Miracle-dz/UniFAR-FTUs huggingface dataset:
Meropenem extraction by ex vivo extracorporeal life support circuits

Background: Meropenem is a broad-spectrum carbapenem-type antibiotic commonly used to treat critically ill patients infected with extended-spectrum β-lactamase (ESBL)-producing Enterobacteriaceae. As many of these patients require extracorporeal membrane oxygenation (ECMO) and/or continuous renal replacement therapy (CRRT), it is important to understand how these extracorporeal life support circuits impact meropenem pharmacokinetics. Based on the physicochemical properties of meropenem, it is expected that ECMO circuits will minimally extract meropenem, while CRRT circuits will rapidly clear meropenem. The present study seeks to determine the extraction of meropenem from ex vivo ECMO and CRRT circuits and elucidate the contribution of different ECMO circuit components to extraction. Methods: Standard doses of meropenem were administered to three different configurations (n = 3 per configuration) of blood-primed ex vivo ECMO circuits and serial sampling was conducted over 24 h. Similarly, standard doses of meropenem were administered to CRRT circuits (n = 4) and serial sampling was conducted over 4 h. Meropenem was administered to separate tubes primed with circuit blood to serve as controls to account for drug degradation. Meropenem concentrations were quantified, and percent recovery was calculated for each sample. Results: Meropenem was cleared at a similar rate in ECMO circuits of different configurations (n = 3) and controls (n = 6), with mean (standard deviation) recovery at 24 h of 15.6% (12.9) in Complete circuits, 37.9% (8.3) in Oxygenator circuits, 47.1% (8.2) in Pump circuits, and 20.6% (20.6) in controls. In CRRT circuits (n = 4) meropenem was cleared rapidly compared with controls (n = 6) with a mean recovery at 2 h of 2.36% (1.44) in circuits and 93.0% (7.1) in controls. Conclusion: Meropenem is rapidly cleared by hemodiafiltration during CRRT. There is minimal adsorption of meropenem to ECMO circuit components; however, meropenem undergoes significant degradation and/or plasma metabolism at physiological conditions. These ex vivo findings will advise pharmacists and physicians on the appropriate dosing of meropenem.
Reconstruction of past atmospheric CO/sub 2/ contents from the chemistry of the contemporary ocean: an evaluation

Previous attempts to reconstruct the preanthropogenic CO/sub 2/ content of the atmosphere from . sigma.CO/sub 2/, alkalinity, and CO/sub 2/ partial pressure (pCO/sub 2/) data for the contemporary ocean are evaluated. Also, new attempts based on the more extensive Transient Tracers in the Ocean (TTO) data for the North Atlantic are made. The conclusion is that such quests are quite likely destined to failure in that the uncertainties in the reconstructions will always be larger than the uncertainties ultimately available from other approaches (i.e., ice cores and /sup 13/C record deconvolutions). The difficulty lies in the fact that successful deconvolution requires the solution of a host of basic problems in the ocean sciences. To solve these problems, we must (1) develop satisfactory ocean mixing models; (2) understand the factors controlling pCO/sub 2/ in surface waters (especially those in the winter for high latitudes); (3) determine the chemical composition of material being oxidized at various places and depths in the ocean; and (4) determine the factors controlling the pattern of alkalinity within the sea. We conclude that the value of the ocean data set lies in understanding processes taking place in the sea rather than in reconstructing past atmospheric CO/sub 2/ contents.more » Once the history of atmospheric CO/sub 2/ has been reconstructed from ice core CO/sub 2/ and /sup 13/C deconvolutions, the ocean . sigma.CO/sub 2/ and pCO/sub 2/ data will become a useful tracer in studies of ocean chemistry and ventilation. 33 references, 32 figures, 10 tables. « less
A UAV Spectrum Classification Framework Based on ResNet50 Algorithm

Unmanned Aerial Vehicle (UAV) category recognition is a significant problem for airport airspace operation. An accurate and efficient identification of UAV is expected to fast predict of UAV behavior and implement the defense. This study presents an UAV spectrum classification framework using radio frequency (RF) signals. In the framework, the spectrum is combined with the ResNet50 neural network model. An open source dataset namely DroneRF is used to test the efficiency of the proposed method. The experimental results show that the highest accuracy rate of the proposed framework reached to 100%. Compared with the state-of-the-art method, this study has a higher improvement of accuracy rates.
Diabetic Endothelial Cell Glycogen Synthase Kinase 3β Activation Induces VCAM1 Ectodomain Shedding

Soluble cell adhesion molecules (sCAMs) are secreted ectodomain fragments of surface adhesion molecules, ICAM1 and VCAM1. sCAMs have diverse immune functions beyond their primary function, impacting immune cell recruitment and activation. Elevated sVCAM1 levels have been found to be associated with poor cardiovascular disease (CVD) outcomes, supporting VCAM1’s role as a potential diagnostic marker and therapeutic target. Inhibiting sVCAM1’s release or its interaction with immune cells could offer cardioprotection in conditions such as diabetes. Membrane-bound surface adhesion molecules are widely expressed in a wide variety of cell types with higher expression in endothelial cells (ECs). Still, the source of sCAMs in the circulation is not clear. Hypothesizing that endothelial cells (ECs) could be a potential source of sCAMs, this study investigated whether dysfunctional EC signaling mechanisms during diabetes cause VCAM1 ectodomain shedding. Our results from samples from an inducible diabetic mouse model revealed increased sVCAM1 plasma levels in diabetes. Protein analysis indicated upregulated VCAM1 expression and metalloproteases ADAM10 and ADAM17 in diabetic ECs. ADAMs are known for proteolytic cleavage of adhesion molecules, contributing to inflammation. GSK3β, implicated in EC VCAM1 expression, was found to be activated in diabetic ECs. GSK3β activation in control ECs increased ADAM10/17 and VCAM1. A GSK3β inhibitor reduced active GSK3β and VCAM1 ectodomain shedding. These findings suggest diabetic ECs with elevated GSK3β activity led to VCAM1 upregulation and ADAM10/17-mediated sVCAM1 shedding. This mechanism underscores the potential therapeutic role of GSK3β inhibition in reducing the levels of circulating sVCAM1. The complex roles of sCAMs extend well beyond CVD. Thus, unraveling the intricate involvement of sCAMs in the initiation and progression of vascular disease, particularly in diabetes, holds significant therapeutic potential.
Class-level and token-level approaches for test impact analysis

The objective of the thesis is to examine the efficacy of test impact analysis in a setting of continuous testing, where automated test cases are routinely run to guarantee the integration of high-quality codes. While continuous testing can enhance code quality and minimize maintenance workload, it also leads to a notable rise in overhead for test execution. In our study on test impact analysis, we developed a novel static class level technique that utilizes JavaParser to create a dependency graph between test cases and source code classes based on abstract syntax trees. We applied this technique to seven Java systems and evaluated it against two configurations and discovered a correlation between the effectiveness of test impact analysis and the degree of interdependence between test cases and source code classes. Our study confirmed previous research indicating that even with minimal code changes in commits, roughly 40% of test cases were impacted and required execution on average in the first configuration (changed source files), while approximately 58% of test cases were impacted on average in the second configuration (changed class tokens). Nevertheless, despite these findings, we assert that test impact analysis is an effective tool for reducing test overhead. Moreover, we compared our proposed test impact analysis (TIA) technique with a state-of-the-art static class-level technique on commonly studied systems and observed that our approach achieved an improvement of approximately 13% in selecting impacted tests in the changed source files configuration, while it underperformed by about 6.5% in the changed class tokens configuration.
Simulation and prototyping of a knee assistive device for improved gait function

This study aimed to develop a knee-assistive device while walking. The research was separated into two sections: the gait support simulation in MATLAB and the prototype of the device. Data on body part positions and ground reaction force were collected from three adult Thai participants walking at a speed of 1.5 m/s to calculate knee moment. The simulation section provides support moments during walking using machine learning and artificial stiffness control strategy (MLASCS), composed of the kNN model and the instantaneous artificial stiffness per body mass (IASPB) equations. The MLASCS was used to determine the proper amount of support moment required to assist walking, and its validation via the recorded data showed that it could reduce the total effort by up to 63.4%. In the prototype section, the posterior-support device was designed using a 3D printing filament and tested for durability. The control system used an actuator replicated from an MIT mini-cheetah servo motor that commanded various parameters such as angular, angular velocity, angular stiffness, angular damping coefficient, and angular moment and provided feedback in the form of angular angular velocity and angular moment. Due to a significant increase in delay time when connecting the microcontroller to the device, the sets of the if-else function called a state classifier combined with the IASPB equations were selected as the control system instead of the MLASCS. Efficiency testing was conducted using electromyography (EMG) sensors, which revealed mixed results that the device was sometimes helpful and sometimes not helpful. These may be due to an imperfect gait cycle, motor command delays, and misalignment of the device, indicating that further data collection and validation with more samples is necessary to verify the device's usefulness.
Pencil lead graphite electrochemically modified with polyglutamic acid as a sensor for detection of enrofloxacin in aqueous media

This study investigates the modification of pencil lead graphite electrodes with polyglutamic acid using an effective and fast static method to develop a sensor for the detection of enrofloxacin (ENR). The successful fabrication of pGA on the electrode surface was confirmed by scanning electron microscopy, energy dispersive X-ray analysis, and Fourier-transform infrared spectroscopy. The conditions of electrochemical modification, including the applied potentials and number of cycles in the potentiostatic process, were systematically investigated to determine their effects on the ENR electrochemical response. The pH of the electrolyte media was also explored to elucidate the electrochemical reaction mechanism of ENR. The developed electrochemical sensor was evaluated using square wave stripping voltammetry for ENR detection. Under optimal conditions, the sensor demonstrated good reproducibility with a relative standard deviation of 4.3% (from five measurements) for ENR signal detection. A linear relationship between ENR concentration and its peak current was observed in the concentration range of 0.1 to 5 μM, with a high correlation coefficient of 0.9988. The limit of detection for ENR using the sensor was 0.12 μM. Our findings provide valuable insights into the design and optimisation of pencil lead graphite electrode-based sensors for ENR detection in aqueous media.
A comparison of imbalanced data handling methods for pre-trained model in multi-label classification of stack overflow

Tag classification is essential in Stack Overflow. Instead of combining through pages or replies of irrelevant information, users can easily and quickly pinpoint relevant posts and answers using tags. Since User-submitted posts can have multiple tags, classifying tags in Stack Overflow can be challenging. This results in an imbalance problem between labels in the whole labelset. Pretrained deep learning models with small datasets can improve tag classification accuracy. Common multi-label resampling techniques with machine learning classifiers can also fix this issue. Still, few studies have explored which resampling technique can improve the performance of pre-trained deep models for predicting tags. To address this gap, we experimented to evaluate the effectiveness of ELECTRA, a powerful deep learning pre-trained model, with various multi-label resampling techniques in decreasing the imbalance that induces mislabeling in Stack Overflow's tagging posts. We compared six resampling techniques, such as�ML-ROS, MLSMOTE, MLeNN, MLTL, ML-SOL, and REMEDIAL, to find the best method to mitigate the imbalance and improve tag prediction accuracy. Our results show that MLTL is the most effective selection to tackle the inequality in multi-label classification for our Stack Overflow data with deep learning scenarios. MLTL achieved 0.517, 0.804, 0.467, and 0.98 from the metrics Precision@1, Recall@5, F1-score@1, and AUC, respectively. Conversely, MLeNN gained only 0.323, 0.648, 0.277, and 0.95 from the same metrics. �
Redesigning Primary Care: The Emergence of Artificial-Intelligence-Driven Symptom Diagnostic Tools

Modern healthcare is facing a juxtaposition of increasing patient demands owing to an aging population and a decreasing general practitioner workforce, leading to strained access to primary care. The coronavirus disease 2019 pandemic has emphasized the potential for alternative consultation methods, highlighting opportunities to minimize unnecessary care. This article discusses the role of artificial-intelligence-driven symptom checkers, particularly their efficiency, utility, and challenges in primary care. Based on a study conducted in Italian general practices, insights from both physicians and patients were gathered regarding this emergent technology, highlighting differences in perceived utility, user satisfaction, and potential challenges. While symptom checkers are seen as potential tools for addressing healthcare challenges, concerns regarding their accuracy and the potential for misdiagnosis persist. Patients generally viewed them positively, valuing their ease of use and the empowerment they provide in managing health. However, some general practitioners perceive these tools as challenges to their expertise. This article proposes that artificial-intelligence-based symptom checkers can optimize medical-history taking for the benefit of both general practitioners and patients, with potential enhancements in complex diagnostic tasks rather than routine diagnoses. It underscores the importance of carefully integrating digital innovations while preserving the essential human touch in healthcare. Symptom checkers offer promising solutions; ensuring their accuracy, reliability, and effective integration into primary care requires rigorous research, clinical guidance, and an understanding of varied user perceptions. Collaboration among technologists, clinicians, and patients is paramount for the successful evolution of digital tools in healthcare.
Computational and experimental characterization of intra-aortic balloon pump support

Because of the large number of interaction factors involved, the effects of the intra‐ aortic balloon pump (IABP) have not been investigated deeply. To enhance its clinical efficiency and to better define indications for use, advanced models are required to test the interaction between the IABP and the cardiovascular system. A patient with mild blood pressure depression and a lowered cardiac output is modeled in a lumped parameter computational model, developed with physiologically representative elements for relevant components of circulation and device. IABP support is applied, and the moments of balloon inflation and deflation are varied around their conventional timing modes. For validation purposes, timing is adapted within acceptable ranges in ten patients undergoing IABP therapy for typical clinical indications. In both model and patients, the IABP induces a diastolic blood pressure augmentation as well as a systolic reduction in afterload. The support capabilities of the IABP benefit the most when the balloon is deflated simultaneously with ventricular contraction, whereas inflation before onset of diastole unconditionally interferes with ejection. The physiologic response makes the model an excellent tool for testing the interaction between the IABP and the cardiovascular system, and how alterations of specific IABP parameters (i.e., timing) affect this coupling. Computational modeling of the IABP | 25
A Comparison of Residential and Visitor Attitudes Toward Experiential Impacts, Environmental Conditions and Management Strategies on the Delaware Inland Bays

This paper compares different attitudes toward experiential impacts on boating, environmental conditions, and proposed management strategies held by permanent residents, seasonal residents and seasonal visitors to the Delaware Inland Bays. The 11 study found variation in opinions held by each group indicating the tourism man_ager should collect information from all groups before developing policy. Additionally, the manager should understand the varying impacts on boater satisfaction depending upon conditions.
Transformer-Based Multi-Modal Data Fusion Method for COPD Classification and Physiological and Biochemical Indicators Identification

As the number of modalities in biomedical data continues to increase, the significance of multi-modal data becomes evident in capturing complex relationships between biological processes, thereby complementing disease classification. However, the current multi-modal fusion methods for biomedical data require more effective exploitation of intra- and inter-modal interactions, and the application of powerful fusion methods to biomedical data is relatively rare. In this paper, we propose a novel multi-modal data fusion method that addresses these limitations. Our proposed method utilizes a graph neural network and a 3D convolutional network to identify intra-modal relationships. By doing so, we can extract meaningful features from each modality, preserving crucial information. To fuse information from different modalities, we employ the Low-rank Multi-modal Fusion method, which effectively integrates multiple modalities while reducing noise and redundancy. Additionally, our method incorporates the Cross-modal Transformer to automatically learn relationships between different modalities, facilitating enhanced information exchange and representation. We validate the effectiveness of our proposed method using lung CT imaging data and physiological and biochemical data obtained from patients diagnosed with Chronic Obstructive Pulmonary Disease (COPD). Our method demonstrates superior performance compared to various fusion methods and their variants in terms of disease classification accuracy.
Discrete-Event Simulation Model for Monitoring Elderly and Patient’s Smart Beds

In this paper, we explore the development of a simulation model to assist in the decision-making process for monitoring the health conditions of elderly patients using data from their smart beds. The main objective of the proposed simulation model is to estimate the transitional condition of the health system by tracking patients’ physiological signals via a growth model. This approach allows the model to mimic the dynamics of various health-related issues faced by patients and the elderly. When combined with real-world data and practical expertise, the discrete-event simulation model can become a valuable tool for improving patient health and streamlining medical treatment processes. The proposed simulation model incorporates input from subject-matter experts and utilises statistical analysis to fit essential parameters based on patient data obtained from smart beds. However, authors should consider rephrasing this section to improve clarity and coherence.
Efficient Removal of Paraquat Pollutants Using Magnetic Biochar Derived from Corn Husk Waste: A Sustainable Approach for Water Remediation

Due to the widespread production of maize, the waste created by this crop has become a serious concern. This study applied the concept of waste circulation to the production of magnetic biochar from corn husk waste to remediate paraquat-contaminated water. Magnetic biochar (MB) was produced by impregnating maize husks with iron and carbonizing the residue in a nitrogen environment. Carbonized MB at the temperature of 850°C (MB-01-850) exhibited a combination of microporous and mesoporous structures ( V meso = 0.30 c m 3 / g , V micro = 0.12 c m 3 / g ), while biochar created only a microporous structure ( V micro = 0.11 c m 3 / g ). According to the findings, Fe(NO3)3 significantly affected the increase in mesopore formation after carbonization. In addition, biochar exhibits excellent magnetic responsiveness. MB-01-850 reached equilibrium within approximately 20 min in synthetic water. Batch adsorption studies showed that MB-01-850 had maximum adsorption capacities ( Q 0 ) of 34.97 mg/g and 31.63 mg/g for synthetic and natural water, respectively. The unmodified biochar (without mesopores) had a Q 0 of 4.08 mg/g. This indicates that the presence of mesopores improves the effectiveness of paraquat adsorption. Additionally, the adsorption performance of magnetic biochar exhibited no statistically significant variance when tested under natural water conditions. Furthermore, magnetic biochar demonstrates impressive regeneration capacity, allowing it to be regenerated almost entirely for a minimum of four cycles using a sodium hydroxide (NaOH) solution with a concentration equal to or greater than 0.5 M.
Databases on Modern Networks: A Decade of Research that now comes into Practice

Modern cloud networks are a fundamental pillar of data-intensive applications. They provide high-speed transaction (packet) rates and low overhead, enabling, for instance, truly scalable database designs. These networks, however, are fundamentally different from conventional ones. Arguably, the two key discerning technologies are RDMA and programmable network devices. Today, these technologies are not niche technologies anymore and are widely deployed across all major cloud vendors. The question is thus not if but how a new breed of data-intensive applications can benefit from modern networks, given the perceived difficulty in using and programming them. This tutorial addresses these challenges by exposing how the underlying principles changed as the network evolved and by presenting the new system design opportunities they opened. In the process, we also discuss several hard-earned lessons accumulated by making the transition first-hand.