bluuebunny/crossref_metadata_2025_split huggingface dataset:
Biological age estimation using circulating blood biomarkers
Authors: Bortz Jordan , Guariglia Andrea , Klaric Lucija , Tang David , Ward Peter , Geer Michael , Chadeau-Hyam Marc , Vuckovic Dragana , Joshi Peter K. 
Published: October 2023

AbstractBiological age captures physiological deterioration better than chronological age and is amenable to interventions. Blood-based biomarkers have been identified as suitable candidates for biological age estimation. This study aims to improve biological age estimation using machine learning models and a feature-set of 60 circulating biomarkers available from the UK Biobank (n = 306,116). We implement an Elastic-Net derived Cox model with 25 selected biomarkers to predict mortality risk (C-Index = 0.778; 95% CI [0.767–0.788]), which outperforms the well-known blood-biomarker based PhenoAge model (C-Index = 0.750; 95% CI [0.739–0.761]), providing a C-Index lift of 0.028 representing an 11% relative increase in predictive value. Importantly, we then show that using common clinical assay panels, with few biomarkers, alongside imputation and the model derived on the full set of biomarkers, does not substantially degrade predictive accuracy from the theoretical maximum achievable for the available biomarkers. Biological age is estimated as the equivalent age within the same-sex population which corresponds to an individual’s mortality risk. Values ranged between 20-years younger and 20-years older than individuals’ chronological age, exposing the magnitude of ageing signals contained in blood markers. Thus, we demonstrate a practical and cost-efficient method of estimating an improved measure of Biological Age, available to the general population.
Feeding Spodoptera exigua larvae with gut-derived Escherichia sp. increases larval juvenile hormone levels inhibiting cannibalism
Authors: Du Xing-Xing , Cao Sheng-Kai , Xiao Hua-Yan , Yang Chang-Jin , Zeng Ai-Ping , Chen Gong , Yu Huan 
Published: October 2023

AbstractFeed quality influences insect cannibalistic behavior and gut microbial communities. In the present study,Spodoptera exigualarvae were fed six different artificial diets, and one of these diets (Diet 3) delayed larval cannibalistic behavior and reduced the cannibalism ratio after ingestion. Diet 3-fed larvae had the highest gut bacterial load (1.396 ± 0.556 × 1014bacteria/mg gut), whereas Diet 2-fed larvae had the lowest gut bacterial load (3.076 ± 1.368 × 1012bacteria/mg gut). The gut bacterial composition and diversity of different diet-fedS. exigualarvae varied according to the 16S rRNA gene sequence analysis. Enterobacteriaceae was specific to the Diet 3-fed larval gut. Fifteen culturable bacterial isolates were obtained from the midgut of Diet 3-fed larvae. Of these, ten belonged toEscherichia sp. After administration with Diet 1- or 2-fedS. exigualarvae, two bacterial isolates (SePC-12and-37) delayed cannibalistic behavior in both tested larval groups. Diet 2-fed larvae had the lowest Juvenile hormone (JH) concentration and were more aggressive against intraspecific predation. However,SePC-12loading increased the JH hormone levels in Diet 2-fed larvae and inhibited their cannibalism. Bacteria in the larval midgut are involved in the stabilization of JH levels, thereby regulating host larval cannibalistic behavior.
Practical N-to-C peptide synthesis with minimal protecting groups
Authors: Tatsumi Toshifumi , Sasamoto Koki , Matsumoto Takuya , Hirano Ryo , Oikawa Kazuki , Nakano Masato , Yoshida Masaru , Oisaki Kounosuke , Kanai Motomu 
Published: October 2023

AbstractAccessible drug modalities have continued to increase in number in recent years. Peptides play a central role as pharmaceuticals and biomaterials in these new drug modalities. Although traditional peptide synthesis using chain-elongation from C- to N-terminus is reliable, it produces large quantities of chemical waste derived from protecting groups and condensation reagents, which place a heavy burden on the environment. Here we report an alternative N-to-C elongation strategy utilizing catalytic peptide thioacid formation and oxidative peptide bond formation with main chain-unprotected amino acids under aerobic conditions. This method is applicable to both iterative peptide couplings and convergent fragment couplings without requiring elaborate condensation reagents and protecting group manipulations. A recyclable N-hydroxy pyridone additive effectively suppresses epimerization at the elongating chain. We demonstrate the practicality of this method by showcasing a straightforward synthesis of the nonapeptide DSIP. This method further opens the door to clean and atom-efficient peptide synthesis.
Deep learning of causal structures in high dimensions under data limitations
Authors: Lagemann Kai , Lagemann Christian , Taschler Bernd , Mukherjee Sach 
Published: October 2023

AbstractCausal learning is a key challenge in scientific artificial intelligence as it allows researchers to go beyond purely correlative or predictive analyses towards learning underlying cause-and-effect relationships, which are important for scientific understanding as well as for a wide range of downstream tasks. Here, motivated by emerging biomedical questions, we propose a deep neural architecture for learning causal relationships between variables from a combination of high-dimensional data and prior causal knowledge. We combine convolutional and graph neural networks within a causal risk framework to provide an approach that is demonstrably effective under the conditions of high dimensionality, noise and data limitations that are characteristic of many applications, including in large-scale biology. In experiments, we find that the proposed learners can effectively identify novel causal relationships across thousands of variables. Results include extensive (linear and nonlinear) simulations (where the ground truth is known and can be directly compared against), as well as real biological examples where the models are applied to high-dimensional molecular data and their outputs compared against entirely unseen validation experiments. These results support the notion that deep learning approaches can be used to learn causal networks at large scale.
Mode switching in organisms for solving explore-versus-exploit problems
Authors: Biswas Debojyoti , Lamperski Andrew , Yang Yu , Hoffman Kathleen , Guckenheimer John , Fortune Eric S. , Cowan Noah J. 
Published: October 2023

AbstractTrade-offs between producing costly movements for gathering information (‘explore’) and using previously acquired information to achieve a goal (‘exploit’) arise in a wide variety of problems, including foraging, reinforcement learning and sensorimotor control. Determining the optimal balance between exploration and exploitation is computationally intractable, necessitating heuristic solutions. Here we show that the electric fish Eigenmannia virescens uses a salience-dependent mode-switching strategy to solve the explore–exploit conflict during a refuge-tracking task in which the same category of movement (fore-aft swimming) is used for both gathering information and achieving task goals. The fish produced distinctive non-Gaussian distributions of movement velocities characterized by sharp peaks for slower, task-oriented ‘exploit’ movements and broad shoulders for faster ‘explore’ movements. The measures of non-normality increased with increased sensory salience, corresponding to a decrease in the prevalence of fast explore movements. We found the same sensory salience-dependent mode-switching behaviour across ten phylogenetically diverse organisms, from amoebae to humans, performing tasks such as postural balance and target tracking. We propose a state-uncertainty-based mode-switching heuristic that reproduces the distinctive velocity distribution, rationalizes modulation by sensory salience and outperforms the classic persistent excitation approach while using less energy. This mode-switching heuristic provides insights into purposeful exploratory behaviours in organisms, as well as a framework for more efficient state estimation and control of robots.
Climate change and cropland management compromise soil integrity and multifunctionality
Authors: Sünnemann Marie , Beugnon Remy , Breitkreuz Claudia , Buscot François , Cesarz Simone , Jones Arwyn , Lehmann Anika , Lochner Alfred , Orgiazzi Alberto , Reitz Thomas , Rillig Matthias C. , Schädler Martin , Smith Linnea C. , Zeuner Anja , Guerra Carlos A. , Eisenhauer Nico 
Published: October 2023

AbstractSoils provide essential ecosystem functions that are threatened by climate change and intensified land use. We explore how climate and land use impact multiple soil function simultaneously, employing two datasets: (1) observational – 456 samples from the European Land Use/Land Cover Area Frame Survey; and (2) experimental – 80 samples from Germany’s Global Change Experimental Facility. We aim to investigate whether manipulative field experiment results align with observable climate, land use, and soil multifunctionality trends across Europe, measuring seven ecosystem functions to calculate soil multifunctionality. The observational data showed Europe-wide declines in soil multifunctionality under rising temperatures and dry conditions, worsened by cropland management. Our experimental data confirmed these relationships, suggesting that changes in climate will reduce soil multifunctionality across croplands and grasslands. Land use changes from grasslands to croplands threaten the integrity of soil systems, and enhancing soil multifunctionality in arable systems is key to maintain multifunctionality in a changing climate.
Suspended clay and surfactants enhance buoyant microplastic settling
Authors: Sutherland Bruce R. , Dhaliwal Maninderpal S. , Thai Dennis , Li Yuhao , Gingras Murray , Konhauser Kurt 
Published: October 2023

AbstractMost of the plastic waste that enters rivers and the oceans is unaccounted for. Approximately half of the world’s produced plastics are buoyant in water, meaning that processes must take place that effectively increase their density, causing them to settle out of solution. One such mechanism is biofouling, in which organic matter grows on the surface of plastics, making them denser. Here we present a new mechanism supported by laboratory experiments for buoyant plastic settling in which particles of clay adhere to the surface of the plastic, mediated by the presence of surfactants. Although the plastic particles in our experiments were a hundred times larger than the micrometer-sized clay particles, we show that clay can adhere to the plastic with sufficient mass to cause the plastic to sink. This occurs even though the plastic is electrically neutral. It is hypothesized that the hydrophobic tails of the surfactant molecules are attracted to the plastic while the hydrophilic heads attract the clay. A greater fraction of plastic sinks if the surfactant concentration is larger. Our findings suggest that microplastic settling is enhanced in muddy rivers due to interactions with naturally occurring or discharged surfactants, even in the absence of biofouling.
Titin governs myocardial passive stiffness with major support from microtubules and actin and the extracellular matrix
Authors: Loescher Christine M. , Freundt Johanna K. , Unger Andreas , Hessel Anthony L. , Kühn Michel , Koser Franziska , Linke Wolfgang A. 
Published: October 2023

AbstractMyocardial passive stiffness is crucial for the heart’s pump function and is determined by mechanical elements, including the extracellular matrix and cytoskeletal filaments; however, their individual contributions are controversially discussed and difficult to quantify. In this study, we targeted the cytoskeletal filaments in a mouse model, which enables the specific, acute and complete cleavage of the sarcomeric titin springs. We show in vitro that each cytoskeletal filament’s stiffness contribution varies depending on whether the elastic or the viscous forces are considered and on strain level. Titin governs myocardial elastic forces, with the largest contribution provided at both low and high strain. Viscous force contributions are more uniformly distributed among the microtubules, titin and actin. The extracellular matrix contributes at high strain. The remaining forces after total target element disruption are likely derived from desmin filaments. Our findings answer longstanding questions about cardiac mechanical architecture and allow better targeting of passive myocardial stiffness in heart failure.
Distributed fault detection for large‐scale interconnected systems
Authors: Zhang Jiarui , Ding Steven X. , Zhang Deyu , Li Linlin 
Published: October 2023

AbstractThe main objective of this paper is to develop a distributed fault detection (FD) approach for large‐scale interconnected systems using sensor networks. Specifically, the one‐step prediction based on the measured data is implemented in a distributed fashion so that each node can receive corresponding estimations and innovation sequences in a real‐time manner. Then the innovation sequences are applied to improve the estimation result delivered from the one‐step prediction by filtering and smoothing. After filtering and smoothing, the residual signals are calculated to detect faults. Finally, a case study shows that the distributed approach can efficiently accomplish the FD task.
A review on the field‐modulated magnetic gears: Development status, potential applications, and existent challenges
Authors: Yan Bo , Li Xianglin , Wang Xiuhe , Yang Yubo 
Published: October 2023

AbstractAs a type of magnetic device being able to realize contact‐free power transmission together with sufficient volumetric torque density, the field‐modulated magnetic gear (FMMG) has become a promising alternative to the mechanical gear having rigid construction yet suffering lots of issues generated by the continuous teeth friction. The conventional magnetic gears (MGs) that behave as simple copies of their mechanical counterparts and can be roughly defined as origination of the FMMG are briefly introduced at first. Subsequently, the topology and material advancements proposed to improve operational performance of the FMMG are comprehensively summarised so as to clarify its current development status. Finally, potential applications of the FMMG due to its inherent advantages are compared against the existent challenges. The review aims to provide referable guidelines for researchers working at the field of high‐performance MGs.
A resilience‐motivated restoration scheme for integrated electricity and natural gas distribution systems using adaptable microgrid formation
Authors: Jafarpour Saeid , Amirioun Mohammad Hassan 
Published: October 2023

AbstractThis paper presents a multi‐objective restoration scheme for improving the resilience of integrated electricity and natural gas distribution systems against extreme weather events. The coupling constraints of electricity and gas networks are tackled properly using a linearized optimal power flow (OPF). Distributed generators, power‐to‐gas facility, rescheduling of generation/storage units, and microgrid formation are employed as operational resources/measures for restoring the integrated energy system after the event landfall. An adaptable directed multi‐commodity flow‐based microgrid formation is utilized, that is, the network configuration is dynamically changed in accordance with time‐variant load priority weights. The proposed method was successfully examined on an integrated electricity and natural gas distribution system comprised of the modified IEEE 33‐bus distribution network and a 14‐node natural gas distribution network. Numerical results showed that using microgrid formation increased the supplied critical load of integrated electricity and natural gas distribution system by about 16%. Moreover, due to making benefit of the power‐to‐gas unit, the supplied critical load increased by about 12.3%. respectively. While utilizing energy storage systems along with the power‐to‐gas unit facilitated the exchange of energy between the power distribution network and natural gas distribution network regarding time‐variant load priority weights, the supplied critical load increased by about 13%.
Stochastic optimal transactive energy management with cloud energy storage using artificial neural networks
Authors: Salehi Mohammad Kazem , Rastegar Mohammad 
Published: October 2023

AbstractResidential buildings may use energy storage, flexible loads, and renewable energy sources to reduce energy consumption and increase demand side flexibility. The flexibility of a single building can be coordinated with other facilities in a transactive energy (TE) market to reduce energy costs. In addition, cloud energy storage (CES) has been proposed to provide storage services for residential buildings with more economic benefits than individual energy storage units in recent years. Although the TE market and CES implementation have received much attention in previous works, a suitable structure for CES participation in TE market has not been addressed. Furthermore, previous studies ignored all or some sources of uncertainties in the TE decision making process. This paper presents a stochastic optimization model in a transactive energy framework based on a distributed optimization algorithm for peer‐to‐peer energy trading using the alternating direction method of multipliers in the presence of CES. This paper considers the uncertainties of the inflexible load demand, renewable energy generations, and market prices using an artificial neural network‐based scenario generation and reduction methodology. Numerical results show improvements toward addressing the challenges of the uncertainties while maximizing the CES's owner revenue and minimizing the customers’ costs in the proposed model.
EfficientUNet: An efficient solution for breast tumour segmentation in ultrasound images
Authors: You Guizeng , Yang Xinwu , Lee Xuanbo , Zhu Kongqiang 
Published: October 2023

AbstractThe accurate segmentation of breast tumours is important for the diagnosis and treatment of breast cancer. When using the classic U‐Net, Attention‐UNet, and UNet++ to segment the tumour, there are problems of oversegmentation, incorrect segmentation, and poor edge continuity. In this paper, an effective tumour segmentation method, EfficientUNet, is proposed. EfficientUNet adopts a step‐by‐step enhancement method, combining ResNet18, a channel attention mechanism and deep supervision. ResNet18, as the encoder of the whole network, solves the problem of gradient disappearanceand improves the feature extraction ability of the model. The channel attention module makes the model more accurate in tumour edge processing. The deep supervision technology accelerates the model training and provides the convergence direction for the model. In addition, it is found that when adjusting the size of the image, the method of image filling before clipping (or zooming) is more conducive to model learning than the direct interpolation method. And a comparative experiment wasperformed on dataset B. Compared to U‐Net, Attention‐UNet and UNet++, EfficientUNet has the highest performance. Finally, the ablation experiment also indicated the effectiveness of each module in EfficientUNet.
Prävention, Diagnostik, Therapie und Nachsorge des Lungenkarzinoms
Authors: Schütte Wolfgang , Gütz Sylvia , Nehls Wiebke , Blum Torsten Gerriet , Brückl Wolfgang , Buttmann-Schweiger Nina , Büttner Reinhard , Christopoulos Petros , Delis Sandra , Deppermann Karl M. , Dickgreber Nikolas , Eberhardt Wilfried , Eggeling Stephan , Fleckenstein Jochen , Flentje Michael , Frost Nikolaj , Griesinger Frank , Grohé Christian , Gröschel Andreas , Guckenberger Matthias , Hecker Erich , Hoffmann Hans , Huber Rudolf M. , Junker Klaus , Kauczor Hans-Ulrich , Kollmeier Jens , Kraywinkel Klaus , Krüger Marcus , Kugler Christian , Möller Miriam , Nestle Ursula , Passlick Bernward , Pfannschmidt Joachim , Reck Martin , Reinmuth Niels , Rübe Christian , Scheubel Robert , Schumann Christian , Sebastian Martin , Serke Monika , Stoelben Erich , Stuschke Martin , Thomas Michael , Tufman Amanda , Vordermark Dirk , Waller Cornelius , Wolf Jürgen , Wolf Martin , Wormanns Dag 
Published: October 2023

ZusammenfassungDie aktuelle Fassung der Leitlinie Lungenkarzinom trägt der Dynamik der Informationen in diesem Fachbereich Rechnung. Insbesondere gelten folgenden Empfehlungen:Die Vorstellung aller neu diagnostizierten Patienten im interdisziplinären pneumoonkologischen Tumorboard ist verpflichtend, das CT-Screening für asymptomatische Risikopersonen (nach Zulassung durch die Behörden), Vorgehen beim inzidentellen Lungenrundherd (außerhalb von Screeningprogrammen), molekulare Testung aller NSCLC unabhängig vom Subtyp, in frühen Stadien auf EGFR-Mutationen und in der Rezidivsituation, adjuvante TKI-Therapie bei Vorliegen einer EGFR-Mutation, adjuvante Konsolidierung mit Checkpointinhibitor bei PD-L1 ≥ 50%, Erhebung des PD-L1-Status, nach Radiochemotherapie bei PD-L1-pos. Tumoren Konsolidierung mit Checkpointinhibitor, adjuvante Konsolidierung mit Checkpointinhibitor bei PD-L1 ≥ 50% im Stadium IIIA, Erweiterung des therapeutischen Spektrums bei PD-L1 ≥ 50%, unabhängig von PD-L1Status, neue zielgerichtete Therapieoptionen sowie die Einführung der Immunchemotherapie in der SCLC Erstlinie.Um eine zeitnahe Umsetzung künftiger Neuerungen zu gewährleisten, wurde die Umstellung auf eine „living guideline“ für das Lungenkarzinom befürwortet.
Outcomes of the Endoscopic Endonasal Approach for the Treatment of Clival Chordomas: A Single-Center Experience
Authors: Alsayed Ahmed , Alrasheed Abdulaziz S. , Aljabab Saif , Alshareef Mohammad , Yahya Buthaina Jaber , Alharbi Abdulmajeed , Alroqi Ahmad , Albaharna Hussain , Alromaih Saud , Alayed Yasir , Alqurashi Ashwag , Alsaleh Saad , Ajlan Abdulrazag 
Published: October 2023

Abstract
Objective Chordoma is a low-grade malignant tumor that originates from the remnant tissue of the embryonic notochord. Postoperative or definitive radiotherapy (RT) has been used to enhance local control. This study aims to assess the outcomes of the expanded endoscopic endonasal approach (EEA) for maximal removal of clival chordomas followed by RT for visualized residual or tumor recurrence.
Materials and Methods A retrospective review was performed on consecutive patients with clival chordoma who underwent endoscopic endonasal resection in the otorhinolaryngology and neurosurgery departments, between 2016 and 2021. We included all patients with pathologically confirmed clival chordoma who were treated using the EEA. Patients who underwent combined external and endoscopic approaches or transcranial surgery were excluded.
Results Seventeen patients were included in this study. Most of them had tumors located in the middle clivus. Regarding RT, the majority of patients underwent postoperative RT. Almost half of them underwent CyberKnife (CK) RT. None of them had severe toxicities (grade 3 or higher). Three patients died, resulting in a mortality rate of 17.6% none of them related to radiation side effect. The 2-year overall survival was 82.4% (mean standard error [SE] = 1.765, 95% confidence interval [CI] = 1.505–2.024), and the progression-free survival (PFS) was 76.5% (mean SE = 3.403, 95% CI = 2.791–4.016). No distal metastasis was reported in our series.
Conclusion This series demonstrates that expanded endoscopic endonasal approach (EEA) for the resection of skull base chordomas, followed by CyberKnife radiosurgery, presents a viable alternative to proton beam therapy; however, further research is necessary to directly compare these modalities.