Weak Ability Research Articles

SEMG Signal-Based Human Lower-Limb Intention Recognition Algorithm Using Improved Extreme Learning Machine

To address the difficulty in identifying human lower-limb movement intentions, low accuracy of classification models, and weak generalization ability, this study proposes a motion intention recognition method that combines an improved pelican optimization algorithm (IPOA) and a hybrid kernel extreme learning machine (HKELM). First, we collect the surface electromyography (sEMG) signals of subjects in six motion modes and perform feature parameter extraction under non-ideal conditions. On this basis, we establish a dataset of the relationships between the feature parameters and gait movements. Second, we build a motion intention classification model based on relational data using the HKELM to solve the problems of low modeling accuracy and weak generalizability. Third, the IPOA is used to optimize the parameters related to the HKELM, and a differential evolution algorithm is introduced to improve the population quality and prevent the algorithm from falling into a local optimal solution. The experimental results show that the IPOA exhibits better optimization accuracy and convergence speed for four classical benchmark functions. Its average classification accuracy, average classification recall, and average F-value are 94.45%, 94.47%, and 94.46%, respectively, which are significantly higher than those of other intention recognition algorithms. Therefore, the proposed method has high classification accuracy and generalization performance.

Crowd counting in domain generalization based on multi-scale attention and hierarchy level enhancement

In order to solve the problem of weak single domain generalization ability in existing crowd counting methods, this study proposes a new crowd counting framework called Multi-scale Attention and Hierarchy level Enhancement (MAHE). Firstly, the model can focus on both the detailed features and the macro information of structural position changes through the fusion of channel attention and spatial attention. Secondly, the addition of multi-head attention feature module facilitates the model’s capacity to effectively capture complex dependency relationships between sequence elements. In addition, the three-stage encoding and decoding processing mode enables the model to effectively represent crowd density information. Finally, the fusion of multi-scale features derived from different receptive fields is further enhanced through multi-scale hierarchy level feature fusion, thereby enabling the model to learn high-level semantic information and low-level multi-scale visual field feature information. This method enhances the model’s capacity to capture key feature information, even in highly differentiated datasets, thereby improving the model’s generalization ability on a single domain. The model has demonstrated strong generalization capabilities through extensive experiments on different datasets. This study not only improves the accuracy of crowd counting, but also introduces a new research approach for single domain generalization of crowd counting.

Chromatin Immunoprecipitation for Standard, Rare, or Weakly Binding Proteins.

Various proteins interact with specific genome regions, playing crucial roles in gene regulation. Chromatin Immunoprecipitation (ChIP) is the most commonly used method to study protein-DNA interactions in vivo. By combining ChIP with high-throughput sequencing, ChIP-seq allows for studying the genome-wide localization of proteins. Although several ChIP protocols are available for plant tissues, they are primarily designed for histone modifications and abundant proteins with high DNA-binding affinity, which are considered as the "standard targets." Here we describe a ChIP protocol for plant tissues not only optimized for the standard targets but also adapted for proteins with low abundances or weak DNA-binding ability. Successful execution of the protocol enables reliable generation of DNA templates for quantitative PCR or libraries for next-generation sequencing, which makes it an effective tool for analyzing genomic interactions of a wide range of proteins.

Recent progress towards the development of fluorescent probes for the detection of disease-related enzymes.

Normal physiological functions as well as regulatory mechanisms for various pathological conditions depend on the activity of enzymes. Thus, determining the in vivo activity of enzymes is crucial for monitoring the physiological metabolism and diagnosis of diseases. Traditional enzyme detection methods are inefficient for in vivo detection, which have different limitations, such as high cost, laborious, and inevitable invasive procedures, low spatio-temporal resolution, weak anti-interference ability, and restricted scope of application. Because of its non-destructive nature, ultra-environmental sensitivity, and high spatiotemporal resolution, fluorescence imaging technology has emerged as a potent tool for the real-time visualization of live cells, thereby imaging the motility of proteins and intracellular signalling networks in tissues and cells and evaluating the binding and attraction of molecules. In the last few years, significant advancements have been achieved in detecting and imaging enzymes in biological systems. In this regard, the high sensitivity and unparalleled spatiotemporal resolution of fluorescent probes in association with confocal microscopy have garnered significant interest. In this review, we focus on providing a concise summary of the latest developments in the design of fluorogenic probes used for monitoring disease-associated enzymes and their application in biological imaging. We anticipate that this study will attract considerable attention among researchers in the relevant field, encouraging them to pursue advances in the development and application of fluorescent probes for the real-time monitoring of enzyme activity in live cells and in vivo models while ensuring excellent biocompatibility.

Dodecylphosphonic acid for the separation of spodumene and albite: flotation behavior and adsorption mechanism

Spodumene is an important lithium-bearing mineral, that is associated with albite and difficult to separate because of their similar surface chemical properties. Traditional fatty acid collectors often have issues, such as poor selectivity or weak collector ability, and thus developing efficient spodumene collectors is essential. In this study, the phosphonic acid surfactant dodecylphosphonic acid (DDPA) was first used as a single collector for the flotation separation of spodumene and albite, showing good selectivity and collection performance. Then, separation and adsorption mechanisms were studied using various characterization methods, including diffuse reflectance infrared Fourier transform spectroscopy, total organic carbon adsorption measurements, X-ray photoelectron spectroscopy, and zeta potential tests. Flotation tests showed that the recovery for spodumene can reach approximately 80% through single-mineral flotation under the following conditions: activation without metal ion, neutral pulp pH of 6.5–7.0, and DDPA dosage of 4 × 10−4 mol/L. By contrast, the recovery of albite was only approximately 10%. The recovery for Li2O in the artificial mixed ore test was 75.03%, and the grade was 6.30%. The separation of spodumene and albite was due to the selective adsorption of DDPA on the surface of spodumene, and more DDPA were adsorbed on the surface of spodumene than on albite under the same conditions. Adsorption mechanism analysis indicated that Al and O on the mineral surface were the active sites for DDPA adsorption. Combined with the species distribution, DDPA existed in various forms in different aqueous environments, indicating that DDPA was mainly adsorbed onto the mineral surface through chemisorption and hydrogen bonding. In summary, DDPA can be used as an effective collector for spodumene.

Rapid adaptive regulation of systemic circulation is suppressed in pulmonary

Pulmonary arterial hypertension (PAH) is characterized by an increase of a pressure in the pulmonary circulation; PAH is accompanied by activation of the sympathetic (SNS) and the renin-angiotensin-aldosterone system (RAAS). However, PAH-associated changes in baroreceptor regulation of systemic circulation, which is tightly interwoven with SNS and RAAS, have not been studied. The baroreceptor response (BRR) was studied in a chronic monocrotaline (MCT) model of PAH in rats (Wistar, 290 ± 30 g, 2–4 months). Phenylephrine as an agonist of α1-adrenergic receptor and sodium nitroprusside as NO donor were gradually administered to chronically catheterized, non-anesthetized control animals and animals with PAH (4 weeks after MCT administration) to induce vasomotor responses. Mean arterial pressure and heart rate (HR) were recorded under the action of vasoactive compounds alone or under the action of vasoactive compounds in presence of angiotensin-II (ATII), atropine. The parameters characterizing baroreceptor change in HR including maximal and minimal heart rate (HRmax, HRmin), reflex tachycardia (TBRR) and bradycardia (BBRR), range (ABBR) and the baroreceptor response sensitivity index (SIBRR) were calculated. A significant decrease in HRmax, TBRR, ABBR (but not BBRR), as well as the sensitivity index of BRR was observed in rats with PAH. ATII induces significant and different changes in the BRR parameters in control rats and in rats with PAH if administered 4 weeks after the start of the experiment. In rats with PAH, ATII causes less pronounced changes in HRmax, TBRR, and BBRR than in control animals. ATII insignificantly affects parasympathetic component of the baroreceptor reflex in rats with PAH. Thus, at least in the MCT-mediated model in rats, PAH significantly deteriorates the baroreceptor regulation of HR. This effect manifests in a decrease in the range and sensitivity of the baroreceptor response. Also, PAH unequally affects the sympathetic and parasympathetic control of the baroreceptor regulation of HR. On the other hand, ATII exhibits weak ability to alter BRR in rats with HAP. In conclusion, PAH leads to a disfunction of immediate, reflex mechanisms HR and systemic circulation control.

The CD4 T cell-independent IgG response during persistent virus infection favors emergence of neutralization-escape variants.

How changes in the quality of anti-viral antibody (Ab) responses due to pre-existing or acquired CD4 T cell insufficiency affect virus evolution during persistent infection are unknown. Using mouse polyomavirus (MuPyV), we found that CD4 T cell depletion before infection results in short-lived plasma cells secreting low-avidity antiviral IgG with limited BCR diversity and weak virus-neutralizing ability. CD4 T cell deficiency during persistent infection incurs a shift from a T-dependent (TD) to T-independent (TI) Ab response, resembling the pre-existing TI Ab response. CD4 T cell loss before infection or during persistent infection is conducive for emergence of Ab-escape variants. Cryo-EM reconstruction of complexes of MuPyV virions with polyclonal IgG directly from infected mice with pre-existing or acquired CD4 T cell deficiency enabled visualization of shortfalls in TI IgG binding. By debilitating the antiviral IgG response, CD4 T cell deficiency sets the stage for outgrowth of variant viruses resistant to neutralization.

Improved artificial rabbits algorithm for global optimization and multi-level thresholding color image segmentation

The Artificial Rabbits Optimization Algorithm is a metaheuristic optimization algorithm proposed in 2022. This algorithm has weak local search ability, which can easily lead to the algorithm falling into local optimal solutions. To overcome these limitations, this paper introduces an Improved Artificial Rabbits Optimization Algorithm (IARO) and demonstrates its effectiveness in multi-level threshold color image segmentation using the Otsu method. Initially, we apply a center-driven strategy to enhance exploration by updating the rabbit’s position during the random hiding phase. Additionally, when the algorithm stalls, a Gaussian Randomized Wandering (GRW) strategy is utilized to enable the algorithm to escape local optima and improve convergence accuracy. The performance of the IARO algorithm is evaluated using 23 standard benchmark functions and CEC2020 benchmark functions, and compared with nine other algorithms. Experimental results indicate that IARO excels in global optimization and demonstrates notable robustness. To assess its effectiveness in multi-threshold color image segmentation, the algorithm is tested on classical Berkeley images. Evaluation metrics including execution time, Peak Signal-to-Noise Ratio (PSNR), Feature Similarity (FSIM), Structural Similarity (SSIM), Boundary Displacement Error (BDE), The Probabilistic Rand Index (PRI), Variation of Information (VOI) and average fitness value are used to measure segmentation quality. The results reveal that IARO achieves high accuracy and fast segmentation speed, validating its efficiency and practical utility in real-world applications.

The effect of workability-related factors in patients with end-stage kidney disease undergoing hemodialysis

BackgroundThe disability of patients with end-stage kidney disease (ESKD) and the possibility of reducing the ability to work for patients who are receiving hemodialysis require extensive investigations worldwide. In this regard, we aimed to investigate employment status and its effect on a large group of work ability-related factors in these patients.MethodsA total of 191 patients with ESKD who were referred to the dialysis department of Guilan Educational and Medical Centers, Rasht, Iran, in 2023 participated. The demographic and occupational data, clinical characteristics, and laboratory findings of the patients were recorded. A work ability index questionnaire was used to record the ability to work.ResultsAccording to the results, 37.7% of people undergoing hemodialysis were employed, 45.4% of those who were not employed, lost their jobs before, and 54.6% lost their jobs after starting hemodialysis. Patients with lower values of work ability index found to be significantly older, illiterate, with lower job satisfaction and high frequency of absence from their job. Also, they were unemployed individuals with high rates of disability and no history of job change (P < 0.05 for all).Additionally, current unemployment, history of job changes, and packed red blood cell transfusion were predictive variables of the ability to work in hemodialysis patients (P < 0.001, P = 0.046, P = 0.046).ConclusionsOur results illustrated that the employment rate is low among patients with ESKD even before starting hemodialysis. Patients with higher age, less education, disability and anemia are at higher risk of weak ability to work.

Adaptive Ramanujan package decomposition method: a novel precise health indicator of rolling bearings

ABSTRACT In this paper, an adaptive Ramanujan packet decomposition (ARPD) method based on precise envelope segmentation is proposed to improve the accuracy of weak fault feature extraction and realise early non-destructive testing and condition evaluation of rolling bearings. Firstly, the power spectral density of the original signal is envelope-processed by accurate envelope segmentation method, so as to reduce the extreme points produced by interference frequency, and then realise the optimal frequency band division and reduce the influence of noise components. Secondly, the Ramanujan spectrum signal-to-noise ratio (RSSNR) index is introduced to quantify the fault characteristic information of the corresponding component in each frequency band. The largest component of the index is selected as the optimal mode, and the current number of layers is defined as the optimal number of modes. Finally, through the idea of iterative decomposition and RSSNR index, the problem that the optimal mode number is difficult to determine accurately is eliminated, and the optimal mode number is determined adaptively. The results of simulation and experimental signal analysis show that ARPD method can accurately divide the frequency bands and has excellent weak fault feature extraction ability, so it is an effective early non-destructive testing and condition evaluation method for rolling bearings.

D3-YOLOv10: Improved YOLOv10-BasedLightweight Tomato Detection Algorithm Under Facility Scenario

Accurate and efficient tomato detection is one of the key techniques for intelligent automatic picking in the area of precision agriculture. However, under the facility scenario, existing detection algorithms still have challenging problems such as weak feature extraction ability for occlusion conditions and different fruit sizes, low accuracy on edge location, and heavy model parameters. To address these problems, this paper proposed D3-YOLOv10, a lightweight YOLOv10-based detection framework. Initially, a compact dynamic faster network (DyFasterNet) was developed, where multiple adaptive convolution kernels are aggregated to extract local effective features for fruit size adaption. Additionally, the deformable large kernel attention mechanism (D-LKA) was designed for the terminal phase of the neck network by adaptively adjusting the receptive field to focus on irregular tomato deformations and occlusions. Then, to further improve detection boundary accuracy and convergence, a dynamic FM-WIoU regression loss with a scaling factor was proposed. Finally, a knowledge distillation scheme using semantic frequency prompts was developed to optimize the model for lightweight deployment in practical applications. We evaluated the proposed framework using a self-made tomato dataset and designed a two-stage category balancing method based on diffusion models to address the sample class-imbalanced issue. The experimental results demonstrated that the D3-YOLOv10 model achieved an mAP0.5 of 91.8%, with a substantial reduction of 54.0% in parameters and 64.9% in FLOPs, compared to the benchmark model. Meanwhile, the detection speed of 80.1 FPS more effectively meets the demand for real-time tomato detection. This study can effectively contribute to the advancement of smart agriculture research on the detection of fruit targets.

Research on Fusion Model Method for Corrosion Damage Detection of Switch Sliding Baseplate

As the core component of railways, the switch sliding baseplate has a bad operating environment, and its surface is prone to corrosion. Existing methods, including traditional methods, ultrasonic detection, and image processing, have difficulty in extracting corrosion features and being applied in practice. To solve the above problems, the Residual Neural Network 50 (ResNet50) model, a deep learning model, is introduced in this paper. To solve the problems of gradient explosion and weak corrosion in the model, a new fusion model, VGG-ResNet50-corrosion (VGGRES50_Corrosion), is proposed in this paper. First of all, for the problem that there is no public dataset, this study conducts a neutral salt spray corrosion test and collects the image features and corrosion depth parameters of skateboard corrosion in different time periods as the dataset to test the performance of the model. Then, corrosion thickness is introduced as a modified variable in the ResNet50 network, and a new network, VGGRES50_Corrosion, is introduced by blending the improved model with the Visual Geometry Group-16 (VGG16) network through a model fusion strategy. Finally, a model test and ultrasonic contrast test are designed to verify the performance of the model. In the model test, the recognition accuracy of the fusion model is 98.98% higher than that of other models, which effectively solves the shortcoming of the gradient explosion's weak generalization ability under a small sample model. In the ultrasonic comparison experiment, the mean relative errors of this method and ultrasonic detection method are 4.08% and 46.14%, respectively, and the mean square errors are 1.86 h and 15.01 h, respectively. The prediction result of deep learning is better than that of ultrasonic piecewise linear fitting. It has been proved that VGGRES50_Corrosion can identify the degree of corrosion of slip switches more effectively, and it has great significance in improving the corrosion detection efficiency of slip switches.

Two-Dimensional Rare-Earth Metal Phosphides: From Weyl Semimetal to Semiconductor.

Two-dimensional (2D) nanomaterials have garnered extensive attention owing to their unique properties and versatile application. Here, a family of 2D rare-earth metal phosphides (M2P, M = Sc, Y, La) and their derivatives M2POT (T = F, OH) is developed to find their topological and electronic properties on the basis of density functional theory simulations. We show that the 2D M2P compounds are most possibly obtained from thermodynamically stable M2InP by chemical exfoliation. The In with a substantial atomic radius of 156 pm exhibits weak polarization ability, resulting in homogeneity of the electron cloud and a weakening of the M-In bond relative to the M-P bond. Upon exfoliation of the In layer, the M22+P3-:e- emerges as an electride with surface electrons, which is attributed to the larger ion radius and lower electronegativity of M2+ ions in M2P. The metallic M2P is found to be a Weyl semimetal derived from the contribution of surface electrons. Further, by leveraging the high reactivity of surface electrons, surface functionalization can produce M2POT compounds with the increased valence state of M3+, which results in their semiconducting properties characterized by high carrier mobilities and strong built-in electronic fields. These distinct topological and electronic characteristics position the 2D M2P and M2POT as promising candidates for a wide range of applications.

Two new species of Late Devonian seed Cosmosperma from Zhejiang and Anhui, South China

AbstractThe earliest ovules or seeds widely reported from the Late Devonian (Famennian) are crucial for understanding the evolution of seed plants. Cosmosperma, the first Devonian seed documented in China and East Asia, is characterized by cupules with multiple segments enclosing a single ovule and covered by prickles. Two new Cosmosperma species, Cosmosperma dicrana sp. nov. and Cosmosperma lepta sp. nov., are now identified from the Upper Devonian (Wutong Formation) of Zhejiang and Anhui provinces, China, respectively. C. dicrana exhibits dichotomized fertile branches terminating in pairs of cupulate ovules, with centrifugally extending cupule segments at their distal portions. C. lepta displays slender cupulate ovules on pinnate fertile branches. Both species possess prickles on their cupules. A comparison with coeval seed plant taxa reveals differences in fertile branch types and suggests different evolutionary levels. The potentially weak dispersal ability of Cosmosperma is suggested by the absence of specialized structures for wind or water transport. Prickles on Cosmosperma cupules do not suggest adaptations for epizoochory but facilitate entanglement and friction. The adaptations for short‐distance dispersal can be advantageous in a disturbed and heterogeneous environment. The presence of Late Devonian seed plant taxa with dispersal‐adapted traits hints at habitat divergence, with some possibly inhabiting uplands or areas away from the coast. The prickles may also be an adaptation for maintaining stability in small‐ to medium‐sized plants of the Late Devonian coastal communities.

Organic modes of occurrence and evolution mechanism of germanium and lithium in coal: Insights from density functional theory

Critical elements in coal, such as Ge and Li, recently have attracted attention due to their economic significance. Modes of occurrence of these critical elements are academically and practically important, because they can not only provide evidence for sources of elements and minerals in coal and regional geological background information, but also help with design of recovery methods. However, conventional analytical methods are unable to observe precisely the organic binding sites of Ge and Li in coal, and this limits the understanding of their enrichment mechanism and the improvements for recovery techniques. In this study, organic modes of occurrence and evolution mechanism of Ge4+ and Li+ were investigated at the atom level by constructing molecular models of Ge- and Li-rich coals combined with density functional theory (DFT) methods. The solid-state 13C nuclear magnetic resonance spectroscopy (NMR), X-ray photoelectron spectroscopy (XPS), Fourier transform infrared spectroscopy (FTIR), and helium pycnometry analyses indicate that the molecular formulas of Ge- and Li-rich coals are C166H162N2O32 and C153H174N2O24S, respectively. The DFT analysis reveals that the binding sites for Ge4+ in Ge-rich coal are located near the carboxyl group (-COOH) and the pyrrole ring, while those for Li+ in Li-rich coal are near the carbonyl group (-C=O) and the pyrrole ring. The Ge4+ is immobilized in the Ge-rich coal molecular model through coordination bonds with the O atom in the -COOH and the C atom in the pyrrole ring, while being away from the N atom in the pyrrole ring. Li+ forms a coordination bond with the O atom in the -C=O and additional coordination bond with the nearby hydroxyl group during binding to the pyrrole ring. The impact of coal rank on the organic modes of occurrence of Ge4+ and Li+ was investigated using Wender coals of different ranks, which were one of the earliest proposed coal models. At the lignite stage, oxygen-containing functional groups and aromatic rings show a strong binding ability to Ge4+ and Li+, facilitating their enrichment in coals. Along with coal rank advance to bituminous coal, the reduction of oxygen-containing functional groups (e.g., -COOH and -C=O) and the relatively low condensation of aromatic rings decrease binding sites for Ge4+ and Li+, and the binding ability also decline, resulting in a decrease in their concentration. In anthracite stage, highly condensed aromatic rings provide binding sites for Ge4+ and Li+. The strong binding ability of aromatic rings to Ge4+ indicates that it is probably enriched in anthracite, whereas Li+ is difficult to enrich owing to its relatively weak binding ability to aromatic rings. The low content of oxygen-containing functional groups in anthracite reduces their effect on the organic modes of occurrence of Ge4+ and Li+. This study elucidates the organic modes of occurrence and evolution mechanism of Ge4+ and Li+ from the perspective of coal structure, providing fundamental insights for future research on the interaction between organic and inorganic matter within coal.

Wi-TCG: a WiFi gesture recognition method based on transfer learning and conditional generative adversarial networks

Abstract With the rapid progress of WiFi technology, WiFi-based wireless sensing technology has opened up new ways for contactless human-computer interaction. However, hand gesture recognition technology faces low-quality data sets, insufficient model robustness, poor universality when the application scenario changes, high training costs, and weak generalization ability. To this end, this study innovatively proposes the Wi-TCG method, which combines transfer learning and conditional generative adversarial network (CGAN) to optimize WiFi gesture recognition. This method uses commercial Wi-Fi devices to collect channel state information (CSI) of gesture actions. It innovatively extracts Doppler shift image data as the input of CGAN to generate virtual data with similar characteristics to expand the training sample set. The network is fine-tuned using transfer learning techniques to recognize multiple gesture action categories in different scenarios accurately. In tests of two new natural scenes and six new gesture categories, the Wi-TCG method achieved a high recognition accuracy of 93.1%, providing strong support for applying WiFi-based wireless sensing technology in contactless human-computer interaction.

Methane production related to microbiota in dairy cattle feces.

Methane (CH4) emission from livestock feces, led by ruminants, shows a profound impact on global warming. Despite this, we have almost no information on the syntrophy of the intact microbiome metabolisms, from carbohydrates to the one-carbon units, covering multiple stages of ruminant development. In this study, syntrophic effects of polysaccharide degradation and acetate-producing bacteria, and methanogenic archaea were revealed through metagenome-assembled genomes from water saturated dairy cattle feces. Although CH4 is thought to be produced by archaea, more edges, nodes, and balanced interaction types revealed by network analysis provided a closed bacteria-archaea network. The CH4 production potential and pathways were further evaluated through dynamic, thermodynamic and 13C stable isotope analysis. The powerful CH4 production potential benefited from the metabolic flux: classical polysaccharides, soluble sugar (glucose, galactose, lactose), acetate, and CH4 produced via typical acetoclastic methanogenesis. In comparison, a cooperative model dominated by hydrogenotrophic methanogenic archaea presented a weak ability to generate CH4. Our findings comprehensively link carbon and CH4 metabolism paradigm to specific microbial lineages which are shaped related to developmental stages of the dairy cattle, directing influencing global warming from livestock and waste treatment.

Клональное микроразмножение сортов земляники садовой (Fragaria х ananassa Duch.) нейтральнодневного типа плодоношения

Abstract. The article presents the results of the evaluation of the efficiency of clonal propagation of commercially promising day-neutral varieties of garden strawberry. Day-neutral strawberry varieties usually form fewer rosettes on a plant during the growing season than regular varieties, and therefore vegetative propagation is ineffective for these varieties. The aim of the study is to increase the efficiency of propagation of day-neutral varieties of garden strawberry (Fragaria x ananassa Duch.), characterized by weak runnerforming ability, by optimizing the process of microclonal propagation in vitro and subsequent adaptation of regenerated plants to ex vitro conditions. The article indicates the features of microclonal propagation technology of neutral-day strawberry varieties Vivara, Sweet Eve and Evis Delight. The studies were conducted in the laboratory of the Federal State Budget Scientific Institution NCFSСHVW in 2022-2023. MurashigeSkoog (MS) agar medium with the addition of plant growth regulator 6-BAP was used as the main medium (at the introduction stage – 6-BAP – 0.2 mg/l, at the micropropagation stage – 0.75 mg/l, hormone-free MS medium – control). As a result of the studies, it was found that the variety of garden strawberry Sweet Eve is characterized by a higher shoot-forming ability. At a 6-BAP concentration of 0.75 mg/l in the medium, an average of 10.5 new shoots are formed from one shoot in the third passage. Evis Delight variety showed a low shoot-forming capacity of up to 3.8 shoots/exp. After the fourth passage, even on a hormone-free medium, Sweet Eve variety showed an aftereffect of 6-BAP, with the formation of an average of 8.8 shoots from one explant. Such a strong aftereffect of 6-BAP was not noted in Vivara and Eve’s Delight varieties. Rooting began after the fifth passage on a hormone-free medium. The root formation efficiency was 95- 100%. At this stage, longer shoots up to 5.9 cm and roots up to 4.8 cm are formed in Vivara variety. Evis Delight variety stood out in terms of the number of leaves (an average of 9.1 pcs.) and roots (7.5 pcs.). The use of the biopreparation Agrinos 1 at the adaptation stage had a positive effect on the formation of the vegetative mass and root system of strawberry plants. The application of the preparation at a concentration of 5 ml/l promotes the growth of the root system, the length of which is 1.6-2.2 times greater than the length of the roots in the control variant. The number of leaves increases by 1.1-1.3 times, and the average height of the bushes is 1.5-1.7 times greater than the control values. Key words: clonal micropropagation, day-neutral strawberry, 6-benzylaminopurine, multiplication factor, microshoots

Antitumor effects of plasma‑activated solution on a murine melanoma model in vivo and in vitro.

Melanoma is a common malignant skin tumor with highly invasive features and a high metastasis rate that can be difficult to treat clinically. Large-scale resection of primary cutaneous melanoma is often used to avoid postoperative recurrence. For advanced patients, radiotherapy, targeted therapy and immunotherapy are often needed. Low-temperature plasma has been proved to have significant antitumor effects on a variety of cancer cell lines cultured in vitro. The main limitation of direct low-temperature plasma treatment is that it has weak penetration ability and can only treat superficial lesions. In recent years, research on low-temperature plasma-activated solution has revealed that it also have good antitumor effects and low-temperature plasma penetration depth problems can be solved by local injection. The present study revealed that low-temperature plasma-activated phosphate buffer solution exhibited good antitumor effects and biosafety against melanoma in vitro and in vivo. It demonstrated that low-temperature plasma-activated solution has antitumor effects due to its regulation of intracellular redox, destruction of mitochondrial function and DNA damage. In vivo experiments demonstrated that treatment with low-temperature plasma-activated solution not only exhibited antitumor effects but also caused no significant damage to hematopoietic function or liver and kidney functions in mice. All these results demonstrated that low-temperature plasma-activated solution represent a promising antitumor treatment strategy.

Don't think of a pink elephant: Individual differences in visualisation predict involuntary imagery and its neural correlates

There are substantial differences in the capacity of people to have imagined visual experiences, ranging from a lifelong inability (Congenital Aphantasia) to people who report having imagined experiences that are as vivid as actually seeing (Hyper-Phantasia). While Congenital Aphantasia has typically been framed as a cognitive deficit, it is possible that a weak or absent ability to have imagined visual sensations is balanced by a heightened resistance to intrusive thoughts – which are experienced as an imagined sensation. Here, we report on a direct test of that proposition. We asked people to either imagine, or to try not to imagine having a range of audio and visual experiences while we recorded their brain activity with electroencephalography (EEG). Ratings describing the subjective vividness of different people's voluntary visualisations predicted if they would also report having involuntary visualisations – such as an imagined experience of seeing a pink elephant when they were asked not to. Both the prevalence of different people's involuntary visualisations and the typical vividness of their visualisations could be predicted by neural correlates of disinhibition, working memory, and neural feedback. Our data suggest that the propensity of people to have involuntary visual experiences can scale with the subjective intensity of their typical experiences of visualisation.