bluuebunny/crossref_metadata_2025_split huggingface dataset:
Inhibition of UHRF1 Improves Motor Function in Mice with Spinal Cord Injury
Authors: Cheng Shuai , Guo Hui , Bai Mingyu , Cui Yang , Tian He , Mei Xifan 
Published: April 2024

AbstractSpinal-cord injury (SCI) is a severe condition that can lead to limb paralysis and motor dysfunction, and its pathogenesis is not fully understood. The objective of this study was to characterize the differential gene expression and molecular mechanisms in the spinal cord of mice three days after spinal cord injury. By analyzing RNA sequencing data, we identified differentially expressed genes and discovered that the immune system and various metabolic processes play crucial roles in SCI. Additionally, we identified UHRF1 as a key gene that plays a significant role in SCI and found that SCI can be improved by suppressing UHRF1. These findings provide important insights into the molecular mechanisms of SCI and identify potential therapeutic targets that could greatly contribute to the development of new treatment strategies for SCI.
Graphical Abstract
Downregulation of UHRF1 promotes the recovery of motor function in mice with spinal cord injury. By analyzing the RNA sequencing results of mice with spinal cord injury for 3 days and selecting UHRF1 for experimental validation, the study found that inhibiting UHRF1 can promote motor function recovery.
Using $$P_\tau $$ property for designing bent functions provably outside the completed Maiorana–McFarland class
Authors: Pasalic Enes , Bapić Amar , Zhang Fengrong , Wei Yongzhuang 
Published: April 2024

AbstractIn this article, we identify certain instances of bent functions, constructed using the so-called $$P_\tau $$

P
τ

property, that are provably outside the completed Maiorana–McFarland ($${\mathcal{M}\mathcal{M}}^\#$$

M
M

#

) class. This also partially answers an open problem in posed by Kan et al. (IEEE Trans Inf Theory, https://doi.org/10.1109/TIT.2022.3140180, 2022). We show that this design framework (using the $$P_\tau $$

P
τ

property), can provide instances of bent functions that are outside the known classes of bent functions, including the classes $${\mathcal{M}\mathcal{M}}^\#$$

M
M

#

, $${{\mathcal {C}}},{{\mathcal {D}}}$$

C
,
D

and $${{\mathcal {D}}}_0$$

D
0

, where the latter three were introduced by Carlet in the early nineties. We provide two generic methods for identifying such instances, where most notably one of these methods uses permutations that may admit linear structures. For the first time, a set of sufficient conditions for the functions of the form $$h(y,z)=Tr(y\pi (z)) + G_1(Tr_1^m(\alpha _1y),\ldots ,Tr_1^m(\alpha _ky))G_2(Tr_1^m(\beta _{k+1}z),\ldots ,Tr_1^m(\beta _{\tau }z))+ G_3(Tr_1^m(\alpha _1y),\ldots ,Tr_1^m(\alpha _ky))$$

h

(
y
,
z
)

=
T
r

(
y
π

(
z
)

)

+

G
1

(
T

r
1
m

(

α
1

y
)

,
…
,
T

r
1
m

(

α
k

y
)

)

G
2

(
T

r
1
m

(

β

k
+
1

z
)

,
…
,
T

r
1
m

(

β
τ

z
)

)

+

G
3

(
T

r
1
m

(

α
1

y
)

,
…
,
T

r
1
m

(

α
k

y
)

)

to be bent and outside $${\mathcal{M}\mathcal{M}}^\#$$

M
M

#

is specified without a strong assumption that the components of the permutation $$\pi $$
π
do not admit linear structures.
Performance of water indices for large-scale water resources monitoring using Sentinel-2 data in Ethiopia
Authors: Tesfaye Mathias , Breuer Lutz 
Published: April 2024

AbstractEvaluating the performance of water indices and water-related ecosystems is crucial for Ethiopia. This is due to limited information on the availability and distribution of water resources at the country scale, despite its critical role in sustainable water management, biodiversity conservation, and ecosystem resilience. The objective of this study is to evaluate the performance of seven water indices and select the best-performing indices for detecting surface water at country scale. Sentinel-2 data from December 1, 2021, to November 30, 2022, were used for the evaluation and processed using the Google Earth Engine. The indices were evaluated using qualitative visual inspection and quantitative accuracy indicators of overall accuracy, producer’s accuracy, and user’s accuracy. Results showed that the water index (WI) and automatic water extraction index with shadow (AWEIsh) were the most accurate ones to extract surface water. For the latter, WI and AWEIsh obtained an overall accuracy of 96% and 95%, respectively. Both indices had approximately the same spatial coverage of surface water with 82,650 km2 (WI) and 86,530 km2 (AWEIsh) for the whole of Ethiopia. The results provide a valuable insight into the extent of surface water bodies, which is essential for water resource planners and decision-makers. Such data can also play a role in monitoring the country’s reservoirs, which are important for the country’s energy and economic development. These results suggest that by applying the best-performing indices, better monitoring and management of water resources would be possible to achieve the Sustainable Development Goal 6 at the regional level.
Review on the matching conditions for the tidal problem: towards the application to more general contexts
Authors: Aranguren Eneko , Vera Raül 
Published: April 2024

AbstractThe tidal problem is used to obtain the tidal deformability (or Love number) of stars. The semi-analytical study is usually treated in perturbation theory as a first order perturbation problem over a spherically symmetric background configuration consisting of a stellar interior region matched across a boundary to a vacuum exterior region that models the tidal field. The field equations for the metric and matter perturbations at the interior and exterior regions are complemented with corresponding boundary conditions. The data of the two problems at the common boundary are related by the so called matching conditions. These conditions for the tidal problem are known in the contexts of perfect fluid stars and superfluid stars modelled by a two-fluid. Here we review the obtaining of the matching conditions for the tidal problem starting from a purely geometrical setting, and present them so that they can be readily applied to more general contexts, such as other types of matter fields, different multiple layers or phase transitions. As a guide on how to use the matching conditions, we recover the known results for perfect fluid and superfluid neutron stars.
Spatio-temporal patterns in the biomass, species composition and nitrogen content of drift macroalgae in an urbanised coastal embayment
Authors: Valero-Rodriguez Juan Manuel , Dempster Tim , Wong Wei Wen , Lewis John A. , Cook Perran L. M. , Swearer Stephen E. 
Published: April 2024

AbstractUnderstanding spatio-temporal variability in the production of drift macroalgae is useful for monitoring and assessing the influence of local anthropogenic nutrient sources on coastal ecosystems. In this study we: (1) assessed how the drift macroalgal community composition and biomass have changed over a 22-year period in Port Phillip Bay (a large coastal embayment in Victoria, Australia); and (2) measured the amount of accumulated nitrogen in drift macroalgae and the contribution of anthropogenic nitrogen sources through isotopic analysis. The surveys revealed little overlap in species composition (<13%) and a significant decrease in standing stock biomass (from 631 to 49 g m-2) between surveys (1995/1996 and 2017/2018). This was associated with a high degree of turnover (β-diversity) at the genus level between surveys (mean – 0.53), resulting in distinct communities at all sites, particularly among red algal genera. In addition, the occurrence of ephemeral macroalgal blooms and a semi-perennial mat of the red macroalga Botryocladia sonderi at 10-15 m depth at locations close to nutrient sources indicate drift macroalgae are important nutrient accumulators in the bay. Isotopic analysis revealed a high contribution of anthropogenic-derived nitrogen (δ15N > 17‰ and ~ 3% N content) at depths and locations close to nutrient sources, particularly at times of the year when large wastewater discharges occur. Harvesting of drift macroalgae could be a cost-effective solution for offsetting nitrogen inputs from anthropogenic sources, but it will be important to first investigate whether harvesting results in negative impacts to associated fauna or nutrient cycling.
De novo drug design as GPT language modeling: large chemistry models with supervised and reinforcement learning
Authors: Ye Gavin 
Published: April 2024

AbstractIn recent years, generative machine learning algorithms have been successful in designing innovative drug-like molecules. SMILES is a sequence-like language used in most effective drug design models. Due to data’s sequential structure, models such as recurrent neural networks and transformers can design pharmacological compounds with optimized efficacy. Large language models have advanced recently, but their implications on drug design have not yet been explored. Although one study successfully pre-trained a large chemistry model (LCM), its application to specific tasks in drug discovery is unknown. In this study, the drug design task is modeled as a causal language modeling problem. Thus, the procedure of reward modeling, supervised fine-tuning, and proximal policy optimization was used to transfer the LCM to drug design, similar to Open AI’s ChatGPT and InstructGPT procedures. By combining the SMILES sequence with chemical descriptors, the novel efficacy evaluation model exceeded its performance compared to previous studies. After proximal policy optimization, the drug design model generated molecules with 99.2% having efficacy pIC50 > 7 towards the amyloid precursor protein, with 100% of the generated molecules being valid and novel. This demonstrated the applicability of LCMs in drug discovery, with benefits including less data consumption while fine-tuning. The applicability of LCMs to drug discovery opens the door for larger studies involving reinforcement-learning with human feedback, where chemists provide feedback to LCMs and generate higher-quality molecules. LCMs’ ability to design similar molecules from datasets paves the way for more accessible, non-patented alternatives to drug molecules.
A generalisable tool path planning strategy for free-form sheet metal stamping through deep reinforcement and supervised learning
Authors: Liu Shiming , Shi Zhusheng , Lin Jianguo , Yu Hui 
Published: April 2024

AbstractDue to the high cost of specially customised presses and dies and the advance of machine learning technology, there is some emerging research attempting free-form sheet metal stamping processes which use several common tools to produce products of various shapes. However, tool path planning strategies for the free forming process, such as reinforcement learning technique, derived from previous path planning experience are not generalisable for an arbitrary new sheet metal workpiece. Thus, in this paper, a generalisable tool path planning strategy is proposed for the first time to realise the tool path prediction for an arbitrary sheet metal part in 2-D space with no metal forming knowledge in prior, through deep reinforcement (implemented with 2 heuristics) and supervised learning technologies. Conferred by deep learning, the tool path planning process is corroborated to have self-learning characteristics. This method has been instantiated and verified by a successful application to a case study, of which the workpiece shape deformed by the predicted tool path has been compared with its target shape. The proposed method significantly improves the generalisation of tool path planning of free-form sheet metal stamping process, compared to strategies using pure reinforcement learning technologies. The successful instantiation of this method also implies the potential of the development of intelligent free-form sheet metal stamping process.
Hybrid intelligence failure analysis for industry 4.0: a literature review and future prospective
Authors: Mokhtarzadeh Mahdi , Rodríguez-Echeverría Jorge , Semanjski Ivana , Gautama Sidharta 
Published: April 2024

AbstractIndustry 4.0 and advanced technology, such as sensors and human–machine cooperation, provide new possibilities for infusing intelligence into failure analysis. Failure analysis is the process of identifying (potential) failures and determining their causes and effects to enhance reliability and manufacturing quality. Proactive methodologies, such as failure mode and effects analysis (FMEA), and reactive methodologies, such as root cause analysis (RCA) and fault tree analysis (FTA), are used to analyze failures before and after their occurrence. This paper focused on failure analysis methodologies intelligentization literature applied to FMEA, RCA, and FTA to provide insights into expert-driven, data-driven, and hybrid intelligence failure analysis advancements. Types of data to establish an intelligence failure analysis, tools to find a failure’s causes and effects, e.g., Bayesian networks, and managerial insights are discussed. This literature review, along with the analyses within it, assists failure and quality analysts in developing effective hybrid intelligence failure analysis methodologies that leverage the strengths of both proactive and reactive methods.
Suppression of Type I Interferon Signaling in Myeloid Cells by Autoantibodies in Severe COVID-19 Patients
Authors: Aoki Ami , Iwamura Chiaki , Kiuchi Masahiro , Tsuji Kaori , Sasaki Atsushi , Hishiya Takahisa , Hirasawa Rui , Kokubo Kota , Kuriyama Sachiko , Onodera Atsushi , Shimada Tadanaga , Nagaoka Tetsutaro , Ishikawa Satoru , Kojima Akira , Mito Haruki , Hase Ryota , Kasahara Yasunori , Kuriyama Naohide , Nakamura Sukeyuki , Urushibara Takashi , Kaneda Satoru , Sakao Seiichiro , Nishida Osamu , Takahashi Kazuhisa , Kimura Motoko Y. , Motohashi Shinichiro , Igari Hidetoshi , Ikehara Yuzuru , Nakajima Hiroshi , Suzuki Takuji , Hanaoka Hideki , Nakada Taka-aki , Kikuchi Toshiaki , Nakayama Toshinori , Yokote Koutaro , Hirahara Kiyoshi 
Published: April 2024

Abstract
Purpose
Auto-antibodies (auto-abs) to type I interferons (IFNs) have been identified in patients with life-threatening coronavirus disease 2019 (COVID-19), suggesting that the presence of auto-abs may be a risk factor for disease severity. We therefore investigated the mechanism underlying COVID-19 exacerbation induced by auto-abs to type I IFNs.

Methods
We evaluated plasma from 123 patients with COVID-19 to measure auto-abs to type I IFNs. We performed single-cell RNA sequencing (scRNA-seq) of peripheral blood mononuclear cells from the patients with auto-abs and conducted epitope mapping of the auto-abs.

Results
Three of 19 severe and 4 of 42 critical COVID-19 patients had neutralizing auto-abs to type I IFNs. Patients with auto-abs to type I IFNs showed no characteristic clinical features. scRNA-seq from 38 patients with COVID-19 revealed that IFN signaling in conventional dendritic cells and canonical monocytes was attenuated, and SARS-CoV-2-specific BCR repertoires were decreased in patients with auto-abs. Furthermore, auto-abs to IFN-α2 from COVID-19 patients with auto-abs recognized characteristic epitopes of IFN-α2, which binds to the receptor.

Conclusion
Auto-abs to type I IFN found in COVID-19 patients inhibited IFN signaling in dendritic cells and monocytes by blocking the binding of type I IFN to its receptor. The failure to properly induce production of an antibody to SARS-CoV-2 may be a causative factor of COVID-19 severity.
Long Time Dynamics of Quasi-linear Hamiltonian Klein–Gordon Equations on the Circle
Authors: Feola Roberto , Giuliani Filippo 
Published: April 2024

AbstractWe consider a class of Hamiltonian Klein–Gordon equations with a quasilinear, quadratic nonlinearity under periodic boundary conditions. For a large set of masses, we provide a precise description of the dynamics for an open set of small initial data of size $$\varepsilon $$
ε
showing that the corresponding solutions remain close to oscillatory motions over a time scale $$\varepsilon ^{{-\frac{9}{4}+\delta }}$$

ε

-

9
4

+
δ

for any $$\delta >0$$

δ
>
0

. The key ingredients of the proof are normal form methods, para-differential calculus and a modified energy approach.
An Implicitly Extended Crank–Nicolson Scheme for the Heat Equation on a Time-Dependent Domain
Authors: Frei Stefan , Singh Maneesh Kumar 
Published: April 2024

AbstractWe consider a time-stepping scheme of Crank–Nicolson type for the heat equation on a moving domain in Eulerian coordinates. As the spatial domain varies between subsequent time steps, an extension of the solution from the previous time step is required. Following Lehrenfeld and Olskanskii (ESAIM: M2AN 53(2):585–614, 2019), we apply an implicit extension based on so-called ghost-penalty terms. For spatial discretisation, a cut finite element method is used. We derive a complete a priori error analysis in space and time, which shows in particular second-order convergence in time under a parabolic CFL condition. Finally, we present numerical results in two and three space dimensions that confirm the analytical estimates, even for much larger time steps.
Reversible jump attack to textual classifiers with modification reduction
Authors: Ni Mingze , Sun Zhensu , Liu Wei 
Published: April 2024

AbstractRecent studies on adversarial examples expose vulnerabilities of natural language processing models. Existing techniques for generating adversarial examples are typically driven by deterministic hierarchical rules that are agnostic to the optimal adversarial examples, a strategy that often results in adversarial samples with a suboptimal balance between magnitudes of changes and attack successes. To this end, in this research we propose two algorithms, Reversible Jump Attack (RJA) and Metropolis–Hasting Modification Reduction (MMR), to generate highly effective adversarial examples and to improve the imperceptibility of the examples, respectively. RJA utilizes a novel randomization mechanism to enlarge the search space and efficiently adapts to a number of perturbed words for adversarial examples. With these generated adversarial examples, MMR applies the Metropolis–Hasting sampler to enhance the imperceptibility of adversarial examples. Extensive experiments demonstrate that RJA-MMR outperforms current state-of-the-art methods in attack performance, imperceptibility, fluency and grammar correctness.
The interplay between membrane viscosity and ligand-binding receptor kinetics in lipid bilayers
Authors: Bernard Chiara , Carotenuto Angelo Rosario , Pugno Nicola Maria , Deseri Luca , Fraldi Massimiliano 
Published: April 2024

AbstractPlasma membranes appear as deformable systems wherein molecules are free to move and diffuse giving rise to condensed microdomains (composed of ordered lipids, transmembrane proteins and cholesterol) surrounded by disordered lipid molecules. Such denser and thicker regions, namely lipid rafts, are important communication hubs for cells. Indeed, recent experiments revealed how the most of active signaling proteins co-localize on such domains, thereby intensifying the biochemical trafficking of substances. From a material standpoint, it is reasonable to assume the bilayer as a visco-elastic body accounting for both in-plane fluidity and elasticity. Consequently, lipid rafts contribute to membrane heterogeneity by typically exhibiting higher stiffness and viscosity and by locally altering the bilayer dynamics and proteins activity. A chemo-mechanical model of lipid bilayer coupled with interspecific dynamics among the resident species (typically transmembrane receptors and trasporters) has been recently formulated to explain and predict how proteins regulate the dynamic heterogeneity of membrane. However, the explicit inclusion of the membrane viscosity in the model was not considered. To this aim, the present work enriches the constitutive description of the bilayer by modeling its visco-elastic behavior. This is done through a strain-level dependent viscosity able to theoretically trace back the alteration of membrane fluidity experimentally observed in lipid phase transitions. This provides new insights into how the quasi-solid and fluid components of lipid membrane response interact with the evolution of resident proteins by affecting the activity of raft domains, with effects on cell mechano-signaling.
Exploring API behaviours through generated examples
Authors: Karlsson Stefan , Hughes John , Jongeling Robbert , Čaušević Adnan , Sundmark Daniel 
Published: April 2024

AbstractUnderstanding the behaviour of a system’s API can be hard. Giving users access to relevant examples of how an API behaves has been shown to make this easier for them. In addition, such examples can be used to verify expected behaviour or identify unwanted behaviours. Methods for automatically generating examples have existed for a long time. However, state-of-the-art methods rely on either white-box information, such as source code, or on formal specifications of the system behaviour. But what if you do not have access to either? This may be the case, for example, when interacting with a third-party API. In this paper, we present an approach to automatically generate relevant examples of behaviours of an API, without requiring either source code or a formal specification of behaviour. Evaluation on an industry-grade REST API shows that our method can produce small and relevant examples that can help engineers to understand the system under exploration.
A Method for Automatic Three-Dimensional Particle Tracing Under Laboratory Conditions Using Dynamic X-Ray Computed Microtomography
Authors: Siebert Judith Marie Undine , Odenbach Stefan 
Published: April 2024

AbstractThis paper presents a method for particle tracing in laboratory X-ray micro-computed tomography (µCT) using an adjusted Random Sample Consensus (RANSAC) algorithm combined with least squares ellipse fitting (LSF). For method testing, a setup for the investigation of deep bed filtration (DBF) has been used as an example of a complex process that can be elucidated with such a method. Particle tracking with tomography systems requires high-temporal resolution which can only be achieved with synchrotron radiation computer tomography. Therefore, in this work, it has been demonstrated that instead of particle tracking, particle tracing in opaque systems such as DBF can be performed in laboratory µCT systems. To achieve particle tracing, dynamic µCT scans with a duration between 30 and 110 s combined with an exposure time of 0.13 s/projection were executed and during the scan time the filtration was performed, causing parabola shaped motion artefacts. The developed method exploits the motion artefacts created by the particle motion during the scan. It could be shown that it is possible to trace particles in complex structures within only one 30 s scan. Furthermore, through trace length and time, it is possible to determine the average velocity. Whereby, the accuracy and limits depend on the particle size, particle velocity/data rate and the X-ray attenuation of particle and medium.