bluuebunny/crossref_metadata_2025 huggingface dataset:
Continual learning fault diagnosis method based on grid-based artificial immune system
Authors: Sun Xin , Wang Haotian , Liu Shulin , Xiao Haihua , Wang Lu
Published: June 2022

In recent years, continual learning for class increments has attracted a great deal of attention. The ontinual-learning classification method (CLCM based on an artificial immune system (AIS) can identify unknown faults during testing. However, the CLCM still has the problem of excessive runtime consumption. Therefore, it is crucial to improve the efficiency of the immune algorithm and take advantage of its continual learning mechanism in the field of fault diagnosis. In this paper, a continual learning fault diagnosis method based on sparse grid and the AIS, which called sparse grid classification method (SGCM), is proposed, which is inspired by grid-based techniques and the CLCM based on an AIS. Firstly, a new cell generation strategy is proposed to reduce the time complexity and improve the diagnosis efficiency; therefore, the problem of dimension explosion is avoided. In addition, the memory cell coding capabilities of the SGCM increases the utilization rate of cells so as to simplify the calculation of affinity. At the same time, the conceived cell backtracking strategy enhances the continual learning ability of the algorithm so that new fault types can be quickly identified through the existing learning results. Ultimately, the model adaptive adjustment method inspired by a single-layer feed-forward neural network improves the generalization power and the accuracy of classification. We conduct experiments on well-known datasets from the UCI repository to assess the performance of the SGCM. To evaluate the fault diagnosis performance of the SGCM, experiments on a reciprocating compressor experimental dataset and the XJTU-SY rolling element bearing dataset were performed. The results show that theSGCM is a fast fault diagnosis method with low time complexity and continual learning ability.
Identification of vehicle axle loads based on visual measurement
Authors: Liu Yingkai , Wang Wei , Deng Lu , Dai Jianjun , He Wei
Published: June 2022

Monitoring vehicle axle loads is very important for preventing infrastructure degradation and traffic accidents. However, developing an accurate, cost-effective, and easy-to-implement axle load identification technology still remains challenging. In this study, a vehicle axle load identification method based on visual measurement is proposed. The principle of the method is that each axle load of the vehicle is determined based on the vehicle unsprung masses, the ratio of vehicular sprung mass to unsprung mass, and the centroid position of the sprung mass. While the vehicle unsprung masses can be treated as a known with the vehicle model identified, the mass ratio can be predicted from the visually captured free damped vibrations of the vehicle as it passes over a speed bump. To be more specific, the vibration responses of a vehicle passing over a speed bump are firstly measured by a camera, and the vehicle system matrix, which is composed of the mass matrix, stiffness matrix, and damping matrix, can be obtained from the vehicle responses. The axle load can then be determined based on the element ratios in the system matrix of the vehicle whose unsprung masses are known. The performance of the proposed method was evaluated by numerical simulations and field tests. The results show that the errors of the simulation results are all less than 1% under ideal conditions. In the field test, the average error in identifying axle loads at different vehicle speeds was 7.01% when the vehicle speed was below 20 m s−1, which proved the accuracy and effectiveness of the proposed method. This study has demonstrated a novel application of computer vision technology to identify the axle loads of moving vehicles. The proposed method does not require installation of sensors on the roadway or the vehicle, making it a promising alternative for traditional weigh-in-motion systems.
Revealing charge carrier dynamics and transport in Te-doped GaAsSb and GaAsSbN nanowires by correlating ultrafast terahertz spectroscopy and optoelectronic characterization
Authors: Yuan Long , Pokharel Rabin , Devkota Shisir , Kuchoor Hirandeep , Dawkins Kendall , Lee Min-Cheol , Huang Yue , Yarotski Dzmitry , Iyer Shanthi , Prasankumar Rohit P
Published: June 2022

Recent advances in the growth of III-V semiconductor nanowires (NWs) hold great promise for nanoscale optoelectronic device applications. It is established that a small amount of nitrogen (N) incorporation in III-V semiconductor NWs can effectively red-shift their wavelength of operation and tailor their electronic properties for specific applications. However, understanding the impact of N incorporation on non-equilibrium charge carrier dynamics and transport in semiconducting NWs is critical in achieving efficient semiconducting NW devices. In this work, ultrafast optical pump-terahertz probe spectroscopy has been used to study non-equilibrium carrier dynamics and transport in Te-doped GaAsSb and dilute nitride GaAsSbN NWs, with the goal of correlating these results with electrical characterization of their equilibrium photo-response under bias and low-frequency noise characteristics. Nitrogen incorporation in GaAsSb NWs led to a significant increase in the carrier scattering rate, resulting in a severe reduction in carrier mobility. Carrier recombination lifetimes of 33 ± 1 picoseconds (ps) and 147 ± 3 ps in GaAsSbN and GaAsSb NWs, respectively, were measured. The reduction in the carrier lifetime and photoinduced optical conductivities are due to the presence of N-induced defects, leading to deterioration in the electrical and optical characteristics of dilute nitride NWs relative to the non-nitride NWs. Finally, we observed a very fast rise time of ∼2 ps for both NW materials, directly impacting their potential use as high-speed photodetectors.
Machine learning for multi-parametric breast MRI: radiomics-based approaches for lesion classification
Authors: Altabella Luisa , Benetti Giulio , Camera Lucia , Cardano Giuseppe , Montemezzi Stefania , Cavedon Carlo
Published: June 2022

In the artificial intelligence era, machine learning (ML) techniques have gained more and more importance in the advanced analysis of medical images in several fields of modern medicine. Radiomics extracts a huge number of medical imaging features revealing key components of tumor phenotype that can be linked to genomic pathways. The multi-dimensional nature of radiomics requires highly accurate and reliable machine-learning methods to create predictive models for classification or therapy response assessment.

Multi-parametric breast magnetic resonance imaging (MRI) is routinely used for dense breast imaging as well for screening in high-risk patients and has shown its potential to improve clinical diagnosis of breast cancer. For this reason, the application of ML techniques to breast MRI, in particular to multi-parametric imaging, is rapidly expanding and enhancing both diagnostic and prognostic power. In this review we will focus on the recent literature related to the use of ML in multi-parametric breast MRI for tumor classification and differentiation of molecular subtypes. Indeed, at present, different models and approaches have been employed for this task, requiring a detailed description of the advantages and drawbacks of each technique and a general overview of their performances.
Effects of phase aberration on transabdominal focusing for a large aperture, low f-number histotripsy transducer
Authors: Yeats Ellen , Gupta Dinank , Xu Zhen , Hall Timothy L
Published: June 2022

Objective. Soft tissue phase aberration may be particularly severe for histotripsy due to large aperture and low f-number transducer geometries. This study investigated how phase aberration from human abdominal tissue affects focusing of a large, strongly curved histotripsy transducer. Approach. A computational model (k-Wave) was experimentally validated with ex vivo porcine abdominal tissue and used to simulate focusing a histotripsy transducer (radius: 14.2 cm, f-number: 0.62, central frequency f
c: 750 kHz) through the human abdomen. Abdominal computed tomography images from 10 human subjects were segmented to create three-dimensional acoustic property maps. Simulations were performed focusing at 3 target locations in the liver of each subject with ideal phase correction, without phase correction, and after separately matching the sound speed of water and fat to non-fat soft tissue. Main results. Experimental validation in porcine abdominal tissue showed that simulated and measured arrival time differences agreed well (average error, ∼0.10 acoustic cycles at f
c). In simulations with human tissue, aberration created arrival time differences of 0.65 μs (∼0.5 cycles) at the target and shifted the focus from the target by 6.8 mm (6.4 mm pre-focally along depth direction), on average. Ideal phase correction increased maximum pressure amplitude by 95%, on average. Matching the sound speed of water and fat to non-fat soft tissue decreased the average pre-focal shift by 3.6 and 0.5 mm and increased pressure amplitude by 2% and 69%, respectively. Significance. Soft tissue phase aberration of large aperture, low f-number histotripsy transducers is substantial despite low therapeutic frequencies. Phase correction could potentially recover substantial pressure amplitude for transabdominal histotripsy. Additionally, different heterogeneity sources distinctly affect focusing quality. The water path strongly affects the focal shift, while irregular tissue boundaries (e.g. fat) dominate pressure loss.
A 3-DOF inertial impact locomotion robot constructed on four piezoelectric bimorph actuators
Authors: Li Jianxing , Zhang Shijing , Liu Yingxiang , Deng Jie , Ma Xuefeng
Published: June 2022

An inertial impact locomotion robot (IILR), consisting of a base, three supporting feet and four vertically distributed piezoelectric bimorph actuators (PBAs), was developed in this work. The IILR could complete plane locomotion with three degrees of freedom (DOFs), including translation along X-axis, translation along Y-axis and rotation around Z-axis. The inertial impact force driving the IILR was generated by the PBAs through vibration. A simplified dynamic model of the IILR was established to predict its locomotion characteristics. Then, a prototype was fabricated, whose size was Φ114 × 14 mm3 and weight was 160.5 g. Then the locomotion performances and carrying capability of the IILR were tested, the results indicated that the maximum linear velocity was 76.66 μm s−1 and the maximum rotary velocity was 161.19 μrad s−1 under voltage of 600 Vp-p and frequency of 9 Hz, respectively. In addition, the maximum carrying load was 1600 g (about 9.97 times of self-weight). The experiment results were also in good agreement with the dynamics simulation results. Overall, the IILR held the characteristics of multiple DOFs, large carrying capability, simple structure and no electromagnetic interference; therefore, it could be suitable for precision positioning and carrying applications such as wafer inspection, microscopic observation and operation.
Phase control of optical Goos–Hänchen shifts in a quantum dot nanostructure via high refractive index
Authors: Raheli Ali , Abdulkareem Sarkew S , Al-Qargholi Basim
Published: June 2022

We proposed a model for adjusting Goos–Hänchen (GH) shifts in a cavity with quantum dot (QD) nanostructure in this letter. The actual component of the susceptibility was studied by analytical solution of the coherence term of the density matrix elements, and the refractive index of the QD nanostructure was explored. We discovered that the intracavity medium became phase sensitive because of the electron tunneling action. As a result, the relative phase of applied lights may be used to manipulate the medium’s refraction index. The GH shifts in reflected and transmitted light beams in high refractive index QD nanostructures with diminishing probe absorption were next examined. We discovered that the GH shifts of reflected and transmitted lights are greatly influenced by the applied lights’ relative phase. We established that greater negative or positive GH shifts in reflected and transmitted photons are conceivable in the presence of electron tunneling.
Characterizing quantum nonlocalities under the Heisenberg XYZ spin model with Dzyaloshinskii–Moriya interaction
Authors: Xing Ling-Ling , Yang Huan , Xu Zong , Hu Xun-Mei , Li Ze-Bin , Kong Min , Zhang Gang
Published: June 2022

In quantum information science, the nontrivial applications of the Heisenberg spin model are to realize quantum communication and quantum computing using the quantum nonlocalities between particles in the spin chain. Here, considering Heisenberg XYZ spin model with Dzyaloshinskii–Moriya (DM) interaction, our attentions are directed to ascertain the nonlocal advantage of quantum coherence (NAQC), and also characterize the Bell nonlocality (BN). As revealed from the results, one can use low temperature to realize situations in which the NAQC and BN are invariant. The NAQC and BN cannot be detected if temperature is high. External temperature strongly influences the two quantum nonlocalities in ferromagnetic systems. The strong coupling parameter

ℑ
x

brings on the fact that the two quantum nonlocalities are invariant. It is very difficult to capture the NAQC and BN if

ℑ
x

is weak. Considering

ℑ
y

>
0

, the strong

ℑ
y

is responsible for freezing quantum nonlocalities, and one cannot witness the NAQC when

ℑ
y

is low. Moreover, the freezing of quantum nonlocalities can be achieved via enhancing

ℑ
z

, and the detection of NAQC is difficult if

ℑ
z

is weak. Of particular note, under the influence of DM interactions, NAQC (BN) cannot (can) be frozen both in antiferromagnetic and ferromagnetic systems. The strong

D
y

and

D
z

give rise to the difficulty of capturing the NAQC.
Detection of 16 Small Glitches in Nine Pulsars
Authors: Zhou Zu-Rong , Wang Jing-Bo , Wang Na , Yuan Jian-Ping , Kou Fei-Fei , Dang Shi-Jun
Published: June 2022

Pulsar timing measurements with a 26 m radio telescope at Nanshan between 2000 and 2014 were used to search for glitch events. The data span of nine pulsars ranges from 11.6 to 14.2 yr, and 16 new glitch events were identified in nine pulsars. Glitch parameters were determined through fitting the timing residuals data. All 16 glitches have a small fractional size. Six new glitches have been detected in PSR J1833−0827, making it another frequent glitching pulsar. Some of the 16 glitches may experience exponential or linear recovery, but it is unlikely for us to make further analyses with the large gap in the data set. All the glitch rates obtained from Nanshan are higher than that from Jodrell Bank Observatory. The small glitch size and high glitch rate could possibly attribute to the high observation cadence.
Adaptive binary multi-objective harmony search algorithm for channel selection and cross-subject generalization in motor imagery-based BCI
Authors: Shi Bin , Yue Zan , Yin Shuai , Wang Weizhen , Yu Haoyong , Huang Zhen , Wang Jing
Published: June 2022

Objective. Multi-channel electroencephalogram data containing redundant information and noise may result in low classification accuracy and high computational complexity, which limits the practicality of motor imagery (MI)-based brain-computer interface (BCI) systems. Therefore, channel selection can improve BCI performance and contribute to user convenience. Additionally, cross-subject generalization is a key topic in the channel selection of MI-based BCI. Approach. In this study, an adaptive binary multi-objective harmony search (ABMOHS) algorithm is proposed to select the optimal set of channels. Furthermore, a new adaptive cross-subject generalization model (ACGM) is proposed. Three public MI datasets were used to validate the effectiveness of the proposed method. Main results. The Wilcoxon signed-rank test was performed on the test accuracies, and the results indicated that the ABMOHS method significantly outperformed all channels (p< 0.001), the C3–Cz–C4 channels (p< 0.001), and 20 channels (p< 0.001) in the sensorimotor cortex. The ABMOHS algorithm based on Fisher’s linear discriminant analysis (FLDA) and support vector machine (SVM) classifiers greatly reduces the number of selected channels, especially for larger channel sizes (Dataset 2), and obtains a comparative classification performance. Although there was no significant difference in test classification performance between ABMOHS and non-dominated sorting genetic algorithm II (NSGA-II) when FLDA and SVM were used, ABMOHS required less computational time than NSGA-II. Furthermore, the number of channels obtained by ABMOHS algorithm were significantly smaller than those obtained by common spatial pattern-Rank and correlation-based channel selection algorithm. Additionally, the generalization of ACGM to untrained subjects shows that the mean test classification accuracy of ACGM created by a small sample of trained subjects is significantly better than that of Special-16 and Special-32. Significance. The proposed method can reduce the calibration time in the training phase and improve the practicability of MI-BCI.
Tutorial: a guide to techniques for analysing recordings from the peripheral nervous system
Authors: Koh Ryan G L , Zariffa José , Jabban Leen , Yen Shih-Cheng , Donaldson Nick , Metcalfe Benjamin W
Published: June 2022

The nervous system, through a combination of conscious and automatic processes, enables the regulation of the body and its interactions with the environment. The peripheral nervous system is an excellent target for technologies that seek to modulate, restore or enhance these abilities as it carries sensory and motor information that most directly relates to a target organ or function. However, many applications require a combination of both an effective peripheral nerve interface (PNI) and effective signal processing techniques to provide selective and stable recordings. While there are many reviews on the design of PNIs, reviews of data analysis techniques and translational considerations are limited. Thus, this tutorial aims to support new and existing researchers in the understanding of the general guiding principles, and introduces a taxonomy for electrode configurations, techniques and translational models to consider.
Occupation time of a renewal process coupled to a discrete Markov chain
Authors: Dessertaine Théo , Godrèche Claude , Bouchaud Jean-Philippe
Published: June 2022

A semi-Markov process is one that changes states in accordance with a Markov chain but takes a random amount of time between changes. We consider the generalisation to semi-Markov processes of the classical Lamperti law for the occupation time of a two-state Markov process. We provide an explicit expression in Laplace space for the distribution of an arbitrary linear combination of the occupation times in the various states of the process. We discuss several consequences of this result. In particular, we infer the limiting distribution of this quantity rescaled by time in the long-time scaling regime, as well as the finite-time corrections to its moments.
Power grid fault classification method of alarm information based on bigru-attention.
Authors: Zhang Fengming , Zhang Xinxin , Yang Xiaofeng , Liu Jiang , Wang Yi
Published: June 2022

With the continuous expansion of the power grid, the number of alarm information collected by the dispatching center is also increasing. How to filter out key information from massive alarm information, delete irrelevant data, classify the importance of alarm information, and make preparations for power grid fault diagnosis based on alarm information has become an urgent problem to be solved in online fault diagnosis. Based on this, this paper proposes an importance classification method of alarm information based on bigru attention to screen the data with the strongest correlation with fault, avoid the impact of irrelevant data on fault diagnosis, and meet the needs of intelligence. Firstly, the two-way gated loop (bigru) neural network layer is used to preprocess the alarm information text and extract the features of the deep-seated information; Secondly, the attention mechanism layer is used to assign corresponding weights to the extracted text deep-seated information; Finally, the text feature information of alarm information with different weights is put into the softmax function layer for alarm information text classification. Finally, an actual fault case in an area is tested to verify the effectiveness and practicability of the proposed method.
Thermal analysis method of optical scanning system drive shaft system
Authors: Weining Chen , Gong Cheng , Sansan Chang , Bo Gao , Xiang Li
Published: June 2022

In order to improve the heating problem of the rotating shaft components of the optical surveillance scanning system due to continuous operation, the feasibility of applying the oil-air lubrication technology to the drive shaft system of the optical surveillance scanning system was studied. Analyzed the contact load of the spindle bearing, obtained the calorific value of the heat source of the spindle component of the drive system, established the finite element analysis model of the temperature field of the spindle component, and calculated the thermal stability of the spindle component under the conditions of no lubrication and lubrication. Temperature rise. It is proposed to apply the oil-air lubrication method to the drive shaft system of the optical monitoring scanning system, and the corresponding lubrication parameters are calculated.
Overtemperature fault diagnosis of front bearing for main spindle based on CNN + LSTM
Authors: Wei Wang , Ruiqing Kang , Yu Zhang
Published: June 2022

The main spindle is an important transmission component of the wind turbine. The overtemperature fault of the front bearing of the main spindle is caused due to mechanical wear, grease failure and other reasons. A neural network based on convolutional neural networks (CNN) and long short memory network is built (LSTM) to judge the early fault. Method used in this paper can find the fault in advance. Compared with BP neural network, support vector machine, the accuracy of the model used in this paper is higher, which is up to 99.77%. The mechanism model of spindle operation will be established to analyse the manifestations of various faults and improve the accuracy in the future.
Design of cryogenic long-wave infrared detection system
Authors: Hongwei Zhang , Weining Chen , Rui Qu , Yingjun Ma , Yalin Ding , Haifeng Xu
Published: June 2022

To address the demand for detection of point / dim targets in complex environments, a cryogenic long-wave infrared detection system was designed. In order to improve the target detection capability, the system adopts high-order aspheric surfaces to reduce the number of optical lenses and improve the system transmittance. At the same time, it corrects on-axis / off-axis aberrations and advanced aberrations to improve the imaging quality of the system. In order to effectively reduce the background radiation and improve the system signal-to-noise ratio, the system adopts cryogenic optical technology. Through the scheme design and calculation analysis of the active refrigeration unit, the requirements of the overall and optical technical indicators are met. The outline of the aircraft image obtained by the field experiment is clear and distinguishable, which meets the requirements of target detection. The system has a good application prospect in the field of infrared imaging in early warning systems.
Analysis on the development of semiconductor manufacturing process
Authors: He Zihan
Published: June 2022

The computing speed of chips depends on the number of transistors. In other words, if you have more transistors, the computing speed of chips will be faster. Supposing the area of a chip is limited, we need to develop ways to fit in more transistors. To put more transistors in chips, minimizing the existing microelectronic device is a good way. To achieve that goal, we need to develop effective semiconductor manufacturing process to develop the performance of chips. In this paper, the research topic is to introduce the development of processes and also show their relevant applications of process. This paper intends to let more people realize the importance of semiconductor manufacturing processes and how it applies to our daily lives. This paper will investigate the following question: until October 2021, which is the most advanced manufacturing process? What are some other areas that we can expand through manufacturing process? The conclusion is that until October 2021, the most advanced process is N5P. Semiconductor manufacturing processes apply to areas like Adaptive Substatistical PCA, complicated material modeling tools and methods.
Scale like Co(OH)2/CoOOH modified platinum catalytic gas diffusion electrode and its application in ammonia detection
Authors: Dong Hanpeng , Wu Zhengwei
Published: June 2022

Platinum (Pt) is sputtered on porous PTFE membrane as the basic electrode. Cobalt hydroxide catalyst is deposited on the porous platinum/PTFE electrode by an electrochemical assisted deposition method with ammonia gas employed as precipitant. SEM image of the catalyst shows an integration of scale like flake of about 0.5 micrometer in diameter. The XRD analysis suggested that the catalyst is a mixture of β-Co(OH)2 and CoOOH. The mixture is regard as the effective constituent in the ammonia electrode reaction according to the electrochemical analysis. The cyclic voltammogram indicate that at higher than 0.4V (v.s. saturated HgO/Hg electrode reference) the catalytic activity of the working electrode increased significantly. With the modified electrode as working electrode and a pure platinum electrode as assistant electrode, a four electrodes sensor is established to measure ammonia in air successfully.
Hybrid composite based on conducting polymers and plasmonic nanomaterials applied to catalysis and sensing
Authors: Alanazi Ahmed T , Rice James H
Published: June 2022

Combining plasmonic and semiconductors offers significant potential in creating sensing and photocatalytic devices. Nanocomposites including both metals and semiconductors can control the charge states in the metals that can enhance catalysis activity along with plasmon-enhanced spectroscopy. Here we demonstrate the use of conducting polymer materials with plasmonic nanomaterials to boost up to five-fold plasmon-enhanced Raman scattering spectroscopy signal strength and support oxidation of target molecules through supporting charge transfer processes. This work demonstrates the use of conducting polymers as a semiconductor platform to support plasmonic catalysis and sensing.
Arithmetic monodromy groups of dynamical Belyi maps
Authors: Ejder Özlem
Published: June 2022

We consider a large family of dynamical Belyi maps of arbitrary degree and study the arithmetic monodromy groups attached to the iterates of such maps. Building on the results of Bouw-Ejder-Karemaker on the geometric monodromy groups of these maps, we show that the quotient of the arithmetic monodromy group by the geometric monodromy group has order either

1
1

or

2
2

. Prior to this article, a result of this kind was only known for quadratic maps (Pink) and a few examples in degree

3
3

.
Global solutions to the stochastic reaction-diffusion equation with superlinear accretive reaction term and superlinear multiplicative noise term on a bounded spatial domain
Authors: Salins Michael
Published: June 2022

We describe sufficient conditions on the reaction terms and multiplicative noise terms of a stochastic reaction-diffusion equation that guarantee that the solutions never explode. Both the reaction term and multiplicative noise terms are allowed to grow superlinearly.
Ageing meets kidney disease
Authors: Ortiz Alberto , Mattace-Raso Francesco , Soler María José , Fouque Denis
Published: June 2022

Chronic kidney disease (CKD) is defined as abnormalities of kidney structure or function, present for >3 months, with implications for health. The most used diagnostic criteria are a urinary albumin: creatinine ratio ≥30 mg/g or an estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2. Either of these diagnostic thresholds is associated with adverse health outcomes. GFR decreases with age and the prevalence of CKD is highest in older adults; moreover, the presence of CKD is associated with an increased risk of all-cause and cardiovascular death related to accelerated ageing in all age ranges, and the absolute increase in risk is highest for those aged >75 years. Indeed, premature death is a more common outcome than CKD progression to kidney failure requiring kidney replacement therapy. The progressive ageing of the world population contributes to the projection that CKD will become the second most common cause of death before the end of the century in countries with long life expectancy. The current collection of selected studies on kidney disease and ageing published in Age&Ageing, NDT and CKJ provides an overview of key topics, including cognitive decline, sarcopaenia, wasting and cardiovascular and non-cardiovascular morbidity and mortality, the management of kidney failure and gender differences in CKD progression.
Characterization, costs, cues and future perspectives of phenotypic plasticity
Authors: Schneider Hannah M
Published: June 2022

Background

Plastic responses of plants to the environment are ubiquitous. Phenotypic plasticity occurs in many forms and at many biological scales, and its adaptive value depends on the specific environment and interactions with other plant traits and organisms. Even though plasticity is the norm rather than the exception, its complex nature has been a challenge in characterizing the expression of plasticity, its adaptive value for fitness and the environmental cues that regulate its expression.

Scope

This review discusses the characterization and costs of plasticity and approaches, considerations, and promising research directions in studying plasticity. Phenotypic plasticity is genetically controlled and heritable; however, little is known about how organisms perceive, interpret and respond to environmental cues, and the genes and pathways associated with plasticity. Not every genotype is plastic for every trait, and plasticity is not infinite, suggesting trade-offs, costs and limits to expression of plasticity. The timing, specificity and duration of plasticity are critical to their adaptive value for plant fitness.

Conclusions

There are many research opportunities to advance our understanding of plant phenotypic plasticity. New methodology and technological breakthroughs enable the study of phenotypic responses across biological scales and in multiple environments. Understanding the mechanisms of plasticity and how the expression of specific phenotypes influences fitness in many environmental ranges would benefit many areas of plant science ranging from basic research to applied breeding for crop improvement.
Accurate identification of bacteriophages from metagenomic data using Transformer
Authors: Shang Jiayu , Tang Xubo , Guo Ruocheng , Sun Yanni
Published: June 2022

Motivation

Bacteriophages are viruses infecting bacteria. Being key players in microbial communities, they can regulate the composition/function of microbiome by infecting their bacterial hosts and mediating gene transfer. Recently, metagenomic sequencing, which can sequence all genetic materials from various microbiome, has become a popular means for new phage discovery. However, accurate and comprehensive detection of phages from the metagenomic data remains difficult. High diversity/abundance, and limited reference genomes pose major challenges for recruiting phage fragments from metagenomic data. Existing alignment-based or learning-based models have either low recall or precision on metagenomic data.

Results

In this work, we adopt the state-of-the-art language model, Transformer, to conduct contextual embedding for phage contigs. By constructing a protein-cluster vocabulary, we can feed both the protein composition and the proteins’ positions from each contig into the Transformer. The Transformer can learn the protein organization and associations using the self-attention mechanism and predicts the label for test contigs. We rigorously tested our developed tool named PhaMer on multiple datasets with increasing difficulty, including quality RefSeq genomes, short contigs, simulated metagenomic data, mock metagenomic data and the public IMG/VR dataset. All the experimental results show that PhaMer outperforms the state-of-the-art tools. In the real metagenomic data experiment, PhaMer improves the F1-score of phage detection by 27%.
ResPAN: a powerful batch correction model for scRNA-seq data through residual adversarial networks
Authors: Wang Yuge , Liu Tianyu , Zhao Hongyu
Published: June 2022

Motivation

With the advancement of technology, we can generate and access large-scale, high dimensional and diverse genomics data, especially through single-cell RNA sequencing (scRNA-seq). However, integrative downstream analysis from multiple scRNA-seq datasets remains challenging due to batch effects.

Results

In this article, we propose a light-structured deep learning framework called ResPAN for scRNA-seq data integration. ResPAN is based on Wasserstein Generative Adversarial Network (WGAN) combined with random walk mutual nearest neighbor pairing and fully skip-connected autoencoders to reduce the differences among batches. We also discuss the limitations of existing methods and demonstrate the advantages of our model over seven other methods through extensive benchmarking studies on both simulated data under various scenarios and real datasets across different scales. Our model achieves leading performance on both batch correction and biological information conservation and maintains scalable to datasets with over half a million cells.

Availability and implementation

An open-source implementation of ResPAN and scripts to reproduce the results can be downloaded from: https://github.com/AprilYuge/ResPAN.

Supplementary information

Supplementary data are available at Bioinformatics online.
i6mA-Caps: a CapsuleNet-based framework for identifying DNA N6-methyladenine sites
Authors: Rehman Mobeen Ur , Tayara Hilal , Zou Quan , Chong Kil To
Published: June 2022

Motivation

DNA N6-methyladenine (6mA) has been demonstrated to have an essential function in epigenetic modification in eukaryotic species in recent research. 6mA has been linked to various biological processes. It’s critical to create a new algorithm that can rapidly and reliably detect 6mA sites in genomes to investigate their biological roles. The identification of 6mA marks in the genome is the first and most important step in understanding the underlying molecular processes, as well as their regulatory functions.

Results

In this article, we proposed a novel computational tool called i6mA-Caps which CapsuleNet based a framework for identifying the DNA N6-methyladenine sites. The proposed framework uses a single encoding scheme for numerical representation of the DNA sequence. The numerical data is then used by the set of convolution layers to extract low-level features. These features are then used by the capsule network to extract intermediate-level and later high-level features to classify the 6mA sites. The proposed network is evaluated on three datasets belonging to three genomes which are Rosaceae, Rice and Arabidopsis thaliana. Proposed method has attained an accuracy of 96.71%, 94% and 86.83% for independent Rosaceae dataset, Rice dataset and A.thaliana dataset respectively. The proposed framework has exhibited improved results when compared with the existing top-of-the-line methods.

Availability and implementation

A user-friendly web-server is made available for the biological experts which can be accessed at: http://nsclbio.jbnu.ac.kr/tools/i6mA-Caps/.

Supplementary information

Supplementary data are available at Bioinformatics online.
InterpolatedXY: a two-step strategy to normalize DNA methylation microarray data avoiding sex bias
Authors: Wang Yucheng , Gorrie-Stone Tyler J , Grant Olivia A , Andrayas Alexandria D , Zhai Xiaojun , McDonald-Maier Klaus D , Schalkwyk Leonard C
Published: June 2022

Motivation

Data normalization is an essential step to reduce technical variation within and between arrays. Due to the different karyotypes and the effects of X chromosome inactivation, females and males exhibit distinct methylation patterns on sex chromosomes; thus, it poses a significant challenge to normalize sex chromosome data without introducing bias. Currently, existing methods do not provide unbiased solutions to normalize sex chromosome data, usually, they just process autosomal and sex chromosomes indiscriminately.

Results

Here, we demonstrate that ignoring this sex difference will lead to introducing artificial sex bias, especially for thousands of autosomal CpGs. We present a novel two-step strategy (interpolatedXY) to address this issue, which is applicable to all quantile-based normalization methods. By this new strategy, the autosomal CpGs are first normalized independently by conventional methods, such as funnorm or dasen; then the corrected methylation values of sex chromosome-linked CpGs are estimated as the weighted average of their nearest neighbors on autosomes. The proposed two-step strategy can also be applied to other non-quantile-based normalization methods, as well as other array-based data types. Moreover, we propose a useful concept: the sex explained fraction of variance, to quantitatively measure the normalization effect.

Availability and implementation

The proposed methods are available by calling the function ‘adjustedDasen’ or ‘adjustedFunnorm’ in the latest wateRmelon package (https://github.com/schalkwyk/wateRmelon), with methods compatible with all the major workflows, including minfi.

Supplementary information

Supplementary data are available at Bioinformatics online.
Ab-CoV: a curated database for binding affinity and neutralization profiles of coronavirus-related antibodies
Authors: Rawat Puneet , Sharma Divya , Prabakaran R , Ridha Fathima , Mohkhedkar Mugdha , Janakiraman Vani , Gromiha M Michael
Published: June 2022

We have developed a database, Ab-CoV, which contains manually curated experimental interaction profiles of 1780 coronavirus-related neutralizing antibodies. It contains more than 3200 datapoints on half maximal inhibitory concentration (IC50), half maximal effective concentration (EC50) and binding affinity (KD). Each data with experimentally known three-dimensional structures are complemented with predicted change in stability and affinity of all possible point mutations of interface residues. Ab-CoV also includes information on epitopes and paratopes, structural features of viral proteins, sequentially similar therapeutic antibodies and Collier de Perles plots. It has the feasibility for structure visualization and options to search, display and download the data.

Availability and implementation

Ab-CoV database is freely available at https://web.iitm.ac.in/bioinfo2/ab-cov/home.

Supplementary information

Supplementary data are available at Bioinformatics online.
Topological analysis as a tool for detection of abnormalities in protein–protein interaction data
Authors: Nowakowska Alicja W , Kotulska Malgorzata
Published: June 2022

Motivation

Protein–protein interaction datasets, which can be modeled as networks, constitute an essential layer in multi-omics approach to biomedical knowledge. This representation gives insight into molecular pathways, help to uncover novel potential drug targets or predict a therapy outcome. Nevertheless, the data that constitute such systems are frequently incomplete, error-prone and biased by scientific trends. Implementation of methods for detection of such shortcomings could improve protein–protein interaction data analysis.

Results

We performed topological analysis of three protein–protein interaction networks (PPINs) from IntAct Molecular Database, regarding cancer, Parkinson’s disease (two most common subjects in PPINs analysis) and Human Reference Interactome. The data collections were shown to be often biased by scientific interests, which highly impact the networks structure. This may obscure correct systematic biological interpretation of the protein–protein interactions and limit their application potential. As a solution to this problem, we propose a set of topological methods for the bias detection, which performed in the first step provides more objective biological conclusions regarding protein–protein interactions and their multi-omics consequences.

Availability and implementation

A user-friendly tool Extensive Tool for Network Analysis (ETNA) is available on https://github.com/AlicjaNowakowska/ETNA. The software includes a graphical Colab notebook: https://githubtocolab.com/AlicjaNowakowska/ETNA/blob/main/ETNAColab.ipynb.

Contact

alicja.nowakowska@pwr.edu.pl or malgorzata.kotulska@pwr.edu.pl

Supplementary information

Supplementary data are available at Bioinformatics online.
A deeper dive into top-down control of pain and itch
Authors: Fields Howard L
Published: June 2022

This scientific commentary refers to ‘Medullary kappa-opioid receptor neurons inhibit pain and itch through a descending circuit’ by Nguyen et al. (https://doi.org/10.1093/brain/awac189).
Full spectrum of vitamin D immunomodulation in multiple sclerosis: mechanisms and therapeutic implications
Authors: Galoppin Manon , Kari Saniya , Soldati Sasha , Pal Arindam , Rival Manon , Engelhardt Britta , Astier Anne , Thouvenot Eric
Published: June 2022

Vitamin D deficiency has been associated with the risk of multiple sclerosis, disease activity and progression. Results from in vitro experiments, animal models and analysis of human samples from randomized controlled trials provide comprehensive data illustrating the pleiotropic actions of Vitamin D on the immune system. They globally result in immunomodulation by decreasing differentiation of effector T and B cells while promoting regulatory subsets. Vitamin D also modulates innate immune cells such as macrophages, monocytes and dendritic cells, and acts at the level of the blood–brain barrier reducing immune cell trafficking. Vitamin D exerts additional activity within the central nervous system reducing microglial and astrocytic activation. The immunomodulatory role of Vitamin D detected in animal models of multiple sclerosis has suggested its potential therapeutic use for treating multiple sclerosis. In this review, we focus on recent published data describing the biological effects of Vitamin D in animal models of multiple sclerosis on immune cells, blood–brain barrier function, activation of glial cells and its potential neuroprotective effects. Based on the current knowledge, we also discuss optimization of therapeutic interventions with Vitamin D in patients with multiple sclerosis, as well as new technologies allowing in-depth analysis of immune cell regulations by vitamin D.
Cortico-cerebellar audio-motor regions coordinate self and other in musical joint action
Authors: Kohler Natalie , Novembre Giacomo , Gugnowska Katarzyna , Keller Peter E , Villringer Arno , Sammler Daniela
Published: June 2022

Joint music performance requires flexible sensorimotor coordination between self and other. Cognitive and sensory parameters of joint action—such as shared knowledge or temporal (a)synchrony—influence this coordination by shifting the balance between self-other segregation and integration. To investigate the neural bases of these parameters and their interaction during joint action, we asked pianists to play on an MR-compatible piano, in duet with a partner outside of the scanner room. Motor knowledge of the partner’s musical part and the temporal compatibility of the partner’s action feedback were manipulated. First, we found stronger activity and functional connectivity within cortico-cerebellar audio-motor networks when pianists had practiced their partner’s part before. This indicates that they simulated and anticipated the auditory feedback of the partner by virtue of an internal model. Second, we observed stronger cerebellar activity and reduced behavioral adaptation when pianists encountered subtle asynchronies between these model-based anticipations and the perceived sensory outcome of (familiar) partner actions, indicating a shift towards self-other segregation. These combined findings demonstrate that cortico-cerebellar audio-motor networks link motor knowledge and other-produced sounds depending on cognitive and sensory factors of the joint performance, and play a crucial role in balancing self-other integration and segregation.
Visual recognition in rhesus monkeys requires area TE but not TEO
Authors: Eldridge Mark A G , Pearl Jonah E , Fomani Grace P , Masseau Evan C , Fredericks J Megan , Chen Gang , Richmond Barry J
Published: June 2022

The primate visual system is often described as a hierarchical feature-conjunction pathway, whereby each level represents an increasingly complex combination of image elements, culminating in the representation of whole coherent images in anterior inferior temporal cortex. Although many models of the ventral visual stream emphasize serial feedforward processing (Poggio et al. 2012; Yamins and DiCarlo 2016) anatomical studies show connections that bypass intermediate areas and that feedback to preceding areas (Distler et al. 1993; Kravitz et al. 2011). Prior studies on visual discrimination and object transforms also provide evidence against a strictly feed-forward serial transfer of information between adjacent areas (Kikuchi and Iwai 1980; Weiskrantz and Saunders 1984; Kar and DiCarlo 2021). Thus, we sought to investigate whether behaviorally relevant propagation of visual information is as strictly sequential as sometimes supposed. We compared the accuracy of visual recognition after selective removal of specific subregions of inferior temporal cortex—area TEO, area TE, or both areas combined. Removal of TEO alone had no detectable effect on recognition memory, whereas removal of TE alone produced a large and significant impairment. Combined removal of both areas created no additional deficit relative to removal of TE alone. Thus, area TE is critical for rapid visual object recognition, and detailed image-level visual information can reach area TE via a route other than through TEO.
A case report of Enterobacter cloacae endocarditis in a patient with a history of cotton fever
Authors: Tarabanis Constantine , Zhang Ruina , Grossman Kelsey , Kaul Christina , Lorin Jeffrey D
Published: June 2022

Background

Cotton fever is a self-limited, febrile syndrome occurring after the injection of trace amounts of drugs, in particular heroin, extracted from reused cotton filters. It is characterized by non-specific findings, such as fever, tachycardia, and leucocytosis. The leading pathophysiologic explanation suggests it is the result of direct inoculation of the bloodstream with endotoxins from Gram-negative bacilli of the genus Enterobacter, known to colonize all parts of the cotton plant. Only one prior case report has suggested cotton fever as a potential risk factor of infective endocarditis (IE).

Case summary

We describe a case of a 57-year-old patient with a history of intravenous heroin use complicated by self-reported episodes of cotton fever. His presentation was notable for Enterobacter cloacae IE with bilateral septic pulmonary emboli. Transthoracic echocardiography findings included new tricuspid regurgitation and two mobile echodensities on the right atrial implantable cardioverter defibrillator (ICD) lead. Despite broad antibiotic coverage and extraction of the ICD leads, the patient passed away from septic shock.

Discussion

The present case report is only the second published report of endocarditis in a patient with a history of cotton fever. In both cases, bacteria of the Enterobacter genus were isolated in patients’ blood cultures. This evidence supports the endotoxin theory as the leading pathophysiologic explanation for cotton fever and suggests cotton fever as a risk factor for Gram-negative IE. In the inpatient setting it informs proper antibiotic coverage, whereas in the outpatient setting it supports harm reduction interventions in the form of sterile cotton balls.