A hybrid approach based on multipath Swin transformer and ConvMixer for white blood cells classification

White blood cells (WBC) play an effective role in the body's defense against parasites, viruses, and bacteria in the human body. Also, WBCs are categorized based on their morphological structures into various subgroups. The number of these WBC types in the blood of non-diseased and diseased people is different. Thus, the study of WBC classification is quite significant for medical diagnosis. Due to the widespread use of deep learning in medical image analysis in recent years, it has also been used in WBC classification. Moreover, the ConvMixer and Swin transformer models, recently introduced, have garnered significant success by attaining efficient long contextual characteristics. Based on this, a new multipath hybrid network is proposed for WBC classification by using ConvMixer and Swin transformer. This proposed model is called Swin Transformer and ConvMixer based Multipath mixer (SC-MP-Mixer). In the SC-MP-Mixer model, firstly, features with strong spatial details are extracted with the ConvMixer. Then Swin transformer effectively handle these features with self-attention mechanism. In addition, the ConvMixer and Swin transformer blocks consist of a multipath structure to obtain better patch representations in the SC-MP-Mixer. To test the performance of the SC-MP-Mixer, experiments were performed on three WBC datasets with 4 (BCCD), 8 (PBC) and 5 (Raabin) classes. The experimental studies resulted in an accuracy of 99.65% for PBC, 98.68% for Raabin, and 95.66% for BCCD. When compared with the studies in the literature and the state-of-the-art models, it was seen that the SC-MP-Mixer had more effective classification results.

The forecasting power of the mucin-microbiome interplay in livestock respiratory diseases

Complex respiratory diseases are a significant challenge for the livestock industry worldwide. These diseases considerably impact animal health and welfare and cause severe economic losses. One of the first lines of pathogen defense combines the respiratory tract mucus, a highly viscous material primarily composed of mucins, and a thriving multi-kingdom microbial ecosystem. The microbiome-mucin interplay protects from unwanted substances and organisms, but its dysfunction may enable pathogenic infections and the onset of respiratory disease. Emerging evidence also shows that noncoding regulatory RNAs might modulate the structure and function of the microbiome-mucin relationship. This opinion paper unearths the current understanding of the triangular relationship between mucins, the microbiome, and noncoding RNAs in the context of respiratory infections in animals of veterinary interest. There is a need to look at these molecular underpinnings that dictate distinct health and disease outcomes to implement effective prevention, surveillance, and timely intervention strategies tailored to the different epidemiological contexts.

Multi-scale window transformer for cervical cytopathology image recognition

Cervical cancer is a major global health issue, particularly in developing countries where access to healthcare is limited. Early detection of pre-cancerous lesions is crucial for successful treatment and reducing mortality rates. However, traditional screening and diagnostic processes require cytopathology doctors to manually interpret a huge number of cells, which is time-consuming, costly, and prone to human experiences. In this paper, we propose a Multi-scale Window Transformer (MWT) for cervical cytopathology image recognition. We design multi-scale window multi-head self-attention (MW-MSA) to simultaneously integrate cell features of different scales. Small window self-attention is used to extract local cell detail features, and large window self-attention aims to integrate features from smaller-scale window attention to achieve window-to-window information interaction. Our design enables long-range feature integration but avoids whole image self-attention (SA) in ViT or twice local window SA in Swin Transformer. We find convolutional feed-forward networks (CFFN) are more efficient than original MLP-based FFN for representing cytopathology images. Our overall model adopts a pyramid architecture. We establish two multi-center cervical cell classification datasets of two-category 192,123 images and four-category 174,138 images. Extensive experiments demonstrate that our MWT outperforms state-of-the-art general classification networks and specialized classifiers for cytopathology images in the internal and external test sets. The results on large-scale datasets prove the effectiveness and generalization of our proposed model. Our work provides a reliable cytopathology image recognition method and helps establish computer-aided screening for cervical cancer. Our code is available at https://github.com/nmyz669/MWT, and our web service tool can be accessed at https://huggingface.co/spaces/nmyz/MWTdemo.

Automated algorithm for medical data structuring, and segmentation using artificial intelligence within secured environment for dataset creation

Objective

Routinely collected electronic health records using artificial intelligence (AI)-based systems bring out enormous benefits for patients, healthcare centers, and its industries. Artificial intelligence models can be used to structure a wide variety of unstructured data.

Methods

We present a semi-automatic workflow for medical dataset management, including data structuring, research extraction, AI-ground truth creation, and updates. The algorithm creates directories based on keywords in new file names.

Results

Our work focuses on organizing computed tomography (CT), magnetic resonance (MR) images, patient clinical data, and segmented annotations. In addition, an AI model is used to generate different initial labels that can be edited manually to create ground truth labels. The manually verified ground truth labels are later included in the structured dataset using an automated algorithm for future research.

Conclusion

This is a workflow with an AI model trained on local hospital medical data with output based/adapted to the users and their preferences. The automated algorithms and AI model could be implemented inside a secondary secure environment in the hospital to produce inferences.

Spontaneous regeneration of cholecystokinergic reticulospinal axons after a complete spinal cord injury in sea lampreys

In contrast to humans, lampreys spontaneously recover their swimming capacity after a complete spinal cord injury (SCI). This recovery process involves the regeneration of descending axons. Spontaneous axon regeneration in lampreys has been mainly studied in giant descending neurons. However, the regeneration of neurochemically distinct descending neuronal populations with small-caliber axons, as those found in mammals, has been less studied. Cholecystokinin (CCK) is a regulatory neuropeptide found in the brain and spinal cord that modulates several processes such as satiety, or locomotion. CCK shows high evolutionary conservation and is present in all vertebrate species. Work in lampreys has shown that all CCKergic spinal cord axons originate in a single neuronal population located in the caudal rhombencephalon. Here, we investigate the spontaneous regeneration of CCKergic descending axons in larval lampreys following a complete SCI. Using anti-CCK-8 immunofluorescence, confocal microscopy and lightning adaptive deconvolution, we demonstrate the partial regeneration of CCKergic axons (81% of the number of axonal profiles seen in controls) 10 weeks after the injury. Our data also revealed a preference for regeneration of CCKergic axons in lateral spinal cord regions. Regenerated CCKergic axons exhibit colocalization with synaptic vesicle marker SV2, indicative of functional synaptic connections. We also extracted swimming dynamics in injured animals by using DeepLabCut. Interestingly, the degree of CCKergic reinnervation correlated with improved swimming performance in injured animals, suggesting a potential role in locomotor recovery. These findings open avenues for further exploration into the role of specific neuropeptidergic systems in post-SCI spinal locomotor networks.

Artificial intelligence for skin permeability prediction: deep learning

BACKGROUND AND OBJECTIVE

Researchers have put in significant laboratory time and effort in measuring the permeability coefficient (Kp) of xenobiotics. To develop alternative approaches to this labour-intensive procedure, predictive models have been employed by scientists to describe the transport of xenobiotics across the skin. Most quantitative structure-permeability relationship (QSPR) models are derived statistically from experimental data. Recently, artificial intelligence-based computational drug delivery has attracted tremendous interest. Deep learning is an umbrella term for machine-learning algorithms consisting of deep neural networks (DNNs). Distinct network architectures, like convolutional neural networks (CNNs), feedforward neural networks (FNNs), and recurrent neural networks (RNNs), can be employed for prediction.

METHODS

In this project, we used a convolutional neural network, feedforward neural network, and recurrent neural network to predict skin permeability coefficients from a publicly available database reported by Cheruvu et al. The dataset contains 476 records of 145 chemicals, xenobiotics, and pharmaceuticals, administered on the human epidermis in vitro from aqueous solutions of constant concentration either saturated in infinite dose quantities or diluted. All the computations were conducted with Python under Anaconda and Jupyterlab environment after importing the required Python, Keras, and Tensorflow modules.

RESULTS

We used a convolutional neural network, feedforward neural network, and recurrent neural network to predict log kp.

CONCLUSION

This research work shows that deep learning networks can be successfully used to digitally screen and predict the skin permeability of xenobiotics.

Identification method of thyroid nodule ultrasonography based on self-supervised learning dual-branch attention learning framework

Thyroid ultrasound is a widely used diagnostic technique for thyroid nodules in clinical practice. However, due to the characteristics of ultrasonic imaging, such as low image contrast, high noise levels, and heterogeneous features, detecting and identifying nodules remains challenging. In addition, high-quality labeled medical imaging datasets are rare, and thyroid ultrasound images are no exception, posing a significant challenge for machine learning applications in medical image analysis. In this study, we propose a Dual-branch Attention Learning (DBAL) convolutional neural network framework to enhance thyroid nodule detection by capturing contextual information. Leveraging jigsaw puzzles as a pretext task during network training, we improve the network's generalization ability with limited data. Our framework effectively captures intrinsic features in a global-to-local manner. Experimental results involve self-supervised pre-training on unlabeled ultrasound images and fine-tuning using 1216 clinical ultrasound images from a collaborating hospital. DBAL achieves accurate discrimination of thyroid nodules, with a 88.5% correct diagnosis rate for malignant and benign nodules and a 93.7% area under the ROC curve. This novel approach demonstrates promising potential in clinical applications for its accuracy and efficiency.

Computational pathology: A survey review and the way forward

Computational Pathology (CPath) is an interdisciplinary science that augments developments of computational approaches to analyze and model medical histopathology images. The main objective for CPath is to develop infrastructure and workflows of digital diagnostics as an assistive CAD system for clinical pathology, facilitating transformational changes in the diagnosis and treatment of cancer that are mainly address by CPath tools. With evergrowing developments in deep learning and computer vision algorithms, and the ease of the data flow from digital pathology, currently CPath is witnessing a paradigm shift. Despite the sheer volume of engineering and scientific works being introduced for cancer image analysis, there is still a considerable gap of adopting and integrating these algorithms in clinical practice. This raises a significant question regarding the direction and trends that are undertaken in CPath. In this article we provide a comprehensive review of more than 800 papers to address the challenges faced in problem design all-the-way to the application and implementation viewpoints. We have catalogued each paper into a model-card by examining the key works and challenges faced to layout the current landscape in CPath. We hope this helps the community to locate relevant works and facilitate understanding of the field's future directions. In a nutshell, we oversee the CPath developments in cycle of stages which are required to be cohesively linked together to address the challenges associated with such multidisciplinary science. We overview this cycle from different perspectives of data-centric, model-centric, and application-centric problems. We finally sketch remaining challenges and provide directions for future technical developments and clinical integration of CPath. For updated information on this survey review paper and accessing to the original model cards repository, please refer to GitHub. Updated version of this draft can also be found from arXiv.

The shell formation mechanism of Turbo argyrostomus based on ultrastructure and transcriptome analysis

The gold inner shell of Turbo argyrostomus is an important morphological classification characteristic in Gastropoda. However, the gene sets responsible for shell formation in gastropods remain poorly explored. In this study, we investigated the microstructure using scanning electron microscopy (SEM), hematoxylin-eosin (HE) and Alcian blue staining-periodic acid-Schiff (AB-PAS) staining. The SEM results illustrated that the T. argyrostomus shell exhibited a special "sandwich" microstructure. The results of histological observation demonstrated two major cell types: adipocytes and mucin cells. A total of 318 differentially expressed genes were identified between edge mantle and central mantle, among which whey acidic protein, N66, and nacre-like proteins, and Lam G and EGF domains may be related to shell microstructure. 22.39% - 25.20% of the mucin genes had biomineralization related domains, which supported for the relationship between mucins and shell formation. Moreover, this study revealed energy distribution differences between the edge mantle and central mantle. These results provide insights for further understanding of the biomineralization mechanism in Gastropoda.

Role of ERβ in the ovary and ovary related diseases

Estrogen and estrogen receptors (ERα and ERβ) regulate a multitude of complicated physiological and pathological processes. Jan-Ake Gustafsson's group discovered ERβ in 1996, this crucial finding gives us new insights into the understanding of estrogen signaling. ERβ is highly expressed in the ovary and particularly exists in granulosa cells (GCs). ERβ is a key transcription factor in the maintenance of ovarian granulosa cell growth, differentiation, and homeostasis, and the ovulation function of ovarian follicles and oocytes. Additionally, ERβ can modulate the steroidogenic transcriptional program through phosphorylation and regulate both gonadotropin response and FOXL2 expression within the ovary. In this review, we focus on the role of ERβ in regulating ovarian granulosa cell development and homeostasis, particularly its significance in ovarian cancer (OC), premature ovarian failure (POF), and polycystic ovary syndrome (PCOS). It also highlights the prospects of small molecule compounds targeting ERβ, providing a new strategy for the treatment of ovarian-related diseases.

Characterization, phytochemical profiling, antioxidant, and cytotoxicity of underutilized medicinal plants and composite flour

In this investigation, three medicinal plant powders and a composite flour developed from them were analyzed. FESEM/EDS illustrated irregularly shaped particles in the plant powders except for Withania, which had round to oval shape particles. XRD analysis displayed a semi-crystalline nature of powders, except for Asparagus, which showed amorphous behavior. Both methanol and ethanol plant extracts exhibited significantly higher antioxidants, total phenols, and cell viability. Amongst, optimized composite flour (OCF) methanolic extract demonstrated the highest total phenolic content (69.2 ± 0.11 μg GAE/ml), potent cell viability against A549 cells (3.35 ± 0.15% at 50 μg/ml), and strong free-radical scavenging activity (48.89 ± 0.67 at 200 μg/ml). GCMS and FTIR analyses of the methanolic extracts demonstrated the presence of essential phytoconstituents and functional groups. In silico studies of the phytocomponents, ethyl isoallocholate, 3-Deoxy-d-mannoic lactone, and 4,5-Diamino-2-hydroxypyrimidine suggested good binding affinity against BAX, P53, and EGFR proteins with no toxicity and a good drug score.

Research advances in detection of food adulteration and application of MALDI-TOF MS: A review

Food adulteration and illegal supplementations have always been one of the major problems in the world. The threat of food adulteration to the health of consumers cannot be ignored. Food of questionable origin causes economic losses to consumers, but the potential health risks cannot be ignored. However, the traditional detection methods are time-consuming and complex. This review mainly discusses the types of adulteration and technologies used to detect adulteration. Matrix-assisted laser desorption ionization-time-of-flight mass spectrometry (MALDI-TOF MS) is also emphasized in the detection of adulteration and authenticity of origin analysis of various types of food (milk, meat, edible oil, etc.), and the future application direction and feasibility of this technology are analyzed. On this basis, MALDI-TOF MS was compared with other detection methods, highlighting the advantages of this technology in the detection of food adulteration. The future development prospect and direction of this technology are also emphasized.

Unlocking the therapeutic treasure of pomegranate leaf: A comprehensive review on phytochemical compounds, health benefits, and future prospects

The exploration of sustainable and valuable by-products from industrial and agricultural processes is increasingly recognized for its economic, environmental and health advantages. This review examines the phytochemical constituents, biological properties, current applications and future directions of pomegranate (Punica granatum L.) leaf (PGL). PGL exhibits broad biological activities, aiding in managing health conditions like chronic diseases, cancer, diabetes, obesity, and neurological disorders. Anti-cancer and anti-diabetic effects are demonstrated in vitro and in vivo using animal models. Anti-inflammatory and neuroprotective properties are also observed in cell cultures and animal studies. Its anti-microbial properties show efficacy against pathogens. However, variability in phytochemical composition due to different extraction methods and environmental conditions poses challenges for standardization. The review underscores the urgent need for comprehensive human clinical trials to confirm PGL's therapeutic benefits and safety, calling for future research to fully harness PGL's potential as a sustainable and bioactive compound in various industrial applications.

Cellulose derivatives as environmentally-friendly additives in water-based drilling fluids: A review

The application of cellulose derivatives including carboxymethyl cellulose (CMC), polyanionic cellulose (PAC), hydroxyethyl cellulose (HEC), cellulose nanofibrils (CNFs), and cellulose nanocrystals (CNCs) has gained enormous interest, especially as environmentally friendly additives for water-based drilling fluids (WBDFs). This is due to their sustainable, biodegradable, and biocompatible nature. Furthermore, cellulose nanomaterials (CNMs), which include both CNFs and CNCs, possess unique properties such as nanoscale dimensions, a large surface area, as well as unique mechanical, thermal, and rheological performance that makes them stand out as compared to other additives used in WBDFs. The high surface hydration capacity, strong interaction with bentonite, and the presence of a complex network within the structure of CNMs enable them to act as efficient rheological modifiers in WBDFs. Moreover, the nano-size dimension and facilely tunable surface chemistry of CNMs make them suitable as effective fluid loss reducers as well as shale inhibitors as they have the ability to penetrate, absorb, and plug the nanopores within the exposed formation and prevent further penetration of water into the formation. This review provides an overview of recent progress in the application of cellulose derivatives, including CMC, PAC, HEC, CNFs, and CNCs, as additives in WBDFs. It begins with a discussion of the structure and synthesis of cellulose derivatives, followed by their specific application as rheological, fluid loss reducer, and shale inhibition additives in WBDFs. Finally, the challenges and future perspectives are outlined to guide further research and development in the effective utilization of cellulose derivatives as additives in WBDFs.

Potential of nature-based solutions to reduce antibiotics, antimicrobial resistance, and pathogens in aquatic ecosystems. a critical review

This comprehensive scientific review evaluates the effectiveness of nature-based solutions (NBS) in reducing antibiotics (ABs), combating antimicrobial resistance (AMR), and controlling pathogens in various aquatic environments at different river catchment levels. It covers conventional and innovative treatment wetland configurations for wastewater treatment to reduce pollutant discharge into the aquatic ecosystems as well as exploring how river restoration and saltmarshes can enhance pollutant removal. Through the analysis of experimental studies and case examples, the review shows NBS's potential for providing sustainable and cost-effective solutions to improve the health of aquatic ecosystems. It also evaluates the use of diagnostic indicators to predict NBS effectiveness in removing specific pollutants such as ABs and AMR. The review concludes that NBS are feasible for addressing the new challenges stemming from human activities such as the presence of ABs, AMR and pathogens, contributing to a better understanding of NBS, highlighting success stories, addressing knowledge gaps, and providing recommendations for future research and implementation.

Exploring dietary patterns and their association with environmental sustainability and body mass index in children and adolescents: Insights from the National Food, Nutrition and Physical Activity Survey 2015-2016

OBJECTIVE

To adapt four dietary patterns for children and adolescents, and study their associations with sustainability (greenhouse-gas emissions-GHGE and land use-LU), and health (body mass index-BMI) indicators.

RESEARCH METHODS & PROCEDURES

Dietary intake of children (3-9y) and adolescents (10-17y) from the National Food, Nutrition and Physical Activity Survey 2015-2016, Portugal (n = 1153) was assessed through two non-consecutive interviews: one-day food diaries (children), and 24-h recalls (adolescents), using an automated multiple-pass method, including a picture book for portion sizes estimation. Adherence to the Eat-Lancet and World Health Organization (WHO) recommendations, Mediterranean and Atlantic diets were evaluated using adapted versions for pediatrics of the World Index for Sustainability and Health (WISH), Diet Quality Index (DQI), Mediterranean Diet Score (MDS) and Southern-European Atlantic Diet (SEAD), respectively. Diet-related GHGE and LU were estimated using the SHARP-Indicators database. BMI (measured) z-scores were classified according to WHO criteria. A standardized weighted health-sustainability composite index was created (BMI + 0.5*GHGE+0.5*LU). Adjusted linear regression models were computed.

RESULTS

WISH, DQI, MDS, and SEAD had weighted mean scores (range) of 50 (0-130), 24 (9-36), 20 (8-32), and 17 (8-32), respectively. All dietary patterns were associated with lower environmental impact, particularly in children, but not with BMI. Only MDS explained both health and sustainability indicators in childhood (composite index: sd.β = -0·223,95%CI:-0·347,-0·072,R2 = 25.1 %), and adolescence (composite index: sd.β = -0·159,95%CI:-0·315,-0·003,R2 = 31.3 %).

CONCLUSION

The Mediterranean diet was the most related to (higher) environmental sustainability and (lower) BMI. However, in children (not adolescents), the WISH, DQI, and SEAD showed then same associations.

Acclimated green microalgae consortium to treat sewage in an alternative urban WWTP in a coastal area of Central Italy

This study exposed a microalgal consortium formed by Auxenochlorella protothecoides, Tetradesmus obliquus, and Chlamydomonas reinhardtii to six mixed wastewater media containing different proportions of primary (P) or secondary (S) effluents diluted in centrate (C). Algae could grow at centrate concentrations up to 50 %, showing no significant differences between effluents. After acclimation, microalgae cultivated in 50%P-50%C and 50%S-50%C grew at a rate similar to that of control cultures (0.59-0.66 d-1). These results suggest that the consortium acclimated to both sewage streams by modulating the proportion of the species and their metabolism. Acclimation also altered the photosynthetic activity of wastewater-grown samples compared to the control, probably due to partial photoinhibition, changes in consortium composition, and changes in metabolic activity. No major differences were observed between the two streams with respect to biochemical composition, biomass yield, or bioremediation capacity of the cultivated algae but algae grown in the secondary effluent showed qualitatively higher exopolysaccharides (EPS) production than algae grown in primary. Regarding wastewater remediation, microalgae grown in both WW media showed proficient nutrient removal efficiencies (close to 100 %); however, the final pH value (close to 11) would be controversial if the system were upscaled as it is over the legal limit and would cause phosphorus precipitation, so that CO2 addition would be required. The theoretical scale-up of the microalgae system could achieve water treatment costs of 0.109 €·m-3, which was significantly lower than the costs of typical activated sludge systems.

Unlocking the potential of Extensin Signal peptide and Elastin-like polypeptide tag fused to Shigella dysenteriae's IpaDSTxB to improve protein expression and purification in Nicotiana tabacum and Medicagosativa

Plants are often seen as a potent tool in the recombinant protein production industry. However, unlike bacterial expression, it is not a popular method due to the low yield and difficulty of protein extraction and purification. Therefore, developing a new high efficient and easy to purify platform is crucial. One of the best approaches to make extraction easier is to utilize the Extensin Signal peptide (EXT) to translocate the recombinant protein to the outside of the cell, along with incorporating an Elastin-like polypeptide tag (ELP) to enhance purification and accumulation rates. In this research, we transiently expressed Shigella dysenteriae's IpaDSTxB fused to both NtEXT and ELP in both Nicotiana tabacum and Medicago sativa. Our results demonstrated that N. tabacum, with an average yield of 6.39 ng/μg TSP, outperforms M. sativa, which had an average yield of 3.58 ng/μg TSP. On the other hand, analyzing NtEXT signal peptide indicated that merging EXT to the constructs facilitates translocation of IpaDSTxB to the apoplast by 78.4% and 65.9% in N. tabacum and M. sativa, respectively. Conversely, the mean level for constructs without EXT was below 25% for both plants. Furthermore, investigation into the orientation of ELP showed that merging it to the C-terminal of IpaDSTxB leads to a higher accumulation rate in both N. tabacum and M. sativa by 1.39 and 1.28 times, respectively. It also facilitates purification rate by over 70% in comparison to 20% of the 6His tag. The results show a highly efficient and easy to purify platform for the expression of heterologous proteins in plant.

Greening of Svalbard

Svalbard, located between 76°30'N and 80°50'N, is among the regions in the world with the most rapid temperature increase. We processed a cloud-free time-series of MODIS-NDVI for Svalbard. The dataset is interpolated to daily data during the 2000-2022 period with 232 m pixel resolution. The onset of growth, with a clear phenological definition, has been mapped each year. Then the integrated NDVI from the onset (O) of growth each year to the time of average (2000-2022) peak (P) of growth (OP NDVI) have been calculated. OP NDVI has previously shown high correlation with field-based tundra productivity. Daily mean temperature data from 11 meteorological stations are compared with the NDVI data. The OP NDVI values show very high and significant correlation with growing degree days computed from onset to time of peak of growth for all the meteorological stations used. On average for the entire Svalbard, the year 2016 first had the highest greening (OP NDVI values) recorded since the year 2000, then the greening in 2018 surpassed 2016, then 2020 surpassed 2018, and finally 2022 was the year with the overall highest greening by far for the whole 2000-2022 period. This shows a rapid recent greening of Svalbard very strongly linked to temperature increase, although there are regional differences: the eastern parts of Svalbard show the largest variability between years, most likely due to variability in the timing of sea-ice break-up in adjacent areas. Finally, we find that areas dominated by manured moss-tundra in the polar desert zone require new methodologies, as moss does not share the seasonal NDVI dynamics of tundra communities.

Periodate oxidation-mediated nanocelluloses: Preparation, functionalization, structural design, and applications

In recent years, the remarkable progress in nanotechnology has ignited considerable interest in investigating nanocelluloses, an environmentally friendly and sustainable nanomaterial derived from cellulosic feedstocks. Current research primarily focuses on the preparation and applications of nanocelluloses. However, to enhance the efficiency of nanofibrillation, reduce energy consumption, and expand nanocellulose applications, chemical pre-treatments of cellulose fibers have attracted substantial interest and extensive exploration. Various chemical pre-treatment methods yield nanocelluloses with diverse functional groups. Among these methods, periodate oxidation has garnered significant attention recently, due to the formation of dialdehyde cellulose derived nanocellulose, which exhibits great promise for further modification with various functional groups. This review seeks to provide a comprehensive and in-depth examination of periodate oxidation-mediated nanocelluloses (PONCs), including their preparation, functionalization, hierarchical structural design, and applications. We believe that PONCs stand as highly promising candidates for the development of novel nano-cellulosic materials.

Investigating the impact of ultrasound on the structural, physicochemical, and emulsifying characteristics of Dioscorin: Insights from experimental data and molecular dynamics simulation

This study investigated the impact of ultrasound treatment on dioscorin, the primary storage protein found in yam tubers. Three key factors, namely ultrasound power, duration, and frequency, were focused on. The research revealed that ultrasound-induced cavitation effects disrupted non-covalent bonds, resulting in a reduction in α-helix and β-sheet contents, decreased thermal stability, and a decrease in the apparent hydrodynamic diameter (Dh) of dioscorin. Additionally, previously hidden amino acid groups within the molecule became exposed on its surface, resulting in increased surface hydrophobicity (Ho) and zeta-potential. Under specific ultrasound conditions (200 W, 25 kHz, 30 min), Dh decreased while Ho increased, facilitating the adsorption of dioscorin molecules onto the oil-water interface. Molecular dynamics (MD) simulations showed that at lower frequencies and pressures, the structural flexibility of dioscorin's main chain atoms increased, leading to more significant fluctuations between amino acid residues. This transformation improved dioscorin's emulsifying properties and its oil-water interface affinity.

Gaussian process regression coupled with mRMR to predict adulterant concentration in cocaine

Street cocaine is often mixed with various substances that intensify its harmful effects. This paper proposes a framework to identify attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR) intervals that best predict the concentration of adulterants in cocaine samples. Wavelengths are ranked according to their relevance through ReliefF and mRMR feature selection approaches, and an iterative process removes less relevant wavelengths based on the ranking suggested by each approach. Gaussian Process (GP) regression models are constructed after each wavelength removal and the prediction performance is evaluated using RMSE. The subset balancing a low RMSE value and a small percentage of retained wavelengths is chosen. The proposed framework was validated using a dataset consisting of 345 samples of cocaine with different amounts of levamisole, caffeine, phenacetin, and lidocaine. Averaged over the four adulterants, the GP regression coupled with the mRMR retained 1.07 % of the 662 original wavelengths, outperforming PLS and SVR regarding prediction performance.

Preclinical evaluation of stereopure antisense oligonucleotides for allele-selective lowering of mutant HTT

Huntington's disease (HD) is an autosomal dominant disease caused by the expansion of cytosine-adenine-guanine (CAG) repeats in one copy of the HTT gene (mutant HTT, mHTT). The unaffected HTT gene encodes wild-type HTT (wtHTT) protein, which supports processes important for the health and function of the central nervous system. Selective lowering of mHTT for the treatment of HD may provide a benefit over nonselective HTT-lowering approaches, as it aims to preserve the beneficial activities of wtHTT. Targeting a heterozygous single-nucleotide polymorphism (SNP) where the targeted variant is on the mHTT gene is one strategy for achieving allele-selective activity. Herein, we investigated whether stereopure phosphorothioate (PS)- and phosphoryl guanidine (PN)-containing oligonucleotides can direct allele-selective mHTT lowering by targeting rs362273 (SNP3). We demonstrate that our SNP3-targeting molecules are potent, durable, and selective for mHTT in vitro and in vivo in mouse models. Through comparisons with a surrogate for the nonselective investigational compound tominersen, we also demonstrate that allele-selective molecules display equivalent potency toward mHTT with improved durability while sparing wtHTT. Our preclinical findings support the advancement of WVE-003, an investigational allele-selective compound currently in clinical testing (NCT05032196) for the treatment of patients with HD.

Soil data augmentation and model construction based on spectral difference and content difference

Soil analysis makes for developing precision agriculture and monitoring land quality, while the models available for spectroscopy-based chemometrics are constrained by limited samples from small areas. The paper proposed sample expansion and model construction based on spectral difference and content difference, realizing data augmentation and deep learning applied to original samples with limited numbers. The spectral subtraction based on maximum or minimum values exploited the maximum or minimum values to acquire the spectral difference and content difference, which provided a new data form for model construction. Keeping enhanced samples whose spectral difference and content difference were all zero was useful for improving model performance. Augmentation of all data or training data based on maximum or minimum values-based spectral subtraction, which sorted the contents and made them the maximum or minimum values in sequence, achieved sample expansion by the spectral difference and content difference. The model utilized the random vector functional link (RVFL) network, extreme learning machine (ELM), and one-dimensional convolutional neural network (1D CNN), which could predict the content of new samples through ensemble averaging when predicting content difference. The experimental result showed the model of the spectral subtraction based on maximum or minimum values had a similar performance to that of the original samples. Augmentation of all data improved model performance by only RVFL and ELM. Augmentation of training data verified 1D CNN was better than RVFL and ELM. The paper implements a new data augmentation method and applies CNN to original samples with inadequate numbers, which lays the foundation for an improved model and applying spectral preprocessing.

Segmenting medical images with limited data

While computer vision has proven valuable for medical image segmentation, its application faces challenges such as limited dataset sizes and the complexity of effectively leveraging unlabeled images. To address these challenges, we present a novel semi-supervised, consistency-based approach termed the data-efficient medical segmenter (DEMS). The DEMS features an encoder-decoder architecture and incorporates the developed online automatic augmenter (OAA) and residual robustness enhancement (RRE) blocks. The OAA augments input data with various image transformations, thereby diversifying the dataset to improve the generalization ability. The RRE enriches feature diversity and introduces perturbations to create varied inputs for different decoders, thereby providing enhanced variability. Moreover, we introduce a sensitive loss to further enhance consistency across different decoders and stabilize the training process. Extensive experimental results on both our own and three public datasets affirm the effectiveness of DEMS. Under extreme data shortage scenarios, our DEMS achieves 16.85% and 10.37% improvement in dice score compared with the U-Net and top-performed state-of-the-art method, respectively. Given its superior data efficiency, DEMS could present significant advancements in medical segmentation under small data regimes. The project homepage can be accessed at https://github.com/NUS-Tim/DEMS.

Association Between Combined Polymetallic Exposure and Osteoporosis

Combined polymetallic exposure may be an influential factor in osteoporosis. This study aimed to explore the association between polymetallic combined exposure and osteoporosis. A total of 2115 participants were included. Plasma concentrations of 22 metals were determined by inductively coupled plasma mass spectrometry. Osteoporosis was defined as a T ≤  - 2.5. The least absolute shrinkage and selection operator (LASSO) regression, binary logistics regression, and Bayesian kernel machine regression (BKMR) model were used to explore the association between plasma metals and osteoporosis. LASSO regression showed that 10 metals were associated with osteoporosis in the total population (magnesium, calcium, manganese, nickel, cobalt, arsenic, selenium, rubidium, cadmium, aluminum) and women (magnesium, calcium, molybdenum, nickel, cobalt, arsenic, selenium, rubidium, cadmium, aluminum), and four metals associated with men (magnesium, cobalt, aluminum, iron). Logistics regression showed that in total population, magnesium (ORQ3 = 0.653, 95% CI = 0.446-0.954) was negatively correlated with osteoporosis, while aluminum (ORQ2 = 1.569, 95% CI = 1.095-2.248, ORQ4 = 1.616, 95% CI = 1.109-2.354) and cadmium (ORQ4 = 1.989, 95% CI = 1.379-2.870) were positively correlated; in women, magnesium (ORQ3 = 0.579, 95% CI = 0.379-0.883) was negatively correlated with osteoporosis, while aluminum (ORQ2 = 1.563, 95% CI = 1.051-2.326, ORQ4 = 1.543, 95% CI = 1.024-2.326) and cadmium (ORQ3 = 1.482, 95% CI = 1.003-2.191, ORQ4 = 1.740, 95% CI = 1.167-2.596) were positively correlated. BKMR model showed that combined polymetallic exposure had an overall positive effect on osteoporosis, magnesium was negatively associated with osteoporosis, and cadmium, selenium, and aluminum were positively associated with osteoporosis. Metal mixtures in plasma were associated with osteoporosis risk. Magnesium may reduce the risk of osteoporosis, while cadmium, selenium, and aluminum may increase the risk of osteoporosis. Future studies needed to explore correlations and mechanisms.

Optimizing reproductive performance in pangasius catfish broodstock: A review of dietary and molecular strategies

Pangasius catfish, a significant player in the global whitefish market, encounters challenges in aquaculture production sustainability. Quality broodstock maintenance and seed production are impeded by growth, maturation, and fecundity issues. This review investigates the efficacy of strategic nutrient composition and molecular strategies in enhancing broodstock conditions and reproductive performance across various fish species. A notable knowledge gap for Pangasius catfish hampers aquaculture progress. The review assesses nutrient manipulation's impact on reproductive physiology, emphasizing pangasius broodstock. A systematic review analysis following PRISMA guidelines was conducted to identify research trends and hotspots quantitatively, revealing a focus on P. bocourti and fertilization techniques. Addressing this gap, the review offers insights into dietary nutrients manipulation and genetic tool utilization for improved seed production, contributing to pangasius catfish aquaculture sustainability.

The study of galactomannans with different molecular weights and their ability to form microparticles suitable for pulmonary delivery

The influence of locust bean gum (LBG) galactomannans (GMs) molecular weight (Mw) to assemble microparticulate systems was evaluated, and carriers for deep lung delivery were developed. A commercial batch of LBG with a mannose/galactose (M/G) ratio of 2.4 (batch 1) was used to study the influence of different microwave partial acid hydrolysis conditions on carbohydrate composition, glycosidic linkages, and aqueous solutions viscosity. The microwave treatment did not affect the composition, presenting 4-Man (36-42 %), 4,6-Man (27-35 %), and T-Gal (24-25 %) as the main glycosidic linkages. Depolymerization led to a viscosity reduction (≤0.005 Pa·s) with no major impact on polysaccharide debranching. The structural composition of the LBG galactomannans were further elucidated with sequence-specific proteins using carbohydrate microarray technologies. A second batch of LBG (M/G 3.3) was used to study the impact of GMs with different Mw on microparticle assembling, characteristics, and insulin release kinetics. The low-Mw GMs microparticles led to a faster release (20 min) than the higher-Mw (40 min) ones, impacting the release kinetics. All microparticles exhibited a safety profile to cells of the respiratory tract. However, only the higher-Mw GMs allowed the assembly of microparticles with sizes suitable for this type of administration.

Joint replacement risk is markedly increased in alkaptonuria (AKU) in those with prior arthroplasty

Background

Increased homogentisic acid (HGA) in alkaptonuria (AKU) causes severe arthritis. Nitisinone reduces the production of HGA, but whether it also decreases arthroplasty was examined in 237 AKU patients.

Patients and methods

Patients attending the United Kingdom National Alkaptonuria Centre (NAC) and the Suitability of Nitisinone in Alkaptonuria 2 (SONIA 2) study were studied. Assessments included questionnaires eliciting details of arthroplasty. Nitisinone was administered from baseline, 2 mg in the NAC and 10 mg in SONIA 2. In SONIA 2, subgroups consisted of those with baseline arthroplasty on and not on nitisinone (BR + N+, BR + N-), as well as those without baseline arthroplasty on and not on nitisinone (BR-N+, BR-N-).

Results

In the SONIA2 subgroups, new joint replacement (JR) probabilities after baseline were significantly different (BR + N+, BR + N-, BR-N+, BR-N-) (χ2 = 23.3, p < 0.001); mean (SD) was 3.8 (0.1) years in BR-N-, 3.7 (0.1) years in BR-N+, 3.4 (0.3) years in BR + N-, and 3.0 (0.3) years in BR + N+. Further, the BR + N- showed more JR than the BR-N- subgroup (p < 0.01), while BR + N+ similarly showed more JR than the BR-N+ subgroup (p < 0.001).In the NAC, the BR- group had a mean age of 51.6 (7.0) years at baseline but 57.7 (8.7) years at final follow up during nitisinone therapy and showed only 7 incident JR. The BR+ group had an age at baseline of 57.4 (8.5) years and had undergone 94 JRs at baseline.

Conclusion

The incidence of arthroplasty was earlier and more frequent after the first JR and was not affected by nitisinone.

The mother-offspring transfer of chlorothalonil through human breast milk: A multi-city cross-sectional study

4-Hydroxychlorothalonil (4-OH CHT), the main metabolite of chlorothalonil and the most widely used fungicide, has been frequently detected in human samples during monitoring. 4-OH CHT may exhibit higher toxicity and persistence in the environment compared to its prototype. In this study, a total of 540 paired serum and breast milk samples from pregnant women in three provinces in China were monitored for contaminant residues. 4-OH CHT was analyzed in the samples using ultra high-performance liquid chromatography - high-resolution mass spectrometry with a detection limit of 20 ng/L. The study investigated the effects of demographic factors, such as BMI, region of residence, and education level, on the levels of 4-OH CHT residues in serum and breast milk. Among the three provinces, the highest median concentration of 4-OH CHT in serum samples was observed in Hebei (1.04 × 103 ng/L), while the highest median concentration of 4-OH CHT in breast milk samples was observed in Hubei and Guangdong (491 ng/L). Multiple linear regression was used to investigate the significant positive correlation between 4-OH CHT in serum and breast milk (p = 0.000) after adjusting for personal characteristics. Based on this, the study further explored the influencing factors of transfer efficiencies (TEs) in conjunction with the individual TEs and the personal characteristics of the participants. Our results demonstrated that the age of the volunteers and their exercise habits had an effect on TEs, but further studies are needed to determine whether exercise leads to an increase in TEs.

Deciphering the enigma of the function of alpha-tocopherol as a vitamin

α-Tocopherol (α-T) is a vitamin, but the reasons for the α-T requirement are controversial. Given that α-T deficiency was first identified in embryos, we studied to the premier model of vertebrate embryo development, the zebrafish embryo. We developed an α-T-deficient diet for zebrafish and used fish consuming this diet to produce α-T deficient (E-) embryos. We showed that α-T deficiency causes increased lipid peroxidation, leading to metabolic dysregulation that impacts both biochemical and morphological changes at very early stages in development. These changes occur at an early developmental window, which takes place prior to an analogous time to when a human knows she is pregnant. We found that α-T limits the chain reaction of lipid peroxidation and protects metabolic pathways and integrated gene expression networks that control embryonic development. Importantly, not only is α-T critical during early development, but the neurodevelopmental process is highly dependent on α-T trafficking by the α-T transfer protein (TTPa). Data from both gene expression and evaluation of the metabolome in E- embryos suggest that the activity of the mechanistic Target of Rapamycin (mTOR) signaling pathway is dysregulated-mTOR is a master regulatory mechanism, which controls both metabolism and neurodevelopment. Our findings suggest that TTPa is needed not only for regulation of plasma α-T in adults but is a key regulator during embryogenesis.

Environmental drivers of phytoplankton community dynamics in an agriculturally-influenced tributary in the lower Great Lakes

Phytoplankton community composition in tributaries differs from that in their receiving waters, due to light limitation from suspended particles and other factors such as nutrient availability and temperature. This study was designed to manipulate light levels in early, mid, and late summer to determine the combined effects of light attenuation and naturally varying nutrient availability on phytoplankton community composition in an agriculturally-influenced tributary of the lower Great Lakes. In all trials, in situ microcosm experiments show that phytoplankton abundance increased under three light attenuation treatments (60 %, 75 %, and 85 % attenuation) relative to time-zero, but higher light attenuation reduced total phytoplankton abundance relative to controls. Highest phytoplankton diversity in terms of richness and evenness occurred in September (late summer), and across all three trials was lowest under the highest light attenuation treatments (85 %). Phytoplankton community composition followed a normal seasonal shift from diatoms dominating in June (early summer), followed by cyanobacteria dominating in mid to late summer. In general, lower light levels (especially 85 % attenuation) corresponded with an increased dominance of cyanobacteria. These findings support the hypothesis that phytoplankton abundance and diversity vary with light and nutrient availability and that light attenuation promotes the shift from buoyant cyanobacteria to other taxa more tolerant of low light levels.

Untargeted metabolomics-based identification of bioactive compounds from Mangifera indica L. seed extracts in drug discovery through molecular docking and assessment of their anticancer potential

BACKGROUND

Mangifera indica L. (mango), a medicinal plant rich in biologically active compounds, has potential to be used in disease-preventing and health-promoting products. The present investigation reveals and uncovers bioactive metabolites with remarkable therapeutic efficiency from mango (family: Anacardiaceae) seeds.

RESULTS

Biological activity was determined by antimicrobial, antioxidant and anticancer assays, and metabolite profiling was performed on gas chromatography coupled to quadrupole time-of-flight mass spectrometry (GC-QTOF-MS) and liquid chromatography coupled to quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) platforms. Validation of active metabolites was carried out by in silico molecular docking (Molinspiration Cheminformatics Server and PASS). Extracted and identified metabolites were screened; 54 compounds associated with various groups were selected for the in silico interaction study.

CONCLUSIONS

Molecular docking revealed lead molecules with a potential binding energy score, efficacy and stable modulation with a selected protein domain. Investigation, directed by in vitro and in silico analysis, confirms mango seeds as an excellent source of potential metabolites as a therapeutic agent. © 2024 Society of Chemical Industry.

Enhanced denitrification and p-nitrophenol removal performance via hydrophilic sponge carriers fixed with dual-bacterial: Optimization, performance, and enhancement mechanism

Effective treatment of industrial wastewater containing complex pollutants, such as nitrate (NO3--N) and organic pollutants, remains a significant challenge to date. Here, a strain Nocardioides sp. ZS2 with denitrification and degradation of p-nitrophenol (PNP) was isolated and its culture conditions were optimized by kinetic analysis. Hydrophilic sponge carriers were prepared using polyvinyl alcohol (PVA), carboxymethyl cellulose (CMC), and chitosan (CS) to construct bioreactors. Furthermore, to further enhance the PNP degradation and denitrification performance of bioreactors, Pseudomonas stutzeri GF2 with denitrification capability was introduced. The results revealed that the removal efficiencies of PNP and NO3--N reached 97.9 % and 91.9 %, respectively, when hydraulic retention time (HRT) of 6 h, C/N of 2.0, and pH of 6.5. The bioreactor exhibited stable denitrification performance even with fluctuations in the influent PNP concentration. The potential functional prediction results revealed that the abundance of amino acids, fatty acids, and carbohydrates increased as the influent C/N decreased, reflecting a tendency of the microbial community to adjust carbon source utilization to maintain cell growth, metabolic balance, and resist adverse C/N environments. This research provides new insights into the effective removal of organic pollutants and NO3--N in wastewater treatment.

Multiomics analysis of the effects of manure-borne doxycycline combined with oversized fiber microplastics on pak choi growth and the risk of antibiotic resistance gene transmission

In this study, oversized microplastics (OMPs) were intentionally introduced into soil containing manure-borne doxycycline (DOX). This strategic approach was used to systematically examine the effects of combined OMP and DOX pollution on the growth of pak choi, analyze alterations in soil environmental metabolites, and explore the potential migration of antibiotic resistance genes (ARGs). The results revealed a more pronounced impact of DOX than of OMPs. Slender-fiber OMPs (SF OMPs) had a more substantial influence on the growth of pak choi than did coarse-fiber OMPs (CF OMPs). Conversely, CF OMPs had a more significant effect on the migration of ARGs within the system. When DOX was combined with OMPs, the negative effects of DOX on pak choi growth were mitigated through the synthesis of indole through the adjustment of carbon metabolism and amino acid metabolism in pak choi roots. In this process, Pseudohongiellaceae and Xanthomonadaceae were key bacteria. During the migration of ARGs, the potential host bacterium Limnobacter should be considered. Additionally, the majority of potential host bacteria in the pak choi endophytic environment were associated with tetG. This study provides insights into the intricate interplay among DOX, OMPs, ARGs, plant growth, soil metabolism, and the microbiome.

Mesenchymal stem cells biological and biotechnological advances: Implications for clinical applications

BACKGROUND

Mesenchymal stem cells (MSCs) are multipotent stem cells that are able to differentiate into multiple lineages including bone, cartilage, muscle and fat. They hold immunomodulatory properties and therapeutic ability to treat multiple diseases, including autoimmune and chronic degenerative diseases. In this article, we reviewed the different biological properties, applications and clinical trials of MSCs. Also, we discussed the basics of manufacturing conditions, quality control, and challenges facing MSCs in the clinical setting.

METHODS

Extensive review of the literature was conducted through the databases PubMed, Google Scholar, and Cochrane. Papers published since 2015 and covering the clinical applications and research of MSC therapy were considered. Furthermore, older papers were considered when referring to pioneering studies in the field.

RESULTS

The most widely studied stem cells in cell therapy and tissue repair are bone marrow-derived mesenchymal stem cells. Adipose tissue-derived stem cells became more common and to a lesser extent other stem cell sources e.g., foreskin derived MSCs. MSCs therapy were also studied in the setting of COVID-19 infections, ischemic strokes, autoimmune diseases, tumor development and graft rejection. Multiple obstacles, still face the standardization and optimization of MSC therapy such as the survival and the immunophenotype and the efficiency of transplanted cells. MSCs used in clinical settings displayed heterogeneity in their function despite their extraction from healthy donors and expression of similar surface markers.

CONCLUSION

Mesenchymal stem cells offer a rising therapeutic promise in various diseases. However, their potential use in clinical applications requires further investigation.

Recognising the role of ruderal species in restoration of degraded lands

Ruderal plants are an important component of plant communities that develop on the range of anthropogenically degraded lands. Yet they were highly neglected and not recognised as desirable for restoration purposes. The aim of this study was to analyse the potential for using ruderal species in restoration processes and to identify preliminary criteria for species selection that could be included in ecological restoration of degraded man-made habitats under future conditions of increased human disturbance and climate changes. The desirable characteristics of the species depend primarily on the type of habitat to be restored, with plant height, specific leaf area, rooting depth and seed characteristics being the most important traits. The recognised ecosystem services of the species analysed show that the provisioning and regulating services are well represented, particularly erosion control, pollination, phytoremediation and other soil quality improvements. Most of the dominant and diagnostic ruderal species from the man-made habitats of the north-western Balkan Peninsula are sensitive to climate change and their potential distribution range is expected to decrease at the European scale. Higher certain ecological indicator values, as well as values for disturbance severity, frequency and soil disturbance indices were found for species that are expected to increase their range. Ruderal species are becoming increasingly important for restoration purposes, as the focus shifts to the significance of early successional species. The inclusion of ruderal species in the restoration of degraded sites should be based on criteria such as: non-invasiveness, plant traits favourable for colonisation (height, SLA, seed traits, rooting depth), values of ecological and disturbance indices, provision of ecosystem services, and change of distribution range under changing climate conditions.

Ethnobotanical uses, phytochemistry and bioactivities of Cymbopogon plants: A review

ETHNOPHARMACOLOGICAL RELEVANCE

Cymbopogon (Poaceae) plants have been used for various purposes by many indigenous peoples in all continents. In particular, almost all species in the genus have traditionally been used as folk medicine to treat ailments. Traditional application records indicated that Cymbopogon might be used extensively to treat cold, dizziness, headache, loss of appetite, abdominal pain, rheumatism, diarrhea, whole grass for cold, sore throat, tracheitis and others.

AIMS OF THE REVIEW

Despite several research confirmed that Cymbopogon includes a range of active components, no review has been undertaken to consolidate information on its traditional uses, phytochemistry, pharmacology, and/or quality control. Thus this article aims to update a comprehensive review about the traditional uses, phytochemistry, pharmacology, cultivation techniques, economic benefits, trade, threats, and future conservation implications of Cymbopogon species. It may provide informative data for future development and further investigation of this important plant group.

MATERIALS AND METHODS

Traditional medicinal books and ethnomedicinal publications related to Cymbopogon from 1992 to 2023 were collated to investigate its ethnobotanical, phytochemical and pharmacological information. The online databases including Google Scholar, SciFinder, Web of Science, Scopus, Springer Link, PubMed, Wiley, China National Knowledge Infrastructure (CNKI), Baidu Scholar, and WanFang Database were screened.

RESULTS

Cymbopogon (Gramineae or Poaceae) plants have been grown worldwide. Traditional Chinese medicine and other medicinal systems believes that Cymbopogon has the effect of relieve a cough, analgesia, treating dizziness, traumatic injury and can relieve abdominal pain. A total of 153 compounds, including flavonoids, terpenoids, fatty acid and other compounds were isolated or identified from Cymbopogon species by phytochemical studies. The extracts or compounds from Cymbopogon have exhibited numerous biological activities such as antibacterial, antiinflammatory, antiviral, antineoplastic, antiarrhythmic, antidiabetic and other activities. The rich contents of citronellal, citronellol and geraniol found in Cymbopogon also provide significant nutritional benefits.

CONCLUSION

Based on their traditional uses, phytochemicals, and pharmacological activities, Cymbopogon plants are potential medicinal and edible resources with diverse pharmacological effects. Due to various advantages of this group, they possess huge application potential in food and pharmaceutical industries, and animal husbandry. Among them, citronella is very important in terms of economic development. Further comprehensive research to evaluate the medicinal properties of Cymbopogon species will be necessary for future development.

White matter tract density index is associated with disability in multiple sclerosis

BACKGROUND

The association between common neuroradiological markers of multiple sclerosis (MS) and clinical disability is weak. Given that the disability in patients with MS may depend on the underlying structural connectivity of the brain, our study aimed to examine the association between white matter tracts affected by MS and the patients' disability using a new tract density index (TDI).

METHOD

This study included 53 patients diagnosed with MS, examined between 2019 and 2020. Manual lesion segmentation was performed on fluid-attenuated inversion recovery (FLAIR) images, and the density of white matter tracts encompassing the lesion (i.e., TDI) was calculated. Correlation analysis was employed to assess the association between TDI and disability. Additionally, the relationship between disability, TDI, and lesion-derived network metrics was examined by computing a partial correlation network.

RESULTS

The TDI significantly correlated with the expanded disability status scale (EDSS) (r = 0.30, p = 0.03). Furthermore, the patient's disability is linked solely through TDI to lesion-derived network metrics -a key metric that 'bridges' the gap between the brain lesion and disability.

CONCLUSIONS

In this study, MS lesions encompassing regions with high white matter tract density were associated and linked with severe physical disability. These findings indicate that TDI may be an outcome predictor that may connect radiologic findings to clinical practice.

The Coronavirus helicase in replication

The coronavirus nonstructural protein (nsp) 13 encodes an RNA helicase (nsp13-HEL) with multiple enzymatic functions, including unwinding and nucleoside phosphatase (NTPase) activities. Attempts for enzymatic inactivation have defined the nsp13-HEL as a critical enzyme for viral replication and a high-priority target for antiviral development. Helicases have been shown to play numerous roles beyond their canonical ATPase and unwinding activities, though these functions are just beginning to be explored in coronavirus biology. Recent genetic and biochemical studies, as well as work in structurally-related helicases, have provided evidence that supports new hypotheses for the helicase's potential role in coronavirus replication. Here, we review several aspects of the coronavirus nsp13-HEL, including its reported and proposed functions in viral replication and highlight fundamental areas of research that may aid the development of helicase inhibitors.

Artificial Intelligence and Multiple Sclerosis

In this paper, we analyse the different advances in artificial intelligence (AI) approaches in multiple sclerosis (MS). AI applications in MS range across investigation of disease pathogenesis, diagnosis, treatment, and prognosis. A subset of AI, Machine learning (ML) models analyse various data sources, including magnetic resonance imaging (MRI), genetic, and clinical data, to distinguish MS from other conditions, predict disease progression, and personalize treatment strategies. Additionally, AI models have been extensively applied to lesion segmentation, identification of biomarkers, and prediction of outcomes, disease monitoring, and management. Despite the big promises of AI solutions, model interpretability and transparency remain critical for gaining clinician and patient trust in these methods. The future of AI in MS holds potential for open data initiatives that could feed ML models and increasing generalizability, the implementation of federated learning solutions for training the models addressing data sharing issues, and generative AI approaches to address challenges in model interpretability, and transparency. In conclusion, AI presents an opportunity to advance our understanding and management of MS. AI promises to aid clinicians in MS diagnosis and prognosis improving patient outcomes and quality of life, however ensuring the interpretability and transparency of AI-generated results is going to be key for facilitating the integration of AI into clinical practice.

Contrastive Learning for Joint Normal Estimation and Point Cloud Filtering

Point cloud filtering and normal estimation are two fundamental research problems in the 3D field. Existing methods usually perform normal estimation and filtering separately and often show sensitivity to noise and/or inability to preserve sharp geometric features such as corners and edges. In this article, we propose a novel deep learning method to jointly estimate normals and filter point clouds. We first introduce a 3D patch based contrastive learning framework, with noise corruption as an augmentation, to train a feature encoder capable of generating faithful representations of point cloud patches while remaining robust to noise. These representations are consumed by a simple regression network and supervised by a novel joint loss, simultaneously estimating point normals and displacements that are used to filter the patch centers. Experimental results show that our method well supports the two tasks simultaneously and preserves sharp features and fine details. It generally outperforms state-of-the-art techniques on both tasks.

Scots pine - panmixia and the elusive signal of genetic adaptation

Scots pine is the foundation species of diverse forested ecosystems across Eurasia and displays remarkable ecological breadth, occurring in environments ranging from temperate rainforests to arid tundra margins. Such expansive distributions can be favored by various demographic and adaptive processes and the interactions between them. To understand the impact of neutral and selective forces on genetic structure in Scots pine, we conducted range-wide population genetic analyses on 2321 trees from 202 populations using genotyping-by-sequencing, reconstructed the recent demography of the species and examined signals of genetic adaptation. We found a high and uniform genetic diversity across the entire range (global FST 0.048), no increased genetic load in expanding populations and minor impact of the last glacial maximum on historical population sizes. Genetic-environmental associations identified only a handful of single-nucleotide polymorphisms significantly linked to environmental gradients. The results suggest that extensive gene flow is predominantly responsible for the observed genetic patterns in Scots pine. The apparent missing signal of genetic adaptation is likely attributed to the intricate genetic architecture controlling adaptation to multi-dimensional environments. The panmixia metapopulation of Scots pine offers a good study system for further exploration into how genetic adaptation and plasticity evolve under gene flow and changing environment.

Assessing avian diversity and red squirrel occurrence in fragmented high-altitude mountain pine forests of the central French Pyrenees: A dataset of point counts

In the spring of 1987, point-count surveys of breeding birds (passerines and picidae) were conducted, resulting in a dataset of 197 counts. The purpose was to analyze the effects of forest fragmentation on bird community composition in a mountain pine forest located in the Néouvielle National Nature Reserve in the central French Pyrenees between 1800 and 2400 metres. The study aimed to differentiate between the impacts of landscape factors (patch area, isolation) and habitat characteristics (altitude, vegetation structure). Additional information was gathered regarding the presence of Common Crossbill (Loxia curvirostra), Great Spotted Woodpecker (Dendrocopos major), Red Squirrel (Sciurus vulgaris), and Capercaillie (Tetrao urogallus) in the forest. The sampling design ensured that the selected patches represented a wide range of sizes and distances to the nearest large pine patch or low-altitude forest stand. Bird sampling utilized the point-count technique [3], focusing on singing passerines and Picidae within a 50-metre radius. The altitude, the percentage of open areas, of stones, boulders and of herbaceous and ligneous plant cover at various heights, the canopy height and number of dead trees, along with landscape variables describing patch size and isolation from large pine stands or low-altitude forests, were assessed for each point count. This dataset offers insight into the breeding bird community and squirrel occurrence in a typical high-altitude mountain pine forest in the Pyrenees in 1987, serving as a baseline for future comparisons to study changes in bird and squirrel populations, the impact of climate change, habitat fragmentation, and conservation priorities. These data aim to inspire further research and enhance our understanding of bird and squirrel ecology in mountain regions.

OnionFoliageSET: Labeled dataset for small onion and foliage flower crop detection

Digital image datasets for Precision Agriculture (PA) still need to be available. Many problems in this field of science have been studied to find solutions, such as detecting weeds, counting fruits and trees, and detecting diseases and pests, among others. One of the main fields of research in PA is detecting different crop types with aerial images. Crop detection is vital in PA to establish crop inventories, planting areas, and crop yields and to have information available for food markets and public entities that provide technical help to small farmers. This work proposes public access to a digital image dataset for detecting green onion and foliage flower crops located in the rural area of Medellín City - Colombia. This dataset consists of 245 images with their respective labels: green onion (Allium fistulosum), foliage flowers (Solidago Canadensis and Aster divaricatus), and non-crop areas prepared for planting. A total of 4315 instances were obtained, which were divided into subsets for training, validation, and testing. The classes in the images were labeled with the polygon method, which allows training machine learning algorithms for detection using bounding boxes or segmentation in the COCO format.

Empirical validation of Conformal Prediction for trustworthy skin lesions classification

BACKGROUND AND OBJECTIVE

Uncertainty quantification is a pivotal field that contributes to realizing reliable and robust systems. It becomes instrumental in fortifying safe decisions by providing complementary information, particularly within high-risk applications. existing studies have explored various methods that often operate under specific assumptions or necessitate substantial modifications to the network architecture to effectively account for uncertainties. The objective of this paper is to study Conformal Prediction, an emerging distribution-free uncertainty quantification technique, and provide a comprehensive understanding of the advantages and limitations inherent in various methods within the medical imaging field.

METHODS

In this study, we developed Conformal Prediction, Monte Carlo Dropout, and Evidential Deep Learning approaches to assess uncertainty quantification in deep neural networks. The effectiveness of these methods is evaluated using three public medical imaging datasets focused on detecting pigmented skin lesions and blood cell types.

RESULTS

The experimental results demonstrate a significant enhancement in uncertainty quantification with the utilization of the Conformal Prediction method, surpassing the performance of the other two methods. Furthermore, the results present insights into the effectiveness of each uncertainty method in handling Out-of-Distribution samples from domain-shifted datasets. Our code is available at: github.com/jfayyad/ConformalDx.

CONCLUSIONS

Our conclusion highlights a robust and consistent performance of conformal prediction across diverse testing conditions. This positions it as the preferred choice for decision-making in safety-critical applications.

Spatial datasets of 30-year (1991-2020) average monthly total precipitation and minimum/maximum temperature for Canada and the United States

Thin plate smoothing spline models, covering Canada and the continental United States, were developed using ANUSPLIN for 30-year (1991-2020) monthly mean maximum and minimum temperature and precipitation. These models employed monthly weather station values from the North American dataset published by National Oceanic and Atmospheric Administration's (NOAA's) National Centers for Environmental Information (NCEI). Maximum temperature mean absolute errors (MAEs) ranged between 0.54 °C and 0.64 °C (approaching measurement error), while minimum temperature MAEs were slightly higher, varying from 0.87 °C to 1.0 °C. On average, thirty-year precipitation estimates were accurate to within approximately 10 % of total precipitation levels, ranging from 9.0 % in the summer to 12.2 % in the winter. Error rates were higher in Canada compared to estimates in the United States, consistent with a less dense station network in Canada relative to the United States. Precipitation estimates in Canada exhibited MAEs representing 14.7 % of mean total precipitation compared to 9.7 % in the United States. The datasets exhibited minimal bias overall; 0.004 °C for maximum temperature, 0.01 °C for minimum temperature, and 0.5 % for precipitation. Winter months showed a greater dry bias (0.8 % of total winter precipitation) compared to other seasons (-0.4 % of precipitation). These 30-year gridded datasets are available at ∼2 km resolution.

Associations between paediatric obesity, chemical mixtures and environmental factors, in a national cross-sectional study of Canadian children

BACKGROUND

Whilst single chemical exposures are suspected to be obesogenic, the combined role of chemical mixtures in paediatric obesity is not well understood.

OBJECTIVES

We aimed to evaluate the potential associations between chemical mixtures and obesity in a population-based sample of Canadian children.

METHODS

We ascertained biomonitoring and health data for children aged 3-11 from the cross-sectional Canadian Health Measures Survey from 2007 to 2019. Several chemicals of interest were measured in blood or urine and paediatric obesity was defined based on measured anthropometrics. Using quantile-based G computational analysis, we quantified the effects of three chemical mixtures selected a priori. Models were adjusted for sociodemographic and environmental factors identified through a directed acyclic graph. Results are presented through adjusted relative risks (RR) with 95% confidence intervals (95% CI).

RESULTS

We included 9147 children. Of these, 24.1% were overweight or obese. Exposure to the mixture of bisphenol A, acrylamide, glycidamide, metals, parabens and arsenic increased the risk of childhood overweight or obesity by 45% (95% CI 1.09, 1.93), obesity by 109% (95% CI 1.27, 3.42) and central obesity by 82% (95% CI 1.30, 2.56).

CONCLUSIONS

Our findings support the role of early childhood chemical exposures in paediatric obesity and the potential combined effects of chemicals.

Heavy metal stress and mitogen activated kinase transcription factors in plants: Exploring heavy metal-ROS influences on plant signalling pathways

Due to their stationary nature, plants are exposed to a diverse range of biotic and abiotic stresses, of which heavy metal (HM) stress poses one of the most detrimental abiotic stresses, targeting diverse plant processes. HMs instigate the overproduction of reactive oxygen species (ROS), and to mitigate the adverse effects of ROS, plants induce multiple defence mechanisms. Besides the negative implications of overproduction of ROS, these molecules play a multitude of signalling roles in plants, acting as a central player in the complex signalling network of cells. One of the ROS-associated signalling mechanisms is the mitogen-activated protein kinase (MAPK) cascade, a signalling pathway which transduces extracellular stimuli into intracellular responses. Plant MAPKs have been implicated in signalling involved in stress response, phytohormone regulation, and cell cycle cues. However, the influence of various HMs on MAPK activation has not been well documented. In this review, we address and summarise several aspects related to various HM-induced ROS signalling. Additionally, we touch on how these signals activate the MAPK cascade and the downstream transcription factors that influence plant responses to HMs. Moreover, we propose a workflow that could characterise genes associated with MAPKs and their roles during plant HM stress responses.

Port calls and vessel trajectory dataset in the Caribbean with accurate port quays survey

With the growth in maritime traffic comes an increased need for precise modelling, analysis, and visualisation to enhance the monitoring capabilities of maritime authorities. To address this need, a range of sensing technologies have been developed to track vessel movements worldwide. Among these, the Automatic Identification System (AIS) is particularly significant, offering high-frequency transmission of both location and identification data. This makes AIS an invaluable tool in the intricate process of modelling maritime traffic that we use in this study. Our study presents a comprehensive dataset for the Caribbean in 2019, including port calls, quay geometries, vessel trajectories, daily locations, a seven-class vessel classification, port statistics, and United Nations reference data for comparison. Beneficial for geomatics, geography, and economics, the dataset provides a versatile tool for visualising data, assessing maritime impact on coastal areas, and enhancing maritime trade analysis. The methodology extracts 1.5 million port calls from 642 million AIS messages, offering detailed data tables and reusable processes. Its granularity down to the single quay allows for flexible data analysis, facilitating in-depth understanding of port and inter-port maritime activities.