bluuebunny/crossref_metadata_2025 huggingface dataset:
Enrichment of lung cancer computed tomography collections with AI-derived annotations
Authors: Krishnaswamy Deepa , Bontempi Dennis , Thiriveedhi Vamsi Krishna , Punzo Davide , Clunie David , Bridge Christopher P. , Aerts Hugo J. W. L. , Kikinis Ron , Fedorov Andrey
Published: January 2024

Public imaging datasets are critical for the development and evaluation of automated tools in cancer imaging. Unfortunately, many do not include annotations or image-derived features, complicating downstream analysis. Artificial intelligence-based annotation tools have been shown to achieve acceptable performance and can be used to automatically annotate large datasets. As part of the effort to enrich public data available within NCI Imaging Data Commons (IDC), here we introduce AI-generated annotations for two collections containing computed tomography images of the chest, NSCLC-Radiomics, and a subset of the National Lung Screening Trial. Using publicly available AI algorithms, we derived volumetric annotations of thoracic organs-at-risk, their corresponding radiomics features, and slice-level annotations of anatomical landmarks and regions. The resulting annotations are publicly available within IDC, where the DICOM format is used to harmonize the data and achieve FAIR (Findable, Accessible, Interoperable, Reusable) data principles. The annotations are accompanied by cloud-enabled notebooks demonstrating their use. This study reinforces the need for large, publicly accessible curated datasets and demonstrates how AI can aid in cancer imaging.
FISHGLOB_data: an integrated dataset of fish biodiversity sampled with scientific bottom-trawl surveys
Authors: Maureaud Aurore A. , Palacios-Abrantes Juliano , Kitchel Zoë , Mannocci Laura , Pinsky Malin L. , Fredston Alexa , Beukhof Esther , Forrest Daniel L. , Frelat Romain , Palomares Maria L. D. , Pecuchet Laurene , Thorson James T. , van Denderen P. Daniël , Mérigot Bastien
Published: January 2024

Scientific bottom-trawl surveys are ecological observation programs conducted along continental shelves and slopes of seas and oceans that sample marine communities associated with the seafloor. These surveys report taxa occurrence, abundance and/or weight in space and time, and contribute to fisheries management as well as population and biodiversity research. Bottom-trawl surveys are conducted all over the world and represent a unique opportunity to understand ocean biogeography, macroecology, and global change. However, combining these data together for cross-ecosystem analyses remains challenging. Here, we present an integrated dataset of 29 publicly available bottom-trawl surveys conducted in national waters of 18 countries that are standardized and pre-processed, covering a total of 2,170 sampled fish taxa and 216,548 hauls collected from 1963 to 2021. We describe the processing steps to create the dataset, flags, and standardization methods that we developed to assist users in conducting spatio-temporal analyses with stable regional survey footprints. The aim of this dataset is to support research, marine conservation, and management in the context of global change.
A multilayered urban tree dataset of point clouds, quantitative structure and graph models
Authors: Yazdi Hadi , Shu Qiguan , Rötzer Thomas , Petzold Frank , Ludwig Ferdinand
Published: January 2024

The significance of urban trees in promoting human health and well-being has been amplified by urbanization and the climate change effects. Simultaneously, advancements in remote sensing techniques have enhanced the opportunities for studying urban trees. The TreeML-Data has been compiled to support these efforts. It consists of labelled point clouds of 40 scanning projects of streets in Munich, 3,755 leaf-off (scans in winter) point clouds of individual trees, quantitative structure models (QSM), tree structure measurements, and tree graph structure models of these trees. The dataset offers valuable data for generating and evaluating models in various scientific disciplines, which include remote sensing, computer vision, machine learning, urban forestry, urban ecosystem, green architecture, and graph analysis. To ensure its quality, the tree structure measurements and QSM have been crosschecked. For instance, the tree diameter at breast height (DBH) in the sample dataset exhibits a deviation of approximately 1.5 cm (4.3%) when compared to manual measurements. In conclusion, the quality checks confirm its reliability for subsequent studies when compared to manual measurements.
Peristaltic transport of Sutterby nanofluid flow in an inclined tapered channel with an artificial neural network model and biomedical engineering application
Authors: Chinnasamy P. , Sivajothi R. , Sathish S. , Abbas Mohamed , Jeyakrishnan V. , Goel Rajat , Alqahtani Mohammed S. , Loganathan K.
Published: January 2024

Modern energy systems are finding new applications for magnetohydrodynamic rheological bio-inspired pumping systems. The incorporation of the electrically conductive qualities of flowing liquids into the biological geometries, rheological behavior, and propulsion processes of these systems was a significant effort. Additional enhancements to transport properties are possible with the use of nanofluids. Due to their several applications in physiology and industry, including urine dynamics, chyme migration in the gastrointestinal system, and the hemodynamics of tiny blood arteries. Peristaltic processes also move spermatozoa in the human reproductive system and embryos in the uterus. The present research examines heat transport in a two-dimensional deformable channel containing magnetic viscoelastic nanofluids by considering all of these factors concurrently, which is vulnerable to peristaltic waves and hall current under ion slip and other situations. Nanofluid rheology makes use of the Sutterby fluid model, while nanoscale effects are modeled using the Buongiorno model. The current study introduces an innovative numerical computing solver utilizing a Multilayer Perceptron feed-forward back-propagation artificial neural network (ANN) with the Levenberg–Marquardt algorithm. Data were collected for testing, certifying, and training the ANN model. In order to make the dimensional PDEs dimensionless, the non-similar variables are employed and calculated by the Homotopy perturbation technique. The effects of developing parameters such as Sutterby fluid parameter, Froude number, thermophoresis, ion-slip parameter, Brownian motion, radiation, Eckert number, and Hall parameter on velocity, temperature, and concentration are demonstrated. The machine learning model chooses data, builds and trains a network, and subsequently assesses its performance using the mean square error metric. Current results declare that the improving Reynolds number tends to increase the pressure rise. Improving the Hall parameter is shown to result in a decrease in velocity. When raising a fluid's parameter, the temperature profile rises.
Partitioning of dense RBC suspensions in single microfluidic bifurcations: role of cell deformability and bifurcation angle
Authors: Stathoulopoulos Antonios , Passos Andreas , Kaliviotis Efstathios , Balabani Stavroula
Published: January 2024

Red blood cells (RBCs) are a key determinant of human physiology and their behaviour becomes extremely heterogeneous as they navigate in narrow, bifurcating vessels in the microvasculature, affecting local haemodynamics. This is due to partitioning in bifurcations which is dependent on the biomechanical properties of RBCs, especially deformability. We examine the effect of deformability on the haematocrit distributions of dense RBC suspensions flowing in a single, asymmetric Y-shaped bifurcation, experimentally. Human RBC suspensions (healthy and artificially hardened) at 20% haematocrit (Ht) were perfused through the microchannels at different flow ratios between the outlet branches, and negligible inertia, and imaged to infer cell distributions. Notable differences in the shape of the haematocrit distributions were observed between healthy and hardened RBCs near the bifurcation apex. These lead to more asymmetric distributions for healthy RBCs in the daughter and outlet branches with cells accumulating near the inner channel walls, exhibiting distinct hematocrit peaks which are sharper for healthy RBCs. Although the hematocrit distributions differed locally, similar partitioning characteristics were observed for both suspensions. Comparisons with RBC distributions measured in a T-shaped bifurcation showed that the bifurcation angle affects the haematocrit characteristics of the healthy RBCs and not the hardened ones. The extent of RBC partitioning was found similar in both geometries and suspensions. The study highlights the differences between local and global characteristics which impact RBC distribution in more complex, multi-bifurcation networks.
Cracking the code: uncovering the factors that drive COVID-19 standard operating procedures compliance among school management in Malaysia
Authors: Ahmad Noor Sakinah , Karuppiah Karmegam , Praveena Sarva Mangala , Ali Nina Fatma , Ramdas Murugadas , Mohammad Yusof Nur Athirah Diyana
Published: January 2024

Malaysia's government's decision to reopen schools during the COVID-19 outbreak, especially for students taking important exams, has alarmed the public. However, the Ministry of Education has implemented a COVID-19 Standard Operating Procedure (SOP) for educational institutions. The school management’s ability to protect children from COVID-19 rests on their understanding, attitudes, and practices regarding COVID-19 SOP compliance. This study investigated Selangor, Kuala Lumpur, and Putrajaya school management’s COVID-19 SOP compliance determinants. Multistage sampling was used to sample 740 school management from Kuala Lumpur, Putrajaya, and Selangor. A self-administered questionnaire collected sociodemographic, occupational, and lifestyle data, knowledge, attitude, and practice of COVID-19 SOP compliance. The school management had good knowledge, attitude, and practice toward COVID-19 SOP. Monthly income, school location, smoking status, and physical activity differed significantly from KAP (p < 0.05). The correlation between KAP showed a significant relationship with the values (r = 0.348, p < 0.001) and (r = 0.358, p = 0.003). Nine independent variables strongly predicted SOPs compliance practice in multiple linear regression: knowledge, attitude, age, source of knowledge; Ministry of Health, physical activities, type of infectious disease exposed; Tuberculosis and Measles (p < 0.05). The data indicate that school management exhibits good knowledge, attitude, and compliance with SOPs during the pandemic. School management oversees SOPs, and to keep schools safe, management must analyse hazards and take action. Therefore, knowledge and attitude are expected to determine factors of practice toward COVID-19 SOP compliance.
High expression of Trop2 is associated with aggressive localized prostate cancer and is a candidate urinary biomarker
Authors: Liu Shiqin , Hawley Sarah J. , Kunder Christian A. , Hsu En-Chi , Shen Michelle , Westphalen Lennart , Auman Heidi , Newcomb Lisa F. , Lin Daniel W. , Nelson Peter S. , Feng Ziding , Tretiakova Maria S. , True Lawrence D. , Vakar-Lopez Funda , Carroll Peter R. , Simko Jeffry , Gleave Martin E. , Troyer Dean A. , McKenney Jesse K. , Brooks James D. , Liss Michael A. , Stoyanova Tanya
Published: January 2024

Distinguishing indolent from clinically significant localized prostate cancer is a major clinical challenge and influences clinical decision-making between treatment and active surveillance. The development of novel predictive biomarkers will help with risk stratification, and clinical decision-making, leading to a decrease in over or under-treatment of patients with prostate cancer. Here, we report that Trop2 is a prognostic tissue biomarker for clinically significant prostate cancer by utilizing the Canary Prostate Cancer Tissue Microarray (CPCTA) cohort composed of over 1100 patients from a multi-institutional study. We demonstrate that elevated Trop2 expression is correlated with worse clinical features including Gleason score, age, and pre-operative PSA levels. More importantly, we demonstrate that elevated Trop2 expression at radical prostatectomy predicts worse overall survival in men undergoing radical prostatectomy. Additionally, we detect shed Trop2 in urine from men with clinically significant prostate cancer. Our study identifies Trop2 as a novel tissue prognostic biomarker and a candidate non-invasive marker for prostate cancer.
A novel few shot learning derived architecture for long-term HbA1c prediction
Authors: Qaraqe Marwa , Elzein Almiqdad , Belhaouari Samir , Ilam Md Shafiq , Petrovski Goran
Published: January 2024

Regular monitoring of glycated hemoglobin (HbA1c) levels is important for the proper management of diabetes. Studies demonstrated that lower levels of HbA1c play an essential role in reducing or delaying microvascular difficulties that arise from diabetes. In addition, there is an association between elevated HbA1c levels and the development of diabetes-related comorbidities. The advanced prediction of HbA1c enables patients and physicians to make changes to treatment plans and lifestyle to avoid elevated HbA1c levels, which can consequently lead to irreversible health complications. Despite the impact of such prediction capabilities, no work in the literature or industry has investigated the futuristic prediction of HbA1c using current blood glucose (BG) measurements. For the first time in the literature, this work proposes a novel FSL-derived algorithm for the long-term prediction of clinical HbA1c measures. More importantly, the study specifically targeted the pediatric Type-1 diabetic population, as an early prediction of elevated HbA1c levels could help avert severe life-threatening complications in these young children. Short-term CGM time-series data are processed using both novel image transformation approaches, as well as using conventional signal processing methods. The derived images are then fed into a convolutional neural network (CNN) adapted from a few-shot learning (FSL) model for feature extraction, and all the derived features are fused together. A novel normalized FSL-distance (FSLD) metric is proposed for accurately separating the features of different HbA1c levels. Finally, a K-nearest neighbor (KNN) model with majority voting is implemented for the final classification task. The proposed FSL-derived algorithm provides a prediction accuracy of 93.2%.
Elevated circulating adiponectin levels do not prevent anxiety-like behavior in a PCOS-like mouse model
Authors: Samad Manisha , Ek Joakim , Börchers Stina , Krieger Jean-Philippe , Stener-Victorin Elisabet , Skibicka Karolina P. , Asterholm Ingrid Wernstedt , Benrick Anna
Published: January 2024

Polycystic ovary syndrome (PCOS) is associated with symptoms of moderate to severe anxiety and depression. Hyperandrogenism is a key feature together with lower levels of the adipocyte hormone adiponectin. Androgen exposure leads to anxiety-like behavior in female offspring while adiponectin is reported to be anxiolytic. Here we test the hypothesis that elevated adiponectin levels protect against the development of androgen-induced anxiety-like behavior. Pregnant mice overexpressing adiponectin (APNtg) and wildtypes were injected with vehicle or dihydrotestosterone to induce prenatal androgenization (PNA) in the offspring. Metabolic profiling and behavioral tests were performed in 4-month-old female offspring. PNA offspring spent more time in the closed arms of the elevated plus maze, indicating anxiety-like behavior. Intriguingly, neither maternal nor offspring adiponectin overexpression prevented an anxiety-like behavior in PNA-exposed offspring. However, adiponectin overexpression in dams had metabolic imprinting effects, shown as lower fat mass and glucose levels in their offspring. While serum adiponectin levels were elevated in APNtg mice, cerebrospinal fluid levels were similar between genotypes. Adiponectin overexpression improved metabolic functions but did not elicit anxiolytic effects in PNA-exposed offspring. These observations might be attributed to increased circulating but unchanged cerebrospinal fluid adiponectin levels in APNtg mice. Thus, increased adiponectin levels in the brain are likely needed to stimulate anxiolytic effects.
Hematologic parameters in female alpacas during age progression: a retrospective study
Authors: Wagener Matthias Gerhard , Kornblum Max , Kiene Frederik , Ganter Martin , Teichmann Ulrike
Published: January 2024

Alpacas, like all camelids, have elliptical red blood cells (RBCs) in contrast to other mammals. This particular shape is important for increased osmotic resistance and stability. Age-related changes in the RBC count are known in other species, with alterations in both red and white blood cells being described. In alpacas, there are few data on age-related changes, and only a comparison of crias with adult animals. We characterized age-related hematologic changes in a study of 21 female alpacas from a research herd. A total of 87 records of clinically healthy alpacas of different ages were statistically analyzed retrospectively from the hematologic records over a nine-year period. Significant positive correlations of age with hemoglobin (Hb), HCT, MCV, MCH, neutrophils, platelet-to-lymphocyte ratio (PLR), and neutrophil-to-lymphocyte ratio (NLR) were found as well as significant negative correlations of age with lymphocytes in addition to lymphocyte-to-monocyte ratio (LMR). A paired comparison of eight older animals in the herd at three different ages also showed significant differences in the parameters Hb, HCT, MCV, MCH, MCHC, lymphocytes, eosinophils and neutrophils. Similar changes in hematologic parameters have been reported in other species and should be taken into account when interpreting hematologic results in alpacas.
Synthesis, in vitro potency of inhibition, enzyme kinetics and in silico studies of quinoline-based α-glucosidase inhibitors
Authors: Ghomi Minoo Khalili , Dastyafteh Navid , Montazer Mohammad Nazari , Noori Milad , Mojtabavi Somayeh , Faramarzi Mohammad Ali , Hashemi Seyedeh Mahdieh , Mahdavi Mohammad
Published: January 2024

Diabetes mellitus is a multifactorial global health disorder that is rising at an alarming rate. One effective therapeutic approach for controlling hyperglycemia associated with type-2 diabetes is to target α-glucosidase, which catalyzes starch hydrolysis in the intestine. In an attempt to find potential α-glucosidase inhibitors, a series of twenty new quinoline linked benzothiazole hybrids (8a–t) were synthesized in good yields from suitable reaction procedures and their chemical structures were analyzed by 1HNMR, 13CNMR, IR, and ESI–MS analysis. The synthesized derivatives further screened for their activity against α-glucosidase. Among them, compounds 8b, 8h, 8n and 8o exhibited remarkable α-glucosidase inhibitory activity with IC50 values ranging from 38.2 ± 0.3 to 79.9 ± 1.2 µM compared with standard drug acarbose (IC50 = 750.0 ± 2.0 µM). Enzyme kinetic studies of the most active compound (8h) indicated a non-competitive inhibition with Ki value of 38.2 µM. Moreover, the homology modeling, molecular docking and molecular dynamics simulation studies were conducted to reveal key interactions between the most active compound 8h and the targeted enzyme. These results are complementary to the experimental observations. In order to predict the druggability of the novel derivatives, the pharmacokinetic properties were also applied. These findings could be useful for the design and development of new α-glucosidase inhibitors.
Divorce prediction using machine learning algorithms in Ha’il region, KSA
Authors: Moumen Abdelkader , Shafqat Ayesha , Alraqad Tariq , Alshawarbeh Etaf Saleh , Saber Hicham , Shafqat Ramsha
Published: January 2024

The application of artificial intelligence (AI) in predictive analytics is growing in popularity. It has the power to offer ground-breaking solutions for a range of social problems and real world societal difficulties. It is helpful in addressing some of the social issues that today’s world seems incapable of solving. One of the most significant phenomena affecting people’s lives is divorce. The goal of this paper is to study the use of machine learning algorithms to determine the effectiveness of divorce predictor scale (DPS) and identify the reasons that usually lead to divorce in the scenario of Hail region, KSA. For this purpose, in this study, the DPS, based on Gottman couples therapy, was used to predict divorce by applying different machine learning algorithms. There were 54 items of the DPS used as features or attributes for data collection. In addition to the DPS, a personal information form was utilized to gather participants’ personal data in order to conduct this study in a more structured and traditional manner. Out of 148 participants 116 participants were married whereas 32 were divorced. With the use of algorithms artificial neural network (ANN), naïve bayes (NB), and random forest (RF), the effectiveness of DPS was examined in this study. The correlation based feature selection method was used to identify the top six features from the same dataset and the highest accuracy rate was 91.66% with RF. The results show that DPS can predict divorce. This scale can help family counselors and therapists in case formulation and intervention plan development process. Additionally, it may be argued that the Hail region, KSA sampling confirmed the Gottman couples treatment predictors.
Electro osmotically interactive biological study of thermally stratified micropolar nanofluid flow for Copper and Silver nanoparticles in a microchannel
Authors: Akbar Noreen Sher , Rafiq Maimona , Muhammad Taseer , Alghamdi Metib
Published: January 2024

A novel mathematical analysis is established that summits the key features of peristaltic propulsion for a non-Newtonian micropolar fluid with the electroosmosis and heat transfer enhancement using nanoparticles. In such physiological models, the channel have a symmetric configuration in accordance with the biological problem. Being mindful of this fact, we have disclosed an integrated analysis on symmetric channel that incorporates major physiological applications. The creeping flow inference is reviewed to model this realistic problem. Flow equations are model using cartesian coordinates and simplified using long wave length and low Reynolds number approximation. Nonlinear linear couple equations are solving numerically. We have studied the variation in the properties of nanofluid developed by two different types of nanoparticles (i.e. Cu and Ag nanoparticles). Graphical illustrations are unveiled to highlight the physical aspects of nanoparticles and flow parameters. The exploration demonstrates that the micro-rotation of the nano-liquid elements enhances the thermal conductivity of the fluid movement. The effect of micropolar fluid parameters on mean flow and pressure variables is also presented.
Development of an ammonia-biodiesel dual fuel combustion engine's injection strategy map using response surface optimization and artificial neural network prediction
Authors: Elumalai R. , Ravi K. , Elumalai P. V. , Sreenivasa Reddy M. , Prakash E. , Sekar Prabhakar
Published: January 2024

The study intends to calibrate the compression ignition (CI) engine split injection parameters as efficiently. The goal of the study is to find the best split injection parameters for a dual-fuel engine that runs on 40% ammonia and 60% biodiesel at 80% load and a constant speed of 1500 rpm with the CRDi system. To optimize and forecast split injection settings, the RSM and an ANN model are created. Based on the experimental findings, the RSM optimization research recommends a per-injection timing of 54 °CA bTDC, a main injection angle of 19 °CA bTDC, and a pilot mass of 42%. As a result, in comparison to the unoptimized map, the split injection optimized calibration map increases BTE by 12.33% and decreases BSEC by 6.60%, and the optimized map reduces HC, CO, smoke, and EGT emissions by 15.68%, 21.40%, 18.82, and 17.24%, while increasing NOx emissions by 15.62%. RSM optimization with the most desirable level was selected for map development, and three trials were carried out to predict the calibrated map using ANN. According to the findings, the ANN predicted all responses with R > 0.99, demonstrating the real-time reproducibility of engine variables in contrast to the RSM responses. The experimental validation of the predicted data has an error range of 1.03–2.86%, which is acceptable.
Psychrophiles to control ice-water phase changes in frost-susceptible soils
Authors: Rahman Rashed , Bheemasetti Tejo V. , Govil Tanvi , Sani Rajesh
Published: January 2024

The phase changes of soil water or porous media have a crucial influence on the performance of natural and man-made infrastructures in cold regions. While various methods have been explored to address the impacts of frost-action arising from these phase changes, conventional approaches often rely on chemicals, mechanical techniques, and the reuse of waste materials, which often exhibit certain limitations and environmental concerns. In contrast, certain organisms produce ice-binding proteins (IBPs) or antifreeze proteins (AFPs) to adapt to low temperatures, which can inhibit ice crystal growth by lowering the freezing point and preventing ice crystallization without the need for external intervention. This study explores the potential of three psychrophilic microbes: Sporosarcina psychrophile, Sporosarcina globispora, and Polaromonas hydrogenivorans, to induce non-equilibrium freezing point depression and thermal hysteresis in order to control ice lens growth in frost-susceptible soils. We hypothesize that the AFPs produced by psychrophiles will alter the phase changes of porous media in frost-susceptible soils. The growth profiles of the microbes, the concentration of released proteins in the extracellular solution, and the thermal properties of the protein-mixed soils are monitored at an interval of three days. The controlled soil showed a freezing point of − 4.59 °C and thermal hysteresis of 4.62 °C, whereas protein-treated soil showed a maximum freezing point depression of − 8.54 °C and thermal hysteresis of 7.71 °C. Interestingly, except for the controlled sample, all the protein-treated soil samples were thawed at a negative temperature (minimum recorded at − 0.85 °C). Further analysis showed that the treated soils compared to porous media mixed soil freeze (1.25 °C vs. 0.51 °C) and thaw (2.75 °C vs. 1.72 °C) at extensive temperature gap. This freezing and thawing temperature gap is the temperature difference between the beginning of ice core formation and completed frozen, and the beginning of ice core thawing and completed thawed for the treated soil samples selected from different incubation days. Overall, this study presents a novel bio-mediated approach using psychrophilic microbes to control ice formation in frost-susceptible soils.
Genome-wide in-silico analysis of ethylene biosynthesis gene family in Musa acuminata L. and their response under nutrient stress
Authors: Tabassum Nosheen , Shafiq Muhammad , Fatima Sameen , Tahir Sana , Tabassum Bushra , Ali Qurban , Javed Muhammad Arshad
Published: January 2024

Ethylene is a gaseous phytohormone involved in plants’ growth and developmental processes, including seed germination, root initiation, fruit ripening, flower and leaf senescence, abscission, and stress responses. Ethylene biosynthesis (EB) gene analysis in response to nitrogen (N) and potassium (K) stress has not yet been conducted in Musa acuminata (banana) roots. The genome mining of banana (Musa acuminata L.) revealed 14 putative 1-aminocyclopropane-1-carboxylate synthase (ACS), 10 1-aminocyclopropane-1-carboxylate oxidase (ACO), and 3 Ethylene overproducer 1 (ETO1) genes. ACS, ACO, and ETO1 proteins possessed amino acid residues ranging from 422–684, 636–2670, and 893–969, respectively, with molecular weight (Mw) ranging from 4.93–7.55 kD, 10.1–8.3 kD and 10.1–10.78 kD. The number of introns present in ACS, ACO, and ETO1 gene sequences ranges from 0–14, 1–6, and 0–6, respectively. The cis-regulatory element analysis revealed the presence of light-responsive, abscisic acid, seed regulation, auxin-responsive, gibberellin element, endosperm-specific, anoxic inducibility, low-temperature responsiveness, salicylic acid responsiveness, meristem-specific and stress-responsive elements. Comprehensive phylogenetic analyses ACS, ACO, and ETO1 genes of Banana with Arabidopsis thaliana revealed several orthologs and paralogs assisting in understanding the putative functions of these genes. The expression profile of Musa acuminata genes in root under normal and low levels of nitrogen and potassium shows that MaACS14 and MaACO6 expressed highly at normal nitrogen supply. MaACS1 expression was significantly upregulated at low potassium levels, whereas, MaACO6 gene expression was significantly downregulated. The functional divergence and site-specific selective pressures on specific gene sequences of banana have been investigated. The bioinformatics-based genome-wide assessment of the family of banana attempted in the present study could be a significant step for deciphering novel ACS, ACO, and ETO1 genes based on genome-wide expression profiling.
Association between weight-adjusted-waist index and urge urinary incontinence: a cross-sectional study from NHANES 2013 to 2018
Authors: Sun Haohao , Huang Jingxi , Tang Hao , Wei Bingbing
Published: January 2024

This study aimed to investigate the association between urge urinary incontinence (UUI) and weight-adjusted waist circumference index (WWI), a newly developed measure of obesity. Data from the 2013–2018 National Health and Nutrition Examination Survey (NHANES) were included in the present cross-sectional study. Urge urinary incontinence was identified by self-reported urine leakage before reaching the toilet. Weighted multivariate logistic regression and generalized additive models were used to investigate the connection between WWI and UUI and its nonlinearity. The nonlinear relationship was explored using smoothed curve fitting. Additionally, further analyses were performed on subgroups and interaction tests were conducted. In the study, a total of 14,118 individuals were enrolled, with a UUI prevalence rate of 21.18%. Overall UUI was more prevalent with elevated WWI (OR 1.20, 95% CI 1.13–12.8, P < 0.0001), which similar results were observed in weekly (OR 1.32, 95% CI 1.18–1.48, P < 0.0001) and daily (OR 1.27, 95% CI 1.06–1.53, P = 0.0091) UUI. And this connection remained steady among all subgroups (P > 0.05 for all interactions). Smoothed curve fitting showed no nonlinear relationship between WWI and UUI. In addition, a stronger correlation was found between WWI and UUI risk than other obesity indicators such as waist circumference (WC) and body mass index (BMI). Among US adults, weight-adjusted waist circumference index values are positively associated with elevated odds of UUI and show stronger associations than WC and BMI. Further studies are required to elucidate the causal relationship between WWI and UUI.
Multiple ageing effects on testicular/epididymal germ cells lead to decreased male fertility in mice
Authors: Endo Tsutomu , Kobayashi Kiyonori , Matsumura Takafumi , Emori Chihiro , Ozawa Manabu , Kawamoto Shimpei , Okuzaki Daisuke , Shimada Keisuke , Miyata Haruhiko , Shimada Kentaro , Kodani Mayo , Ishikawa-Yamauchi Yu , Motooka Daisuke , Hara Eiji , Ikawa Masahito
Published: January 2024

In mammals, females undergo reproductive cessation with age, whereas male fertility gradually declines but persists almost throughout life. However, the detailed effects of ageing on germ cells during and after spermatogenesis, in the testis and epididymis, respectively, remain unclear. Here we comprehensively examined the in vivo male fertility and the overall organization of the testis and epididymis with age, focusing on spermatogenesis, and sperm function and fertility, in mice. We first found that in vivo male fertility decreased with age, which is independent of mating behaviors and testosterone levels. Second, overall sperm production in aged testes was decreased; about 20% of seminiferous tubules showed abnormalities such as germ cell depletion, sperm release failure, and perturbed germ cell associations, and the remaining 80% of tubules contained lower number of germ cells because of decreased proliferation of spermatogonia. Further, the spermatozoa in aged epididymides exhibited decreased total cell numbers, abnormal morphology/structure, decreased motility, and DNA damage, resulting in low fertilizing and developmental rates. We conclude that these multiple ageing effects on germ cells lead to decreased in vivo male fertility. Our present findings are useful to better understand the basic mechanism behind the ageing effect on male fertility in mammals including humans.
Stanford Hypnosis Integrated with Functional Connectivity-targeted Transcranial Stimulation (SHIFT): a preregistered randomized controlled trial
Authors: Faerman Afik , Bishop James H. , Stimpson Katy H. , Phillips Angela , Gülser Merve , Amin Heer , Nejad Romina , DeSouza Danielle D. , Geoly Andrew D. , Kallioniemi Elisa , Jo Booil , Williams Nolan R. , Spiegel David
Published: January 2024

Hypnotizability, one’s ability to experience cognitive, emotional, behavioral and physical changes in response to suggestions in the context of hypnosis, is a stable neurobehavioral trait associated with improved treatment outcomes from hypnosis-based therapy. Increasing hypnotizability in people who are low-to-medium hypnotizable individuals could improve both the efficacy and effectiveness of therapeutic hypnosis as a clinical intervention. Hypnotizability is associated with dorsolateral prefrontal cortex (DLPFC) functions and connectivity with the salience network, yet there is conflicting evidence as to whether unilateral inhibition of the DLPFC changes hypnotizability. We hypothesized that using personalized neuroimaging-guided targeting to non-invasively stimulate the left DLPFC with transcranial magnetic stimulation (TMS) would temporarily increase hypnotizability. In a preregistered, double-blinded, randomized controlled trial, we recruited a sample of 80 patients with fibromyalgia syndrome, a functional pain disorder for which hypnosis has been a demonstrated beneficial non-pharmacological treatment option. All participants were TMS-naive. Participants were randomly assigned to active or sham continuous theta-burst stimulation over a personalized neuroimaging-derived left-DLPFC target, a technique termed SHIFT (Stanford Hypnosis Integrated with Functional Connectivity-targeted Transcranial Stimulation). We tested our hypothesis using the hypnotic induction profile scores, a standardized measure of hypnotizability. Pre-to-post SHIFT change in the hypnotic induction profile scores was significantly greater in the active versus sham group after 92 s of stimulation (P = 0.046). Only the active SHIFT group showed a significant increase in hypnotizability following stimulation (active: P < 0.001; sham: P = 0.607). As such, modulation of trait hypnotizability is possible in humans using non-invasive neuromodulation. Our findings support a relationship between the inhibition of the left DLPFC and an increase in hypnotizability. Dose–response optimization of spaced SHIFT should be explored to understand the optimal dose–response relationship. ClinicalTrials.gov registration: NCT02969707.
Data-driven Model Construction
Authors: Zhang Zhaoyan , Zhang Dongda , del Rio Chanona Ehecatl Antonio
Published: January 2024

The amount of data generated by modern reaction engineering systems has increased exponentially. A natural progression is to apply the data to gain information about the process, be it for scale-up, control, or optimisation. Data-driven models enable this acquisition of knowledge by transforming data into predictions. These predictions are increasingly important as systems become complex beyond human comprehension and logical reasoning fails. Data-driven models come in two main forms: parametric models and non-parametric models. Parametric models contain parameters similar to kinetic models. While a modeller carefully considers the placement of each parameter and its meaning in their kinetic model, a parametric data-driven model often bears no relation to the underlying system. In deliberately defining such a general parameterisation, the model gives data a chance to ‘speak for itself’ without the inductive bias of a human modeller. Non-parametric models directly apply the data without using parameters to make predictions. In this chapter, key parametric and non-parametric data-driven models for reaction engineering will be introduced. Examples will be given of many popular use cases, and the benefits of each method will be described.
Model Structure Identification
Authors: de Carvalho Servia Miguel Ángel , del Rio Chanona Ehecatl Antonio
Published: January 2024

Identifying an appropriate model structure for a particular system, such as the kinetics of a chemical reaction, is of the utmost importance both industrially and academically. In this chapter, we introduce different performance metrics, called information criteria, that are capable of ranking candidate models in a model class from best to worst. The presented information criteria are scrutinized and their performance is examined on a simple case study, exploring how various variables (e.g. amount of additive noise, number of datapoints) can influence the behaviour of the criteria and their ability to identify correct model structures. This chapter also introduces different model-based design of experiments techniques for model discrimination techniques, where optimal discriminatory experiments are determined based on a specific objective function.
Model Uncertainty Analysis
Authors: Wang Haiting , Iraola Eduardo , Kontoravdi Cleo , del Rio Chanona Ehecatl Antonio
Published: January 2024

Uncertainty analysis of model parameters is critical in assessing the reliability and usefulness of a model’s predictions. In this chapter, we illustrate and discuss different uncertainty analysis methods. We present both frequentist and Bayesian approaches for uncertainty quantification. Frequentist approaches, such as the construction of individual confidence intervals or ellipsoids for parameter pairs, have been widely applied due to their intuitive nature and easy computation. However, in high dimensions this intuition might be misleading, and the nonlinear relationship between parameters is often ignored. Algorithms derived from Bayesian inference can sample the posterior distribution of model parameters and show better performance when the kinetic model is nonlinear with complex parameter distributions and correlations. This approach, however, can be intractable for large numbers of parameters. We introduce Bayesian sampling methods such as Markov chain Monte Carlo and show how the confidence region obtained by Bayesian methods can reveal the nonlinear relationship between kinetic parameters, at the expense of a higher computational cost.
Surrogate and Multiscale Modelling for (Bio)reactor Scale-up and Visualisation
Authors: Anye Cho Bovinille
Published: January 2024

Bioresource production in bioreactors presents a sustainable biotechnology for tackling the ever-increasing energy and mass demands of the world’s surging population. To attain commercial viability, reaction engineers must efficiently design and upscale these bioreactors for the industrial production of high value biochemicals, fuels, and materials. These engineers utilise computational fluid dynamics (CFD) to visualise bioreactor fluid flow and optimise dead zones with poor mixing, leading to promising bioreactor configurations. An advanced route, yet to be widely deployed, is the integration of bioreaction kinetics within the CFD framework for multiscale optimisation and upscaling. To demonstrate its potential, a two-step coupling strategy of CFD hydrodynamics to light transmission and bioreaction transport was comprehensively demonstrated herein for photobioreactors (PBRs) of different configurations and scales. The problem of prohibitively high computational cost of simulating long lasting fermentation experiments was addressed with a recently published accelerated growth kinetics strategy. To further cut the simulation cost stemming from the computationally expensive objective evaluation during multiscale CFD optimisation, a Gaussian process model was trained as a surrogate of the expensive multiscale CFD model and utilised within a Bayesian optimisation (BO) framework. BO suggested a near-optimal static mixer configuration for a flat plate PBR yielding over a 95.3% increase in biomass concentration compared to the baseline without static mixers. This robust and sample efficient optimisation strategy provides enormous cost savings and presents a step forward towards the efficient design, optimisation, and upscaling of bioreactors.
Machine learning for hypothesis generation in biology and medicine: exploring the latent space of neuroscience and developmental bioelectricity
Authors: O'Brien Thomas , Stremmel Joel , Pio-Lopez Léo , McMillen Patrick , Rasmussen-Ivey Cody , Levin Michael
Published: January 2024

FieldSHIFT uses in-context learning to translate neuroscience abstracts into developmental biology abstracts based on example concept mappings, creating new research hypotheses at scale.
Characterization and dermal bioaccessibility of residual- and listed PFAS ingredients in cosmetic products
Authors: Namazkar Shahla , Ragnarsdottir Oddny , Josefsson Anton , Branzell Felice , Abel Sebastian , Abou-Elwafa Abdallah Mohamed , Harrad Stuart , Benskin Jonathan P.
Published: January 2024

Estimation of dermal bioaccessibility and characterization of listed-ingredient and residual per- and polyfluoroalkyl substances (PFAS) in cosmetic products.
Cholesterol lowering in diet-induced hypercholesterolemic mice using Lactobacillus bile salt hydrolases with different substrate specificities
Authors: Zhao Menghuan , Kuang Weijia , Yang Jiaxin , Liu Yanrong , Yang Miao , Chen Ying , Zhu Huanjing , Yang Yao
Published: January 2024

Lactobacillus BSH with TCA or GCA substrate specificity, the same cholesterol-lowering effect in diet-induced hypercholesterolemia mice with different mechanism.
Enhancement of thermal conducting properties in epoxy thermoset systems using an aligned liquid-crystalline mesophase
Authors: Trinh Thi En , Yeo Hyeonuk
Published: January 2024

Liquid crystalline epoxy resins (LCERs) with high thermal conductivity (T/C) are an attractive solution that meet the thermal management requirements of electronic devices.
Correction: A novel study on the preferential attachment of chromophore and auxochrome groups in azo dye adsorption on different greenly synthesized magnetite nanoparticles: investigation of the influence of the mediating plant extract's acidity
Authors: Ahmouda Kaouthar , Boudiaf Moussa , Benhaoua Boubaker
Published: January 2024

Correction for ‘A novel study on the preferential attachment of chromophore and auxochrome groups in azo dye adsorption on different greenly synthesized magnetite nanoparticles: investigation of the influence of the mediating plant extract's acidity’ by Kaouthar Ahmouda et al., Nanoscale Adv., 2022, 4, 3250–3271, DOI: https://doi.org/10.1039/D2NA00302C.
Catalytic selectivity of nanorippled graphene
Authors: Liu Yu , Xiong Wenqi , Bera Achintya , Ji Yu , Yu Miao , Chen Shi , Lin Li , Yuan Shengjun , Sun Pengzhan
Published: January 2024

Experiments have shown that nanoscale ripples in a graphene membrane exhibit high catalytic selectivity toward hydrogen dissociation over other reactions involving hydrogen.
Lewis acid-induced homo- and heterogeneous nickel catalysts for ethylene polymerization and copolymerization with polar monomers
Authors: Tian Wanlu , Li Chao , Tan Chen , Chen Min
Published: January 2024

By introducing soluble and supported Lewis acids, homogeneous and heterogeneous nickel-catalyzed ethylene (co)polymerization was designed and studied.
Preconcentration and selective extraction of trace Hg(ii) by polymeric g-C3N4 nanosheet-packed SPE column
Authors: Haseen Uzma , Ali Syed Ghazanfar , Khan Rais Ahmad , Alsalme Ali , Koo Bon Heun , Ahmad Hilal
Published: January 2024

In this study, we successfully synthesized polymeric graphitic carbon nitride (g-C3N4) nanosheets through thermal means and proposed their application in solid-phase extraction (SPE) for the enrichment of trace Hg(ii).
Application of 3D printing technology for green synthesis of Fe2O3using ABS/TPU/chlorella skeletons for methyl orange removal
Authors: Liu Dingyong , Cai Hongjie , Zhou Weiming , Lei Dandan , Cao Changlin , Xia Xinshu , Xiao Liren , Qian Qingrong , Chen Qinghua
Published: January 2024

Photocatalysis is widely acknowledged as an efficient and environmentally friendly method for treating dye-contaminated wastewater.
Total synthesis of (±)-auranthine confirmed its refined structure
Authors: Reinker Marie , Kalinina Svetlana A. , Kalinin Dmitrii V.
Published: January 2024

Auranthine is a fungal benzodiazepine. Through the successful total synthesis of (±)-auranthine, we confirmed the refined structure of natural (−)-auranthine.
Rational design of galactopyranoside-substituted N-heterocyclic carbene palladium(ii) complexes. Stable and efficient catalyst for C–C coupling in aqueous media
Authors: Hobsteter Ariana W. , Lo Fiego Marcos J. , Silbestri Gustavo F.
Published: January 2024

Three palladium(ii) complexes bearing galactopyranoside-based NHC ligands have been synthesized. Catalytic studies reveal that the complexes are active and reusable.
General synthesis of secondary imines via reductive coupling of carbonyl and nitro compounds employing a reusable cobalt catalyst
Authors: Zhang Chaochong , He Yurong , Zhou Peng , Ma Yuandie , Yuan Ziliang , She Guoqiang , Zheng Zhe , Hu Juncheng , Jiang Qingqing , Lam Jason Chun-Ho , Han Bo , Zhang Zehui , Liu Bing
Published: January 2024

Reusable nitrogen-doped carbon-encapsulated Co nanoparticle catalysts are found robust for the synthesis of secondary imines via the reductive amination of carbonyl compounds with NH3 or directly with nitro compounds.
Correction: Synthetic ramoplanin analogues are accessible by effective incorporation of arylglycines in solid-phase peptide synthesis
Authors: Marschall Edward , Cass Rachel W. , Prasad Komal M. , Swarbrick James D. , McKay Alasdair I. , Payne Jennifer A. E. , Cryle Max J. , Tailhades Julien
Published: January 2024

Correction for ‘Synthetic ramoplanin analogues are accessible by effective incorporation of arylglycines in solid-phase peptide synthesis’ by Edward Marschall et al., Chem. Sci., 2024, 15, 195–203, https://doi.org/10.1039/D3SC01944F.
A nanoscale surface engineered magneto-mechano-triboelectric nanogenerator enabled by reliable pattern replication for self-powered IoT devices
Authors: Pattipaka Srinivas , Park Tae Wan , Bae Young Min , Na Yujin , Chung Kyeongwoon , Park Kwi-Il , Ryu Jungho , Park Woon Ik , Hwang Geon-Tae
Published: January 2024

This paper provides a nanoscale surface-replicated magneto-mechano-triboelectric nanogenerator (MMTENG) for demonstration of a self-powered IoT system.
Synthesis of an SBA-15 supported NiPtN catalyst for dehydrogenation of hydrazine hydrate
Authors: Ridwan Muhammad , Afifah Risda Maulida , Yati Indri , Yunarti Rika Tri
Published: January 2024

In this study, a NiPtN/SBA-15 catalyst was synthesized by a wet-impregnation method, which was validated using EDXRF, CHN analysis, XRD, SAA, and FESEM-EDS analysis and showed good catalytic activity towards dehydrogenation of hydrazine hydrate with a selectivity of 96.18% and TOF of 1133 h−1.
Multifunctional conductive hydrogels for wearable sensors and supercapacitors
Authors: Li Quancai , Tian Bin , Tang Guilin , Zhan Haoye , Liang Jing , Guo Panwang , Liu Qun , Wu Wei
Published: January 2024

Conductive hydrogels, which combine the adhesive properties of polydopamine with the conductivity and low melting point features of deep eutectic solvents, are ideal green materials for bioelectrodes, flexible sensors, and supercapacitors.