bluuebunny/crossref_metadata_2025 huggingface dataset:
Biodiversity of mosquitoes (Diptera: Culicidae) in phytotelmata from Car Nicobar Island, India
Authors: Sunish I. P. , Prem Kumar Addepalli , Thiruvengadam Kannan , Shriram A. N.
Published: May 2024

The Culicidae mosquito family breeds in various natural and artificial water bodies. Phytotelmata are plant structures that hold water and are used by many mosquito species during their life cycle. Mosquitoes are disease vectors, and phytotelmata play an important role in harbouring them. Investigating these phytotelmata will facilitate to develop appropriate site specific vector control strategy. Approximately 750 phytotelmata in 16 locations on Car Nicobar Island was examined. Mosquito larvae were collected from different habitats such as tree holes, root holes, leaf axils, and bamboo stumps. On average, the number of larvae per 10 ml of water ranged from 0.003 to 3.833. Among the 16 mosquito species identified, there were vectors of malaria (Anopheles sundaicus), dengue (Aedes aegypti and Aedes albopictus) and filariasis (Culex quinquefasciatus), belonging to seven genera. The most common phytotelmata were coconut trees (17.5%) followed by Java apple trees (10.3%) and bamboo stumps (7.1%). The most widespread mosquito species was Aedes edwardsi, followed by Aedes malayensis and Ae. albopictus, and the per cent of phytotelmata that contained these species were 42.6, 22.0, and 16.8% respectively. Anopheles mosquitoes had the highest container index (6.1%) in root holes, while Aedes and Culex mosquitoes were more frequently found in bamboo stumps. The alpha biodiversity indices indicated that the mosquito population in Car Nicobar Island was highly diverse (S = 16; DMg = 36.9). Tree holes showed the highest diversity, as all 16 species were collected from them. Assessment of phytotelmata, there is a significant relationship observed between mosquito genus, village, habitat, and tree families, on the remote island of Car Nicobar. Considering the presence of disease-transmitting mosquito vectors in the phytotelmata, these habitats should be considered when implementing control measures to eliminate mosquito-borne infections.
Effect of corn–soybean meal-based diets with low calcium and available phosphorus in male broilers on performance, tibia criteria and jejunum histomorphology
Authors: Olgun Osman , Cufadar Yusuf , Gül Esra Tuğçe , Ahmet Gökmen Seyit , Sevim Behlül
Published: May 2024

This study was conducted to determine the effect of reducing calcium (Ca) and available phosphorus (AvP) on performance, carcass yield, tibia traits and jejunum histomorphology in broilers. For this purpose, 480 one-day-old Ross 308 male chicks were distributed into four trial groups with eight subgroups. During the starter period, birds were fed with recommended or reduced Ca and AvP contents of 66.7 and 62.5 g/kg, respectively. Calcium and AvP contents of the groups were as follows: control: 8.70 g/kg Ca, 4.40 g/kg AvP for grower and 7.80 g/kg Ca, 3.90 g/kg AvP for finisher; LCP1: 8.30 g/kg Ca, 4.20 g/kg AvP for grower and 7.10 g/kg Ca, 3.50 g/kg AvP for finisher; LCP2: 7.90 g/kg Ca, 4.00 g/kg AvP for grower and 6.00 g/kg Ca, 3.00 g/kg AvP for finisher; LCP3: 7.00 g/kg Ca, 3.80 g/kg for grower and 5.00 g/kg Ca, 3.00 g/kg AvP for finisher. Performance variables have been calculated from the data of each period, and samples were obtained from the slaughtered birds on the final day of the trial (42nd day) for carcass and tibia traits and jejunum histomorphology. Reducing dietary Ca and AvP did not affect the broiler performance, carcass yield and mortality. Tibia ash decreased in LCP2 and LCP3 groups (P < 0.01). Villus width and villus surface area increased in LCP2 and LCP3. Overall, feeding with a diet 100 g/kg lower than the recommended Ca and AvP did not affect performance in broilers, but improved jejunal development.
Management of supraglottic stenosis using interarytenoid Z-plasty: how I do it
Authors: Jaffal Hussein , Tumlin Parker , McArdle Erica
Published: May 2024

Background

Supraglottoplasty is the primary surgical treatment of congenital laryngomalacia. Supraglottic stenosis is a rare complication of supraglottoplasty that is difficult to manage.

Methods

This study presents a new endoscopic mucosa-sparing Z-plasty double transposition flap technique that was used to manage supraglottic stenosis following supraglottoplasty for severe congenital laryngomalacia in an eight-month-old infant.

Results

At 10 months post-operatively, the patient remained asymptomatic and flexible laryngoscopy showed adequate supraglottic patency.

Conclusion

Endoscopic interarytenoid Z-plasty is a safe and effective technique in the management of paediatric supraglottic stenosis.
Apocalyptic praxis in Evagrius of Pontus and Francis of Assisi
Authors: Lambelet Kyle B. T.
Published: May 2024

The Christian mystical tradition approaches the apocalyptic as praxis – a way of living that renounces the world as it is, lives proleptically into a counter-world of God's reign and practices indifferent freedom in the meantime to love God and neighbour. Although concerns about the ethical viability of such a disposition have merit, this essay demonstrates its constructive possibility through recourse to two archives: the writings of Evagrius of Pontus and the witness of Francis of Assisi. By recovering a scriptural distinction between world and creation, and by emphasising the posture of holy indifference, apocalyptic praxis offers a resource and guide.
AI and the future of IR: Disentangling flesh-and-blood, institutional, and synthetic moral agency in world politics
Authors: Erskine Toni
Published: May 2024

Intelligent machines – from automated robots to algorithmic systems – can create images and poetry, steer our preferences, aid decision making, and kill. Our perception of their capacities, relative autonomy, and moral status will profoundly affect not only how we interpret and address practical problems in world politics over the next 50 years but also how we prescribe and evaluate individual and state responses. In this article, I argue that we must analyse this emerging synthetic agency in order to effectively navigate – and theorise – the future of world politics. I begin by outlining the ways that agency has been under-theorised within the discipline of International Relations (IR) and suggest that artificial intelligence (AI) disrupts prevailing conceptions. I then examine how individual human beings and formal organisations – purposive actors with which IR is already familiar – qualify as moral agents, or bearers of duties, and explore what criteria intelligent machines would need to meet to also qualify. After demonstrating that synthetic agents currently lack the ‘reflexive autonomy’ required for moral agency, I turn to the context of war to illustrate how insights drawn from this comparative analysis counter our tendency to elide different manifestations of moral agency in ways that erode crucial notions of responsibility in world politics.
Reproducing socio-ecological life from below: Towards a planetary political economy of the global majority
Authors: Tansel Cemal Burak , Tilley Lisa
Published: May 2024

Confronting the coming five decades from our present conjuncture demands – to paraphrase Antonio Gramsci’s famous mantra – both critical pessimism and a wilful politics of hope. In this article, we engage with the politics of climate breakdown and the responses to wider socio-ecological crises with a necessary critical pessimism. Specifically, we confront the capture of green transition imperatives by finance capital, as well as the troubling orientation of transition towards building new structures of accumulation around the vision of an electrified consumer society. We also see the coming decades being marked by the ever-increasing wealth of global asset-owning classes – who, by definition, enclose the atmospheric commons faster than any other community. Against this dystopian picture of increasingly concentrated wealth, corporate excess, and terrestrial crisis, we focus on the stubborn reproduction of socio-ecological life through various grounded projects across the world. We engage with communities who work against structural constraints to reproduce life from below through urban commoning, food sovereignty, Indigenous organising, and caretaking economies – all of which are scaling out their visions through alternative internationals. All of these projects, we argue, present a planetary and multiscalar political economy in practice, which connects grounded experience with resistance to the dynamics of capitalism at the state, corporate, and transnational levels. With lessons from these communities in mind, we call for a ‘planetary political economy of the global majority’, which prioritises the reproduction of socio-ecological life according to the visions of grounded anti-systemic projects.
Musical humour and caricatures in The Book of Mormon
Authors: Braae Nick
Published: May 2024

This article examines the musical techniques used to create humour in the contemporary Broadway musical The Book of Mormon. Drawing on existing theories of parody, I argue that most songs initially rely on ‘straight’ and accurate imitations of other Broadway composers and pop-rock artists to set up an incongruity with the characters’ words or actions. There is a tendency then for these intertextual references to be layered or exaggerated, giving the joke in question an overblown quality. I adopt the novel label of a ‘musical caricature’ to describe such instances. This analysis thus presents a new methodological tool for studying musical humour; it also offers insights into The Book of Mormon's critical success as well as the writers’ ability to evade censure for the crude and potentially offensive thematic content.
Molecular detection of Histoplasma capsulatum in organ samples from bats randomly captured in urban areas of Araraquara, São Paulo state, Brazil
Authors: Ruiz-Muñoz Jessica A. , Rodríguez-Arellanes Gabriela , Ramírez José A. , Carreto-Binaghi Laura E. , Fusco-Almeida Ana M. , Mendes-Giannini Maria J. S. , García-Pérez Blanca E. , Taylor Maria L.
Published: May 2024

The mycosis histoplasmosis is also considered a zoonosis that affects humans and other mammalian species worldwide. Among the wild mammals predisposed to be infected with the etiologic agent of histoplasmosis, bats are relevant because they are reservoir of Histoplasma species, and they play a fundamental role in maintaining and spreading fungal propagules in the environments since the infective mycelial phase of Histoplasma grows in their accumulated guano. In this study, we detected the fungal presence in organ samples of bats randomly captured in urban areas of Araraquara City, São Paulo, Brazil. Fungal detection was performed using a nested polymerase chain reaction to amplify a molecular marker (Hcp100) unique to H. capsulatum, which revealed the pathogen presence in organ samples from 15 out of 37 captured bats, indicating 40.5% of infection. Out of 22 Hcp100-amplicons generated, 41% corresponded to lung and trachea samples and 59% to spleen, liver, and kidney samples. Data from these last three organs suggest that bats develop disseminated infections. Considering that infected bats create environments with a high risk of infection, it is important to register the percentage of infected bats living in urban areas to avoid risks of infection to humans, domestic animals, and wildlife.
A Flexible Framework for Simulating the Water Balance of Lakes and Reservoirs From Local to Global Scales: mizuRoute‐Lake
Authors: Gharari Shervan , Vanderkelen Inne , Tefs Andrew , Mizukami Naoki , Kluzek Erik , Stadnyk Tricia , Lawrence David , Clark Martyn P.
Published: May 2024

Lakes and reservoirs are an integral part of the terrestrial water cycle. In this work, we present the implementation of different water balance models of lakes and reservoirs into mizuRoute, a vector‐based routing model, termed mizuRoute‐Lake. As the main advantage of mizuRoute‐Lake, users can choose between various parametric models implemented in mizuRoute‐Lake. So far, three parametric models of lake and reservoir water balance, namely Hanasaki, HYPE, and Döll are implemented in mizuRoute‐Lake. In general, the parametric models relate the outflow from lakes or reservoirs to the storage and various parameters including inflow, demand, volume of storage, etc. Additionally, this flexibility allows users to easily evaluate and compare the effect of various water balance models for a lake without needing to reconfigure the routing model or change the parameters of other lakes or reservoirs in the modeling domain. Users can also use existing data such as historical observations or water management models to specify the behavior of a selected number of lakes and reservoirs within the modeling domain using the data‐driven capability of mizuRoute‐Lake. We demonstrate the flexibility of mizuRoute‐Lake by presenting global, regional, and local scale applications. The development of mizuRoute‐Lake paves the way for better integration of water management models, locally measured, and remotely sensed data sets in the context of Earth system modeling.
Groundwater Level Forecasting Using Machine Learning: A Case Study of the Baekje Weir in Four Major Rivers Project, South Korea
Authors: Yi Sooyeon , Kondolf G. Mathias , Sandoval Solis Samuel , Dale Larry
Published: May 2024

Understanding the impact of human‐made structures on groundwater levels is essential, with structures like dams or weirs presenting unique challenges and opportunities for study. The Baekje weir in South Korea presents an interesting case as the weir has undergone full gate opening, which is generally not the case for weirs and reservoirs, providing valuable opportunity for simulating weir removal conditions. The main objectives are investigation of groundwater level fluctuations under various weir operations, distances from the weir, and seasonal variations. The study utilizes observed data that simulates conditions with and without the weir, including scenarios of full gate opening. Multiple machine learning algorithms—Random Forest (RF), Artificial Neural Network, Support Vector Regression (SVR), Gradient Boosting, and Extreme Gradient Boosting (XGBoost)—are used to develop accurate groundwater level prediction models. The models' performance is assessed using coefficient of determination, Root mean square error (RMSE), Mean Absolute Error (MAE) indices, and visualized through Taylor diagrams. Results indicate that XGBoost outperforms other models in all three groups during both training and testing phases. Specifically, XGBoost surpasses RF by 2.09% (R2), 5.66% (RMSE), and 10.1% (MAE) in training, and outperforms SVR by 11.2% (R2), 42.0% (RMSE), and 129.2% (MAE) in testing. Additionally, the study generates groundwater level maps, providing a practical tool for managing groundwater systems and informing decision‐making in weir operations. This study not only sheds light on the dynamic relationship between weir operations and groundwater levels but also provides actionable insights for effective water management in similar hydrological settings.
Observation and Numerical Simulation of Cold Ions Energized by EMIC Waves
Authors: Kim K.‐H. , Jun C.‐W. , Kwon J.‐W. , Lee J. , Shiokawa K. , Miyoshi Y. , Kim E.‐H. , Min K. , Seough J. , Asamura K. , Shinohara I. , Matsuoka A. , Yokota S. , Kasahara Y. , Kasahara S. , Hori T. , Keika K. , Kumamoto A. , Tsuchiya F.
Published: May 2024

This is the first report of significant energization (up to 7,000 eV) of low‐energy He+ ions, which occurred simultaneously with H‐band electromagnetic ion cyclotron (EMIC) wave activity, in a direction mostly perpendicular to the ambient magnetic field. The event was detected by the Arase satellite in the dayside plasmatrough region off the magnetic equator on 15 May 2019. The peak energy of the He+ flux enhancements is mostly above 1,000 eV. At some interval, the He+ ions are energized up to ∼7,000 eV. The H‐band waves are excited in a frequency band between the local crossover and helium gyrofrequencies and are close to a linear polarization state with weakly left‐handed or right‐handed polarization. The normal angle of the waves exhibits significant variation between 0° and 80°, indicating a non‐parallel propagation. We run a hybrid code with parameters estimated from the Arase observations to examine the He+ energization. The simulations show that cold He+ ions are energized up to more than 1,000 eV, similar to the spacecraft observations. From the analysis of the simulated wave fields and cold plasma motions, we found that the ratio of the wave frequency to He+ gyrofrequency is a primary factor for transverse energization of cold He+ ions. As a consequence of the numerical analysis, we suggest that the significant transverse energization of He+ ions observed by Arase is attributed to H‐band EMIC waves excited near the local helium gyrofrequency.
AI‐Based Ensemble Flood Forecasts and Its Implementation in Multi‐Objective Robust Optimization Operation for Reservoir Flood Control
Authors: Guo Yuxue , Xu Yue‐Ping , Yu Xinting , Liu Li , Gu Haiting
Published: May 2024

Informing reservoirs with forecasts is highly important for real‐time flood control. This study proposed a forecast‐informed methodology framework for reservoir flood control operation under uncertainty. A new combination of two post‐processing methods, that is, the Cloud model and error‐based copula functions, were developed to merge individual AI‐based forecasts to ensemble flood forecasts, so called stochastic errors‐based Cloud (SE‐Cloud). A multi‐objective robust optimization model (MRO) integrating the risk, resilience, and vulnerability was then proposed to tackle flood control problems under ensemble forecasts; for comparison, a two‐objective stochastic optimization model (TSO) was developed to minimize the expected highest reservoir level and peak release. The proposed methodology was applied to the Lishimen reservoir in the Shifeng River subbasin, China, aiming to comprehensively verify the relationships among deterministic forecasts, ensemble forecasts, and flood control performance. Results showed that the Cloud model could effectively integrate different models and improve forecast accuracy. But a higher deterministic forecast quality did not consistently result in improved flood control performance. SE‐Cloud could capture the peak flow and effectively characterize forecast uncertainties and increased hypervolume values by 13.14%–39.65% compared to the Cloud model, indicating the superiority of ensemble forecasts in generating robust solutions over individual deterministic forecasts. MRO released more inflow than TSO, decreasing the expected highest water level by 0.05 m and incrementing the expected peak release by 4.29%. However, with downstream resilience value remaining at zero, it is demonstrated that MRO improving upstream vulnerability did not necessarily diminish resilience. The enhanced robustness highlights the potential of AI‐based ensemble forecasts in flood control.
DeepARV: ensemble deep learning to predict drug-drug interaction of clinical relevance with antiretroviral therapy
Authors: Pham Thao , Ghafoor Mohamed , Grañana-Castillo Sandra , Marzolini Catia , Gibbons Sara , Khoo Saye , Chiong Justin , Wang Dennis , Siccardi Marco
Published: May 2024

Drug-drug interaction (DDI) may result in clinical toxicity or treatment failure of antiretroviral therapy (ARV) or comedications. Despite the high number of possible drug combinations, only a limited number of clinical DDI studies are conducted. Computational prediction of DDIs could provide key evidence for the rational management of complex therapies. Our study aimed to assess the potential of deep learning approaches to predict DDIs of clinical relevance between ARVs and comedications. DDI severity grading between 30,142 drug pairs was extracted from the Liverpool HIV Drug Interaction database. Two feature construction techniques were employed: 1) drug similarity profiles by comparing Morgan fingerprints, and 2) embeddings from SMILES of each drug via ChemBERTa, a transformer-based model. We developed DeepARV-Sim and DeepARV-ChemBERTa to predict four categories of DDI: i) Red: drugs should not be co-administered, ii) Amber: interaction of potential clinical relevance manageable by monitoring/dose adjustment, iii) Yellow: interaction of weak relevance and iv) Green: no expected interaction. The imbalance in the distribution of DDI severity grades was addressed by undersampling and applying ensemble learning. DeepARV-Sim and DeepARV-ChemBERTa predicted clinically relevant DDI between ARVs and comedications with a weighted mean balanced accuracy of 0.729 ± 0.012 and 0.776 ± 0.011, respectively. DeepARV-Sim and DeepARV-ChemBERTa have the potential to leverage molecular structures associated with DDI risks and reduce DDI class imbalance, effectively increasing the predictive ability on clinically relevant DDIs. This approach could be developed for identifying high-risk pairing of drugs, enhancing the screening process, and targeting DDIs to study in clinical drug development.
Proactive vaccination using multiviral Quartet Nanocages to elicit broad anti-coronavirus responses
Authors: Hills Rory A. , Tan Tiong Kit , Cohen Alexander A. , Keeffe Jennifer R. , Keeble Anthony H. , Gnanapragasam Priyanthi N. P. , Storm Kaya N. , Rorick Annie V. , West Anthony P. , Hill Michelle L. , Liu Sai , Gilbert-Jaramillo Javier , Afzal Madeeha , Napier Amy , Admans Gabrielle , James William S. , Bjorkman Pamela J. , Townsend Alain R. , Howarth Mark R.
Published: May 2024

Defending against future pandemics requires vaccine platforms that protect across a range of related pathogens. Nanoscale patterning can be used to address this issue. Here, we produce quartets of linked receptor-binding domains (RBDs) from a panel of SARS-like betacoronaviruses, coupled to a computationally designed nanocage through SpyTag/SpyCatcher links. These Quartet Nanocages, possessing a branched morphology, induce a high level of neutralizing antibodies against several different coronaviruses, including against viruses not represented in the vaccine. Equivalent antibody responses are raised to RBDs close to the nanocage or at the tips of the nanoparticle’s branches. In animals primed with SARS-CoV-2 Spike, boost immunizations with Quartet Nanocages increase the strength and breadth of an otherwise narrow immune response. A Quartet Nanocage including the Omicron XBB.1.5 ‘Kraken’ RBD induced antibodies with binding to a broad range of sarbecoviruses, as well as neutralizing activity against this variant of concern. Quartet nanocages are a nanomedicine approach with potential to confer heterotypic protection against emergent zoonotic pathogens and facilitate proactive pandemic protection.
Anisotropic exchange interaction of two hole-spin qubits
Authors: Geyer Simon , Hetényi Bence , Bosco Stefano , Camenzind Leon C. , Eggli Rafael S. , Fuhrer Andreas , Loss Daniel , Warburton Richard J. , Zumbühl Dominik M. , Kuhlmann Andreas V.
Published: May 2024

Semiconductor spin qubits offer the potential to employ industrial transistor technology to produce large-scale quantum computers. Silicon hole spin qubits benefit from fast all-electrical qubit control and sweet spots to counteract charge and nuclear spin noise. However, the demonstration of a two-qubit interaction has remained an open challenge. One missing factor is an understanding of the exchange coupling in the presence of a strong spin–orbit interaction. Here we study two hole-spin qubits in a silicon fin field-effect transistor, the workhorse device of today’s semiconductor industry. We demonstrate electrical tunability of the exchange splitting from above 500 MHz to close-to-off and perform a conditional spin-flip in 24 ns. The exchange is anisotropic because of the spin–orbit interaction. Upon tunnelling from one quantum dot to the other, the spin is rotated by almost 180 degrees. The exchange Hamiltonian no longer has the Heisenberg form and can be engineered such that it enables two-qubit controlled rotation gates without a trade-off between speed and fidelity. This ideal behaviour applies over a wide range of magnetic field orientations, rendering the concept robust with respect to variations from qubit to qubit, indicating that it is a suitable approach for realizing a large-scale quantum computer.
Targeting pathological cells with senolytic drugs reduces seizures in neurodevelopmental mTOR-related epilepsy
Authors: Ribierre Théo , Bacq Alexandre , Donneger Florian , Doladilhe Marion , Maletic Marina , Roussel Delphine , Le Roux Isabelle , Chassoux Francine , Devaux Bertrand , Adle-Biassette Homa , Ferrand-Sorbets Sarah , Dorfmüller Georg , Chipaux Mathilde , Baldassari Sara , Poncer Jean-Christophe , Baulac Stéphanie
Published: May 2024

Cortical malformations such as focal cortical dysplasia type II (FCDII) are associated with pediatric drug-resistant epilepsy that necessitates neurosurgery. FCDII results from somatic mosaicism due to post-zygotic mutations in genes of the PI3K-AKT-mTOR pathway, which produce a subset of dysmorphic cells clustered within healthy brain tissue. Here we show a correlation between epileptiform activity in acute cortical slices obtained from human surgical FCDII brain tissues and the density of dysmorphic neurons. We uncovered multiple signatures of cellular senescence in these pathological cells, including p53/p16 expression, SASP expression and senescence-associated β-galactosidase activity. We also show that administration of senolytic drugs (dasatinib/quercetin) decreases the load of senescent cells and reduces seizure frequency in an MtorS2215F FCDII preclinical mouse model, providing proof of concept that senotherapy may be a useful approach to control seizures. These findings pave the way for therapeutic strategies selectively targeting mutated senescent cells in FCDII brain tissue.
Metagenomics datasets of water and sediments from eutrophication-impacted artificial lakes in South Africa
Authors: Ijoma Grace Nkechinyere , Ogola Henry Joseph Oduor , Pillay Preenan , Tshisekedi Kalonji Abondance , Tekere Memory
Published: May 2024

We present metagenomes of 16 samples of water and sediment from two lakes, collected from eutrophic and non-eutrophic areas, including pooled samples enriched with phosphate and nitrate. Additionally, we assembled 167 bacterial metagenome-assembled genomes (MAGs). These MAGs were de-replicated into 83 unique genomes representing different species found in the lakes. All the MAGs exhibited >70% completeness and <10% contamination, with 79 MAGs being classified as ‘nearly complete’ (completeness >90%), while 54 falling within 80–90% range and 34 between 75–80% complete. The most abundant MAGs identified across all samples were Proteobacteria (n = 80), Firmicutes_A (n = 35), Firmicutes (n = 13), and Bacteriodota (n = 22). Other groups included Desulfobacteria_I (n = 2), Verrucomicrobiota (n = 4), Campylobacterota (n = 4) and Actinobacteriota (n = 6). Importantly, phylogenomic analysis identified that approximately 50.3% of the MAGs could not be classified to known species, suggesting the presence of potentially new and unknown bacteria in these lakes, warranting further in-depth investigation. This study provides valuable new dataset on the diverse and often unique microbial communities living in polluted lakes, useful in developing effective strategies to manage pollution.
Conventional and frugal methods of estimating COVID-19-related excess deaths and undercount factors
Authors: Dedhe Abhishek M. , Chowkase Aakash A. , Gogate Niramay V. , Kshirsagar Manas M. , Naphade Rohan , Naphade Atharv , Kulkarni Pranav , Naik Mrunmayi , Dharm Aarya , Raste Soham , Patankar Shravan , Jogdeo Chinmay M. , Sathe Aalok , Kulkarni Soham , Bapat Vibha , Joshi Rohinee , Deshmukh Kshitij , Lele Subhash , Manke-Miller Kody J. , Cantlon Jessica F. , Pandit Pranav S.
Published: May 2024

Across the world, the officially reported number of COVID-19 deaths is likely an undercount. Establishing true mortality is key to improving data transparency and strengthening public health systems to tackle future disease outbreaks. In this study, we estimated excess deaths during the COVID-19 pandemic in the Pune region of India. Excess deaths are defined as the number of additional deaths relative to those expected from pre-COVID-19-pandemic trends. We integrated data from: (a) epidemiological modeling using pre-pandemic all-cause mortality data, (b) discrepancies between media-reported death compensation claims and official reported mortality, and (c) the “wisdom of crowds” public surveying. Our results point to an estimated 14,770 excess deaths [95% CI 9820–22,790] in Pune from March 2020 to December 2021, of which 9093 were officially counted as COVID-19 deaths. We further calculated the undercount factor—the ratio of excess deaths to officially reported COVID-19 deaths. Our results point to an estimated undercount factor of 1.6 [95% CI 1.1–2.5]. Besides providing similar conclusions about excess deaths estimates across different methods, our study demonstrates the utility of frugal methods such as the analysis of death compensation claims and the wisdom of crowds in estimating excess mortality.
Synthesis and in silico inhibitory action studies of azo-anchored imidazo[4,5-b]indole scaffolds against the COVID-19 main protease (Mpro)
Authors: Geedkar Deepika , Kumar Ashok , Sharma Pratibha
Published: May 2024

The present work elicits a novel approach to combating COVID-19 by synthesizing a series of azo-anchored 3,4-dihydroimidazo[4,5-b]indole derivatives. The envisaged methodology involves the l-proline-catalyzed condensation of para-amino-functionalized azo benzene, indoline-2,3-dione, and ammonium acetate precursors with pertinent aryl aldehyde derivatives under ultrasonic conditions. The structures of synthesized compounds were corroborated through FT-IR, 1H NMR, 13C NMR, and mass analysis data. Molecular docking studies assessed the inhibitory potential of these compounds against the main protease (Mpro) of SARS-CoV-2. Remarkably, in silico investigations revealed significant inhibitory action surpassing standard drugs such as Remdesivir, Paxlovid, Molnupiravir, Chloroquine, Hydroxychloroquine (HCQ), and (N3), an irreversible Michael acceptor inhibitor. Furthermore, the highly active compound was also screened for cytotoxicity activity against HEK-293 cells and exhibited minimal toxicity across a range of concentrations, affirming its favorable safety profile and potential suitability. The pharmacokinetic properties (ADME) of the synthesized compounds have also been deliberated. This study paves the way for in vitro and in vivo testing of these scaffolds in the ongoing battle against SARS-CoV-2.
Improved random forest classification model combined with C5.0 algorithm for vegetation feature analysis in non-agricultural environments
Authors: Wang Tianyu
Published: May 2024

In response to the challenges posed by the high computational complexity and suboptimal classification performance of traditional random forest algorithms when dealing with high-dimensional and noisy non-agricultural vegetation satellite data, this paper proposes an enhanced random forest algorithm based on the C5.0 algorithm. The paper focuses on the Liaohe Plain, selecting two distinct non-agricultural landscape patterns in Shenbei New District and Changtu County as research objects. High-resolution satellite data from GF-2 serves as the experimental dataset. This paper introduces an ensemble feature method based on the bagging concept to improve the original random forest classification model. This method enhances the likelihood of selecting features beneficial to classifying positive class samples, avoiding excessive removal of useful features from negative samples. This approach ensures feature importance and model diversity. The C5.0 algorithm is then employed for feature selection, and the enhanced vegetation index (EVI) is utilized for vegetation coverage estimation. Results indicate that employing a multi-scale parameter selection tool, combined with limited field-measured data, facilitates the identification and classification of plant species in forest landscapes. The C5.0 algorithm effectively selects classification features, minimizing information redundancy. The established object-oriented random forest classification model achieves an impressive accuracy of 94.02% on the aerial imagery for forest classification dataset, with EVI-based vegetation coverage estimation demonstrating high accuracy. In experiments on the same test set, the proposed algorithm attains an average accuracy of 90.20%, outperforming common model algorithms such as bidirectional encoder representation from transformer, FastText, and convolutional neural network, which achieve average accuracies ranging from 84.41 to 88.33% in identifying non-agricultural artificial habitat vegetation features. The proposed algorithm exhibits a competitive edge compared to other algorithms. These research findings contribute scientific evidence for protecting agricultural ecosystems and restoring agricultural ecosystem biodiversity.
Scenarios for the Altamira cave CO2 concentration from 1950 to 2100
Authors: Sáez Marina , Benavente David , Cuezva Soledad , Huc Mireille , Fernández-Cortés Ángel , Mialon Arnaud , Kerr Yann , Sánchez-Moral Sergio , Mangiarotti Sylvain
Published: May 2024

A data-driven approach insensitive to the initial conditions was developed to extract governing equations for the concentration of CO2in the Altamira cave (Spain) and its two main drivers: the outside temperature and the soil moisture. This model was then reformulated in order to use satellite observations and meteorological predictions, as a forcing. The concentration of CO2inside the cave was then investigated from 1950 to 2100 under various scenarios. It is found that extreme levels of CO2were reached during the period 1950–1972 due to the massive affluence of visitors. It is demonstrated that it is possible to monitor the CO2in the cave in real time using satellite information as an external forcing. For the future, it is shown that the maximum values of CO2will exceed the levels reached during the 1980s and the 1990s when the CO2introduced by the touristic visits, although intentionally reduced, still enhanced considerably the micro corrosion of walls and pigments.
Essentiality of dietary cholesterol and its interactions with phospholipid in juvenile slipper lobster (Thenus australiensis)
Authors: Landman Michael J. , Codabaccus Basseer M. , Nichols David S. , Carter Chris G. , Fitzgibbon Quinn P. , Smith Gregory G.
Published: May 2024

This study was conducted to verify the essentiality of dietary cholesterol for early juvenile slipper lobster, Thenus australiensis (initial weight 4.50 ± 0.72 g, mean ± SD, CV = 0.16), and to explore the potential for interactions between dietary cholesterol and phospholipid. An 8-week experiment was conducted using six experimental feeds containing three supplemental cholesterol concentrations (0, 0.2 and 0.4% dry matter) at two supplemental phospholipid concentrations (0% and 1.0% dry matter). Dietary cholesterol concentrations of ≥ 0.2% resulted in up to threefold greater weight gain compared to 0% dietary cholesterol, but without any significant main or interactive dietary phospholipid effect. An interaction was observed for lobster survival with lowest survival (46%) recorded for combined 0% cholesterol and 0% phospholipid compared to every other treatment (71–100%). However, all surviving lobsters at 0% dietary cholesterol, regardless of dietary phospholipid level, were in poor nutritional condition. Apparent feed intake (AFI) was significantly higher at dietary cholesterol ≥ 0.2% but was lower for each corresponding dietary cholesterol level at 1% dietary phospholipid. This implied that the feed conversion ratio was improved with supplemental phospholipid. In conclusion, this study confirms the essential nature of dietary cholesterol and that dietary phospholipid can provide additional benefits.
Trefoil factor 3 can stimulate Th17 cell response in the development of type 2 diabetes mellitus
Authors: Lin Ziyang , Zhang Jinyuan , Duan Tingting , Yang Junzheng , Yang Yiqi
Published: May 2024

This study aims to evaluate the role of trefoil factor 3 (TFF3) peptides in type 2 diabetes mellitus (T2DM) from an inflammatory perspective. The focus was on exploring how TFF3 affects the function of T cells. TFF3 overexpression model was constructed using lentivirus in Jurkat cell lines. We evaluated the impact of TFF3 on the proliferation, apoptosis, and IL-17A levels of Jurkat cells cultured in high glucose. The T2DM model was induced in TFF3 knockout (KO) mice through streptozotocin combined with high-fat diet. The measurements included glucose tolerance, insulin tolerance, inflammation markers, Th17 cell proportion, and pancreatic pathological changes. The T2DM modeling led to splenomegaly in mice, and increased expression of TFF3 in their spleens. Overexpression of TFF3 increased the proportion of IL-17+ T cells and the levels of Th17-related cytokines in Jurkat cells. There was no difference in body weight and blood glucose levels between wild-type and TFF3 KO mice. However, T2DM mice lacking the TFF3 gene showed improved glucose utilization, ameliorated pancreatic pathology, decreased inflammation levels, and reduced Th17 cell ratio. TFF3 may be involved in the chronic inflammatory immune response in T2DM. Its mechanism may be related to the regulation of the RORγt/IL-17 signaling pathway and its impact on T cell proliferation and apoptosis.
Neurological features of Hansen disease: a retrospective, multicenter cohort study
Authors: Chen Xiaohua , Di Li , Qian Min , Shen Dongchao , Feng Xinhong , Zhang Xiqing
Published: May 2024

To elucidate the neurological features of Hansen disease. The medical records of patients with confirmed Hansen disease transferred from the neurology department were reviewed, and all medical and neurological manifestations of Hansen disease were assessed. Eleven patients with confirmed Hansen disease, 10 with newly detected Hansen disease and 1 with relapsed Hansen disease, who visited neurology departments were enrolled. The newly detected patients with Hansen disease were classified as having lepromatous leprosy (LL, n = 1), borderline lepromatous leprosy (BL, n = 2), borderline leprosy (BB, n = 2), borderline tuberculoid leprosy (BT, n = 1), tuberculoid leprosy (TT, n = 2), or pure neural leprosy (PNL, n = 2). All of the patients with confirmed Hansen were diagnosed with peripheral neuropathy (100.00%, 11/11). The symptoms and signs presented were mainly limb numbness (100.00%, 11/11), sensory and motor dysfunction (100.00%, 11/11), decreased muscle strength (90.90%, 10/11), and skin lesions (81.81%, 9/11). Nerve morphological features in nerve ultrasonography (US) included peripheral nerve asymmetry and segmental thickening (100.00%, 9/9). For neuro-electrophysiology feature, the frequency of no response of sensory nerves was significantly higher than those of motor nerves [(51.21% 42/82) vs (24.70%, 21/85)(P = 0.0183*)] by electrodiagnostic (EDX) studies. Nerve histological features in nerve biopsy analysis included demyelination (100.00%, 5/5) and axonal damage (60.00%, 3/5). In addition to confirmed diagnoses by acid-fast bacteria (AFB) staining (54.54%, 6/11) and skin pathology analysis (100.00%, 8/8), serology and molecular technology were positive in 36.36% (4/11) and 100.00% (11/11) of confirmed patients of Hansen disease, respectively. It is not uncommon for patients of Hansen disease to visit neurology departments due to peripheral neuropathy. The main pathological features of affected nerves are demyelination and axonal damage. The combination of nerve US, EDX studies, nerve biopsy, and serological and molecular tests can improve the diagnosis of Hansen disease.
Future groundwater potential mapping using machine learning algorithms and climate change scenarios in Bangladesh
Authors: Sarkar Showmitra Kumar , Rudra Rhyme Rubayet , Talukdar Swapan , Das Palash Chandra , Nur Md. Sadmin , Alam Edris , Islam Md Kamrul , Islam Abu Reza Md. Towfiqul
Published: May 2024

The aim of the study was to estimate future groundwater potential zones based on machine learning algorithms and climate change scenarios. Fourteen parameters (i.e., curvature, drainage density, slope, roughness, rainfall, temperature, relative humidity, lineament density, land use and land cover, general soil types, geology, geomorphology, topographic position index (TPI), topographic wetness index (TWI)) were used in developing machine learning algorithms. Three machine learning algorithms (i.e., artificial neural network (ANN), logistic model tree (LMT), and logistic regression (LR)) were applied to identify groundwater potential zones. The best-fit model was selected based on the ROC curve. Representative concentration pathways (RCP) of 2.5, 4.5, 6.0, and 8.5 climate scenarios of precipitation were used for modeling future climate change. Finally, future groundwater potential zones were identified for 2025, 2030, 2035, and 2040 based on the best machine learning model and future RCP models. According to findings, ANN shows better accuracy than the other two models (AUC: 0.875). The ANN model predicted that 23.10 percent of the land was in very high groundwater potential zones, whereas 33.50 percent was in extremely high groundwater potential zones. The study forecasts precipitation values under different climate change scenarios (RCP2.6, RCP4.5, RCP6, and RCP8.5) for 2025, 2030, 2035, and 2040 using an ANN model and shows spatial distribution maps for each scenario. Finally, sixteen scenarios were generated for future groundwater potential zones. Government officials may utilize the study’s results to inform evidence-based choices on water management and planning at the national level.
Designing of a new transdermal antibiotic delivery polymeric membrane modified by functionalized SBA-15 mesoporous filler
Authors: Samari Mahya , Kashanian Soheila , Zinadini Sirus , Derakhshankhah Hossein
Published: May 2024

A new drug delivery system using an asymmetric polyethersulfone (PES) membrane modified by SBA-15 and glutamine-modified SBA-15 (SBA-Q) was prepared in this study by the aim of azithromycin delivery enhancement in both in vitro and ex vivo experiments. The research focused on optimizing membrane performance by adjusting critical parameters including drug concentration, membrane thickness, modifier percentage, polymer percentage, and pore maker percentage. To characterize the fabricated membranes, various techniques were employed, including scanning electron microscopy, water contact angle, and tensile strength assessments. Following optimization, membrane composition of 17% PES, 2% polyvinylpyrrolidone, 1% SBA-15, and 0.5% SBA-Q emerged as the most effective. The optimized membranes demonstrated a substantial increase in drug release (906 mg/L) compared to the unmodified membrane (440 mg/L). The unique membrane structure, with a dense top layer facilitating sustained drug release and a porous sub-layer acting as a drug reservoir, contributed to this improvement. Biocompatibility assessments, antibacterial activity analysis, blood compatibility tests, and post-diffusion tissue integrity evaluations confirmed the promising biocompatibility of the optimized membranes. Moreover, long-term performance evaluations involving ten repeated usages underscored the reusability of the optimized membrane, highlighting its potential for sustained and reliable drug delivery applications.
Oral microbiome dysbiosis among cigarette smokers and smokeless tobacco users compared to non-users
Authors: Chattopadhyay Suhana , Malayil Leena , Chopyk Jessica , Smyth Eoghan , Kulkarni Prachi , Raspanti Greg , Thomas Stephen B. , Sapkota Amir , Mongodin Emmanuel F. , Sapkota Amy R.
Published: May 2024

Tobacco use significantly influences the oral microbiome. However, less is known about how different tobacco products specifically impact the oral microbiome over time. To address this knowledge gap, we characterized the oral microbiome of cigarette users, smokeless tobacco users, and non-users over 4 months (four time points). Buccal swab and saliva samples (n = 611) were collected from 85 participants. DNA was extracted from all samples and sequencing was carried out on an Illumina MiSeq, targeting the V3–V4 region of the 16S rRNA gene. Cigarette and smokeless tobacco users had more diverse oral bacterial communities, including a higher relative abundance of Firmicutes and a lower relative abundance of Proteobacteria, when compared to non-users. Non-users had a higher relative abundance of Actinomyces, Granulicatella, Haemophilus, Neisseria, Oribacterium, Prevotella, Pseudomonas, Rothia, and Veillonella in buccal swab samples, compared to tobacco users. While the most abundant bacterial genera were relatively constant over time, some species demonstrated significant shifts in relative abundance between the first and last time points. In addition, some opportunistic pathogens were detected among tobacco users including Neisseria subflava, Bulleidia moorei and Porphyromonas endodontalis. Overall, our results provide a more holistic understanding of the structure of oral bacterial communities in tobacco users compared to non-users.
Pseudo-class part prototype networks for interpretable breast cancer classification
Authors: Choukali Mohammad Amin , Amirani Mehdi Chehel , Valizadeh Morteza , Abbasi Ata , Komeili Majid
Published: May 2024

Interpretability in machine learning has become increasingly important as machine learning is being used in more and more applications, including those with high-stakes consequences such as healthcare where Interpretability has been regarded as a key to the successful adoption of machine learning models. However, using confounding/irrelevant information in making predictions by deep learning models, even the interpretable ones, poses critical challenges to their clinical acceptance. That has recently drawn researchers’ attention to issues beyond the mere interpretation of deep learning models. In this paper, we first investigate application of an inherently interpretable prototype-based architecture, known as ProtoPNet, for breast cancer classification in digital pathology and highlight its shortcomings in this application. Then, we propose a new method that uses more medically relevant information and makes more accurate and interpretable predictions. Our method leverages the clustering concept and implicitly increases the number of classes in the training dataset. The proposed method learns more relevant prototypes without any pixel-level annotated data. To have a more holistic assessment, in addition to classification accuracy, we define a new metric for assessing the degree of interpretability based on the comments of a group of skilled pathologists. Experimental results on the BreakHis dataset show that the proposed method effectively improves the classification accuracy and interpretability by respectively $$8 \%$$

8
%

and $$18 \%$$

18
%

. Therefore, the proposed method can be seen as a step toward implementing interpretable deep learning models for the detection of breast cancer using histopathology images.