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Abstract The Intertropical Convergence Zone (ITCZ) is a narrow tropical belt of deep convective clouds, intense precipitation, and monsoon circulations encircling the Earth. Complex interactions between the ITCZ and local geophysical dynamics result in high climate variability, making weather forecasting and prediction of extreme rainfall or drought events challenging. We unravel the complex spatio-temporal dynamics of the ITCZ and the resulting teleconnection patterns via a novel tropical climate classification achieved using complex network analysis and community detection. We reduce the high-dimensional complex ITCZ dynamics into a simple yet insightful community structure that classifies the tropics into seven regions representing distinct ITCZ dynamics. The two largest communities, encompassing landmasses over the Northern and Southern hemispheres, are associated with coherent seasonal ITCZ dynamics and have significant long-range connections. Temporal analysis of the community structure highlights that the tropical Pacific and Atlantic Oceans communities exhibit substantial variation on multidecadal scales. Further, these communities exhibit incoherent dynamics due to atmosphere-ocean interactions driven by equatorial and coastal oceanic upwelling.

Abstract Contract governance ensures that the agreed outcomes between customers and vendors are fulfilled. Information Technology (IT) outsourcing organizations enter thousands of contractual relationships each month leading to a high volume of business-critical contractual text that must be reviewed and deciphered for effective governance. The key to effective governance of contracts is a model that facilitates assigning ownership of the obligations to the right departments in an organization and allocating their accountability to the right stakeholders. For this, the contractual obligations need to be identified and classified so that details such as actions to be taken by departments in an organization and their ownership as per a given clause are brought out for the purpose of governance. In this paper, we present our work on automated extraction and classification of obligations present in Software Engineering (SE) contracts for the purpose of contracts governance. We propose a novel data decomposition-based hierarchical classification method for a multi-label classification of contractual obligations. We conducted experiments for a fine-grained automated classification of more than 55,000 statements from 50 large real-life SE contract documents received from a large vendor organization into 152 governance-specific classes. The results indicate that the proposed method can bring about a 7–8% improvement in accuracies when compared to state-of-the-art classification baselines such as BERT, RoBERTa, and generative models such as GPT-2.

Abstract Wireless charging of Electric Vehicles (EVs) has been extensively researched in the realm of electric cars, offering a convenient method. Nonetheless, there has been a scarcity of experiments conducted on low-power electric vehicles. To establish a wireless power transfer system for an electric vehicle, optimal power and transmission efficiency necessitate arranging the coils coaxially. In wireless charging systems, coils often experience angular and lateral misalignments. In this paper, a new alignment strategy is introduced to tackle the misalignment problem between the transmitter and receiver coils in the wireless charging of Electric Vehicles (EVs). The study involves the design and analysis of a coil, considering factors such as mutual inductance and efficiency. Wireless coils with angular misalignment are modelled in Ansys Maxwell simulation software. The proposed practical EV system aims to align the coils using angular motion, effectively reducing misalignment during the parking of two-wheelers. This is achieved by tilting the transmitter coil in the desired direction. Furthermore, micro sensing coils are employed to identify misalignment and facilitate automatic alignment. Additionally, adopting a power control technique becomes essential to achieve both constant current (CC) and constant voltage (CV) modes during battery charging. Integrating CC and CV modes is crucial for efficiently charging lithium-ion batteries, ensuring prolonged lifespan and optimal capacity utilization. The developed system can improve the efficiency of the wireless charging system to 90.3% with a 24 V, 16 Ah Lithium Ion Phosphate (LiFePO4) battery at a 160 mm distance between the coils.

Abstract Loratadine (LoR) is a highly lipophilic and practically insoluble in water, hence having a low oral bioavailability. As it is formulated as topical gel, it competitively binds with the receptors, thus reducing the side-effects. The objective of this study was to prepare LoR loaded nanosponge (LoR-NS) in gel for topical delivery. Nine different formulations of emulsion were prepared by solvent evaporation method with polyvinyl alcohol (PVA), ethyl cellulose (EC), and dichloromethane (DCM). Based on 32 Full Factorial Design (FFD), optimization was carried out by varying the concentration of LOR:EC ratio and stirring rate. The preparations were subjected for the evaluation of particle size (PS), in vitro release, zeta potential (ZP) and entrapment efficiency (EE). The results revealed that the NS dispersion was nanosized with sustained release profiles and significant PS. The optimised formulation was formulated and incorporated into carbopol 934P hydrogel. The formulation was then examined to surface morphological characterizations using scanning electron microscopy (SEM) which depicted spherical NS. Stability studies, undertaken for 2 months at 40 ± 2 °C/75 ± 5% RH, concluded to the stability of the formulation. The formulation did not cause skin irritation. Therefore, the prepared NS hydrogel proved to be a promising applicant for LoR as a novel drug delivery system (NDDS) for safe, sustained and controlled topical application.

Abstract Vector control is a key intervention against mosquito borne diseases. However, conventional methods have several limitations and alternate strategies are in urgent need. Vector control with endectocides such as ivermectin is emerging as a novel strategy. The short half-life of ivermectin is a limiting factor for its application as a mass therapy tool for vector control. Isoxazoline compounds like fluralaner, a class of veterinary acaricides with long half-life hold promise as an alternative. However, information about their mosquitocidal effect is limited. We explored the efficacy of fluralaner against laboratory reared vector mosquitoes—Aedes aegypti, Anopheles stephensi, and, Culex quinquefasciatus. 24 h post-blood feeding, fluralaner showed a significant mosquitocidal effect with LC50 values in the range of 24.04–49.82 ng/mL for the three different mosquito species tested. Effects on life history characteristics (fecundity, egg hatch success, etc.) were also observed and significant effects were noted at drug concentrations of 20, 25 and 45 ng/mL for Ae. aegypti, An. stephensi, and, Cx. quinquefasciatus respectively. At higher drug concentration of 250 ng/mL, significant mortality was observed within 1–2 h of post blood feeding. Potent mosquitocidal effect coupled with its long half-life makes fluralaner an excellent candidate for drug based vector control strategies.

Abstract Inadequate conservation of medicinal plants can affect their productivity. Traditional assessments and strategies are often time-consuming and linked with errors. Utilizing herbs has been an integral part of the traditional system of medicine for centuries. However, its sustainability and conservation are critical due to climate change, over-harvesting and habitat loss. The study reveals how machine learning algorithms, geographic information systems (GIS) being a powerful tool for mapping and spatial analysis, and soil information can contribute to a swift decision-making approach for actual forethought and intensify the productivity of vulnerable curative plants of specific regions to promote drug discovery. The data analysis based on machine learning and data mining techniques over the soil, medicinal plants and GIS information can predict quick and effective results on a map to nurture the growth of the herbs. The work incorporates the construction of a novel dataset by using the quantum geographic information system tool and recommends the vulnerable herbs by implementing different supervised algorithms such as extra tree classifier (EXTC), random forest, bagging classifier, extreme gradient boosting and k nearest neighbor. Two unique approaches suggested for the user by using EXTC, firstly, for a given subregion type, its suitable soil classes and secondly, for soil type from the user, its respective subregion labels are revealed, finally, potential medicinal herbs and their conservation status are visualised using the choropleth map for classified soil/subregion. The research concludes on EXTC as it showcases outstanding performance for both soil and subregion classifications compared to other models, with an accuracy rate of 99.01% and 98.76%, respectively. The approach focuses on serving as a comprehensive and swift reference for the general public, bioscience researchers, and conservationists interested in conserving medicinal herbs based on soil availability or specific regions through maps.

Abstract The pedestrians’ feeling of comfort while walking on footpaths varies according to the time of day, environment, and the purpose of the trip. The quality of service offered by pedestrian facilities such as walkways, intersections, and public places is evaluated by the Pedestrian level of service (PLOS) and has been measured from time to time, to upgrade and maintain the sustainable travel choice of people. This paper aims to focus on the level of service based on three main trip purposes such as work, education, and recreation, while considering various path characteristics and pedestrian flow characteristics that affect the pedestrian’s feeling of comfort on the walkways. The data has been collected using pedestrian questionnaire surveys and pedestrian sensors in the Melbourne central business district and the significant factors that influence the PLOS for each trip purpose will be chosen using the Mutual Information gain, which is found to be different for each trip purpose. The major influencing factors that affect the PLOS will be used to develop machine learning models for three trip purposes separately using Random Forest and Light-GBM algorithm in Python. The accuracy of prediction using the light GBM model is 0.74 for education, 0.80 for recreation, and 0.70 for work trip purposes. It is found using SHAP which stands for Shapely Additive explanations that the factors such as interpersonal distance, distance from vehicles, construction sites, vehicle volume, traffic noise, and footpath surface are the most influencing variables that affect the PLOS based on three different trip purposes.

Abstract One potential approach for treating obesity is to increase energy expenditure in brown and white adipose tissue. Here we aimed to achieve this outcome by targeting mitochondrial uncoupler compounds selectively to adipose tissue, thus avoiding side effects from uncoupling in other tissues. Selective drug accumulation in adipose tissue has been observed with many lipophilic compounds and dyes. Hence, we explored the feasibility of conjugating uncoupler compounds with a lipophilic C8-hydrocarbon chain via an ether bond. We found that substituting the trifluoromethoxy group in the uncoupler FCCP with a C8-hydrocarbon chain resulted in potent uncoupling activity. Nonetheless, the compound did not elicit therapeutic effects in mice, likely as a consequence of metabolic instability resulting from rapid ether bond cleavage. A lipophilic analog of the uncoupler compound 2,6-dinitrophenol, in which a C8-hydrocarbon chain was conjugated via an ether bond in the para-position (2,6-dinitro-4-(octyloxy)phenol), exhibited increased uncoupling activity compared to the parent compound. However, in vivo pharmacokinetics studies suggested that 2,6-dinitro-4-(octyloxy)phenol was also metabolically unstable. In conclusion, conjugation of a hydrophobic hydrocarbon chain to uncoupler compounds resulted in sustained or improved uncoupling activity. However, an ether bond linkage led to metabolic instability, indicating the need to conjugate lipophilic groups via other chemical bonds.

Abstract In this paper, we propose hybrid consensus algorithms that combine machine learning (ML) techniques to address the challenges and vulnerabilities in blockchain networks. Consensus Protocols make ensuring agreement among the applicants in the distributed systems difficult. However, existing mechanisms are more vulnerable to cyber-attacks. Previous studies extensively explore the influence of cyber attacks and highlight the necessity for effective preventive measures. This research presents the integration of ML techniques with the proposed hybrid consensus algorithms and advantages over predicting cyber-attacks, anomaly detection, and feature extraction. Our hybrid approaches leverage and optimize the proposed consensus protocols' security, trust, and robustness. However, this research also explores the various ML techniques with hybrid consensus algorithms, such as Delegated Proof of Stake Work (DPoSW), Proof of Stake and Work (PoSW), Proof of CASBFT (PoCASBFT), Delegated Byzantine Proof of Stake (DBPoS) for security enhancement and intelligent decision making in consensus protocols. Here, we also demonstrate the effectiveness of the proposed methodology within the decentralized networks using the ProximaX blockchain platform. This study shows that the proposed research framework is an energy-efficient mechanism that maintains security and adapts to dynamic conditions. It also integrates privacy-enhancing features, robust consensus mechanisms, and ML approaches to detect and prevent security threats. Furthermore, the practical implementation of these ML-based hybrid consensus models faces significant challenges, such as scalability, latency, throughput, resource requirements, and potential adversarial attacks. These challenges must be addressed to ensure the successful implementation of the blockchain network for real-world scenarios.

Abstract The green methodologies of nanoparticles with plant extracts have received an increase of interest. Copper oxide nanoparticles (CuO NPs) have been utilized in a many of applications in the last few decades. The current study presents the synthesis of CuO NPs with aqueous extract of Morinda citrifolia as a stabilizing agent. The leaf extract of Morinda citrifolia was mixed with a solution of copper sulphate (CuSO4·5H2O) and sodium hydroxide as a catalyst. UV–visible spectroscopy, FTIR, XRD, SEM, TEM, and EDAX analysis were performed to study the synthesized CuO NPs. Particle size distribution of the synthesized CuO NPs have been measured with dynamic light scattering. The CuO NPs synthesized were highly stable, sphere-like, and have size of particles from 20 to 50 nm. Furthermore, as-formed CuO NPs shown strong antibacterial activity against the Gram-positive bacteria (Bacillus subtilis, and Staphylococcus aureus), and Gram-negative bacteria (Escherichia coli). CuO NPs revealed a similar trend was analysed for antifungal activity. The zone of inhibition for the fungi evaluated for Aspergillus flavus (13.0 ± 1.1), Aspergillus niger (14.3 ± 0.7), and Penicillium frequentans (16.8 ± 1.4). According to the results of this investigation, green synthesized CuO NPs with Morinda citrifolia leaf extract may be used in biomedicine as a replacement agent for biological applications.

Abstract Maturity Onset Diabetes of the Young (MODY) is a monogenic form of diabetes which is detected by genetic testing. We looked at clinical and biochemcial variables that could help detect possible MODY among Asian Indians with youth-onset diabetes. From the diabetes electronic medical records of a diabetes care centre in Chennai in southern India, demographic, anthropometric, and biochemical details of 34 genetically confirmed MODY participants were extracted. They were compared with patients with type 1 diabetes (T1D) (n = 1011) and type 2 diabetes (T2D) (n = 1605), diagnosed below 30 years of age. Clinical and biochemical variables including body mass index (BMI), glycated hemoglobin, HDL cholesterol, and C-peptide (fasting and stimulated) were analyzed to determine whether cut points could be derived to identify individuals who could be sent for genetic testing to diagnose or rule out MODY in this ethnic group. The age at diagnosis was higher for T2D (26.5 ± 4.0 years) compared to T1D (18.2 ± 6.1 years) and MODY (17.8 ± 6.0 years). Individuals with MODY had BMI, glycated hemoglobin, total cholesterol, triglycerides, HDL cholesterol, and C-peptide levels which were intermediate between T1D and T2D. The identified probable parameters and their cut points to identify cases for MODY genetic screening were BMI 21.2–22.7 kg/m2, glycated hemoglobin 7.2–10%, HDL cholesterol 43–45 mg/dl, fasting C -peptide, 1.2–2.1 ng/ml and stimulated C-peptide, 2.1–4.5 ng/ml. Asian Indians with MODY have clinical features that are intermediate between T1D and T2D and selected biochemical parameters, especially stimulated C peptide cut points were the most useful to diagnose MODY.

Abstract Frequent mutation and variable immunological protection against vaccination is a common feature for COVID-19 pandemic. Early detection and confinement remain key to controlling further spread of infection. In response, we have developed an aptamer-based system that possesses both diagnostic and therapeutic potential towards the virus. A random aptamer library (~ 1017 molecules) was screened using systematic evolution of ligands by exponential enrichment (SELEX) and aptamer R was identified as a potent binder for the SARS-CoV-2 spike receptor binding domain (RBD) using in vitro binding assay. Using a pseudotyped viral entry assay we have shown that aptamer R specifically inhibited the entry of a SARS-CoV-2 pseudotyped virus in HEK293T-ACE2 cells but did not inhibit the entry of a Vesicular Stomatitis Virus (VSV) glycoprotein (G) pseudotyped virus, hence establishing its specificity towards SARS-CoV-2 spike protein. The antiviral potential of aptamers R and J (same central sequence as R but lacking flanked primer regions) was tested and showed 95.4% and 82.5% inhibition, respectively, against the SARS-CoV-2 virus. Finally, intermolecular interactions between the aptamers and the RBD domain were analyzed using in silico docking and molecular dynamics simulations that provided additional insight into the binding and inhibitory action of aptamers R and J.

Abstract We represent the results of a study on as the chelators used in the environmentally friendly electroless deposition bath changed depending on the amounts of hydroxides were present. The baths were prepared using polyhydroxides, glycerol and sorbitol, as chelators with copper methanesulfonate as the metal ion. Dimethylamine borane (DMAB) was used as the reducing agent with N-methylthiourea and cytosine, as additives in both the glycerol and sorbitol contained baths. Potassium hydroxide was used as the pH adjuster, with glycerol and sorbitol baths maintained at a pH of 11.50 and 10.75 respectively at a room temperature of 28 ± 2 °C. XRD, SEM, AFM, cyclic voltammetry studies, Tafel and Impedance studies, as well as additional methods, were employed to monitor and record the surface, structural, and electrochemical characteristics of the deposits and bath. The reports of the study gave interesting results, which clearly the effect of chelators on additives in the nano deposition of copper in an electroless deposition bath.

Abstract Carbon dots have stimulated the curiosity of biomedical researchers due to their unique properties, such as less toxicity and high biocompatibility. The synthesis of carbon dots for biomedical application is a core area in research. In the current research, an eco-friendly hydrothermal technique was employed to synthesize high fluorescent, plant-derived carbon dots from Prosopis juliflora leaves extract (PJ-CDs). The synthesized PJ-CDs were investigated by physicochemical evaluation instruments such as fluorescence spectroscopy, SEM, HR-TEM, EDX, XRD, FTIR, and UV-Vis. The UV-Vis absorption peaks obtained at 270 nm due to carbonyl functional groups shifts of n→π*. In addition, a quantum yield of 7.88 % is achieved. The synthesized PJ-CDs showing the presence of carious functional groups O–H, C–H, C=O, O–H, C–N and the obtained particles in spherical shape with an average size of 8 nm. The fluorescence PJ-CDs showed stability against various environmental factors such as a broad range of ionic strength and pH gradient. The antimicrobial activity of PJ-CDs was tested against a Staphylococcus aureus, and a Escherichia coli. The results suggest that the PJ-CDs could substantially inhibit the growth of Staphylococcus aureus. The findings also indicate that PJ-CDs are effective materials for bio-imaging in Caenorhabditis elegans and they can be also used for pharmaceutical applications.

Abstract Home use tests to monitor hormone trends during the menstrual cycle have been available over-the-counter for a long time. However, these tests often depend upon manual readouts and hence may lead to false analysis. Furthermore, a lot of these tests are also not quantitative. The aim of this study was to evaluate the accuracy of the quantitative home-based fertility monitor, Inito Fertility Monitor (IFM) and to use it to identify novel hormone trends in natural menstrual cycles. There were two aspects to our analysis: (i) Evaluating the efficacy of Inito Fertility Monitor in the measurement of urinary Estrone-3-glucuronide (E3G), Pregnanediol glucuronide (PdG) and Luteinizing hormone (LH), and (ii) A retrospective study of patients' hormone profiles using IFM. To evaluate the efficacy, the recovery percentage of the three hormones from IFM was evaluated using standard spiked solutions, the accuracy of measurement was calculated and the correlation between reproducible values from IFM and ELISA was established. During the validation of IFM, novel hormone trends were also observed. In order to reinforce the observations, a second group of 52 women was recruited. Assessment of the accuracy of IFM and evaluation of the volunteer urine samples was performed in a laboratory. Home assessment of hormone analysis was carried out using IFM. For the validation study, 100 women aged 21–45 years with cycle lengths ranging from 21 to 42 days were recruited. The participants had no previously diagnosed infertility conditions and their cycle lengths did not vary for more than 3 days from the expected cycle length. Daily first morning urine samples were collected from these 100 women. For the second group, 52 women were selected meeting the same criteria set for the validation study and IFM was provided to these women for testing at home. Coefficient of variation and recovery percentage of IFM with respect to laboratory based ELISA. Percentage occurrence of novel hormone trends and AUC analysis of a novel criteria identified for confirming ovulation. We observed that with all three hormones, IFM had an accurate recovery percentage. We found that the assay has an average CV of 5.05% in PdG measurement, 4.95% in E3G measurement and 5.57% in LH measurement. Furthermore, in predicting the concentration of E3G, PdG and LH in urine samples, we show that IFM has a high correlation with ELISA. In this study, we could also reproduce hormones trends across the menstrual cycle that have been observed by previous studies. We also identified a novel criterion for earlier confirmation of ovulation which could accurately distinguish ovulatory from anovulatory cycles with 100% specificity and had an area under the ROC curve of 0.98. In addition, we identified a new hormone trend which could be observed in 94.5% of the ovulatory cycles. The Inito Fertility Monitor is an effective tool for calculating the urinary concentrations of E3G, PdG and LH and can also be used to provide accurate fertility scores and confirm ovulation. We show that certain hormone trends associated with urinary E3G, PdG and LH could be accurately captured using IFM. In addition, we report a novel criterion for earlier confirmation of ovulation compared to existing criteria. Finally, we present a novel hormone pattern associated with most of the menstrual cycles by examining hormone profiles from the volunteers recruited for the clinical trial. Trial registration: The trial is registered at the current controlled trials ISRCTN registry #ISRCTN15534557.

Abstract Amyotrophic lateral sclerosis (ALS) is a devastating neurodegenerative disease, characterized by degeneration of upper and lower motor neurons that leads to muscle weakness, paralysis, and death, but the effects of disease-causing mutations on axonal outgrowth of neurons derived from human induced pluripotent stem cells (iPSC)-derived motor neurons (hiPSC-MN) are poorly understood. The use of hiPSC-MN is a promising tool to develop more relevant models for target identification and drug development in ALS research, but questions remain concerning the effects of distinct disease-causing mutations on axon regeneration. Mutations in superoxide dismutase 1 (SOD1) were the first to be discovered in ALS patients. Here, we investigated the effect of the SOD1 A4V mutation on axonal regeneration of hiPSC-MNs, utilizing compartmentalized microfluidic devices, which are powerful tools for studying hiPSC-MN distal axons. Surprisingly, SOD1+/A4V hiPSC-MNs regenerated axons more quickly following axotomy than those expressing the native form of SOD1. Though initial axon regrowth was not significantly different following axotomy, enhanced regeneration was apparent at later time points, indicating an increased rate of outgrowth. This regeneration model could be used to identify factors that enhance the rate of human axon regeneration.

Abstract Organelles play important roles in human health and disease, such as maintaining homeostasis, regulating growth and aging, and generating energy. Organelle diversity in cells not only exists between cell types but also between individual cells. Therefore, studying the distribution of organelles at the single-cell level is important to understand cellular function. Mesenchymal stem cells are multipotent cells that have been explored as a therapeutic method for treating a variety of diseases. Studying how organelles are structured in these cells can answer questions about their characteristics and potential. Herein, rapid multiplexed immunofluorescence (RapMIF) was performed to understand the spatial organization of 10 organelle proteins and the interactions between them in the bone marrow (BM) and umbilical cord (UC) mesenchymal stem cells (MSCs). Spatial correlations, colocalization, clustering, statistical tests, texture, and morphological analyses were conducted at the single cell level, shedding light onto the interrelations between the organelles and comparisons of the two MSC subtypes. Such analytics toolsets indicated that UC MSCs exhibited higher organelle expression and spatially spread distribution of mitochondria accompanied by several other organelles compared to BM MSCs. This data-driven single-cell approach provided by rapid subcellular proteomic imaging enables personalized stem cell therapeutics.

Plants deploy both primary and species-specific, specialized metabolites to communicate with other organisms and adapt to environmental challenges, including interactions with soil-dwelling microbial communities. However, the role of specialized metabolites in modulating plant-microbiome interactions often remains elusive. In this study, we report that maize (Zea mays) diterpenoid metabolites with known antifungal bioactivities also influence rhizosphere bacterial communities. Metabolite profiling showed that dolabralexins, antibiotic diterpenoids that are highly abundant in roots of some maize varieties, can be exuded from the roots. Comparative 16S rRNA gene sequencing determined the bacterial community composition of the maize mutant Zman2 (anther ear 2), which is deficient in dolabralexins and closely related bioactive kauralexin diterpenoids. The Zman2 rhizosphere microbiome differed significantly from the wild-type sibling with the most significant changes observed for Alphaproteobacteria of the order Sphingomonadales. Metabolomics analyses support that these differences are attributed to the diterpenoid deficiency of the Zman2 mutant, rather than other large-scale metabolome alterations. Together, these findings support physiological functions of maize diterpenoids beyond known chemical defenses, including the assembly of the rhizosphere microbiome.

Abstract Particle size, shape and morphology can be considered as the most significant functional parameters, their effects on increasing the performance of oral solid dosage formulation are indisputable. Supercritical Carbon dioxide fluid (SCCO2) technology is an effective approach to control the above-mentioned parameters in oral solid dosage formulation. In this study, drug solubility measuring is investigated based on artificial intelligence model using carbon dioxide as a common supercritical solvent, at different pressure and temperature, 120–400 bar, 308–338 K. The results indicate that pressure has a strong effect on drug solubility. In this investigation, Decision Tree (DT), Adaptive Boosted Decision Trees (ADA-DT), and Nu-SVR regression models are used for the first time as a novel model on the available data, which have two inputs, including pressure, X1 = P(bar) and temperature, X2 = T(K). Also, output is Y = solubility. With an R-squared score, DT, ADA-DT, and Nu-SVR showed results of 0.836, 0.921, and 0.813. Also, in terms of MAE, they showed error rates of 4.30E−06, 1.95E−06, and 3.45E−06. Another metric is RMSE, in which DT, ADA-DT, and Nu-SVR showed error rates of 4.96E−06, 2.34E−06, and 5.26E−06, respectively. Due to the analysis outputs, ADA-DT selected as the best and novel model and the find optimal outputs can be shown via vector: (x1 = 309, x2 = 317.39, Y1 = 7.03e−05).

Abstract The mitochondrial calcium (Ca2+) uniporter (MCU) channel is responsible for mitochondrial Ca2+ influx. Its expression was found to be upregulated in endothelial cells (ECs) under cardiovascular disease conditions. Since the role of MCU in regulating cytosolic Ca2+ homeostasis in ECs exposed to shear stress (SS) is unknown, we studied mitochondrial Ca2+ dynamics (that is known to decode cytosolic Ca2+ signaling) in sheared ECs. To understand cause-and-effect, we ectopically expressed MCU in ECs. A higher percentage of MCU-transduced ECs exhibited mitochondrial Ca2+ transients/oscillations, and at higher frequency, under SS compared to sheared control ECs. Transients/oscillations correlated with mitochondrial reactive oxygen species (mROS) flashes and mitochondrial membrane potential (ΔΨm) flickers, and depended on activation of the mechanosensitive Piezo1 channel and the endothelial nitric oxide synthase (eNOS). A positive feedback loop composed of mitochondrial Ca2+ uptake/mROS flashes/ΔΨm flickers and endoplasmic reticulum Ca2+ release, in association with Piezo1 and eNOS, provided insights into the mechanism by which SS, under conditions of high MCU activity, may shape vascular EC energetics and function.

Abstract Computational analysis of drug solubility was carried out using machine learning approach. The solubility of Decitabine as model drug in supercritical CO2 was studied as function of pressure and temperature to assess the feasibility of that for production of nanomedicine to enhance the solubility. The data was collected for solubility optimization of Decitabine at the temperature 308–338 K, and pressure 120–400 bar used as the inputs to the machine learning models. A dataset of 32 data points and two inputs (P and T) have been applied to optimize the solubility. The only output is Y = solubility, which is Decitabine mole fraction solubility in the solvent. The developed models are three models including Kernel Ridge Regression (KRR), Decision tree Regression (DTR), and Gaussian process (GPR), which are used for the first time as a novel model. These models are optimized using their hyper-parameters tuning and then assessed using standard metrics, which shows R2-score, KRR, DTR, and GPR equal to 0.806, 0.891, and 0.998. Also, the MAE metric shows 1.08E−04, 7.40E−05, and 9.73E−06 error rates in the same order. The other metric is MAPE, in which the KRR error rate is 4.64E−01, DTR shows an error rate equal to 1.63E−01, and GPR as the best mode illustrates 5.06E−02. Finally, analysis using the best model (GPR) reveals that increasing both inputs results in an increase in the solubility of Decitabine. The optimal values are (P = 400, T = 3.38E + 02, Y = 1.07E−03).

Abstract The abyssal ocean is generally considered an aseasonal environment decoupled from the variabilities observed at and just below the ocean's surface. Herein, we describe the first in-situ timeseries record of seasonal warming and cooling in the Arabian Sea at a depth of 4000 m. The seasonal cycle was observed over the nearly four-year-long record (from November 2018 to March 2022). The abyssal seasonal temperature cycle also exhibited noticeable interannual variability. We investigate whether or not surface processes influence the near-seabed temperature through deep meridional overturning circulation modulated by the Indian monsoon or by Rossby wave propagation. We also consider if bottom water circulation variability and discharge of the dense Persian Gulf and Red Sea Water may contribute to the observed seasonality.

Abstract Numerous genes exert multifaceted roles in hematopoiesis. Therefore, we generated novel lineage-specific RNA interference (RNAi) lentiviral vectors, H23B-Ery-Lin-shRNA and H234B-Ery-Lin-shRNA, to probe the functions of these genes in erythroid cells without affecting other hematopoietic lineages. The lineage specificity of these vectors was confirmed by transducing multiple hematopoietic cells to express a fluorescent protein. Unlike the previously reported erythroid lineage RNAi vector, our vectors were designed for cloning the short hairpin RNAs (shRNAs) for any gene, and they also provide superior knockdown of the target gene expression with a single shRNA integration per cell. High-level lineage-specific downregulation of BCL11A and ZBTB7A, two well-characterized transcriptional repressors of HBG in adult erythroid cells, was achieved with substantial induction of fetal hemoglobin with a single-copy lentiviral vector integration. Transduction of primary healthy donor CD34+ cells with these vectors resulted in >80% reduction in the target protein levels and up to 40% elevation in the γ-chain levels in the differentiated erythroid cells. Xenotransplantation of the human CD34+ cells transduced with H23B-Ery-Lin-shBCL11A LV in immunocompromised mice showed ~ 60% reduction in BCL11A protein expression with ~ 40% elevation of γ-chain levels in the erythroid cells derived from the transduced CD34+ cells. Overall, the novel erythroid lineage-specific lentiviral RNAi vectors described in this study provide a high-level knockdown of target gene expression in the erythroid cells, making them suitable for their use in gene therapy for hemoglobinopathies. Additionally, the design of these vectors also makes them ideal for high-throughput RNAi screening for studying normal and pathological erythropoiesis.

Abstract These days, many efforts have been made to increase and develop the solubility and bioavailability of novel therapeutic medicines. One of the most believable approaches is the operation of supercritical carbon dioxide fluid (SC-CO2). This operation has been used as a unique method in pharmacology due to the brilliant positive points such as colorless nature, cost-effectives, and environmentally friendly. This research project is aimed to mathematically calculate the solubility of Oxaprozin in SC-CO2 through artificial intelligence. Oxaprozin is a nonsteroidal anti-inflammatory drug which is useful in arthritis disease to improve swelling and pain. Oxaprozin is a type of BCS class II (Biopharmaceutical Classification) drug with low solubility and bioavailability. Here in order to optimize and improve the solubility of Oxaprozin, three ensemble decision tree-based models including random forest (RF), Extremely random trees (ET), and gradient boosting (GB) are considered. 32 data vectors are used for this modeling, moreover, temperature and pressure as inputs, and drug solubility as output. Using the MSE metric, ET, RF, and GB illustrated error rates of 6.29E−09, 9.71E−09, and 3.78E−11. Then, using the R-squared metric, they demonstrated results including 0.999, 0.984, and 0.999, respectively. GB is selected as the best fitted model with the optimal values including 33.15 (K) for the temperature, 380.4 (bar) for the pressure and 0.001242 (mole fraction) as optimized value for the solubility.

Abstract Accurate specification of the drugs’ solubility is known as an important activity to appropriately manage the supercritical impregnation process. Over the last decades, the application of supercritical fluids (SCFs), mainly CO2, has found great interest as a promising solution to dominate the limitations of traditional methods including high toxicity, difficulty of control, high expense and low stability. Oxaprozin is an efficient off-patent nonsteroidal anti-inflammatory drug (NSAID), which is being extensively used for the pain management of patients suffering from chronic musculoskeletal disorders such as rheumatoid arthritis. In this paper, the prominent purpose of the authors is to predict and consequently optimize the solubility of Oxaprozin inside the CO2SCF. To do this, the authors employed two basic models and improved them with the Adaboost ensemble method. The base models include Gaussian process regression (GPR) and decision tree (DT). We optimized and evaluated the hyper-parameters of them using standard metrics. Boosted DT has an MAE error rate, an R2-score, and an MAPE of 6.806E-05, 0.980, and 4.511E-01, respectively. Also, boosted GPR has an R2-score of 0.998 and its MAPE error is 3.929E-02, and with MAE it has an error rate of 5.024E-06. So, boosted GPR was chosen as the best model, and the best values were: (T = 3.38E + 02, P = 4.0E + 02, Solubility = 0.001241).

Abstract Idiopathic pulmonary fibrosis (IPF) is the most common and fatal form of interstitial lung disease. IPF is characterized by irreversible scarring of the lungs leading to lung function decline. Although the etiology remains poorly understood, dysregulated autophagy in alveolar-epithelial cells (AECs) together with interplay between apoptotic-AECs and proliferative-myofibroblasts have been strongly implicated in IPF pathogenesis. Recent studies have revealed that a caveolin-1-derived 7-mer peptide, CSP7, mitigates established PF at least in part by improving AEC viability. In the present study, we aimed to determine whether and how CSP7 regulates autophagy in fibrotic-lung AECs. We found that p53 and autophagic proteins were markedly upregulated in AECs from mice with single/multi-doses of bleomycin—or silica-induced PF. This was abolished following treatment of PF-mice with CSP7. Further, CSP7 abrogated silica- or bleomycin-induced p53 and autophagy proteins in AECs. Immunoprecipitation further revealed that CSP7 abolishes the interaction of caveolin-1 with LC3BII and p62 in AECs. AEC-specific p53-knockout mice resisted silica- or bleomycin-induced changes in autophagy proteins, or CSP7 treatment. Our findings provide a novel mechanism by which CSP7 inhibits dysregulated autophagy in injured AECs and mitigates existing PF. These results affirm the potential of CSP7 for treating established PF, including IPF and silicosis.

Abstract Synacinn is a standardized polyherbal extract formulated for the treatment of diabetes mellitus and its complications. This study aims to assess the mutagenicity potential of Synacinn by Ames assay and in vivo bone marrow micronucleus (MN) test on Sprague Dawley rat. Human ether-a-go-go-related gene (hERG) assay and Functional Observation Battery (FOB) were done for the safety pharmacology tests. In the Ames assay, Dose Range Finding (DRF) study and mutagenicity assays (+/− S9) were carried out. For the MN test, a preliminary and definitive study were conducted. In-life observations and number of immature and mature erythrocytes in the bone marrow cells were recorded. The hERG assay was conducted to determine the inhibitory effect on hERG potassium channel current expressed in human embryonic kidney cells (HEK293). FOB tests were performed orally (250, 750, and 2000 mg/kg) on Sprague Dawley rats. Synacinn is non-mutagenic against all tested strains of Salmonella typhimurium and did not induce any clastogenicity in the rat bone marrow. Synacinn also did not produce any significant inhibition (p ≤ 0.05) on hERG potassium current. Synacinn did not cause any neurobehavioural changes in rats up to 2000 mg/kg. Thus, no mutagenicity, cardiotoxicity and neurotoxicity effects of Synacinn were observed in this study.

Abstract Twitter is a renowned microblogging site that allows users to interact using tweets and it has almost reached 206 million daily active users by the second quarter of 2021. The ratio of Twitter bots has risen in tandem with their popularity. Bot detection is critical for combating misinformation and protecting the credibility of online disclosures. Current bot detection approaches rely on the Twitosphere’s topological structure, ignoring the heterogeneity among the profiles. Moreover, most techniques incorporate supervised learning, which depends strongly on large-scale training sets. Therefore, to overcome these issues, we proposed a novel entropy-based framework to detect correlated bots leveraging only user behavior. Specifically, real-time data of users is collected and their online behaviors are modeled as DNA sequences. We then determine the probability distribution of DNA sequences and compute relative entropy to evaluate the distance between the distributions. Accounts with entropy values less than a fixed threshold represent bots. Extensive experiments conducted in real-time Twitter data prove that the proposed detection technique outperforms state-of-the-art approaches with precision = 0.9471, recall = 0.9682, F1 score = 0.9511, and accuracy = 0.9457.

Abstract Metastatic prostate cancer colonizes the bone to pave the way for bone metastasis, leading to skeletal complications associated with poor prognosis and morbidity. This study demonstrates the feasibility of Raman imaging to differentiate between cancer cells at different stages of tumorigenesis using a nanoclay-based three-dimensional (3D) bone mimetic in vitro model that mimics prostate cancer bone metastasis. A comprehensive study comparing the classification of as received prostate cancer cells in a two-dimensional (2D) model and cancer cells in a 3D bone mimetic environment was performed over various time intervals using principal component analysis (PCA). Our results showed distinctive spectral differences in Raman imaging between prostate cancer cells and the cells cultured in 3D bone mimetic scaffolds, particularly at 1002, 1261, 1444, and 1654 cm−1, which primarily contain proteins and lipids signals. Raman maps capture sub-cellular responses with the progression of tumor cells into metastasis. Raman feature extraction via cluster analysis allows for the identification of specific cellular constituents in the images. For the first time, this work demonstrates a promising potential of Raman imaging, PCA, and cluster analysis to discriminate between cancer cells at different stages of metastatic tumorigenesis.