bluuebunny/crossref_metadata_2025_split huggingface dataset:
Leveraging Machine Learning Approaches to Predict Organic Carbon Abundance in Mars‐Analog Hypersaline Lake Sediments
Authors: Nichols Floyd , Pontefract Alexandra , Masterson Andrew L. , Thompson Mia L. , Carr Christopher E. , Tuccillo Mia T. , Osburn Magdalena R. 
Published: June 2024

AbstractModern advancements in laboratory and instrumental techniques in astrobiology have improved our life detection capabilities on both Earth and beyond. These advancements have also increased the complexity of data often resulting in data sets that are characterized by complex and non‐linear relationships. Machine learning methods are underutilized in astrobiology; however, these methods are extremely effective at revealing structure and patterns in complex data sets when paired with the right algorithms. Here, we employ a series of classification and regression algorithms to predict the abundance of organic carbon (OC) from X‐ray fluorescence (XRF) heavy element (>Mg) data in dynamic Mars‐analog hypersaline lake sediments. More specifically, we constructed models using the random forest, k‐nearest neighbors (KNN), support vector machine, and logistic regression algorithms. Overall, our trained models showed good performance with predicting the abundance of OC, with accuracies from 80% to 94%. Machine learning approaches such as classification and regression algorithms offer insight into complex data while providing agnostic insights, ultimately creating a more efficient search for OC. We applied our trained model on XRF data from Martian soil using rover‐based (PIXL) and orbital (Odyssey) data sets to produce probability predictions of OC abundance. Our predictions show a high probability that OC abundance is low which is comparable to OC data from recently landed missions. These results highlight the potential for predictive machine learning models to be trained on data from analog environments on Earth and then applied to extraterrestrial targets, ultimately, improving life detection efforts.
MEMS reservoir computing system with stiffness modulation for multi-scene data processing at the edge
Authors: Guo Xiaowei , Yang Wuhao , Xiong Xingyin , Wang Zheng , Zou Xudong 
Published: June 2024

AbstractReservoir computing (RC) is a bio-inspired neural network structure which can be implemented in hardware with ease. It has been applied across various fields such as memristors, and electrochemical reactions, among which the micro-electro-mechanical systems (MEMS) is supposed to be the closest to sensing and computing integration. While previous MEMS RCs have demonstrated their potential as reservoirs, the amplitude modulation mode was found to be inadequate for computing directly upon sensing. To achieve this objective, this paper introduces a novel MEMS reservoir computing system based on stiffness modulation, where natural signals directly influence the system stiffness as input. Under this innovative concept, information can be processed locally without the need for advanced data collection and pre-processing. We present an integrated RC system characterized by small volume and low power consumption, eliminating complicated setups in traditional MEMS RC for data discretization and transduction. Both simulation and experiment were conducted on our accelerometer. We performed nonlinearity tuning for the resonator and optimized the post-processing algorithm by introducing a digital mask operator. Consequently, our MEMS RC is capable of both classification and forecasting, surpassing the capabilities of our previous non-delay-based architecture. Our method successfully processed word classification, with a 99.8% accuracy, and chaos forecasting, with a 0.0305 normalized mean square error (NMSE), demonstrating its adaptability for multi-scene data processing. This work is essential as it presents a novel MEMS RC with stiffness modulation, offering a simplified, efficient approach to integrate sensing and computing. Our approach has initiated edge computing, enabling emergent applications in MEMS for local computations.
An acoustofluidic device for the automated separation of platelet-reduced plasma from whole blood
Authors: Ma Zhehan , Xia Jianping , Upreti Neil , David Emeraghi , Rufo Joseph , Gu Yuyang , Yang Kaichun , Yang Shujie , Xu Xiangchen , Kwun Jean , Chambers Eileen , Huang Tony Jun 
Published: June 2024

AbstractSeparating plasma from whole blood is an important sample processing technique required for fundamental biomedical research, medical diagnostics, and therapeutic applications. Traditional protocols for plasma isolation require multiple centrifugation steps or multiunit microfluidic processing to sequentially remove large red blood cells (RBCs) and white blood cells (WBCs), followed by the removal of small platelets. Here, we present an acoustofluidic platform capable of efficiently removing RBCs, WBCs, and platelets from whole blood in a single step. By leveraging differences in the acoustic impedances of fluids, our device generates significantly greater forces on suspended particles than conventional microfluidic approaches, enabling the removal of both large blood cells and smaller platelets in a single unit. As a result, undiluted human whole blood can be processed by our device to remove both blood cells and platelets (>90%) at low voltages (25 Vpp). The ability to successfully remove blood cells and platelets from plasma without altering the properties of the proteins and antibodies present creates numerous potential applications for our platform in biomedical research, as well as plasma-based diagnostics and therapeutics. Furthermore, the microfluidic nature of our device offers advantages such as portability, cost efficiency, and the ability to process small-volume samples.
Monolithic integrated optoelectronic chip for vector force detection
Authors: Feng Jiansong , Wang Zhongqi , Zhanghu Mengyuan , Zhang Xu , Shen Yong , Yang Jing , Li Zhibin , Chen Bin , Wang Taihong , Chen Xiaolong , Liu Zhaojun 
Published: June 2024

AbstractSensors with a small footprint and real-time detection capabilities are crucial in robotic surgery and smart wearable equipment. Reducing device footprint while maintaining its high performance is a major challenge and a significant limitation to their development. Here, we proposed a monolithic integrated micro-scale sensor, which can be used for vector force detection. This sensor combines an optical source, four photodetectors, and a hemispherical silicone elastomer component on the same sapphire-based AlGaInP wafer. The chip-scale optical coupling is achieved by employing the laser lift-off techniques and the flip-chip bonding to a processed sapphire substrate. This hemispherical structure device can detect normal and shear forces as low as 1 mN within a measurement range of 0–220 mN for normal force and 0–15 mN for shear force. After packaging, the sensor is capable of detecting forces over a broader range, with measurement capabilities extending up to 10 N for normal forces and 0.2 N for shear forces. It has an accuracy of detecting a minimum normal force of 25 mN and a minimum shear force of 20 mN. Furthermore, this sensor has been validated to have a compact footprint of approximately 1.5 mm2, while maintaining high real-time response. We also demonstrate its promising potential by combining this sensor with fine surface texture perception in the fields of compact medical robot interaction and wearable devices.
Prevalence of obesity and associated sociodemographic and lifestyle factors in Ecuadorian children and adolescents
Authors: López-Gil José Francisco , Chen Sitong , López-Bueno Rubén , Gutiérrez-Espinoza Hector , Duarte Junior Miguel Angelo , Galan-Lopez Pablo , Palma-Gamiz José Luis , Smith Lee 
Published: June 2024

Abstract

Background
Given the increasing prevalence of obesity in young people in Ecuador, there is a need to understand the factors associated with this condition. The aim of this study was to assess the prevalence of obesity in Ecuadorian children and adolescents aged 5–17 years and identify its associated sociodemographic and lifestyle factors.

Methods
This cross-sectional study was conducted using data from the Encuesta Nacional de Salud y Nutrición (ENSANUT-2018). The final sample consisted of 11,980 participants who provided full information on the variables of interest.

Results
The prevalence of obesity was 12.7%. A lower odd of having obesity was observed for adolescents; for those with a breadwinner with an educational level in middle/high school or higher; for each additional day with 60 or more minutes of daily moderate-to-vigorous physical activity; and for those with greater daily vegetable consumption (one, two, or three or more servings). Conversely, there were greater odds of obesity in participants from families with medium, poor, and very poor wealth and those from the coast and insular region.

Conclusions
The high prevalence of obesity in Ecuadorian children and adolescents is a public health concern. Sociodemographic and lifestyle behavior differences in young people with obesity should be considered when developing specific interventions.

Impact

As the prevalence of obesity among children and adolescents increases in Latin America, with a particular focus on Ecuador, it becomes crucial to delve into the factors linked to this condition and identify the most successful strategies for its mitigation.

The elevated prevalence of obesity among young individuals in Ecuador raises significant public health concerns.

To develop targeted interventions, it is crucial to account for sociodemographic variables and lifestyle behaviors that contribute to obesity in this population.
SMAD4 depletion contributes to endocrine resistance by integrating ER and ERBB signaling in HR + HER2− breast cancer
Authors: Li Kang , Shu Dan , Li Han , Lan Ailin , Zhang Wenjie , Tan Zhaofu , Huang Man , Tomasi Maria Lauda , Jin Aishun , Yu Haochen , Shen Meiying , Liu Shengchun 
Published: June 2024

AbstractEndocrine resistance poses a significant clinical challenge for patients with hormone receptor-positive and human epithelial growth factor receptor 2-negative (HR + HER2−) breast cancer. Dysregulation of estrogen receptor (ER) and ERBB signaling pathways is implicated in resistance development; however, the integration of these pathways remains unclear. While SMAD4 is known to play diverse roles in tumorigenesis, its involvement in endocrine resistance is poorly understood. Here, we investigate the role of SMAD4 in acquired endocrine resistance in HR + HER2− breast cancer. Genome-wide CRISPR screening identifies SMAD4 as a regulator of 4-hydroxytamoxifen (OHT) sensitivity in T47D cells. Clinical data analysis reveals downregulated SMAD4 expression in breast cancer tissues, correlating with poor prognosis. Following endocrine therapy, SMAD4 expression is further suppressed. Functional studies demonstrate that SMAD4 depletion induces endocrine resistance in vitro and in vivo by enhancing ER and ERBB signaling. Concomitant inhibition of ER and ERBB signaling leads to aberrant autophagy activation. Simultaneous inhibition of ER, ERBB, and autophagy pathways synergistically impacts SMAD4-depleted cells. Our findings unveil a mechanism whereby endocrine therapy-induced SMAD4 downregulation drives acquired resistance by integrating ER and ERBB signaling and suggest a rational treatment strategy for endocrine-resistant HR + HER2− breast cancer patients.
Associations of combined genetic and lifestyle risks with hypertension and home hypertension
Authors: Takase Masato , Hirata Takumi , Nakaya Naoki , Nakamura Tomohiro , Kogure Mana , Hatanaka Rieko , Nakaya Kumi , Chiba Ippei , Kanno Ikumi , Nochioka Kotaro , Tsuchiya Naho , Narita Akira , Metoki Hirohito , Satoh Michihiro , Obara Taku , Ishikuro Mami , Ohseto Hisashi , Uruno Akira , Kobayashi Tomoko , Kodama Eiichi N. , Hamanaka Yohei , Orui Masatsugu , Ogishima Soichi , Nagaie Satoshi , Fuse Nobuo , Sugawara Junichi , Kuriyama Shinichi , Tamiya Gen , Hozawa Atsushi , Yamamoto Masayuki ,   the ToMMo investigators
Published: June 2024

AbstractNo study, to our knowledge, has constructed a polygenic risk score based on clinical blood pressure and investigated the association of genetic and lifestyle risks with home hypertension. We examined the associations of combined genetic and lifestyle risks with hypertension and home hypertension. In a cross-sectional study of 7027 Japanese individuals aged ≥20 years, we developed a lifestyle score based on body mass index, alcohol consumption, physical activity, and sodium-to-potassium ratio, categorized into ideal, intermediate, and poor lifestyles. A polygenic risk score was constructed with the target data (n = 1405) using publicly available genome-wide association study summary statistics from BioBank Japan. Using the test data (n = 5622), we evaluated polygenic risk score performance and examined the associations of combined genetic and lifestyle risks with hypertension and home hypertension. Hypertension and home hypertension were defined as blood pressure measured at a community-support center ≥140/90 mmHg or at home ≥135/85 mmHg, respectively, or self-reported treatment for hypertension. In the test data, 2294 and 2322 participants had hypertension and home hypertension, respectively. Both polygenic risk and lifestyle scores were independently associated with hypertension and home hypertension. Compared with those of participants with low genetic risk and an ideal lifestyle, the odds ratios for hypertension and home hypertension in the low genetic risk and poor lifestyle group were 1.94 (95% confidence interval, 1.34–2.80) and 2.15 (1.60–2.90), respectively. In summary, lifestyle is important to prevent hypertension; nevertheless, participants with high genetic risk should carefully monitor their blood pressure despite a healthy lifestyle.
Longitudinal single-cell data informs deterministic modelling of inflammatory bowel disease
Authors: Kilian Christoph , Ulrich Hanna , Zouboulis Viktor A. , Sprezyna Paulina , Schreiber Jasmin , Landsberger Tomer , Büttner Maren , Biton Moshe , Villablanca Eduardo J. , Huber Samuel , Adlung Lorenz 
Published: June 2024

AbstractSingle-cell-based methods such as flow cytometry or single-cell mRNA sequencing (scRNA-seq) allow deep molecular and cellular profiling of immunological processes. Despite their high throughput, however, these measurements represent only a snapshot in time. Here, we explore how longitudinal single-cell-based datasets can be used for deterministic ordinary differential equation (ODE)-based modelling to mechanistically describe immune dynamics. We derived longitudinal changes in cell numbers of colonic cell types during inflammatory bowel disease (IBD) from flow cytometry and scRNA-seq data of murine colitis using ODE-based models. Our mathematical model generalised well across different protocols and experimental techniques, and we hypothesised that the estimated model parameters reflect biological processes. We validated this prediction of cellular turnover rates with KI-67 staining and with gene expression information from the scRNA-seq data not used for model fitting. Finally, we tested the translational relevance of the mathematical model by deconvolution of longitudinal bulk mRNA-sequencing data from a cohort of human IBD patients treated with olamkicept. We found that neutrophil depletion may contribute to IBD patients entering remission. The predictive power of IBD deterministic modelling highlights its potential to advance our understanding of immune dynamics in health and disease.
Extended-release ketamine tablets for treatment-resistant depression: a randomized placebo-controlled phase 2 trial
Authors: Glue Paul , Loo Colleen , Fam Johnson , Lane Hsien-Yuan , Young Allan H. , Surman Peter ,   BEDROC study investigators, Glozier Nick , Fitzgerald Paul , Liu Dennis , Sharma Shanthi , Grunfeld Jennifer , Barton David , Hopwood Malcolm , Miles Wayne , Williams Mike , Carson Simon , Fam Johnson , Tor Phern-Chern , Lane Hsien-Yuan , Chen Chun-Hsin , Huang Yu-Jui 
Published: June 2024

AbstractKetamine has rapid-onset antidepressant activity in patients with treatment-resistant major depression (TRD). The safety and tolerability of racemic ketamine may be improved if given orally, as an extended-release tablet (R-107), compared with other routes of administration. In this phase 2 multicenter clinical trial, male and female adult patients with TRD and Montgomery–Asberg Depression Rating Scale (MADRS) scores ≥20 received open-label R-107 tablets 120 mg per day for 5 days and were assessed on day 8 (enrichment phase). On day 8, responders (MADRS scores ≤12 and reduction ≥50%) were randomized on a 1:1:1:1:1 basis to receive double-blind R-107 doses of 30, 60, 120 or 180 mg, or placebo, twice weekly for a further 12 weeks. Nonresponders on day 8 exited the study. The primary endpoint was least square mean change in MADRS for each active treatment compared with placebo at 13 weeks, starting with the 180 mg dose, using a fixed sequence step-down closed test procedure. Between May 2019 and August 2021, 329 individuals were screened for eligibility, 231 entered the open-label enrichment phase (days 1–8) and 168 responders were randomized to double-blind treatment. The primary objective was met; the least square mean difference of MADRS score for the 180 mg tablet group and placebo was −6.1 (95% confidence interval 1.0 to 11.16, P = 0.019) at 13 weeks. Relapse rates during double-blind treatment showed a dose response from 70.6% for placebo to 42.9% for 180 mg. Tolerability was excellent, with no changes in blood pressure, minimal reports of sedation and minimal dissociation. The most common adverse events were headache, dizziness and anxiety. During the randomized phase of the study, most patient dosing occurred at home. R-107 tablets were effective, safe and well tolerated in a patient population with TRD, enriched for initial response to R-107 tablets. ClinicalTrials.gov registration: ACTRN12618001042235.
Prediction of hepatic metastasis in esophageal cancer based on machine learning
Authors: Wan Jun , Zeng Yukai 
Published: June 2024

AbstractThis study aimed to establish a machine learning (ML) model for predicting hepatic metastasis in esophageal cancer. We retrospectively analyzed patients with esophageal cancer recorded in the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2020. We identified 11 indicators associated with the risk of liver metastasis through univariate and multivariate logistic regression. Subsequently, these indicators were incorporated into six ML classifiers to build corresponding predictive models. The performance of these models was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. A total of 17,800 patients diagnosed with esophageal cancer were included in this study. Age, primary site, histology, tumor grade, T stage, N stage, surgical intervention, radiotherapy, chemotherapy, bone metastasis, and lung metastasis were independent risk factors for hepatic metastasis in esophageal cancer patients. Among the six models developed, the ML model constructed using the GBM algorithm exhibited the highest performance during internal validation of the dataset, with AUC, accuracy, sensitivity, and specificity of 0.885, 0.868, 0.667, and 0.888, respectively. Based on the GBM algorithm, we developed an accessible web-based prediction tool (accessible at https://project2-dngisws9d7xkygjcvnue8u.streamlit.app/) for predicting the risk of hepatic metastasis in esophageal cancer.
Correlation between normally aerated lung and respiratory system compliance at clinical high positive end-expiratory pressure in patients with COVID-19
Authors: Ogura Keishi , Nakayama Ryuichi , Bunya Naofumi , Katayama Shinshu , Yama Naoya , Goto Yuya , Sawamoto Keigo , Uemura Shuji , Narimatsu Eichi 
Published: June 2024

AbstractNormally aerated lung tissue on computed tomography (CT) is correlated with static respiratory system compliance (Crs) at zero end-expiratory pressure. In clinical practice, however, patients with acute respiratory failure are often managed using elevated PEEP levels. No study has validated the relationship between lung volume and tissue and Crs at the applied positive end-expiratory pressure (PEEP). Therefore, this study aimed to demonstrate the relationship between lung volume and tissue on CT and Crs during the application of PEEP for the clinical management of patients with acute respiratory distress syndrome due to COVID-19. Additionally, as a secondary outcome, the study aimed to evaluate the relationship between CT characteristics and Crs, considering recruitability using the recruitment-to-inflation ratio (R/I ratio). We analyzed the CT and respiratory mechanics data of 30 patients with COVID-19 who were mechanically ventilated. The CT images were acquired during mechanical ventilation at PEEP level of 15 cmH2O and were quantitatively analyzed using Synapse Vincent system version 6.4 (Fujifilm Corporation, Tokyo, Japan). Recruitability was stratified into two groups, high and low recruitability, based on the median R/I ratio of our study population. Thirty patients were included in the analysis with the median R/I ratio of 0.71. A significant correlation was observed between Crs at the applied PEEP (median 15 [interquartile range (IQR) 12.2, 15.8]) and the normally aerated lung volume (r = 0.70 [95% CI 0.46–0.85], P < 0.001) and tissue (r = 0.70 [95% CI 0.46–0.85], P < 0.001). Multivariable linear regression revealed that recruitability (Coefficient = − 390.9 [95% CI − 725.0 to − 56.8], P = 0.024) and Crs (Coefficient = 48.9 [95% CI 32.6–65.2], P < 0.001) were significantly associated with normally aerated lung volume (R-squared: 0.58). In this study, Crs at the applied PEEP was significantly correlated with normally aerated lung volume and tissue on CT. Moreover, recruitability indicated by the R/I ratio and Crs were significantly associated with the normally aerated lung volume. This research underscores the significance of Crs at the applied PEEP as a bedside-measurable parameter and sheds new light on the link between recruitability and normally aerated lung.
Preliminary clinical and radiological evaluation of osteosynthesis using the Femoral Neck System (FNS) for subcapital fractures of the femur
Authors: Hernández-Naranjo Jose Manuel , Campuzano-Bitterling Borja , Renau-Cerrillo Marina , Vives-Barquiel Marian , Camacho-Carrasco María Pilar , Muñoz-Mahamud Ernesto 
Published: June 2024

AbstractAddressing subcapital fractures of the femur poses a substantial clinical challenge, complicated by the diverse range of available osteosynthesis materials. This study is dedicated to a comprehensive analysis of the clinical and radiological implications linked with the implementation of the Femoral Neck System (FNS) in osteosynthesis procedures. A descriptive study was conducted involving patients who underwent osteosynthesis for subcapital fractures of the femur utilizing the FNS during the period from 2019 to 2022. The investigation encompassed various facets, including the classification of fractures according to the Garden and Pauwells classifications, criteria for achieving precise reduction based on the Garden criteria and Tip Apex Distance (TAD). At the one-year follow-up, factors such as fracture consolidation, loss of reduction, fracture collapse, complications, and functional outcomes were evaluated utilizing the Harris Hip Score (HHS) scale. The study cohort included a total of 26 patients, among whom 22 exhibited non-displaced subcapital femur fractures categorized as Garden I and II. Successful reduction was accomplished in 23 cases, in which 24 cases (92.3%) displayed a TAD measurement below 25 mm. According to the HHS, patients achieved an average score of 90.9 (ranging from 63 to 100) following the surgical intervention, with predominantly "excellent" and "good" outcomes. The outcomes derived from our investigation corroborate the viability of the Femoral Neck System (FNS) as a reliable option for osteosynthesis in femoral neck fractures. The results obtained are comparable to those achieved with other available implants, as highlighted by previous studies.