Miracle-dz/UniFAR-FTUs huggingface dataset:
Exosomal Proteins and Lipids as Potential Biomarkers for Lung Cancer Diagnosis, Prognosis, and Treatment

Simple Summary Exosomes (or extracellular vesicles) are known to mediate intercellular communication and to transmit molecular signals between cells. Molecules carried by exosomes have their own molecular roles in affecting surrounding and distant environment, as well as recipient cells. Molecular components of exosomes can be used as cancer biomarkers for diagnosis and prognosis, being promising therapeutic targets for the interruption of cellular signals. Therefore, the understanding of the molecular compositions and their functional indications of exosomes has the potential to help doctors to diagnose and monitor diseases and to allow researchers to design and develop potential targeted therapies. This review aims to provide a comprehensive protein and lipid characterization of lung cancer exosomes and to explore their molecular functions and mechanisms regulating physiological and pathological processes. This organization offers informative insight for lung cancer diagnosis and treatment. Abstract Exosomes participate in cell–cell communication by transferring molecular components between cells. Previous studies have shown that exosomal molecules derived from cancer cells and liquid biopsies can serve as biomarkers for cancer diagnosis and prognosis. The exploration of the molecules transferred by lung cancer-derived exosomes can advance the understanding of exosome-mediated signaling pathways and mechanisms. However, the molecular characterization and functional indications of exosomal proteins and lipids have not been comprehensively organized. This review thoroughly collected data concerning exosomal proteins and lipids from various lung cancer samples, including cancer cell lines and cancer patients. As potential diagnostic and prognostic biomarkers, exosomal proteins and lipids are available for clinical use in lung cancer. Potential therapeutic targets are mentioned for the future development of lung cancer therapy. Molecular functions implying their possible roles in exosome-mediated signaling are also discussed. Finally, we emphasized the importance and value of lung cancer stem cell-derived exosomes in lung cancer therapy. In summary, this review presents a comprehensive description of the protein and lipid composition and function of lung cancer-derived exosomes for lung cancer diagnosis, prognosis, and treatment.
Exploring DC/DC Converters with PowerESIM: A Laboratory Manual

Summary Report for 4 Corners Test . Here, other than the original report, four more reports under the testing conditions, a) minimum input voltage and maximum output current; b) maximum input voltage and maximum output current; c) minimum input voltage and minimum output current; d) maximum input voltage and minimum output current; will be generated. These operating conditions can be modified by clicking Detail Spec. in the main page of PowerESim. 3. Check on the checkbox “ Use Thermal Analysis result for all analytical tools ”. Click on Recalculate . This ensures that the verification test includes the actual thermal property of the components due to their orientations and placements. 4. The Result column gives the status of the verification. Go through the list for any rejected components. Click on the left blue button of rejected components. Change to components that pass the verification. Note that the message “ warning” represents a marginal pass, and it does not signify reject. 5.
A Tabu Search Heuristic for Building Aircraft Maintenance Personnel Rosters

This paper presents a fast heuristic approach to optimize staffing and scheduling of the workforce at an aircraft maintenance company. Two linked Tabu Search algorithms are proposed to minimize the labour costs and to maximize employee satisfaction. To enhance the performance of the algorithm, strategic oscillation and pattern creation are used. We illustrate the performance of the algorithms with a computational experiment based on real life data from Sabena Technics, a large aircraft maintenance company located at Brussels Airport in Belgium. We analyze the results in different scenarios and compare them to a mixed integer linear programming model. The experiments demonstrate that Tabu Search can deliver acceptable solutions faster than the integer programming technique for small instances and outperforms the integer programming model for larger and more realistic problem sizes.
Adaptive Authentication Protocol Based on Zero-Knowledge Proof

Authentication protocols are expanding their application scope in wireless information systems, among which are low-orbit satellite communication systems (LOSCS) for the OneWeb space Internet, automatic object identification systems using RFID, the Internet of Things, intelligent transportation systems (ITS), Vehicular Ad Hoc Network (VANET). This is due to the fact that authentication protocols effectively resist a number of attacks on wireless data transmission channels in these systems. The main disadvantage of most authentication protocols is the use of symmetric and asymmetric encryption systems to ensure high cryptographic strength. As a result, there is a problem in delivering keys to the sides of the prover and the verifier. At the same time, compromising of keys will lead to a decrease in the level of protection of the transmitted data. Zero-knowledge authentication protocols (ZKAP) are able to eliminate this disadvantage. However, most of these protocols use multiple rounds to authenticate the prover. Therefore, ZKAP, which has minimal time costs, is developed in the article. A scheme for adapting protocol parameters has been developed in this protocol to increase its efficiency. Reductions in the level of confidentiality allow us to reduce the time spent on the execution of the authentication protocol. This increases the volume of information traffic. At the same time, an increase in the confidentiality of the protocol entails an increase in the time needed for authentication of the prover, which reduces the volume of information traffic. The FPGA Artix-7 xc7a12ticsg325-1L was used to estimate the time spent implementing the adaptive ZKAP protocol. Testing was performed for 32- and 64-bit adaptive authentication protocols.
Nanostructured Metal Oxide Semiconductors towards Greenhouse Gas Detection

Climate change and global warming are two huge current threats due to continuous anthropogenic emissions of greenhouse gases (GHGs) in the Earth’s atmosphere. Accurate measurements and reliable quantifications of GHG emissions in air are thus of primary importance to the study of climate change and for taking mitigation actions. Therefore, the detection of GHGs should be the first step when trying to reduce their concentration in the environment. Throughout recent decades, nanostructured metal oxide semiconductors have been found to be reliable and accurate for the detection of many different toxic gases in air. Thus, the aim of this article is to present a comprehensive review of the development of various metal oxide semiconductors, as well as to discuss their strong and weak points for GHG detection.
Quantifying the Carbon Export and Sequestration Pathways of the Ocean's Biological Carbon Pump

The ocean's biological carbon pump transfers carbon from the surface ocean to the deep ocean by several distinct pathways, including gravitational settling of organic particles, mixing and advection of suspended organic carbon, and active transport by vertically migrating metazoans. Carbon exported by these pathways can be sequestered as respired CO2 in the deep ocean for years to centuries. However, the contribution of each pathway to carbon export and sequestration remains highly uncertain. Here, satellite and in situ ocean biogeochemical observations are assimilated in an ensemble numerical model of the biological pump to quantify global and regional carbon export and sequestration. The ensemble mean global carbon export is 10.2 Pg C yr−1 and the total amount of carbon sequestered via the biological pump is 1,300 Pg C. The gravitational pump is responsible for 70% of the total global carbon export, 85% of which is zooplankton fecal pellets and 15% is sinking phytoplankton aggregates, while migrating zooplankton account for 10% of total export and physical mixing is responsible for the remaining 20%. These pathways have different sequestration times, with an average of 140 years for the gravitational pump, 150 years for the migrant pump, and only 50 years for the mixing pump. Regionally, the largest sequestration inventories and longest sequestration times are found in the northern high latitudes, while the shortest sequestration times are found in the subtropical gyres. These results suggest that ocean carbon storage will weaken as the oceans stratify and the subtropical gyres expand due to anthropogenic climate change.
A Learning-Based Service Function Chain Early Fault Diagnosis Mechanism Based on In-Band Network Telemetry

SUMMARY Network virtualization has become a promising paradigm for supporting diverse vertical services in Software Deﬁned Networks (SDNs). Each vertical service is carried by a virtual network (VN), which normally has a chaining structure. In this way, a Service Function Chain (SFC) is composed by an ordered set of virtual network functions (VNFs) to provide tailored network services. Such new programmable ﬂexibilities for future networks also bring new network management challenges: how to collect and analyze network measurement data, and further predict and diagnose the performance of SFCs? This is a fundamental problem for the management of SFCs, because the VNFs could be migrated in case of SFC performance degradation to avoid Service Level Agreement (SLA) violation. Despite the importance of the problem, SFC performance analysis has not attracted much research attention in the literature. In this current paper, enabled by a novel detailed network debugging technology, In-band Network Telemetry (INT), we propose a learning based framework for early SFC fault prediction and diagnosis. Based on the SFC tra ﬃ c ﬂow measurement data provided by INT, the framework ﬁrstly extracts SFC performance features. Then, Long Short-Term Memory (LSTM) networks are utilized to predict the upcoming values for these features in the next time slot. Finally, Support Vector Machine (SVM) is utilized as network fault classiﬁer to predict possible SFC faults. We also discuss the practical utilization relevance of the proposed framework, and conduct a set of network emulations to validate the performance of the proposed framework.
A Matrix Approach to Homeland Security Professional Education

The field of Homeland Security (HLS) has entered a stage where it is not yet a fully established profession. Drawing on the experience of Public Administration (PA) it is suggested that HLS act like a profession to advance the knowledge, skills, and values essential to its role. To do this, the case example of HLS education at Pennsylvania State University is reviewed. Penn State has chosen a matrix form of organization to provide a core of professional identity while allowing students to choose from five options to guide their program of study. It is argued that this approach provides a balance of common and specific subject matter that corresponds to the need for HLS to evolve as a loosely coupled but emergent profession.
Second Harmonic Weighted Reconstruction for Non-Contact Monitoring Heart Rate

Achieving high-accuracy and non-contact heart rate monitoring via radar is a non-trivial task under the interference from respiration and its harmonics. In order to eliminate the interference, we propose a weighted reconstruction method of the second harmonic signal of the heartbeat for the heart rate estimation. The proposed method combines the Variational Modal Decomposition (VMD) algorithm with the prior information about the second harmonic of the heartbeat. Namely, after a sophisticated endeavor for preprocessing, the chest wall displacement signal is decomposed into several quasi-orthogonal components by VMD and then those components whose spectrum are within the second harmonic frequency band of the heartbeat are selected to reconstruct the second harmonic signal of the heartbeat. For highlighting more likely second harmonic components and suppressing undesired noise components, the energy of each selected component is used as a weight to adjust its proportion in the reconstructed signal. Consequently, the heart rate estimation is achieved indirectly by estimating the spectral peak of the second harmonic signal. The proposed method not only avoids the strong interference from the respiratory signal and its higher harmonics on heart rate estimation, but also predigests the parameter setting in the spectral peak estimation model. Experimental results from 15 subjects demonstrate that the proposed method can achieve higher measurement accuracy compared with other several methods based on a 77 GHz FMCW radar.
Modeling and Attitude Control Analysis of a Spherical VTOL Aerial Vehicle

The aim of this paper is to present a con guration for a multi-functional spherical robotic vehicle designed for search and rescue purposes, which combines the advantages of hover, level ight, and ground mobility. The spherical frame allows the vehicle to encounter an object without the risk of damaging the propeller and onboard components, and can land anywhere without the need of coordinated landing maneuverability. Called the Unmanned Flying and Rolling Orb (UFRO), the Japan Flying Sphere (JFS) developed by the Technical Research and Development Institute (TRDI) of Japan is used as the template for the preliminary design. The current prototype consists of a single propeller and eight control vanes enclosed in a reinforced spherical frame. A detailed dynamic model is established using the Newton-Euler formulation and linearized to estimate the attitude for hovering ight in trim conditions. Successful attitude stability during takeo and maintaining hover-mode ight is demonstrated using a COTS PID controller.
Design of a recharge station for UAVs using non-contact wireless power transfer

Power infrastructure inspection is a requirement task to assure proper functioning of its distribution. Comparing to current practice, this task can be performed by safer and more economical alternatives, like the application of electrical UAVa. The main problem with this type of vehicles is the low endurance/range capabilities due to the high expend rate and the batteries characteristics. This research proposes a possible way to achieve longer distance& and flight times by means of wireless recharging. We made the study and preliminary design of a strongly coupled magnetic resonance (SCAR) system that can be located inside the current infrastructure and that can transmit electric power to the UAV for battery recharge.
Systematic literature review on the neonatal outcome of preeclampsia

Preeclampsia is a pregnancy-specific multisystem disorder that is a leading cause of maternal and foetal/neonatal morbidity and mortality. Thus this systematic review aims to identify the neonatal outcomes of preeclamptic patients. A systematic literature review of works published between January 2015 and March 2021 written in the English language and freely accessed online were used considering the PRISMA guidelines. The results from the search were managed using the endnote X7 software and extracted data from the full articles were documented in Microsoft Word. The neonatal outcomes of preeclampsia identified are; preterm birth, stillbirth, low birth weight (LBW), low Apgar score, intrauterine growth reduction (IUGR), neonatal intensive care unit (NICU) admission are foetal/neonatal outcomes of preeclampsia and were subsequently classified into six groups according to the similarities of their outcome; group 1: death related neonatal outcomes, group 2: weight-related neonatal outcomes, group 3: prematurity related neonatal outcomes, group 4: respiratory related neonatal outcomes, group 5: injury-related neonatal outcomes, and Group 6: internal organ related outcome. The magnitude of occurrence of the classified neonatal outcomes is; respiratory-related neonatal outcome, death-related neonatal outcome, weight-related neonatal outcome, prematurity related neonatal outcome, internal related neonatal outcome and injury-related outcome in that sequence. All round interventions to improve neonatal morbidity and mortality of preeclamptic mothers should be targeted in addition to adequate provision of health/ medical resources for the tending of preterm neonates.
Covid-19 Detection by using Deep learning-based Custom Convolution Neural Network (CNN)

The early diagnosis and treatment of COVID-19 has been a challenge all over the world. It is challenging to manufacture many testing kits and even then, their accuracy rate is very low. Studies carried out recently show that chest x-ray images are of great help in the diagnosis of COVID-19. In this study, we have developed a COVID-19 detection model that by observing the chest x-ray images of the patient, detects that either the patient is affected by COVID-19 or not. The model is developed using a custom Convolutional Neural Network (CNN) that differentiates between COVID-19 and healthy x-ray images so that the patient can be diagnosed and quarantined on time to prevent the spread of the pandemic. We used two different datasets which are publicly available for the training and validation of this model. Upon completion, the proposed model yields an accuracy of almost 98%. Upon further training, our model will be able to be used as a COVID-19 detection tool in hospitals worldwide and will play a vital role in early detection and timely containment of the pandemic.
Left Atrial Appendage Thrombus in Patients with Nonvalvular Atrial Fibrillation before Catheter Ablation and Cardioversion: Risk Factors beyond the CHA2DS2-VASc Score

Left atrial appendage thrombus (LAAT) is a surrogate of thromboembolic events in patients with nonvalvular atrial fibrillation (NVAF). We aimed to investigate the risk factors for LAAT formation before catheter ablation and cardioversion beside the CHA2DS2-VASc score. In this case-control study, patients with NVAF who underwent transesophageal echocardiography (TEE) were included. Demographic data, laboratory results, and echocardiographic measurements were retrospectively collected. Logistic regression analysis was performed to determine risk factors predicting LAAT. Of the 543 included patients, LAAT was identified in 50 patients (9.2%). Multivariable logistic regression analysis for the entire cohort showed that NT-proBNP (per 500 ng/L increase, OR (95% CI): 1.09 (1.00–1.19), p = 0.038) and LDL-C (per 1 mmol/L increase, OR (95% CI): 1.70 (1.05–2.77), p = 0.032) were independently correlated with the presence of LAAT after the adjustment for CHA2DS2-VASc score and anticoagulant therapy. The subgroup analysis of patients without anticoagulant therapy also yielded similar results. Regarding patients with CHA2DS2-VASc scores ≤ 1, a higher level of LDL-C (per 1 mmol/L increase, OR (95% CI): 6.31 (2.38–16.74), p < 0.001) independently correlated with the presence of LAAT. The present study suggests that beyond CHA2DS2-VASc score, raised NT-proBNP and LDL-C are additional predictors for LAAT in NVAF patients.
Local Policy-sharing Systems for Multi-agent Reinforcement Learning : An Approach from the Learning Classifier System

Multi-agent reinforcement learning features many problems, one of these being state-space explosion based on combinations of policies that each agent has. Generally, if agents can share their policies, they can effectively search their enormous state space; that, however, simultaneously produces a risk of local optimality. Hence we propose a novel policy-sharing system based on the Learning Classifier System, on which agents locally share their policies. The aim of this system is to decrease the probability of falls into local optimality, and to effectively reduce state space by policy sharing. To verify the above, we use simplified soccer, which has discrete space and time.
Source attribution of cloud condensation nuclei and their impact on stratocumulus clouds and radiation in the south-eastern Atlantic

Abstract. The semi-permanent stratocumulus clouds over the South-eastern Atlantic Ocean (SEA) can act as an “air conditioners” to the regional and global climate system. The interaction of aerosols and clouds become important in this region, and can lead to negative radiative effects, partially offsetting the positive radiative forcing of greenhouse gases. A key pathway of aerosols affecting cloud properties is by acting as cloud condensation nuclei (CCN). In this paper, we use the United Kingdom Earth System Model to investigate the sources of CCN (from atmospheric processes and emission sources) in the SEA, and the response of cloud droplet number concentration (CDNC), cloud liquid water path (LWP), and radiative forcing to those sources. Overall, total nucleation (binary nucleation) is the most important source of CCN0.2 % in the marine boundary layer, contributing an annual average of 50 % of CCN0.2 %. In terms of emission sources, anthropogenic emissions (from energy, industry, agriculture, etc.) contribute the most to the annual average CCN0.2 % in the marine boundary layer, followed by BB. In the free troposphere, however, BB becomes the dominant source of CCN0.2 %, accounting for 64 % of the annual average. The contribution of aerosols from different sources to CDNC is consistent with their contribution to CCN0.2 % within the marine boundary layer, with total nucleation being the most important source of CDNC overall. In terms of emissions, anthropogenic sources are also the largest contributors to the annual average of CDNC, closely followed by BB. The contribution of BB to CDNC is more significant than its increase to CCN0.2 %, mainly because BB aerosol also can increase CDNC by enhancing the maximum supersaturation through the radiative effect of shortwave absorption. For an aerosol source that shows an increase in CDNC, it also shows an increase in LWP resulting from a reduction in autoconversion. BB aerosol, due to the absorption effect, can enhance existing temperature inversions and reduce the entrainment of sub-saturated air, leading to a further increase in LWP. As a result, the contribution of BB to LWP is second only to total nucleation. These findings demonstrate that BB is not the dominant source of CCN within the marine boundary layer from an emission source perspective. However, its contribution to clouds increases due to its absorption effect (about the same as anthropogenic sources for CDNC and more than anthropogenic sources for LWP), highlighting the crucial role of its radiative effect on clouds. The results on the radiative effects of aerosols show that BB aerosol exhibits an overall positive RFari (radiative forcing associated with aerosol-radiation interaction), but its net effective radiative forcing remains negative due to its effect on clouds (mainly by absorbing effect). By quantifying aerosol and cloud properties affected by different sources, this paper provides a framework to understand aerosol sources effects on the marine cirrocumulus clouds and radiation in the SEA.