bluuebunny/crossref_metadata_2025_split huggingface dataset:
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

AbstractMolecular 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

AbstractHadron 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

AbstractTrillium 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

AbstractWorms 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

AbstractPollution 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

AbstractThe 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

AbstractSuspended 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

AbstractThe 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

AbstractAnaesthetics 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

AbstractDistribution 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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). 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

AbstractPosttraumatic 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

AbstractPromiscuous 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

AbstractMany 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

AbstractFor 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.