Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic Contrast-Enhanced Magnetic Resonance Imaging for the Prediction of Monoclonal Antibody Tumor Disposition

The prediction of monoclonal antibody (mAb) disposition within solid tumors for individual patients is difficult due to inter-patient variability in tumor physiology. Improved a priori prediction of mAb pharmacokinetics in tumors may facilitate the development of patient-specific dosing protocols and facilitate improved selection of patients for treatment with anti-cancer mAb. Here, we report the use of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), with tumor penetration of the contrast agent gadobutrol used as a surrogate, to improve physiologically based pharmacokinetic model (PBPK) predictions of cetuximab pharmacokinetics in epidermal growth factor receptor (EGFR) positive xenografts. In the initial investigations, mice bearing Panc-1, NCI-N87, and LS174T xenografts underwent DCE-MRI imaging with the contrast agent gadobutrol, followed by intravenous dosing of an 125Iodine-labeled, non-binding mAb (8C2). Tumor concentrations of 8C2 were determined following the euthanasia of mice (3 h–6 days after 8C2 dosing). Potential predictor relationships between DCE-MRI kinetic parameters and 8C2 PBPK parameters were evaluated through covariate modeling. The addition of the DCE-MRI parameter Ktrans alone or Ktrans in combination with the DCE-MRI parameter Vp on the PBPK parameters for tumor blood flow (QTU) and tumor vasculature permeability (σTUV) led to the most significant improvement in the characterization of 8C2 pharmacokinetics in individual tumors. To test the utility of the DCE-MRI covariates on a priori prediction of the disposition of mAb with high-affinity tumor binding, a second group of tumor-bearing mice underwent DCE-MRI imaging with gadobutrol, followed by the administration of 125Iodine-labeled cetuximab (a high-affinity anti-EGFR mAb). The MRI-PBPK covariate relationships, which were established with the untargeted antibody 8C2, were implemented into the PBPK model with considerations for EGFR expression and cetuximab-EGFR interaction to predict the disposition of cetuximab in individual tumors (a priori). The incorporation of the Ktrans MRI parameter as a covariate on the PBPK parameters QTU and σTUV decreased the PBPK model prediction error for cetuximab tumor pharmacokinetics from 223.71 to 65.02%. DCE-MRI may be a useful clinical tool in improving the prediction of antibody pharmacokinetics in solid tumors. Further studies are warranted to evaluate the utility of the DCE-MRI approach to additional mAbs and additional drug modalities.
The Microstructure and Property of a Titanium-Carbon Steel Clad Plate Prepared Using Explosive Welding

The microstructure and properties of pure titanium (Ti)-carbon steel clad plate prepared using explosive welding were characterized. The bonding of the welding interface was inspected using C-scanning imaging technique. The microstructure and composition of the clad were characterized with optical microscopy and scanning electron microscopy. Mechanical and corrosion properties of the clad plate were investigated using tensile test, shearing test, and potentiodynamic polarization measurement. The results show that the pure titanium and carbon steel plate are joined successfully without visible defects. The interface wave is uniform. SEM observation and EDS analyses show that some melt blocks distribute at the interface waves vortices. Hardness testes results show that after heat treating, the hardness values in the titanium layer of the clad plate are similar to the original titanium plate, whereas the values at carbon steel layer increase from the interface to 300 μm away. Tensile and shearing test results indicate that the mechanical properties of the clad meet the requirements of ASTM B898 standard. Corrosion test shows that the Ecorr of the clad plate is more positive, and icorr is 1 order of magnitude lower compared to carbon steel material, suggesting that the corrosion resistance of clad plate is better than that of carbon steel material. These results suggest that the clad plate has good bonding quality and properties to meet the processing requirement and can be safely applicable in the petrochemical field.
Mental Health Symptomatology and Posttraumatic Growth among Those with Multimorbidity in COVID Pandemic: Cross-Sectional Findings from the STRONG Study in Greece

Background: Individuals with physical or mental health conditions represent a vulnerable population, especially during the COVID-19 pandemic. However, limited information is available concerning posttraumatic growth and common mental health symptoms of this vulnerable health group during COVID-19. Methods: An online cross-sectional study (STRONG study; psychological changes and effects after COVID-19 quarantine in Greece) was conducted from 28 September 2020 (no lockdown restrictions) to 2 November 2020, just before the second lockdown in Greece. Main outcomes were depressive and anxiety symptoms as well as posttraumatic growth. Results: A total of 860 adults participated in the study. A high proportion of participants did not report any pre-existing health condition (61%), while 334 individuals reported one or more physical or mental health conditions. Overall, 20.2% of the participants reported significant depressive symptoms, and 27.9% reported moderate to high posttraumatic growth. The presence of physical and mental health conditions, either as single diagnosis or as a multimorbidity, was significantly associated with the development of depressive symptomatology (either physical or mental health conditions: OR = 1.12; 95% CI, 1.07–1.17, p < 0.001; both physical and mental health conditions: OR = 1.23; 95% CI, 1.14–1.33, p < 0.001). Posttraumatic growth did not differ between those with or without any pre-existing health issue. Conclusions: Although having a physical or/and a mental health condition predicted the development of depressive symptomatology in a post-lockdown period, the presence of pre-existing conditions was not associated with posttraumatic growth development. Clinicians should be aware of depressive symptoms among their multimorbid patients, even after exiting lockdown.
Evaluation of multiple myeloma measurable residual disease by high sensitivity flow cytometry: An international harmonized approach for data analysis

Multiple myeloma (MM) measurable residual disease (MRD) evaluated by flow cytometry is a surrogate for progression‐free and overall survival in clinical trials. However, analysis and reporting between centers lack uniformity. We designed and evaluated a consensus protocol for MM MRD analysis to reduce inter‐laboratory variation in MM MRD reporting.
Review of contemporary energy harvesting techniques and their feasibility in wireless geophones

Energy harvesting converts ambient energy to electrical energy providing numerous opportunities to realize wireless sensors. Seismic exploration is a prime avenue to benefit from it as energy harvesting equipped geophones would relieve the burden of cables which account for the biggest chunk of exploration cost and equipment weight. Since numerous energies are abundantly available in seismic fields, these can be harvested to power up geophones. However, due to the random and intermittent nature of the harvested energy, it is important that geophones must be equipped to tap from several energy sources for a stable operation. It may involve some initial installation cost but in the long run, it is cost-effective and beneficial as the sources for energy harvesting are available naturally. Extensive research has been carried out in recent years to harvest energies from various sources. However, there has not been a thorough investigation of utilizing these developments in the seismic context. In this survey, a comprehensive literature review is provided on the research progress in energy harvesting methods suitable for direct adaptation in geophones. Specifically, the focus is on small form factor energy harvesting circuits and systems capable of harvesting energy from wind, sun, vibrations, temperature difference, and radio frequencies. Furthermore, case studies are presented to assess the suitability of the studied energy harvesting methods. Finally, a design of energy harvesting equipped geophone is also proposed.
Detecting Mental Disorders through Social Media Content

Mental illness affects millions of people around the world. The popularity of social media platforms and their rapid insertion into nearly all the facets of our lives have not ceased to increase. The abundance and availability of social media content in conjunction with Machine Learning can aid the development of a suicide and depression detector by uncovering specific behavioral cues of individuals from their online posts. The study consists of building an application that uses a deep neural network model trained on the collected dataset to help create a prediction model in real-time. This application acts as a monitoring tool that can help in reducing the effects of mental illness by early detection. In this article, we developed six deep learning models in which half of them were trained with word embedding. Results demonstrated that the CNN+LSTM with word embeddings achieved the best performance with an accuracy of 97.56% after 15 epochs, followed by the LSTM model with 97.48% accuracy.
Maternal and Neonatal Polyunsaturated Fatty Acid Intake and Risk of Neurodevelopmental Impairment in Premature Infants

The N3 and N6 long chain polyunsaturated fatty acids (LCPUFA) docosahexaenoic acid (DHA) and arachidonic acid (AA) are essential for proper neurodevelopment in early life. These fatty acids are passed from mother to infant via the placenta, accreting into fetal tissues such as brain and adipose tissue. Placental transfer of LCPUFA is highest in the final trimester, but this transfer is abruptly severed with premature birth. As such, efforts have been made to supplement the post-natal feed of premature infants with LCPUFA to improve neurodevelopmental outcomes. This narrative review analyzes the current body of evidence pertinent to neurodevelopmental outcomes after LCPUFA supplementation in prematurely born infants, which was identified via the reference lists of systematic and narrative reviews and PubMed search engine results. This review finds that, while the evidence is weakened by heterogeneity, it may be seen that feed comprising 0.3% DHA and 0.6% AA is associated with more positive neurodevelopmental outcomes than LCPUFA-deplete feed. While no new RCTs have been performed since the most recent Cochrane meta-analysis in 2016, this narrative review provides a wider commentary; the wider effects of LCPUFA supplementation in prematurely born infants, the physiology of LCPUFA accretion into preterm tissues, and the physiological effects of LCPUFA that affect neurodevelopment. We also discuss the roles of maternal LCPUFA status as a modifiable factor affecting the risk of preterm birth and infant neurodevelopmental outcomes. To better understand the role of LCPUFAs in infant neurodevelopment, future study designs must consider absolute and relative availabilities of all LCPUFA species and incorporate the LCPUFA status of both mother and infant in pre- and postnatal periods.
Optimization of Modelling and Scene Drive of Channel System Based on OpenGL&Creator/Vega

Based on the previous simulation system of second branch channels in Yangling Baojixia,an optimization method for channels simulation system was put forward.Using MultiGen Creator,the hierarchy model was constructed and the control of complex models was simplified.Through combination of Vega and openGL,the problem about interactive control between 2D map and 3D scene,Lens flare effect,dynamic water control and simulation of DieShui effect was effectively solved,and the function of collision detection,realtime picking and 3d querying was realized with the Vega class and ODBC technology.The results show that,after using this method,the channel system is optimized,its development circle is shortened and the scene of Baojixa channel is rendered faithfully.
Design and Analysis of a Voltage-Mode Non-Linear Control of a Non-Minimum-Phase Positive Output Elementary Luo Converter

The positive output elementary Luo (POEL) converter is a fourth-order DC–DC converter having highly non-linear dynamic characteristics. In this paper, a new dynamic output voltage feedback controller is proposed to achieve output voltage regulation of the POEL converter. In contrast to the state-of-the-art current-mode controllers for the high-order boost converters, the proposed control strategy uses only the output voltage state variable for feedback purposes. This eliminates the need for the inductor current sensor to reduce the cost and complexity of implementation. The controller design is accompanied by a strong theoretical foundation and detailed stability analyses to obtain some insight into the controlled system. The performance of the proposed controller is then compared with a multi-loop hysteresis-based sliding-mode controller (SMC) to achieve the output voltage-regulation of the same POEL converter. The schemes are compared concerning ease of implementation, in particular, the number of state variables and current sensors required for implementation and the closed-loop dynamic performance. Experimental results illustrating the features of both controllers in the presence of input reference and load changes are presented.
The Mediating Effect of Self-Esteem on the Relationship Between Smartphone Dependency and Academic Helplessness of Adolescents According to the Smartphone Dependence REGULATION

Purpose: This study tried to verify the mediating effect of self-esteem in the effects of smartphone dependence among adolescents on academic helplessness. Method: 2,404 first-year middle school students were selected as research subjects using data form the sec-ond year of the Korea Children and Youth Panel Survey 2018. For the analysis method, the mediating effect was analyzed using the PROCESS macro 3.5(Model 4) suggested by Hayes(2017). Results: Results of the study First, there was a significant positive relationship and influence between smartphone dependence and academic helplessness of adolescents, Second, a significant negative relationship and influence between smartphone dependence and self-esteem, and Third, there was a significant negative relationship between academic helplessness and self-esteem. It has been found to influence negative relationships. Fourth, smartphone dependence indirectly affects academic helplessness through self-esteem and plays a partial mediating role. Conclusion: These results are meaningful in that they revealed the indirect mediating effect of self-esteem in the path that smartphone dependence leads to academic helplessness. Therefore, through the results of this study, it was suggested that various programs are needed to reduce smartphone dependence and academic helplessness, and to recognize and improve the importance of self-esteem in the educational field for correct study and growth of adolescents. non-face-to-face classes and culture spread due to COVID-19, smartphones have become an essential tool for adolescents to study and interact in their peer communities.
Exosomes in Parkinson: Revisiting Their Pathologic Role and Potential Applications

Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by bradykinesia, rigidity, and tremor. Considerable progress has been made to understand the exact mechanism leading to this disease. Most of what is known comes from the evidence of PD brains’ autopsies showing a deposition of Lewy bodies—containing a protein called α-synuclein (α-syn)—as the pathological determinant of PD. α-syn predisposes neurons to neurotoxicity and cell death, while the other associated mechanisms are mitochondrial dysfunction and oxidative stress, which are underlying precursors to the death of dopaminergic neurons at the substantia nigra pars compacta leading to disease progression. Several mechanisms have been proposed to unravel the pathological cascade of these diseases; most of them share a particular similarity: cell-to-cell communication through exosomes (EXOs). EXOs are intracellular membrane-based vesicles with diverse compositions involved in biological and pathological processes, which their secretion is driven by the NLR family pyrin domain-containing three proteins (NLRP3) inflammasome. Toxic biological fibrils are transferred to recipient cells, and the disposal of damaged organelles through generating mitochondrial-derived vesicles are suggested mechanisms for developing PD. EXOs carry various biomarkers; thus, they are promising to diagnose different neurological disorders, including neurodegenerative diseases (NDDs). As nanovesicles, the applications of EXOs are not only restricted as diagnostics but also expanded to treat NDDs as therapeutic carriers and nano-scavengers. Herein, the aim is to highlight the potential incrimination of EXOs in the pathological cascade and progression of PD and their role as biomarkers and therapeutic carriers for diagnosing and treating this neuro-debilitating disorder.
Design of a Condition Monitoring System for Wind Turbines

Renewable energy is being adopted worldwide, and the proportion of offshore wind turbines is increasing. Offshore wind turbines operate in harsh weather conditions, resulting in various failures and high maintenance costs. In this paper, a condition diagnosis model for condition monitoring of an offshore wind turbine has been developed. The generator, main bearing, pitch system, and yaw system were selected as components subject to the condition monitoring by considering the failure rate and downtime of the wind turbine. The condition diagnosis model works by comparing real-time and predictive operating data of the wind turbine, and about four years of Supervisory Control and Data Acquisition (SCADA) data from a 2 MW wind turbine was used to develop the model. A deep neural network and an artificial neural network were used as machine learning to predict the operational data in the condition diagnosis model, and a confusion matrix was used to measure the accuracy of the failure determination. As a result of the condition monitoring derived by inputting SCADA data to the designed system, it was possible to maintain the failure determination accuracy of more than 90%. The proposed condition monitoring system will be effectively utilized for the maintenance of wind turbines.
A 3D Agent-Based Model of Lung Fibrosis

Understanding the pathophysiology of lung fibrosis is of paramount importance to elaborate targeted and effective therapies. As it onsets, the randomly accumulating extracellular matrix (ECM) breaks the symmetry of the branching lung structure. Interestingly, similar pathways have been reported for both idiopathic pulmonary fibrosis and radiation-induced lung fibrosis (RILF). Individuals suffering from the disease, the worldwide incidence of which is growing, have poor prognosis and a short mean survival time. In this context, mathematical and computational models have the potential to shed light on key underlying pathological mechanisms, shorten the time needed for clinical trials, parallelize hypotheses testing, and improve personalized drug development. Agent-based modeling (ABM) has proven to be a reliable and versatile simulation tool, whose features make it a good candidate for recapitulating emergent behaviors in heterogeneous systems, such as those found at multiple scales in the human body. In this paper, we detail the implementation of a 3D agent-based model of lung fibrosis using a novel simulation platform, namely, BioDynaMo, and prove that it can qualitatively and quantitatively reproduce published results. Furthermore, we provide additional insights on late-fibrosis patterns through ECM density distribution histograms. The model recapitulates key intercellular mechanisms, while cell numbers and types are embodied by alveolar segments that act as agents and are spatially arranged by a custom algorithm. Finally, our model may hold potential for future applications in the context of lung disorders, ranging from RILF (by implementing radiation-induced cell damage mechanisms) to COVID-19 and inflammatory diseases (such as asthma or chronic obstructive pulmonary disease).
A Combined Atomic and Molecular Probe Characterization of Aromatic Hydrocarbons via PALS and ESR: Methylbenzene

A combined study of one of the simplest aromatic hydrocarbons, i.e., methylbenzene (toluene) (TOL), via the annihilation of an ortho-positronium (o-Ps) probe via positron annihilation lifetime spectroscopy (PALS) and the rotation dynamics of nitroxide spin probe 2,2,6,6-tetramethyl-piperidinyl-1-oxy (TEMPO) using electron spin resonance (ESR) over a wide temperature range, 10–300 K, is reported. The o-Ps lifetime, τ3, and the relative o-Ps intensity, I3, as a function of temperature exhibit changes defining several characteristic PALS temperatures in the slowly and rapidly cooled samples. Similarly, the spectral parameter of TEMPO mobility in TOL, 2Azz‘, and its correlation time, τc, reveal several effects at a set of the characteristic ESR temperatures, which were determined and compared with the PALS results. Finally, the physical origins of the changes in free volume expansion and spin probe mobility are revealed. They are reflected in a series of the mutual coincidences between the characteristic PALS and ESR temperatures and appropriate complementary thermodynamic and dynamic techniques.
Impact of contracting on occupational injuries and fatalities in underground coal mining

Contracting out of mining operations has increased significantly in the underground coal mining industry over the last two decades. Contract miners are hired by coal operators to extract coal, usually for a specified price and using equipment often leased from the operator or mine controller. We hypothesize that the market and organizational incentives facing contractors increase the likelihood of traumatic injuries and fatalities to their workforce relative to miners working directly for operators in comparable mines. We test this hypothesis using data from the Mine Safety and Health Administration for the period 2000-2010. We statistically model the determinants of injuries and fatalities based on contract status along with mining methods, geologic characteristics, union status, size of mine and controller, and prior history of violations. Our preliminary results suggest increased risk exposure for contract operations and mines with high contractor utilization. These findings have important implications for enforcement and public policies and extend the evidence of the adverse impact of forms of subcontracting on health and safety to the mining sector.
Artificial neural networks in cardiology: analysis of graphic data

Aim. To consider application of convolutional neural networks for processing medical images in various fields of cardiology and cardiac surgery using publications from 2016 to 2019 as an example.Materials and methods. In the study, we used the following scientific databases: PubMed Central, ArXiv, ResearchGate. The cited publications were grouped by the area of interest (heart, aorta, carotid arteries). Results. The general principle of work of the technology under consideration was described, the results were shown, and the main areas of application of this technology in the studies under consideration were described. For most of the studies, sample sizes were given. The author’s view on the development of convolutional neural networks in medicine was presented and some limiting factors for their distribution were listed.Conclusion. A brief overview shows possible areas of application of convolutional neural networks in the fields of cardiology and cardiac surgery. Without denying the existing problems, this type of artificial neural networks may help many doctors and researchers in the future.