrations in the serum increased by 92% and the beta-zearalanol in the urine decreased by 48% compared to the first stage (p < 0.05). The beta-zearalenol and zearalanone concentrations in the urine were higher compared to that of the alpha-zearalenol. The zearalenone concentration in the milk at the second stage was 35% higher than at the first stage (p < 0.05). A significant negative correlation (r = –0.540) was determined between the beta-zearalenol and beta-zearalanol concentrations in the urine and the positive significant correlation (r = 0.826) between the beta-zearalanol and alpha-zearalenol concentrations in the serum (p < 0.05). During the study, it was determined that feeding cows for two weeks with a TMR without mycotoxins can reduce concentrations of alpha-zearalanol, beta-zearalenol, and beta-zearalanol in the biological fluids and can reduce the concentrations of ZEN in the milk, but does not reduce the concentration of zearalanone.
Identification of Estrus in Sows Based on Salivary Proteomics
Authors: Li Chenlei , Song Chenglei , Qi Kunlong , Liu Yingke , Dou Yaqing , Li Xiuling , Qiao Ruimin , Wang Kejun , Han Xuelei , Li Xinjian
Published: June 2022

The estrus cycle of multiparous Large White sows was divided into three stages to solve the problems of heavy workload and low accuracy of the traditional estrus identification method in pig production. Saliva protein was extracted from the oral saliva of multiparous sows. Label-free quantitative proteomics was used to detect salivary proteome, and MaxQuant software was used for quality control. Results showed that 246 proteins were identified in the three stages, where 40 proteins were significantly different (p < 0.05). The total proteins identified were enriched by STEM software and the protein function was annotated by using the ClueGO plug-in in the Cytoscape software. The results were enriched to eight different trends. The annotated items were related to protein synthesis and processing and estrogen response. Gene ontology and the Kyoto Encyclopedia of Genes and Genomes enrichment analysis of differential proteins involved in the pathways and entries included oocyte meiosis, response to estradiol, and oogenesis. Further interaction analysis showed that an interaction occurred between P00355, F1SHL9, P28491, F1SDR7, F2Z558, F1RYY6, and F2Z5G3 proteins. The findings served as a basis for revealing the changes in salivary protein content in the sow estrus cycle and provided a reference for the development of an estrus identification kit/test strip in the next step.
Whole-Genome Sequence of Multidrug-Resistant Methicillin-Resistant Staphylococcus epidermidis Carrying Biofilm-Associated Genes and a Unique Composite of SCCmec
Authors: Altayb Hisham N. , Elbadawi Hana S. , Baothman Othman , Kazmi Imran , Alzahrani Faisal A. , Nadeem Muhammad Shahid , Hosawi Salman , Chaieb Kamel
Published: June 2022

Staphylococcus epidermidis is part of the normal human flora that has recently become an important opportunistic pathogen causing nosocomial infections and tends to be multidrug-resistant. In this investigation, we aimed to study the genomic characteristics of methicillin-resistant S. epidermidis isolated from clinical specimens. Three isolates were identified using biochemical tests and evaluated for drug susceptibility. Genomic DNA sequences were obtained using Illumina, and were processed for analysis using various bioinformatics tools. The isolates showed multidrug resistance to most of the antibiotics tested in this study, and were identified with three types (III(3A), IV(2B&5), and VI(4B)) of the mobile genetic element SCCmec that carries the methicillin resistance gene (mecA) and its regulators (mecI and mecR1). A total of 11 antimicrobial resistance genes (ARGs) was identified as chromosomally mediated or in plasmids; these genes encode for proteins causing decreased susceptibility to methicillin (mecA), penicillin (blaZ), fusidic acid (fusB), fosfomycin (fosB), tetracycline (tet(K)), aminoglycosides (aadD, aac(6′)-aph(2′’)), fluoroquinolone (MFS antibiotic efflux pump), trimethoprim (dfrG), macrolide (msr(A)), and chlorhexidine (qacA)). Additionally, the 9SE strain belongs to the globally disseminated ST2, and harbors biofilm-formation genes (icaA, icaB, icaC, icaD, and IS256) with phenotypic biofilm production capability. It also harbors the fusidic acid resistance gene (fusB), which could increase the risk of device-associated healthcare infections, and 9SE has been identified as having a unique extra SCC gene (ccrB4); this new composite element of the ccr type needs more focus to better understand its role in the drug resistance mechanism.
Inactivation of Bacteria and Residual Antimicrobials in Hospital Wastewater by Ozone Treatment
Authors: Azuma Takashi , Katagiri Miwa , Sekizuka Tsuyoshi , Kuroda Makoto , Watanabe Manabu
Published: June 2022

The emergence and spread of antimicrobial resistance (AMR) has become a persistent problem globally. In this study, an ozone treatment facility was established for an advanced hospital wastewater treatment in a core hospital facility in an urban area in Japan to evaluate the inactivation of antimicrobial-resistant bacteria and antimicrobials. Metagenomic DNA-seq analysis and the isolation of potential extended-spectrum β-lactamase (ESBL)-producing bacteria suggested that ozone exposure for at least 20 min is required for the adequate inactivation of DNA and ESBL-producing bacteria. Escherichia coli and Klebsiella species were markedly susceptible to 20-min ozone exposure, whereas Raoultella ornithinolytica and Pseudomonas putida were isolated even after an 80-min exposure. These ozone-resistant bacteria might play a pivotal role as AMR reservoirs in the environment. Nine antimicrobials (ampicillin, cefdinir, cefpodoxime, ciprofloxacin, levofloxacin, clarithromycin, chlortetracycline, minocycline, and vancomycin) were detected at 373 ng/L to 27 μg/L in the hospital wastewater, and these were removed (96–100% removal) after a 40-min treatment. These results facilitate a comprehensive understanding of the AMR risk posed by hospital wastewater and provides insights for devising strategies to eliminate or mitigate the burden of antimicrobial-resistant bacteria and the flow of antimicrobials into the environment. To the best of our knowledge, this is the first report on the implementation of a batch-type, plant-scale ozone treatment system in a hospital facility to execute and evaluate the inactivation of drug-resistant bacteria and antimicrobials.
Prevention of Surgical Site Infections in Neonates and Children: Non-Pharmacological Measures of Prevention
Authors: Meoli Aniello , Ciavola Lorenzo , Rahman Sofia , Masetti Marco , Toschetti Tommaso , Morini Riccardo , Dal Canto Giulia , Auriti Cinzia , Caminiti Caterina , Castagnola Elio , Conti Giorgio , Donà Daniele , Galli Luisa , La Grutta Stefania , Lancella Laura , Lima Mario , Lo Vecchio Andrea , Pelizzo Gloria , Petrosillo Nicola , Simonini Alessandro , Venturini Elisabetta , Caramelli Fabio , Gargiulo Gaetano Domenico , Sesenna Enrico , Sgarzani Rossella , Vicini Claudio , Zucchelli Mino , Mosca Fabio , Staiano Annamaria , Principi Nicola , Esposito Susanna ,   on behalf of the Peri-Operative Prophylaxis in Neonatal and Paediatric Age (POP-NeoPed) Study Group
Published: June 2022

A surgical site infection (SSI) is an infection that occurs in the incision created by an invasive surgical procedure. Although most infections are treatable with antibiotics, SSIs remain a significant cause of morbidity and mortality after surgery and have a significant economic impact on health systems. Preventive measures are essential to decrease the incidence of SSIs and antibiotic abuse, but data in the literature regarding risk factors for SSIs in the pediatric age group are scarce, and current guidelines for the prevention of the risk of developing SSIs are mainly focused on the adult population. This document describes the current knowledge on risk factors for SSIs in neonates and children undergoing surgery and has the purpose of providing guidance to health care professionals for the prevention of SSIs in this population. Our aim is to consider the possible non-pharmacological measures that can be adopted to prevent SSIs. To our knowledge, this is the first study to provide recommendations based on a careful review of the available scientific evidence for the non-pharmacological prevention of SSIs in neonates and children. The specific scenarios developed are intended to guide the healthcare professional in practice to ensure standardized management of the neonatal and pediatric patients, decrease the incidence of SSIs and reduce antibiotic abuse.
Development of Wash-Durable Antimicrobial Cotton Fabrics by In Situ Green Synthesis of Silver Nanoparticles and Investigation of Their Antimicrobial Efficacy against Drug-Resistant Bacteria
Authors: Jain Ashu , Kongkham Bhani , Puttaswamy Hariprasad , Butola Bhupendra Singh , Malik Hitendra Kumar , Malik Anushree
Published: June 2022

An environment friendly and wash-durable silver nanoparticle treatment of cotton fabrics was carried out by in situ reduction of silver nitrate using Azadirachta indica leaf extract. The wash durability of the silver nanoparticles treatment on the cotton fabric was improved by pretreating the fabrics by mercerization and by adopting hydrothermal conditions of 120 °C temperature and 15 psi pressure for the in situ synthesis. The silver nanoparticle treated fabrics were characterized using scanning electron microscopy, colorimetric analysis and inductively coupled plasma mass spectroscopy. The coating of silver nanoparticles was seen to be dense and uniform in the scanning electron micrographs of the treated fabrics. An evaluation of the antibacterial efficacy of the silver nanoparticle treated fabric against antibiotic-resistant Gram-positive and Gram-negative strains was carried out. The antibacterial efficacy was found to be the highest against Bacillus licheniformis, showing 93.3% inhibition, whereas it was moderate against Klebsiella pneumoniae (20%) and Escherichia coli (10%). The transmittance data of a UV spectrophotometer (290–400nm) was used for measuring the UV protection factor of the silver nanoparticle treated fabrics. All the silver nanoparticle treated fabrics showed good antimicrobial and UV protection activity. The treatment was also seen to be durable against repeated laundering. This paper contributes the first report on a novel green synthesis approach integrating mercerization of cotton fabrics and in situ synthesis of nanoparticles under hydrothermal conditions using Azadirachta indica leaf extract for improved wash durability of the multifunctional fabric.
Mixed-Flow Load-Balanced Scheduling for Software-Defined Networks in Intelligent Video Surveillance Cloud Data Center
Authors: Song Biao , Chang Yue , Zhang Xinchang , Al-Dhelaan Abdullah , Al-Dhelaan Mohammed
Published: June 2022

As the large amount of video surveillance data floods into cloud data center, achieving load balancing in a cloud network has become a challenging problem. Meanwhile, we hope the cloud data center maintains low latency, low consumption, and high throughput performance when transmitting massive amounts of data. OpenFlow enables a software-defined solution through programing to control the scheduling of data flow in the cloud data center. However, the existing scheduling algorithm of the data center cannot cope with the congestion of the network center effectively. Even for some dynamic scheduling algorithms, adjustments can only be made after congestion occurs. Hence, we propose a proactive and dynamically adjusted mixed-flow load-balanced scheduling (MFLBS) algorithm, which not only takes into account the different sizes of flows in the network but also maintains maximum throughput while balancing the load. In this paper, the MFLBS problem was formulated, along with a set of heuristic algorithms for real-time feedback and adjustment. Experiments with mesh and tree network models show that our MFLBS is significantly better than other dynamic scheduling algorithms, including one-hop DLBS and static scheduling algorithm FCFS. The MFLBS algorithm can effectively reduce the delay of small flows and average delay while maintaining high throughput.
A Hybrid Early Warning Method for the Landslide Acceleration Process Based on Automated Monitoring Data
Authors: Bai Dongxin , Lu Guangyin , Zhu Ziqiang , Zhu Xudong , Tao Chuanyi , Fang Ji
Published: June 2022

The data collection in the automated monitoring of landslides is often characterized by large amounts of data, periodic fluctuations, many outliers, and different collection intervals. The traditional method of calculating velocity and acceleration using the differential algorithm for landslide acceleration relies on experience to select thresholds and produces a large number of false early warnings. A hybrid early warning method for the landslide acceleration process based on automated monitoring data is proposed to solve this problem. The method combines the conventional warning method, based on cumulative displacement, velocity, and acceleration, and the critical sliding warning method based on normalized tangent angle according to different strategies. On the one hand, the least-squares fitting of monitoring data inside a given time window is used to calculate various early warning parameters, improving data usage and lowering calculation error. On the other hand, a dynamic semi-quantitative and semi-empirical method is provided for the determination of the thresholds, which is more reliable than the purely empirical method. The validation experiments at the Lishanyuan landslide in southern China show that the hybrid method can accurately identify the accelerating deformation of the landslide and gives very few false warnings. The proposed method is practical and effective for systems that require automated monitoring and warnings for a large number of landslides.
Smooth Bend Structures Using Hybrid Compensation for Common-Mode Noise Reduction
Authors: Lin Ding-Bing , Wang Mei-Hui , Huang Yong-Lun
Published: June 2022

The proposed smooth bend structure was implemented with a 150-degree bend to reduce the asymmetrical part of the differential bend and thus prevent mode conversion loss and common-mode noise. The smooth bend structure maintained the differential signal integrity. In addition, we proposed several hybrid compensation methods to enhance common-mode noise suppression and concluded that a smooth bend using the “L-C-L” compensation method offered the best performance. The frequency-domain analysis, from direct current (DC) to 6 GHz, was suppressed below 17.3 dB for the differential to common-mode conversion loss (Scd21) and maintained a differential insertion loss (Sdd21) above 2.18 dB. Furthermore, the 150-degree bend, using the “L-C-L” compensation method, reduced common-mode noise by 48.9% compared with a 150-degree bend structure in the time-domain analysis. The significant advantage of the proposed structure is that it can be easily fabricated using the printed circuit board (PCB) manufacturing process, reducing costs. The measurement results of the proposed design showed good consistency with the simulations. The proposed hybrid compensation structure successfully reduced the undesirable effects caused by asymmetry; as a result, mode conversion suppression was improved, and common-mode noise was reduced.
Dynamic Hand Gesture Recognition for Smart Lifecare Routines via K-Ary Tree Hashing Classifier
Authors: Ansar Hira , Ksibi Amel , Jalal Ahmad , Shorfuzzaman Mohammad , Alsufyani Abdulmajeed , Alsuhibany Suliman A. , Park Jeongmin
Published: June 2022

In the past few years, home appliances have been influenced by the latest technologies and changes in consumer trends. One of the most desired gadgets of this time is a universal remote control for gestures. Hand gestures are the best way to control home appliances. This paper presents a novel method of recognizing hand gestures for smart home appliances using imaging sensors. The proposed model is divided into six steps. First, preprocessing is done to de-noise the video frames and resize each frame to a specific dimension. Second, the hand is detected using a single shot detector-based convolution neural network (SSD-CNN) model. Third, landmarks are localized on the hand using the skeleton method. Fourth, features are extracted based on point-based trajectories, frame differencing, orientation histograms, and 3D point clouds. Fifth, features are optimized using fuzzy logic, and last, the H-Hash classifier is used for the classification of hand gestures. The system is tested on two benchmark datasets, namely, the IPN hand dataset and Jester dataset. The recognition accuracy on the IPN hand dataset is 88.46% and on Jester datasets is 87.69%. Users can control their smart home appliances, such as television, radio, air conditioner, and vacuum cleaner, using the proposed system.
Generation of Decoy Signals Using Optical Amplifiers for a Plug-and-Play Quantum Key Distribution System
Authors: Woo Min-Ki , Park Chang-Hoon , Kim Sangin , Han Sang-Wook
Published: June 2022

In most quantum key distribution (QKD) systems, a decoy-state protocol is implemented for preventing potential quantum attacks and higher mean photon rates. An optical intensity modulator attenuating the signal intensity is used to implement it in a QKD system adopting a one-way architecture. However, in the case of the plug-and-play (or two-way) architecture, there are technical issues, including random polarization of the input signal pulse and long-term stability. In this study, we propose a method for generating decoy pulses through amplification using an optical amplifier. The proposed scheme operates regardless of the input signal polarization. In addition, a circulator was added to adjust the signal intensity when the signal enters the input and exits the QKD transmitter by monitoring the intensity of the output signal pulse. It also helps to defend against Trojan horse attacks. A test setup for the proof-of-principle experiment was implemented and tested, and it was shown that the system operated stably with a quantum bit error rate (QBER) value of less than 5% over 26 h using a quantum channel (QC) of 25 km.
Derivation of a Universally Valid Array Factor of a Conformal Arrays Based on Phase Compensation and Genetic Learning Particle Swarm Optimization
Authors: Park Jinsu , Lim Hong Jun , Trinh-Van Son , Park Daesung , Jung Youn Kwon , Lim Dongju , Hwang Keum Cheol
Published: June 2022

In this study, we investigated the recent deterioration of the radiation pattern performance of conformal arrays, which are applied to fields such as aircraft and vehicles. We analyzed the radiation pattern of conformal arrays in the array factor stage by combining previous studies on various beam-forming techniques for conformal arrays. To efficiently calculate and utilize the radiation pattern of conformal arrays, we derived an array factor based on phase composition for nonplanar arrays of three-dimensional (3D) coordinate systems. As an amplitude tapering method for controlling the sidelobe level of the derived 3D array factor, we propose a Bernstein polynomial generalization method based on Genetic Learning Particle Swarm Optimization. The proposed 3D array factor was verified using a cavity-backed patch antenna operating at the X-band through EM simulation of conformal arrays as a single element.
A Novel Online Correlation Noise Model Based on Band Coefficients Mean to Achieve Low Computational and Coding-Efficient Distributed Video Codec
Authors: Khursheed Shahzad , Badruddin Nasreen , Jeoti Varun , Hashmani Manzoor Ahmed
Published: June 2022

Distributed video coding (DVC) is a novel coding paradigm that offers low computational encoding relative to conventional video-coding framework at the expense of high-decoding computational complexity. The challenging part of this video-coding framework is achieving better rate-distortion (RD) compared with conventional codec performance. A suitable and accurate correlation noise model (CNM) is crucial in improving the RD performance by achieving high coding efficiency and making decoding less computationally demanding. Since the correlation is nonstationary and time-variant and can vary from frame to frame, offline CNM estimation is not feasible for practical applications and real-time decoding. An online CNM may be the solution to this problem. In DVC, neither Wyner–Ziv frame (WZF) nor estimated side information (SI) of the corresponding WZF is available at the encoder. Therefore, online estimation of the CNM and its parameters can be quite challenging. The contribution of this research work is a novel online CNM which is computed by taking the mean of each transformed coefficient band and deployed for two different codecs. Our proposed codec, DIVCOM, which stands for “Distributed Video Coding with Online Band Mean Correlation Noise Model”, outperforms the existing baseline codec, DISCOVER (DIS), in both coding efficiency and peak signal-to-noise ratio (PSNR). The DIVCOM codec achieves coding efficiency of up to 8.05 kbps, and PSNR ranges from 0.0245 dB to 0.18 dB. An extended version of DIVCOM incorporating phase-based side information called PDIVCOM achieves coding efficiency up to 10.9 kbps, and PSNR ranges from 0.019 to 0.17 dB compared to DIS.
Explainable Machine Learning Solution for Observing Optimal Surgery Timings in Thoracic Cancer Diagnosis
Authors: Cozma Gabriel V. , Onchis Darian , Istin Codruta , Petrache Ioan Adrian
Published: June 2022

In this paper, we introduce an AI-based procedure to estimate and assist in choosing the optimal surgery timing, in the case of a thoracic cancer diagnostic, based on an explainable machine learning model trained on a knowledge base. This decision is usually taken by the surgeon after examining a set of clinical parameters and their evolution in time. Therefore, it is sometimes subjective, it depends heavily on the previous experience of the surgeon, and it might not be confirmed by the histopathological exam. Therefore, we propose a pipeline of automatic processing steps with the purpose of inferring the prospective result of the histopathologic exam, generating an explanation of why this inference holds, and finally, evaluating it against the conclusive opinion of an experienced surgeon. To obtain an accurate practical result, the training dataset is labeled manually by the thoracic surgeon, creating a training knowledge base that is not biased towards clinical practice. The resulting intelligent system benefits from both the precision of a classical expert system and the flexibility of deep neural networks, and it is supposed to avoid, at maximum, any possible human misinterpretations and provide a factual estimate for the proper timing for surgical intervention. Overall, the experiments showed a 7% improvement on the test set compared with the medical opinion alone. To enable the reproducibility of the AI system, complete handling of a case study is presented from both the medical and technical aspects.
Machine-Learning-Based Digital Twin in Manufacturing: A Bibliometric Analysis and Evolutionary Overview
Authors: Sheuly Sharmin Sultana , Ahmed Mobyen Uddin , Begum Shahina
Published: June 2022

The Digital Twin (DT) concept in the manufacturing industry has received considerable attention from researchers because of its versatile application potential. Machine Learning (ML) adds a new dimension to DT by enhancing its functionality. Many studies on DT in the manufacturing industry have recently been published. However, there is still a lack of a systematic literature review on different aspects of ML-based DT in the manufacturing industry from a bibliometric and evolutionary perspective. Therefore, the proposed study is mainly aimed at reviewing DT in the manufacturing industry to identify the contribution of ML, current methods, and future research directions. According to the findings, the contribution of ML to this domain is significant. Additionally, the results show that the latest ML technologies are being used in the DT domain; neural networks have evolved based on application-specific requirements. The total number of papers and citations per paper on ML-based DT is increasing. The relevance of ML in DT has increased over time. The current trend is to use ML-based DT for data analytics. Additionally, there are many unfilled gaps; certain gaps include industrial applications of DT, synchronisation with real-time data through sensors, heterogeneous data management, and benchmarking.
Reliability Modeling and Analysis of a Diesel Engine Design Phase Based on 4F Integration Technology
Authors: Zhang Meng , Liu Shuangfeng , Hou Xinrong , Dong Haiping , Cui Chunsheng , Li Yafen
Published: June 2022

As one of the most important components within a vehicle, diesel engines have high requirements for reliability due to the harsh operating environments. However, previous studies have mainly focused on the reliability assessment of diesel engines, while less research has been conducted on the modeling of the diesel engine reliability analysis and its management. For this reason, this paper proposes a comprehensive method for reliability analysis and its management based on the use of 4F integration technology in the early stages of diesel engine design. First of all, an expert group used FEMCA (failure mode, effects and criticality analysis) and FHA (functional hazard analysis) to find the most harmful level of fault mode. At the same time, a new method for the repair of dynamic fault trees to find the weak links at the component level was developed. Finally, a FRACAS (fracture report analysis and corrective action system) was used during the above analysis process. By applying this method to the reliability assessment of a diesel engine in the design stage, the problems of failure information feedback and the reuse of failure information in the actual reliability assessment can be solved.
Unsharp Mask Guided Filtering for Acoustic Point Cloud of Water-Conveyance Tunnel
Authors: Wang Jisong , Zhang Xuewu , Zhang Zhuo , Xu Xiaolong
Published: June 2022

The inner-surface damage of water conveyance tunnels is the main hidden danger that threatens their safety and leads to serious accidents. The method based on the principle of acoustic reflection is the main means of inspecting damage to water-conveyance tunnels. However, affected by the tunnel environment and equipment noise, the obtained acoustic point cloud model inevitably suffers from noise, which can produce erroneous results. Therefore, we proposed a novel filtering method, called unsharp-mask-guided filtering for 3D point cloud, to reduce the impact of noise on the acoustic point cloud model of water-conveyance tunnels. The proposed method fuses the ideas of guided filtering and the unsharp masking technique and extends them to the 3D point cloud model by considering the position of the point. In addition, edge-aware weighting mean is also used to retain the edge features of the point cloud model while smoothing the noise points. The experimental results show that our method can obtain impressive results and a better performance in both the acoustic point cloud model of the tunnel and the simulated point cloud model than many state-of-the-art methods.
Reliability and Validity of IMU-Based Foot Progression Angle Measurement under Different Gait Retraining Strategies
Authors: Urbanus Francine C. A. , Grayson Jane , Harlaar Jaap , Simic Milena
Published: June 2022

Load modifying gait retraining strategies, such as changing the foot progression angle (FPA) to toe-in and toe-out gait, are used for people with medial knee osteoarthritis. The FPA can be measured using a pressure sensitive walkway (PSW), but inertial measurement units (IMUs) are considered more suitable for clinical use. This study evaluated the reliability and validity of an IMU system, to measure FPA under different gait retraining strategies. Twenty healthy participants walked a 10-m-long path using different gait strategies (natural (2), toe-out gait (1), toe-in gait (1)) during four 90-s trials. FPA was measured simultaneously with IMUs and a PSW, the latter considered the reference standard. There was good and excellent reliability for the IMUs and PSW FPA measurements, respectively (ICC: IMU, 0.89; PSW, 0.97). Minimal detectable change (MDC) was 4.5° for the IMUs and 2.7° for the PSW. Repeated measures ANOVA indicated a significant effect of gait type on FPA (p < 0.001), but not the measurement instrument (p = 0.875). Bland–Altman plots demonstrated the good agreement of both systems for the baseline condition, though the IMUs seemed to consistently overestimate the FPA value compared to the PSW. In conclusion, IMUs are a reliable and valid measurement system for measuring FPA under different gait retraining strategies. The differences between the systems are significant for all gait strategies, so the systems should not be used interchangeably.
Covariant Space-Time Line Elements in the Friedmann–Lemaitre–Robertson–Walker Geometry
Authors: Escors David , Kochan Grazyna
Published: June 2022

Most quantum gravity theories quantize space-time on the order of Planck length (ℓp ). Some of these theories, such as loop quantum gravity (LQG), predict that this discreetness could be manifested through Lorentz invariance violations (LIV) over travelling particles at astronomical length distances. However, reports on LIV are controversial, and space discreetness could still be compatible with Lorentz invariance. Here, it is tested whether space quantization on the order of Planck length could still be compatible with Lorentz invariance through the application of a covariant geometric uncertainty principle (GeUP) as a constraint over geodesics in FRW geometries. Space-time line elements compatible with the uncertainty principle are calculated for a homogeneous, isotropic expanding Universe represented by the Friedmann–Lemaitre–Robertson–Walker solution to General Relativity (FLRW or FRW metric). A generic expression for the quadratic proper space-time line element is derived, proportional to Planck length-squared, and dependent on two contributions. The first is associated to the energy–time uncertainty, and the second depends on the Hubble function. The results are in agreement with space-time quantization on the expected length orders, according to quantum gravity theories, and within experimental constraints on putative LIV.
Multigenerational Social Housing and Group-Rearing Enhance Female Reproductive Success in Captive Rhesus Macaques (Macaca mulatta)
Authors: Rox Astrid , Waasdorp Sophie , Sterck Elisabeth H. M. , Langermans Jan A. M. , Louwerse Annet L.
Published: June 2022

To optimize costs and reproductive success, rhesus macaques in biomedical primate research facilities are often peer-reared. Older, dependent infants are typically removed from their natal group to enhance female reproduction. The minimal husbandry age-norm of infant removal is ten months. These practices deviate from species-specific behavior and may reduce welfare, suggesting a trade-off between female reproduction and welfare. However, the effect of breeding group type and rearing history on female reproductive success (i.e., birth rate; inter-birth interval (IBI); offspring survival) is unclear. This retrospective study investigated whether group type (i.e., peer groups versus multigenerational groups) and rearing history (i.e., peer- or hand-reared; group-reared with peer- or hand-reared mother; group-reared) affected female reproductive success in captive rhesus macaques. Data on female reproduction between 1996 and 2019 were collected at the Biomedical Primate Research Centre, Rijswijk. Birth rates were higher in multigenerational breeding groups than in peer groups. Moreover, group-reared females had higher offspring survival than peer- or hand-reared females. IBI was not affected by breeding group type or female rearing history. However, females in both peer and multigenerational breeding groups typically conceived earlier after giving birth than the husbandry infant removal age-norm of ten months. Thus, infant removal at an age of ten months does not enhance a female’s reproduction. Altogether, female reproduction and non-human primate welfare can simultaneously be optimized through multigenerational breeding groups and group-rearing.
Regulation of Neural Circuitry under General Anesthesia: New Methods and Findings
Authors: Zhang Kai , Pan Jiacheng , Yu Yonghao
Published: June 2022

General anesthesia has been widely utilized since the 1840s, but its underlying neural circuits remain to be completely understood. Since both general anesthesia and sleep are reversible losses of consciousness, studies on the neural-circuit mechanisms affected by general anesthesia have mainly focused on the neural nuclei or the pathways known to regulate sleep. Three advanced technologies commonly used in neuroscience, in vivo calcium imaging, chemogenetics, and optogenetics, are used to record and modulate the activity of specific neurons or neural circuits in the brain areas of interest. Recently, they have successfully been used to study the neural nuclei and pathways of general anesthesia. This article reviews these three techniques and their applications in the brain nuclei or pathways affected by general anesthesia, to serve as a reference for further and more accurate exploration of other neural circuits under general anesthesia and to contribute to other research fields in the future.
Transcriptome Analysis of Lycoris chinensis Bulbs Reveals Flowering in the Age-Mediated Pathway
Authors: Zhang Fengjiao , Cheng Guanghao , Shu Xiaochun , Wang Ning , Wang Zhong
Published: June 2022

Lycoris is a summer bulbous flower that commonly needs to go through a long period of vegetative growth for 3 to 5 years before flowering. Plant flowering is regulated by a complex genetic network. Compared with most perennial flowers, knowledge on the molecular mechanism responsible for floral transition in bulbous flowers is lacking, and only a few genes that regulate flowering have been identified with few reports on the floral transition in Lycoris. In this study, we identified many differentially expressed genes (DEGs) and transcription factors (TFs) by RNA-Seq in L. chinensis bulbs of different ages, including one- to four-year-old nonflowering bulbs and four-year-old flowering bulbs. Some DEGs were enriched in Gene Ontology (GO) terms between the three- and four-year-old bulbs, and there most genes were enriched in terms of metabolic process and catalytic activity. In the four-year old bulbs, most of the DEGs that may be involved in flowering were classified under the GO term biological process, which was a totally different result from the vegetative bulbs. Some DEGs between flowering and nonflowering bulbs were enriched in plant hormone signal transduction, including the hormones auxin, cytokinin, abscisic acid, and ethylene, but no DEGs were enriched in the gibberellin pathway. Auxin is the main endogenous phytohormone involved in bulb growth and development, but cytokinin, abscisic acid, and ethylene were shown to increase in flowering bulbs. In addition, energy-metabolism-related genes maintain a high expression level in large bulbs, and some positive regulators (SPL, COL, and AP1) and early flowering genes were also shown to be highly expressed in the meristems of flowering bulbs. It suggested that sugar molecules may be the energy source that regulates the signal transduction of flowering by connecting with phytohormone signaling in Lycoris. A total of 1911 TFs were identified and classified into 89 categories, where the top six families with the largest gene numbers were C2H2, NAC, AP2/ERF-ERF, C3H, MYB-related, and WRKY. Most DEGs were in the AP2/ERF-ERF family, and most of them were downregulated in 4-year-old flowering bulbs. A number of families were reported to be involved in plant flowering, including NAC, AP2/ERF, MYB, WRKY, bZIP, MADS, and NF-Y. These results can act as a genetic resource to aid in the explanation of the genetic mechanism responsible for the flowering of Lycoris and other bulbous flowers.
The Clinical Utility of Soluble Serum Biomarkers in Autoimmune Pancreatitis: A Systematic Review
Authors: Dugic Ana , Verdejo Gil Cristina , Mellenthin Claudia , Vujasinovic Miroslav , Löhr J.-Matthias , Mühldorfer Steffen
Published: June 2022

Autoimmune pancreatitis (AIP) is a rare etiological type of chronic pancreatitis. The clinical and radiological presentation of AIP often resembles that of pancreatic cancer. Identifying non-invasive markers for their early distinction is of utmost importance to avoid unnecessary surgery or a delay in steroid therapy. Thus, this systematic review was conducted to revisit all current evidence on the clinical utility of different serum biomarkers in diagnosing AIP, distinguishing AIP from pancreatic cancer, and predicting disease course, steroid therapy response, and relapse. A systematic review was performed for articles published up to August 2021 by searching electronic databases such as MEDLINE, Web of Science, and EMBASE. Among 5123 identified records, 92 studies were included in the qualitative synthesis. Apart from immunoglobulin (Ig) G4, which was by far the most studied biomarker, we identified autoantibodies against the following: lactoferrin, carboanhydrase II, plasminogen-binding protein, amylase-α2A, cationic (PRSS1) and anionic (PRSS2) trypsinogens, pancreatic secretory trypsin inhibitor (PSTI/SPINK1), and type IV collagen. The identified novel autoantigens were laminin 511, annexin A11, HSP-10, and prohibitin. Other biomarkers included cytokines, decreased complement levels, circulating immune complexes, N-glycan profile changes, aberrant miRNAs expression, decreased IgA and IgM levels, increased IgE levels and/or peripheral eosinophil count, and changes in apolipoprotein isoforms levels. To our knowledge, this is the first systematic review that addresses biomarkers in AIP. Evolving research has recognized numerous biomarkers that could help elucidate the pathophysiological mechanisms of AIP, bringing us closer to AIP diagnosis and its preoperative distinction from pancreatic cancer.
CGK4PM: Towards a Methodology for the Systematic Generation of Clinical Guideline Process Models and the Utilization of Conformance Checking
Authors: Grüger Joscha , Geyer Tobias , Bergmann Ralph , Braun Stephan A.
Published: June 2022

In the context of improving clinical treatments and certifying clinics, guideline-compliant care has become more important. However, verifying the compliance of treatment procedures with Clinical Guidelines remains difficult, as guidelines are mostly available in non-computer interpretable form and previous computer-interpretable approaches neglect the process perspective with its potential to gain medical insight. In this paper, we present our transformation framework CGK4PM, which addresses the procedural nature of treatment processes and which guides the transformation of clinical explicit and implicit guideline knowledge into process models. The procedural representation enables the use of process mining techniques such as conformance checking to verify guideline compliance and the opportunity to gain insights from complex clinical treatment processes. In collaboration with physicians from Münster University Hospital, the practical applicability of the framework is demonstrated in a case study by transforming the guideline for the treatment of malignant melanoma. The case study findings demonstrate the need for structured and guided transformation and highlight the difficulties in developing a guideline-based process model.
Point-of-Care Diagnostics for Farm Animal Diseases: From Biosensors to Integrated Lab-on-Chip Devices
Authors: Manessis Georgios , Gelasakis Athanasios I. , Bossis Ioannis
Published: June 2022

Zoonoses and animal diseases threaten human health and livestock biosecurity and productivity. Currently, laboratory confirmation of animal disease outbreaks requires centralized laboratories and trained personnel; it is expensive and time-consuming, and it often does not coincide with the onset or progress of diseases. Point-of-care (POC) diagnostics are rapid, simple, and cost-effective devices and tests, that can be directly applied on field for the detection of animal pathogens. The development of POC diagnostics for use in human medicine has displayed remarkable progress. Nevertheless, animal POC testing has not yet unfolded its full potential. POC devices and tests for animal diseases face many challenges, such as insufficient validation, simplicity, and portability. Emerging technologies and advanced materials are expected to overcome some of these challenges and could popularize animal POC testing. This review aims to: (i) present the main concepts and formats of POC devices and tests, such as lateral flow assays and lab-on-chip devices; (ii) summarize the mode of operation and recent advances in biosensor and POC devices for the detection of farm animal diseases; (iii) present some of the regulatory aspects of POC commercialization in the EU, USA, and Japan; and (iv) summarize the challenges and future perspectives of animal POC testing.
Highly Sensitive Plasmonic Waveguide Biosensor Based on Phase Singularity-Enhanced Goos–Hänchen Shift
Authors: Hedhly Manel , Wang Yuye , Zeng Shuwen , Ouerghi Faouzi , Zhou Jun , Humbert Georges
Published: June 2022

The detection for small molecules with low concentrations is known to be challenging for current chemical and biological sensors. In this work, we designed a highly sensitive plasmonic biosensor based on the symmetric metal cladding plasmonic waveguide (SMCW) structure for the detection of biomolecules. By precisely designing the configuration and tuning the thickness of the guiding layer, ultra-high order modes can be excited, which generates a steep phase change and a large position shift from the Goos–Hänchen effect (with respect to refractive index changes). This position shift is related to the sharpness of the optical phase change from the reflected signal of the SPR sensing substrate and can be directly measured by a position sensor. Based on our knowledge, this is the first experimental study done using this configuration. Experimental results showed a lateral position signal change > 90 µm for glycerol with a sensitivity figure-of-merit of 2.33 × 104 µm/RIU and more than 15 µm for 10−4 M biotin, which is a low molecular weight biomolecule (less than 400 Da) and difficult to be detected with traditional SPR sensing techniques. Through integrating the waveguide with a guiding layer, a strong improvement in the electric field, as well as sensitivity have been achieved. The lateral position shift has been further improved from 14.17 µm to 284 µm compared with conventional SPR substrate with 50 nm gold on single side. The as-reported sensing technique allows for the detection of ultra-small biological molecules and will play an important role in biomedical and clinical diagnostics.
Effects of Non-Invasive Brain Stimulation on Post-Stroke Spasticity: A Systematic Review and Meta-Analysis of Randomized Controlled Trials
Authors: Wang Xiaohan , Ge Le , Hu Huijing , Yan Li , Li Le
Published: June 2022

In recent years, the potential of non-invasive brain stimulation (NIBS) for the therapeutic effect of post-stroke spasticity has been explored. There are various NIBS methods depending on the stimulation modality, site and parameters. The purpose of this study is to evaluate the efficacy of NIBS on spasticity in patients after stroke. This systematic review and meta-analysis was conducted according to Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. PUBMED (MEDLINE), Web of Science, Cochrane Library and Excerpta Medica Database (EMBASE) were searched for all randomized controlled trials (RCTs) published before December 2021. Two independent researchers screened relevant articles and extracted data. This meta-analysis included 14 articles, and all included articles included 18 RCT datasets. The results showed that repetitive transcranial magnetic stimulation (rTMS) (MD = −0.40, [95% CI]: −0.56 to −0.25, p < 0.01) had a significant effect on improving spasticity, in which low-frequency rTMS (LF-rTMS) (MD = −0.51, [95% CI]: −0.78 to −0.24, p < 0.01) and stimulation of the unaffected hemisphere (MD = −0.58, [95% CI]: −0.80 to −0.36, p < 0.01) were beneficial on Modified Ashworth Scale (MAS) in patients with post-stroke spasticity. Transcranial direct current stimulation (tDCS) (MD = −0.65, [95% CI]: −1.07 to −0.22, p < 0.01) also had a significant impact on post-stroke rehabilitation, with anodal stimulation (MD = −0.74, [95% CI]: −1.35 to −0.13, p < 0.05) being more effective in improving spasticity in patients. This meta-analysis revealed moderate evidence that NIBS reduces spasticity after stroke and may promote recovery in stroke survivors. Future studies investigating the mechanisms of NIBS in addressing spasticity are warranted to further support the clinical application of NIBS in post-stroke spasticity.