bluuebunny/crossref_metadata_2025_split huggingface dataset:
Predictive enrichment for the need of renal replacement in sepsis-associated acute kidney injury: combination of furosemide stress test and urinary biomarkers TIMP-2 and IGFBP-7
Authors: Palmowski Lars , Lindau Simone , Henk Laura Contreras , Marko Britta , Witowski Andrea , Nowak Hartmuth , Stoll Sandra E. , Zacharowski Kai , Böttiger Bernd W. , Peters Jürgen , Adamzik Michael , Dusse Fabian , Rahmel Tim 
Published: July 2024

Abstract
Background
In sepsis, initial resuscitation with fluids is followed by efforts to achieve a negative fluid balance. However, patients with sepsis-associated acute kidney injury (SA-AKI) often need diuretic or renal replacement therapy (RRT). The dilemma is to predict whether early RRT might be advantageous or diuretics will suffice. Both the Furosemide Stress Test (FST) and measurements of the urinary biomarkers TIMP-2*IGFBP-7, if applied solely, do not provide sufficient guidance. We tested the hypothesis that a combination of two tests, i.e., an upstream FST combined with downstream measurements of urinary TIMP-2*IGFBP-7 concentrations improves the accuracy in predicting RRT necessity.

Methods
In this prospective, multicenter study 100 patients with sepsis (diagnosed < 48h), AKI stage ≥ 2, and an indication for negative fluid balance were included between 02/2020 and 12/2022. All patients received a standardized FST and urinary biomarkers TIMP-2*IGFBP-7 were serially measured immediately before and up to 12 h after the FST. The primary outcome was the RRT requirement within 7 days after inclusion.

Results
32% (n = 32/99) of SA-AKI patients eventually required RRT within 7 days. With the FST, urine TIMP-2*IGFBP-7 decreased within 2 h from 3.26 ng2/mL2/1000 (IQR: 1.38–5.53) to 2.36 ng2/mL2/1000 (IQR: 1.61–4.87) in RRT and 1.68 ng2/mL2/1000 (IQR: 0.56–2.94) to 0.27 ng2/mL2/1000 (IQR: 0.12–0.89) and non-RRT patients, respectively. While TIMP-2*IGFBP-7 concentrations remained low for up to 12 h in non-RRT patients, we noted a rebound in RRT patients after 6 h. TIMP-2*IGFBP-7 before FST (accuracy 0.66; 95%-CI 0.55–0.78) and the FST itself (accuracy 0.74; 95%-CI: 0.64–0.82) yielded moderate test accuracies in predicting RRT requirement. In contrast, a two-step approach, utilizing FST as an upstream screening tool followed by TIMP-2*IGFBP-7 quantification after 2 h improved predictive accuracy (0.83; 95%-CI 0.74–0.90, p = 0.03) compared to the FST alone, resulting in a positive predictive value of 0.86 (95%-CI 0.64–0.97), and a specificity of 0.96 (95%-CI 0.88–0.99).

Conclusions
The combined application of an upstream FST followed by urinary TIMP-2*IGFBP-7 measurements supports highly specific identification of SA-AKI patients requiring RRT. Upcoming interventional trials should elucidate if this high-risk SA-AKI subgroup, identified by our predictive enrichment approach, benefits from an early RRT initiation.
Adaptive multi-task learning for speech to text translation
Authors: Feng Xin , Zhao Yue , Zong Wei , Xu Xiaona 
Published: July 2024

AbstractEnd-to-end speech to text translation aims to directly translate speech from one language into text in another, posing a challenging cross-modal task particularly in scenarios of limited data. Multi-task learning serves as an effective strategy for knowledge sharing between speech translation and machine translation, which allows models to leverage extensive machine translation data to learn the mapping between source and target languages, thereby improving the performance of speech translation. However, in multi-task learning, finding a set of weights that balances various tasks is challenging and computationally expensive. We proposed an adaptive multi-task learning method to dynamically adjust multi-task weights based on the proportional losses incurred during training, enabling adaptive balance in multi-task learning for speech to text translation. Moreover, inherent representation disparities across different modalities impede speech translation models from harnessing textual data effectively. To bridge the gap across different modalities, we proposed to apply optimal transport in the input of end-to-end model to find the alignment between speech and text sequences and learn the shared representations between them. Experimental results show that our method effectively improved the performance on the Tibetan-Chinese, English-German, and English-French speech translation datasets.
Trade-offs in biomethane production by substrate mixture optimization under German market conditions
Authors: Güsewell Joshua , Rousta Milad , Eltrop Ludger 
Published: July 2024

Abstract
Background
New regulations and market conditions in Germany affect the profitability of biomethane upgrading as a repowering option for existing biogas plants following on-site CHP utilization. These conditions present trade-off challenges between higher sustainability requirements, maintaining production capacity and new revenue opportunities. Optimization methods, such as linear programming (LP), are essential for determining the ideal substrate mixture and profitable solutions amidst multiple market conditions, plant-specific process constraints, and substrate properties.

Methods
We updated a substrate mixture optimization model within an assessment framework for the repowering of existing biogas plants (BGPs), which focuses on the operator’s perspective. By integrating multiple German biomethane markets for various BGPs, we assessed changes in the substrate mixture, GHG emissions, contribution margins, and constraint parameters to derive conclusions for operators and future framework design.

Results
Integrating market revenues and constraints can increase contribution margins by 12–55%. Additional gains can be achieved by considering multiple markets simultaneously but limited to a few BGPs. The plant-specific LP solution space and used benchmark market are decisive. The former limits the potential of high substrate-specific contribution margins, which has a significantly higher impact than the relation between plant-specific characteristics and process constraints. The advanced fuel market is currently the lead market for biomethane, incentivizing GHG-emission extensive substrates, decreasing gas production and GHG emissions but increasing levelized cost of energy (LCOE) and partially CO2 abatement costs.

Conclusions
The key to improve profitability and to supply an increasing biomethane demand while fulfilling new requirements is a large LP solution space. Increasing market options, substrate availability, and digestion system capacity achieve this on the operator’s side. Policy makers could reduce normative requirements such as the maize cap or double counting of advanced fuels and favor high but uniform GHG requirements. Operators can prepare robustly for the future substrate mixture by adding digester volume and pre-treatment tech, ensuring long-term and diverse substrate availability, and contracts with flexible components. Although current market conditions can improve specific GHG emissions, they do not necessarily increase manure usage when other options, such as straw, are viable. Other regulatory support systems will be required to do so.
Dietary silymarin improves performance by altering hepatic lipid metabolism and cecal microbiota function and its metabolites in late laying hens
Authors: Guo Yanghao , Xu Yudong , Wang Derun , Yang Shihao , Song Zehe , Li Rui , He Xi 
Published: July 2024

Abstract

Background
Liver lipid dysregulation is one of the major factors in the decline of production performance in late-stage laying hens. Silymarin (SIL), a natural flavonolignan extracted from milk thistle, is known for its hepatoprotective and lipid-lowering properties in humans. This study evaluates whether SIL can provide similar benefits to late-stage laying hens. A total of 480 68-week-old Lohmann Pink laying hens were randomly assigned into 5 groups, each group consisting of 6 replicates with 16 hens each. The birds received a basal diet either without silymarin (control) or supplemented with silymarin at concentrations of 250, 500, 750, or 1,000 mg/kg (SIL250, SIL500, SIL750, SIL1000) over a 12-week period.

Results
The CON group exhibited a significant decline in laying rates from weeks 9 to 12 compared to the initial 4 weeks (P = 0.042), while SIL supplementation maintained consistent laying rates throughout the study (P > 0.05). Notably, the SIL500 and SIL750 groups showed higher average egg weight than the CON group during weeks 5 to 8 (P = 0.049). The SIL750 group had a significantly higher average daily feed intake across the study period (P < 0.05), and the SIL500 group saw a marked decrease in the feed-to-egg ratio from weeks 5 to 8 (P = 0.003). Furthermore, the SIL500 group demonstrated significant reductions in serum ALT and AST levels (P < 0.05) and a significant decrease in serum triglycerides and total cholesterol at week 12 with increasing doses of SIL (P < 0.05). SIL also positively influenced liver enzyme expression (FASN, ACC, Apo-VLDL II, FXR, and CYP7A1; P < 0.05) and altered the cecal microbiota composition, enhancing species linked to secondary bile acid synthesis. Targeted metabolomics identified 9 metabolites predominantly involved in thiamin metabolism that were significantly different in the SIL groups (P < 0.05).

Conclusions
Our study demonstrated that dietary SIL supplementation could ameliorate egg production rate in late stage laying hens, mechanistically, this effect was via improving hepatic lipid metabolism and cecal microbiota function to achieve. Revealed the potentially of SIL as a feed supplementation to regulate hepatic lipid metabolism dysregulation. Overall, dietary 500 mg/kg SIL had the best effects.

Graphical Abstract
Post-implantation analysis of genomic variations in the progeny from developing fetus to birth
Authors: Zheng Yingming , Lin Chuanping , Wang Wen-Jing , Wang Liya , Qian Yeqing , Mao Luna , Li Baohua , Lou Lijun , Mao Yuchan , Li Na , Zheng Jiayong , Jiang Nan , He Chaying , Wang Qijing , Zhou Qing , Chen Fang , Jin Fan 
Published: July 2024

AbstractThe analysis of genomic variations in offspring after implantation has been infrequently studied. In this study, we aim to investigate the extent of de novo mutations in humans from developing fetus to birth. Using high-depth whole-genome sequencing, 443 parent-offspring trios were studied to compare the results of de novo mutations (DNMs) between different groups. The focus was on fetuses and newborns, with DNA samples obtained from the families’ blood and the aspirated embryonic tissues subjected to deep sequencing. It was observed that the average number of total DNMs in the newborns group was 56.26 (54.17–58.35), which appeared to be lower than that the multifetal reduction group, which was 76.05 (69.70–82.40) (F = 2.42, P = 0.12). However, after adjusting for parental age and maternal pre-pregnancy body mass index (BMI), significant differences were found between the two groups. The analysis was further divided into single nucleotide variants (SNVs) and insertion/deletion of a small number of bases (indels), and it was discovered that the average number of de novo SNVs associated with the multifetal reduction group and the newborn group was 49.89 (45.59–54.20) and 51.09 (49.22–52.96), respectively. No significant differences were noted between the groups (F = 1.01, P = 0.32). However, a significant difference was observed for de novo indels, with a higher average number found in the multifetal reduction group compared to the newborn group (F = 194.17, P < 0.001). The average number of de novo indels among the multifetal reduction group and the newborn group was 26.26 (23.27–29.05) and 5.17 (4.82–5.52), respectively. To conclude, it has been observed that the quantity of de novo indels in the newborns experiences a significant decrease when compared to that in the aspirated embryonic tissues (7–9 weeks). This phenomenon is evident across all genomic regions, highlighting the adverse effects of de novo indels on the fetus and emphasizing the significance of embryonic implantation and intrauterine growth in human genetic selection mechanisms.
The other side of the coin: expectations of Polish and Hungarian students on soft skills in the labour market – a futures perspective
Authors: Laurisz Norbert , Gáspár Tamás , Gałat Wioleta , Juhász Tímea 
Published: July 2024

AbstractThere is common agreement that we are facing a great transition, which poses a challenge for the labour market, too. The expending literature on soft skills reflects the restructuring of the competition factors, and several papers discuss the future of the labour market: what new professions may emerge and what new skills these jobs demand. However, the attention on the expectations of the future generation of the labour market is more neglected, mainly in terms of what they perceive of companies’ expectations. The present research seeks information what soft skills university students think to possess and their views on companies' expectations for the future. In addition, we aimed at detecting the possible cultural differences between two similar countries. The study makes a comparative quantitative analysis of 931 Polish and Hungarian students by analysis of variance, logistic regression and cluster analysis with the PS Imago Pro and PAST programmes. The results reflect that students have a relevant perception of company needs; however, Polish and Hungarian future employees clearly differ in terms of both possession and expectations on soft skills. Additionally, these differences create patterns as well. Polish students are more team-related and prefer collaborative skills, while being more present-oriented. In terms of the future, the preferred skills rather help avoid uncertainties, and serve problem-solving, and hence, planning. Hungarian students highlight professional and individualistic skills to improve competitive abilities. They are more open to the future and recognise the importance of skills that help to unfold optional futures.
Is creativity a reality in Bulgaria cities? An empirical study in the pre-pandemic period
Authors: Rodrigues Margarida , Franco Mário , Filipova Hristina , Silva Rui Jorge , Kazandzhieva Velina , Nacheva Radka , Koleva Velina , Lyubenov Lyubomir , Oliveira Cidália , Borges Ana Pinto , Braga José , Ivanova Pavlina , Antonova Katya 
Published: July 2024

AbstractThis study aims to obtain a composite index characterizing Bulgarian districts about their creativity by presenting results for the pre-pandemic. The composite index developed by Rodrigues and Franco (2019), use of quantitative method and multivariate statistical techniques have been used to answer this objective. From the data gathered primarily from NSI- The National Statistical Institute of Bulgaria, the results show that creativity is one of the pillars of economic growth, and financial resources and local and foreign investment for its development are still very limited. The government and cultural organisations supported initiatives to showcase the country's cultural diversity and historical significance. The creative sector, including design, arts, media, and technology started to be seen as a driving force for the country's economic growth. Startups and creative entrepreneurs are increasingly gaining weight, which has led to the creation of creative centers, and co-working spaces. Therefore, this study contributes to the intelligent application of creative cities’ ideas in the creative industries and contemporary cities’ management.
Thermal Earth model for the conterminous United States using an interpolative physics-informed graph neural network
Authors: Aljubran Mohammad J. , Horne Roland N. 
Published: July 2024

AbstractThis study presents a data-driven spatial interpolation algorithm based on physics-informed graph neural networks used to develop a thermal Earth model for the conterminous United States. The model was trained to approximately satisfy Fourier’s Law of conductive heat transfer by simultaneously predicting subsurface temperature, surface heat flow, and rock thermal conductivity. In addition to bottomhole temperature measurements, we incorporated other spatial and physical quantities as model inputs, such as depth, geographic coordinates, elevation, sediment thickness, magnetic anomaly, gravity anomaly, gamma-ray flux of radioactive elements, seismicity, electrical conductivity, and proximity to faults and volcanoes. With a spatial resolution of $$18 \ km^2$$

18

k

m
2

per grid cell, we predicted heat flow at surface as well as temperature and rock thermal conductivity across depths of $$0-7 \ km$$

0
-
7

k
m

at an interval of $$1 \ km$$

1

k
m

. Our model showed temperature, surface heat flow and thermal conductivity mean absolute errors of $$6.4^\circ C$$

6
.

4
∘

C

, $$6.9 \ mW/m^2$$

6.9

m
W
/

m
2

and $$0.04 \ W/m-K$$

0.04

W
/
m
-
K

, respectively. This thorough modeling of the Earth’s thermal processes is crucial to understanding subsurface phenomena and exploiting natural underground resources. Our thermal Earth model is available as web application at https://stm.stanford.edu, feature layers on ArcGIS at https://arcg.is/nLzzT0, and tabulated data on the Geothermal Data Repository at https://gdr.openei.org/submissions/1592.
A low-cost robotic system for simultaneous localization and mapping
Authors: Kassem Ayman Hamdy , Asem Muhammad 
Published: July 2024

AbstractThis paper presents a low-cost system for simultaneous localization and mapping (SLAM) for unknown indoor environments. The system is based on a low-cost mobile-robot platform. The low-cost mobile robot is designed and fabricated in our control laboratory. The Rao-Blackwellized particle filter algorithm is used for SLAM computations, Xbox 360 Kinect module is utilized for stereo-camera imaging, and a Linux-based microcomputer (Raspberry Pi3) was used as the main onboard processing unit. An Arduino board is used to control the DC motors for mobile robot wheels. Raspberry Pi unit was wirelessly connected to a ground station machine that processes the information sent by the robot to build the environment map and estimate its pose. ROS (Robot Operating System) is used for map visualization, data-handling, and communication between different software nodes. The system has been tested virtually on a simulator and in real indoor environments and has successfully identified objects greater than 30 cm × 30 cm × 30 cm and added it to the map. It also shows promising capability to work autonomous missions independently without aid from any external sensors and with a fraction of the cost of similar systems based on Lidars.
Optimality Conditions in Control Problems with Random State Constraints in Probabilistic or Almost Sure Form
Authors: Geiersbach Caroline , Henrion René 
Published: July 2024

In this paper, we discuss optimality conditions for optimization problems involving random state constraints, which are modeled in probabilistic or almost sure form. Although the latter can be understood as the limiting case of the former, the derivation of optimality conditions requires substantially different approaches. We apply them to a linear elliptic partial differential equation with random inputs. In the probabilistic case, we rely on the spherical-radial decomposition of Gaussian random vectors in order to formulate fully explicit optimality conditions involving a spherical integral. In the almost sure case, we derive optimality conditions and compare them with a model based on robust constraints with respect to the (compact) support of the given distribution. Funding: The authors thank the Deutsche Forschungsgemeinschaft [Projects B02 and B04 in the “Sonderforschungsbereich/Transregio 154 Mathematical Modelling, Simulation and Optimization Using the Example of Gas Networks”] for support. C. Geiersbach acknowledges support from the Deutsche Forschungsgemeinschaft [Germany’s Excellence Strategy–the Berlin Mathematics Research Center MATH+ Grant EXC-2046/1, Project 390685689]. R. Henrion acknowledges support from the Fondation Mathématique Jacques Hadamard [Program Gaspard Monge in Optimization and Operations Research, including support to this program by Electricité de France].
Improving Blockchain Consistency Bound by Assigning Weights to Random Blocks
Authors: Gong Xueping , Zhang Qing , Li Huizhong , Zhang Jiheng 
Published: July 2024

Ironclad Method Boosts Blockchain Consistency and Speeds up Block Production Researchers have developed a novel method called Ironclad to address the scalability limitations of blockchains based on the Nakamoto consensus protocol. Although these blockchains have shown promise in various applications, the consensus properties of the protocol impose inherent scalability constraints. A key property, known as consistency, poses a challenge by presenting a trade-off between block production speed and the system’s security against adversarial attacks. To overcome this, the researchers propose the Ironclad method, which introduces a unique approach by assigning different weights to randomly selected blocks. By applying the Ironclad method to the original Nakamoto protocol and conducting rigorous analysis of its consensus properties, the researchers demonstrate a significant improvement in the consistency bound. This advancement allows for a much faster block production rate compared with the original protocol while maintaining the same level of security guarantees. The Ironclad method presents a promising solution to enhance blockchain scalability, overcoming the limitations of the Nakamoto consensus protocol. This breakthrough has the potential to unlock new possibilities for blockchain applications, paving the way for improved transaction speeds and increased efficiency in various industries.