bluuebunny/crossref_metadata_2025 huggingface dataset:
An intensity-based self-supervised domain adaptation method for intervertebral disc segmentation in magnetic resonance imaging
Authors: Fiorentino Maria Chiara , Villani Francesca Pia , Benito Herce Rafael , González Ballester Miguel Angel , Mancini Adriano , López-Linares Román Karen
Published: July 2024

Background and objective:

Accurate IVD segmentation is crucial for diagnosing and treating spinal conditions. Traditional deep learning methods depend on extensive, annotated datasets, which are hard to acquire. This research proposes an intensity-based self-supervised domain adaptation, using unlabeled multi-domain data to reduce reliance on large annotated datasets.

Methods:

The study introduces an innovative method using intensity-based self-supervised learning for IVD segmentation in MRI scans. This approach is particularly suited for IVD segmentations due to its ability to effectively capture the subtle intensity variations that are characteristic of spinal structures. The model, a dual-task system, simultaneously segments IVDs and predicts intensity transformations. This intensity-focused method has the advantages of being easy to train and computationally light, making it highly practical in diverse clinical settings. Trained on unlabeled data from multiple domains, the model learns domain-invariant features, adeptly handling intensity variations across different MRI devices and protocols.

Results:

Testing on three public datasets showed that this model outperforms baseline models trained on single-domain data. It handles domain shifts and achieves higher accuracy in IVD segmentation.

Conclusions:

This study demonstrates the potential of intensity-based self-supervised domain adaptation for IVD segmentation. It suggests new directions for research in enhancing generalizability across datasets with domain shifts, which can be applied to other medical imaging fields.
A multimodal cancer rehabilitation programme promoting sense of coherence for women treated for female reproductive cancers: a pilot randomised controlled trial
Authors: Chow Ka Ming , Chan Carmen Wing Han , McCarthy Alexandra Leigh , Zhu Jiemin , Choi Kai Chow , Siu Ka Yi , Leung Alice Wai Yi , Nguyen Khanh Thi
Published: July 2024

Purpose

To investigate the feasibility, acceptability, and preliminary effects of a theory-driven multimodal cancer rehabilitation intervention (MCRI) programme among Hong Kong Chinese women treated for female reproductive cancers (FRC).

Methods

A single-blinded randomised controlled trial was conducted in two regional hospitals in Hong Kong involving 35 women treated for FRC. The intervention group (n = 18) received a 12-week MCRI which included 30 modules of app-based health education and three nurse-led individual counselling sessions. The control group (n = 17) received attention from the research nurse through telephone calls. Sense of coherence, health-related quality of life, and cancer-specific distress were measured at baseline (T0), immediately after completion of the intervention (T1) and 12 weeks post-intervention (T2). Twelve intervention completers were interviewed to explore the acceptability of the programme.

Results

Recruitment, consent, and retention rates, counselling session attendance rate, and app usage were satisfactory. The intervention participants reported to have significant improvement in physical well-being at T1 (Cohen’s d effect size (d) = 1.04, 95% CI 0.24, 1.83), sense of coherence (d = 0.76, 95% CI − 0.03, 1.54), and cancer-specific distress (d = 1.03, 95% CI − 1.83, − 0.21) at T2. Interviewed participants acknowledged the benefits of the programme and provided comments for improvement.

Conclusions

The MCRI is found to be feasible and acceptable and may improve their sense of coherence, distress, and physical health. A full-scale trial using a larger and more representative sample is warranted to confirm the effects of the programme.

Implications for Cancer Survivors

Women treated for FRC may be benefited from the MCRI in improving sense of coherence, physical well-being, and distress.

Trial registration

This trial was registered on ISRCTN registry with ID ISRCTN73177277.
Godfather Marketing: offering favors before products
Authors: Guido Gianluigi
Published: July 2024

Disruptive shifts in the current environment are engendering uncertainty, radically changing market relationships and consumers’ priorities. This challenging-the-boundaries article introduces a new marketing paradigm, Godfather Marketing, according to which firms evolve into organizations reminiscent of ‘mafia families,’ yet completely devoid of criminal connotations. Their aim is to deeply fulfill customer needs and desires through favors, not just product sales. This approach requires customers to adhere to a mutual ‘code of honor,’ where merit is rewarded and wrongdoing punished, participating in the firm’s favor exchange network. Through a theoretical approach grounded in historical cultural factors, this article explores firm credibility, favor conditions, reciprocation methods, customer traits, and organizational dynamics. In an era where the quality of information will supersede its quantity, Godfather Marketing offers a distinct perspective, giving marketers a competitive edge for fostering consumer loyalty and local policymakers a potential tool for community governance, within a shared framework of careful controls ensuring the protection of individual freedoms.
A cellular senescence-related signature for predicting prognosis, immunotherapy response, and candidate drugs in patients treated with transarterial chemoembolization (TACE)
Authors: He Ning , Zhao Wenjing , Tian Wenlong , Wu Ying , Xu Jian , Lu Yunyan , Chen Xudong , Zhao Hui
Published: July 2024

Background

Cellular senescence is essential to TME development, progression, and remodeling. Few studies have examined cellular senescence in HCC after TACE. Investigating the relationship between cellular senescence, post-TACE prognosis, the TME, and immune treatment responses is crucial.

Methods

We analyzed the GSE104580 dataset to identify DEGs. A cellular senescence-related signature was developed using LASSO Cox regression in the GSE14520 dataset and validated in the ICGC dataset. High- and low-risk subgroups were compared using GSVA and GSEA. Correlation studies were conducted to explore the relationship between the prognostic model, immune infiltration, immunotherapy response, and drug sensitivity.

Results

A cellular senescence-related signature comprising FOXM1, CDK1, CHEK1, and SERPINE1 was created and validated. High-risk patients showed significantly lower OS than low-risk patients. High-risk patients had carcinogenetic pathways activated, immunosuppressive cells infiltrated, and immunomodulatory genes overexpressed. They also showed higher sensitivity to EPZ004777_1237 and MK-2206_1053 and potential benefits from GSK-3 inhibitor IX, nortriptyline, lestaurtinib, and JNK-9L.

Conclusions

This study constructed a cellular senescence-related signature that could be used to predict HCC patients’ responses to and prognosis after TACE treatment, aiding in the development of personalized treatment plans.
Enhancement of Overlimiting Current in a Three-Dimensional Hierarchical Micro/Nanofluidic System by Non-uniform Compartmentalization
Authors: Park Hyungjoo , Kim Misun , Kang Seunghyun , Kim Taewan , Yoon Sehyuk , Park Jihee , Ha Sungjae , Kim Sung Jae
Published: July 2024

Overlimiting current (OLC) is a non-linear current response that occurs related to an ion concentration polarization (ICP) phenomenon in micro/nanofluidic systems and holds great importance since it represents the rate of selective ion transportation through perm-selective structure. For last two decades, numerous studies of OLC have been reported about understanding the fundamentals of nanoelectrokinetics and enhancing ion transportation through perm-selective membranes. Recent study reported that the alignment of non-uniform microspace near the perm-selective membranes in two-dimensional micro/nanofluidic systems can significantly enhance OLC, i.e., overlimiting conductance (σOLC). This is attributed to recirculation flow induced by combination of unbalanced electroosmosis and induced pressure driven flow among non-uniform microspaces. However, 2D micro/nanofluidic systems have limited practicality due to their small volume and low throughput. Herein, we tested the OLC enhancement using 3D-printed hierarchical micro/nanofluidic systems with respect to the non-uniformity of microspaces. The 3D microspaces were fabricated as a mesh structure using a conventional 3D printer. By comparing current–voltage measurement with each type of mesh, we experimentally confirmed the generation of recirculation flow among non-uniform meshes and ionic current enhancement in 3D hierarchical micro/nanofluidic system. Also, we further investigated the enhancement of overlimiting conductance depending on the mesh pattern. Furthermore, we validated that this effect of microscale non-uniform compartmentalization, both increasing surface area and aligning non-uniform spaces, appears not only at low molar concentration but at high molar concentrations. This demonstration can offer a strategy to design optimal electrochemical systems where a perm-selective ion transportation is crucial.
Biofabrication of TiO2 nanoparticles via Aspergillus niger DS22 supernatant: bioreactor optimization and multi-activity profiling
Authors: Hamid Dalia K. Abd El , Desouky Enayat M. , Ellatif Sawsan Abd , Abed Nermine N. , Mahfouz Amira Y.
Published: July 2024

The study presents a safe and eco-friendly green synthesis of titanium dioxide nanoparticles (TiO2 NPs) using Aspergillus niger DS22 (ON076463.1) cell-free filtrate, focusing on optimizing factors affecting nitrate reductase enzyme production within the framework TiO2 NP biosynthesis. Maximum enzyme activity was accomplished by growing A. niger DS22 in a modified MYGP medium at pH 6, 0.5% peptone, 0.15% yeast extract, 0.25% KNO3, 2% glucose, and 200 rpm for 4 days at 30 °C. Statistical optimization takes place, where a central composite design was employed for testing the reaction variables. The individual and interactive effects of process variables lead to optimal biosynthesis conditions with 10−4 M (K2TiF6) concentration, for 96 h, 28 °C, pH 9, and Ti+4 salt solution:filtrate ratio (10%, v/v). Kinetic conversion rates in 1-L shake flask and 10-L stirred tank bioreactor were calculated and compared. Current findings revealed that the yield coefficient of biomass dry weight (Yx/s) and the yield coefficient of TiO2 NP dry weight (Y pn/s) in the bioreactor exceed those of the shake flask (0.85 g/L and 0.51 g/L; 0.04 g/L and 0.11 g/L, respectively). TiO2 NPs showed anticancer activities with high biocompatibility (at 1000 µg/mL) against MCF-7 and HepG-2 cell lines, with 97.35% and 97.71% cytotoxicity, respectively. TiO2 NPs had a moderate antioxidant activity of 57.8% recorded by DPPH assay. Moreover, TiO2 NPs had anticoagulant activities and decolorization efficiency for methyl orange dye. The current study paves the way for maximizing TiO2 NP production, which can be used in industrial and medical sectors.

Graphical Abstract
Smart city and sustainability indicators: a bibliometric literature review
Authors: da Silva Tomadon Leonardo , do Couto Edivando Vitor , de Vries Walter Timo , Moretto Yara
Published: July 2024

This study delves into the pivotal role that indicators play in designing, assessing, and guiding policies for sustainable urban development. Indicators, encompassing both quantitative and qualitative measures, serve as essential tools in evaluating efforts toward sustainable development, providing a practical and objective means of understanding the complex urban environment. The lack of a robust database is identified as a hindrance to monitoring sustainable development progress, underscoring the importance of comprehensive indicators. The study employs a bibliometric literature review methodology, focusing on smart city and sustainability indicators (SSCI) from 2015 to 2022. A total of 818 articles were narrowed down to 191 through rigorous criteria. The study showcases a growing interest in this field, with the number of articles published experiencing a remarkable 288% increase from 2015 to 2022. China emerges as a focal point, leading in both article production and citations, emphasizing its commitment to sustainable development and smart city initiatives. The keywords "sustainable development", "sustainability" and “urban development” had the most occurrences in text analysis. We found three different clusters with k-means analysis, and the circular economy indicators were the most representative category. In conclusion, the study underscores the holistic vision of SSCI in the current scenario, balancing technology and sustainability to improve urban quality of life while safeguarding the planet. Encouraging further research into integrating resilience-focused indicators and innovative solutions is crucial for enhancing sustainable urban development and informing policy decisions.
ExaAUAC: Arabic Twitter user age prediction corpus based on language and metadata features
Authors: Sadeghi Reyhaneh , Akbari Ahmad , Jaziriyan Mohammad Mehdi
Published: July 2024

Twitter is a rich resource for analyzing the contents of social media and extracting the age groups of users can be beneficial for recommender systems, marketing and advertising. Age detection task is an aspect of demographic information of users. In this study a large-scale corpus of Arabic Twitter users including 181k user profiles with diverse age groups consisting of −18, 18–24, 25–34, 35–49, 50–64, +65 is presented. The corpus is created by four methods: (1) collecting publicly available birthday announcement tweets using the Twitter Search application programming interface, (2) augmenting data, (3) fetching verified accounts, and (4) manual annotation. To have a best age detection model on the presented corpus, different evaluations are tested to find the model with highest accuracy and efficiency. Number of tweets, regression vs. classification, using metadata of users and tweets, using LSTM+CNN model vs. BERT are some parts of examinations done. Presented methodology is based on language and metadata features and final model is fine-tuned with BERT on 70k users and evaluated on 8200 manually annotated users. We show that our best model, compared with LSTM+CNN model and BERT-based similar model yields an improvement of up to 9% in F1-score and increment of 5% in accuracy, respectively. The model achieved macro-averaged F1-score of 44 on six age groups, and F1-score of 58 on three age groups of −25, 25–34, +35. The link of our proposed data is provided here: www.github.com/exaco/ExaAUAC.
Root system of Coffea canephora cv. Robusta genotypes cultivated in the Brazilian Amazon
Authors: Schmidt Raquel , Silva Larícia Olária Emerick , Ferreira Adésio , Rodrigues Weverton Pereira , Vieira Henrique Duarte , Tomaz Marcelo Antonio , Partelli Fábio Luiz
Published: July 2024

The root system distribution of coffee, especially Coffea canephora cv. Robusta, remains poorly characterized. The aim of this study were: (i) to analyze the distribution of the root system in various genotypes of C. canephora cv. Robusta cultivated in the Amazon region; (ii) to evaluate genetic variability based on root traits; and (iii) to test the correlation between root traits, aboveground part, and production capacity. Roots were collected up to a depth of 60 cm and 30 cm away from the stems. The roots were washed, scanned, and processed to quantify root volume (mm3 cm−3), surface area (mm2 cm−3), diameter (mm), and length (mm cm−3). The variations in root systems reveled phenotypic variability among different genotypes of C. canephora cv. Robusta. Most roots were concentrated in the surface layer of the soil (0–30 cm), with LB015 standing out. At a depth of 50–60 cm, AS10, AS4, A106, and GJ08 had more roots than other genotypes, suggesting a promising capacity to avoid drought. There were significant negative correlations between root traits, aboveground part, and production capacity. This study provides valuable information for the selection of genotypes with desirable root traits for different environmental conditions, contributing to the improvement of management practices and breeding programs for Robusta coffee.
NATURE, LANDSCAPE AND IDENTITY IN SILIUS ITALICUS’ ACCOUNT OF THE BATTLE AT THE TREBIA
Authors: Beltramini Luca
Published: July 2024

This article deals with the fight between the river Trebia and Scipio the Elder in Silius Italicus’ Punica (4.525–703), notoriously based on the Homeric battle between Achilles and Scamander (Il. 21.1–382). By means of a close reading of the geographical details of Silius’ account, this article aims at highlighting the peculiar role given to the landscape in this episode. By intertwining well-established epic topoi and historiographical reflections, the poet imbues Italy's landscape with a profound ideological meaning. His depiction of the natural environment thematizes key issues relating to the Second Punic War, such as the disruptive effects of Hannibal's invasion on the bond between Italian communities, the problematic nature of shared Italian identity, and the contagious nature of rebellion.
Assessing the Helium Potential of Variscan Batholiths: Insight From Corsica Island
Authors: Dutoit H. , Donzé F. V. , Cardoso C. , Charroy J. , Riba Pereyra C. , Taillefer A. , Dusséaux C. , Tisserand D. , Monnier L. , Byrdina S. , Pik R. , Truche L.
Published: July 2024

Most of the current helium (He) reserves originate from fortuitous discoveries, mainly made during oil and gas exploration in sedimentary basins. As helium generation depends on U and Th α‐decay, old geological provinces gather key ingredients for high He accumulation. However, numerous He‐rich springs have also been documented in much younger rocks, such as Variscan granites (320–250 Ma). These latter discoveries question the current exploration guidelines and require revisiting some of the longstanding paradigms. Here, is investigated He migration along a major fault rooted in the Corso‐Sardinian batholith (France). Two thermal springs, Caldanelle and Guagno‐Les‐Bains, show significant outgassing activities of crustal sourced He with concentrations up to 1.45 vol% and flow rates of 110 m3 STP 4He/year. Besides He, the gas phase is dominated by N2 (≈98 vol%) and minor CH4. Based on a survey employing multidisciplinary methodologies, it is revealed that (a) Variscan rocks represent efficient 4He source rocks, (b) the main source of He comes from the underlying Eo‐Variscan basement, (c) A deeply rooted fault and dense fractures networks drain the He, (d) the helium loss is limited, (e) faults and fractures may act as partial traps, and finally (f) the presence of an efficient trap could promote a He‐rich reservoir with high flux but low reserves. In that sense, young post‐orogenic granites represent promising helium plays. The geological context in which Caldanelle and Guagno‐Les‐Bains are embedded is ubiquitous in European Variscan batholiths. This case study is therefore intended to serve as a guide for helium exploration and to provide insights into helium behavior within a Variscan geological context.
A Numerical Study of the High Latitudinal Ion‐Neutral Coupling Time Scale Under Disturbed Conditions
Authors: Tan Yusha , Lei Jiuhou , Dang Tong , Wang Wenbin , Zhang Binzheng , Luan Xiaoli , Dou Xiankang
Published: July 2024

When solar wind and interplanetary magnetic field (IMF) disturb, thermospheric winds change accordingly. Among the momentum forces driving high‐latitude thermospheric winds, ion drag is supposed to greatly affect wind variations through ion‐neutral coupling when abrupt and strong changes in ion drifts occur. However, due to the great inertia of thermospheric winds it needs a certain period of time for the wind changes to be prominent both in speed and direction. How long the neutral winds take to change from one steady state to another through the ion‐neutral coupling process is currently still a controversial issue. In this paper, we examine the high latitudinal ion‐neutral coupling time scale based on the Thermosphere Ionosphere Electrodynamics General Circulation Model simulations, which can determine whether wind variations are dominantly driven by ion drag by analyzing the relative contribution of each momentum force. It is found that the spatial variation of ion‐neutral coupling time scale is primarily determined by local electron density, but also varies with neutral density and ion‐neutral collision frequency. Simulations during periods of medium solar activity at ∼250 km altitude show that the ion drag‐dominated region is generally located at the dayside convection inverse boundary and the coupling time scale (e‐folding time) is ∼1 hr when IMF By is the dominant component of the IMF and changes direction. Meanwhile, the southward component of IMF Bz enlarges the ion drag‐dominated region. When IMF Bz is southward with a large magnitude, ion drag‐dominated region is primarily located in the nightside auroral oval with ∼2 hr coupling time scale.
Velocity Dependence of Rate‐And‐State Friction in Granular Fault Gouge and Implications for Slow‐Slip Events
Authors: Mei Cheng , Wang Lu
Published: July 2024

The standard rate‐and‐state friction (RSF) has extensively captured frictional behaviors, but it fails to explain the velocity dependence of frictional stability transition and widespread slow‐slip events (SSEs) in experiments and nature adequately. An alternative microphysical Chen‐Niemeijer‐Spiers (CNS) model can well describe the velocity dependence of frictional behaviors of granular gouges. Using the original CNS model, standard RSF parameters can be quantified microphysically. However, some micro‐parameters are not easy to estimate quantitatively, making it difficult to extrapolate to natural and experimental conditions. Here, we simplify the microphysically‐derived RSF parameters including direct effect a, evolution effect b, and critical slip distance Dc, as well as equivalent values (aeq, beq, and Deq). The simplified friction parameters directly illustrate their velocity dependence, namely the essentially constant a, aeq, and Dc, negatively velocity‐dependent b and beq, as well as varying Deq for different laws. They are roughly consistent with experimental results in various fault gouges. A modified CNS model is further derived from the original CNS model, establishing a direct link between the standard RSF and CNS models. The modified CNS model exhibits virtually identical frictional behaviors to the original CNS, but differs from the standard RSF at large velocity perturbations. Moreover, the linearized stability analysis indicates that the critical stiffness for the modified CNS model is velocity‐dependent. Compared with the standard RSF, the modified CNS model not only explains the velocity dependence of frictional stability transition, but also exhibits a more gradual transition for SSEs with a broader range of stiffness ratios.
Mountain Peatlands and Drought: Carbon Cycling in the Pyrenees Amidst Global Climate Change
Authors: Garisoain Raphael , Jacotot Adrien , Delire Christine , Binet Stéphane , Le Roux Gael , Gascoin Simon , Rosset Thomas , Gogo Sébastien , Granouillac Franck , Payre‐Suc Virginie , Gandois Laure
Published: July 2024

This study provides a multi‐year (2017–2022) Net Ecosystem Carbon Balance (NECB) of a Pyrenean mountainous peatland through the integration of field data, satellite imagery, and statistical modeling. Fluvial organic carbon export was measured at 30 min frequency, while gaseous (CO2 and CH4) exchanges were measured monthly using closed chambers. These measurements were combined with Sentinel‐2 derived chlorophyll index and in situ high frequency (1 hr) measurements of key environmental variables such as air temperature, photosynthetically active radiation, and water table level, to develop hourly gaseous carbon flux models (R2 = 0.69 for GPP, R2 = 0.84 for ER, R2 = 0.59 for CH4). Over the 2017–2022 period, modeled average GPP (610 ± 39 gC.m−2.year−1) and ER (641 ± 59 gC.m−2.year−1) showed that the peatland acted as a weak source of CO2 to the atmosphere, releasing 31 ± 73 gC.m−2.year−1. Considering fluvial carbon export and CH4 exchanges, the loss of carbon from the peatland increased to 55 ± 73 gC.m−2.year−1. Dissolved organic carbon constituted 8%–106% of the NECB. The estimated long‐term organic accumulation rate indicated a steady carbon accumulation rate of 16.4 gC.m−2.year−1, contrasting with the contemporary NECB, suggesting a recent shift in ecosystem functioning from a carbon sink to a source. The study underscores the role of water availability and air temperature through a drought index (DI), in shaping the NECB. The DI correlated significantly with annual carbon gaseous fluxes, except for 2022, marked by an intense drought. During this year the peatland became a large source of carbon (189 gC.m−2.year−1) to the atmosphere.
Effect of transcranial direct current stimulation with concurrent cognitive performance targeting posterior parietal cortex vs prefrontal cortex on working memory in schizophrenia: a randomized clinical trial
Authors: Hou Wenpeng , Zhou Fuchun , Wang Qi , Li Hang , Qin Xiangqin , Ding Yushen , Dong Fang , Bo Qijing , Li Anning , Zhang Liang , Chen Zhenzhu , Wang Zhimin , Li Xianbin , Lee Jimmy , Wang Chuanyue
Published: July 2024

Working memory deficits are linked to irregularities in the dorsolateral prefrontal cortex (DLPFC) and the posterior parietal cortex (PPC) in schizophrenia, effective intervention strategies are lacking. We evaluated the differential efficacy and underlying neuromechanisms of targeting transcranial direct current stimulation (tDCS) at the DLPFC and the PPC with concurrent cognitive performance for working memory in schizophrenia. In a randomized and double-blind clinical trial, sixty clinically stable schizophrenic patients with below-average working memory were randomly assigned to active DLPFC, active PPC, and sham tDCS groups. Two sessions of tDCS during N-back task were delivered daily for five days. The primary outcome was changes in spatial span test scores from baseline to week 1. The secondary outcomes included changes in scores of color delay-estimation task, other cognitive tasks, and mismatch negativity (biomarker of N-methyl-d-aspartate receptor functioning). Compared with the active DLPFC group, the active PPC group demonstrated significantly greater improvement in spatial span test scores (p = 0.008, d = 0.94) and an augmentation in color delay-estimation task capacity at week 1; the latter sustained to week 2. Compared with the sham tDCS group, the active PPC group did not show a significant improvement in spatial span test scores at week 1 and 2; however, significant enhancement was observed in their color delay-estimation task capacity at week 2. Additionally, mismatch negativity amplitude was enhanced, and changes in theta band measures were positively correlated with working memory improvement in the active PPC group, while no such correlations were observed in the active DLPFC group or the sham tDCS group. Our results suggest that tDCS targeting the PPC relative to the DLPFC during concurrent cognitive performance may improve working memory in schizophrenia, meriting further investigation. The improvement in working memory appears to be linked to enhanced N-methyl-d-aspartate receptor functioning.
Long-term severe hypoxia adaptation induces non-canonical EMT and a novel Wilms Tumor 1 (WT1) isoform
Authors: Quenneville Jordan , Feghaly Albert , Tual Margaux , Thomas Kiersten , Major François , Gagnon Etienne
Published: July 2024

The majority of cancer deaths are caused by solid tumors, where the four most prevalent cancers (breast, lung, colorectal and prostate) account for more than 60% of all cases (1). Tumor cell heterogeneity driven by variable cancer microenvironments, such as hypoxia, is a key determinant of therapeutic outcome. We developed a novel culture protocol, termed the Long-Term Hypoxia (LTHY) time course, to recapitulate the gradual development of severe hypoxia seen in vivo to mimic conditions observed in primary tumors. Cells subjected to LTHY underwent a non-canonical epithelial to mesenchymal transition (EMT) based on miRNA and mRNA signatures as well as displayed EMT-like morphological changes. Concomitant to this, we report production of a novel truncated isoform of WT1 transcription factor (tWt1), a non-canonical EMT driver, with expression driven by a yet undescribed intronic promoter through hypoxia-responsive elements (HREs). We further demonstrated that tWt1 initiates translation from an intron-derived start codon, retains proper subcellular localization and DNA binding. A similar tWt1 is also expressed in LTHY-cultured human cancer cell lines as well as primary cancers and predicts long-term patient survival. Our study not only demonstrates the importance of culture conditions that better mimic those observed in primary cancers, especially with regards to hypoxia, but also identifies a novel isoform of WT1 which correlates with poor long-term survival in ovarian cancer.
Lithium-ion battery components are at the nexus of sustainable energy and environmental release of per- and polyfluoroalkyl substances
Authors: Guelfo Jennifer L. , Ferguson P. Lee , Beck Jonathan , Chernick Melissa , Doria-Manzur Alonso , Faught Patrick W. , Flug Thomas , Gray Evan P. , Jayasundara Nishad , Knappe Detlef R. U. , Joyce Abigail S. , Meng Pingping , Shojaei Marzieh
Published: July 2024

Lithium-ion batteries (LiBs) are used globally as a key component of clean and sustainable energy infrastructure, and emerging LiB technologies have incorporated a class of per- and polyfluoroalkyl substances (PFAS) known as bis-perfluoroalkyl sulfonimides (bis-FASIs). PFAS are recognized internationally as recalcitrant contaminants, a subset of which are known to be mobile and toxic, but little is known about environmental impacts of bis-FASIs released during LiB manufacture, use, and disposal. Here we demonstrate that environmental concentrations proximal to manufacturers, ecotoxicity, and treatability of bis-FASIs are comparable to PFAS such as perfluorooctanoic acid that are now prohibited and highly regulated worldwide, and we confirm the clean energy sector as an unrecognized and potentially growing source of international PFAS release. Results underscore that environmental impacts of clean energy infrastructure merit scrutiny to ensure that reduced CO2 emissions are not achieved at the expense of increasing global releases of persistent organic pollutants.
Temporal stratification of amyotrophic lateral sclerosis patients using disease progression patterns
Authors: M. Amaral Daniela , Soares Diogo F. , Gromicho Marta , de Carvalho Mamede , Madeira Sara C. , Tomás Pedro , Aidos Helena
Published: July 2024

Identifying groups of patients with similar disease progression patterns is key to understand disease heterogeneity, guide clinical decisions and improve patient care. In this paper, we propose a data-driven temporal stratification approach, ClusTric, combining triclustering and hierarchical clustering. The proposed approach enables the discovery of complex disease progression patterns not found by univariate temporal analyses. As a case study, we use Amyotrophic Lateral Sclerosis (ALS), a neurodegenerative disease with a non-linear and heterogeneous disease progression. In this context, we applied ClusTric to stratify a hospital-based population (Lisbon ALS Clinic dataset) and validate it in a clinical trial population. The results unravelled four clinically relevant disease progression groups: slow progressors, moderate bulbar and spinal progressors, and fast progressors. We compared ClusTric with a state-of-the-art method, showing its effectiveness in capturing the heterogeneity of ALS disease progression in a lower number of clinically relevant progression groups.
Fluorinated chlorin chromophores for red-light-driven CO2 reduction
Authors: Yang Shuang , Yuan Huiqing , Guo Kai , Wei Zuting , Ming Mei , Yi Jinzhi , Jiang Long , Han Zhiji
Published: July 2024

The utilization of low-energy photons in light-driven reactions is an effective strategy for improving the efficiency of solar energy conversion. In nature, photosynthetic organisms use chlorophylls to harvest the red portion of sunlight, which ultimately drives the reduction of CO2. However, a molecular system that mimics such function is extremely rare in non-noble-metal catalysis. Here we report a series of synthetic fluorinated chlorins as biomimetic chromophores for CO2 reduction, which catalytically produces CO under both 630 nm and 730 nm light irradiation, with turnover numbers of 1790 and 510, respectively. Under appropriate conditions, the system lasts over 240 h and stays active under 1% concentration of CO2. Mechanistic studies reveal that chlorin and chlorinphlorin are two key intermediates in red-light-driven CO2 reduction, while corresponding porphyrin and bacteriochlorin are much less active forms of chromophores.
Plasma metabolomics reveals the shared and distinct metabolic disturbances associated with cardiovascular events in coronary artery disease
Authors: Lv Jiali , Pan Chang , Cai Yuping , Han Xinyue , Wang Cheng , Ma Jingjing , Pang Jiaojiao , Xu Feng , Wu Shuo , Kou Tianzhang , Ren Fandong , Zhu Zheng-Jiang , Zhang Tao , Wang Jiali , Chen Yuguo
Published: July 2024

Risk prediction for subsequent cardiovascular events remains an unmet clinical issue in patients with coronary artery disease. We aimed to investigate prognostic metabolic biomarkers by considering both shared and distinct metabolic disturbance associated with the composite and individual cardiovascular events. Here, we conducted an untargeted metabolomics analysis for 333 incident cardiovascular events and 333 matched controls. The cardiovascular events were designated as cardiovascular death, myocardial infarction/stroke and heart failure. A total of 23 shared differential metabolites were associated with the composite of cardiovascular events. The majority were middle and long chain acylcarnitines. Distinct metabolic patterns for individual events were revealed, and glycerophospholipids alteration was specific to heart failure. Notably, the addition of metabolites to clinical markers significantly improved heart failure risk prediction. This study highlights the potential significance of plasma metabolites on tailed risk assessment of cardiovascular events, and strengthens the understanding of the heterogenic mechanisms across different events.
Environment random interaction of rime optimization with Nelder-Mead simplex for parameter estimation of photovoltaic models
Authors: Shi Jinge , Chen Yi , Heidari Ali Asghar , Cai Zhennao , Chen Huiling , Chen Yipeng , Liang Guoxi
Published: July 2024

As countries attach importance to environmental protection, clean energy has become a hot topic. Among them, solar energy, as one of the efficient and easily accessible clean energy sources, has received widespread attention. An essential component in converting solar energy into electricity are solar cells. However, a major optimization difficulty remains in precisely and effectively calculating the parameters of photovoltaic (PV) models. In this regard, this study introduces an improved rime optimization algorithm (RIME), namely ERINMRIME, which integrates the Nelder-Mead simplex (NMs) with the environment random interaction (ERI) strategy. In the later phases of ERINMRIME, the ERI strategy serves as a complementary mechanism for augmenting the solution space exploration ability of the agent. By facilitating external interactions, this method improves the algorithm’s efficacy in conducting a global search by keeping it from becoming stuck in local optima. Moreover, by incorporating NMs, ERINMRIME enhances its ability to do local searches, leading to improved space exploration. To evaluate ERINMRIME's optimization performance on PV models, this study conducted experiments on four different models: the single diode model (SDM), the double diode model (DDM), the three-diode model (TDM), and the photovoltaic (PV) module model. The experimental results show that ERINMRIME reduces root mean square error for SDM, DDM, TDM, and PV module models by 46.23%, 59.32%, 61.49%, and 23.95%, respectively, compared with the original RIME. Furthermore, this study compared ERINMRIME with nine improved classical algorithms. The results show that ERINMRIME is a remarkable competitor. Ultimately, this study evaluated the performance of ERINMRIME across three distinct commercial PV models, while considering varying irradiation and temperature conditions. The performance of ERINMRIME is superior to existing similar algorithms in different irradiation and temperature conditions. Therefore, ERINMRIME is an algorithm with great potential in identifying and recognizing unknown parameters of PV models.
Complete realization of energy landscapes and non-equilibrium trapping dynamics in small spin glass and optimization problems
Authors: Po Ho Fai , Yeung Chi Ho
Published: July 2024

Energy landscapes are high-dimensional surfaces underlie all physical systems, which determine crucially the energetic and behavioral dependence of the systems on variable configurations, but are difficult to be analyzed due to their high-dimensional nature. Here we introduce an approach to reveal for the complete energy landscapes of spin glasses and Boolean satisfiability problems with a small system size, and unravels their non-equilibrium dynamics at an arbitrary temperature for an arbitrarily long time. Remarkably, our results show that it can be less likely for the system to attain ground states when temperature decreases, due to trapping in individual local minima, which ceases at a different time, leading to multiple abrupt jumps in the ground-state probability. For large systems, we introduce a variant approach to extract partially the energy landscapes and observe both semi-analytically and in simulations similar phenomena. This work introduces new methodology to unravel the energy landscapes and non-equilibrium dynamics of glassy systems, and provides us with a clear, complete and new physical picture on their long-time behaviors inaccessible by existing approaches.
Assessing the impacts of vaccination and viral evolution in contact networks
Authors: Blanco-Rodríguez Rodolfo , Tetteh Josephine N. A. , Hernández-Vargas Esteban
Published: July 2024

A key lesson learned with COVID-19 is that public health measures were very different from country to country. In this study, we provide an analysis of epidemic dynamics using three well-known stochastic network models—small-world networks (Watts–Strogatz), random networks (Erdös–Rényi), and scale-free networks (Barabási–Albert)—to assess the impact of different viral strains, lockdown strategies, and vaccination campaigns. We highlight the significant role of highly connected nodes in the spread of infections, particularly within Barabási–Albert networks. These networks experienced earlier and higher peaks in infection rates, but ultimately had the lowest total number of infections, indicating their rapid transmission dynamics. We also found that intermittent lockdown strategies, particularly those with 7-day intervals, effectively reduce the total number of infections, serving as viable alternatives to prolonged continuous lockdowns. When simulating vaccination campaigns, we observed a bimodal distribution leading to two distinct outcomes: pandemic contraction and pandemic expansion. For WS and ER networks, rapid mass vaccination campaigns significantly reduced infection rates compared to slower campaigns; however, for BA networks, differences between vaccination strategies were minimal. To account for the evolution of a virus into a more transmissible strain, we modeled vaccination scenarios that varied vaccine efficacy against the wild-type virus and noted a decline in this efficacy over time against a second variant. Our results showed that vaccination coverage above 40% significantly flattened infection peaks for the wild-type virus, while at least 80% coverage was required to similarly reduce peaks for variant 2. Furthermore, the effect of vaccine efficacy on reducing the peak of variant 2 infection was minimal. Although vaccination strategies targeting hub nodes in scale-free networks did not substantially reduce the total number of infections, they were effective in increasing the probability of preventing pandemic outbreaks. These findings underscore the need to consider the network structure for effective pandemic control.
Effect of electromigration on microstructure and properties of CeO2 nanopartical-reinforced Sn58Bi/Cu solder joints
Authors: Chen Weiming , Zhang Keke , Fan Yuchun , Zhang Chao , Wang Nannan
Published: July 2024

To mitigate the decrease in mechanical performance of Sn58Bi/Cu solder joints resulting from electromigration-induced damage. The CeO2 nanoparticles were incorporated into Sn58Bi solder by a melt-casting method, and their effects on the microstructure and properties of Sn58Bi/Cu solder joints under electromigration were investigated. The study results demonstrate that the addition of 0.125 ~ 0.5 wt% CeO2 nanoparticles refines the eutectic microstructure of Sn58Bi solder alloy. At an addition amount of 0.5 wt%, the composite solder alloy exhibits the maximum tensile strength of 68.9 MPa, which is 37% higher than that of the base solder. CeO2 nanoparticle-reinforced Sn58Bi solder can achieve excellent solderbility with Cu substrates and the joints can significantly inhibit the growth of the anodic Bi-rich layer, which is responsible for electromigration. With the extension of current stressing time, Bi-rich and Sn-rich layer are respectively formed on the anode and cathode in the joints. The intermetallic compound (IMC) layer grows asymmetrically, transitioning from a fan-shaped morphology to a flattened structure at the anode and to a thickened mountain-like morphology at the cathode. Adding the CeO2 nanoparticles helps to mitigate the decrease in mechanical performance caused by electromigration damage during current application to some extent. Over the current stressing period of 288 ~ 480 h, the fracture position shifts from the anodic IMC/Bi-rich interface to the cathodic Sn-rich/IMC interface. The fracture mechanism transitions from a brittle fracture characterized by plate-like cleavage to a ductile–brittle mixed fracture with fine dimples and cleavage.
Refining silicon nitride waveguide quality through femtosecond laser annealing
Authors: Wang Pei-Hsun , Chen Chien-Hung , Hou Nien-Lin , Cao Jia-Hao , Zheng He-Yuan , Chen Hung-Wen
Published: July 2024

We present a method for modification of silicon nitride (Si3N4) waveguide resonators using femtosecond laser annealing. The quality (Q) factor of the waveguide resonators can be improved by approximately 1.3 times after annealing. Notably, waveguides that originally had a high Q value maintained their quality after the annealing process. However, those with a lower initial Q value experienced a noticeable improvement post-annealing. To characterize the annealing effect, the surface morphologies of Si3N4 films, both pre- and post-annealing, were analyzed using atomic force microscopy. The findings suggest a potential enhancement in surface refinement. Furthermore, Raman spectroscopy confirmed that the Si3N4 film's composition remains largely consistent with its original state within the annealing power range of 0.6–1.6 W. This research underscores the potential of femtosecond laser annealing as an efficient, cost-effective, and localized technique for fabricating low-loss integrated photonics.
Predictive modeling of lower extreme deep vein thrombosis following radical gastrectomy for gastric cancer: based on multiple machine learning methods
Authors: Zhou Haiyan , Jin Yongyan , Chen Guofeng , Jin Xiaoli , Chen Jian , Wang Jun
Published: July 2024

Postoperative venous thromboembolic events (VTEs), such as lower extremity deep vein thrombosis (DVT), are major risk factors for gastric cancer (GC) patients following radical gastrectomy. Accurately predicting and managing these risks is crucial for optimal patient care. This retrospective case‒control study involved 693 GC patients from our hospital who underwent radical gastrectomy. We collected plentiful and comprehensive clinical indicators including a total of 49 baseline, preoperative, surgical and pathological clinical data. Using univariate logistic regression, we identified potential risk factors, followed by feature selection through the Boruta algorithm. We then constructed the final predictive model using multivariate logistic regression and evaluated it using receiver operating characteristic (ROC) curve analysis, calibration plots, decision curve analysis, and other methods. Additionally, we applied various machine learning techniques, including decision trees and random forests, to assess our model’s predictive strength. This retrospective case‒control study involved 693 GC patients from our hospital who underwent radical gastrectomy. We collected plentiful and comprehensive clinical indicators including a total of 49 baseline, preoperative, surgical and pathological clinical data. Using univariate logistic regression, we identified potential risk factors, followed by feature selection through the Boruta algorithm. We then constructed the final predictive model using multivariate logistic regression and evaluated it using receiver operating characteristic (ROC) curve analysis, calibration plots, decision curve analysis, and other methods. Additionally, we applied various machine learning techniques, including decision trees and random forests, to assess our model’s predictive strength. Univariate logistic analysis revealed 14 risk factors associated with postoperative lower limb DVT. Based on the Boruta algorithm, six significant clinical factors were selected, namely, age, D-dimer (D-D) level, low-density lipoprotein, CA125, and calcium and chloride ion levels. A nomogram was developed using the outcomes from the multivariate logistic regression analysis. The predictive model showed high accuracy, with an area under the curve of 0.936 in the training set and 0.875 in the validation set. Various machine learning algorithms confirmed its strong predictive capacity. MR analysis revealed meaningful causal relationships between key clinical factors and DVT risk. Based on various machine learning methods, we developed an effective predictive diagnostic model for postoperative lower extremity DVT in GC patients. This model demonstrated excellent predictive value in both the training and validation sets. This novel model is a valuable tool for clinicians to use in identifying and managing thrombotic risks in this patient population.