bluuebunny/crossref_metadata_2025 huggingface dataset:
Correlating activities and defects in (photo)electrocatalysts using in-situ multi-modal microscopic imaging
Authors: Mesa Camilo A. , Sachs Michael , Pastor Ernest , Gauriot Nicolas , Merryweather Alice J. , Gomez-Gonzalez Miguel A. , Ignatyev Konstantin , Giménez Sixto , Rao Akshay , Durrant James R. , Pandya Raj
Published: May 2024

Photo(electro)catalysts use sunlight to drive chemical reactions such as water splitting. A major factor limiting photocatalyst development is physicochemical heterogeneity which leads to spatially dependent reactivity. To link structure and function in such systems, simultaneous probing of the electrochemical environment at microscopic length scales and a broad range of timescales (ns to s) is required. Here, we address this challenge by developing and applying in-situ (optical) microscopies to map and correlate local electrochemical activity, with hole lifetimes, oxygen vacancy concentrations and photoelectrode crystal structure. Using this multi-modal approach, we study prototypical hematite (α-Fe2O3) photoelectrodes. We demonstrate that regions of α-Fe2O3, adjacent to microstructural cracks have a better photoelectrochemical response and reduced back electron recombination due to an optimal oxygen vacancy concentration, with the film thickness and extended light exposure also influencing local activity. Our work highlights the importance of microscopic mapping to understand activity, in even seemingly homogeneous photoelectrodes.
Neural network kinetics for exploring diffusion multiplicity and chemical ordering in compositionally complex materials
Authors: Xing Bin , Rupert Timothy J. , Pan Xiaoqing , Cao Penghui
Published: May 2024

Diffusion involving atom transport from one location to another governs many important processes and behaviors such as precipitation and phase nucleation. The inherent chemical complexity in compositionally complex materials poses challenges for modeling atomic diffusion and the resulting formation of chemically ordered structures. Here, we introduce a neural network kinetics (NNK) scheme that predicts and simulates diffusion-induced chemical and structural evolution in complex concentrated chemical environments. The framework is grounded on efficient on-lattice structure and chemistry representation combined with artificial neural networks, enabling precise prediction of all path-dependent migration barriers and individual atom jumps. To demonstrate the method, we study the temperature-dependent local chemical ordering in a refractory NbMoTa alloy and reveal a critical temperature at which the B2 order reaches a maximum. The atomic jump randomness map exhibits the highest diffusion heterogeneity (multiplicity) in the vicinity of this characteristic temperature, which is closely related to chemical ordering and B2 structure formation. The scalable NNK framework provides a promising new avenue to exploring diffusion-related properties in the vast compositional space within which extraordinary properties are hidden.
A common polymorphism in the Intelectin-1 gene influences mucus plugging in severe asthma
Authors: Everman Jamie L. , Sajuthi Satria P. , Liegeois Maude A. , Jackson Nathan D. , Collet Erik H. , Peters Michael C. , Chioccioli Maurizio , Moore Camille M. , Patel Bhavika B. , Dyjack Nathan , Powell Roger , Rios Cydney , Montgomery Michael T. , Eng Celeste , Elhawary Jennifer R. , Mak Angel C. Y. , Hu Donglei , Huntsman Scott , Salazar Sandra , Feriani Luigi , Fairbanks-Mahnke Ana , Zinnen Gianna L. , Michel Cole R. , Gomez Joe , Zhang Xing , Medina Vivian , Chu Hong Wei , Cicuta Pietro , Gordon Erin D. , Zeitlin Pamela , Ortega Victor E. , Reisdorph Nichole , Dunican Eleanor M. , Tang Monica , Elicker Brett M. , Henry Travis S. , Bleecker Eugene R. , Castro Mario , Erzurum Serpil C. , Israel Elliot , Levy Bruce D. , Mauger David T. , Meyers Deborah A. , Sumino Kaharu , Gierada David S. , Hastie Annette T. , Moore Wendy C. , Denlinger Loren C. , Jarjour Nizar N. , Schiebler Mark L. , Wenzel Sally E. , Woodruff Prescott G. , Rodriguez-Santana Jose , Pearson Chad G. , Burchard Esteban G. , Fahy John V. , Seibold Max A.
Published: May 2024

By incompletely understood mechanisms, type 2 (T2) inflammation present in the airways of severe asthmatics drives the formation of pathologic mucus which leads to airway mucus plugging. Here we investigate the molecular role and clinical significance of intelectin-1 (ITLN-1) in the development of pathologic airway mucus in asthma. Through analyses of human airway epithelial cells we find that ITLN1 gene expression is highly induced by interleukin-13 (IL-13) in a subset of metaplastic MUC5AC+ mucus secretory cells, and that ITLN-1 protein is a secreted component of IL-13-induced mucus. Additionally, we find ITLN-1 protein binds the C-terminus of the MUC5AC mucin and that its deletion in airway epithelial cells partially reverses IL-13-induced mucostasis. Through analysis of nasal airway epithelial brushings, we find that ITLN1 is highly expressed in T2-high asthmatics, when compared to T2-low children. Furthermore, we demonstrate that both ITLN-1 gene expression and protein levels are significantly reduced by a common genetic variant that is associated with protection from the formation of mucus plugs in T2-high asthma. This work identifies an important biomarker and targetable pathways for the treatment of mucus obstruction in asthma.
Hyphal Als proteins act as CR3 ligands to promote immune responses against Candida albicans
Authors: Zhou Tingting , Solis Norma V. , Marshall Michaela , Yao Qing , Garleb Rachel , Yang Mengli , Pearlman Eric , Filler Scott G. , Liu Haoping
Published: May 2024

Patients with decreased levels of CD18 (β2 integrins) suffer from life-threatening bacterial and fungal infections. CD11b, the α subunit of integrin CR3 (CD11b/CD18, αMβ2), is essential for mice to fight against systemic Candida albicans infections. Live elongating C. albicans activates CR3 in immune cells. However, the hyphal ligands that activate CR3 are not well defined. Here, we discovered that the C. albicans Als family proteins are recognized by the I domain of CD11b in macrophages. This recognition synergizes with the β-glucan-bound lectin-like domain to activate CR3, thereby promoting Syk signaling and inflammasome activation. Dectin-2 activation serves as the “outside-in signaling” for CR3 activation at the entry site of incompletely sealed phagosomes, where a thick cuff of F-actin forms to strengthen the local interaction. In vitro, CD18 partially contributes to IL-1β release from dendritic cells induced by purified hyphal Als3. In vivo, Als3 is vital for C. albicans clearance in mouse kidneys. These findings uncover a novel family of ligands for the CR3 I domain that promotes fungal clearance.
Contemporary HIV-1 consensus Env with AI-assisted redesigned hypervariable loops promote antibody binding
Authors: Bai Hongjun , Lewitus Eric , Li Yifan , Thomas Paul V. , Zemil Michelle , Merbah Mélanie , Peterson Caroline E. , Thuraisamy Thujitha , Rees Phyllis A. , Hajduczki Agnes , Dussupt Vincent , Slike Bonnie , Mendez-Rivera Letzibeth , Schmid Annika , Kavusak Erin , Rao Mekhala , Smith Gabriel , Frey Jessica , Sims Alicea , Wieczorek Lindsay , Polonis Victoria , Krebs Shelly J. , Ake Julie A. , Vasan Sandhya , Bolton Diane L. , Joyce M. Gordon , Townsley Samantha , Rolland Morgane
Published: May 2024

An effective HIV-1 vaccine must elicit broadly neutralizing antibodies (bnAbs) against highly diverse Envelope glycoproteins (Env). Since Env with the longest hypervariable (HV) loops is more resistant to the cognate bnAbs than Env with shorter HV loops, we redesigned hypervariable loops for updated Env consensus sequences of subtypes B and C and CRF01_AE. Using modeling with AlphaFold2, we reduced the length of V1, V2, and V5 HV loops while maintaining the integrity of the Env structure and glycan shield, and modified the V4 HV loop. Spacers are designed to limit strain-specific targeting. All updated Env are infectious as pseudoviruses. Preliminary structural characterization suggests that the modified HV loops have a limited impact on Env’s conformation. Binding assays show improved binding to modified subtype B and CRF01_AE Env but not to subtype C Env. Neutralization assays show increases in sensitivity to bnAbs, although not always consistently across clades. Strikingly, the HV loop modification renders the resistant CRF01_AE Env sensitive to 10-1074 despite the absence of a glycan at N332.
Exploiting in silico modelling to enhance translation of liver cell therapies from bench to bedside
Authors: Ashmore-Harris Candice , Antonopoulou Evangelia , Finney Simon M. , Vieira Melissa R. , Hennessy Matthew G. , Muench Andreas , Lu Wei-Yu , Gadd Victoria L. , El Haj Alicia J. , Forbes Stuart J. , Waters Sarah L.
Published: May 2024

Cell therapies are emerging as promising treatments for a range of liver diseases but translational bottlenecks still remain including: securing and assessing the safe and effective delivery of cells to the disease site; ensuring successful cell engraftment and function; and preventing immunogenic responses. Here we highlight three therapies, each utilising a different cell type, at different stages in their clinical translation journey: transplantation of multipotent mesenchymal stromal/signalling cells, hepatocytes and macrophages. To overcome bottlenecks impeding clinical progression, we advocate for wider use of mechanistic in silico modelling approaches. We discuss how in silico approaches, alongside complementary experimental approaches, can enhance our understanding of the mechanisms underlying successful cell delivery and engraftment. Furthermore, such combined theoretical-experimental approaches can be exploited to develop novel therapies, address safety and efficacy challenges, bridge the gap between in vitro and in vivo model systems, and compensate for the inherent differences between animal model systems and humans. We also highlight how in silico model development can result in fewer and more targeted in vivo experiments, thereby reducing preclinical costs and experimental animal numbers and potentially accelerating translation to the clinic. The development of biologically-accurate in silico models that capture the mechanisms underpinning the behaviour of these complex systems must be reinforced by quantitative methods to assess cell survival post-transplant, and we argue that non-invasive in vivo imaging strategies should be routinely integrated into transplant studies.
Multiplex profiling of developmental cis-regulatory elements with quantitative single-cell expression reporters
Authors: Lalanne Jean-Benoît , Regalado Samuel G. , Domcke Silvia , Calderon Diego , Martin Beth K. , Li Xiaoyi , Li Tony , Suiter Chase C. , Lee Choli , Trapnell Cole , Shendure Jay
Published: May 2024

The inability to scalably and precisely measure the activity of developmentalcis-regulatory elements (CREs) in multicellular systems is a bottleneck in genomics. Here we develop a dual RNA cassette that decouples the detection and quantification tasks inherent to multiplex single-cell reporter assays. The resulting measurement of reporter expression is accurate over multiple orders of magnitude, with a precision approaching the limit set by Poisson counting noise. Together with RNA barcode stabilization via circularization, these scalable single-cell quantitative expression reporters provide high-contrast readouts, analogous to classic in situ assays but entirely from sequencing. Screening >200 regions of accessible chromatin in a multicellular in vitro model of early mammalian development, we identify 13 (8 previously uncharacterized) autonomous and cell-type-specific developmental CREs. We further demonstrate that chimeric CRE pairs generate cognate two-cell-type activity profiles and assess gain- and loss-of-function multicellular expression phenotypes from CRE variants with perturbed transcription factor binding sites. Single-cell quantitative expression reporters can be applied in developmental and multicellular systems to quantitatively characterize native, perturbed and synthetic CREs at scale, with high sensitivity and at single-cell resolution.
InsectSound1000 An insect sound dataset for deep learning based acoustic insect recognition
Authors: Branding Jelto , von Hörsten Dieter , Böckmann Elias , Wegener Jens Karl , Hartung Eberhard
Published: May 2024

InsectSound1000 is a dataset comprising more than 169000 labelled sound samples of 12 insects. The insect sound level spans from very loud (Bombus terrestris) to inaudible to human ears (Aphidoletes aphidimyza). The samples were extracted from more than 1000 h of recordings made in an anechoic box with a four-channel low-noise measurement microphone array. Each sample is a four-channel wave-file of 2500 kHz length, at 16 kHz sample rate and 32 bit resolution. Acoustic insect recognition holds great potential to form the basis of a digital insect sensor. Such sensors are desperately needed to automate pest monitoring and ecological monitoring. With its significant size and high-quality recordings, InsectSound1000 can be used to train data-hungry deep learning models. Used to pretrain models, it can also be leveraged to enable the development of acoustic insect recognition systems on different hardware or for different insects. Further, the methodology employed to create the dataset is presented in detail to allow for the extension of the published dataset.
Co-located ecological data for exploring top- and subsoil carbon dynamics across grassland-woodland contrasts
Authors: Reinsch Sabine , Lebron Inma , Brentegani Michele , Brooks Milo , Busi Susheel Bhanu , Cagnarini Claudia , Cooper David , Day John , Emmett Bridget A. , Fitos Eleonora , Goodall Tim , Griffiths Robert , Jones Briony , Keenan Patrick , Keith Aidan , Lopes-Mazzetto Josiane M. , Mason Kelly E. , Pallett Denise , Pereira M. Glória , Pinder Adam , Robinson David A. , Smart Simon M. , Thomas Amy , Benham Sue , Vanguelova Elena , Cosby Bernhard J.
Published: May 2024

Soil organic carbon (SOC) is a soil health indicator and understanding dynamics changing SOC stocks will help achieving net zero goals. Here we present four datasets featuring 11,750 data points covering co-located aboveground and below-ground metrics for exploring ecosystem SOC dynamics. Five sites across England with an established land use contrast, grassland and woodland next to each other, were rigorously sampled for aboveground (n = 109), surface (n = 33 soil water release curves), topsoil, and subsoil metrics. Commonly measured soil metrics were analysed in five soil increments for 0–1 metre (n = 4550). Less commonly measured soil metrics which were assumed to change across the soil profile were measured on a subset of samples only (n = 3762). Additionally, we developed a simple method for soil organic matter fractionation using density fractionation which is part of the less common metrics. Finally, soil metrics which may impact SOC dynamics, but with less confidence as to their importance across the soil profile were only measured on topsoil (~5–15 cm = mineral soil) and subsoil (below 50 cm) samples (n = 2567).
CH4 emissions from runoff water of Alaskan mountain glaciers
Authors: Konya Keiko , Sueyoshi Tetsuo , Iwahana Go , Morishita Tomoaki , Uetake Jun , Wakita Masahide
Published: May 2024

Recent studies have observed high methane concentrations in runoff water and the ambient air at various glacier sites, including the Greenland Ice Sheet, the glacier forefield in Svalbard, and the ice cap in Iceland. This study extends these findings to smaller mountain glaciers in Alaska. Methane and carbon dioxide concentrations in the ambient air near the meltwater outlet, fluxes of these gases at the surface of runoff water and riverbank sediments, and dissolved methane content in the runoff water were measured at four glaciers. Three of the four glaciers showed conspicuous signals of methane emissions from runoff water, with the Castner Glacier terminus exhibiting a methane concentration three times higher than background levels, along with elevated dissolved methane levels in the runoff water. This study marks the detection of significant methane emissions from small mountain glacier runoff, contributing to the understanding that mountain glaciers also release methane into the atmosphere.
A novel type-II–II heterojunction for photocatalytic degradation of LEV based on the built-in electric field: carrier transfer mechanism and DFT calculation
Authors: Li Jiaquan , Tu Peng , Yang Qian , Cui Yanjun , Gao Chenyang , Zhou Hui , Lu Jun , Bian Hongxia
Published: May 2024

Heterojunctions play a crucial role in improving the absorption of visible light and performance of photocatalysts for organic contaminants degradation in water. In this work, a novel type-II–II Ag2CO3/Bi2WO6 (AB) heterojunction was synthesized by hydrothermal reaction and in situ-precipitation methods. The mechanisms of charge transfer and carrier separation at the interface of heterojunctions and the influence on the photocatalytic activity were investigated. The degradation of levofloxacin (LEV) under visible light irradiation was employed to evaluate the photocatalytic performance of AB. The results showed that 85.4% LEV was degraded by AB, which was 1.38 and 1.39 times higher than that of Bi2WO6 and Ag2CO3, respectively. The work functions of the different crystal planes in the AB heterojunction, which was calculated by density functional theory, are a significant difference. The Fermi energy (Ef) of Ag2CO3 (− 6.005 eV) is lower than Bi2WO6 (− 3.659 eV), but the conduction band (CB) is higher. Therefore, using AB heterojunctions as an example, the research explored the mechanism of type-II–II which CB and Ef of one semiconductor cannot simultaneously surpass those of another material, based on the built-in electric field theory. Through this analysis, a deeper understanding of type-II heterojunctions was achieved, and providing valuable insights into the behavior of this specific heterojunction system.
An ensemble learning model for forecasting water-pipe leakage
Authors: Warad Ahmed Ali Mohamed , Wassif Khaled , Darwish Nagy Ramadan
Published: May 2024

Based on the benefits of different ensemble methods, such as bagging and boosting, which have been studied and adopted extensively in research and practice, where bagging and boosting focus more on reducing variance and bias, this paper presented an optimization ensemble learning-based model for a large pipe failure dataset of water pipe leakage forecasting, something that was not previously considered by others. It is known that tuning the hyperparameters of each base learned inside the ensemble weight optimization process can produce better-performing ensembles, so it effectively improves the accuracy of water pipe leakage forecasting based on the pipeline failure rate. To evaluate the proposed model, the results are compared with the results of the bagging ensemble and boosting ensemble models using the root-mean-square error (RMSE), the mean square error (MSE), the mean absolute error (MAE), and the coefficient of determination (R2) of the bagging ensemble technique, the boosting ensemble technique and optimizable ensemble technique are higher than other models. The experimental result shows that the optimizable ensemble model has better prediction accuracy. The optimizable ensemble model has achieved the best prediction of water pipe failure rate at the 14th iteration, with the least RMSE = 0.00231 and MAE = 0.00071513 when building the model that predicts water pipe leakage forecasting via pipeline failure rate.
Exploring toilet plume bioaerosol exposure dynamics in public toilets using a Design of Experiments approach
Authors: Paddy Elizabeth N. , Afolabi Oluwasola O. D. , Sohail M.
Published: May 2024

Bioaerosols generated during toilet flushing can contribute to the spread of airborne pathogens and cross-contamination in indoor environments. This presents an increased risk of fomite-mediated or aerosol disease transmission. This study systematically investigated the factors contributing to increased bioaerosol exposure following toilet flushing and developed an empirical model for predicting the exposure-relevant bioaerosol concentration. Air in a toilet cubicle was sampled by impaction after seeding with Clostridium difficile spores. Design of Experiments (DoE) main effects screening and full factorial design approaches were then employed to investigate the significant factors that heighten the risk of exposure to bioaerosols post-flush. Our findings reveal that the inoculated bacterial concentration (C), time elapsed after flushing (t), lateral distance (d), and mechanical ventilation (v) are significant predictors of bioaerosol concentration, with p-values < 0.05. The interaction term, C × d showed a marked increase in bioaerosol concentration up to 232 CFU/m3 at the closest proximity and highest pathogen load. The interplay of C and t (C × t) demonstrated a time-dependent attenuation of bioaerosol viability, with concentrations peaking at 241 CFU/m3 immediately post-flush and notably diminishing over time. The lateral distance and time post-flush (d × t) interaction also revealed a gradual decrease in bioaerosol concentration, highlighting the effectiveness of spatial and temporal dilution in mitigating bioaerosol exposure risks. Furthermore, there is an immediate rise in relative humidity levels post-flush, impacting the air quality in the toilet environment. This study not only advances our understanding of exposure pathways in determining bioaerosol exposure, but also offers pivotal insights for designing targeted interventions to reduce bioaerosol exposure. Recommendations include designing public toilets with antimicrobial surfaces, optimizing ventilation, and initiating timely disinfection protocols to prioritise surfaces closest to the toilet bowl during peak exposure periods, thereby promoting healthier indoor environments and safeguarding public health in high-traffic toilet settings.
Mechanically-flexible wafer-scale integrated-photonics fabrication platform
Authors: Notaros Milica , Dyer Thomas , Garcia Coleto Andres , Hattori Ashton , Fealey Kevin , Kruger Seth , Notaros Jelena
Published: May 2024

The field of integrated photonics has advanced rapidly due to wafer-scale fabrication, with integrated-photonics platforms and fabrication processes being demonstrated at both infrared and visible wavelengths. However, these demonstrations have primarily focused on fabrication processes on silicon substrates that result in rigid photonic wafers and chips, which limit the potential application spaces. There are many application areas that would benefit from mechanically-flexible integrated-photonics wafers, such as wearable healthcare monitors and pliable displays. Although there have been demonstrations of mechanically-flexible photonics fabrication, they have been limited to fabrication processes on the individual device or chip scale, which limits scalability. In this paper, we propose, develop, and experimentally characterize the first 300-mm wafer-scale platform and fabrication process that results in mechanically-flexible photonic wafers and chips. First, we develop and describe the 300-mm wafer-scale CMOS-compatible flexible platform and fabrication process. Next, we experimentally demonstrate key optical functionality at visible wavelengths, including chip coupling, waveguide routing, and passive devices. Then, we perform a bend-durability study to characterize the mechanical flexibility of the photonic chips, demonstrating bending a single chip 2000 times down to a bend diameter of 0.5 inch with no degradation in the optical performance. Finally, we experimentally characterize polarization-rotation effects induced by bending the flexible photonic chips. This work will enable the field of integrated photonics to advance into new application areas that require flexible photonic chips.
Genomic analysis of a spontaneous unifoliate mutant reveals gene candidates associated with compound leaf development in Vigna unguiculata [L] Walp
Authors: Edet Offiong Ukpong , Ubi Benjamin Ewa , Ishii Takayoshi
Published: May 2024

Molecular mechanisms which underpin compound leaf development in some legumes have been reported, but there is no previous study on the molecular genetic control of compound leaf formation in Vigna unguiculata (cowpea), an important dryland legume of African origin. In most studied species with compound leaves, class 1 KNOTTED-LIKE HOMEOBOX genes expressed in developing leaf primordia sustain morphogenetic activity, allowing leaf dissection and the development of leaflets. Other genes, such as, SINGLE LEAFLET1 in Medicago truncatula and Trifoliate in Solanum lycopersicum, are also implicated in regulating compound leaf patterning. To set the pace for an in-depth understanding of the genetics of compound leaf development in cowpea, we applied RNA-seq and whole genome shotgun sequence datasets of a spontaneous cowpea unifoliate mutant and its trifoliate wild-type cultivar to conduct comparative reference-based gene expression, de novo genome-wide isoform switch, and genome variant analyses between the two genotypes. Our results suggest that genomic variants upstream of LATE ELONGATED HYPOCOTYL and down-stream of REVEILLE4, BRASSINOSTERIOD INSENSITIVE1 and LATERAL ORGAN BOUNDARIES result in down-regulation of key components of cowpea circadian rhythm central oscillator and brassinosteroid signaling, resulting in unifoliate leaves and brassinosteroid-deficient-like phenotypes. We have stated hypotheses that will guide follow-up studies expected to provide more insights.
A high-resolution large-area detector for quality assurance in radiotherapy
Authors: Oliveira Andreia Maia , Akkerman Hylke B. , Braccini Saverio , van Breemen Albert J. J. M. , Gelinck Gerwin H. , Heracleous Natalie , Leidner Johannes , Murtas Fabrizio , Peeters Bart , Silari Marco
Published: May 2024

Hadron therapy is an advanced radiation modality for treating cancer, which currently uses protons and carbon ions. Hadrons allow for a highly conformal dose distribution to the tumour, minimising the detrimental side-effects due to radiation received by healthy tissues. Treatment with hadrons requires sub-millimetre spatial resolution and high dosimetric accuracy. This paper discusses the design, fabrication and performance tests of a detector based on Gas Electron Multipliers (GEM) coupled to a matrix of thin-film transistors (TFT), with an active area of 60 × 80 mm2 and 200 ppi resolution. The experimental results show that this novel detector is able to detect low-energy (40 kVp X-rays), high-energy (6 MeV) photons used in conventional radiation therapy and protons and carbon ions of clinical energies used in hadron therapy. The GEM-TFT is a compact, fully scalable, radiation-hard detector that measures secondary electrons produced by the GEMs with sub-millimetre spatial resolution and a linear response for proton currents from 18 pA to 0.7 nA. Correcting known detector defects may aid in future studies on dose uniformity, LET dependence, and different gas mixture evaluation, improving the accuracy of QA in radiotherapy.
Organs-specific metabolomics and anticholinesterase activity suggests a trade-off between metabolites for therapeutic advantages of Trillium govanianum Wall. ex D. Don
Authors: Kumar Dinesh , Kumari Vandana , Kumar Dinesh
Published: May 2024

Trillium govanianum is traditionally used to treat innumerable alignments like sexual disorders, cancer, inflammation etc. Mainly rhizomes of T. govanianum have been explored for phytochemical profiling but comprehensive metabolomics of other parts has not been yet deeply investigated. Thus, current study was aimed for organs-specific (roots, rhizomes, rhizomatous buds, stems, leaves, and fruits) phytochemical profiling of T. govanianum via metabolomics approach. Targeted (steroidal saponins and free sugars) and non-targeted metabolomics were performed by UPLC-PDA/ELSD & UHPLC-Q-TOF-IMS. Among steroidal compounds, 20-hydroxyecdysone, pennogenin-3-O-β-chacotrioside, dioscin were found predominantly in all samples while diosgenin was identified only in rhizomes. Further, four free sugars viz. 2-deoxyribose (116.24 ± 1.26 mg/g: leaves), fructose (454.76 ± 12.14 mg/g: rhizomes), glucose (243.21 ± 7.53 mg/g: fruits), and galactose (69.06 ± 2.14 mg/g: fruits) were found significant in respective parts of T. govanianum. Elemental analysis of targeted samples was determined by atomic absorption spectrophotometer. Heavy metals (Cd, Hg, Pd, As) were absent while micro- (Mn, Na, Zn, Cu) and macro- (Ca, Fe, Mg, K) elements were found in all samples. Furthermore, UHPLC-Q-TOF-IMS had identified 103 metabolites based on their mass fragmentation patterns and 839 were tentatively predicted using METLIN database. The multivariate statistical analysis showed organs specific clustering and variance of metabolites. Apart from this, extracts were evaluated for in vitro anticholinesterase activity, and found potentials inhibitors with IC50 values 2.02 ± 0.15 to 27.65 ± 0.89 mg/mL and 3.58 ± 0.12 to 16.81 ± 2.48 mg/mL of acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) enzyme, respectively. Thus, comprehensive metabolomics and anti-cholinesterase activity of different parts of T. govanianum would lay the foundation for improving medicinal importance and health benefits of T. govanianum.
Simulating worm feeding patterns with computational models
Authors: Vaughan Neil
Published: May 2024

Worms create complex paths when moving through sediment to feed. This research applies computer simulation models to provide a unique approach to visualise and quantify the process by which complex worm paths can emerge from simple local movement decisions. A grid environment is proposed in which worms can move with choice of up to 8 directions at each step. This uses a square grid with diagonal paths which has not been investigated before and the resulting number of complex paths is increased compared to triangular grids. Results identify many novel worm paths. Some of the resulting paths are symmetrical, others produce repetitive looping paths, others return to the origin. Interesting worm paths are identified with chaotic movement. Some include oscillating between chaotic and ordered movement for which the outcome is still unknown after millions of steps. A conclusion that may be extrapolated to other creatures is that local movement decisions of a species substantially determine the overall global search strategy that emerges.
Heavy metals immobilization and bioavailability in multi-metal contaminated soil under ryegrass cultivation as affected by ZnO and MnO2 nanoparticle-modified biochar
Authors: Ghandali Mahboobeh Varnaseri , Safarzadeh Sedigheh , Ghasemi-Fasaei Reza , Zeinali Sedigheh
Published: May 2024

Pollution by heavy metals (HMs) has become a global problem for agriculture and the environment. In this study, the effects of pristine biochar and biochar modified with manganese dioxide (BC@MnO2) and zinc oxide (BC@ZnO) nanoparticles on the immobilization and bioavailability of Pb, Cd, Zn, and Ni in soil under ryegrass (Lolium perenne L.) cultivation were investigated. The results of SEM–EDX, FTIR, and XRD showed that ZnO and MnO2 nanoparticles were successfully loaded onto biochar. The results showed that BC, BC@MnO2 and BC@ZnO treatments significantly increased shoots and roots dry weight of ryegrass compared to the control. The maximum dry weight of root and shoot (1.365 g pot−1 and 4.163 g pot−1, respectively) was reached at 1% BC@MnO2. The HMs uptake by ryegrass roots and shoots decreased significantly after addition of amendments. The lowest Pb, Cd, Zn and Ni uptake in the plant shoot (13.176, 24.92, 32.407, and 53.88 µg pot−1, respectively) was obtained in the 1% BC@MnO2 treatment. Modified biochar was more successful in reducing HMs uptake by ryegrass and improving plant growth than pristine biochar and can therefore be used as an efficient and cost effective amendment for the remediation of HMs contaminated soils. The lowest HMs translocation (TF) and bioconcentration factors were related to the 1% BC@MnO2 treatment. Therefore, BC@MnO2 was the most successful treatment for HMs immobilization in soil. Also, a comparison of the TF values of plant showed that ryegrass had a good ability to accumulate all studied HMs in its roots, and it is a suitable plant for HMs phytostabilization.
Quantitative analysis of hemodynamic changes induced by the discrepancy between the sizes of the flow diverter and parent artery
Authors: Kim Sunghan , Yang Hyeondong , Oh Je Hoon , Kim Yong Bae
Published: May 2024

The efficacy of flow diverters is influenced by the strut configuration changes resulting from size discrepancies between the stent and the parent artery. This study aimed to quantitatively analyze the impact of size discrepancies between flow diverters and parent arteries on the flow diversion effects, using computational fluid dynamics. Four silicone models with varying parent artery sizes were developed. Real flow diverters were deployed in these models to assess stent configurations at the aneurysm neck. Virtual stents were generated based on these configurations for computational fluid dynamics analysis. The changes in the reduction rate of the hemodynamic parameters were quantified to evaluate the flow diversion effect. Implanting 4.0 mm flow diverters in aneurysm models with parent artery diameters of 3.0–4.5 mm, in 0.5 mm increments, revealed that a shift from oversized to undersized flow diverters led to an increase in the reduction rates of hemodynamic parameter, accompanied by enhanced metal coverage rate and pore density. However, the flow diversion effect observed transitioning from oversizing to matching was less pronounced when moving from matching to undersizing. This emphasizes the importance of proper sizing of flow diverters, considering the benefits of undersizing and not to exceed the threshold of advantages.
A comparative survey between cascade correlation neural network (CCNN) and feedforward neural network (FFNN) machine learning models for forecasting suspended sediment concentration
Authors: Joshi Bhupendra , Singh Vijay Kumar , Vishwakarma Dinesh Kumar , Ghorbani Mohammad Ali , Kim Sungwon , Gupta Shivam , Chandola V. K. , Rajput Jitendra , Chung Il-Moon , Yadav Krishna Kumar , Mirzania Ehsan , Al-Ansari Nadhir , Mattar Mohamed A.
Published: May 2024

Suspended sediment concentration prediction is critical for the design of reservoirs, dams, rivers ecosystems, various operations of aquatic resource structure, environmental safety, and water management. In this study, two different machine models, namely the cascade correlation neural network (CCNN) and feedforward neural network (FFNN) were applied to predict daily-suspended sediment concentration (SSC) at Simga and Jondhara stations in Sheonath basin, India. Daily-suspended sediment concentration and discharge data from 2010 to 2015 were collected and used to develop the model to predict suspended sediment concentration. The developed models were evaluated using statistical indices like Nash and Sutcliffe efficiency coefficient (NES), root mean square error (RMSE), Willmott’s index of agreement (WI), and Legates–McCabe’s index (LM), supplemented by a scatter plot, density plots, histograms and Taylor diagram for graphical representation. The developed model was evaluated and compared with CCNN and FFNN. Nine input combinations were explored using different lag-times for discharge (Qt-n) and suspended sediment concentration (St-n) as input variables, with the current suspended sediment concentration as the desired output, to develop CCNN and FFNN models. The CCNN4 model with 4 lagged inputs (St-1, St-2, St-3, St-4) outperformed the other developed models with the lowest RMSE = 95.02 mg/l and the highest NES = 0.0.662, WI = 0.890 and LM = 0.668 for the Jondhara Station while the same CCNN4 model secure as the best with the lowest RMSE = 53.71 mg/l and the highest NES = 0.785, WI = 0.936 and LM = 0.788 for the Simga Station. The result shows the CCNN model was better than the FFNN model for predicting daily-suspended sediment at both stations in the Sheonath basin, India. Overall, CCNN showed better forecasting potential for suspended sediment concentration compared to FFNN at both stations, demonstrating their applicability for hydrological forecasting with complex relationships.
A new wide input voltage DC-DC converter for solar PV systems with hybrid MPPT controller
Authors: Kumar Sunkara Sunil. , Balakrishna K.
Published: May 2024

The present working conventional power generation systems utilization is reducing day by day because of their demerits are more functioning cost, high carbon dioxide emission, more complexity in handling, and required high installation area. So, the current power generation company focuses on Renewable Energy Sources (RES) which are wind, tidal, and solar. Here, the solar power network is utilized for supplying electricity to the electrical vehicle battery charging system. The Solar photovoltaic (PV) modules supply nonlinear power which is not useful for automotive systems. To maximize the supply power of the solar PV system, an Adaptive Step Genetic Algorithm Optimized (ASGAO) Radial Basis Functional Network (RBFN) is utilized for tracking the working point of the solar PV module thereby enhancing the operating efficiency of the overall system. The features of this proposed hybrid Maximum Power Point Tracking (MPPT) controller are quick system dynamic response, easy operation, quick convergence speed, more robustness, and high operating efficiency when equalized with the basic MPPT controllers. The major issue of solar PV modules is low supply voltage which is increased by introducing the wide input voltage DC-DC converter. The merits of this introduced converter are low-level voltage stress on diodes, good quality supply power, high voltage gain, plus low implementation cost. Here, the introduced converter along with the AGAO-RBFN controller is analyzed by selecting the MATLAB/Simulink environment. Also, the proposed converter is tested with the help of a programable DC source.
O-GlcNAcylation levels remain stable regardless of the anaesthesia in healthy rats
Authors: Dupas Thomas , Vergnaud Amandine , Pelé Thomas , Blangy-Letheule Angélique , Aillerie Virginie , Bouaud Martin , Erraud Angélique , Maillard Anaïs , Hassoun Dorian , Persello Antoine , Lecomte Jules , Rivière Matthieu , Tessier Arnaud , Leroux Aurélia A. , Rozec Bertrand , Denis Manon , Lauzier Benjamin
Published: May 2024

Anaesthetics are used daily in human and veterinary medicine as well as in scientific research. Anaesthetics have an impact on cell homeostasis especially through modulation of protein post-translational modifications. O-GlcNAcylation, a ubiquitous post-translational modification, plays a role in many biological processes. The aims of this study were to evaluate whether (1) anaesthesia influences O-GlcNAcylation and (2) its stimulation affects physiological parameters. Male Wistar rats (n = 38) were anaesthetized with ketamine-xylazine or isoflurane. They randomly received either an intravenous injection of Ringer's lactate or NButGT (10mg/kg) in order to increase O-GlcNAcylation levels. One hour after induction of anaesthesia, haemodynamic parameters and plasmatic markers were evaluated. Heart, brain and lungs were harvested and O-GlcNAcylation levels and O-GlcNAc-related enzymes were evaluated by western blot. Cardiac and pulmonary O-GlcNAcylation levels and cardiac, cerebral and pulmonary O-GlcNAc associated enzyme expression were not impacted with anaesthesia. Compared with ketamine-xylazine, isoflurane had a lower impact on blood pressure, heart rate and glycaemia. Pharmacological stimulation of O-GlcNAcylation by NButGT did not affect the physiological parameters. This study offers unprecedented insights into the regulation of O-GlcNAcylation and O-GlcNAc related enzymes during anaesthesia. Pharmacological stimulation of O-GlcNAcylation over a 1-h period did not disrupt the physiological balance in healthy anaesthetized rats.
Distribution shift detection for the postmarket surveillance of medical AI algorithms: a retrospective simulation study
Authors: Koch Lisa M. , Baumgartner Christian F. , Berens Philipp
Published: May 2024

Distribution shifts remain a problem for the safe application of regulated medical AI systems, and may impact their real-world performance if undetected. Postmarket shifts can occur for example if algorithms developed on data from various acquisition settings and a heterogeneous population are predominantly applied in hospitals with lower quality data acquisition or other centre-specific acquisition factors, or where some ethnicities are over-represented. Therefore, distribution shift detection could be important for monitoring AI-based medical products during postmarket surveillance. We implemented and evaluated three deep-learning based shift detection techniques (classifier-based, deep kernel, and multiple univariate kolmogorov-smirnov tests) on simulated shifts in a dataset of 130’486 retinal images. We trained a deep learning classifier for diabetic retinopathy grading. We then simulated population shifts by changing the prevalence of patients’ sex, ethnicity, and co-morbidities, and example acquisition shifts by changes in image quality. We observed classification subgroup performance disparities w.r.t. image quality, patient sex, ethnicity and co-morbidity presence. The sensitivity at detecting referable diabetic retinopathy ranged from 0.50 to 0.79 for different ethnicities. This motivates the need for detecting shifts after deployment. Classifier-based tests performed best overall, with perfect detection rates for quality and co-morbidity subgroup shifts at a sample size of 1000. It was the only method to detect shifts in patient sex, but required large sample sizes ($$> 30^{\prime} 000$$

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3

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). All methods identified easier-to-detect out-of-distribution shifts with small (≤300) sample sizes. We conclude that effective tools exist for detecting clinically relevant distribution shifts. In particular classifier-based tests can be easily implemented components in the post-market surveillance strategy of medical device manufacturers.
The application of machine learning techniques in posttraumatic stress disorder: a systematic review and meta-analysis
Authors: Wang Jing , Ouyang Hui , Jiao Runda , Cheng Suhui , Zhang Haiyan , Shang Zhilei , Jia Yanpu , Yan Wenjie , Wu Lili , Liu Weizhi
Published: May 2024

Posttraumatic stress disorder (PTSD) recently becomes one of the most important mental health concerns. However, no previous study has comprehensively reviewed the application of big data and machine learning (ML) techniques in PTSD. We found 873 studies meet the inclusion criteria and a total of 31 of those in a sample of 210,001 were included in quantitative analysis. ML algorithms were able to discriminate PTSD with an overall accuracy of 0.89. Pooled estimates of classification accuracy from multi-dimensional data (0.96) are higher than single data types (0.86 to 0.90). ML techniques can effectively classify PTSD and models using multi-dimensional data perform better than those using single data types. While selecting optimal combinations of data types and ML algorithms to be clinically applied at the individual level still remains a big challenge, these findings provide insights into the classification, identification, diagnosis and treatment of PTSD.
Super-resolution proximity labeling with enhanced direct identification of biotinylation sites
Authors: Shin Sanghee , Lee Song-Yi , Kang Myeong-Gyun , Jang Dong-Gi , Kim Jeesoo , Rhee Hyun-Woo , Kim Jong-Seo
Published: May 2024

Promiscuous labeling enzymes, such as APEX2 or TurboID, are commonly used in in situ biotinylation studies of subcellular proteomes or protein–protein interactions. Although the conventional approach of enriching biotinylated proteins is widely implemented, in-depth identification of specific biotinylation sites remains challenging, and current approaches are technically demanding with low yields. A novel method to systematically identify specific biotinylation sites for LC-MS analysis followed by proximity labeling showed excellent performance compared with that of related approaches in terms of identification depth with high enrichment power. The systematic identification of biotinylation sites enabled a simpler and more efficient experimental design to identify subcellular localized proteins within membranous organelles. Applying this method to the processing body (PB), a non-membranous organelle, successfully allowed unbiased identification of PB core proteins, including novel candidates. We anticipate that our newly developed method will replace the conventional method for identifying biotinylated proteins labeled by promiscuous labeling enzymes.
Multimodal communication and audience directedness in the greeting behaviour of semi-captive African savannah elephants
Authors: Eleuteri Vesta , Bates Lucy , Rendle-Worthington Jake , Hobaiter Catherine , Stoeger Angela
Published: May 2024

Many species communicate by combining signals into multimodal combinations. Elephants live in multi-level societies where individuals regularly separate and reunite. Upon reunion, elephants often engage in elaborate greeting rituals, where they use vocalisations and body acts produced with different body parts and of various sensory modalities (e.g., audible, tactile). However, whether these body acts represent communicative gestures and whether elephants combine vocalisations and gestures during greeting is still unknown. Here we use separation-reunion events to explore the greeting behaviour of semi-captive elephants(Loxodonta africana). We investigate whether elephants use silent-visual, audible, and tactile gestures directing them at their audience based on their state of visual attention and how they combine these gestures with vocalisations during greeting. We show that elephants select gesture modality appropriately according to their audience’s visual attention, suggesting evidence of first-order intentional communicative use. We further show that elephants integrate vocalisations and gestures into different combinations and orders. The most frequent combination consists of rumble vocalisations with ear-flapping gestures, used most often between females. By showing that a species evolutionarily distant to our own primate lineage shows sensitivity to their audience’s visual attention in their gesturing and combines gestures with vocalisations, our study advances our understanding of the emergence of first-order intentionality and multimodal communication across taxa.
Genie: the first open-source ISO/IEC encoder for genomic data
Authors: Müntefering Fabian , Adhisantoso Yeremia Gunawan , Chandak Shubham , Ostermann Jörn , Hernaez Mikel , Voges Jan
Published: May 2024

For the last two decades, the amount of genomic data produced by scientific and medical applications has been growing at a rapid pace. To enable software solutions that analyze, process, and transmit these data in an efficient and interoperable way, ISO and IEC released the first version of the compression standard MPEG-G in 2019. However, non-proprietary implementations of the standard are not openly available so far, limiting fair scientific assessment of the standard and, therefore, hindering its broad adoption. In this paper, we present Genie, to the best of our knowledge the first open-source encoder that compresses genomic data according to the MPEG-G standard. We demonstrate that Genie reaches state-of-the-art compression ratios while offering interoperability with any other standard-compliant decoder independent from its manufacturer. Finally, the ISO/IEC ecosystem ensures the long-term sustainability and decodability of the compressed data through the ISO/IEC-supported reference decoder.