bluuebunny/crossref_metadata_2025_split huggingface dataset:
Terpenoids: Unlocking Their Potential on Cancer Glucose Metabolism
Authors: Muhammad Siti Nur Hasyila , Ramli Redzyque Ramza , Nik Mohamed Kamal Nik Nur Syazni , Fauzi Agustine Nengsih 
Published: December 2024

ABSTRACTCancer incidence has increased globally and has become the leading cause of death in the majority of countries. Many cancers have altered energy metabolism pathways, such as increased glucose uptake and glycolysis, as well as decreased oxidative phosphorylation. This is known as the Warburg effect, where cancer cells become more reliant on glucose to generate energy and produce lactate as an end product, even when oxygen is present. These are attributed to the overexpression of key glycolytic enzymes, glucose transporters, and related signaling pathways that occur in cancer cells. Therefore, overcoming metabolic alterations in cancer cells has recently become a target for therapeutic approaches. Natural products have played a key role in drug discovery, especially for cancer and infectious diseases. In this review, we are going to focus on terpenoids, which are gradually gaining popularity among drug researchers due to their reported anti‐cancer effects via cell cycle arrest, induction of apoptosis, reduction of proliferation, and metastasis. This review summarizes the potential of 13 terpenoid compounds as anti‐glycolytic inhibitors in different cancer models, primarily by inhibiting the glucose uptake and the generation of lactate, as well as by downregulating enzymes associated to glycolysis. As a conclusion, disruption of cancer cell glycolysis may be responsible for the anti‐cancer activity of terpenoids.
A Simple Information Criterion for Variable Selection in High‐Dimensional Regression
Authors: Pluntz Matthieu , Dalmasso Cyril , Tubert‐Bitter Pascale , Ahmed Ismaïl 
Published: December 2024

ABSTRACTHigh‐dimensional regression problems, for example with genomic or drug exposure data, typically involve automated selection of a sparse set of regressors. Penalized regression methods like the LASSO can deliver a family of candidate sparse models. To select one, there are criteria balancing log‐likelihood and model size, the most common being AIC and BIC. These two methods do not take into account the implicit multiple testing performed when selecting variables in a high‐dimensional regression, which makes them too liberal. We propose the extended AIC (EAIC), a new information criterion for sparse model selection in high‐dimensional regressions. It allows for asymptotic FWER control when the candidate regressors are independent. It is based on a simple formula involving model log‐likelihood, model size, the total number of candidate regressors, and the FWER target. In a simulation study over a wide range of linear and logistic regression settings, we assessed the variable selection performance of the EAIC and of other information criteria (including some that also use the number of candidate regressors: mBIC, mAIC, and EBIC) in conjunction with the LASSO. Our method controls the FWER in nearly all settings, in contrast to the AIC and BIC, which produce many false positives. We also illustrate it for the automated signal detection of adverse drug reactions on the French pharmacovigilance spontaneous reporting database.
Linear Mixed Modeling of Federated Data When Only the Mean, Covariance, and Sample Size Are Available
Authors: Limpoco Marie Analiz April , Faes Christel , Hens Niel 
Published: December 2024

ABSTRACTIn medical research, individual‐level patient data provide invaluable information, but the patients' right to confidentiality remains of utmost priority. This poses a huge challenge when estimating statistical models such as a linear mixed model, which is an extension of linear regression models that can account for potential heterogeneity whenever data come from different data providers. Federated learning tackles this hurdle by estimating parameters without retrieving individual‐level data. Instead, iterative communication of parameter estimate updates between the data providers and analysts is required. In this article, we propose an alternative framework to federated learning for fitting linear mixed models. Specifically, our approach only requires the mean, covariance, and sample size of multiple covariates from different data providers once. Using the principle of statistical sufficiency within the likelihood framework as theoretical support, this proposed strategy achieves estimates identical to those derived from actual individual‐level data. We demonstrate this approach through real data on 15 068 patient records from 70 clinics at the Children's Hospital of Pennsylvania. Assuming that each clinic only shares summary statistics once, we model the COVID‐19 polymerase chain reaction test cycle threshold as a function of patient information. Simplicity, communication efficiency, generalisability, and wider scope of implementation in any statistical software distinguish our approach from existing strategies in the literature.
Selectively Self‐Aligned Sol‐Gel Copper Oxide for Large‐Area Multi‐Valued Logic Devices
Authors: Baek Seokhyeon , Kim Wonsik , Lee Won‐June , Choi Jun‐Gyu , Park Sungjun 
Published: December 2024

AbstractRapid expansion of digital information density has led to a growing demand for multi‐valued logic (MVL) systems, which aim to minimize energy and time consumption for computations. Heterojunction transistors represent a class of device architectures for MVL circuits; however, partially layered structures can be realized only for vacuum‐deposited organic and transferred 2D materials due to the constraints of patterning processes. In this study, a novel CuxO/IGZO heterojunction‐based ternary inverter is presented via a sol‐gel technique and direct patterning process using a self‐assembled monolayer (SAM). This approach allows for a promising alternative to conventional photolithography, with the electrical characteristics of SAM‐processed oxide thin‐film transistors closely matching those of pristine devices. Structural investigations validate the partially overlapped heterojunction and its smoothness. Depth profiling with x‐ray photoelectronspectroscopy highlights an oxidation gradient in CuxO, suggesting enhanced hole introduction at the IGZO interface. This mechanism potentially supports efficient hole transport and negative differential transconductance, foundational for MVL systems. Such advancements signify the potential for solution‐processable digital electronics that offer streamlined fabrication at an affordable budget.
Anisotropic Strain Evolution of Layered LiNi0.5Co0.2Mn0.3O2 Cathode in Formation Cycle
Authors: Li Kaili , Chen Weixin , Liu Zhiling , Shi Yuansheng , Qian Guoyu , Lu Xueyi , Wei Bin , Zhang Qinghua , Sun Yang , Lu Xia 
Published: December 2024

AbstractThe formation process plays a key role in developing long life and high energy density materials, during which the phase transition, strain evolution, and structural self‐adaption interact rationally with each other to produce a qualified electrode for Li‐ion batteries (LIBs). Taking the layered LiNi0.5Co0.2Mn0.3O2 cathode as an example, the substantial structural changes, both in the bulk and at the surface are thoroughly documented during the formation cycle. Upon initial charging, different phases emerge continuously, resulting in lattice mismatches, inhomogeneous strain, and microstructural distortions. The irreversible loss of lattice oxygen, combined with the spontaneous cation mixing and the formation of rock‐salt phase at the surface, contributes to the initial capacity loss. As a self‐adjustment process, this initial capacity loss facilitates the development of a robust LixNi0.5Co0.2Mn0.3O2 electrode by relieving internal strain and stabilizing the rippled layered structure, thereby ensuring highly reversible electrochemical behavior in subsequent cycles. These findings lay a solid foundation for the design and optimization of layered cathodes for rechargeable batteries.
Remotely Sequential Activation of Biofunctional MXenes for Spatiotemporally Controlled Photothermal Cancer Therapy Integrated with Multimodal Imaging
Authors: Jia Jing , Zhang Xiaobo , Li Yiran , Wang Tian , An Ying , Yan Xinrong , Liu Bin , Yang Chaoyi , Ju Huangxian 
Published: December 2024

AbstractSpatiotemporally controlled cancer therapy may offer great advantages in precision medicine, but still remains some challenges in programmed sequential release and co‐localization of components at target sites. Herein, a MXene‐based nanoprobe (TCC@M) is meticulously designed by engineering of photodynamically activated CRISPR‐Cas9 and cancer cell membrane‐camouflaged Ti3C2 MXenes for targeting delivery and spatiotemporally controlled gene regulation followed by enhanced photothermal therapy (PTT) via two near‐infrared irradiations. The first irradiation can activate the photosensitizer bound in cancer cells internalized TCC@M to release Cas9 ribonucleoprotein (RNP) by photodynamic effect. The released Cas9 RNP then enters the nuclei directed by the fused nuclear localization sequence in Cas9 to cleave the heat shock protein (HSP) 90α gene, which greatly reduces the expression of HSP90α protein and thus effectively sensitizes cancer cells to heat, leading to enhanced PTT at a mild temperature (<45 °C) risen by Ti₃C₂ MXenes under the second irradiation. Simultaneously, TCC@M can produce fluorescence, photoacoustic, and thermal imaging signals to guide the optimal irradiation timing. The in vivo studies have demonstrated the spatiotemporally selective therapeutic efficacy of the designed TCC@M. This innovative approach presents an effective integration of gene regulation and enhanced PTT, exemplifying a precise cancer treatment strategy.
Quiver algebras and their representations for arbitrary quivers
Authors: Li Wei 
Published: December 2024

Abstract
The quiver Yangians were originally defined for the quiver and superpotential from string theory on general toric Calabi-Yau threefolds, and serve as BPS algebras of these systems. Their characters reproduce the unrefined BPS indices, which are related to classical Donaldson-Thomas (DT) invariants. We generalize this construction in two directions. First, we show that this definition extends to arbitrary quivers with potentials. Second, we explore how one can refine the characters of the quiver Yangian to incorporate the refined BPS indices, which are related to motivic DT invariants. We focus on two main classes of quivers: the BPS quivers of 4D $$ \mathcal{N} $$
N
= 2 theories and the quivers from the knot-quiver correspondence. The entire construction allows for straightforward generalizations to trigonometric, elliptic, and generalized cohomologies.
Anomaly-aware summary statistic from data batches
Authors: Grosso G. 
Published: December 2024

Abstract
Signal-agnostic data exploration based on machine learning could unveil very subtle statistical deviations of collider data from the expected Standard Model of particle physics. The beneficial impact of a large training sample on machine learning solutions motivates the exploration of increasingly large and inclusive samples of acquired data with resource efficient computational methods. In this work we consider the New Physics Learning Machine (NPLM), a multivariate goodness-of-fit test built on the Neyman-Pearson maximum-likelihood-ratio construction, and we address the problem of testing large size samples under computational and storage resource constraints. We propose to perform parallel NPLM routines over batches of the data, and to combine them by locally aggregating over the data-to-reference density ratios learnt by each batch. The resulting data hypothesis defining the likelihood-ratio test is thus shared over the batches, and complies with the assumption that the expected rate of new physical processes is time invariant. We show that this method outperforms the simple sum of the independent tests run over the batches, and can recover, or even surpass, the sensitivity of the single test run over the full data. Beside the significant advantage for the offline application of NPLM to large size samples, the proposed approach offers new prospects toward the use of NPLM to construct anomaly-aware summary statistics in quasi-online data streaming scenarios.
Stabilization of a cyclic network of strings by nodal control
Authors: Gugat Martin 
Published: December 2024

AbstractWe consider a network of coupled strings that contains a cycle. We show that boundary feedback stabilization that uses only data of the state at boundary nodes that are not contained in the cycle is in general impossible. We demonstrate that also with control actuators that act at the interior nodes contained in the cycle with certain standard control laws that use only Neumann control action the situation does not improve and the system is still not stable. We prove that with an actuator that uses also Dirichlet control action and is located within the cycle, exponential stabilization is possible.
The influence of electrode array design, scalar dislocation and insertion technique on postoperative vertigo in CI surgery – a prospective study
Authors: Ketterer Manuel Christoph , Rauch A. K. , Beck R. L. , Jakob T. F. , Fries L. , Aschendorff A. , Arndt S. , Everad F. 
Published: December 2024

Abstract
Objectives
This study aimed to examine the effect of electrode array design, insertion angle, scalar position, and insertion technique on the occurrence of postoperative subjective vertigo following cochlear implant (CI) surgery using questionnaires in conjunction with objective vestibular functional measurements.

Materials and methods
We prospectively evaluated subjective vertigo using the Dizziness Handicap Inventory (DHI). Additionally, we performed videonystagmography, video head-impulse tests, and vestibular-evoked myogenic potentials to assess the objective vestibular function preoperatively, at four weeks and 12 months after CI. These results were compared with those of postoperative imaging using digital volume tomography.

Results
Postoperative vertigo was observed in 2 out of 62 patients (3%). Cochleostomy (n = 8) did not lead to an increase in postoperative vertigo. Functional diagnostics revealed abnormalities in up to 23% of patients without subjective dizziness. In our patient cohort, neither electrode array dislocation nor increasing insertion depth was associated with an increase in postoperative vertigo.

Conclusion
Both postoperative vertigo occurrence and electrode array dislocation rates have significantly decreased due to the optimized atraumatic electrode array design and improved surgical insertion techniques. Neither dislocation nor cochleostomy appeared to induce vertigo but the sample size was too small to draw definitive conclusions.
A novel method based on hybrid deep learning with explainability for olive fruit pest forecasting
Authors: Chacón-Maldonado A. M. , Melgar-García L. , Asencio-Cortés G. , Troncoso A. 
Published: December 2024

Abstract
Predicting the occurrence of crop pests is becoming a crucial task in modern agriculture to facilitate farmers’ decision-making. One of the most significant pests is the olive fruit fly, a public concern because it causes damage that compromises oil quality, increasing acidity and altering its flavor. This paper proposes a hybrid deep learning model to predict the presence of olive flies in crops. This model is based on an autoencoder and an automated deep feed-forward neural network. First, the autoencoder neural network learns a representation of the data and then the automated deep feed-forward neural network automatically determines the best values for the hyperparameters in order to obtain the prediction of the number of flies caught in traps from the dataset generated by the autoencoder. On the other hand, farmers to trust the proposed deep learning models need these models to be explainable. Thus, explainable artificial intelligence techniques are applied to the produced models to interpret the results. Results using a dataset from different sources such as satellite image band data, vegetation indices, and meteorological variables are reported. The performance of the proposed model has been compared with classical benchmark algorithms and a deep learning model recently published in the literature. In addition, the comparison includes the automated deep feed-forward neural network individually to show how the autoencoder network improves the accuracy of predictions.
Chemical environment adaptive learning for optical band gap prediction of doped graphitic carbon nitride nanosheets
Authors: Chen Chen , Xu Enze , Yang Defu , Yan Chenggang , Wei Tao , Chen Hanning , Wei Yong , Chen Minghan 
Published: December 2024

Abstract
This study presents a new machine learning algorithm, named Chemical Environment Graph Neural Network (ChemGNN), designed to accelerate materials property prediction and advance new materials discovery. Graphitic carbon nitride (g-C3N4) and its doped variants have gained significant interest for their potential as optical materials. Accurate prediction of their band gaps is crucial for practical applications; however, traditional quantum simulation methods are computationally expensive and challenging to explore the vast space of possible doped molecular structures. The proposed ChemGNN leverages the learning ability of current graph neural networks (GNNs) to satisfactorily capture the characteristics of atoms' chemical environment underlying complex molecular structures. Our experimental results demonstrate more than 100% improvement in band gap prediction accuracy over existing GNNs on g-C3N4. Furthermore, the general ChemGNN model can precisely foresee band gaps of various doped g-C3N4 structures, making it a valuable tool for performing high-throughput prediction in materials design and development.
The potential of precision diabetology for type 2 diabetes treatment—evidence from a meta-regression for all-cause mortality from large cardiovascular outcome trials
Authors: Kuss Oliver , Roden Michael , Schlesinger Sabrina , Hoyer Annika 
Published: December 2024

Abstract
Aims
Two prerequisites must be met for the precision treatment approach to be beneficial for treated individuals. First, there must be treatment heterogeneity; second, in case of treatment heterogeneity, clinical predictors to identify people who would benefit from one treatment more than from others must be available. There is an established meta-regression approach to assess these two prerequisites that relies on measuring the variability of a clinical outcome after treatment in placebo-controlled randomised trials. We recently applied this approach to the treatment of type 2 diabetes for the clinical outcomes of glycaemic control and body weight and repeat it for the clinical outcome of all-cause mortality.

Methods
We performed a meta-regression analysis using digitalized individual participant information on time to death from 10 large cardiovascular outcome trials (7563 deaths from 99,746 participants) on DPP-4 inhibitors, GLP-1 receptor agonists, and SGLT-2 inhibitors with respect to the variability of all-cause mortality and its potential predictors after treatment.

Results
The adjusted difference in log(SD) values of time to death between the verum and placebo arms was −0.036 (95%-CI: −0.059; −0.013), showing larger variability of time to death in the placebo arms. No clinical predictors were found to explain treatment heterogeneity.

Conclusions
This analysis suggests that the potential of the precision treatment approach in type 2 diabetes is low, at least with regard to improvement of all-cause mortality in population with high cardiovascular risk. This extends our previous findings for the clinical outcomes of glycaemic control and body weight.
A Novel In Silico–Ex Vivo Model for Correlating Coating Transfer to Tissue with Local Drug-Coated Balloon-Vessel Contact Pressures
Authors: Stratakos Efstathios , Tscheuschner Linnea , Vincenzi Lorenzo , Pedrinazzi Edoardo , Sigala Fragiska , D’Andrea Luca , Gastaldi Dario , Berti Francesca , Tzafriri Abraham Rami , Pennati Giancarlo 
Published: December 2024

Abstract
Drug-coated balloons (DCBs) aim to deliver drug-loaded surface coating upon inflation at specific vascular sites, yet the role of inflation pressure remains to be defined. We implement a new approach combining ex vivo stamping experiments with in silico simulations to study acute coating transfer by commercial DCBs. This methodology comprises 3 essential pillars: (I) DCB resin inflation and slicing into cylindrical segments for subsequent stamping onto porcine-excised tissue, (II) Numerical inflation of a full DCB replica in an idealized porcine vessel to predict in vivo interfacial contact pressures (CPs) and subsequent interfacial-level numerical stamping to calculate appropriate benchtop forces that recreate these in vivo CP values, and (III) ex vivo stamping experiments and optical analysis of the stamped surfaces (DCB segment and arterial tissue), using a standard high-resolution camera to visualize coating. High-performance liquid chromatography (HPLC) was employed as a validated assay for quantifying drug in tissue samples post-stamping. HPLC analysis revealed a significant correlation with image processing, confirming the validity of the optical method as a tool to quantify DCB coating. Image and HPLC analysis revealed a statistically significant twofold rise in coating area and drug content to tissue, respectively, when the average CP roughly doubled (0.16–0.35 atm) and a non-statistically significant increase in coating area and drug content with a further rough doubling of average CP (0.35 to 0.75 atm). Imaging of DCB segments pre- and post-stamping showed transfer of partial coating thickness at low CP, contrasting with complete transfer at high CP at the same site. 3D confocal images of DCB surfaces revealed variable thickness in the transferred coating. This study introduces a comprehensive methodology for evaluating the efficacy of commercial DCB coating transfer to arterial tissue—a crucial precursor to drug elution studies—while minimizing the number of DCBs needed and improving variable control and realism.
Predicting Occupational Outcomes for Individuals with ADHD: The Role of Hyperactivity/Impulsivity and Executive Functioning
Authors: Chan Elizabeth S. M. , Langberg Joshua M. 
Published: December 2024

Abstract
Purpose
The present study adds to the literature by evaluating the differential risk of ADHD symptom dimensions and executive functioning (EF; time management, organization, motivation) to key occupational outcomes.

Methods
Participants were adults (N = 100; 51% male) with ADHD working full-time between the ages of 19–30 (M = 26.61, SD = 2.28). Participants reported on their ADHD symptoms and EF as well as on five areas of occupational functioning (income, written warnings, boredom at work, satisfaction with coworkers and supervisors).

Results
Over and above medication status, sex, and age, path analyses revealed hyperactivity/impulsivity and time management were the most consistent predictors of outcomes. Higher levels of each predictor were associated with lower income, more written warnings, greater boredom at work, as well as lower satisfaction with coworkers and supervisors. Executive function difficulties with organization and motivation were risk factors for all evaluated outcomes, except written warnings. Higher levels of inattention symptoms were associated with more written warnings and lower satisfaction with coworkers.

Conclusion
The present study is one of the first to document the differential risks of ADHD symptoms and EF to a comprehensive set of occupational functioning outcomes. Future research is needed to replicate the present findings and expand this line of work to identify factors that may protect against these ADHD-related risks on workplace success.