bluuebunny/crossref_metadata_2025 huggingface dataset:
Electric field simulation and appropriate electrode positioning for optimized transcranial direct current stimulation of stroke patients: an in Silico model
Authors: Yoon Mi-Jeong , Park Hye Jung , Yoo Yeun Jie , Oh Hyun Mi , Im Sun , Kim Tae-Woo , Lim Seong Hoon
Published: February 2024

Transcranial Direct Current Stimulation (tDCS) has benefits for motor rehabilitation in stroke patients, but its clinical application is limited due to inter-individual heterogeneous effects. Recently, optimized tDCS that considers individual brain structure has been proposed, but the utility thereof has not been studied in detail. We explored whether optimized tDCS provides unique electrode positions for each patient and creates a higher target electric field than the conventional approach. A comparative within-subject simulation study was conducted using data collected for a randomized controlled study evaluating the effect of optimized tDCS on upper extremity function in stroke patients. Using Neurophet tES LAB 3.0 software, individual brain models were created based on magnetic resonance images and tDCS simulations were performed for each of the conventional and optimized configurations. A comparison of electrode positions between conventional tDCS and optimized tDCS was quantified by calculation of Euclidean distances. A total of 21 stroke patients were studied. Optimized tDCS produced a higher electric field in the hand motor region than conventional tDCS, with an average improvement of 20% and a maximum of 52%. The electrode montage for optimized tDCS was unique to each patient and exhibited various configurations that differed from electrode placement of conventional tDCS. Optimized tDCS afforded a higher electric field in the target of a stroke patient compared to conventional tDCS, which was made possible by appropriately positioning the electrodes. Our findings may encourage further trials on optimized tDCS for motor rehabilitation after stroke.
Intersectional strategy to study cortical inhibitory parvalbumin-expressing interneurons
Authors: Palicz Rebeka , Pater Bettina , Truschow Pavel , Witte Mirko , Staiger Jochen F.
Published: February 2024

Parvalbumin-expressing (PV) interneurons are key neuronal elements to a global excitatory-inhibitory balance in normal cortical functioning. To better understand the circuit functions of PV interneurons, reliable animal models are needed. This study investigated the sensitivity and specificity of the most frequently used PV-Cre/tdTomato mouse line in this regard. The colocalization of the transgene (tdTomato) with the parvalbumin protein, with GAD1 (a conclusive inhibitory cell marker) and Vglut1 (a conclusive excitatory cell marker) as well as with a marker for perineuronal nets (WFA) was assessed and a substantial proportion of layer 5 PV neurons was found to be excitatory and not inhibitory in the PV-Cre/tdTomato mouse. The intersectional transgenic mouse line Vgat-Cre/PV-Flp/tdTomato provided a solution, since no colocalization of tdTomato with the Vglut1 probe was found there. In conclusion, the Vgat-Cre/PV-Flp/tdTomato mouse line seems to be a more reliable animal model for functional studies of GABAergic PV interneurons.
Exposure to arsenic and other potentially toxic elements: health risk assessment and source analysis in the Wuming Basin, Guangxi Province, China
Authors: Hu Bo , Li Jie , Liu Rui , Lei Guoxin , Wang Xinyu , Wang Lei
Published: February 2024

Guangxi, China, is one of the world's largest karst regions where potential toxic elements tend to accumulate, resulting in high soil background values. This study explores the ecological risk, elemental baseline values, and sources of potential toxic elements in karst regions, expanding the research to include 21 common elements. The significance of this research lies in its implications for the management of potential toxic element pollution, the formulation of environmental quality standards, and soil remediation in karst areas. In this study, 12,547 topsoil samples (0–20 cm) were collected in the study area. Pollution assessment and ecological risk evaluation of eight potential toxic elements (Zn, Ni, Cu, Pb, Cd, Hg, Cr, and As) were conducted using the geo-accumulation index method and potential ecological risk index method. Multivariate statistical analysis was applied to analyze the total content of 21 common elements (Zn, Ni, Cu, Pb, P, Cd, Hg, Co, Mn, Cr, V, I, S, As, pH, Se, N, CaO, Corg, Mo, and F). Additionally, the potential sources of 21 soil elements were preliminarily quantitatively analyzed using the principal component analysis-absolute principal component scores-multiple linear regression receptor model. The results showed that (1) Zn, Ni, Cu, Pb, Cd, Cr, V, and As were enriched in the research area and Ca, Cd, Mn, Mo, Hg, As, and Cu might have been influenced by human activities; (2) Cr, Pb, As, and Zn were generally lightly polluted, with Hg having a moderate potential ecological risk level; and (3) Ni and Zn have contributions of 37.99% and 35.07% from geological sources, agricultural fertilization, and pesticides. Mo, V, Cr, Se, Hg, and As exhibit contributions ranging from 39.44 to 59.22% originating from geological backgrounds and human activities. Corg, S, N, and P show contributions of 45.39% to 80.33% from surface vegetation. F, Co, Mn, and Pb have contributions ranging from 31.63 to 47.93% from acidic rocks in the soil parent material, mining activities, and transportation. Cd and CaO derive 31.67% and 40.23%, respectively, from soil parent material and industrial sources. I has 31.94% from geological background and human activities, and 31.95% from soil parent material and atmospheric sources. Cu has 30.56% from geological sources. The study results can serve as a scientific basis for element research in karst areas domestically and internationally.
Prognosis prediction and risk stratification of breast cancer patients based on a mitochondria-related gene signature
Authors: Wang Yang , Wang Ding-yuan , Bu Ke-na , Gao Ji-dong , Zhang Bai-lin
Published: February 2024

As the malignancy with the highest global incidence, breast cancer represents a significant threat to women’s health. Recent advances have shed light on the importance of mitochondrial function in cancer, particularly in metabolic reprogramming within tumors. Recognizing this, we developed a novel risk signature based on mitochondrial-related genes to improve prognosis prediction and risk stratification in breast cancer patients. In this study, transcriptome data and clinical features of breast cancer samples were extracted from two sources: the TCGA, serving as the training set, and the METABRIC, used as the independent validation set. We developed the signature using LASSO-Cox regression and assessed its prognostic efficacy via ROC curves. Furthermore, the signature was integrated with clinical features to create a Nomogram model, whose accuracy was validated through clinical calibration curves and decision curve analysis. To further elucidate prognostic variations between high and low-risk groups, we conducted functional enrichment and immune infiltration analyses. Additionally, the study encompassed a comparison of mutation landscapes and drug sensitivity, providing a comprehensive understanding of the differing characteristics in these groups. Conclusively, we established a risk signature comprising 8 mitochondrial-related genes—ACSL1, ALDH2, MTHFD2, MRPL13, TP53AIP1, SLC1A1, ME3, and BCL2A1. This signature was identified as an independent risk predictor for breast cancer patient survival, exhibiting a significant high hazard ratio (HR = 3.028, 95%CI 2.038–4.499, P < 0.001). Patients in the low-risk group showed a more favorable prognosis, with enhanced immune infiltration, distinct mutation landscapes, and greater sensitivity to anti-tumor drugs. In contrast, the high-risk group exhibited an adverse trend in these aspects. This risk signature represents a novel and effective prognostic indicator, suggesting valuable insights for patient stratification in breast cancer.
Mechanical activation and expression of HSP27 in epithelial ovarian cancer
Authors: Buckley Molly , Kramer Maranda , Johnson Bronte , Huskin Gillian , Berry Joel , Sewell-Loftin Mary Kathryn
Published: February 2024

Understanding the complex biomechanical tumor microenvironment (TME) is of critical importance in developing the next generation of anti-cancer treatment strategies. This is especially true in epithelial ovarian cancer (EOC), the deadliest of the gynecologic cancers due to recurrent disease or chemoresistance. However, current models of EOC progression provide little control or ability to monitor how changes in biomechanical parameters alter EOC cell behaviors. In this study, we present a microfluidic device designed to permit biomechanical investigations of the ovarian TME. Using this microtissue system, we describe how biomechanical stimulation in the form of tensile strains upregulate phosphorylation of HSP27, a heat shock protein implicated in ovarian cancer chemoresistance. Furthermore, EOC cells treated with strain demonstrate decreased response to paclitaxel in the in vitro vascularized TME model. The results provide a direct link to biomechanical regulation of HSP27 as a mediator of EOC chemoresistance, possibly explaining the failure of such therapies in some patients. The work presented here lays a foundation to elucidating mechanobiological regulation of EOC progression, including chemoresistance and could provide novel targets for anti-cancer therapeutics.
Alterations in colorectal cancer virome and its persistence after surgery
Authors: Ho Si Xian , Law Jia-Hao , Png Chin-Wen , Alberts Rudi , Zhang Yongliang , Chu Justin Jang Hann , Tan Ker-Kan
Published: February 2024

Viruses are a key component of the colon microbiome, but the relationship between virome and colorectal cancer (CRC) remains poorly understood. We seek to identify alterations in the viral community that is characteristic of CRC and examine if they persist after surgery. Forty-nine fecal samples from 25 non-cancer (NC) individuals and 12 CRC patients, before and 6-months after surgery, were collected for metagenomic analysis. The fecal virome of CRC patients demonstrated an increased network connectivity as compared to NC individuals. Co-exclusion of influential viruses to bacterial species associated with healthy gut status was observed in CRC, suggesting an altered virome induced a change in the healthy gut bacteriome. Network analysis revealed lower connectivity within the virome and trans-kingdom interactions in NC. After surgery, the number of strong correlations decreased for trans-kingdom and within the bacteria and virome networks, indicating lower connectivity within the microbiome. Some co-occurrence patterns between dominant viruses and bacteria were also lost after surgery, suggesting a possible return to the healthy state of gut microbiome. Microbial signatures characteristic of CRC include an altered virome besides an altered bacterial composition. Elevated viral correlations and network connectivity were observed in CRC patients relative to healthy individuals, alongside distinct changes in the cross-kingdom correlation network unique to CRC patients. Some patterns of dysbiosis persist after surgery. Future studies should seek to verify if dysbiosis truly persists after surgery in a larger sample size with microbiome data collected at various time points after surgery to explore if there is field-change in the remaining colon, as well as to examine if persistent dysbiosis correlates with patient outcomes.
Effect of using nano-particles of magnesium oxide and titanium dioxide to enhance physical and mechanical properties of hip joint bone cement
Authors: Gamal Safaa , Mikhail Mina , Salem Nancy , El-Wakad Mohamed Tarek , Abdelbaset Reda
Published: February 2024

In this work, the effect of adding Magnesium Oxide (MgO) and Titanium Dioxide (TiO2) nanoparticles to enhance the properties of the bone cement used for hip prosthesis fixation. Related to previous work on enhanced bone cement properties utilizing MgO and TiO2, samples of composite bone cement were made using three different ratios (0.5%:1%, 1.5%:1.5%, and 1%:0.5%) w/w of MgO and TiO2 to determine the optimal enhancement ratio. Hardness, compression, and bending tests were calculated to check the mechanical properties of pure and composite bone cement. The surface structure was studied using Fourier transform infrared spectroscopy (FTIR) and Field emission scanning electron microscopy (FE-SEM). Setting temperature, porosity, and degradation were calculated for each specimen ratio to check values matched with the standard range of bone cement. The results demonstrate a slight decrease in porosity up to 2.2% and degradation up to 0.17% with NP-containing composites, as well as acceptable variations in FTIR and setting temperature. The compression strength increased by 2.8% and hardness strength increased by 1.89% on adding 0.5%w/w of MgO and 1.5%w/w TiO2 NPs. Bending strength increases by 0.35% on adding 1.5% w/w of MgO and 0.5% w/w TiO2 NPs, however, SEM scan shows remarkable improvement for surface structure.
In-situ multicore fibre-based pH mapping through obstacles in integrated microfluidic devices
Authors: Chandrasekharan Harikumar K. , Wlodarczyk Krystian L. , MacPherson William N. , Maroto-Valer M. Mercedes
Published: February 2024

Microfluidic systems with integrated sensors are ideal platforms to study and emulate processes such as complex multiphase flow and reactive transport in porous media, numerical modeling of bulk systems in medicine, and in engineering. Existing commercial optical fibre sensing systems used in integrated microfluidic devices are based on single-core fibres, limiting the spatial resolution in parameter measurements in such application scenarios. Here, we propose a multicore fibre-based pH system for in-situ pH mapping with tens of micrometer spatial resolution in microfluidic devices. The demonstration uses custom laser-manufactured glass microfluidic devices (called further micromodels) consisting of two round ports. The micromodels comprise two lintels for the injection of various pH buffers and an outlet. The two-port system facilitates the injection of various pH solutions using independent pressure pumps. The multicore fibre imaging system provides spatial information about the pH environment from the intensity distribution of fluorescence emission from the sensor attached to the fibre end facet, making use of the cores in the fibre as independent measurement channels. As proof-of-concept, we performed pH measurements in micromodels through obstacles (glass and rock beads), showing that the particle features can be clearly distinguishable from the intensity distribution from the fibre sensor.
The impact of COVID-19 on the diagnosis and treatment of HCC: analysis of a nationwide registry for advanced liver diseases (REAL)
Authors: Okushin Kazuya , Tateishi Ryosuke , Hirakawa Shinya , Tachimori Hisateru , Uchino Koji , Nakagomi Ryo , Yamada Tomoharu , Nakatsuka Takuma , Minami Tatsuya , Sato Masaya , Fujishiro Mitsuhiro , Hasegawa Kiyoshi , Eguchi Yuichiro , Kanto Tatsuya , Yoshiji Hitoshi , Izumi Namiki , Kudo Masatoshi , Koike Kazuhiko
Published: February 2024

The number of cancer cases diagnosed during the coronavirus disease 2019 (COVID-19) pandemic has decreased. This study investigated the impact of the pandemic on the clinical practice of hepatocellular carcinoma (HCC) using a novel nationwide REgistry for Advanced Liver diseases (REAL) in Japan. We retrieved data of patients initially diagnosed with HCC between January 2018 and December 2021. We adopted tumor size as the primary outcome measure and compared it between the pre-COVID-19 (2018 and 2019) and COVID-19 eras (2020 and 2021). We analyzed 13,777 patients initially diagnosed with HCC (8074 in the pre-COVID-19 era and 5703 in the COVID-19 era). The size of the maximal intrahepatic tumor did not change between the two periods (mean [SD] = 4.3 [3.6] cm and 4.4 [3.6] cm), whereas the proportion of patients with a single tumor increased slightly from 72.0 to 74.3%. HCC was diagnosed at a similar Barcelona Clinic Liver Cancer stage. However, the proportion of patients treated with systemic therapy has increased from 5.4 to 8.9%. The proportion of patients with a non-viral etiology significantly increased from 55.3 to 60.4%. Although the tumor size was significantly different among the etiologies, the subgroup analysis showed that the tumor size did not change after stratification by etiology. In conclusion, the characteristics of initially diagnosed HCC remained unchanged during the COVID-19 pandemic in Japan, regardless of differences in etiology. A robust surveillance system should be established particularly for non-B, non-C etiology to detect HCC in earlier stages.
Interactive curriculum learning increases and homogenizes motor smoothness
Authors: Sungeelee Vaynee , Loriette Antoine , Sigaud Olivier , Caramiaux Baptiste
Published: February 2024

One of the challenges of technology-assisted motor learning is how to adapt practice to facilitate learning. Random practice has been shown to promote long-term learning. However, it does not adapt to the learner’s specific learning requirements. Previous attempts to adapt learning considered the skill level of learners from past training sessions. This study investigates the effects of personalizing practice in real time, through a curriculum learning approach, where a curriculum of tasks is built by considering consecutive performance differences for each task. 12 participants were allocated to each of three training conditions in an experiment which required performing a steering task to drive a cursor in an arc channel. The curriculum learning approach was compared to two other conditions: random practice and another adaptive practice, which does not consider the learning evolution. The curriculum learning practice outperformed the random practice in effectively increasing movement smoothness at post-test and outperformed both the random practice and the adaptive practice on transfer tests. The adaptation of practice through the curriculum learning approach also made learners’ skills more uniform. Based on these findings, we anticipate that future research will explore the use of curriculum learning in interactive training tools to support motor skill learning, such as rehabilitation.
Digital health interventions to promote healthy lifestyle in hemodialysis patients: an interventional pilot study
Authors: Li Wen-Yi , Yeh Jiang-Chou , Cheng Cheng-Chih , Huang Su-Hua , Yeh Hui-Chin , Cheng Bor-Wen , Lin Jou-Wei , Yang Feng-Jung
Published: February 2024

Low physical activity has been associated with poor prognosis in hemodialysis (HD) patients. Interventions to maintain healthy lifestyle in this population are important to reduce mortality. This study aimed to evaluate the effectiveness of digital health interventions (DHIs) for improving the physical activity and health-related quality of life (HRQoL) in HD patients. The 24-week prospective study enrolled 31 clinically stable HD patients. All participants were assigned home exercises and provided with wearable devices. Dietary and exercise information was uploaded to a health management platform. Suggestions about diet and exercise were provided, and a social media group was created. Physical performance testing was performed at baseline and during weeks 4, 8, 12, 16 and 24. HRQoL and nutritional status were evaluated. A total of 25 participants completed the study. After the interventions, the daily step count increased 1658 steps. The 10-time-repeated sit-to-stand test reduced by 4.4 s, the sit-to-stand transfers in 60 s increased 12 repetitions, the distance of six-minute walk test (6MWT) increased by 55.4 m. The mental health components and burden of kidney disease of the Kidney Disease Quality of Life survey, and subjective global assessment (SGA) scores improved. By Spearman correlation, the monthly step count correlated positively with 6MWT and SGA. DHIs that combined wearable devices, a health management platform, and social media could strengthen physical activity and improve the HRQoL and nutrition of maintenance HD patients. The results outline a new model to promote healthy lifestyle behaviors in HD patients.
Exploring the status and associated factors of the readiness for return-to-work in young and middle-aged stroke patients
Authors: Liu Ziwei , Li Jiaxin , Liu Fangli , Guan Ningxiao , Li Ye , Zhang Yu , Hou Linlin , Jiang Qiuhuan
Published: February 2024

Stroke increasingly affects individuals of working age. An accurate assessment of Readiness for Return-to-Work (RRTW) can help determine the optimal timing for RRTW and facilitate an early reintegration into society. This study investigates the current state of RRTW and the influencing factors among young and middle-aged stroke patients in China. A sample of young and middle-aged stroke patients hospitalized in a tertiary hospital in Henan Province between December 2021 and May 2022 were included in this study. A general information questionnaire and the Readiness for RRTW scale, the Social Support Rate Scale, the Stroke Self-Efficacy Scale, and the Fatigue Severity Scale were administered to the patients. Of the 203 patients successfully surveyed, 60 (29.6%) were in the pre-contemplation stage, 35 (17.2%) in the contemplation stage, 81 (39.9%) in the prepared for action-self-evaluative stage, and 27 (13.3%) in the prepared for action- behavior stage. Logistic regression analysis identified education level, monthly income, time to start rehabilitation therapy, social support, stroke self-efficacy, and fatigue severity as key factors affecting RRTW scale readiness in young and middle-aged stroke patients. The readiness of young and middle-aged stroke patients to Return-to-Work needs to be increased further. Healthcare professionals should consider the influencing factors of RRTW and design targeted intervention programs to facilitate a successful Return-to-Work and normal life.
Phenotypic characterization of liver tissue heterogeneity through a next-generation 3D single-cell atlas
Authors: Martínez-Torres Dilan , Maldonado Valentina , Pérez-Gallardo Cristian , Yañez Rodrigo , Candia Valeria , Kalaidzidis Yannis , Zerial Marino , Morales-Navarrete Hernán , Segovia-Miranda Fabián
Published: February 2024

Three-dimensional (3D) geometrical models are potent tools for quantifying complex tissue features and exploring structure–function relationships. However, these models are generally incomplete due to experimental limitations in acquiring multiple (> 4) fluorescent channels in thick tissue sections simultaneously. Indeed, predictive geometrical and functional models of the liver have been restricted to few tissue and cellular components, excluding important cellular populations such as hepatic stellate cells (HSCs) and Kupffer cells (KCs). Here, we combined deep-tissue immunostaining, multiphoton microscopy, deep-learning techniques, and 3D image processing to computationally expand the number of simultaneously reconstructed tissue structures. We then generated a spatial single-cell atlas of hepatic architecture (Hep3D), including all main tissue and cellular components at different stages of post-natal development in mice. We used Hep3D to quantitatively study 1) hepatic morphodynamics from early post-natal development to adulthood, and 2) the effect on the liver's overall structure when changing the hepatic environment after removing KCs. In addition to a complete description of bile canaliculi and sinusoidal network remodeling, our analysis uncovered unexpected spatiotemporal patterns of non-parenchymal cells and hepatocytes differing in size, number of nuclei, and DNA content. Surprisingly, we found that the specific depletion of KCs results in morphological changes in hepatocytes and HSCs. These findings reveal novel characteristics of liver heterogeneity and have important implications for both the structural organization of liver tissue and its function. Our next-gen 3D single-cell atlas is a powerful tool to understand liver tissue architecture, opening up avenues for in-depth investigations into tissue structure across both normal and pathological conditions.
Combined effects of naringin and doxorubicin on the JAK/STAT signaling pathway reduce the development and spread of breast cancer cells
Authors: Effat Heba , Abosharaf Hamed A. , Radwan Aliaa M.
Published: February 2024

Breast cancer therapy options are limited due to its late diagnosis and poor prognosis. Doxorubicin is the fundamental therapy approach for this disease. Because chemotherapy has numerous adverse effects, the scope of the existing research was to appraise the synergetic effect of doxorubicin and naringin and explore the underlying mechanism. The cytotoxicity of doxorubicin and naringin on MCF-7 was monitored. Furthermore, the expression of STAT3 and JAK1 as well as the apoptotic and metastatic related genes (Bax, Bcl-2, Survivin, and VEGF) were conducted by immunoblotting assay and qRT-PCR. In addition, a wound healing test was utilized to appraise the migration and metastasis of MCF-7. Our results revealed that naringin and doxorubicin had a synergetic inhibitory influence on MCF-7 cells growth and migration. The synergetic action of doxorubicin and naringin effectively hindered the expression of STAT3, JAK1, Bcl-2, Survivin, and VEGF, with a boost in the level of Bax compared to cells treated with either doxorubicin or naringin. In conclusion, our findings imply that combining doxorubicin with naringin may be a favorable strategy for inhibiting the growth of breast cancer.
Endotracheal tubes with dexamethasone eluting electrospun coating improve tissue mechanical function after upper airway injury
Authors: Gonzales Gabriela , Malka Ronit , Marinelli Lisa , Lee Christine M. , Miar Solaleh , Cook Stacy , Dion Gregory R. , Guda Teja
Published: February 2024

Corticosteroid-eluting endotracheal tubes (ETTs) were developed and employed in a swine laryngotracheal injury model to maintain airway patency and provide localized drug delivery to inhibit fibrotic scarring. Polycaprolactone (PCL) fibers with or without dexamethasone were electrospun onto the ETT surface PCL-only coated ETTs and placed in native airways of 18 Yorkshire swine. Regular and dexamethasone-PCL coated ETTs were placed in airways of another 18 swine injured by inner laryngeal mucosal abrasion. All groups were evaluated after 3, 7 and 14 days (n = 3/treatment/time). Larynges were bisected and localized stiffness determined by normal indentation, then sequentially matched with histological assessment. In the native airway, tissue stiffness with PCL-only ETT placement increased significantly from 3 to 7 days (p = 0.0016) and 3 to 14 days (p < 0.0001) while dexamethasone-PCL ETT placement resulted in stiffness decreasing from 7 to 14 days (p = 0.031). In the injured airway, localized stiffness at 14 days was significantly greater after regular ETT placement (23.1 ± 0.725 N/m) versus dexamethasone-PCL ETTs (17.10 ± 0.930 N/m, p < 0.0001). Dexamethasone-loaded ETTs were found to reduce laryngotracheal tissue stiffening after simulated intubation injury compared to regular ETTs, supported by a trend of reduced collagen in the basement membrane in injured swine over time. Findings suggest localized corticosteroid delivery allows for tissue stiffness control and potential use as an approach for prevention and treatment of scarring caused by intubation injury.
Mental simulation of the factual and the illusory in negation processing: evidence from anticipatory eye movements on a blank screen
Authors: Vanek Norbert , Matić Škorić Ana , Košutar Sara , Matějka Štěpán , Stone Kate
Published: February 2024

How do comprehenders process negative statements such as The fish is not jumping out of the water? Opinions vary. Some argue for two steps, namely that processing starts off with the representation of the positive/illusory [fish jumping out of the water] and then shifts to the (f)actual. To test this idea, we measured fixations on the factual (fish not jumping) versus the illusory (fish jumping) during auditory processing of negation and affirmation. We tested speakers of English (single-cued negation) and Croatian (double-cued negation) and focused on anticipatory fixations in the absence of pictures to indicate the strength of mental simulations. Our findings contribute to negation processing research in two ways. First, dominant anticipatory fixations on the factual suggest a direct rather than a two-step process. Second, time-sensitive insights from two languages call for a finer-grained account of negation processing with negation-specific support of inferences of the factual over the illusory.
Identification of EMT-associated prognostic features among grade II/III gliomas
Authors: Yang Wenyong , Lin Liangbin , Lu Tianqi , Yu Hui , Zhang Sunfu
Published: February 2024

Grade II/III gliomas have a highly heterogeneous clinical course. Identifying prognostic biomarkers in grade II/III gliomas is essential to guide clinical management. We explored epithelial-mesenchymal transition (EMT)-related genes to uncover prognostic features in grade II/III gliomas. Consensus cluster analysis of 200 EMT-related genes classified 512 grade II/III glioma samples into two molecular subtypes, C1 and C2. The C1 subtype had significantly worse overall survival compared to the C2 subtype. Pathway analysis revealed C1 tumors were highly associated with tumor progression pathways and demonstrated higher immune cell infiltration scores. Differential expression analysis identified four genes (ACTN1, AQP1, LAMC3, NRM) that discriminated the two subtypes. Validation in external datasets confirmed that high expression of this four-gene signature predicted poor prognosis in grade II/III gliomas. Cellular experiments showed ACTN1, AQP1 and NRM promoted glioma cell proliferation, migration and invasion. We examined correlations of the signature genes with T cell exhaustion markers and found ACTN1 expression had the strongest association. Immunohistochemistry analysis further demonstrated that ACTN1 protein expression in grade II/III gliomas was negatively correlated with patient overall survival. In summary, our study identified a concise four-gene signature that robustly predicts grade II/III gliomas prognosis across multiple datasets. The signature provides clinical relevance in distinguishing more aggressive grade II/III glioma tumors. Targeting the ACTN1, AQP1 and NRM genes may offer new therapeutic opportunities to improve grade II/III gliomas patient outcomes.
Longitudinal assessment of sweat-based TNF-alpha in inflammatory bowel disease using a wearable device
Authors: Hirten Robert P. , Lin Kai-Chun , Whang Jessica , Shahub Sarah , Helmus Drew , Muthukumar Sriram , Sands Bruce E. , Prasad Shalini
Published: February 2024

Wearable devices can non-invasively monitor patients with chronic diseases. Sweat is an easily accessible biofluid for continuous sampling of analytes, including inflammatory markers and cytokines. We evaluated a sweat sensing wearable device in subjects with and without inflammatory bowel disease (IBD), a chronic inflammatory condition of the gastrointestinal tract. Participants with an IBD related hospital admission and a C-reactive protein level above 5 mg/L wore a sweat sensing wearable device for up to 5 days. Tumor necrosis factor-alpha (TNF-α) levels were continually assessed in the sweat via the sensor, and daily in the blood. A second cohort of healthy subjects without chronic diseases wore the device for up to 48 h. Twenty-eight subjects were enrolled. In the 16 subjects with IBD, a moderate linear relationship between serum and sweat TNF-α levels was observed (R2 = 0.72). Subjects with IBD were found to have a mean sweat TNF-α level of 2.11 pg/mL, compared to a mean value of 0.19 pg/mL in 12 healthy controls (p < 0.0001). Sweat TNF-α measurements differentiated subjects with active IBD from healthy subjects with an AUC of 0.962 (95% CI 0.894–1.000). A sweat sensing wearable device can longitudinally measure key sweat-based markers of IBD. TNF-α levels in the sweat of subjects with IBD correlate with serum values, suggesting feasibility in non-invasive disease monitoring.
Deep representation learning of tissue metabolome and computed tomography annotates NSCLC classification and prognosis
Authors: Boubnovski Martell Marc , Linton-Reid Kristofer , Hindocha Sumeet , Chen Mitchell , Moreno Paula , Álvarez‐Benito Marina , Salvatierra Ángel , Lee Richard , Posma Joram M. , Calzado Marco A. , Aboagye Eric O.
Published: February 2024

The rich chemical information from tissue metabolomics provides a powerful means to elaborate tissue physiology or tumor characteristics at cellular and tumor microenvironment levels. However, the process of obtaining such information requires invasive biopsies, is costly, and can delay clinical patient management. Conversely, computed tomography (CT) is a clinical standard of care but does not intuitively harbor histological or prognostic information. Furthermore, the ability to embed metabolome information into CT to subsequently use the learned representation for classification or prognosis has yet to be described. This study develops a deep learning-based framework -- tissue-metabolomic-radiomic-CT (TMR-CT) by combining 48 paired CT images and tumor/normal tissue metabolite intensities to generate ten image embeddings to infer metabolite-derived representation from CT alone. In clinical NSCLC settings, we ascertain whether TMR-CT results in an enhanced feature generation model solving histology classification/prognosis tasks in an unseen international CT dataset of 742 patients. TMR-CT non-invasively determines histological classes - adenocarcinoma/squamous cell carcinoma with an F1-score = 0.78 and further asserts patients’ prognosis with a c-index = 0.72, surpassing the performance of radiomics models and deep learning on single modality CT feature extraction. Additionally, our work shows the potential to generate informative biology-inspired CT-led features to explore connections between hard-to-obtain tissue metabolic profiles and routine lesion-derived image data.
AI-derived epicardial fat measurements improve cardiovascular risk prediction from myocardial perfusion imaging
Authors: Miller Robert J. H. , Shanbhag Aakash , Killekar Aditya , Lemley Mark , Bednarski Bryan , Van Kriekinge Serge D. , Kavanagh Paul B. , Feher Attila , Miller Edward J. , Einstein Andrew J. , Ruddy Terrence D. , Liang Joanna X. , Builoff Valerie , Berman Daniel S. , Dey Damini , Slomka Piotr J.
Published: February 2024

Epicardial adipose tissue (EAT) volume and attenuation are associated with cardiovascular risk, but manual annotation is time-consuming. We evaluated whether automated deep learning-based EAT measurements from ungated computed tomography (CT) are associated with death or myocardial infarction (MI). We included 8781 patients from 4 sites without known coronary artery disease who underwent hybrid myocardial perfusion imaging. Of those, 500 patients from one site were used for model training and validation, with the remaining patients held out for testing (n = 3511 internal testing, n = 4770 external testing). We modified an existing deep learning model to first identify the cardiac silhouette, then automatically segment EAT based on attenuation thresholds. Deep learning EAT measurements were obtained in <2 s compared to 15 min for expert annotations. There was excellent agreement between EAT attenuation (Spearman correlation 0.90 internal, 0.82 external) and volume (Spearman correlation 0.90 internal, 0.91 external) by deep learning and expert segmentation in all 3 sites (Spearman correlation 0.90–0.98). During median follow-up of 2.7 years (IQR 1.6–4.9), 565 patients experienced death or MI. Elevated EAT volume and attenuation were independently associated with an increased risk of death or MI after adjustment for relevant confounders. Deep learning can automatically measure EAT volume and attenuation from low-dose, ungated CT with excellent correlation with expert annotations, but in a fraction of the time. EAT measurements offer additional prognostic insights within the context of hybrid perfusion imaging.
Exploring the combinatorial explosion of amine–acid reaction space via graph editing
Authors: Zhang Rui , Mahjour Babak , Outlaw Andrew , McGrath Andrew , Hopper Tim , Kelley Brian , Walters W. Patrick , Cernak Tim
Published: February 2024

Amines and carboxylic acids are abundant chemical feedstocks that are nearly exclusively united via the amide coupling reaction. The disproportionate use of the amide coupling leaves a large section of unexplored reaction space between amines and acids: two of the most common chemical building blocks. Herein we conduct a thorough exploration of amine–acid reaction space via systematic enumeration of reactions involving a simple amine–carboxylic acid pair. This approach to chemical space exploration investigates the coarse and fine modulation of physicochemical properties and molecular shapes. With the invention of reaction methods becoming increasingly automated and bringing conceptual reactions into reality, our map provides an entirely new axis of chemical space exploration for rational property design.
Enhanced oxygen reduction reaction on caffeine-modified platinum single-crystal electrodes
Authors: Hoshi Nagahiro , Nakamura Masashi , Kubo Ryuta , Suzuki Rui
Published: February 2024

Enhancing the activity of the oxygen reduction reaction (ORR) is crucial for fuel cell development, and hydrophobic species are known to increase the ORR activity. This paper reports that caffeine enhanced the specific ORR activity of Pt(111) 11-fold compared to that without caffeine in a 0.1 M HClO4 aqueous solution. Moreover, caffeine increased the ORR activity of Pt(110) 2.5-fold; however, the activity of Pt(100) was unaffected. The infrared (IR) band of PtOH (blocking species of the ORR) decreased for all the surfaces. Caffeine was adsorbed with its molecular plane perpendicular to the Pt(111) and Pt(110) surfaces and tilted relative to the Pt(100) surface. Thus, the effects of caffeine on the ORR activity can be rationalized by a decrease in PtOH coverage and the difference in adsorption geometry of caffeine.
Dynamics of inertialess sedimentation of a rigid U-shaped disk
Authors: Miara Tymoteusz , Vaquero-Stainer Christian , Pihler-Puzović Draga , Heil Matthias , Juel Anne
Published: February 2024

When particles sediment in a viscous fluid, the character of their trajectories depends sensitively on the particles’ shape. Here we study the sedimentation of U-shaped rigid disks in a regime where inertia can be neglected. We show that, unlike the case of planar disks which settle in a fixed orientation relative to the direction of gravity, U-shaped disks tend to perform a periodic sequence of pitching and rolling motions which cause their centre of mass to sediment along complex trajectories that range from quasi-periodic spirals to helices. Thus, we demonstrate that particles of achiral shape can sediment along chiral paths whose handedness is determined by their initial orientation rather than their geometry. Our analysis provides a framework in which to interpret the motion of sedimenting particles of arbitrary shape.
N-doped branched metal–organic framework derivatives to boost multiband microwave absorption with ultrathin thickness
Authors: Zhang Zhe , Cui Jiewu , Yu Dongbo , Liu Jiaqin , Zhang Pengjie , Zhang Yong , Ma Song , Wang Linjie , Deng Guangsheng , Wu Yucheng
Published: February 2024

CoNi@NPC/NCNTs composites with a rod-like N-doped carbon matrix and branched N-doped CNTs, prepared via melamine-assisted CVD pyrolysis, demonstrate superior dual-band electromagnetic wave absorption (2–18 and 26.5–40 GHz) with ultrathin thickness.
Co-catalyst design to control charge transfer and product composition for photocatalytic H2 production and biomass reforming
Authors: Nguyen Van-Can , Sanoe Meyta , Putri Novy Pralisa , Lee Yuh-Lang , Teng Hsisheng
Published: February 2024

Pt co-catalyst results in strong H2 evolution and piece-by-piece peeling of xylose; Ag co-catalyst results in cleavage of C2–C3 bond.
Engineering Fe–N–C sites onto Fe nanoparticles for synergistically boosting Cr(vi) reduction: performance, mechanism, and applications
Authors: Li Minghui , Liu Wenjing , Liu Panxinchen , Lin Xiaoqin , Zhu Hongjie , Fang Caixia , Li Wenwen , Liu Chang
Published: February 2024

Hexavalent chromium (Cr(vi)) is well known for its high toxicity to humans and the environment.
Unveiling the mechanism of thermal catalytic oxidation of HCHO from the kiln exhaust gas using Sc-decorated Cr2CO2-MXenes
Authors: Yang Jinkai , Zhang Zhongyong , Peng Jiahe , Wan Jieshuo , Liu Zhaohui , Zhang Peng , Li Neng
Published: February 2024

Sc/Cr2CO2 is a novel efficient thermal catalyst for HCHO oxidation.
Graph neural network‐based attack prediction for communication‐based train control systems
Authors: Zhao Junyi , Tang Tao , Bu Bing , Li Qichang
Published: February 2024

The Advanced Persistent Threats (APTs) have emerged as one of the key security challenges to industrial control systems. APTs are complex multi‐step attacks, and they are naturally diverse and complex. Therefore, it is important to comprehend the behaviour of APT attackers and anticipate the upcoming attack actions. GNN‐AP is proposed, a framework utilising an alert log to predict potential attack targets. Firstly, GNN‐AP uses causality to eliminate confounding elements from the alert dataset and then uses an encoder‐decoder model to reconstruct an attack scenario graph. Based on the chronological characteristics of APT attacks, GNN‐AP identifies APT attack sequences from attack scenario graphs and integrates these attack sequences with communication‐based train control (CBTC) devices topology information to construct an Attack‐Target Graph. Based on the attack‐target graph, a graph neural network approach is used to identify the attack intent and transforms the attack prediction problem into a link prediction problem that predicts the connected edges of the attack and target nodes. The simulation results obtained using DARPA data show that the proposed method can improve the comparison methods by 4% of accuracy in terms of prediction. Furthermore, the method was applied to the CBTC system dataset with a prediction accuracy of 88%, demonstrating the efficacy of the proposed method for industrial control systems.
A multi‐layer–multi‐player game model in electricity market
Authors: Kafshian Hajar , Monfared Mohammad Ali Saniee
Published: February 2024

Here, a novel tri‐level energy market model aimed at addressing the challenges posed by demand side management (DSM) in the electricity distribution company (EDC) is introduced. DSM has emerged as a new strategy employed by EDCs to manage and control electricity demand by encouraging end‐users to modify their electricity consumption patterns. This is achieved through the participation of demand response (DR) aggregators, which play a crucial role in assisting end‐users with strategies and technologies to reduce their electricity consumption during peak hours. The proposed tri‐level energy market model consists of four distinct players: EDC, microgrids, aggregators, customers. The interactions between these four actors are modelled within a tri‐level game framework, where the EDC and aggregators act as leaders, and the micro‐grids and customers are followers. This multi‐level and multi‐player game structure allows for a more realistic representation of the complexities involved in DSM programs within the energy market. To demonstrate the effectiveness of the proposed model, a real case study is utilized, showing that the new model better resembles real‐life market conditions. The results illustrate how the tri‐level energy market model can significantly reduce demand fluctuations during peak hours, leading to improved efficiency and effectiveness within DSM programs.
An reconstruction bidirectional recurrent neural network ‐based deinterleaving method for known radar signals in open‐set scenarios
Authors: Zheng Haiping , Xie Kai , Zhu Yingshen , Lin Jinjian , Wang Lihong
Published: February 2024

In electronic warfare, radar signal deinterleaving is a critical task. While many researchers have applied deep learning and utilised known radar classes to construct interleaved pulse sequences training sets for deinterleaving models, these models face challenges in distinguishing between known and unknown radar classes in open‐set scenarios. To address this challenge, the authors propose a novel model, the Reconstruction Bidirectional Recurrent Neural Network (RBi‐RNN). RBi‐RNN utilises input reconstruction and employs a joint training strategy incorporating cross‐entropy loss, reconstruction loss, and centre loss. These strategies aim to maximise inter‐class latent representation distances while minimising intra‐class disparities. By incorporating an open‐set recognition method based on extreme value theory, RBi‐RNN adapts to open‐set scenarios. Simulation results demonstrate the superiority of RBi‐RNN over conventional models in both closed‐set and open‐set scenarios. In open‐set scenarios, it successfully discriminates between known and unknown radar signals within interleaved pulse sequences, deinterleaving known radar classes with high stability. The authors lay the foundation for future unsupervised deinterleaving methods designed specifically for unknown radar pulses.
Open space radar specific emitter identification using MSAK‐CNN‐LSTM network
Authors: Zheng Yuanhao , Wang Jiantao , Huang Jie
Published: February 2024

To enhance the capability of identifying unknown emitters in open spaces, an open‐multiscale attention kernel (MSAK)‐convolutional neural network‐long short‐term memory (CNN‐LSTM) structure is proposed. To this end, first, a MSAK module and CNN‐LSTM structure are introduced, and then, the depth and complexity of the feature extraction network are improved to enhance its representation capability. To classify unknown emitters accurately, the MSAK‐CNN‐LSTM model is improved to obtain an open‐MSAK‐CNN‐LSTM model with open‐set recognition capability. Additionally, the two preprocessing procedures are summarised, and their strengths and weaknesses are compared. Experimental results show that the proposed open‐MSAK‐CNN‐LSTM model achieves satisfactory accuracy in identifying unknown emitters in open space. In addition, it has significant advantages in low signal‐to‐noise ratio (SNR) scenarios.