High Consistency Research Articles

Information mismatch in PHH3-assisted mitosis annotation leads to interpretation shifts in H&E slide analysis

The count of mitotic figures (MFs) observed in hematoxylin and eosin (H&E)-stained slides is an important prognostic marker, as it is a measure for tumor cell proliferation. However, the identification of MFs has a known low inter-rater agreement. In a computer-aided setting, deep learning algorithms can help to mitigate this, but they require large amounts of annotated data for training and validation. Furthermore, label noise introduced during the annotation process may impede the algorithms’ performance. Unlike H&E, where identification of MFs is based mainly on morphological features, the mitosis-specific antibody phospho-histone H3 (PHH3) specifically highlights MFs. Counting MFs on slides stained against PHH3 leads to higher agreement among raters and has therefore recently been used as a ground truth for the annotation of MFs in H&E. However, as PHH3 facilitates the recognition of cells indistinguishable from H&E staining alone, the use of this ground truth could potentially introduce an interpretation shift and even label noise into the H&E-related dataset, impacting model performance. This study analyzes the impact of PHH3-assisted MF annotation on inter-rater reliability and object level agreement through an extensive multi-rater experiment. Subsequently, MF detectors, including a novel dual-stain detector, were evaluated on the resulting datasets to investigate the influence of PHH3-assisted labeling on the models’ performance. We found that the annotators’ object-level agreement significantly increased when using PHH3-assisted labeling (F1: 0.53 to 0.74). However, this enhancement in label consistency did not translate to improved performance for H&E-based detectors, neither during the training phase nor the evaluation phase. Conversely, the dual-stain detector was able to benefit from the higher consistency. This reveals an information mismatch between the H&E and PHH3-stained images as the cause of this effect, which renders PHH3-assisted annotations not well-aligned for use with H&E-based detectors. Based on our findings, we propose an improved PHH3-assisted labeling procedure.

Trajectory Tracking Control of Unmanned Vehicles via Front-Wheel Driving

Automated Guided Vehicles (AGVs) are the fastest commercially available application of unmanned driving technology, and the research significance of unmanned vehicle technology remains substantial. This paper investigates the driving mode of AGVs and proposes a method to extend the kinematic model of center-driven unmanned vehicles to front-wheel drive. This change in driving force enables unmanned vehicles to achieve faster tracking and higher consistency, solving the problems of long tracking time and insufficient accuracy in complex environments and reducing production costs. By analyzing the posture relationship of the unmanned vehicle system during movement, we established a posture error system to analyze the trajectory tracking problem. Utilizing Lyapunov stability theory and the concept of backstepping, we designed a control scheme that uses linear velocity and heading angular velocity as variables for the posture error system. This control scheme aims to stabilize the system and achieve synchronized trajectory tracking control of the unmanned vehicle. The impact of control parameters in the controller on tracking performance is also discussed. The final experimental simulation results show that the system error stabilizes, and the unmanned vehicle accurately follows the predetermined trajectory, verifying the feasibility of our proposed method and control scheme.

A Data-Driven Cognitive Composite Sensitive to Amyloid-β for Preclinical Alzheimer's Disease.

Integrating scores from multiple cognitive tests into a single cognitive composite has been shown to improve sensitivity to detect AD-related cognitive impairment. However, existing composites have little sensitivity to amyloid-β status (Aβ +/-) in preclinical AD. Evaluate whether a data-driven approach for deriving cognitive composites can improve the sensitivity to detect Aβ status among cognitively unimpaired (CU) individuals compared to existing cognitive composites. Based on the data from the Anti-Amyloid Treatment in the Asymptomatic Alzheimer's Disease (A4) study, a novel composite, the Data-driven Preclinical Alzheimer's Cognitive Composite (D-PACC), was developed based on test scores and response durations selected using a machine learning algorithm from the Cogstate Brief Battery (CBB). The D-PACC was then compared with conventional composites in the follow-up A4 visits and in individuals from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The D-PACC showed a comparable or significantly higher ability to discriminate Aβ status [median Cohen's d = 0.172] than existing composites at the A4 baseline visit, with similar results at the second visit. The D-PACC demonstrated the most consistent sensitivity to Aβ status in both A4 and ADNI datasets. The D-PACC showed similar or improved sensitivity when screening for Aβ+ in CU populations compared to existing composites but with higher consistency across studies.

Structural Characteristics of the Pine Stands on Degraded Lands in the South-East of Romania, in the Context of Climate Changes

The present research was carried out in stands of Scots pine and black pine, pure or mixed with deciduous trees, installed on degraded lands from the Curvature Subcarpathian area, Romania, in a representative network of permanent research plots and followed the analysis of the structural diversity and stability indicators of these stands at different ages and in different conditions of degraded lands. The relationships between the quantitative variables with reference to the structure were established by analyzing the significance of the Pearson correlation coefficient (r) and also including datasets of slenderness indexes, which were classed into three domains of vulnerability to abiotic factors (like wind and snow). The compositional diversity of pine stands (pure or mixed with deciduous ones) is different in relation to age and is correlated with the structural diversity. The obtained correlation coefficients (r Pearson) express very strong and significant relationships between biometric parameters (h x Dbh, h x Lc%, Dc x Dbh, and Lc% x Dbh) of the structural diversity (r = 0.800–0.930), which is important for the analysis of the stability and vulnerability of pine forests. The strong correlation between the analyzed variables expresses a weak vulnerability to the action of harmful abiotic factors and the increase in the stability and resilience of the studied stands, especially of over 50 years old. In the old pine stands, the low-vulnerability domain (I < 0.80) is the best represented one, with an average of 64.01% from the total number of trees. At this age, trees with DBH > 22 cm fall into the low-vulnerability category. The explanation is that the stands were affected in their youth by the action of snow and wind, which, combined with the silvotechnical works performed, led to their compositional and structural diversification and increased stability. The young (<45 years) and pure-pine stands with higher consistency (>0.8) and even-aged structure are the most vulnerable to abiotic factors due to the fact that a large number of trees are passing gradually into the higher cenotic classes.

Spring-reinforced pneumatic actuator and soft robotic applications

Abstract Compared to rigid-structure robots, soft robots possess higher degrees of freedom and stronger environmental adaptability, which has aroused increasing attention in the robotic field. Among them, soft pneumatic robots have excellent performances in various practical applications. However, the nonlinearity and instability of pressure response of soft actuators caused by lateral expansion come to a great challenge. To address this problem, we proposed to embed a spring constraint layer around each single air chamber. Following the design concept, we obtained single-cavity and multi-DoF pneumatic actuators and evaluated their elongation and bending characteristics. Experimental results demonstrated that our proposed actuators have more linear pressure response as well as higher consistency. Eventually, through robotic applications, including soft robotic hand and gripper our proposed actuators could facilitate flexible manipulation and elaborate performance.

Beat-by-Beat Estimation of Hemodynamic Parameters in Left Ventricle Based on Phonocardiogram and Photoplethysmography Signals Using a Deep Learning Model: Preliminary Study.

Beat-by-beat monitoring of hemodynamic parameters in the left ventricle contributes to the early diagnosis and treatment of heart failure, valvular heart disease, and other cardiovascular diseases. Current accurate measurement methods for ventricular hemodynamic parameters are inconvenient for monitoring hemodynamic indexes in daily life. The objective of this study is to propose a method for estimating intraventricular hemodynamic parameters in a beat-to-beat style based on non-invasive PCG (phonocardiogram) and PPG (photoplethysmography) signals. Three beagle dogs were used as subjects. PCG, PPG, electrocardiogram (ECG), and invasive blood pressure signals in the left ventricle were synchronously collected while epinephrine medicine was injected into the veins to produce hemodynamic variations. Various doses of epinephrine were used to produce hemodynamic variations. A total of 40 records (over 12,000 cardiac cycles) were obtained. A deep neural network was built to simultaneously estimate four hemodynamic parameters of one cardiac cycle by inputting the PCGs and PPGs of the cardiac cycle. The outputs of the network were four hemodynamic parameters: left ventricular systolic blood pressure (SBP), left ventricular diastolic blood pressure (DBP), maximum rate of left ventricular pressure rise (MRR), and maximum rate of left ventricular pressure decline (MRD). The model built in this study consisted of a residual convolutional module and a bidirectional recurrent neural network module which learnt the local features and context relations, respectively. The training mode of the network followed a regression model, and the loss function was set as mean square error. When the network was trained and tested on one subject using a five-fold validation scheme, the performances were very good. The average correlation coefficients (CCs) between the estimated values and measured values were generally greater than 0.90 for SBP, DBP, MRR, and MRD. However, when the network was trained with one subject's data and tested with another subject's data, the performance degraded somewhat. The average CCs reduced from over 0.9 to 0.7 for SBP, DBP, and MRD; however, MRR had higher consistency, with the average CC reducing from over 0.9 to about 0.85 only. The generalizability across subjects could be improved if individual differences were considered. The performance indicates the possibility that hemodynamic parameters could be estimated by PCG and PPG signals collected on the body surface. With the rapid development of wearable devices, it has up-and-coming applications for self-monitoring in home healthcare environments.

Mechano-chemical, high-consistency activation of kraft pulp in deep eutectic solvent of choline chloride and urea

Deep eutectic solvents (DESs) offer an appealing green medium for the activation of cellulose fibres to promote their swelling, reactivity, hydrolysis, disintegration, and solubility for further processing. Typically, DES treatments are carried out below 5 wt% consistency even though a higher solids content could enhance the fibre activation and reduce the solvent consumption. In this work, a high-consistency (HC) mechano-chemical activation of bleached softwood kraft pulp was elucidated using a simultaneous fibre treatment with DES of choline chloride-urea and a sigma-type kneader or a twin-screw extruder at a solids content of 15–35 wt% and 30 wt%, respectively. Both HC treatments efficiently triggered fibre swelling, which was indicated by an increase in the fibre width, and loosened the cell wall structure which was indicated by an increase in the mesopore volume. Mechano-chemical HC processing generated fibre fines and external fibrillation, while the molecular-level structural alteration or changes in chemical composition were minor; the intrinsic viscosity and the crystallinity of the pulp remained at their initial level and only a small amount of xylan was dissolved. Overall, HC treatment in a twin-screw extruder caused notably more severe morphological changes in the fibres than batch treatment in a sigma-type kneader. Thus, the mechano-chemical HC treatment with DES provides an industrially relevant technology for cellulose modification and opens possibilities to enhance heterogeneous cellulose modification processes in which the highly available surface area of pulp is a key parameter.

Artificial Intelligence Measurement of Preoperative Radiographs in Adolescent Idiopathic Scoliosis Based on Multiple-View Semantic Segmentation.

Cross-sectional study. Imaging classification of adolescent idiopathic scoliosis (AIS) is directly related to the surgical strategy, but the artificial classification is complex and depends on doctors' experience. This study investigated deep learning-based automated classification methods (DL group) for AIS and validated the consistency of machine classification and manual classification (M group). A total of 506 cases (81 males and 425 females) and 1812 AIS full spine images in the anteroposterior (AP), lateral (LAT), left bending (LB) and right bending (RB) positions were retrospectively used for training. The mean age was 13.6 ± 1.8. The mean maximum Cobb angle was 46.8 ± 12.0. U-Net semantic segmentation neural network technology and deep learning methods were used to automatically segment and establish the alignment relationship between multiple views of the spine, and to extract spinal features such as the Cobb angle. The type of each test case was automatically calculated according to Lenke's rule. An additional 107 cases of adolescent idiopathic scoliosis imaging were prospectively used for testing. The consistency of the DL group and M group was compared. Automatic vertebral body segmentation and recognition, multi-view alignment of the spine and automatic Cobb angle measurement were implemented. Compare to the M group, the consistency of the DL group was significantly higher in 3 aspects: type of lateral convexity (0.989 vs 0.566), lumbar curvature modifier (0.932 vs 0.738), and sagittal plane modifier (0.987 vs 0.522). Deep learning enables automated Cobb angle measurement and automated Lenke classification of idiopathic scoliosis whole spine radiographs with higher consistency than manual measurement classification.

Oral structural breakdown and sensory perception of plant-based emulsions

Emulsion-based matrices correspond to an interesting structure for designing healthy and plant-based foods since they can simulate the texture of traditional foods, protect lipids from oxidation, and carry bioactive compounds. However, oral processing could modify their structure, affecting their sensory performance. So, it is necessary to understand the changes that occur during food consumption and their relationship with sensory perception. For that purpose, this research aimed to study the effect of droplet size and thickener type of plant-based emulsions on their structural breakdown under oral conditions and their sensory perception. Four oil-in-water (O/W) emulsions with different droplet sizes (nanoemulsions and conventional emulsions) and thickener types (starch and xanthan gum) were prepared. In vitro oral structural breakdown of emulsions was characterized using a rheological approach (saliva + sharing). The samples were later subjected to a sensory evaluation of aroma, flavor, consistency, and creaminess intensity by multiple paired comparison test (n = 50). Results showed different oral breakdown patterns due to droplet size and thickener type. Xanthan gum matrices were less susceptible to structure breakdown than starch-based matrices, particularly those with microscale droplet size (emulsions), due mainly to salivary α-amylase activity. Regarding sensory perception, xanthan gum-based matrices were perceived to have a higher consistency (nanoemulsion) and creaminess (conventional emulsion) than starch-based ones. Meanwhile, starch-based samples showed higher flavor (nanoemulsion) and aroma intensities (conventional emulsion). Therefore, the sensory characteristics of plant-based emulsions can be modulated by thickener type and droplet size. For example, xanthan gum-based nanoemulsions can increase the perceived consistency, while starch-based emulsions can increase the perceived flavor intensity.

DOES N-ACETYLCYSTEINE SUPPLEMENTATION DURING AN ALTITUDE TRAINING CAMP ENABLE TRAINING QUALITY AND SUCCESSFUL PERFORMANCE OUTCOMES? A COMPARATIVE CASE STUDY

INTRODUCTION & AIMS Elite athletes regularly incorporate training at altitude into their programs to enhance performance. However, the additional stress of exposure to a hypoxic environment needs to be carefully managed by coaches and sport scientists. Supplementation with antioxidants, such as N-acetylcysteine (NAC) may support the athlete’s immune system to cope with this stress. Preliminary evidence also suggests NAC may further promote haematological adaptations to hypoxia. Combined, the potential health and adaptive benefits of NAC warrant further investigation. Conducting ecologically valid and scientifically robust research in an elite sporting setting can be challenging due to small sample sizes, an inability to experimentally manipulate training, and the inherent complexity of the training environment. To account for these challenges, a comparative case study approach was used. METHODS Thirteen swimmers completed a three-week ‘Live-High, Train-High’ camp in Flagstaff, USA (elevation 2,100m). Beginning four days prior to the camp commencing, swimmers where randomly assigned in matched groups to consume NAC (1,200mg per day; n = 6) or a placebo supplement (n = 7) for 14 days. With each athlete defined as a case, fuzzy-set qualitative comparative analysis was used to examine the configurational effects of attributes (i.e., antioxidant support, sufficient ferritin, maintained body mass) previously associated with successful altitude training camps on physiological and performance outcomes. RESULTS NAC was not a solely sufficient or necessary attribute for increased haemoglobin mass. Higher consistency in haemoglobin mass was observed with high training quality. NAC and high training quality contributed to performance improvements. CONCLUSION Swimmer responses were highly individual with successful physiological and performance outcomes achieved by some, irrespective of having attributes that can impair adaptation to altitude training. Subjective training quality provided important contextual information. Fuzzy-set qualitative comparative analysis is a novel approach in sports science to evaluate the interrelationship of attributes that can impact performance.

Exopolysaccharides isolated from fermented milk-associated lactic acid bacteria and applied to produce functional value-added probiotic yogurt

The current study aimed to isolate and characterize the lactic acid bacteria (LAB)-derived exopolysaccharide (EPS) and apply it to enhance yogurt's functional and technological quality. After screening and isolation of 52 different LAB strains from fermented milk products, Lactobacillus (L.) rhamnosus KF5, L. plantarum C182, Pediococcus (P.) acidilactici CCFM18, and Leuconostoc (Leu.) mesenteroides F27, with high EPS production yield, were nominated for further evaluation. EPS-derived strains were evaluated based on production yield, protein content, antioxidant activity, emulsification, and anti-inflammatory ability. After comparing isolated EPS samples, the Leu. mesenteroides F27 products were chosen in 3 concentrations (100, 250, and 500 mg EPS/L) to supplement the yogurt. EPS at all concentrations enhanced LAB growth, water-holding capacity, viscosity, and antioxidant activity. The electronic nose and tongue revealed that EPS did not affect the yogurt flavor profile. The confocal laser scanning microscopy showed a homogenously distributed protein and EPS network in the supplemented yogurt samples. However, in the rheology and consumer analysis, the 250 mg/L concentration showed better attributes, such as higher consistency, lower flow behavior, and a higher flavor score. Leu. mesenteroides F27-derived EPS could be used as a biological additive to enhance yogurt's technological and functional quality.

Recycled glass powder as an alternative to fly ash in non-proprietary UHPC: A comparative study of resource-efficient design, mechanical and durability properties

Fly ash (FA) is one of the preferable supplementary cementitious materials used in the design of resource-efficient ultra-high performance concrete (UHPC). Recent changes in energy production have led to a reduced availability of FA which increases the need to find an alternative replacement. One suitable, innovative, and sustainable pozzolan with higher consistency in material properties is recycled glass powder (RGP), produced from 100% post-consumer glass. To fill the knowledge gap whether RGP is a suitable alternative to FA in non-proprietary UHPC, this research emphasizes the comparative investigation of material properties of UHPC using FA versus RGP. The comprehensive material characterization and analysis included workability, mechanical behavior such as compressive strength, single fiber pull out and direct tensile behavior, static elastic modulus, and long-term strength. Durability properties included electrical surface resistivity and freeze-thaw resistance. In addition, a comparative analysis for their resource-efficiency and service life was also performed. The results were compared to two commercial UHPCs, available in the United States. This research showed that UHPC with RGP can be designed to achieve comparable mechanical and durability properties as UHPC with FA. Compressive strength up to 176 MPa (25.5ksi) were obtained without the use of special treatment, at current material cost of about US$1145/m3 with 2% of fiber volume fraction of steel fibers. Over time, the compressive strength of UHPC with RGP increased by 15%, resulting in more than 200 MPa (29.0ksi) after three years. Both UHPCs designed with either FA or RGP showed strain-hardening under direct tension with maximum tensile strength of about 14.8 MPa (2.1ksi). Both the UHPC with RGP and FA achieved electrical surface resistivity of more than 870kΩ × cm and 822kΩ × cm in 28 days and relative dynamic modulus of 102% after 600 freeze-thaw cycles that ensure long term durability with design service life of 364 and 344 years respectively.

Exploring the impacts of senescence on implantation and early embryonic development using totipotent cell-derived blastoids

IntroductionAdvanced maternal age is associated with reduced implantation and pregnancy rates, yet the underlying mechanisms remain poorly understood, and research models are limited. ObjectivesHere, we aim to elucidate the impacts of senescence on implantation ability by employing blastoids to construct a novel research model. MethodsWe used a novel three-dimensional system with totipotent blastomere-like cells (TBLCs) to construct TBL-blastoids and established senescence-related embryo models derived from oxidative stress-induced TBLCs. ResultsMorphological and transcriptomic analyses revealed that TBL-blastoids exhibited characteristic blastocyst morphology, cell lineages, and a higher consistency in developmental rate. TBL-blastoids demonstrated the ability to develop into postimplantation structures in vitro and successfully implanted into mouse uteri, inducing decidualization and forming embryonic tissues. Importantly, senescence impaired the implantation potential of TBL-blastoids, effectively mimicking the impaired implantation ability and reduced pregnancy rates associated with advanced age. Furthermore, analysis of differentially expressed genes (DEGs) in human homologous deciduae revealed enrichment in multiple fertility-related diseases and other complications of pregnancy. The genes implicated in these diseases and the common DEGs identified in the lineage-like cells of the two types of TBL-blastoids and deciduae may represent potential targets for addressing impaired implantation potential. ConclusionThese results unveiled that TBL blastoids are an improved model for investigating implantation and early postimplantation, offering valuable insights into pregnancy-related disorders in women with advanced age and potential targets for therapeutic interventions.

Interpretable artificial intelligence-based app assists inexperienced radiologists in diagnosing biliary atresia from sonographic gallbladder images

BackgroundA previously trained deep learning-based smartphone app provides an artificial intelligence solution to help diagnose biliary atresia from sonographic gallbladder images, but it might be impractical to launch it in real clinical settings. This study aimed to redevelop a new model using original sonographic images and their derived smartphone photos and then test the new model’s performance in assisting radiologists with different experiences to detect biliary atresia in real-world mimic settings.MethodsA new model was first trained retrospectively using 3659 original sonographic gallbladder images and their derived 51,226 smartphone photos and tested on 11,410 external validation smartphone photos. Afterward, the new model was tested in 333 prospectively collected sonographic gallbladder videos from 207 infants by 14 inexperienced radiologists (9 juniors and 5 seniors) and 4 experienced pediatric radiologists in real-world mimic settings. Diagnostic performance was expressed as the area under the receiver operating characteristic curve (AUC).ResultsThe new model outperformed the previously published model in diagnosing BA on the external validation set (AUC 0.924 vs 0.908, P = 0.004) with higher consistency (kappa value 0.708 vs 0.609). When tested in real-world mimic settings using 333 sonographic gallbladder videos, the new model performed comparable to experienced pediatric radiologists (average AUC 0.860 vs 0.876) and outperformed junior radiologists (average AUC 0.838 vs 0.773) and senior radiologists (average AUC 0.829 vs 0.749). Furthermore, the new model could aid both junior and senior radiologists to improve their diagnostic performances, with the average AUC increasing from 0.773 to 0.835 for junior radiologists and from 0.749 to 0.805 for senior radiologists.ConclusionsThe interpretable app-based model showed robust and satisfactory performance in diagnosing biliary atresia, and it could aid radiologists with limited experiences to improve their diagnostic performances in real-world mimic settings.

Utilizing ChatGPT in Telepharmacy.

ChatGPT is an artificial intelligence-powered chatbot that has demonstrated capabilities in numerous fields, including medical and healthcare sciences. This study evaluates the potential for ChatGPT application in telepharmacy, the delivering of pharmaceutical care via means of telecommunications, through assessing its interactions, adherence to instructions, and ability to role-play as a pharmacist while handling a series of life-like scenario questions. Two versions (ChatGPT 3.5 and 4.0, OpenAI) were assessed using two independent trials each. ChatGPT was instructed to act as a pharmacist and answer patient inquiries, followed by a set of 20 assessment questions. Then, ChatGPT was instructed to stop its act, provide feedback and list its sources for drug information. The responses to the assessment questions were evaluated in terms of accuracy, precision and clarity using a 4-point Likert-like scale. ChatGPT demonstrated the ability to follow detailed instructions, role-play as a pharmacist, and appropriately handle all questions. ChatGPT was able to understand case details, recognize generic and brand drug names, identify drug side effects, interactions, prescription requirements and precautions, and provide proper point-by-point instructions regarding administration, dosing, storage and disposal. The overall means of pooled scores were 3.425 (0.712) and 3.7 (0.61) for ChatGPT 3.5 and 4.0, respectively. The rank distribution of scores was not significantly different (P>0.05). None of the answers could be considered directly harmful or labeled as entirely or mostly incorrect, and most point deductions were due to other factors such as indecisiveness, adding immaterial information, missing certain considerations, or partial unclarity. The answers were similar in length across trials and appropriately concise. ChatGPT 4.0 showed superior performance, higher consistency, better character adherence and the ability to report various reliable information sources. However, it only allowed an input of 40 questions every three hours and provided inaccurate feedback regarding the number of assessed patients, compared to 3.5 which allowed unlimited input but was unable to provide feedback. Integrating ChatGPT in telepharmacy holds promising potential; however, a number of drawbacks are to be overcome in order to function effectively.

Water hammer in pipelines based on different friction models

Water hammer in pipelines is a difficult problem in fluid transmission field. Especially, there exists some friction items of pipeline transient model such that the simulation model is not consistent to the experimental results. By using the friction model proposed by Kagawa and the model of impulse response function, the pressure transients are calculated with and without cavitation. The corresponding simulation results involving pressure, velocity, steady and dynamic frictions, cavitation volume are analyzed to reveal the effect of friction item on pressure transients. Moreover, the features of steady and dynamic frictions are captured in pipelines with upstream and downstream valves. The comparative simulation results of two friction models have verified that the friction model using an impulse response function has higher consistency between simulation and experimental results of pipeline transients.

Hybrid bioinspired approach for secret sharing algorithm and ownership transfer optimization in cloud based models

It is widely accepted that secret sharing models like Shamir cryptosystem allows cloud designers to deploy highly secure multichannel data transfer mechanisms. But these systems are majorly static, and do not incorporate context-aware share-selection techniques. Moreover, SaaS based cloud deployments require ownership transfers for efficient real-time operations. These ownership transfer mechanisms are backed by selection encryption models assisting in permission-aware data sharing. Scholars have put forward pre-emptive and bioinspired models to accomplish these tasks, but nearly all of them are still sophisticated or demonstrate limited flexibility in real-time scenarios. The proposed structure first gathers cloud logs for various temporal requests and groups them based on IP & resource constraints with the goal of getting around these problems. The modified rules are initially tested on dummy ownership requests, and are later on real-time ownership-transfer scenarios for validation of their efficiency levels based on accuracy of misconfiguration detection, delay needed for processing the requests, consistency of blocking invalid ownership requests, and request throughput levels. Based on this evaluation, The accuracy might be increased by 3.5% using the suggested model, reducing delay by 2.9%, while having 6.4% higher consistency, and 8.3% higher throughput than existing secret sharing & ownership transfer methods under similar deployment scenarios.

From genes to fields: Environmental compatibility of herbicide tolerant transgenic cotton

IntroductionThere are several enticing reasons to use genetic engineering to develop transgenic cotton plants with economically relevant new traits. The use of transgenic cotton to protect crops from biotic stressors like weeds may prove to be a cost-effective solution. ObjectivesThe development of herbicide-resistant cotton variants is the primary motivation for gene transfer in this crop. In the current study, we conducted a battery of experiments on the herbicide-tolerant cotton variety MNH-786 to investigate its expression patterns and environmental adaptation. Material and methodsThe 35S promoter controls the expression of the codon-optimized Cp4-EPSPS gene that is present in this variety. Several different molecular approaches, such as polymerase chain reaction (PCR), Florescent In-Situ Hybridization analysis, and enzyme-linked immunosorbent assay (ELISA), are used to confirm the presence of transgenic cotton plants. The glyphosate spray assay was performed using molecular analysis, employing a dosage of 2000 ml per acre. The suitability of transgenic and non-transgenic cotton plants in four unique environments—the Multan, Vehari, Bahawalpur, and Faisalabad Districts of Punjab province, Pakistan was assessed using a comparative comparison. A gene x environment interaction (GEI) biplot was used to analyze the correlation between genes and their environments. ResultsTransgenic cotton plants screened using PCR to target 1.467 kb regions showed effective integration into cotton genomes. Results showed that Cp4-EPSPS proteins fluctuated and associated with environmental variables across all three generations (T0, T1, and T2). In the herbicide spray assay, all transgenic cotton plants survived, but non-transgenic cotton and all weeds died. According to the "which-won-where" polygon, transgenic cotton plant Events 3, 4, 5, 6, and 7 performed better in three settings, except Faisalabad. According to correlation and heat-map yield research, transgenic cotton variety MNH-786 Events 4, 5 and 6 have a higher consistency and influence in Vehari district's natural habitats. Multan and Bahawalpur also show encouraging results. In Faisalabad, no transgenic events perform well. ConclusionWe conclude that Events 4 and 5 show a substantial abundance of genetic potential that is robust over a wide range of environments thanks to our thorough study of the outcomes obtained under these conditions. SignificanceOur study emphasises the importance of transgenic cotton, its performance, and its exposure to appropriate agroecological zones. This knowledge can be advantageous for farmers in attaining optimal conditions to maximize productivity.

Variation in personality shaped by evolutionary history, genotype and developmental plasticity in response to feeding modalities in the Arctic charr

Animal personality has been shown to be influenced by both genetic and environmental factors and shaped by natural selection. Currently, little is known about mechanisms influencing the development of personality traits. This study examines the extent to which personality development is genetically influenced and/or environmentally responsive (plastic). We also investigated the role of evolutionary history, assessing whether personality traits could be canalized along a genetic and ecological divergence gradient. We tested the plastic potential of boldness in juveniles of five Icelandic Arctic charr morphs ( Salvelinus alpinus ), including two pairs of sympatric morphs, displaying various degrees of genetic and ecological divergence from the ancestral anadromous charr, split between treatments mimicking benthic versus pelagic feeding modalities. We show that differences in mean boldness are mostly affected by genetics. While the benthic treatment led to bolder individuals overall, the environmental effect was rather weak, suggesting that boldness lies under strong genetic influence with reduced plastic potential. Finally, we found hints of differences by morphs in boldness canalization through reduced variance and plasticity, and higher consistency in boldness within morphs. These findings provide new insights on how behavioural development may impact adaptive diversification.

The Effect of Giving Human Umbilical Cord Mesenchymal Stem Cells (HUMSC) on the Expression of Collagen I in Menopause Model Mice

Background: Patients with pelvic floor dysfunction (PFD) who fail conservative treatment will undergo surgery. In recent years, various therapeutic approaches for PFD using stem cells have been investigated in vivo. One component of stem cells is the human umbilical cord mesenchymal stem cell (HUMSC). It is hoped that HUMSC can modify the collagen and elastin content in the extracellular matrix, leading to improvements in pelvic floor strength and PFD.
 Methods: This research is an experimental study with a randomized posttest-only controlled group design with the aim of determining the effect of giving HUMSC on the expression of Collagen I in a female mouse model of menopause. There are inclusion criteria, namely female Balb/c mice, healthy, never mated, aged 8 - 12 weeks, and body weight 18 - 22 grams. The research will be carried out with statistical analysis using SPSS; a p-value <0.05 is considered significant.
 Results: The results of the analysis of the distribution of sample means show that there are significant differences between the two groups. The treatment group had a higher mean expression of type I collagen compared to the control group. In addition, variation in the control group was lower compared with the treatment group, which could be interpreted as higher consistency in the response to HUMSC treatment.
 Conclusion: Human umbilical cord (HUMSC) has a positive impact on type I collagen expression in menopausal model mice.