For the tracking control of n order MIMO affine systems with uncertainty, a nonlinear control method of command filtering backstepping with self-organizing approximation is proposed. A command filter with a saturation structure is used to eliminate the analytic computation of command derivatives, and compensated tracking errors are redefined to compensate the discrepancy between the command filtered signals and analytic computation of command derivatives. Command Filtered Backstepping is used to decouple the higher system and simplify the control structure. Self-organizing approximation is used to on-line approximate the uncertain and eliminate the effect of the uncertain to the tracking performance. The uncertain is approximated by a linear product between a set of basis functions and weighted functions on the local region. The weighted sum of compensated tracking errors is used as the prespecified tracking accuracy performance. When the prespecified tracking accuracy performance can not be achieved, a new local approximator is added to the control system according to the criterion based on Lyapunov theory to enhance the capacity of the approximator. Finally, an applications for a simple system is analyzed by simulations.

Performing complex tasks in open environments remains challenging for robots, even when using large language models (LLMs) as the core planner. Many LLM-based planners are inefficient due to their large number of parameters and prone to inaccuracies because they operate in open-loop systems. We think the reason is that only applying LLMs as planners is insufficient. In this work, we propose DaDu-E, a robust closed-loop planning framework for embodied AI robots. Specifically, DaDu-E is equipped with a relatively lightweight LLM, a set of encapsulated robot skill instructions, a robust feedback system, and memory augmentation. Together, these components enable DaDu-E to (i) actively perceive and adapt to dynamic environments, (ii) optimize computational costs while maintaining high performance, and (iii) recover from execution failures using its memory and feedback mechanisms. Extensive experiments on real-world and simulated tasks show that DaDu-E achieves task success rates comparable to embodied AI robots with larger models as planners like COME-Robot, while reducing computational requirements by 6 .6×. Users are encouraged to explore our system at: https://rlc-lab.github.io/dadu-e/.

Purpose: To investigate fetal cardiac functions and remodeling in pregnancies conceived via in vitro fertilization (IVF). Methods: This prospective case-control study included 40 singleton IVF pregnancies and 46 uncomplicated control pregnancies at 28–36 weeks of gestation. The IVF group consisted of pregnancies admitted to the hospital, excluding those with anatomical or chromosomal abnormalities. Fetal cardiac morphological measurements, left myocardial performance index, cardiac output, spectral, tissue Doppler, and M-mode measurements were recorded. Ventricular and great vessel size were assessed for fetal cardiac morphology, while myocardial performance index, spectral Doppler and tissue Doppler parameters were assessed for cardiac function. Results: Cardiothoracic circumference ratio and both ventricular areas were found to be significantly smaller in the IVF group than in the control group. The right ventricular basal sphericity index was also smaller in the IVF group. The mitral and aortic valves were smaller in the IVF group, while tricuspid and pulmonary valve measurements were similar. Left ventricular ejection time was statistically lower in the IVF group, although the myocardial performance index was similar. The IVF group had higher right fetal myocardial performance index on tissue Doppler imaging and lower cardiac output than the control group, but the difference was not statistically significant. Conclusion: This study demonstrated the presence of cardiac morphological remodeling and mild systolic dysfunction in IVF pregnancies.

Depositions of protein aggregates are typical pathological hallmarks of various neurodegenerative diseases (NDs). For example, amyloid-beta (Aβ) and tau aggregates are present in the brain and plasma of patients with Alzheimer’s disease (AD); α-synuclein in Parkinson’s disease (PD), dementia with Lewy Bodies (DLB), and multiple system atrophy (MSA); mutant huntingtin protein (Htt) in Huntington’s disease (HD); and DNA-binding protein 43 kD (TDP-43) in amyotrophic lateral sclerosis (ALS), frontotemporal dementia (FTD), and limbic-predominant age-related TDP-43 encephalopathy (LATE). The same misfolded proteins can be present in multiple diseases (mixed proteinopathies), like Aβ in AD, DLB, and LATE; α-synuclein in AD, DLB, and PD; and TDP-43 in AD, FTD, DLB, and LATE. Since there is no cure for all these diseases, understanding the mechanisms of protein aggregation becomes imperative in modern medicine, especially for developing diagnostics and therapeutics. A Multimer Detection System (MDS) was designed to distinguish and quantify the multimeric/oligomeric forms from the monomeric form of aggregated proteins. As the unique epitope of the monomer is already occupied by capturing or detecting antibodies, the aggregated proteins with multiple epitopes would be accessible to both capturing and detection antibodies simultaneously, and signals will be generated from the multimers rather than the monomers. Hence, MDS could present a simple solution for measuring various conformations of aggregated proteins with high sensitivity and specificity, which may help to explore diagnostic and treatment strategies for developing anti-aggregation therapeutics.

Background: To improve accessibility and financial support for outpatient services, China introduced a scheme to decrease cost-sharing for outpatient care under the Urban Employee Basic Medical Insurance. This study evaluates the health impacts of this policy and examines its heterogeneous effects. Methods: Utilizing data from the 2018 China Health and Retirement Longitudinal Study, we analyzed 2,896 individual-level observations across 105 prefectures. Propensity score matching and a causal forest model were applied to evaluate the effects on chronic disease status, body pain, self-rated health, and hospitalization, while accounting for various demographic, socioeconomic, residential, health-related behaviors, and prefecture-specific factors. Results: The reduction in cost-sharing was significantly linked to decreased probabilities of chronic disease (Average Treatment Effect (ATE) = -0.0619, p < 0.01), body pain (ATE = -0.0715, p < 0.05), and hospitalization (ATE = -0.0592, p < 0.001), as well as improved self-rated health (ATE = 0.1557, p < 0.001). These benefits may be attributed to reduced out-of-pocket payments for outpatient care (ATE = -287.6112, p < 0.01) and increased outpatient visits (ATE = 0.0414 visits, p < 0.05). Causal forest analyses revealed that older individuals, those with higher educational attainment, higher household income, urban residents, and those engaging in healthier behaviors exhibited larger treatment effects. Conclusions: Decreasing outpatient cost-sharing in China has beneficial health outcomes, with variations in its impact based on socio-economic status and health behaviors. It is advisable to further increase reimbursement rates and broaden benefit packages for outpatient care, while addressing the unequal distribution of benefits.

Nitrogen (N) is a key limiting element for plant photosynthesis in boreal forests. Thus, N fertilization is proposed as an effective management strategy to increase forest productivity and associated carbon (C) sink in the N-limited boreal biome. However, there is a limited understanding of how N fertilization can affect the sensitivity of the C sink to drought stress, which is predicted to occur more frequently in the boreal region in a changing climate. This study was based on a 15-year controlled N fertilization experiment in a boreal Scots pine stand. Ecosystem light-saturated photosynthetic capacity (GPP2000) is a good indicator of forest photosynthesis response to environmental stress. Here, we used eddy covariance measurements of C fluxes data and environmental data from paired sites to investigate whether long-term N fertilization altered the drought sensitivity of the GPP2000. We found that long-term N fertilization significantly increased ecosystem GPP2000 even on dry days during summer. However, a significantly divergent drought sensitivity of GPP2000 between the N Fertilized and Reference sites was detected. Specifically, N fertilization increased the sensitivity of GPP2000 to both atmospheric and soil drought to the extent that it may offset the positive effect of N fertilization on GPP2000. Moreover, using the random forest model, we found that the absolute GPP2000 difference between fertilization and control sites was mainly determined by air and soil drought proxies and followed by canopy conductance rather than the air temperature. These results advance our understanding of the mechanisms of forest response to drought with long-term N fertilization.

African wild dog (Lycaon pictus) populations are difficult to assess effectively and scalable strategies for population monitoring are lacking, often because of low detection rates. Scat detection dogs (Canis lupus familiaris) have emerged as a suitable tool to detect the presence of wide-ranging carnivores. In this study, we employed a detection dog to locate African wild dog scat in an unfenced, understudied region of the Kavango-Zambezi Transfrontier Conservation Area. Over two weeks of fieldwork, the detection dog-team found 21 African wild dog scats across a 2,304 km² study area. Six of those scats were detected at a marking site shared by multiple African wild dog individuals, as determined through genetic identification. The marking site discovered by the scat detection dog facilitated the collaring of two African wild dogs in close proximity, the repeat detection of wild dog individuals on camera trap, the collection of additional scat samples, and the highest recording of individuals per site from camera traps (n = 5) and genetic verification (n = 5). This highlights the value of marking sites for improved long-term monitoring for this elusive species. To our knowledge, we report the first use of a detection dog to find wild dog scat and discover a marking site, and our findings hold promise for the potential of detection dogs to rapidly survey this wide-ranging, endangered canid.

Plantago hakusanensis (2n = 4x = 24) is an endangered endemic species that occurs in subalpine zones in Japan. To clarify the unresolved taxonomic status of P. hakusanensis within the subgenus Plantago, we conducted a phylogenetic analysis based on the nuclear-encoded single-copy gene sucrose–proton symporter 1 (SUC1) using 60 previously reported alleles from 24 taxa in the subgenus Plantago. We found that P. hakusanensis was closely related to P. asiatica var. densiuscula. The phylogenetic relationships between P. hakusanensis and P. asiatica var. densiuscula were examined by analyses of the SUC1 nuclear regions and the internal transcribed spacer (ITS) of rDNA, genome-wide single-nucleotide polymorphism genotyping (via multiplexed inter-simple sequence repeat genotyping by sequencing), as well as additional analyses of three chloroplast (cp) regions (trnL-F, ndhF-rpl32, and rpl32-trnL) in 25 individuals of P. hakusanensis and 53 individuals of P. asiatica var. densiuscula. Monophyly of P. hakusanensis was suggested by the nuclear marker analyses, whereas the cp haplotypes of P. hakusanensis were shared with P. asiatica var. densiuscula and P. asiatica in China. The disparity between the nuclear and cp data may be explained by introgression of the cp genome (cp capture) during Quaternary climate changes. Our results provide (i) a molecular phylogenetic basis for the taxonomy and (ii) insight into the intraspecific diversification history of P. hakusanensis.

Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation technique that can alter the excitability of targeted brain regions and influence motor learning. This study investigates the effects of concurrent M1 anodal and cerebellar cathodal tDCS (M1a+CBc) on motor learning in a complex rhythm-timing video game task. Forty-two participants practiced the game with their non-dominant hand while receiving either M1a+CBc (n = 24) or sham tDCS (n = 18). Performance was assessed using a performance index (PI) incorporating keystroke timing accuracy, tap distribution ratio, and key error rate. A 2 x 5 mixed ANOVA revealed a significant main effect of stimulation group on PI gain scores across practice blocks (P = 0.021, ηp2 = 0.126), with M1a+CBc showing greater gains than sham. An ANCOVA, controlling for baseline performance, showed that the M1a+CBc group had significantly higher post-test PI scores compared to the sham group (P = 0.034, ηp2 = 0.110). These results suggest that concurrent M1a+CBc tDCS significantly enhances motor learning in complex tasks.

We estimated the effectiveness of the adapted monovalent XBB.1.5 COVID-19 vaccines against PCR-confirmed SARS-CoV-2 hospitalisation during the BA.2.86/JN.1 lineage-predominant period using a multicentre test-negative case-control study in Europe. We included older adults (≥65 years) hospitalised with severe acute respiratory infection from November 2023 to May 2024. Vaccine effectiveness was 46% at 14–59 days, 34% at 60–119 days, with no effect thereafter. The XBB.1.5 COVID-19 vaccines conferred protection against BA.2.86 lineage hospitalisation in the first 4 months post vaccination.

Increased homogenisation (decreased beta-diversity) among biological assemblages is often interpreted as being caused by already-widespread species increasing, but individual species contributions are poorly understood. Here, we consider species contributions to beta-diversity over decades to millennia, within different taxonomic groups and environments. Species occurring in around half of sites provided the greatest contributions to differentiation at a given time, but not through time. They had a slightly homogenising effect if they declined below 0.5 or increased above 0.5 (in strongly nested communities, changes above 0.5 have little impact on differentiation). The most widespread species (>0.75 of sites) contributed little to beta-diversity change. In contrast, localised species (initially in <0.25 of sites) contributed most to both homogenisation (when declining) and differentiation (when increasing). Conservation interventions to increase localised species occurrence would do more to limit homogenisation than attempts to control already-widespread species or prevent others becoming widespread.

This research paper analyses brain tumor MRI segmentation and classification using modified convolutional neural networks (CNN) and transfer learning with VGG16 techniques. These advanced methods improve detection, classification, and accuracy over conventional techniques, which typically involve feature extraction, pre-processing, image acquisition, and classification. MRI scans are utilized to diagnose various types of brain tumors, focusing on tumor type recognition and position identification. The study shows that the modified CNN and VGG16 techniques enhance accuracy, precision, recall, F1-score, macro average, and weighted average metrics compared to conventional methods. Fine-tuning the network algorithms’ hyper-parameters further increases precision. Specifically, the proposed methods achieve an average accuracy of 99.50% and 99.60% for weighted and macro averages, respectively, significantly outperforming traditional techniques.

The wind energy industry presents a green-green dilemma whereby it aims to reduce CO2 emissions and combat climate change which benefits biodiversity, but biodiversity can be negatively impacted through its development. In order to reconcile this dilemma, the first action in the mitigation hierarchy is to avoid development in areas considered too risky for vulnerable species. For eagles, development has often been restricted within a certain distance from nests, or more recently, by using predictive habitat use models to define site-specific areas of high risk. One such model has been used to predict risky areas for Verreaux’s Eagles (Aquila verreauxii) in South Africa, but there has been declining use of this tool by industry (research-implementation gap, RIG). Uncertainty over the model outputs is a likely cause of the RIG, because the model results in variably sized exclusion areas for each development. To reduce this uncertainty and increase implementation of the model, we explore if limiting this predictive model to protect the same amount of space, or less, as a circular buffer around the nest, could still provide improved protection of the species. We found that by fixing the area of risk to be equal to the area of current circular buffer recommendation, eagle protection was improved by around 6–7 % compared to circular buffers, or by 2–3 % compared to our previous threshold-based model. This fixed-area approach removes variation in the amount of area excluded from development. This ensures that by applying the collision risk model there is no unexpected loss in the developable area for wind energy development. Our study demonstrates the importance of understanding and adapting tools that aim to promote sustainable development of renewable energy. Responding to stakeholder needs and balancing conservation with practical applications is critical, particularly in countries where policy enforcement is lacking.

Anthracnose, caused by Colletotrichum species, is one of the most damaging fungal diseases affecting the cultivation of Polygonatum sibiricum, a traditional Chinese medicinal plant. To investigate the effects of Colletotrichum spaethianum infection on the growth and physiological metabolism of Polygonatum sibiricum, we conducted an experiment by root-irrigation the plants with the pathogen. The results indicated that after root treatment, the leaf margins of the plants were the first to show symptoms, beginning with chlorosis and the formation of yellow-brown lesions. These lesions then expanded, eventually leading to complete desiccation and easy detachment of the leaves. Following pathogen infection, there was a decrease in stomatal conductance, transpiration rate, and intercellular CO 2 concentration in the leaves, yet the maximum photosynthetic efficiency was observed to increase. After infection, the chlorophyll content in leaves remained relatively stable, while the content of soluble protein decreased. The activity of superoxide dismutase (SOD), peroxidase (POD), and the content of malondialdehyde (MDA) all exhibited an initial increase followed by a decrease. Notably, the activity of SOD changed earlier than that of POD and MDA, suggesting that Polygonatum sibiricum has some capacity to resist Colletotrichum spaethianum. However, the growth indicators of Polygonatum sibiricum plants were largely unaffected by the Colletotrichum spaethianum stress. These findings contribute significantly to the understanding of the physiological changes in Polygonatum sibiricum following infection by Colletotrichum spaethianum and provide insights for disease diagnosis and control measures during Polygonatum sibiricum cultivation.

A document by SERGIO VERD. Click on the document to view its contents.

The purpose of this study was to assess and contrast the anti-diabetic properties of chitosan-silver oxide nanocomposite (CS-Ag 2O NC) and silver oxide nanoparticles (Ag 2O NPs). A green synthesis approach was used to create the nanoparticles and nanocomposite, The vitro models were used to assess the anti-diabetic effects. According to the findings, CS-Ag 2O nanocomposite demonstrated better glucose absorption, insulin sensitivity, and alpha-glucosidase inhibition than Ag 2ONPs, demonstrating greater anti-diabetic action. Additionally, the nanocomposite demonstrated enhanced biocompatibility and decreased toxicity. According to the study, CS-Ag 2O NC shows promise as a material for the creation of anti-diabetic medications. The nanoparticles and nanocomposite were characterized by UV- vis spectroscopy, infrared spectroscopy (FTIR) and, X ray diffraction (XRD) and EDAX analysis. The results showed a considerable Ag 2ONPs and CS-Ag 2O NC demonstrating that strong anti-diabetic potential

Infectious diseases present a growing risk to wildlife biodiversity. Predicting outbreak dynamics and developing effective management tools requires knowledge on the mechanisms of infection transmission. Specifically, understanding the relative contributions of direct host-to-host transmission versus indirect environmental transmission is critical. Here, we leverage a large dataset from long-term monitoring of a free-living population of eastern water dragons (Intellagama lesueurii) experiencing an outbreak of the fungal pathogen Nannizziopsis barbatae, to investigate this pathogens’ transmission. Using different host behaviours to model direct and indirect transmission of infection in this population, we demonstrate N. barbatae’s ability to infect hosts through indirect transmission. Our findings shed light on the danger N. barbatae poses to Australian reptiles, but also provide insights into how outbreaks should be managed. Additionally, we provide a methodology to elucidate the transmission mechanisms of driving the spread of emerging fungal pathogens.

Background and Purpose: Cefiderocol (FDC) application on critically ill patients is still limited: recommended dosing regimens are usually derived from studies on healthy subjects, but critical illness is always associated with physio/pathological alterations in patients, resulting in drug concentration perturbation. Furthermore, there is a scant real-life data addressing particular sub-populations, like morbidly/obese and augmented renal clearance patients, making pharmacokinetics/pharmacodynamics (PK/PD) target recommendation debated. The aim of this study was to present FDC PK/PD values in patients from morbidly to pathologically obese and with augmented renal clearance. Experimental Approach: Ten patients with K. pneumoniae NDM infections were enrolled. Plasma therapeutic drug monitoring was performed after steady-state at different timings. The conventional PK/PD target of fT>MIC has been considered, along with a more aggressive target of fT>6 x MIC; evaluated clinical outcomes were microbiological eradication and 30 days mortality. Key Results: A correlation between weight and 30 days mortality was highlighted. The percentage of failure in achieving the microbiological eradication progressively increased with higher BMI. Furthermore, 100% patients reached the conventional PK/PD target of fCmin>4 mg/L; while, when evaluating a more aggressive target of 100% fT>6 x MIC (2 mg/L), different factors showed an influence as FDC minimum free concentration, area under the concentration time curve and drug clearance. Conclusion and Implications: Our study may be useful in real-world evidence, highlighting the important influence of BMI and high value of clearance in achieving the microbiological target, suggesting that further study and the use of therapeutic drug monitoring in this context are needed.

Akash V. Ramanathan, B.S.1, Sukesh Burjonroppa M.D.1, Jeffery J. Wu M.D.2,3, and Mohanakrishnan Sathyamoorthy, M.D. 1,3,4#1 Department of Medicine, Burnett School of Medicine at TCU, Fort Worth, TX, 761042 Department of Surgery, Burnett School of Medicine at TCU, Fort Worth, TX, 761043 Baylor Heart and Vascular Hospital – Fort Worth Division, Fort Worth, TX 761044 Consultants in Cardiovascular Medicine and Science, Fort Worth, TX, 76104, USA#Corresponding and senior authorAddress for correspondence: Mohanakrishnan Sathyamoorthy, MD, Burnett School of Medicine at TCU, TCU Box 297085 , Fort Worth, TX, 76129. M.sathyamoorthy@tcu.edu +1-817-257-8450Conflicts of Interest: The authors declare no conflicts of interest.Funding: This research had no supportWord count: 2,723

A document by Ming Xu. Click on the document to view its contents.

Abstract

Classification is one of the main areas of pattern recognition research, and within it, Support Vector Machine (SVM) is one of the most popular methods outside of field of deep learning – and a de-facto reference for many Machine Learning approaches. Its performance is determined by parameter selection, which is usually achieved by a time-consuming grid search cross-validation procedure (GSCV). That method, however relies on the availability and quality of labelled examples and thus, when those are limited can be hindered. To address that problem, there exist several unsupervised heuristics that take advantage of the characteristics of the dataset for selecting parameters instead of using class label information. While an order of magnitude faster, they are scarcely used under the assumption that their results are significantly worse than those of grid search. To challenge that assumption we have proposed an improved heuristics for SVM parameter selection and tested it against GSCV and state of art heuristic on over 30 standard classification datasets. The results show not only its advantage over state-of-art heuristics but also that it is statistically no worse than GSCV.

A document by Aasma Hameed. Click on the document to view its contents.

Large wind energy plants can significantly impact the environment if not managed properly. This study employs Geographic Information Systems (GIS) and spatial multi-criteria analysis (SMCA) to evaluate the carrying capacity of territories to host wind energy production. Carrying capacity, defined as a spatial category of suitability, is derived from integrating multiple layers. The proposed approach incorporates 34 aptitude and 21 impact variables, addressing wind behavior, land use, and physical, natural, and historical-cultural characteristics. The aptitude variables were ranked by a panel of experts. This methodology was applied to regions in northwest Spain to assess their capacity for hosting wind energy projects. Two carrying capacity maps were generated: one with absolute intervals and another with relative intervals. Positive carrying capacity, indicating a value above zero, varied between 0.00% and 43.85% of the area depending on the region. Strikingly, only 0.09% of the total area exhibited both optimal energy potential and Maximum relative carrying capacity. The maps were compared to the binding zonings proposed by national and regional authorities, which were found to be overly restrictive. Alarmingly, more than 50 wind turbines across eight projected wind energy plants are authorized or in the approval process for locations with negative carrying capacity under the new classification. This spatial approach offers a valuable tool for future territorial planning, ensuring a balance between energy production and environmental conservation. It provides a replicable framework for sustainable land management in regions facing similar challenges.