bluuebunny/crossref_metadata_2025_split huggingface dataset:
Path planning in three-dimensional space based on butterfly optimization algorithm
Authors: Mazaheri Hakimeh , Goli Salman , Nourollah Ali 
Published: January 2024

AbstractPath planning is one of the most critical issues in many related fields including UAVs. Many researchers have addressed this problem according to different conditions and limitations, but modelling the 3-D space and routing with an evolutional algorithm in such spaces is an open issue. So, in this paper, we first, introduce a method to grids the environment using geometrical shapes. This can reduce the random states of cell decomposition and increases the computational speed. We then propose an effective routing algorithm based on the butterfly optimization algorithm (BOA). It can simultaneously optimize multiple path planning objectives. It uses an objective function to compute the shortest path, based on obstacle avoidance and the UAV’s operational power minimization. A novel concept, the intelligent throwing agent, used in this algorithm prevents getting stuck in local optima and increases the network coverage in path planning. The throwing agent prevents the collision of the UAV with the obstacles using geometrical techniques and contour lines. The simulation results show that BOA has the least and second-least cost in best-case and worst-case scenarios in comparison with ant colony and particle swarm. Its run time and the optimal value of the fitting function are also better than the two other algorithms.
Effect of time-dependent dialysate bicarbonate concentrations on acid–base and uremic solute kinetics during hemodialysis treatments
Authors: Wieliczko Monika , Twardowska-Kawalec Malgorzata , Debowska Malgorzata , Pietribiasi Mauro , Bialonczyk Urszula , Waniewski Jacek , Leypoldt John K. , Matuszkiewicz-Rowinska Joanna , Malyszko Jolanta 
Published: January 2024

AbstractRecent studies have suggested benefits for time-dependent dialysate bicarbonate concentrations (Dbic) during hemodialysis (HD). In this clinical trial, we compared for the first time in the same HD patients the effects of time-dependent changes with constant Dbic on acid–base and uremic solute kinetics. Blood acid–base and uremic solute concentration were measured in twenty chronic HD patients during 4-h treatments with A) constant Dbic of 35 mmol/L; B) Dbic of 35 mmol/L then 30 mmol/L; and C) Dbic of 30 mmol/L then 35 mmol/L (change of Dbic after two hours during Treatments B and C). Arterial blood samples were obtained predialysis, every hour during HD and one hour after HD, during second and third treatments of the week with each Dbic concentration profile. Blood bicarbonate concentration (blood [HCO3]) during Treatment C was lower only during the first three HD hours than in Treatment A. Overall blood [HCO3] was reduced during Treatment B in comparison to Treatment A at each time points. We conclude that a single change Dbic in the middle of HD can alter the rate of change in blood [HCO3] and pH during HD; time-dependent Dbic had no influence on uremic solute kinetics.
NKG2D knockdown improves hypoxic-ischemic brain damage by inhibiting neuroinflammation in neonatal mice
Authors: Liu Lin , Yang Yuxin , Wu Ting , Du Junrong , Long Fangyi 
Published: January 2024

AbstractHypoxic-ischemic brain damage (HIBD) is a leading cause of neonatal death and neurological dysfunction. Neuroinflammation is identified as one of the crucial pathological mechanisms after HIBD, and natural killer group 2 member D (NKG2D) is reported to be implicated in the pathogenesis of immunoinflammatory diseases. However, the role of NKG2D in neonatal HIBD is seldomly investigated. In this study, a neonatal mice model of HIBD was induced, and the role of the NKG2D in neuroinflammation and brain injury was explored by intracerebroventricular injection of lentivirus to knockdown NKG2D in neonatal mice with HIBD. The results showed that a significant increase in NKG2D protein level in the brain of neonatal mice with HIBD. The NKG2D knockdown in the brain significantly alleviated cerebral infarction, neurobehavioral deficits, and neuronal loss in neuronal HIBD. Moreover, the neuroprotective effect of NKG2D knockdown was associated with inhibition of the activation of microglia and astrocytes, expression of NKG2D ligands (NKG2DLs) and DAP10, and the nuclear translocation of NF-κB p65. Our findings reveal NKG2D knockdown may exert anti-inflammatory and neuroprotective effects in the neonatal mice with HIBD through downregulation of NKG2D/NKG2DLs/DAP10/NF-κB pathway. These results suggest that NKG2D may be a potential target for the treatment of neonatal HIBD.
Follow-up short and long-term mortalities of tracheostomized critically ill patients in an Italian multi-center observational study
Authors: Vargas Maria , Battaglini Denise , Antonelli Massimo , Corso Ruggero , Frova Giulio , Merli Guido , Petrini Flavia , Ranieri Marco V. , Sorbello Massimiliano , Di Giacinto Ida , Terragni Pierpaolo , Brunetti Iole , Servillo Giuseppe , Pelosi Paolo 
Published: January 2024

AbstractThe effects of tracheostomy on outcome as well as on intra or post-operative complications is yet to be defined. Admission of patients with tracheostomy to rehabilitation facility is at higher risk of suboptimal care and increased mortality. The aim of the study was to investigate ICU mortality, clinical outcome and quality of life up to 12 months after ICU discharge in tracheostomized critically ill patients. This is a prospective, multi-center, cohort study endorsed by Italian Society of Anesthesia, Analgesia, Reanimation, and Intensive Care (SIAARTI Prot. n° 643/13) registered in Clinicaltrial.gov (NCT01899352). Patients admitted to intensive care unit (ICU) and requiring elective tracheostomy according to physician in charge decision were included in the study. The primary outcome was ICU mortality. Secondary outcomes included risk factors for ICU mortality, prevalence of mortality at follow-up, rate of discharge from the hospital and rehabilitation, quality of life, performance status, and management of tracheostomy cannula at 3-, 6, 12-months from the day of tracheostomy. 694 critically ill patients who were tracheostomized in the ICU were included. ICU mortality was 15.8%. Age, SOFA score at the day of the tracheostomy, and days of endotracheal intubation before tracheostomy were risk factors for ICU mortality. The regression tree analysis showed that SOFA score at the day of tracheostomy and age had a preeminent role for the choice to perform the tracheostomy. Of the 694 ICU patients with tracheostomy, 469 completed the 12-months follow-up. Mortality was 33.51% at 3-months, 45.30% at 6-months, and 55.86% at 12-months. Patients with tracheostomy were less likely discharged at home but at hospital facilities or rehabilitative structures; and quality of life of patients with tracheostomy was severely compromised at 3–6 and 12 months when compared with patients without tracheostomy. In patients admitted to ICU, tracheostomy is associated with high mortality, difficult rehabilitation, and decreased quality of life. The choice to perform a tracheostomy should be carefully weighed on family burden and health-related quality of life.Clinical trial registration: Clinicaltrial.gov (NCT01899352).
Gravity-induced seismicity modulation on planetary bodies and their natural satellites
Authors: Senapati Batakrushna , Kundu Bhaskar , Jha Birendra , Jin Shuanggen 
Published: January 2024

AbstractGround-based monitoring of seismicity and modulation by external forces in the field of planetary seismology remains equivocal due to the lack of natural observations. Constrained by the natural observations (including Earthquakes, Moonquakes, and Marsquakes) and theoretical models, we present the variation in gravitational acceleration “g” of different solar system objects, combined with external harmonic forcings that are responsible for seismicity modulation on the planetary bodies and their natural satellites. From the global diversity in seismicity modulation, it has been observed that the plate-boundary regions on the Earth exhibit both short and long-period seismicity modulation. In contrast, the stable plate interior regions appear to be more sensitive to long-period seismicity modulation, however, lacking in short-period modulation. The deep Moonquakes are susceptible for both the lunar tidal period (13.6 days and 27 days) and long-period pole wobble modulation (206 days), whereas shallow emergent type moonquakes show a seismic periodicity at the lunation period (29.5 days). Further, the seasonal variation with an annual seismicity burst and seismic periodicity at polar wobble periods for high-frequency Marsquakes captured by InSight lander indicate a natural origin. Whereas diurnal and semi-diurnal periodicity along with Phobos’ tidal period, indicate possible artifacts due to different detection probabilities and non-seismic noise in the Martian environment. We argue that, in the context of rate-state-dependent fault friction, the gravity-induced resonance destabilization model appears to be better agreement with the contrast and relative diversity in seismicity modulation linked to the Earth, Moon, and Mars.
Ventilation control of road tunnels towards disturbance suppression
Authors: Wang Yimeng , Zhou Changxuan , Zhao Qitao , Jia Ruihan , Wu Wei 
Published: January 2024

AbstractIn recent years, research on ventilating tunnels has become increasingly important. However, the impact of external disturbances on ventilating systems has been largely ignored. To address this issue of frequent airflow fluctuations caused by external perturbations, which cannot be fully compensated using conventional control methods, this study proposes a perturbation-compensated ventilation control approach. A disturbance compensator is developed by incorporating the tunnel’s airflow velocity and the number of jet fan start-stop events as input parameters. By compensating for external disturbances, the disturbance to the system is reduced. The Simulink model of the tunnel controller was used for simulation experiments. The compensator demonstrated good tracking results in comparison experiments with different disturbances. The ventilation approach based on disturbance compensator is capable of regulating the fluctuation of CO concentration within a justifiable range compared to using PID control and ADRC. This not only improves the stability of the entire control system but also significantly prolongs the service life of the jet fan by reducing the frequency of start-stop cycles.
Optimized network based natural language processing approach to reveal disease comorbidities in COVID-19
Authors: Duman Emre Taylan , Tuna Gizem , Ak Enes , Avsar Gülben , Pir Pinar 
Published: January 2024

AbstractA novel virus emerged from Wuhan, China, at the end of 2019 and quickly evolved into a pandemic, significantly impacting various industries, especially healthcare. One critical lesson from COVID-19 is the importance of understanding and predicting underlying comorbidities to better prioritize care and pharmacological therapies. Factors like age, race, and comorbidity history are crucial in determining disease mortality. While clinical data from hospitals and cohorts have led to the identification of these comorbidities, traditional approaches often lack a mechanistic understanding of the connections between them. In response, we utilized a deep learning approach to integrate COVID-19 data with data from other diseases, aiming to detect comorbidities with mechanistic insights. Our modified algorithm in the mpDisNet package, based on word-embedding deep learning techniques, incorporates miRNA expression profiles from SARS-CoV-2 infected cell lines and their target transcription factors. This approach is aligned with the emerging field of network medicine, which seeks to define diseases based on distinct pathomechanisms rather than just phenotypes. The main aim is discovery of possible unknown comorbidities by connecting the diseases by their miRNA mediated regulatory interactions. The algorithm can predict the majority of COVID-19's known comorbidities, as well as several diseases that have yet to be discovered to be comorbid with COVID-19. These potentially comorbid diseases should be investigated further to raise awareness and prevention, as well as informing the comorbidity research for the next possible outbreak.
Boosting-based ensemble machine learning models for predicting unconfined compressive strength of geopolymer stabilized clayey soil
Authors: Abdullah Gamil M. S. , Ahmad Mahmood , Babur Muhammad , Badshah Muhammad Usman , Al-Mansob Ramez A. , Gamil Yaser , Fawad Muhammad 
Published: January 2024

AbstractThe present research employs new boosting-based ensemble machine learning models i.e., gradient boosting (GB) and adaptive boosting (AdaBoost) to predict the unconfined compressive strength (UCS) of geopolymer stabilized clayey soil. The GB and AdaBoost models were developed and validated using 270 clayey soil samples stabilized with geopolymer, with ground-granulated blast-furnace slag and fly ash as source materials and sodium hydroxide solution as alkali activator. The database was randomly divided into training (80%) and testing (20%) sets for model development and validation. Several performance metrics, including coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), and mean squared error (MSE), were utilized to assess the accuracy and reliability of the developed models. The statistical results of this research showed that the GB and AdaBoost are reliable models based on the obtained values of R2 (= 0.980, 0.975), MAE (= 0.585, 0.655), RMSE (= 0.969, 1.088), and MSE (= 0.940, 1.185) for the testing dataset, respectively compared to the widely used artificial neural network, random forest, extreme gradient boosting, multivariable regression, and multi-gen genetic programming based models. Furthermore, the sensitivity analysis result shows that ground-granulated blast-furnace slag content was the key parameter affecting the UCS.
A complex system health state assessment method with reference value optimization for interpretable BRB
Authors: Zhang Qingxi , Li Kangle , Zhang Guangling , Zhu Hailong , He Wei 
Published: January 2024

AbstractHealth condition assessment is the basis for formulating and optimizing maintenance strategies of complex systems, which is crucial for ensuring the safe and stable operation of these systems. In complex system health condition assessment, it is not only necessary for the model to handle various uncertainties to ensure the accuracy of assessment results, but also to have a transparent and reasonable assessment process and interpretable, traceable assessment results. belief rule base (BRB) has been widely used as an interpretable modeling method in health condition assessment. However, BRB-based models currently face two issues: (1) inaccuracies in expert-provided parameters that can affect the model's accuracy, and (2) after model optimization, interpretability may be reduced. Therefore, this paper proposes a new method for complex system health condition assessment called interpretable BRB with reference value optimization (I-BRB). Firstly, to address the issue of inaccurate reference values, a reference value optimization algorithm with interpretability constraints is designed, which optimizes the reference values without compromising expert knowledge. Secondly, the remaining parameters are optimized using the projection covariance matrix adaptation evolution strategy (P-CMA-ES) with interpretability constraints to improve the model's accuracy. Finally, a case study evaluating the bearing components of a flywheel system is conducted to validate the proposed method. Experimental results demonstrate that I-BRB achieves higher accuracy in health condition assessment.
Dual contrastive learning based image-to-image translation of unstained skin tissue into virtually stained H&E images
Authors: Asaf Muhammad Zeeshan , Rao Babar , Akram Muhammad Usman , Khawaja Sajid Gul , Khan Samavia , Truong Thu Minh , Sekhon Palveen , Khan Irfan J. , Abbasi Muhammad Shahmir 
Published: January 2024

AbstractStaining is a crucial step in histopathology that prepares tissue sections for microscopic examination. Hematoxylin and eosin (H&E) staining, also known as basic or routine staining, is used in 80% of histopathology slides worldwide. To enhance the histopathology workflow, recent research has focused on integrating generative artificial intelligence and deep learning models. These models have the potential to improve staining accuracy, reduce staining time, and minimize the use of hazardous chemicals, making histopathology a safer and more efficient field. In this study, we introduce a novel three-stage, dual contrastive learning-based, image-to-image generative (DCLGAN) model for virtually applying an "H&E stain" to unstained skin tissue images. The proposed model utilizes a unique learning setting comprising two pairs of generators and discriminators. By employing contrastive learning, our model maximizes the mutual information between traditional H&E-stained and virtually stained H&E patches. Our dataset consists of pairs of unstained and H&E-stained images, scanned with a brightfield microscope at 20 × magnification, providing a comprehensive set of training and testing images for evaluating the efficacy of our proposed model. Two metrics, Fréchet Inception Distance (FID) and Kernel Inception Distance (KID), were used to quantitatively evaluate virtual stained slides. Our analysis revealed that the average FID score between virtually stained and H&E-stained images (80.47) was considerably lower than that between unstained and virtually stained slides (342.01), and unstained and H&E stained (320.4) indicating a similarity virtual and H&E stains. Similarly, the mean KID score between H&E stained and virtually stained images (0.022) was significantly lower than the mean KID score between unstained and H&E stained (0.28) or unstained and virtually stained (0.31) images. In addition, a group of experienced dermatopathologists evaluated traditional and virtually stained images and demonstrated an average agreement of 78.8% and 90.2% for paired and single virtual stained image evaluations, respectively. Our study demonstrates that the proposed three-stage dual contrastive learning-based image-to-image generative model is effective in generating virtual stained images, as indicated by quantified parameters and grader evaluations. In addition, our findings suggest that GAN models have the potential to replace traditional H&E staining, which can reduce both time and environmental impact. This study highlights the promise of virtual staining as a viable alternative to traditional staining techniques in histopathology.
Molecular determinants of ASIC1 modulation by divalent cations
Authors: Liu Yi , Ma Jichun , DesJarlais Renee L. , Hagan Rebecca , Rech Jason , Liu Changlu , Miller Robyn , Schoellerman Jeffrey , Luo Jinquan , Letavic Michael , Grasberger Bruce , Maher Michael P. 
Published: January 2024

AbstractAcid-sensing ion channels (ASICs) are proton-gated cation channels widely expressed in the nervous system. ASIC gating is modulated by divalent cations as well as small molecules; however, the molecular determinants of gating modulation by divalent cations are not well understood. Previously, we identified two small molecules that bind to ASIC1a at a novel site in the acidic pocket and modulate ASIC1 gating in a manner broadly resembling divalent cations, raising the possibility that these small molecules may help to illuminate the molecular determinants of gating modulation by divalent cations. Here, we examined how these two groups of modulators might interact as well as mutational effects on ASIC1a gating and its modulation by divalent cations. Our results indicate that binding of divalent cations to an acidic pocket site plays a key role in gating modulation of the channel.
Intention to leave, depersonalisation and job satisfaction in physicians and nurses: a cross-sectional study in Europe
Authors: Maniscalco L. , Enea M. , de Vries N. , Mazzucco W. , Boone A. , Lavreysen O. , Baranski K. , Miceli S. , Savatteri A. , Fruscione S. , Kowalska M. , de Winter P. , Szemik S. , Godderis L. , Matranga D. 
Published: January 2024

AbstractThe European healthcare sector faces a significant shortage of healthcare workers. Assessing the prevalence of this issue and understanding its direct and indirect determinants are essential for formulating effective recruitment programs and enhancing job retention strategies for physicians and nurses. A multicentric cross-sectional study was conducted, involving 381 physicians and 1351 nurses recruited from eight European hospitals in Belgium, the Netherlands, Italy, and Poland. The study focused on assessing turnover intentions among healthcare workers based on the Job Demands-Resources model, using an online questionnaire. Structural equation models were employed to test the data collection questionnaires’ construct validity and internal consistency. The turnover intention was assessed by agreement with the intention to leave either the hospital or the profession. Among physicians, 17% expressed an intention to leave the hospital, while 9% intended to leave the profession. For nurses, the figures were 8.9% and 13.6%, respectively. The internal consistency of the questionnaires exceeded 0.90 for both categories of health workers. Depersonalization and job dissatisfaction were identified as direct determinants of turnover intention, with work engagement being particularly relevant for nurses. We found a higher intention to leave the hospital among physicians, while nurses were more prone to leave their profession. To mitigate turnover intentions, it is recommended to focus on improving job satisfaction, work engagement and fostering a positive working climate, thereby addressing depersonalisation and promoting job retention.
Effects of bathing in different hot spring types on Japanese gut microbiota
Authors: Takeda Midori , Choi Jungmi , Maeda Toyoki , Managi Shunsuke 
Published: January 2024

AbstractHot springs have been used for a variety of purposes, including the treatment and amelioration of illness and recreation. Japan has ten different types of therapeutic springs (described here as spa types), which are traditionally believed to have different efficacy. However, more research must be conducted to determine how they affect healthy people. Therefore, this study focused on the gut microbiota and aimed to investigate changes in the gut microbiota in healthy people after bathing in different spa types. Using Beppu's hot springs (simple, chloride, bicarbonate, sulfur, and sulfate types), 136 healthy Japanese adults living in the Kyushu area participated in the study and bathed in the same hot spring for seven days. Fecal samples were collected before and after the 7-day bathing period, and the relative abundance of the gut microbiota was determined by 16S rRNA sequencing. The results showed that the relative abundance of Bifidobacterium bifidum increased significantly after seven consecutive days of bathing in the bicarbonate spring. Significant increases in other gut microbiota were also observed after bathing in simple, bicarbonate, and sulfur springs. These results suggest that bathing in different hot springs may affect the gut microbiota in healthy individuals differently.