bluuebunny/crossref_metadata_2025 huggingface dataset:
Ecological dynamics and conservation implications of Indo-Pacific Tarpon (Megalops cyprinoides) in Lake Siombak, North Sumatra, Indonesia
Authors: Muhtadi Ahmad , Maiyah Nur , Suryanti Ani , Abdelrahman Amal Saber , Akbar Helmy , Putra Singgih Afifa
Published: June 2024

This study presents a comprehensive examination of the Indo-Pacific Tarpon (IPT) population in Lake Siombak, North Sumatra Province, Indonesia. Through a year-long investigation, we analyzed various facets of IPT ecology, encompassing population structure, growth dynamics, reproductive behavior, feeding habits, and mortality parameters. Male dominance in collected specimens, coupled with consistently immature gonads in females, suggests a unique reproductive pattern influenced by the brackish nature of Lake Siombak. Utilizing the von Bertalanffy method, we determined a moderate growth rate in IPT, with a predominance of medium-sized, sexually immature individuals. The dietary analysis revealed an omnivorous nature, with a notable reliance on zooplankton, particularly Cladocera and Copepods. However, the most significant concern arises from the estimation of the exploitation rate (E), surpassing the annual optimum exploitation threshold, indicating a state of overexploitation. This underscores the urgent need for regulatory measures to ensure the sustainability of IPT resources in Lake Siombak. Our findings contribute to a nuanced understanding of IPT ecology in this specific ecosystem, emphasizing the importance of balanced conservation and management practices. The documented reproductive, growth, and feeding patterns provide crucial insights for informing targeted conservation strategies and safeguarding the enduring viability of the IPT population in Lake Siombak. This research serves as a foundational contribution to the broader field of fisheries science, advocating for evidence-based management to preserve the ecological balance of aquatic ecosystems.
Landslide Deformation Prediction and Automatic Warning by Coupling Machine Learning and Physical Models
Authors: Wang Xianmin , Diao Mupeng , Guo Haonan , Wang Lizhe , Guo Haixiang , Li Dongdong
Published: June 2024

Automatic deformation forecast and warning of a catastrophic landslide can effectively avoid significant casualties and economic losses. However, currently it has not come to a comprehensive forecast framework covering all the deformation stages of a landslide. Moreover, landslide deformation prediction possesses high error and false alarm rates. This work suggests a novel integrated framework of landslide deformation forecast and warning by coupling machine learning and physical models. The framework can relatively accurately predict all the deformation stages from creeping deformation to critical sliding and features 4 advantages. (a) The forecast indices are established by combining deformation and disaster‐triggering characteristics to improve the prediction accuracy. (b) It leverages the advantage of C5.0 decision tree algorithm in knowledge interpretability to automatically extract deformation forecast criteria. (c) It capitalizes on the precision superiority of a graph convolutional network in time‐series data learning to predict the four deformation stages from creeping deformation to rapidly accelerated deformation. (d) It utilizes the physical and mechanical bases of Morgenstern‐Price method to forecast the critical sliding stage. Zhujiatang Landslide is a large‐scale deep‐seated soil landslide with significant deformation. It is selected as a case study because it has endangered 1,131 persons and may cause a direct financial loss of 100 million RMB. The validating and predicting Accuracy values attain 97.39% and 95.72%, respectively, and the Kappa values reach 0.91 and 0.93, respectively. The landslide will run out when it suffers from a rainstorm with cumulative rainfall of 79.57 mm and an earthquake with a horizontal coefficient of 0.04.
Drivers of the Extreme Early Spring Glacier Melt of 2022 on the Central Tibetan Plateau
Authors: Zhu Fei , Zhu Meilin , Yang Wei , Wang Zhongyan , Guo Yanhong , Yao Tandong
Published: June 2024

Extreme glacier melt has important implications for climate change studies and glacier‐related hazards. Few attempts have been made to study the characteristics and mechanisms of extreme melt on the Tibetan Plateau (TP) and surroundings, one of the largest concentrations of glaciers beyond the polar regions. To address this gap, we investigated the magnitude and possible drivers of anomalous early spring glacier melt in 2022 on the central TP using multi‐source observations (October 2020–July 2022) of Kuoqionggangri and Sangqu glaciers and an energy and mass balance model. Our results show that anomalous melt of both glaciers occurred in early spring (18 March–10 April 2022), when glaciers are traditionally expected to be frozen. There were 89 cumulative hours with air temperatures above 0°C, with a positive cumulative air temperature of 121.80°C hour at 5,700 m on Kuoqionggangri Glacier. The glacier‐wide cumulative melts on Kuoqionggangri and Sangqu glaciers from 18 March to 10 April 2022 were 22 ± 14.2 and 48 ± 37.5 mm w.e., respectively. Such anomalous glacier melt was driven by high incoming shortwave radiation and sensible heat flux, which were influenced by low cloud fraction and precipitation, and high air temperature during early spring. The weakening and northward‐shift of the westerlies, coupled with descending air motions over the central TP, reduced cloud fraction and hindered the transport of cold, moist air to the central TP. These changes occurred both in the central TP and the Himalaya, indicating the influence of macroscale atmospheric circulation patterns on this extreme glacier melt.
An Omnidirectional Filtering Method for Destriping Lunar Satellite Gravity Anomalies Data
Authors: Yang Jing , Guo Lianghui , Tang Hanhan , Yao Shuo
Published: June 2024

High‐resolution gravity field models of the Moon established from Gravity Recovery and Interior Laboratory satellite gravity data have been playing an important role in understanding the interior structure and tectonic evolution of the Moon. Due to the correlations in the high degree and order term coefficients of the gravity field models, the difference between satellite flight orbits, and the random noise of instruments, the high‐resolution gravity anomalies data derived from the models of high degree and order usually present serious multi‐directional striping and random noise, which clearly affect the subsequent interpretation of the data. We provide an omnidirectional filtering method based on the polynomial‐fitting principle to remove multi‐directional striping noise in the lunar satellite gravity anomalies data. A set of azimuth parameters are chosen to suppress all the directions of striping noise. The algorithms of data partitioning and iterative optimization are utilized to make our method suitable and stable for large‐scale data sets. Tests on the synthetic data and the real data from the Moon's Rümker region and globally verified the feasibility of our method with a better destriping effect than the traditional Gaussian filtering or degree‐order‐truncation methods.
Deciphering the Biophysical Impact of Permafrost Greening on Summer Surface Offset
Authors: Wang Jian , Liu Desheng
Published: June 2024

Satellite observations have shown widespread greening during the last few decades over the northern permafrost region, but the impact of vegetation greening on permafrost thermal dynamics remains poorly understood, hindering the understanding of permafrost‐vegetation‐climate feedbacks. Summer surface offset (SSO), defined as the difference between surface soil temperature and near‐surface air temperature in summer (June‐August), is often predicted as a function of surface thermal characteristics for permafrost modeling. Here we examined the impact of leaf area index (LAI), detected by satellite as a proxy to permafrost vegetation dynamics, on SSO variations from 2003 to 2021 across the northern permafrost region. We observed latitude‐ and biome‐dependent patterns of SSO changes, with a pronounced increase in Siberian shrublands and a decrease in Tibetan grasslands. Based on partial correlation and sensitivity analyses, we found a strong LAI signal (∼30% of climatic signal) on SSO with varying elevation‐ and canopy height‐dependent patterns. Positive correlations or sensitivities, that is, increases in LAI lead to higher SSO, were distributed in relatively cold and wet areas. Biophysical effects of permafrost greening on surface albedo, evapotranspiration, and soil moisture (SM) could link the connection between LAI and SSO. Increased LAI substantially reduced surface albedo and enhanced evapotranspiration, influenced energy redistribution, and further controlled interannual variability of SSO. We also found contrasting effects of LAI on surface SM, consequently leading to divergent impacts on SSO. The results offer a fresh perspective on how greening affects the thermal balance and dynamics of permafrost, which is enlightening for improved permafrost projections.
Is Only the Wind Field Controlling the Maximum Sea Ice Area in the Bering Sea?
Authors: Wang Weibo , Jing Chunsheng , Guo Xiaogang
Published: June 2024

Over the past four decades, temporal variability in March sea ice area (SIA) within the Bering Sea occasionally contradicts prevailing northeasterly wind, raising doubt about the primary regulatory role of wind speed in governing maximum SIA. We argue that at least two spatial modes, extracted through the Empirical Orthogonal Function analysis, are necessary to explain the variations in the maximum SIA in the Bering Sea. Wind field emerges as the primary regulator of EOF1, governing both the direct influence of wind divergence (WD) and the indirect influence of meridional heat transport on sea ice. EOF2 is directly governed by ocean heat transport (OHT). Considering only the direct impact on sea ice, the OHT is directly responsible for the maximum SIA changes in 1995–2007, while in 2008–2014, the wind field is the largest driver. Since 2015, historically low SIA is attributed to significantly enhanced wind convergence and reduced zonal heat transport in the Gulf of Anadyr. Wind field directly controls the maximum SIA for less than half of the observation time, with OHT driving the rest. Declining trends observed in both WD and OHT suggests a future distribution pattern for the maximum SIA in the Bering Sea, characterized by an increase in the east and a decrease in the west. Long‐term retreat of sea ice on the western side of the Bering Sea is anticipated to exert significant impacts on local ecosystems, commercial activities, and even indigenous communities.
Reply to: “Comment on ‘Stratospheric Aerosol Composition Observed by the Atmospheric Chemistry Experiment Following the 2019 Raikoke Eruption’ by Boone et al.” by Ansmann et al.
Authors: Boone C. D.
Published: June 2024

The question of stratospheric aerosol type following the Raikoke eruption is revisited. Raman lidar measurements suggest the aerosols are predominately smoke, while Atmospheric Chemistry Experiment Fourier transform spectrometer (ACE‐FTS) results indicate the aerosols are predominately sulfate aerosols. The suggested mechanism of smoke particles self‐lofting into the stratosphere is inconsistent with observations in 2020, when more severe Siberian fires failed to invoke a response even vaguely similar to 2019. A side‐by side comparison of the Sarychev and Raikoke eruptions invalidates model calculations that suggest sulfate aerosols should be at levels too low to explain the observed aerosol loading. Structure in infrared absorption spectra provides conclusive evidence of composition, a unique fingerprint for identifying aerosol type. Such information cannot be misinterpreted so long as there is sufficient resolution and spectral coverage. ACE‐FTS infrared aerosol spectra often have an order of magnitude stronger absorption than that of background sulfate aerosols. These spectra can be accurately reproduced by laboratory measured sulfate aerosol spectroscopic information, providing unambiguous identification of the aerosols as sulfate. Visual inspection of thousands of infrared aerosol spectra from the period following the Raikoke eruption indicates the aerosols in the lower stratosphere are predominately sulfate, with no indication of smoke. The lidar study's identification of the aerosols as smoke was based primarily on observed lidar ratios that were more consistent with a material that absorbed significantly at the lidar wavelengths, inconsistent with expectations for sulfate aerosols. However, this could indicate the presence of a substance dissolved in the sulfate aerosols absorbing at those wavelengths rather than smoke particles.
Four Years of Meander‐Bend Evolution Captured by Drone‐Based Lidar Reveals Lack of Width Maintenance on the White River, Indiana, USA
Authors: Martin H. K. , Edmonds D. A. , Lewis Q. W.
Published: June 2024

Meandering rivers experience fluctuations in width whenever riverbanks migrate in different directions or at different rates, which can be observed after individual floods. However, meandering rivers maintain approximately constant widths over decadal timescales. This implies some timescale below which width fluctuates as banks migrate independently, and above which width is maintained by a bank‐coupling process. This coupling is thought to occur either as point bar deposition events induce cutbank erosion (bar‐push), or as cutbank erosion events induce point bar deposition (bank‐pull). This coupling, however, has been challenging to observe in natural rivers due to limited event‐scale field data. We present results from a 4.5‐year campaign with 22 drone‐based lidar surveys of a single point bar and cutbank (∼0.35 km2 in area) on the White River near Worthington, Indiana, USA. The middle point bar experienced net erosion (5,400 m3), but net aggradation (17,100 m3) between 2019 and 2022 when including perennially submerged regions. This aggradation was less than the 35,700 m3 of cutbank erosion over the same period. Combined, we have observed widening (1.58 m/yr bend‐averaged; 3.08 m/yr near apex) over the study period as point bar deposition has not kept up with cutbank erosion. Finally, we suggest that the difference between bar‐push and bank‐pull as width‐maintenance mechanisms may not be resolvable by observing bend widening or narrowing alone without an advancement of current theory, such as determining a long‐term equilibrium width and measuring deviations relative thereto.
The Growth and Carbon Sink of Tundra Peat Patches in Arctic Alaska
Authors: Cleary Kathleen G. , Xia Zhengyu , Yu Zicheng
Published: June 2024

Recent amplified climate warming in the Arctic has caused profound changes in terrestrial ecosystems, with the potential for strong feedback on climate change. Arctic tundra landscapes have developed patchy and thin organic soil (peat) layers at the surface that may continue to grow into mature peatlands and become a larger carbon sink under future warming. Here we use paleoecological analyses of multiple soil and peat cores collected from the North Slope of Alaska to document and understand the formation and development histories of tundra peat patches and permafrost peatlands. We find a consistent peat development sequence for peat patches, first from mineral soils to sedge peat during the Little Ice Age, and then to Sphagnum peat during the recent warming with high carbon accumulation rates. These findings suggest that climate cooling is likely critical for the initial peat buildup on tundra soils due to reduced decomposition, whereas climate warming triggers the regime shift into an increased abundance of Sphagnum mosses that are likely central to enhancing their carbon sink capacity. Additionally, peat patches become similar to permafrost peatlands in the vicinity in terms of ecosystem processes and carbon dynamics, and therefore may have developed the same ecohydrological feedback system to maintain their long‐term stability. This study implies that the potential future expansion of peat patches into peatlands may strongly alter the carbon balance of Arctic tundra, supporting the new United Nations Environment Programme's report that calls for incorporating widespread shallow peat into understanding the peatland–carbon–climate nexus.
FaIRGP: A Bayesian Energy Balance Model for Surface Temperatures Emulation
Authors: Bouabid Shahine , Sejdinovic Dino , Watson‐Parris Duncan
Published: June 2024

Emulators, or reduced complexity climate models, are surrogate Earth system models (ESMs) that produce projections of key climate quantities with minimal computational resources. Using time‐series modeling or more advanced machine learning techniques, data‐driven emulators have emerged as a promising avenue of research, producing spatially resolved climate responses that are visually indistinguishable from state‐of‐the‐art ESMs. Yet, their lack of physical interpretability limits their wider adoption. In this work, we introduce FaIRGP, a data‐driven emulator that satisfies the physical temperature response equations of an energy balance model. The result is an emulator that (a) enjoys the flexibility of statistical machine learning models and can learn from data, and (b) has a robust physical grounding with interpretable parameters that can be used to make inference about the climate system. Further, our Bayesian approach allows a principled and mathematically tractable uncertainty quantification. Our model demonstrates skillful emulation of global mean surface temperature and spatial surface temperatures across realistic future scenarios. Its ability to learn from data allows it to outperform EBMs, while its robust physical foundation safeguards against the pitfalls of purely data‐driven models. We also illustrate how FaIRGP can be used to obtain estimates of top‐of‐atmosphere radiative forcing and discuss the benefits of its mathematical tractability for applications such as detection and attribution or precipitation emulation. We hope that this work will contribute to widening the adoption of data‐driven methods in climate emulation.
Development of a Distributed Physics‐Informed Deep Learning Hydrological Model for Data‐Scarce Regions
Authors: Zhong Liangjin , Lei Huimin , Yang Jingjing
Published: June 2024

Climate change has exacerbated water stress and water‐related disasters, necessitating more precise streamflow simulations. However, in the majority of global regions, a deficiency of streamflow data constitutes a significant constraint on modeling endeavors. Traditional distributed hydrological models and regionalization approaches have shown suboptimal performance. While current deep learning (DL)‐related models trained on large data sets excel in spatial generalization, the direct applicability of these models in certain regions with unique hydrological processes can be challenging due to the limited representativeness within the training data set. Furthermore, transfer learning DL models pre‐trained on large data sets still necessitate local data for retraining, thereby constraining their applicability. To address these challenges, we present a physics‐informed DL model based on a distributed framework. It involves spatial discretization and the establishment of differentiable hydrological models for discrete sub‐basins, coupled with a differentiable Muskingum method for channel routing. By introducing upstream‐downstream relationships, model errors in sub‐basins propagate through the river network to the watershed outlet, enabling the optimization using limited downstream streamflow data, thereby achieving spatial simulation of ungauged internal sub‐basins. The model, when trained solely on the downstream‐most station, outperforms the distributed hydrological model in streamflow simulation at both the training station and upstream held‐out stations. Additionally, in comparison to transfer learning models, our model requires fewer gauge stations for training, but achieves higher precision in simulating streamflow on spatially held‐out stations, indicating better spatial generalization ability. Consequently, this model offers a novel approach to hydrological simulation in data‐scarce regions, especially those with poor hydrological representativeness.
Smart mid-infrared metasurface microspectrometer gas sensing system
Authors: Meng Jiajun , Balendhran Sivacarendran , Sabri Ylias , Bhargava Suresh K. , Crozier Kenneth B.
Published: June 2024

Smart, low-cost and portable gas sensors are highly desired due to the importance of air quality monitoring for environmental and defense-related applications. Traditionally, electrochemical and nondispersive infrared (IR) gas sensors are designed to detect a single specific analyte. Although IR spectroscopy-based sensors provide superior performance, their deployment is limited due to their large size and high cost. In this study, a smart, low-cost, multigas sensing system is demonstrated consisting of a mid-infrared microspectrometer and a machine learning algorithm. The microspectrometer is a metasurface filter array integrated with a commercial IR camera that is consumable-free, compact ( ~ 1 cm3) and lightweight ( ~ 1 g). The machine learning algorithm is trained to analyze the data from the microspectrometer and predict the gases present. The system detects the greenhouse gases carbon dioxide and methane at concentrations ranging from 10 to 100% with 100% accuracy. It also detects hazardous gases at low concentrations with an accuracy of 98.4%. Ammonia can be detected at a concentration of 100 ppm. Additionally, methyl-ethyl-ketone can be detected at its permissible exposure limit (200 ppm); this concentration is considered low and nonhazardous. This study demonstrates the viability of using machine learning with IR spectroscopy to provide a smart and low-cost multigas sensing platform.
Cost-effectiveness and threshold analysis of deep brain stimulation vs. treatment-as-usual for treatment-resistant depression
Authors: Kabotyanski Katherine E. , Najera Ricardo A. , Banks Garrett P. , Sharma Himanshu , Provenza Nicole R. , Hayden Benjamin Y. , Mathew Sanjay J. , Sheth Sameer A.
Published: June 2024

Treatment-resistant depression (TRD) affects approximately 2.8 million people in the U.S. with estimated annual healthcare costs of $43.8 billion. Deep brain stimulation (DBS) is currently an investigational intervention for TRD. We used a decision-analytic model to compare cost-effectiveness of DBS to treatment-as-usual (TAU) for TRD. Because this therapy is not FDA approved or in common use, our goal was to establish an effectiveness threshold that trials would need to demonstrate for this therapy to be cost-effective. Remission and complication rates were determined from review of relevant studies. We used published utility scores to reflect quality of life after treatment. Medicare reimbursement rates and health economics data were used to approximate costs. We performed Monte Carlo (MC) simulations and probabilistic sensitivity analyses to estimate incremental cost-effectiveness ratios (ICER; USD/quality-adjusted life year [QALY]) at a 5-year time horizon. Cost-effectiveness was defined using willingness-to-pay (WTP) thresholds of $100,000/QALY and $50,000/QALY for moderate and definitive cost-effectiveness, respectively. We included 274 patients across 16 studies from 2009–2021 who underwent DBS for TRD and had ≥12 months follow-up in our model inputs. From a healthcare sector perspective, DBS using non-rechargeable devices (DBS-pc) would require 55% and 85% remission, while DBS using rechargeable devices (DBS-rc) would require 11% and 19% remission for moderate and definitive cost-effectiveness, respectively. From a societal perspective, DBS-pc would require 35% and 46% remission, while DBS-rc would require 8% and 10% remission for moderate and definitive cost-effectiveness, respectively. DBS-pc will unlikely be cost-effective at any time horizon without transformative improvements in battery longevity. If remission rates ≥8–19% are achieved, DBS-rc will likely be more cost-effective than TAU for TRD, with further increasing cost-effectiveness beyond 5 years.
Gasdermin D silencing alleviates airway inflammation and remodeling in an ovalbumin-induced asthmatic mouse model
Authors: Wu Jinxiang , Wang Pin , Xie Xinyu , Yang Xiaoqi , Tang Shuangmei , Zhao Jiping , Liu Tian , Wang Junfei , Zhang Jintao , Xia Tongliang , Feng Xin
Published: June 2024

Emerging evidence demonstrates that pyroptosis has been implicated in the pathogenesis of asthma. Gasdermin D (GSDMD) is the pyroptosis executioner. The mechanism of GSDMD in asthma remains unclear. The aim of this study was to elucidate the potential role of GSDMD in asthmatic airway inflammation and remodeling. Immunofluorescence staining was conducted on airway epithelial tissues obtained from both asthma patients and healthy controls (HCs) to evaluate the expression level of N-GSDMD. ELISA was used to measure concentrations of cytokines (IL-1β, IL-18, IL-17A, and IL-10) in serum samples collected from asthma patients and healthy individuals. We demonstrated that N-GSDMD, IL-18, and IL-1β were significantly increased in samples with mild asthma compared with those from the controls. Then, wild type and Gsdmd-knockout (Gsdmd−/−) mice were used to establish asthma model. We performed histopathological staining, ELISA, and flow cytometry to explore the function of GSDMD in allergic airway inflammation and tissue remodeling in vivo. We observed that the expression of N-GSDMD, IL-18, and IL-1β was enhanced in OVA-induced asthma mouse model. Gsdmd knockout resulted in attenuated IL-18, and IL-1β production in both bronchoalveolar lavage fluid (BALF) and lung tissue in asthmatic mice. In addition, Gsdmd−/− mice exhibit a significant reduction in airway inflammation and remodeling, which might be associated with reduced Th17 inflammatory response and M2 polarization of macrophages. Further, we found that GSDMD knockout may improve asthmatic airway inflammation and remodeling through regulating macrophage adhesion, migration, and macrophage M2 polarization by targeting Notch signaling pathway. These findings demonstrate that GSDMD deficiency profoundly alleviates allergic inflammation and tissue remodeling. Therefore, GSDMD may serve as a potential therapeutic target against asthma.
Omecamtiv mecarbil and Mavacamten target the same myosin pocket despite opposite effects in heart contraction
Authors: Auguin Daniel , Robert-Paganin Julien , Réty Stéphane , Kikuti Carlos , David Amandine , Theumer Gabriele , Schmidt Arndt W. , Knölker Hans-Joachim , Houdusse Anne
Published: June 2024

Inherited cardiomyopathies are common cardiac diseases worldwide, leading in the late stage to heart failure and death. The most promising treatments against these diseases are small molecules directly modulating the force produced by β-cardiac myosin, the molecular motor driving heart contraction. Omecamtiv mecarbil and Mavacamten are two such molecules that completed phase 3 clinical trials, and the inhibitor Mavacamten is now approved by the FDA. In contrast to Mavacamten, Omecamtiv mecarbil acts as an activator of cardiac contractility. Here, we reveal by X-ray crystallography that both drugs target the same pocket and stabilize a pre-stroke structural state, with only few local differences. All-atom molecular dynamics simulations reveal how these molecules produce distinct effects in motor allostery thus impacting force production in opposite way. Altogether, our results provide the framework for rational drug development for the purpose of personalized medicine.
Seasonal switching of integrated leaf senescence controls in an evergreen perennial Arabidopsis
Authors: Yumoto Genki , Nishio Haruki , Muranaka Tomoaki , Sugisaka Jiro , Honjo Mie N. , Kudoh Hiroshi
Published: June 2024

Evergreeness is a substantial strategy for temperate and boreal plants and is as common as deciduousness. However, whether evergreen plants switch foliage functions between seasons remains unknown. We conduct an in natura study of leaf senescence control in the evergreen perennial, Arabidopsis halleri. A four-year census of leaf longevity of 102 biweekly cohorts allows us to identify growth season (GS) and overwintering (OW) cohorts characterised by short and extended longevity, respectively, and to recognise three distinct periods in foliage functions, i.e., the growth, overwintering, and reproductive seasons. Photoperiods during leaf expansion separate the GS and OW cohorts, providing primal control of leaf senescence depending on the season, with leaf senescence being shut down during winter. Phenotypic and transcriptomic responses in field experiments indicate that shade-induced and reproductive-sink-triggered senescence are active during the growth and reproductive seasons, respectively. These secondary controls of leaf senescence cause desynchronised and synchronised leaf senescence during growth and reproduction, respectively. Conclusively, seasonal switching of leaf senescence optimises resource production, storage, and translocation for the season, making the evergreen strategy adaptively relevant.
Quantitative profiling of m6A at single base resolution across the life cycle of rice and Arabidopsis
Authors: Wang Guanqun , Li Haoxuan , Ye Chang , He Kayla , Liu Shun , Jiang Bochen , Ge Ruiqi , Gao Boyang , Wei Jiangbo , Zhao Yutao , Li Aixuan , Zhang Di , Zhang Jianhua , He Chuan
Published: June 2024

N6-methyladenosine (m6A) plays critical roles in regulating mRNA metabolism. However, comprehensive m6A methylomes in different plant tissues with single-base precision have yet to be reported. Here, we present transcriptome-wide m6A maps at single-base resolution in different tissues of rice and Arabidopsis using m6A-SAC-seq. Our analysis uncovers a total of 205,691 m6A sites distributed across 22,574 genes in rice, and 188,282 m6A sites across 19,984 genes in Arabidopsis. The evolutionarily conserved m6A sites in rice and Arabidopsis ortholog gene pairs are involved in controlling tissue development, photosynthesis and stress response. We observe an overall mRNA stabilization effect by 3’ UTR m6A sites in certain plant tissues. Like in mammals, a positive correlation between the m6A level and the length of internal exons is also observed in plant mRNA, except for the last exon. Our data suggest an active m6A deposition process occurring near the stop codon in plant mRNA. In addition, the MTA-installed plant mRNA m6A sites correlate with both translation promotion and translation suppression, depicting a more complicated regulatory picture. Our results therefore provide in-depth resources for relating single-base resolution m6A sites with functions in plants and uncover a suppression-activation model controlling m6A biogenesis across species.
Fully exposed Pt clusters for efficient catalysis of multi-step hydrogenation reactions
Authors: Si Yang , Jiao Yueyue , Wang Maolin , Xiang Shengling , Diao Jiangyong , Chen Xiaowen , Chen Jiawei , Wang Yue , Xiao Dequan , Wen Xiaodong , Wang Ning , Ma Ding , Liu Hongyang
Published: June 2024

For di-nitroaromatics hydrogenation, it is a challenge to achieve the multi-step hydrogenation with high activity and selectivity due to the complexity of the process involving two nitro groups. Consequently, many precious metal catalysts suffer from low activity for this multi-step hydrogenation reaction. Herein, we employ a fully exposed Pt clusters catalyst consisting of an average of four Pt atoms on nanodiamond@graphene (Ptn/ND@G), demonstrating excellent catalytic performance for the multi-step hydrogenation of 2,4-dinitrotoluene. The TOF (40647 h−1) of Ptn/ND@G is significantly superior to that of single Pt atoms catalyst, Pt nanoparticles catalyst, and even all the known catalysts. Density functional theory calculations and absorption experiments reveal that the synergetic interaction between the multiple active sites of Ptn/ND@G facilitate the co-adsorption/activation of reactants and H2, as well as the desorption of intermediates/products, which is the key for the higher catalytic activity than single Pt atoms catalyst and Pt nanoparticles catalyst.
Microbial community organization designates distinct pulmonary exacerbation types and predicts treatment outcome in cystic fibrosis
Authors: Widder Stefanie , Carmody Lisa A. , Opron Kristopher , Kalikin Linda M. , Caverly Lindsay J. , LiPuma John J.
Published: June 2024

Polymicrobial infection of the airways is a hallmark of obstructive lung diseases such as cystic fibrosis (CF), non-CF bronchiectasis, and chronic obstructive pulmonary disease. Pulmonary exacerbations (PEx) in these conditions are associated with accelerated lung function decline and higher mortality rates. Understanding PEx ecology is challenged by high inter-patient variability in airway microbial community profiles. We analyze bacterial communities in 880 CF sputum samples collected during an observational prospective cohort study and develop microbiome descriptors to model community reorganization prior to and during 18 PEx. We identify two microbial dysbiosis regimes with opposing ecology and dynamics. Pathogen-governed PEx show hierarchical community reorganization and reduced diversity, whereas anaerobic bloom PEx display stochasticity and increased diversity. A simulation of antimicrobial treatment predicts better efficacy for hierarchically organized communities. This link between PEx, microbiome organization, and treatment success advances the development of personalized clinical management in CF and, potentially, other obstructive lung diseases.
The role of promiscuous molecular recognition in the evolution of RNase-based self-incompatibility in plants
Authors: Erez Keren , Jangid Amit , Feldheim Ohad Noy , Friedlander Tamar
Published: June 2024

How do biological networks evolve and expand? We study these questions in the context of the plant collaborative-non-self recognition self-incompatibility system. Self-incompatibility evolved to avoid self-fertilization among hermaphroditic plants. It relies on specific molecular recognition between highly diverse proteins of two families: female and male determinants, such that the combination of genes an individual possesses determines its mating partners. Though highly polymorphic, previous models struggled to pinpoint the evolutionary trajectories by which new specificities evolved. Here, we construct a novel theoretical framework, that crucially affords interaction promiscuity and multiple distinct partners per protein, as is seen in empirical findings disregarded by previous models. We demonstrate spontaneous self-organization of the population into distinct “classes” with full between-class compatibility and a dynamic long-term balance between class emergence and decay. Our work highlights the importance of molecular recognition promiscuity to network evolvability. Promiscuity was found in additional systems suggesting that our framework could be more broadly applicable.
Physical networks as network-of-networks
Authors: Pete Gábor , Timár Ádám , Stefánsson Sigurdur Örn , Bonamassa Ivan , Pósfai Márton
Published: June 2024

Physical networks are made of nodes and links that are physical objects embedded in a geometric space. Understanding how the mutual volume exclusion between these elements affects the structure and function of physical networks calls for a suitable generalization of network theory. Here, we introduce a network-of-networks framework where we describe the shape of each extended physical node as a network embedded in space and these networks are bound together by physical links. Relying on this representation, we introduce a minimal model of network growth and we show for a general class of physical networks that volume exclusion induces heterogeneity in both node volume and degree, with the two becoming correlated. These emergent properties strongly affect the dynamics on physical networks: by calculating their Laplacian spectrum as a function of the coupling strength between the nodes we show that degree-volume correlations suppress the role of hubs as early spreaders in diffusive dynamics. We apply the network-of-networks framework to describe several real systems and find properties analog to the minimal model networks. The prevalence of these properties points towards general growth mechanisms that do not depend on the specifics of the systems.
Covalently bridging graphene edges for improving mechanical and electrical properties of fibers
Authors: Ding Ling , Xu Tianqi , Zhang Jiawen , Ji Jinpeng , Song Zhaotao , Zhang Yanan , Xu Yijun , Liu Tong , Liu Yang , Zhang Zihan , Gong Wenbin , Wang Yunong , Shi Zhenzhong , Ma Renzhi , Geng Jianxin , Ngo Huynh Thien , Geng Fengxia , Liu Zhongfan
Published: June 2024

Assembling graphene sheets into macroscopic fibers with graphitic layers uniaxially aligned along the fiber axis is of both fundamental and technological importance. However, the optimal performance of graphene-based fibers has been far lower than what is expected based on the properties of individual graphene. Here we show that both mechanical properties and electrical conductivity of graphene-based fibers can be significantly improved if bridges are created between graphene edges through covalent conjugating aromatic amide bonds. The improved electrical conductivity is likely due to extended electron conjugation over the aromatic amide bridged graphene sheets. The larger sheets also result in improved π-π stacking, which, along with the robust aromatic amide linkage, provides high mechanical strength. In our experiments, graphene edges were bridged using the established wet-spinning technique in the presence of an aromatic amine linker, which selectively reacts to carboxyl groups at the graphene edge sites. This technique is already industrial and can be easily upscaled. Our methodology thus paves the way to the fabrication of high-performance macroscopic graphene fibers under optimal techno-economic and ecological conditions.
Closed-loop recycling of tough epoxy supramolecular thermosets constructed with hyperbranched topological structure
Authors: Zhang Junheng , Jiang Can , Deng Guoyan , Luo Mi , Ye Bangjiao , Zhang Hongjun , Miao Menghe , Li Tingcheng , Zhang Daohong
Published: June 2024

The regulation of topological structure of covalent adaptable networks (CANs) remains a challenge for epoxy CANs. Here, we report a strategy to develop strong and tough epoxy supramolecular thermosets with rapid reprocessability and room-temperature closed-loop recyclability. These thermosets were constructed from vanillin-based hyperbranched epoxy resin (VanEHBP) through the introduction of intermolecular hydrogen bonds and dual dynamic covalent bonds, as well as the formation of intramolecular and intermolecular cavities. The supramolecular structures confer remarkable energy dissipation capability of thermosets, leading to high toughness and strength. Due to the dynamic imine exchange and reversible noncovalent crosslinks, the thermosets can be rapidly and effectively reprocessed at 120 °C within 30 s. Importantly, the thermosets can be efficiently depolymerized at room temperature, and the recovered materials retain the structural integrity and mechanical properties of the original samples. This strategy may be employed to design tough, closed-loop recyclable epoxy thermosets for practical applications.
Pitting corrosion characteristics of sintered Type 316 L stainless steel: relationship between pores and MnS
Authors: Saito Haruka , Nishimoto Masashi , Muto Izumi
Published: June 2024

In Type 316 L stainless steel fabricated from gas-atomized powder via spark plasma sintering, lack-of-fusion pores and MnS inclusions were identified as possible pitting initiation sites. Through potentiodynamic polarization with different working electrode areas, the distribution density of the identified pit initiation sites was compared with that of sites found on wrought Type 316 L stainless steel. Surface observations of the sintered Type 316 L after polarization suggest that pitting corrosion was initiated at a location where both MnS and pores existed. By reducing the porosity and removing MnS, the roles of pores and MnS inclusions in the initiation of pitting corrosion were investigated.
Genome-wide determinants of mortality and motor progression in Parkinson’s disease
Authors: Tan Manuela M. X. , Lawton Michael A. , Pollard Miriam I. , Brown Emmeline , Real Raquel , Carrasco Alejandro Martinez , Bekadar Samir , Jabbari Edwin , Reynolds Regina H. , Iwaki Hirotaka , Blauwendraat Cornelis , Kanavou Sofia , Hubbard Leon , Malek Naveed , Grosset Katherine A. , Bajaj Nin , Barker Roger A. , Burn David J. , Bresner Catherine , Foltynie Thomas , Wood Nicholas W. , Williams-Gray Caroline H. , Andreassen Ole A. , Toft Mathias , Elbaz Alexis , Artaud Fanny , Brice Alexis , Corvol Jean-Christophe , Aasly Jan , Farrer Matthew J. , Nalls Michael A. , Singleton Andrew B. , Williams Nigel M. , Ben-Shlomo Yoav , Hardy John , Hu Michele T. M. , Grosset Donald G. , Shoai Maryam , Pihlstrøm Lasse , Morris Huw R.
Published: June 2024

There are 90 independent genome-wide significant genetic risk variants for Parkinson’s disease (PD) but currently only five nominated loci for PD progression. The biology of PD progression is likely to be of central importance in defining mechanisms that can be used to develop new treatments. We studied 6766 PD patients, over 15,340 visits with a mean follow-up of between 4.2 and 15.7 years and carried out genome-wide survival studies for time to a motor progression endpoint, defined by reaching Hoehn and Yahr stage 3 or greater, and death (mortality). There was a robust effect of the APOE ε4 allele on mortality in PD. We also identified a locus within the TBXAS1 gene encoding thromboxane A synthase 1 associated with mortality in PD. We also report 4 independent loci associated with motor progression in or near MORN1, ASNS, PDE5A, and XPO1. Only the non-Gaucher disease causing GBA1 PD risk variant E326K, of the known PD risk variants, was associated with mortality in PD. Further work is needed to understand the links between these genomic variants and the underlying disease biology. However, these may represent new candidates for disease modification in PD.
An automated, high-resolution phenotypic assay for adult Brugia malayi and microfilaria
Authors: Kalwa Upender , Park Yunsoo , Kimber Michael J. , Pandey Santosh
Published: June 2024

Brugia malayi are thread-like parasitic worms and one of the etiological agents of Lymphatic filariasis (LF). Existing anthelmintic drugs to treat LF are effective in reducing the larval microfilaria (mf) counts in human bloodstream but are less effective on adult parasites. To test potential drug candidates, we report a multi-parameter phenotypic assay based on tracking the motility of adult B. malayi and mf in vitro. For adult B. malayi, motility is characterized by the centroid velocity, path curvature, angular velocity, eccentricity, extent, and Euler Number. These parameters are evaluated in experiments with three anthelmintic drugs. For B. malayi mf, motility is extracted from the evolving body skeleton to yield positional data and bending angles at 74 key point. We achieved high-fidelity tracking of complex worm postures (self-occlusions, omega turns, body bending, and reversals) while providing a visual representation of pose estimates and behavioral attributes in both space and time scales.
An efficient low cost means of biophysical gene transfection in primary cells
Authors: Huang Shudi , Henderson Tyler R. , Dojo Soeandy Chesarahmia , Lezhanska Anastasiya , Henderson Jeffrey T.
Published: June 2024

Efficient, facile gene modification of cells has become an indispensable part of modern molecular biology. For the majority of cell lines and several primary populations, such modifications can be readily performed through a variety of methods. However, many primary cell lines such as stem cells frequently suffer from poor transfection efficiency. Though several physical approaches have been introduced to circumvent these issues, they often require expensive/specialized equipment and/or consumables, utilize substantial cell numbers and often still suffer from poor efficiency. Viral methods are capable of transducing difficult cellular populations, however such methods can be time consuming for large arrays of gene targets, present biohazard concerns, and result in expression of viral proteins; issues of concern for certain experimental approaches. We report here a widely applicable, low-cost (< $100 CAD) method of electroporation, applicable to small (1–10 μl) cell volumes and composed of equipment readily available to the average investigator. Using this system we observe a sixfold increase in transfection efficiency in embryonic stem cell lines compared to commercial devices. Due to efficiency gains and reductions in volume and applied voltage, this process improves the survival of sensitive stem cell populations while reducing reagent requirements for protocols such as Cas9/gRNAs transfections.
Optogenetic inhibition of the limbic corticothalamic circuit does not alter spontaneous oscillatory activity, auditory-evoked oscillations, and deviant detection
Authors: Gonzalez-Burgos Irene , Valencia Miguel , Redondo Roger , Janz Philipp
Published: June 2024

Aberrant neuronal circuit dynamics are at the core of complex neuropsychiatric disorders, such as schizophrenia (SZ). Clinical assessment of the integrity of neuronal circuits in SZ has consistently described aberrant resting-state gamma oscillatory activity, decreased auditory-evoked gamma responses, and abnormal mismatch responses. We hypothesized that corticothalamic circuit manipulation could recapitulate SZ circuit phenotypes in rodent models. In this study, we optogenetically inhibited the mediodorsal thalamus-to-prefrontal cortex (MDT-to-PFC) or the PFC-to-MDT projection in rats and assessed circuit function through electrophysiological readouts. We found that MDT–PFC perturbation could not recapitulate SZ-linked phenotypes such as broadband gamma disruption, altered evoked oscillatory activity, and diminished mismatch negativity responses. Therefore, the induced functional impairment of the MDT–PFC pathways cannot account for the oscillatory abnormalities described in SZ.
Basketball technique action recognition using 3D convolutional neural networks
Authors: Wang Jingfei , Zuo Liang , Cordente Martínez Carlos
Published: June 2024

This research investigates the recognition of basketball techniques actions through the implementation of three-dimensional (3D) Convolutional Neural Networks (CNNs), aiming to enhance the accurate and automated identification of various actions in basketball games. Initially, basketball action sequences are extracted from publicly available basketball action datasets, followed by data preprocessing, including image sampling, data augmentation, and label processing. Subsequently, a novel action recognition model is proposed, combining 3D convolutions and Long Short-Term Memory (LSTM) networks to model temporal features and capture the spatiotemporal relationships and temporal information of actions. This facilitates the facilitating automatic learning of the spatiotemporal features associated with basketball actions. The model’s performance and robustness are further improved through the adoption of optimization algorithms, such as adaptive learning rate adjustment and regularization. The efficacy of the proposed method is verified through experiments conducted on three publicly available basketball action datasets: NTURGB + D, Basketball-Action-Dataset, and B3D Dataset. The results indicate that this approach achieves outstanding performance in basketball technique action recognition tasks across different datasets compared to two common traditional methods. Specifically, when compared to the frame difference-based method, this model exhibits a significant accuracy improvement of 15.1%. When compared to the optical flow-based method, this model demonstrates a substantial accuracy improvement of 12.4%. Moreover, this method showcases strong robustness, accurately recognizing actions under diverse lighting conditions and scenes, achieving an average accuracy of 93.1%. The research demonstrates that the method reported here effectively captures the spatiotemporal relationships of basketball actions, thereby providing reliable technical assessment tools for basketball coaches and players.