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Gas adsorption and separation/desorption are two pivotal stages in gas capture, demanding additional energy for liberating gas molecules from the adsorbent. Hence, it’s essential to engineer an adsorbent with minimal energy consumption yet effective gas adsorption–desorption characteristics. Accordingly, this research introduces a carbon nano-network (CNN) material capable of inhaling and exhaling gases via self-deformation. Molecular dynamics simulations demonstrate that the gas adsorption–desorption capabilities of CNN can be efficiently regulated by its deformation. While the size of CNN minimally affects the gas sorption rate, a larger CNN necessitates a prolonged duration to achieve saturation under identical gas conditions. Incorporating elongated appendages in CNN enhances both its stability during contraction and its efficiencies in gas adsorption and desorption. We discerned that attaining a critical gas density within a confined space is imperative to initiate self-desorption of CNN. Specifically, if the gas density dips below this critical threshold–set at 5.2 times that of CO2 at room temperature (300 K) and atmospheric pressure (1 Bar)–CNN exhibits no self-desorption capability. Nevertheless, even a marginal elevation in gas density triggers and maintains self-desorption with a consistently surpassing desorption ratio of 98 %. This discovery offers valuable insights for designing automatic self-desorption materials or apparatuses.

Abstract Background Falls are a global public problem and may be an important cause of death in older adults. However, older adults with mild cognitive impairment(MCI) are more likely to fall and suffer more damage than older adults with normal cognitive function, which shows the importance of preventing falls. More and more evidence shows that Baduanjin can improve the balance function of the elderly and reduce the risk of falls in the elderly with MCI, but the mechanism is still unclear. The main purpose of this study is to verify the intervention effect of Baduanjin training on the risk of falls in elderly people with MCI and to elucidate the underlying mechanism of Baduanjin training in reducing the risk of falls in MCI patients. Methods In this prospective study, outcome assessor-blind, three-arm randomized controlled trial, a total of 72 eligible participants will be randomly allocated (1:1:1) into the 12-week Baduanjin exercise intervention (60 min per session, three sessions per week), the 12-week brisk walking group(60 min per session, three sessions per week) or the 12-week health education group. Primary outcome is the Fall-Risk Self-Assessment Questionnaire(FRQ), and secondary outcomes are fall efficacy index, gait assessment, balance function, lower limb muscle strength, cognitive function, activities of daily living(ADL) and MRI scans. In addition to the MRI scans, which will be measured before and after the intervention,other primary and secondary outcomes will be assessed at baseline, 6 weeks, and 12 weeks (at the end of the intervention) and after an additional 12-week follow-up period. The mixed linear model will be conducted to observe the intervention effects. Discussion This trial will investigate the effect of Baduanjin exercise on the prevention of falls in elderly individuals with MCI, explore the imaging mechanism of Baduanjin exercise to reduce the risk of falls in elderly individuals with MCI from the perspective of vestibular neural network, and provide strong evidence for Baduanjin exercise to reduce the risk of falls in elderly individuals with MCI, as well as provide new ideas and approaches for the central mechanism of Traditional Chinese Medicine(TRC) rehabilitation methods to intervene in falls in elderly. Trial registration Chictr.org.cn, ID: ChiCTR2200057520. Registered on 14 March 2022, https://www.chictr.org.cn/showproj.html?proj=146592 .

Background. Mild cognitive impairment (MCI), an intermediate stage between normal aging and dementia, has emerged as a prominent research area in geriatric care due to its heightened propensity for progressing toward dementia. Sleep plays a pivotal role in cognitive function, with dyssomnias not only exacerbating cognitive and affective symptoms associated with neurodegenerative diseases but also contributing to disease progression. Aim. This bibliometric analysis investigates the global research on MCI with dyssomnias over the past two decades, aiming to discern key findings, research domains, and emerging trends in this field. Methods. In this study, a bibliometric analysis was conducted using the search terms “MCI” and “sleep”. Data were extracted from the Web of Science Core Collection database, and visualization and collaborative analysis were performed using CiteSpace and VOSviewer. Results. This study encompassed 546 publications from 2003 to 2023. The publication volume and citation rate consistently increased over time. Neurosciences, Clinical Neurology, and Geriatrics Gerontology emerged as the top three research fields. The Journal of Alzheimer’s Disease had the highest publication count, while Sleep Medicine received the most citations. USA, China, and Italy led in publication output. Collaborative clusters among authors and institutions were identified, but cooperation between clusters was limited. Active cocited reference clusters included “obstructive sleep apnea”, “possible mediating pathways”, and “isolated rapid eye movement sleep behaviour disorder”. The top frequently mentioned keywords, besides “MCI”, were “Alzheimer’s disease”, “dementia”, “risk factor”, and “Parkinson’s Disease”. Notable keyword clusters spanned circadian rhythm, Parkinson’s disease, MCI, dementia with Lewy body, subjective cognitive impairment, Lewy body disease, Alzheimer’s disease, and dietary patterns. Conclusion. The field of MCI with dyssomnias is rapidly expanding, encompassing a wide range of neurodegenerative disorders and sleep disturbances. Current research endeavors are primarily focused on elucidating the underlying pathogenesis, predicting disease progression, and developing innovative treatment strategies for individuals affected by MCI with dyssomnias.

Objective: This study aims to systematically evaluate the effect of breathing exercises on the low back pain (LBP). Methods: The studies of relevant randomized controlled trials (RCTs) testing the effect of breathing exercises on LBP were selected after strict screening from the establishment of PubMed, EMBASE, Cochrane Library, Web of Science, CBM, and CNKI databases until September 2022. The studies included were then independently assessed for risk bias by two investigators. The PRISMA 2020 statement was followed in this study. Results: 11 RCTs involving 383 patients were included in this analysis. Results showed that the effective rate of LBP patients after breathing exercises was significantly higher than those in the control group, and the VAS (Visual Analogue Score) and ODI (Oswestry Disability Index) scores of LBP patients were significantly lower than those in the control group [VAS: MD = −0.50, 95% CI (−0.88, −0.11), I2 = 76%, p = 0.0009; ODI: MD = −2.46, 95% CI (−3.41, −1.52), I2 = 20%, p = 0.28]. The results of subgroup showed that the duration of treatment had little effect on the effect of breathing exercises, and breathing exercises alone could also have a positive effect on LBP. However, there were methodological limitations in the included studies, future studies should ensure blinded outcome assessors and full reporting to reduce bias risks. Because this review is a study of breathing exercises as an intervention without any adverse events, all studies did not involve safety assessments. Conclusions: The results indicated that breathing exercises have a positive effect on alleviating LBP, but due to the lack of methodological rigor and some limitations of the included studies, more critical RCTs are still needed in the future to verify the precision of this conclusion.The protocol was registered in PROSPERO (No. CRD42022345561).

Gastric cancer (GC) is an aggressive malignant disease which still lacks effective early diagnosis markers and targeted therapies, representing the fourth-leading cause of cancer-associated death worldwide. The Hippo signaling pathway plays crucial roles in organ size control and tissue homeostasis under physiological conditions, yet its aberrations have been closely associated with several hallmarks of cancer. The last decade witnessed a burst of investigations dissecting how Hippo dysregulation contributes to tumorigenesis, highlighting the therapeutic potential of targeting this pathway for tumor intervention. In this review, we systemically document studies on the Hippo pathway in the contexts of gastric tumor initiation, progression, metastasis, acquired drug resistance, and the emerging development of Hippo-targeting strategies. By summarizing major open questions in this field, we aim to inspire further in-depth understanding of Hippo signaling in GC development, as well as the translational implications of targeting Hippo for GC treatment.

Abstract Introduction Nearly one in seven women worldwide suffers from chronic pelvic pain syndrome (CPPS) each year. Often, CPPS necessitates a combination of treatments. Studies have shown the good therapeutic effects of repetitive transcranial magnetic stimulation (rTMS) upon CPPS. We wish to undertake a randomized controlled trial (RCT) to observe the effect of high-frequency rTMS at different intensities upon CPPS. Methods and analyses In this prospective, double-blinded RCT, 63 female CPPS participants will be recruited and randomized (1:1:1) to high-intensity rTMS, low-intensity rTMS, or sham rTMS. The control group will receive a 10-day course of conventional pelvic floor (PF) rehabilitation (neuromuscular stimulation, magnetic therapy, or light therapy of the PF). On the basis of conventional treatment, participants in the high-intensity rTMS group will receive pulses of 10 Hz with a resting motor threshold (RMT) of 110% for a total of 15,000 pulses. Participants in the low-intensity rTMS group will receive pulses of 10 Hz with an RMT of 80% with 15,000 pulses. The sham rTMS group will be subjected to sham stimulation with the same sound as produced by the real magnetic stimulation coil. The primary outcome will be determined using a visual analog scale, the Genitourinary Pain Index, Zung Self-Rating Anxiety Scale, and Zung Self-Rating Depression Scale. The secondary outcome will be determined by electromyography of the surface of PF muscles at baseline and after treatment completion. Ethics and dissemination This study is approved by the Ethics Committee of Bao’an People’s Hospital, Shenzhen, Guangdong Province (approval number: BYL20211203). The results will be submitted for publication in peer-reviewed journals and disseminated at scientific conferences (Protocol version 1.0-20220709). Trial registration Chictr.org.cn, ID: ChiCTR2200055615. Registered on 14 January 2022, http://www.chictr.org.cn/showproj.aspx?proj=146720 . Protocol version 1.0-20220709.

Objective: This study focuses on in-depth research and development of the utilization of Coreopsis tinctoria resources. Methods: The Coreopsis samples from Hetian, Urumqi, Aba, Luding and Xining were hydrolyzed with hydrochloric acid, and the contents of 17 kinds of amino acids were determined by automatic amino acid analyzer. On this basis, the characteristics of amino acid flavor were analyzed and compared with the amino acid pattern spectrum issued by FAO/WHO. RAA, RC and SRC were calculated, and correlation analysis and cluster analysis were performed on 17 kinds of amino acids of chrysanthemum. Thereafter, the nutritional value of Coreopsis protein was comprehensively evaluated. Results: 17 kinds of amino acids were detected in all the 5 species from different habitats. The taste characteristics of Luding and Aba Coreopsis was mainly bitter, while the other three kinds of Coreopsis showed umami, bitter and sweet taste from the highest to the lowest. Compared with the amino acid pattern profile, the Met+Cys in Coreopsis from the five producing areas were lower than the pattern spectrum standard, and the proportions of other essential amino acids in the total amino acids in Luding, Hetian and Urumqi were close to and higher than the pattern spectrum standard. According to the RC value, Met and Cys were identified as as the first limiting amino acids of Coreopsis. The SRC value ranged from 75 to 80, the ratio of essential amino acids to total amino acids (E/T value) ranged from 31% to 36%, and the ratio of essential amino acids to non-essential amino acids (E/N value) ranged from 46% to 55%. The E/N values of Coreopsis in all five regions are slightly lower than the FAO/WHO ideal protein standard. Conclusion: Coreopsis is rich in amino acids and has high nutritional value with certain medicinal value. It has good taste and can be used as a beneficial product resource for human body.

Significant wave height (SWH) is a key parameter for monitoring the state of waves. Accurate and long-term SWH forecasting is significant to maritime shipping and coastal engineering. This study proposes a transformer model based on an attention mechanism to achieve the forecasting of SWHs. The transformer model can capture the contextual information and dependencies between sequences and achieves continuous time series forecasting. Wave scale classification is carried out according to the forecasting results, and the results are compared with gated recurrent unit (GRU) and long short-term memory (LSTM) machine-learning models and the key laboratory of MArine Science and NUmerical Modeling (MASNUM) numerical wave model. The results show that the machine-learning models outperform the MASNUM within 72 h, with the transformer being the best model. For continuous 12 h, 24 h, 36 h, 48 h, 72 h, and 96 h forecasting, the average mean absolute errors (MAEs) of the test sets were, respectively, 0.139 m, 0.186 m, 0.223 m, 0.254 m, 0.302 m, and 0.329 m, and the wave scale classification accuracies were, respectively, 91.1%, 99.4%, 86%, 83.3%, 78.9%, and 77.5%. The experimental results validate that the transformer model can achieve continuous and accurate SWH forecasting, as well as accurate wave scale classification and early warning of waves, providing technical support for wave monitoring.

Allergic rhinitis (AR) is one of the popular childhood diseases, bringing physical and metal burdens to the children and their families. The study was performed to detect common allergens eliciting AR in children, to investigate the prevalence of allergens in different age and gender cohorts, and to provide a reliable basis for clinical prevention and treatment of AR during childhood. We measured serum-specific IgE and performed inhalant and ingestion allergen examinations in 2,316 children with AR, in collaboration with BioSciTec GmbH. The prevalence of different allergens was determined according to gender, age, severity, and season. Among the 2,316 AR cases, the top five inhalant allergens were Dermatophagoides pteronyssinus (1,674 cases, 72.3%), Dermatophagoides farinae (1,520 cases, 65.6%), Blomia tropicalis (1,477 cases, 63.8%), Cockroach (602 cases, 26.0%), and Dog hair (602 cases, 26.0%). The top five ingestive allergens were Milk (1,111 cases, 48.0%), Egg white (543 cases, 23.4%), Shrimp/Crab (425 cases, 18.4%), Beef/Mutton (422 cases, 18.2%), and Egg yold (329 cases, 14.2%). AR severity analyses showed that 50.9% (1,180 cases) of D. pteronyssinus allergies were above level three, 47.9% (1,109 cases) of D. farinae allergies were above level three, only 23.3% (539 cases) of B. tropicalis allergies were level three, and B. tropicalis allergies were mainly of level 2. Other AR-inducing allergens mainly produced level one or two reactions. Regarding ingestion allergens, 7.9% (183 cases) of milk allergies and 4.7% (108 cases) of Shrimp/Crab allergies were above level three, and other allergens induced AR mainly of level one or two. The study investigated the major allergens eliciting AR in children from Guangdong, China, assessed the prevalence and severity among cohorts regarding age, gender, and season, and produced essential information on childhood AR, laying important references for AR prevention and treatment in the future.

Iturin A is a biosurfactant with various applications, and its low synthesis capability limits its production and application development. Fatty acids play a critical role in cellular metabolism and target product syntheses, and the relationship between fatty acid supplies and iturin A synthesis is unclear. In this study, we attempted to increase iturin A production via strengthening fatty acid synthesis pathways in Bacillus amyloliquefaciens. First, acetyl-CoA carboxylase AccAD and ACP S-malonyltransferase fabD were overexpressed via promoter replacement, and iturin A yield was increased to 1.36 g/L by 2.78-fold in the resultant strain HZ-ADF1. Then, soluble acyl-ACP thioesterase derived from Escherichia coli showed the best performance for iturin A synthesis, as compared to those derived from B. amyloliquefaciens and Corynebacterium glutamicum, the introduction of which in HZ-ADF1 further led to a 57.35% increase of iturin A yield, reaching 2.14 g/L. Finally, long-chain fatty acid-CoA ligase LcfA was overexpressed in HZ-ADFT to attain the final strain HZ-ADFTL2, and iturin A yield reached 2.96 g/L, increasing by 6.59-fold, and the contents of fatty acids were enhanced significantly in HZ-ADFTL2, as compared to the original strain HZ-12. Taken together, our results implied that strengthening fatty acid supplies was an efficient approach for iturin A production, and this research provided a promising strain for industrial production of iturin A.

Neural network models for hyperspectral images classification are complex and therefore difficult to deploy directly onto mobile platforms. Neural network model compression methods can effectively optimize the storage space and inference time of the model while maintaining the accuracy. Although automated pruning methods can avoid designing pruning rules, they face the problem of search efficiency when optimizing complex networks. In this paper, a network collaborative pruning method is proposed for hyperspectral image classification based on evolutionary multi-task optimization. The proposed method allows classification networks to perform the model pruning task on multiple hyperspectral images simultaneously. Knowledge (the important local sparse structure of the network) is automatically searched and updated by using knowledge transfer between different tasks. The self-adaptive knowledge transfer strategy based on historical information and dormancy mechanism is designed to avoid possible negative transfer and unnecessary consumption of computing resources. The pruned networks can achieve high classification accuracy on hyperspectral data with limited labeled samples. Experiments on multiple hyperspectral images show that the proposed method can effectively realize the compression of the network model and the classification of hyperspectral images.

Water and fertilizer play an important role in crop growth in dryland areas. It is a necessity to improve the water use efficiency (WUE) of the crop once the water resource is limited. In northern China, where there is a wide shortage of water resources, it is therefore necessary to investigate how fertilization affects the WUE of spring maize and to quantify the effects. A total of 33 published peer-reviewed papers were collected, and a meta-analysis and random forest model analysis were performed with 364 WUE comparisons, aiming to explore the effects of fertilization on the WUE of spring maize and to clarify the optimal conditions for WUE under fertilizer management. The results showed that fertilization significantly increased the WUE of spring maize by 56.72% (P < 0.01) when compared with non-fertilization. The WUE effect under the organic–inorganic fertilizer combination (MNPK) was approximately twice as high as that under inorganic fertilizer (NPK) or organic fertilizer (M). The greatest increase in WUE occurred at 0–100 kg ha−1 of nitrogen application (NA). Under environmental conditions including 7 ≤ mean annual temperature in the test year (T) ≤ 10 °C, 400 ≤ mean annual precipitation in the test year (P) ≤ 600 mm, and mean altitude (A) > 1500 m, and soil conditions including 10 ≤ soil organic matter content (SOM) ≤ 14 g kg−1 and available phosphorus (AP) < 5 mg kg−1, the fertilization optimally enhanced the WUE of spring maize when the agronomic measures of ridge–furrow planting (RFP) and mulching film (MF) were used. The random forest model analysis indicated that the influence factors (i.e., fertilizer regimes, environmental factors, soil factors, and agronomic measures) caused 65.62% of the variation in spring maize WUE effects, while in all influence factors, fertilizer types related to fertilizer regimes caused the most variation. The initial available potassium (AK) and available nitrogen (AN) of the soil were negatively correlated to the WUE effect, indicating that fertilization imposed a better effect on the WUE of spring maize when the soil was infertile. Fertilization significantly increased the WUE of spring maize, and organic and inorganic fertilizer application provided an effective measure for the sustainable development of spring maize in northern China. After clarifying the required conditions for fertilization increasing WUE, high-efficiency water use may be achieved.

Image hashing embeds the image to binary codes which can boost the efficiency of approximately nearest neighbors search. F-measure is a widely-used metric for evaluating the performance of hashing methods. However, it is non-differentiable and hence it has not been used as an object function for hashing. Heuristic algorithms, e.g. evolutionary computation and particle swarm optimization (PSO), are good at optimizing non-differentiable objectives, while they are inefficient in very high-dimensional variables which are commonly used in hashing models. To address this contradict, we propose a scheme to bridge hashing methods and F-measure objective using PSO. The hashing methods are used to generate real-valued codes for images and then the parameters of quantization procedure are optimized by PSO. Our scheme can incorporate a wide range of hashing methods, heuristic optimization algorithms and non-differentiable metrics. Experimental results demonstrate that our scheme can be used to further improve the performance of existing hashing methods.

Recently, ensemble learning paradigm has shown great potential to achieve better prediction performance in the hyperspectral image classification. However, in the traditional methods, each classifier independently searches for the optimal spectral feature subspace in series and some important features are searched repeatedly, which leads to high computing redundancy and low effective utilization of features. In this article, an evolutionary multitask ensemble learning model (EMT_EL) for hyperspectral image classification is designed. First, the model formulates the spectral feature subspaces generation into a multitask optimization problem to concurrently search for optimal feature subspaces for multiple classifiers, which would be capable to select more informative and representative feature subspaces effectively. Second, seeking the optimal feature subspace for one base classifier can assist in the optima-seeking process for some other base classifiers via sharing the useful features, which can accelerate converge toward the direction of the optimal feature subspace, avoid trapping in local optimal subspace and improve searching capability. Third, randomization-enhanced genetic operators are designed for effective and reasonable feature selection, which can facilitate the exchange of information and improve the joint searching efficiency of the feature subspace. Eventually, the quality of generated spectral feature subspaces for each base classifier is improved and the feature sharing can parse HSI data by knowing which spectral features are important. Experimental results demonstrate that the proposed method can generate the appropriate feature subspace for each base classifier, thus it has outstanding classification performance on the different hyperspectral datasets.

Abstract Nanotube can be used as a mass sensor. To design a mass sensor for evaluating a high-speed nanoparticle, in this study, we investigated the impact vibration of a cantilever nanobeam being transversally collided by a high-speed C60 at the beam's free end with an incident velocity of v In. The capped beam contains alternately two boron nitride zones and two carbon zones on its cross section. Hence, the relaxed beam has elliptic cross section. The vibration properties were demonstrated by molecular dynamics simulation results. Beat vibration of a slim beam can be found easily. The 1st and the 2nd order natural frequencies (f 1 and f 2) of the beam illustrate the vibration of beam along the short and the long axes of its elliptic cross section, respectively. f 2 decreases with increasing temperature. A minimal value of v In leads to the local buckling of the beam, and a different minimal v In leading to damage of the beam. For the same system at a specified temperature, f 2 varies with v In. When the beam bends almost uniformly, f 2 decreases linearly with v In. If v In becomes higher, the beam has a cross section which buckles locally, and the buckling position varies during vibration. If v In approaches the damage velocity, a fixed contraflexture point may appear on the beam due to its strong buckling. Above the damage velocity, f 2 decreases sharply. These results have a potential application in design of a mass sensor.

In this paper, a novel change detection technique is proposed based on multiscale superpixel segmentation and stacked denoising autoencoders (SDAE). This approach is designed to achieve superpixel-based change detection, in which the basic analysis unit is between pixel-based and object-based ones. Given two original images, the difference image (DI) is obtained by conventional DI generation methods. Then, we propose a multiscale superpixel segmentation which is guided by the changing degrees estimated from the DI. Different from traditional multiscale superpixel, the proposed multiscale superpixel segmentation is employed in a single map. In the proposed method, SDAE is used to learn the difference representation between bi-temporal superpixels. Bi-temporal superpixels are stacked and fed into SDAE for its pre-training, and then SDAE is fine-tuned according to pseudo labels generated by traditional unsupervised methods. After fine-tuned with back propagation, the SDAE can be used to classify all superpixel pairs into changed or unchanged ones. The experimental results on real remote sensing datasets have demonstrated the effectiveness of the proposed approach.

Although the deep neural network (DNN) has shown a powerful ability in hyperspectral image (HSI) classification, its learning requires a large number of labeled training samples; otherwise, it is prone to over-fitting and has a poor classification performance. However, this requirement is impractical for HSIs due to the difficulty in obtaining class labels. To make DNNs suitable for HSI classification with few labeled samples, we propose a graph-based deep multitask few-shot learning (GDMFSL) framework that learns the intrinsic relationships among all samples (labeled and unlabeled) of HSIs with the assistance of graph information to alleviate the over-fitting caused by few labeled training samples. Firstly, a semi-supervised graph is constructed to generate graph information. Secondly, a deep multitask network (DMN) is designed, which contains two subnetworks (tasks): a classifier subnetwork for learning class information from labeled samples and a Siamese subnetwork for learning sample relationships from the semi-supervised graph. To effectively learn graph information, a loss function suitable for the Siamese subnetwork is designed that shortens (and expands) the distance between the target sample and its nearest (and farthest) neighbors. Finally, since the number of training samples of the two subnetworks is severely imbalanced, a multitask few-shot learning strategy is designed to make two subnetworks converge simultaneously. Experimental results on the Indian Pines, University of Pavia and Salinas datasets demonstrate that GDMFSL achieves a better classification performance relative to existing competitors in few-shot settings. In particular, when only five labels per class are involved in training, the classification accuracy of GDMFSL on the three datasets reaches 87.58%, 86.42% and 98.85%, respectively.

For improving probiotics’ survivability under harsh conditions, this study used Lactiplantibacillus plantarum GIM1.648 as a model microorganism to investigate its ability to produce biofilms on electrospun ethyl cellulose nanofiber mats. SEM observations confirmed that biofilm was successfully formed on the nanofibers, with the latter being an excellent scaffold material. The optimal cultivation conditions for biofilm formation were MRS medium without Tween 80, a culture time of 36 h, a temperature of 30 °C, a pH of 6.5, and an inoculum concentration of 1% (v/v). The sessile cells in the biofilm exhibited improved gastrointestinal and thermal tolerance compared to the planktonic cells. Additionally, the RT-qPCR assay indicated that the luxS gene played a crucial role in biofilm formation, with its relative expression level being 8.7-fold higher compared to the planktonic cells. In conclusion, biofilm formation on electrospun nanofiber mat has great potential for improving the viability of probiotic cells under harsh conditions.

In this study, we build a nanostirrer, in which the circular graphene nanoflake can be driven to rotate on graphite substrate by four wedged diamond needles (DNs). Molecular dynamics simulations reveal that the rotationally distributed DNs provide strong repulsion to the nanoflake via their tip atoms. Once the circumferential component of the repulsive force is non-zero, it actuates the flake to rotate at gigahertz. The stable rotational frequency (SRF) of the flake can be obtained when the friction moment from substrate balances the driving moment from DNs. Results also demonstrated that, at temperature lower than 100 K, stronger compression from DNs will introduce larger SRF to the flake due to stronger driving force from the DNs. Under the same conditions, a smaller flake has higher SRF. When changing the layout angles of DNs in a range with width of ~90°, both the SRF and rotational direction of the nanoflake change monotonously. These characteristics suggest a clue for design of a nano-stirrer with controllable rotation for potential applications, e.g., separation and purification or generating nanoflow field through the gigahertz rotary flake.

The structures and properties of disordered systems are very challenging in material science. In the present first-principles molecular dynamics study, the results show that the liquid gold at 2000 K undergoes a continuous liquid–liquid transition under pressure. The icosahedral short range order was found to be initially enhanced by compression and then started to decay and became less dominant. At 18 GPa, it was solidified and became a crystal. The present theoretical results provide a new perspective to understand the evolution of the atomic packing in liquid metals under compression and may be generalized for other disordered systems.

A model of two-dimensional knitting system is constructed from graphene ribbons (GRs) aligned in the three isometric directions. It then recommends a process for fabrication of the nanotexture. Based on the model, its thermal stability is assessed via molecular dynamics simulations. The nanotexture may experience out-of-plane fold or in-plane shrinkage at different temperatures due to the three factors including bending of the ribbons, relative sliding between the ribbons at their overlapping areas, and strong van der Waals forces between the GRs. In addition to the global deformation of the nanotexture, it has experienced local deformation at pores which depends on the chirality and gaps of the GRs. For a nanotexture formed from armchair GRs, it is relaxed to be a quasi-periodic porous medium with similar pore size. The mechanism is that the armchair GRs have non-smooth edges, and are locked at their contact areas in relative sliding. In water, the in-plane shrunk nanotexture does not expand to be a plate even at 500 K. However, in argon, it expands to be a plate with periodic hexagon pores. The proposed nanotexture may have significant applications in the area of controllable shrinkage-expansion oriented nanomaterial/nanodevice. Keywords: 2D nanoporous material, Nanotexture, In-plane shrinkage, Out-of-plane fold, Shrinkage-expansion control, Molecular dynamics

Abstract Background Poly-γ-glutamic acid (γ-PGA) is a natural polymer with great potential applications in areas of agriculture, industry, and pharmaceutical. The biodiesel-derived glycerol can be used as an attractive feedstock for γ-PGA production due to its availability and low price; however, insufficient production of γ-PGA from glycerol is limitation. Results The metabolic pathway of Bacillus licheniformis WX-02 was rewired to improve the efficiency of glycerol assimilation and the supply of NADPH for γ-PGA synthesis. GlpK, GlpX, Zwf, and Tkt1 were found to be the key enzymes for γ-PGA synthesis using glycerol as a feedstock. Through combinational expression of these key enzymes, the γ-PGA titer increased to 19.20 ± 1.57 g/L, which was 1.50-fold of that of the wild-type strain. Then, we studied the flux distributions, gene expression, and intracellular metabolites in WX-02 and the recombinant strain BC4 (over-expression of the above quadruple enzymes). Our results indicated that over-expression of the quadruple enzymes redistributed metabolic flux to γ-PGA synthesis. Furthermore, using crude glycerol as carbon source, the BC4 strain showed a high productivity of 0.38 g/L/h, and produced 18.41 g/L γ-PGA, with a high yield of 0.46 g γ-PGA/g glycerol. Conclusions The approach to rewiring of metabolic pathways enables B. licheniformis to efficiently synthesize γ-PGA from glycerol. The γ-PGA productivity reported in this work is the highest obtained in glutamate-free medium. The present study demonstrates that the recombinant B. licheniformis strain shows significant potential to produce valuable compounds from crude glycerol.

In the distributed power system with more sources, the application of multiple-input converter instead of using several single-input converters can simplify the circuit , decrease the cost of the power system, but also improve the reliability of the power system. A new double-input Sepic DC/DC converter is proposed in this paper. The proposed topology has the following advantages: the circuit is simple，the voltage gain is high, the voltage stress of the switches is low and the sources can deliver power to the load individually or simultaneously. The operation modes and the steady-state analysis are presented. Finally, the MATLAB simulation and experiments on an output power 200 W prototype verify the correctness of the theoretical analysis.

Abstract Objective To explore the precise mechanism of electroacupuncture (EA) to delay cognitive impairment in Alzheimer disease. Methods N-Acetylaspartate (NAA), glutamate (Glu) and myoinositol (mI) metabolism were measured by magnetic resonance spectroscopy, learning and memory of APP/PS1 mouse was evaluated by the Morris water maze test and the step-down avoidance test, neuron survival number and neuronal structure in the hippocampus were observed by Nissl staining, and BDNF and phosphorylated TrkB detected by Western blot. Results EA at DU20 acupuncture significantly improve learning and memory in behavioral tests, up-regulate NAA, Glu and mI metabolism, increase the surviving neurons in hippocampus, and promote the expression of BDNF and TrkB in the APP/PS1 transgenic mice. Conclusion These findings suggested that EA is a potential therapeutic for ameliorate cognitive dysfunction, and it might be due to EA could improve NAA and Glu metabolism by upregulation of BDNF in APP/PS1 mice.

Abstract When argon is used as a protecting gas in the fabrication or working environment of a nanodevice, absorption of some argon atoms onto the surface of the device lead to different responses. In this work, the rotation of the rotor in a carbon nanotube (CNT)-based rotary nanomotor in argon environment is investigated. In the rotary nanomotor, two outer CNTs act as the stator and are used to constrain the inner CNT (i.e., the rotor). The rotor is driven to rotate by the stator due to their collision during thermal vibration of their atoms. A stable rotational frequency (SRF) of the rotor occurs when the rotor reaches a dynamic equilibrium state. The value of the SRF decreases exponentially with an increase in the initial argon density. At dynamic equilibrium date, some of the argon atoms rotate synchronously with the rotor when they are absorbed onto either internal or external surface of the rotor. The interaction between the rest of the argon atoms and the rotor is stronger at higher densities of argon, resulting in lower values of the SRF. These principles provide insight for future experimentation and fabrication of such rotary nanomotor.

Population based search techniques have been developed and applied to wide applications for their good performance, such as the optimization of the unmanned aerial vehicle (UAV) path planning problems. However, the search for optimal solutions for an optimization problem is usually expensive. For example, the UAV problem is a large-scale optimization problem with many constraints, which makes it hard to get exact solutions. Especially, it will be time-consuming when multiple UAV problems are waiting to be optimized at the same time. Evolutionary multi-task optimization (EMTO) studies the problem of utilizing the population-based characteristics of evolutionary computation techniques to optimize multiple optimization problems simultaneously, for the purpose of further improving the overall performance of resolving all these problems. EMTO has great potential in solving real-world problems more efficiently. Therefore, in this paper, we develop a novel EMTO algorithm using a classical PSO algorithm, in which the developed knowledge transfer strategy achieves knowledge transfer between task by synthesizing the transferred knowledges from a selected set of component tasks during the updating of the velocities of population. Two knowledge transfer strategies are developed along with two versions of the proposed algorithm. The proposed algorithm is compared with the multifactorial PSO algorithm, the SREMTO algorithm, the popular multifactorial evolutionary algorithm and a classical PSO algorithm on nine popular single-objective MTO problems and six five-task MTO problems, which demonstrates its superiority.

Deep neural networks have achieved significant development and wide applications for their amazing performance. However, their complex structure, high computation and storage resource limit their applications in mobile or embedding devices such as sensor platforms. Neural network pruning is an efficient way to design a lightweight model from a well-trained complex deep neural network. In this paper, we propose an evolutionary multi-objective one-shot filter pruning method for designing a lightweight convolutional neural network. Firstly, unlike some famous iterative pruning methods, a one-shot pruning framework only needs to perform filter pruning and model fine-tuning once. Moreover, we built a constraint multi-objective filter pruning problem in which two objectives represent the filter pruning ratio and the accuracy of the pruned convolutional neural network, respectively. A non-dominated sorting-based evolutionary multi-objective algorithm was used to solve the filter pruning problem, and it provides a set of Pareto solutions which consists of a series of different trade-off pruned models. Finally, some models are uniformly selected from the set of Pareto solutions to be fine-tuned as the output of our method. The effectiveness of our method was demonstrated in experimental studies on four designed models, LeNet and AlexNet. Our method can prune over 85%, 82%, 75%, 65%, 91% and 68% filters with little accuracy loss on four designed models, LeNet and AlexNet, respectively.

Dimensionality reduction (DR) plays an important role in hyperspectral image (HSI) classification. Unsupervised DR (uDR) is more practical due to the difficulty of obtaining class labels and their scarcity for HSIs. However, many existing uDR algorithms lack the comprehensive exploration of spectral-locational-spatial (SLS) information, which is of great significance for uDR in view of the complex intrinsic structure in HSIs. To address this issue, two uDR methods called SLS structure preserving projection (SLSSPP) and SLS reconstruction preserving embedding (SLSRPE) are proposed. Firstly, to facilitate the extraction of SLS information, a weighted spectral-locational (wSL) datum is generated to break the locality of spatial information extraction. Then, a new SLS distance (SLSD) excavating the SLS relationships among samples is designed to select effective SLS neighbors. In SLSSPP, a new uDR model that includes a SLS adjacency graph based on SLSD and a cluster centroid adjacency graph based on wSL data is proposed, which compresses intraclass samples and approximately separates interclass samples in an unsupervised manner. Meanwhile, in SLSRPE, for preserving the SLS relationship among target pixels and their nearest neighbors, a new SLS reconstruction weight was defined to obtain the more discriminative projection. Experimental results on the Indian Pines, Pavia University and Salinas datasets demonstrate that, through KNN and SVM classifiers with different classification conditions, the classification accuracies of SLSSPP and SLSRPE are approximately 4.88%, 4.15%, 2.51%, and 2.30%, 5.31%, 2.41% higher than that of the state-of-the-art DR algorithms.

Abstract In a short nanobearing system made from carbon nanotubes, the rotor with high rotational frequency may escape from the stator, which may cause a stability problem to the system of a nanodevice with such a nanobearing. In the present work, nanobearings with tri-walled nanotubes are investigated to reveal the conditions for the moving away of the free inner tube from the high-speed rotating middle tube. Experimental results show that the escape happens when the radii difference between the two rotors is larger than 0.34 nm and the rotational frequency of the middle tube is higher than a critical value. And before the escape occurs, the rotational frequency of the inner tube is lower than this critical value. Due to the radii difference being larger than 0.34 nm, the two rotors are non-coaxial, and the centrifugal force of the inner tube results in strong radial and axial interactions between the edges of the two rotors. When the relative sliding speed is relatively high, an edge of the inner rotor will pass through the potential barrier at the adjacent edge of the middle rotor, and further escape from the middle rotor occurs. The selection of a longer middle rotor with smaller radius can increase the critical rotational frequency of the middle rotor.

Functional magnetic resonance imaging has been widely used to investigate the effects of acupuncture on neural activity. However, most functional magnetic resonance imaging studies have focused on acute changes in brain activation induced by acupuncture. Thus, the time course of the therapeutic effects of acupuncture remains unclear. In this study, 32 patients with amnestic mild cognitive impairment were randomly divided into two groups, where they received either Tiaoshen Yizhi acupuncture or sham acupoint acupuncture. The needles were either twirled at Tiaoshen Yizhi acupoints, including Sishencong (EX-HN1), Yintang (EX-HN3), Neiguan (PC6), Taixi (KI3), Fenglong (ST40), and Taichong (LR3), or at related sham acupoints at a depth of approximately 15 mm, an angle of ± 60°, and a rate of approximately 120 times per minute. Acupuncture was conducted for 4 consecutive weeks, five times per week, on weekdays. Resting-state functional magnetic resonance imaging indicated that connections between cognition-related regions such as the insula, dorsolateral prefrontal cortex, hippocampus, thalamus, inferior parietal lobule, and anterior cingulate cortex increased after acupuncture at Tiaoshen Yizhi acupoints. The insula, dorsolateral prefrontal cortex, and hippocampus acted as central brain hubs. Patients in the Tiaoshen Yizhi group exhibited improved cognitive performance after acupuncture. In the sham acupoint acupuncture group, connections between brain regions were dispersed, and we found no differences in cognitive function following the treatment. These results indicate that acupuncture at Tiaoshen Yizhi acupoints can regulate brain networks by increasing connectivity between cognition-related regions, thereby improving cognitive function in patients with mild cognitive impairment.

Due to the superior spatial–spectral extraction capability of the convolutional neural network (CNN), CNN shows great potential in dimensionality reduction (DR) of hyperspectral images (HSIs). However, most CNN-based methods are supervised while the class labels of HSIs are limited and difficult to obtain. While a few unsupervised CNN-based methods have been proposed recently, they always focus on data reconstruction and are lacking in the exploration of discriminability which is usually the primary goal of DR. To address these issues, we propose a deep fully convolutional embedding network (DFCEN), which not only considers data reconstruction but also introduces the specific learning task of enhancing feature discriminability. DFCEN has an end-to-end symmetric network structure that is the key for unsupervised learning. Moreover, a novel objective function containing two terms—the reconstruction term and the embedding term of a specific task—is established to supervise the learning of DFCEN towards improving the completeness and discriminability of low-dimensional data. In particular, the specific task is designed to explore and preserve relationships among samples in HSIs. Besides, due to the limited training samples, inherent complexity and the presence of noise in HSIs, a preprocessing where a few noise spectral bands are removed is adopted to improve the effectiveness of unsupervised DFCEN. Experimental results on three well-known hyperspectral datasets and two classifiers illustrate that the low dimensional features of DFCEN are highly separable and DFCEN has promising classification performance compared with other DR methods.

Deep neural networks have evolved significantly in the past decades and are now able to achieve better progression of sensor data. Nonetheless, most of the deep models verify the ruling maxim in deep learning—bigger is better—so they have very complex structures. As the models become more complex, the computational complexity and resource consumption of these deep models are increasing significantly, making them difficult to perform on resource-limited platforms, such as sensor platforms. In this paper, we observe that different layers often have different pruning requirements, and propose a differential evolutionary layer-wise weight pruning method. Firstly, the pruning sensitivity of each layer is analyzed, and then the network is compressed by iterating the weight pruning process. Unlike some other methods that deal with pruning ratio by greedy ways or statistical analysis, we establish an optimization model to find the optimal pruning sensitivity set for each layer. Differential evolution is an effective method based on population optimization which can be used to address this task. Furthermore, we adopt a strategy to recovery some of the removed connections to increase the capacity of the pruned model during the fine-tuning phase. The effectiveness of our method has been demonstrated in experimental studies. Our method compresses the number of weight parameters in LeNet-300-100, LeNet-5, AlexNet and VGG16 by 24×, 14×, 29× and 12×, respectively.

A concentric twin tube (CTT) can be built by placing a carbon nanotube (CNT) in another identical CNT. Different from diamondene nanotubes, a stable CTT has no inter-shell covalent bond. As a prestressed double-walled nanotube, CTT has a lower structural stability at a finite temperature. According to the molecular dynamics and first-principle calculations, (a) CTTs have three types of relaxed configurations. In a type III CTT, the inner tube buckles to produce a V-shaped cross-section, and the outer tube may be convex or concave. (b) The minimal radii of relaxed zigzag and armchair CTTs with concave outer tubes were found. (c) After relaxation, the circumferences and areas of the two tubes in a type III CTT are different from those of the corresponding ideal CNT. The area change rate (A-CR) and circumference change rate (C-CR) of the outer tube are the first-order Gaussian function of the radius of the ideal CNT (which forms the CTT), and tends to be 73.3% of A-CR or 95.3% of C-CR, respectively. For the inner tube of a CTT, the A-CR is between 29.3% and 37.0%, and the C-CR is close to 95.8%. (d) The temperature slightly influences the findings given above.

It was discovered that large-amplitude axial oscillation can occur on a rotor with an internally hydrogenated deformable part (HDP) in a rotation–translation nanoconverter. The dynamic outputs of the system were investigated using molecular dynamics simulations. When an input rotational frequency (100 GHz > ω > 20 GHz) was applied at one end of the rotor, the HDP deformed under the centrifugal and van der Waals forces, which simultaneously led to the axial translation of the other end of the rotor. Except at too high an input rotational frequency (e.g., >100 GHz), which led to eccentric rotation and even collapse of the system, the present system could generate a periodic axial oscillation with an amplitude above 0.5 nm at a temperature below 50 K. In other ranges of temperature and amplitude, the oscillation dampened quickly due to the drastic thermal vibrations of the atoms. Furthermore, the effects of the hydrogenation scheme and the length of HDP on the equilibrium position, amplitude, and frequency of oscillation were investigated. The conclusions can be applied to the design of an ideal nano-oscillator based on the present rotation–translation converter model.

Active contour models are always designed on the assumption that images are approximated by regions with piecewise-constant intensities. This assumption, however, cannot be satisfied when describing intensity inhomogeneous images which frequently occur in real world images and induced considerable difficulties in image segmentation. A milder assumption that the image is statistically homogeneous within different local regions may better suit real world images. By taking local image information into consideration, an enhanced active contour model is proposed to overcome difficulties caused by intensity inhomogeneity. In addition, according to curve evolution theory, only the region near contour boundaries is supposed to be evolved in each iteration. We try to detect the regions near contour boundaries adaptively for satisfying the requirement of curve evolution theory. In the proposed method, pixels within a selected region near contour boundaries have the opportunity to be updated in each iteration, which enables the contour to be evolved gradually. Experimental results on synthetic and real world images demonstrate the advantages of the proposed model when dealing with intensity inhomogeneity images.

How to maintain the population diversity is an important issue in designing a multiobjective evolutionary algorithm. This paper presents an enhanced nondominated neighbor-based immune algorithm in which a multipopulation coevolutionary strategy is introduced for improving the population diversity. In the proposed algorithm, subpopulations evolve independently; thus the unique characteristics of each subpopulation can be effectively maintained, and the diversity of the entire population is effectively increased. Besides, the dynamic information of multiple subpopulations is obtained with the help of the designed cooperation operator which reflects a mutually beneficial relationship among subpopulations. Subpopulations gain the opportunity to exchange information, thereby expanding the search range of the entire population. Subpopulations make use of the reference experience from each other, thereby improving the efficiency of evolutionary search. Compared with several state-of-the-art multiobjective evolutionary algorithms on well-known and frequently used multiobjective and many-objective problems, the proposed algorithm achieves comparable results in terms of convergence, diversity metrics, and running time on most test problems.

Lactylation has been recently identified as a new type of posttranslational modification occurring widely on lysine residues of both histone and nonhistone proteins. The acetyltransferase p300 is thought to mediate protein lactylation, yet the cellular concentration of the proposed lactyl-donor, lactyl-coenzyme A, is about 1,000 times lower than that of acetyl-CoA, raising the question of whether p300 is a genuine lactyltransferase. Here, we report that alanyl-tRNA synthetase 1 (AARS1) moonlights as a bona fide lactyltransferase that directly uses lactate and ATP to catalyze protein lactylation. Among the candidate substrates, we focused on the Hippo pathway, which has a well-established role in tumorigenesis. Specifically, AARS1 was found to sense intracellular lactate and translocate into the nucleus to lactylate and activate the YAP-TEAD complex; and AARS1 itself was identified as a Hippo target gene that forms a positive-feedback loop with YAP-TEAD to promote gastric cancer (GC) cell proliferation. Consistently, the expression of AARS1 was found to be upregulated in GC, and elevated AARS1 expression was found to be associated with poor prognosis for patients with GC. Collectively, this work found AARS1 with lactyltransferase activity in vitro and in vivo and revealed how the metabolite lactate is translated into a signal of cell proliferation., Introduction Lactate is a well-known metabolite found in almost all types of cells, and is highly abundant in proliferating tumor cells owing to the Warburg effect (1, 2). Despite many [...]