bluuebunny/crossref_metadata_2025 huggingface dataset:
Nickel phosphorous trisulfide: A ternary 2D material with an ultra-low coefficient of friction
Authors: Deng Haoyu , Yu Tongtong , Du Changhe , Shen Ruilin , Zhao Yongkang , He Xinjian , Feng Yange , Zhang Liqiang , Wang Daoai
Published: July 2024

Ultra-low friction is crucial for the anti-friction, anti-wear, and long-life operation of nanodevices. However, very few two-dimensional materials can achieve ultra-low friction, and they have some limitations in their applications. Therefore, exploring novel materials with ultra-low friction properties is greatly significant. The emergence of ternary two-dimensional materials has opened new opportunities for nanoscale ultra-low friction. This study introduced nickel phosphorous trisulfide (NiPS3, referred to as NPS), a novel two-dimensional ternary material capable of achieving ultralow friction in a vacuum, into the large nanotribology family. Large-size and high-quality NPS crystals with up to 14 mm × 6 mm × 0.3 mm dimensions were grown using the chemical vapor transport method. The NPS nanosheets were obtained using mechanical exfoliation. The dependence of the NPS nanotribology on layer, velocity, and angle was systematically investigated using lateral force microscopy. Interestingly, the coefficient of friction (COF) of NPS with multilayers was decreased to about 0.0045 under 0.005 Pa vacuum condition (with load up to 767.8 nN), achieving the ultra-low friction state. The analysis of the frictional dissipation energy and adhesive forces showed that NPS with multilayers had minimum frictional dissipation energy and adhesive forces since the interlayer interactions were weak and the meniscus force was excluded under vacuum conditions. This study on the nanoscale friction of a ternary two-dimensional material lays a foundation for exploring the nanoscale friction and friction origin of other two-dimensional materials in the future.
Kosten von COVID-19 – Auswertungen von Routinedaten einer Unfallversicherung
Authors: Dulon Madeleine , Wendeler Dana , Nienhaus Albert
Published: July 2024

Hintergrund

Für Beschäftigte im Gesundheitswesen, die 2020 an COVID-19 erkrankten, wird die Entwicklung der Folgekosten über einen Zeitraum von 3,5 Jahren beschrieben.

Methodik

Auf Basis der Routinedaten der Berufsgenossenschaft für Gesundheitsdienst und Wohlfahrtspflege (BGW) werden Versicherungsfälle ausgewählt, die im Jahr 2020 an COVID-19 erkrankt waren. Für diese Fälle werden die Leistungen mit Buchungsdatum bis zum 30.06.2023 ausgewertet. Für die Analyse soziodemografischer Merkmale wird die Stichprobe entsprechend ihres Erkrankungsdatums der ersten und zweiten Pandemiewelle zugeordnet.

Ergebnisse

Die Auswertung basiert auf 47.697 Fällen. In der Gruppe aus der ersten Welle kommen Indikatoren für schwere Krankheitsverläufe signifikant häufiger vor, wie stationäre Behandlung, Zahlung von Verletztengeld und Anteil an Verstorbenen. Die Kosten belaufen sich über den betrachteten Buchungszeitraum auf 117,2 Mio. Euro mit einem Gipfel der jährlichen Aufwendungen in 2022. Den größten Anteil an den Gesamtkosten haben Verletztengeld und stationäre Behandlung (52,2 % bzw. 27,5 %). Bei beiden zeigen sich die deutlichsten Zuwächse in den Jahren 2021 und 2022. Für ambulante Heilbehandlungen nehmen die Kosten bezogen auf die Gesamtkosten kontinuierlich von 3,1 % in 2020 auf 14,9 % in 2023 zu. Dies ist auf eine Zunahme bei den physikalischen Therapien zurückzuführen.

Diskussion

Für Versicherte mit COVID-19 im Jahr 2020 entstehen auch im Jahr 2023 noch Kosten. Der überwiegende Anteil der Kosten entfällt auf Verletztengeld und stationäre Behandlung. Die Kosten für Maßnahmen zur Rehabilitation im ambulanten Versorgungsbereich nehmen in den Folgejahren nach der Erkrankung deutlich zu.
TCohPrompt: task-coherent prompt-oriented fine-tuning for relation extraction
Authors: Long Jun , Yin Zhuoying , Liu Chao , Huang Wenti
Published: July 2024

Prompt-tuning has emerged as a promising approach for improving the performance of classification tasks by converting them into masked language modeling problems through the insertion of text templates. Despite its considerable success, applying this approach to relation extraction is challenging. Predicting the relation, often expressed as a specific word or phrase between two entities, usually requires creating mappings from these terms to an existing lexicon and introducing extra learnable parameters. This can lead to a decrease in coherence between the pre-training task and fine-tuning. To address this issue, we propose a novel method for prompt-tuning in relation extraction, aiming to enhance the coherence between fine-tuning and pre-training tasks. Specifically, we avoid the need for a suitable relation word by converting the relation into relational semantic keywords, which are representative phrases that encapsulate the essence of the relation. Moreover, we employ a composite loss function that optimizes the model at both token and relation levels. Our approach incorporates the masked language modeling (MLM) loss and the entity pair constraint loss for predicted tokens. For relation level optimization, we use both the cross-entropy loss and TransE. Extensive experimental results on four datasets demonstrate that our method significantly improves performance in relation extraction tasks. The results show an average improvement of approximately 1.6 points in F1 metrics compared to the current state-of-the-art model. Codes are released at https://github.com/12138yx/TCohPrompt.
Controlling carbon emissions through modeling and optimization: addressing an earth system and environment challenge
Authors: Shahid Iqra , Naqvi Rehana Ali , Yousaf M. , Siddiqui A. M. , Sohail A.
Published: July 2024

This study aims to analyze the trend of carbon dioxide CO2 emissions from various sources in Pakistan between 1990 and 2020 to effectively model the underlying dynamics of carbon emissions. The design and fitting of the model to historical emissions data reveal significant trends and patterns, highlighting the alarming increase in CO2 emissions. These findings underscore the necessity for robust policy interventions to mitigate emissions and achieve sustainable development goals (SDGs). This work can contribute to addressing the challenges of recent plans targeting global warming and the climate emergency. By controlling these parameters, the mean reversion can be managed, allowing for the control of the increasing rate of carbon emissions in regions threatened by climate change. The O-U model provides a valuable framework for understanding the stochastic nature of carbon emissions, offering insights into the persistence and variability of emission levels over time. The optimized parametric thresholds of the stochastic model, after synchronizing it with the real data, reveal that the challenge of carbon emissions cannot be naturally resolved and serious interventions are highly desired. These include robust policy measures to mitigate emissions, improve air quality, and combat climate change.
Next stop carbon dioxide removal? German climate policies and the risky road to negative emission technologies
Authors: Haas Tobias , Schoppek Dorothea Elena
Published: July 2024

Both the global and the German climate targets are ambitious given recent emission reduction rates. In addition to emission reduction measures, carbon dioxide removal has been increasingly discussed recently and initial measures have been developed to potentially scale-up carbon removals in order to meet net zero targets. In this article, we undertake a political economy-based analysis of the historical development and structural conditions of German climate policy measures and their enabling role for the emergence and strengthening of carbon dioxide removal policy options. We refer to regulation theory and the Modell Deutschland approach and argue that within this model the concept of ecological modernization has been established as the dominant approach to regulate society-nature relations. In light of the ambitious climate policy goals, such as net zero by 2045, ecological modernization reaches its current limits and must be recalibrated. Against this background, we conclude by discussing the risk of mitigation deterrence associated with carbon dioxide removals and considering alternative pathways, such as degrowth, to social ecological transformation.
A review on the application of biopolymers (xanthan, agar and guar) for sustainable improvement of soil
Authors: Kumar Sumit , Yadav Brahm Deo , Raj Rohit
Published: July 2024

This review explores the use of biopolymers as sustainable alternatives for soil improvement in geotechnical engineering. Specifically, focusing on three commonly used biopolymers: xanthan gum, agar gum, and guar gum, this review highlights their potential applications and effects on the geotechnical properties of different soil types. Xanthan gum exhibits improved impermeability and water storage capacity, making it suitable for anti-wind erosion, soil remediation, and grouting vegetation growth. Agar gum shows promise in liquefaction remediation with its gelation process and absence of chemical reactions during soil cementation. It is resistant to thermal degradation, oxidation, acid-alkaline environments, and salt concentrations. Its hygroscopicity and stability make it useful for soil stabilization. Guar gum enhances shear strength, reduces permeability, and improves soil stability, making it effective for mine tailing stabilisation, slope stability, and other geotechnical applications. Being a polymeric molecule, as it breaks down, it becomes more biopolymer-rich, making it more resistant to wetting and drying processes. This review discusses biopolymer-treated soils’ strengthening mechanisms, such as hydrogel formation and cross-links between soil particles. The utilisation of biopolymers offers advantages in terms of abundance, non-toxicity, and potential for reducing greenhouse gas emissions. The review also identifies the use of biopolymers in mine tailings. Although further research is needed to optimise their application and explore their full potential in sustainable improvement practices, This integration of biopolymers in soil engineering would provide a more environmentally friendly approach.
Perovskite multiple quantum wells: toward artificial construction and lasing
Authors: Chin Sang-Hyun
Published: July 2024

Halide perovskites (HPs) are rising semiconductors in optoelectronics, such as photovoltaic and light-emitting applications. However currently, laser diode with HPs is still not realized yet. When coherent emissions are targeted as the applications, materials with lower lasing thresholds and increased stabilities must be ensured to realize the device. Perovskite multiple-quantum-wells (MQWs) can potentially ease the population inversion by confining photoexcitation, but there must be several more progress on fabrication process and structural design are required to realize electrically pumped laser. Hence, in this perspective, current research trend and progress in this field are summarized and some suggestions to design HP MQW lasers, whether self-assembled or artificially fabricated, ground on recent literature are casted.
Intervertebral disc creep behaviour through viscoelastic models: an in-vitro study
Authors: Sciortino Vincenza , Jansen Jan Ulrich , Cerniglia Donatella , Ingrassia Tommaso , Wilke Hans-Joachim
Published: July 2024

The intervertebral disc (IVD) is a complex biological structure that ensures the spine strength, stability, mobility, and flexibility. This is achieved due to its biphasic nature which is attained by its solid phase (annulus fibrosus) and fluid phases (nucleus pulposus). Hence, the IVD biomechanical response to long-term loads, which is critical as it affects hydration, and nutrients-water transport influencing disc height reduction, has been further explored and mathematically modelled in this paper. An in-vitro study was performed on seven human lumbar spine specimens (L4-5), to assess if the classical rheological models and Nutting's power law can model in a simple way the intermediate characteristics between solid and fluid of the IVD. Creep tests were conducted by applying a static compression load of 500 N for 15 min. A correlation analysis was done (Pearson, p < 0.05) between the model parameters and the maximum value of Disc Height Reduction, followed by a linear regression analysis. In summary, the long-term IVD mechanical behavior was modeled in a simple way, emphasizing that yet there is no mathematical certainty about this mechanical behavior. Hence, a future aim might be to develop intervertebral disc prostheses capable of replicating only the disc mechanical response, without necessarily considering the microscopic-level biological drivers. Therefore, a future goal is to fully understand and model the disc's mechanical response toward the design of new disc prostheses that would consider only the macroscopic aspect, without considering the biological aspects.
Graph representation learning for parameter transferability in quantum approximate optimization algorithm
Authors: Falla Jose , Langfitt Quinn , Alexeev Yuri , Safro Ilya
Published: July 2024

The quantum approximate optimization algorithm (QAOA) is one of the most promising candidates for achieving quantum advantage through quantum-enhanced combinatorial optimization. Optimal QAOA parameter concentration effects for special MaxCut problem instances have been observed, but a rigorous study of the subject is still lacking. Due to clustering of optimal QAOA parameters for MaxCut, successful parameter transferability between different MaxCut instances can be explained and predicted based on local properties of the graphs, including the type of subgraphs (lightcones) from which graphs are composed as well as the overall degree of nodes in the graph (parity). In this work, we apply five different graph embedding techniques to determine good donor candidates for parameter transferability, including parameter transferability between different classes of MaxCut instances. Using this technique, we effectively reduce the number of iterations required for parameter optimization, obtaining an approximate solution to the target problem with an order of magnitude speedup. This procedure also effectively removes the problem of encountering barren plateaus during the variational optimization of parameters. Additionally, our findings demonstrate that the transferred parameters maintain effectiveness when subjected to noise, supporting their use in real-world quantum applications. This work presents a framework for identifying classes of combinatorial optimization instances for which optimal donor candidates can be predicted such that QAOA can be substantially accelerated under both ideal and noisy conditions.
Machine Learning in Lithium-Ion Battery: Applications, Challenges, and Future Trends
Authors: Valizadeh Alireza , Amirhosseini Mohammad Hossein
Published: July 2024

Machine Learning has garnered significant attention in lithium-ion battery research for its potential to revolutionize various aspects of the field. This paper explores the practical applications, challenges, and emerging trends of employing Machine Learning in lithium-ion battery research. Delves into specific Machine Learning techniques and their relevance, offering insights into their transformative potential. The applications of Machine Learning in lithium-ion-battery design, manufacturing, service, and end-of-life are discussed. The challenges including data availability, data preprocessing and cleaning challenges, limited sample size, computational complexity, model generalization, black-box nature of Machine Learning models, scalability of the algorithms for large datasets, data bias, and interdisciplinary nature and their mitigations are also discussed. Accordingly, by discussing the future trends, it provides valuable insights for researchers in this field. For example, a future trend is to address the challenge of small datasets by techniques such as Transfer Learning and N-shot Learning. This paper not only contributes to our understanding of Machine Learning applications but also empowers professionals in this field to harness its capabilities effectively.
The importance of adjusting the processing parameters for the resulting material density of PBF-LB AISI 316L lattice structures
Authors: Szcześniak Krzysztof , Pawlak Andrzej , Dybała Bogdan , Kras Aleksander
Published: July 2024

Lattice structures are becoming more commonly used in the design of components for additive manufacturing. This is due to their ability to reduce the weight of manufactured parts, minimize material consumption, and achieve specific properties by modifying their geometry. As the applications of lattice structures continue to evolve, it is essential to determine whether the process parameters used in the PBF-LB (Laser Beam Powder Bed Fusion) process for manufacturing these structures should be the same as or different from those used for larger cross-sectional components. An analysis of the existing literature revealed insufficient data on this subject, which inspired this study. Experiments conducted using AISI 316L stainless steel showed that lattice structures can be produced with significantly lower volumetric energy density, while maintaining a high relative material density. In the experiment on lattice structures made of BCCZ and gyroid unit cells, a relative material density of over 99.5% was achieved with a volumetric energy density of approximately 33 J/mm3. These findings are significant for the fabrication of lattice structures. The lower volumetric energy density typically allows for greater geometric accuracy and reduced internal stresses. Furthermore, it has been proven that the nodes of the structure are critical places exposed to porosity formation.
Memory recall can increase authenticity and engagement: understanding the role of work context and memory function in autobiographical memories
Authors: Sutton Anna
Published: July 2024

Authenticity is positively associated with important psychological outcomes in the workplace, such as well-being and engagement, and is reliant on having a coherent sense of self. This coherent self is maintained by recalling autobiographical memories (AM). But the specific relationships between authenticity and memory functions are unclear. This study aims to first, identify differences in memory function and content in the recall of authentic and inauthentic moments and second, address how these AM functions are related to authenticity, engagement and well-being. Participants completed measures of authenticity, engagement and well-being before and after a memory recall task. For the recall task, they were randomly assigned to recall either an authentic, inauthentic, or control memory. Recall narratives were coded for memory functions (self-identity, social, directive) and for physical/social context features. Authentic moments, inauthentic moments and controls differed in terms of social and physical contexts. Authentic moments were most likely to involve being alone while inauthentic moments were most likely to happen at work. Authentic or inauthentic moments were both more likely than controls to involve work colleagues, with inauthentic moments also involving more distant acquaintances such as bosses or customers. In addition, recalling a memory, no matter what type, increased engagement and authentic self-awareness. While there was no difference between authentic or inauthentic moments in influencing these outcomes, individuals’ pre-recall levels of engagement and authenticity influenced the memory functions used. This taxonomy of (in)authentic moment recall can guide future research in authenticity and memory function. In addition, these findings begin to address the interactive role that authenticity plays in memory recall and its influence on other psychological outcomes.
Eye-Tracking Devices for Virtual and Augmented Reality Metaverse Environments and Their Compatibility with the European Union General Data Protection Regulation
Authors: Menéndez González Natalia , Bozkir Efe
Published: July 2024

Even though the Metaverse from science fiction is not a reality yet, it is possible to take a glimpse into how it might look like. However, the current vision of the Metaverse does not only encompass software. A great deal of companies is complementing their Metaverse projects with Virtual and Augmented Reality devices such as headsets and glasses. In this line, one of the last technological advancements in virtual and augmented reality devices included the introduction of eye-tracking technology. However, when new and additional kinds of data are processed, emerging risks for data protection might arise. This paper will, therefore, discuss the compatibility of eye-tracking devices for virtual and augmented reality environments with the European Union General Data Protection Regulation (GDPR). Being the GDPR considered a worldwide role model in terms of fundamental rights protection, the compatibility of such devices with one of the most severe data protection regimes will be put to the hardest test. The paper will do so by analyzing the state of the art of the technology, its use in headsets and glasses for virtual and augmented reality Metaverse environments, and the potential risks that such use might entail for data protection. After that, such risks will be confronted with the relevant applicable provisions of the GDPR. Finally, the paper will issue policy recommendations.
Microstructure Design of Multiphase Compositionally Complex Alloys I: Effects of Strength Contrast and Strain Hardening
Authors: Bhattacharyya Jishnu J. , Agnew Sean R.
Published: July 2024

This work addresses the question, “Which microstructure yields the highest “toughness” for a two phase, compositionally complex alloy (CCA) composed of a ductile, face centered cubic (FCC) matrix phase and a stronger, intermetallic reinforcing phase?” A polycrystal plasticity model is used, which takes as input the phase fraction, grain sizes, and parameters describing the mechanical properties (stiffness, strength and strain hardening behavior) of each phase. In addition to assuming an elasto-viscoplastic matrix, the reinforcing phase is also assumed to be elasto-viscoplastic, albeit with a higher strength and lower strain hardening rate. The results reveal that, if the reinforcement remains elastic, the addition of such a reinforcement leads to an increase in strain hardening rate, in addition to the obvious increase in yield strength. If the reinforcement yields, the hardening rate sharply decreases, leading to earlier tensile instability. Therefore, when the strength contrast between the phases is high, addition of reinforcement is beneficial to toughness as well as strength. For lower contrast cases, the optimal toughness case may be the unreinforced matrix material. More specifically, only if the FCC matrix exhibits sustained linear hardening-type behavior typical of TWIP or TRIP effects (which correlate with very low stacking fault energies achievable in CCAs), will it exhibit an increase in toughness, as well as an increase in strength. Finally, the hard reinforcement is under a lower stress triaxiality due to pressure imposed by the ductile matrix, and this causes it to yield at a lower uniaxial stress than it would in isolation. The findings of this work provide a mechanistic understanding of the dependence of toughness on the microstructure of two-phase aggregates, and the approach is equally applicable to multiphase alloys.
Synergistic impact of Rhizobium and arbuscular mycorrhizal fungi on lentil plant tolerance to heavy metal-rich fly ash amended soil
Authors: Shaher Hina , Naushin Fauzia , Hasan Mudassara , Bagyaraj D. J.
Published: July 2024

The purpose of the study was to investigate the impact of microbes [symbiotic bacteria viz. Rhizobium (Frank) and the arbuscular mycorrhizal fungi (AMF) viz. Funneliformis caledonius (Nicolson & Gerd.) and Glomus bagyarajii Mehrotra] on the growth and physiology of lentil (Lens culinaris Medik.) cultivated in soil alone and soil amended with fly ash. The experiment had twenty-four treatments, twelve in sterilized soil and twelve in unsterilized soil (with six treatments in soil alone and six in soil amended with fly ash in both the sets). Amendment of soil with 25% fly ash significantly increased plant growth. Microbial inoculation further increased the plant growth and physiological parameters studied (plant length, fresh and dry weight, chlorophyll and protein content, and nitrate reductase activity). Microbial parameters like nodule number and fresh weight, mycorrhizal root colonization and spore numbers were also significantly higher in plants inoculated with Rhizobium + AMF. Soil amendment with 25% fly ash together with inoculation of Rhizobium + AMF further improved the growth of lentil. Plant heavy metal (Cd, Pb, Zn) content was significantly more in soil amended with fly ash, but microbial inoculation significantly decreased heavy metal uptake. Of the two AM fungus studied F. caledonius proved to be better, resulting in higher plant growth and physiological parameters studied with reduced heavy metal uptake.
A Comprehensive Evaluation of Black Carbon in Snow and Its Radiative Forcing in CMIP5 and CMIP6 Models Based on Global Field Observations
Authors: Chen Yang , Xing Yuxuan , Yan Shirui , Hou Yaliang , Li Xuejing , Shi Tenglong , Cui Jiecan , Wu Dongyou , Zhou Yue , Hao Dalei , Pu Wei , Wang Xin
Published: July 2024

Black carbon in snow (BCS) is a crucial parameter in Earth System modeling, as it influences global radiative balance. Here, simulated BCS from Coupled Model Intercomparison Project Phase 5 and 6 (CMIP5 and CMIP6) that provided BCS as a model output were evaluated. In comparison with global BCS observations, CMIP5/6 models successfully reproduced long‐term historical trends linked to human activities, but struggled capturing decadal variability caused by natural climate variability. CMIP6 models NorESM2‐MM, NorESM2‐LM, and TaiESM1 yielded the most accurate simulations of BCS concentration with modest overestimation of <50%, while the four CESM2 models underestimated concentrations by up to ∼80%. These errors effectively balanced for the CMIP6 multi‐model ensemble mean (MME), which had a relative error (RE) of −37%. However, the CMIP5 MME was less reliable due to extreme overestimation by up to 8,000% in the three MIROC models. The significant BCS concentration errors in the MIROC and CESM2 models were linked mainly to errors in handling of BC in snow processes. Conversely, marked improvements in NorESM, the only common to both CMIP5 and CMIP6, were due to improved simulation of black carbon deposition. BCS errors significantly impacted radiative forcing estimates, particularly at the poles, where model errors reached several thousandfold. CMIP6 exhibited superior results compared to CMIP5, achieving global MME RE of −33% in radiative forcing estimates. However, it's worth noting BCS output is currently limited, with only seven models available for each of CMIP5 and CMIP6 here. Additional models simulating BCS are desirable in the next CMIP generations.
Influence of Western Pacific Madden–Julian Oscillation on New York City's Record‐Breaking Air Pollution in Early June 2023
Authors: Zhu Yan , Hsu Pang‐chi , Qian Yitian
Published: July 2024

In early June 2023, New York City (NYC) and other cities in the northeastern US experienced a severe air pollution event. Although reports associated this hazardous pollution event with the smoke from Canadian wildfires, the factors triggering the southward waft of the smoke remain unclear. We found the northerly anomaly that transported the smoke was linked to the Rossby wave train excited by the Madden–Julian Oscillation (MJO) over the Philippine Sea, which led to the formation of an enhanced northerly at the western edge of the cyclonic anomaly over the East Coast–North Atlantic. When the MJO convection left the western Pacific, the disorganized teleconnection caused the pollution to dissipate. Observational findings were further supported by model simulations and predictions. These results suggest that monitoring and predictions of MJO activity may help mitigate air pollution events over the northeastern US during Canadian wildfire seasons.
Contribution of El Niño Southern Oscillation (ENSO) Diversity to Low‐Frequency Changes in ENSO Variance
Authors: Schlör Jakob , Strnad Felix , Capotondi Antonietta , Goswami Bedartha
Published: July 2024

El Niño Southern Oscillation (ENSO) diversity is characterized based on the longitudinal location of maximum sea surface temperature anomalies (SSTA) and amplitude in the tropical Pacific, as Central Pacific events are typically weaker than Eastern Pacific events. SSTA pattern and intensity undergo low‐frequency modulations, affecting ENSO prediction skill and remote impacts, and resulting in low‐frequency changes in ENSO variance. Yet, how different ENSO types contribute to these decadal variance changes remains unclear. Here, we decompose the low‐frequency changes of ENSO variance into contributions from ENSO diversity categories. We propose a fuzzy clustering of monthly SSTA to allow for non‐binary event category memberships, where each event can belong to different clusters. Our approach identifies two La Niña and three El Niño categories and shows that the major shift of ENSO variance in the mid‐1970s was associated with an increasing likelihood of strong La Niña and extreme El Niño events.
Shape-position perceptive fusion electronic skin with autonomous learning for gesture interaction
Authors: Wang Qian , Li Mingming , Guo Pingping , Gao Liang , Weng Ling , Huang Wenmei
Published: July 2024

Wearable devices, such as data gloves and electronic skins, can perceive human instructions, behaviors and even emotions by tracking a hand's motion, with the help of knowledge learning. The shape or position single-mode sensor in such devices often lacks comprehensive information to perceive interactive gestures. Meanwhile, the limited computing power of wearable applications restricts the multimode fusion of different sensing data and the deployment of deep learning networks. We propose a perceptive fusion electronic skin (PFES) with a bioinspired hierarchical structure that utilizes the magnetization state of a magnetostrictive alloy film to be sensitive to external strain or magnetic field. Installed at the joints of a hand, the PFES realizes perception of curvature (joint shape) and magnetism (joint position) information by mapping corresponding signals to the two-directional continuous distribution such that the two edges represent the contributions of curvature radius and magnetic field, respectively. By autonomously selecting knowledge closer to the user's hand movement characteristics, the reinforced knowledge distillation method is developed to learn and compress a teacher model for rapid deployment on wearable devices. The PFES integrating the autonomous learning algorithm can fuse curvature-magnetism dual information, ultimately achieving human machine interaction with gesture recognition and haptic feedback for cross-space perception and manipulation.
Prognostic value of quantitative EEG in early hours of life for neonatal encephalopathy and neurodevelopmental outcomes
Authors: Kota Srinivas , Kang Shu , Liu Yu-Lun , Liu Hanli , Montazeri Saeed , Vanhatalo Sampsa , Chalak Lina F.
Published: July 2024

Background

The ability to determine severity of encephalopathy is crucial for early neuroprotective therapies and for predicting neurodevelopmental outcome. The objective of this study was to assess a novel brain state of newborn (BSN) trend to distinguish newborns with presence of hypoxic ischemic encephalopathy (HIE) within hours after birth and predict neurodevelopmental outcomes at 2 years of age.

Method

This is a prospective cohort study of newborns at 36 weeks’ gestation or later with and without HIE at birth. The Total Sanart Score (TSS) was calculated based on a modified Sarnat exam within 6 h of life. BSN was calculated from electroencephalogram (EEG) measurements initiated after birth. The primary outcome at 2 year of age was a diagnosis of death or disability using the Bayley Scales of Infant Development III.

Results

BSN differentiated between normal and abnormal neurodevelopmental outcomes throughout the entire recording period from 6 h of life. Additionally, infants with lower BSN values had higher odds of neurodevelopmental impairment and HIE. BSN distinguished between normal (n = 86) and HIE (n = 46) and showed a significant correlation with the concomitant TSS.

Conclusion

BSN is a sensitive real-time marker for monitoring dynamic progression of encephalopathy and predicting neurodevelopmental impairment.

Impact

This is a prospective cohort study to investigate the ability of brain state of newborn (BSN) trend to predict neurodevelopmental outcome within the first day of life and identify severity of encephalopathy.

BSN predicts neurodevelopmental outcomes at 2 years of age and the severity of encephalopathy severity. It also correlates with the Total Sarnat Score from the modified Sarnat exam.

BSN could serve as a promising bedside trend aiding in accurate assessment and identification of newborns who may benefit from additional neuroprotection therapies.
The evolution of organic material on Asteroid 162173 Ryugu and its delivery to Earth
Authors: Changela H. G. , Kebukawa Y. , Petera L. , Ferus M. , Chatzitheodoridis E. , Nejdl L. , Nebel R. , Protiva V. , Krepelka P. , Moravcova J. , Holbova R. , Hlavenkova Z. , Samoril T. , Bridges J. C. , Yamashita S. , Takahashi Y. , Yada T. , Nakato A. , Sobotkova K. , Tesarova H. , Zapotok D.
Published: July 2024

The recent return of samples from asteroid 162173 Ryugu provides a first insight into early Solar System prebiotic evolution from known planetary bodies. Ryugu’s samples are CI chondrite-like, rich in water and organic material, and primarily composed of phyllosilicate. This phyllosilicate surrounds micron to submicron macromolecular organic particles known as insoluble organic matter. Using advanced microscopy techniques on Hayabusa-2 samples, we find that aqueous alteration on Ryugu produced organic particles richer in aromatics compared to less altered carbonaceous chondrites. This challenges the view that aromatic-rich organic matter formed pre-accretion. Additionally, widespread diffuse organic material occurs in phyllosilicate more aliphatic-, carboxylic-rich, and aromatic-poor than the discrete organic particles, likely preserving the soluble organic material. Some organic particles evolved to encapsulate phyllosilicate, indicating that aqueous alteration on Ryugu led to the containment of soluble organic matter within these particles. Earth therefore has been, and continues to be, delivered micron-sized polymeric organic objects containing biologically relevant molecules.
Targeted degradation of extracellular mitochondrial aspartyl-tRNA synthetase modulates immune responses
Authors: Johnson Benjamin S. , Farkas Daniela , El-Mergawy Rabab , Adair Jessica A. , Elhance Ajit , Eltobgy Moemen , Coan Francesca M. , Chafin Lexie , Joseph Jessica A. , Cornwell Alex , Johns Finny J. , Rosas Lorena , Rojas Mauricio , Farkas Laszlo , Bednash Joseph S. , Londino James D. , Ray Prabir , Ray Anuradha , Kagan Valerian , Lee Janet S. , Chen Bill B. , Mallampalli Rama K.
Published: July 2024

The severity of bacterial pneumonia can be worsened by impaired innate immunity resulting in ineffective pathogen clearance. We describe a mitochondrial protein, aspartyl-tRNA synthetase (DARS2), which is released in circulation during bacterial pneumonia in humans and displays intrinsic innate immune properties and cellular repair properties. DARS2 interacts with a bacterial-induced ubiquitin E3 ligase subunit, FBXO24, which targets the synthetase for ubiquitylation and degradation, a process that is inhibited by DARS2 acetylation. During experimental pneumonia, Fbxo24 knockout mice exhibit elevated DARS2 levels with an increase in pulmonary cellular and cytokine levels. In silico modeling identified an FBXO24 inhibitory compound with immunostimulatory properties which extended DARS2 lifespan in cells. Here, we show a unique biological role for an extracellular, mitochondrially derived enzyme and its molecular control by the ubiquitin apparatus, which may serve as a mechanistic platform to enhance protective host immunity through small molecule discovery.
Tetrapod sperm length evolution in relation to body mass is shaped by multiple trade-offs
Authors: Koçillari Loren , Cattelan Silvia , Rasotto Maria Berica , Seno Flavio , Maritan Amos , Pilastro Andrea
Published: July 2024

Sperm length is highly variable across species and many questions about its variation remain open. Although variation in body mass may affect sperm length evolution through its influence on multiple factors, the extent to which sperm length variation is linked to body mass remains elusive. Here, we use the Pareto multi-task evolution framework to investigate the relationship between sperm length and body mass across tetrapods. We find that tetrapods occupy a triangular Pareto front, indicating that trade-offs shape the evolution of sperm length in relation to body mass. By exploring the factors predicted to influence sperm length evolution, we find that sperm length evolution is mainly driven by sperm competition and clutch size, rather than by genome size. Moreover, the triangular Pareto front is maintained within endotherms, internal fertilizers, mammals and birds, suggesting similar evolutionary trade-offs within tetrapods. Finally, we demonstrate that the Pareto front is robust to phylogenetic dependencies and finite sampling bias. Our findings provide insights into the evolutionary mechanisms driving interspecific sperm length variation and highlight the importance of considering multiple trade-offs in optimizing reproductive traits.
Optical vortex-antivortex crystallization in free space
Authors: Lin Haolin , Liao Yixuan , Liu Guohua , Ren Jianbin , Li Zhen , Chen Zhenqiang , Malomed Boris A. , Fu Shenhe
Published: July 2024

Stable vortex lattices are basic dynamical patterns which have been demonstrated in physical systems including superconductor physics, Bose-Einstein condensates, hydrodynamics and optics. Vortex-antivortex (VAV) ensembles can be produced, self-organizing into the respective polar lattices. However, these structures are in general highly unstable due to the strong VAV attraction. Here, we demonstrate that multiple optical VAV clusters nested in the propagating coherent field can crystallize into patterns which preserve their lattice structures over distance up to several Rayleigh lengths. To explain this phenomenon, we present a model for effective interactions between the vortices and antivortices at different lattice sites. The observed VAV crystallization is a consequence of the globally balanced VAV couplings. As the crystallization does not require the presence of nonlinearities and appears in free space, it may find applications to high-capacity optical communications and multiparticle manipulations. Our findings suggest possibilities for constructing VAV complexes through the orbit-orbit couplings, which differs from the extensively studied spin-orbit couplings.
Molecular insights into degron recognition by CRL5ASB7 ubiquitin ligase
Authors: Zhou Mengyu , Wang Xiaolu , Li Jiangtao , Ma Jinfeng , Bao Ziyu , Yan Xiaojie , Zhang Bing , Liu Tong , Yu Ying , Mi Wenyi , Dong Cheng
Published: July 2024

The ankyrin (ANK) SOCS box (ASB) family, encompassing ASB1–18, is the largest group of substrate receptors of cullin 5 Ring E3 ubiquitin ligase. Nonetheless, the mechanism of substrate recognition by ASB family proteins has remained largely elusive. Here we present the crystal structure of ASB7-Elongin B-Elongin C ternary complex bound to a conserved helical degron. ASB7 employs its ANK3-6 to form an extended groove, effectively interacting with the internal α-helix-degron through a network of side-chain-mediated electrostatic and hydrophobic interactions. Our structural findings, combined with biochemical and cellular analyses, identify the key residues of the degron motif and ASB7 required for their recognition. This will facilitate the identification of additional physiological substrates of ASB7 by providing a defined degron motif for screening. Furthermore, the structural insights provide a basis for the rational design of compounds that can specifically target ASB7 by disrupting its interaction with its cognate degron.
Understanding and tuning negative longitudinal piezoelectricity in hafnia
Authors: Jing Huirong , Guan Chaohong , Zhu Hong
Published: July 2024

Most piezoelectric materials exhibit a positive longitudinal piezoelectric effect (PLPE), while a negative longitudinal piezoelectric effect (NLPE) is rarely reported or paid much attention. Here, utilizing first-principles calculations, we unveil the origin of negative longitudinal piezoelectricity in ferroelectric hafnia by introducing the concept of weighted projected bond strength around cation in the c direction (WPBc), which is proposed to quantitatively characterize the asymmetric bonding stiffness along the strain direction. When the WPBc is anti-parallel to the direction of bulk spontaneous polarization, the polarization decreases with respect to tensile strain and leads to a negative piezoelectricity. Furthermore, to confirm the influence of WPBc on the piezoelectric effect and understand how the value of WPBc influences the piezoelectric coefficient e33, we acquire both the piezoelectric coefficient of doped hafnia and the corresponding bonding environment around each cation. The finding reveals that the more negative piezoelectric coefficient can be achieved through a concurrent achievement of the more negative average WPBc and the lower standard deviation (STD) of WPBc. In addition, the Sn-doped hafnia with the lowest average WPBc and smaller STD-WPBc is identified to have the highest piezoelectric coefficient (−2.04 C/m2) compared to other dopants, showing great potential in next-generation electromechanical devices.
A trivalent protein-based pan-Betacoronavirus vaccine elicits cross-neutralizing antibodies against a panel of coronavirus pseudoviruses
Authors: Thimmiraju Syamala Rani , Adhikari Rakesh , Redd JeAnna R. , Villar Maria Jose , Lee Jungsoon , Liu Zhuyun , Chen Yi-Lin , Sharma Suman , Kaur Amandeep , Uzcategui Nestor L. , Ronca Shannon E. , Chen Wen-Hsiang , Kimata Jason T. , Zhan Bin , Strych Ulrich , Bottazzi Maria Elena , Hotez Peter J. , Pollet Jeroen
Published: July 2024

The development of broad-spectrum coronavirus vaccines is essential to prepare for future respiratory virus pandemics. We demonstrated broad neutralization by a trivalent subunit vaccine, formulating the receptor-binding domains of SARS-CoV, MERS-CoV, and SARS-CoV-2 XBB.1.5 with Alum and CpG55.2. Vaccinated mice produced cross-neutralizing antibodies against all three human Betacoronaviruses and others currently exclusive to bats, indicating the epitope preservation of the individual antigens during co-formulation and the potential for epitope broadening.
Neural general circulation models for weather and climate
Authors: Kochkov Dmitrii , Yuval Janni , Langmore Ian , Norgaard Peter , Smith Jamie , Mooers Griffin , Klöwer Milan , Lottes James , Rasp Stephan , Düben Peter , Hatfield Sam , Battaglia Peter , Sanchez-Gonzalez Alvaro , Willson Matthew , Brenner Michael P. , Hoyer Stephan
Published: July 2024

General circulation models (GCMs) are the foundation of weather and climate prediction1,2. GCMs are physics-based simulators that combine a numerical solver for large-scale dynamics with tuned representations for small-scale processes such as cloud formation. Recently, machine-learning models trained on reanalysis data have achieved comparable or better skill than GCMs for deterministic weather forecasting3,4. However, these models have not demonstrated improved ensemble forecasts, or shown sufficient stability for long-term weather and climate simulations. Here we present a GCM that combines a differentiable solver for atmospheric dynamics with machine-learning components and show that it can generate forecasts of deterministic weather, ensemble weather and climate on par with the best machine-learning and physics-based methods. NeuralGCM is competitive with machine-learning models for one- to ten-day forecasts, and with the European Centre for Medium-Range Weather Forecasts ensemble prediction for one- to fifteen-day forecasts. With prescribed sea surface temperature, NeuralGCM can accurately track climate metrics for multiple decades, and climate forecasts with 140-kilometre resolution show emergent phenomena such as realistic frequency and trajectories of tropical cyclones. For both weather and climate, our approach offers orders of magnitude computational savings over conventional GCMs, although our model does not extrapolate to substantially different future climates. Our results show that end-to-end deep learning is compatible with tasks performed by conventional GCMs and can enhance the large-scale physical simulations that are essential for understanding and predicting the Earth system.
Genome-wide association meta-analysis identifies five loci associated with postpartum hemorrhage
Authors: Westergaard David , Steinthorsdottir Valgerdur , Stefansdottir Lilja , Rohde Palle Duun , Wu Xiaoping , Geller Frank , Tyrmi Jaakko , Havulinna Aki S. , Solé-Navais Pol , Flatley Christopher , Ostrowski Sisse Rye , Pedersen Ole Birger , Erikstrup Christian , Sørensen Erik , Mikkelsen Christina , Bruun Mie Topholm , Aagaard Jensen Bitten , Brodersen Thorsten , Ullum Henrik ,   FinnGen,   Danish Blood Donor Study Genomic Consortium,   Estonian Biobank Research Team,   Nordic Collaboration for Womens and Reproductive Health, Magnus Per , Andreassen Ole A. , Njolstad Pål R. , Kolte Astrid Marie , Krebs Lone , Nyegaard Mette , Hansen Thomas Folkmann , Feenstra Bjarke , Daly Mark , Lindgren Cecilia M. , Thorleifsson Gudmar , Stefansson Olafur A. , Sveinbjornsson Gardar , Gudbjartsson Daniel F. , Thorsteinsdottir Unnur , Banasik Karina , Jacobsson Bo , Laisk Triin , Laivuori Hannele , Stefansson Kari , Brunak Søren , Nielsen Henriette Svarre
Published: July 2024

Bleeding in early pregnancy and postpartum hemorrhage (PPH) bear substantial risks, with the former closely associated with pregnancy loss and the latter being the foremost cause of maternal death, underscoring the severe impact on maternal–fetal health. We identified five genetic loci linked to PPH in a meta-analysis. Functional annotation analysis indicated candidate genesHAND2,TBX3andRAP2C/FRMD7at three loci and showed that at each locus, associated variants were located within binding sites for progesterone receptors. There were strong genetic correlations with birth weight, gestational duration and uterine fibroids. Bleeding in early pregnancy yielded no genome-wide association signals but showed strong genetic correlation with various human traits, suggesting a potentially complex, polygenic etiology. Our results suggest that PPH is related to progesterone signaling dysregulation, whereas early bleeding is a complex trait associated with underlying health and possibly socioeconomic status and may include genetic factors that have not yet been identified.