Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantitative phosphoproteomic analysis reveals unique cAMP signaling pools emanating from AC2 and AC6 in human airway smooth muscle cells

Human airway smooth muscle (HASM) is the primary target of ßAR agonists used to control airway hypercontractility in asthma and chronic obstructive pulmonary disease (COPD). ßAR agonists induce the production of cAMP by adenylyl cyclases (ACs), activate PKA and cause bronchodilation. Several other G-protein coupled receptors (GPCR) expressed in human airway smooth muscle cells transduce extracellular signals through cAMP but these receptors elicit different cellular responses. Some G-protein coupled receptors couple to distinct adenylyl cyclases isoforms with different localization, partly explaining this compartmentation, but little is known about the downstream networks that result. We used quantitative phosphoproteomics to define the downstream signaling networks emanating from cAMP produced by two adenylyl cyclases isoforms with contrasting localization in uman airway smooth muscle. After a short stimulus of adenylyl cyclases activity using forskolin, phosphopeptides were analyzed by LC-MS/MS and differences between cells overexpressing AC2 (localized in non-raft membranes) or AC6 (localized in lipid raft membranes) were compared to control human airway smooth muscle. The degree of AC2 and AC6 overexpression was titrated to generate roughly equal forskolin-stimulated cAMP production. 14 Differentially phosphorylated proteins (DPPs) resulted from AC2 activity and 34 differentially phosphorylated proteins resulted from AC6 activity. Analysis of these hits with the STRING protein interaction tool showed that AC2 signaling is more associated with modifications in RNA/DNA binding proteins and microtubule/spindle body proteins while AC6 signaling is associated with proteins regulating autophagy, calcium-calmodulin (Ca2+/CaM) signaling, Rho GTPases and cytoskeletal regulation. One protein, OFD1, was regulated in opposite directions, with serine 899 phosphorylation increased in the AC6 condition 1.5-fold but decreased to 0.46-fold by AC2. In conclusion, quantitative phosphoproteomics is a powerful tool for deciphering the complex signaling networks resulting from discreet signaling events that occur in cAMP compartments. Our data show key differences in the cAMP pools generated from AC2 and AC6 activity and imply that distinct cellular responses are regulated by these two compartments.
Modeling Crossing Behaviors of E-Bikes at Intersection With Deep Maximum Entropy Inverse Reinforcement Learning Using Drone-Based Video Data

The crossing behaviors modeling of non-networked road users can improve Connected and Autonomous Vehicles (CAVs)’s awareness of the imminent hazards in shared space while planning routes. In this study, an agent-based microsimulation model is utilized to simulate the crossing behavior of e-bikes which is difficult to model due to their greater maneuverability. To deeply understand the crossing mechanism of e-bikes, a customized neural network-based nonlinear reward function is developed from five dimensions, including travel efficiency, travel direction, travel destination, risk avoidance, and travel location. A Deep Maximum Entropy Inverse Reinforcement Learning(Deep MEIRL), which can recover the nonlinear reward function, is introduced to predict trajectories of e-bikes from the real drone-based video dataset, collected at the intersection of Jixiangcun, Xi’an (China). The results reveal that the Deep MEIRL can simulate the e-bike crossing trajectory more precisely, particularly in the microscopic behaviors of riders. Comparing Deep MEIRL with the baseline model MEIRL, it can be found that the nonlinear reward function designed in this paper is more advantageous in terms of continuous space modeling, with an improvement of 19.77%. Notably, Deep MEIRL outperforms MEIRL in modeling distance to potentially risk target states for left- and right-turn crossing behavior, improving Mean Absolute Error (MAE) by 71% and 30%, respectively. It means that the Deep MEIRL with a designed neural network is of great value to provide a logical result in modeling e-bike interaction with other road users. Therefore, the research is conducive to CAVs to understand e-bike behaviors in complex traffic scenarios, thereby assisting CAVs in making decisions efficiently.
Generalised empirical likelihood-based kernel density estimation

If additional information about the distribution of a random variable is available in the form of moment conditions, a weighted kernel density estimate reflecting the extra information can be constructed by replacing the uniform weights with the generalised empirical likelihood probabilities. It is shown that the resultant density estimator provides an improved approximation to the moment constraints. Moreover, a reduction in variance is achieved due to the systematic use of the extra moment information.
An Analysis on the Coordination Development of Low-carbon Economy in Mongolia

Currently, the global economic growth model is based on the input of resources, especially the input of energy. Throughout the energy structures all over the world, mostly coal, oil, natural gas and other high-carbon fossil fuels, and those high-carbon fossil fuels have become the main source of atmospheric carbon dioxide and other greenhouse gases. Economic growth and energy consumption, there should be causal relationship between economic growth and carbon dioxide emissions, the economic growth model can get rid of the fossil energy constraints, whereas carbon dioxide emissions can be disconnected from the economic growth, to cope with these problems facing the development of low-carbon economy in Mongolia. The research makes use of time-series model to test the causal relationship between economic growth and energy consumption, economic growth and carbon dioxide emissions as well as between energy consumption and carbon dioxide emissions.
Development of an acquisition system to monitoring asynchronous tele-ultrasound for lung patients

Lung ultrasound imaging allows the detection and evaluation of the lung damage generated by COVID-19. However, several infrastructure and logistical limitations prevent them from being carried out in isolated and remote areas. In this work, a system for the acquisition of medical images through asynchronous tele-ultrasounds was developed. The system is based on a graphical user interface, which records the three video cameras, the ultrasound image and the accelerometer simultaneously. The interface was developed according to the Volume Sweep Imaging acquisition protocol. The translational and rotational movement of the transducer are tracked and monitored by the accelerometer and the position of the transducer is obtained from the images acquired by the three video cameras. The results show a correct functioning of the system overall, being viable to be implemented for data acquisition and calculation of error, although in order to validate the error calculation there is still more research to be done.
Single Hydrogel Particle Mechanics and Dynamics Studied by Combining Capillary Micromechanics with Osmotic Compression

Hydrogels can exhibit a remarkably complex response to external stimuli and show rich mechanical behavior. Previous studies of the mechanics of hydrogel particles have generally focused on their static, rather than dynamic, response, as traditional methods for measuring single particle response at the microscopic scale cannot readily measure time-dependent mechanics. Here, we study both the static and the time-dependent response of a single batch of polyacrylamide (PAAm) particles by combining direct contact forces, applied by using Capillary Micromechanics, a method where particles are deformed in a tapered capillary, and osmotic forces are applied by a high molecular weight dextran solution. We found higher values of the static compressive and shear elastic moduli for particles exposed to dextran, as compared to water (KDex≈63 kPa vs. Kwater≈36 kPa, and GDex≈16 kPa vs. Gwater≈7 kPa), which we accounted for, theoretically, as being the result of the increased internal polymer concentration. For the dynamic response, we observed surprising behavior, not readily explained by poroelastic theories. The particles exposed to dextran solutions deformed more slowly under applied external forces than did those suspended in water (τDex≈90 s vs. τwater≈15 s). The theoretical expectation was the opposite. However, we could account for this behaviour by considering the diffusion of dextran molecules in the surrounding solution, which we found to dominate the compression dynamics of our hydrogel particles suspended in dextran solutions.
Validation of a Statistical Forecast Model for Zonda Wind in West Argentina Based on the Vertical Atmospheric Structure

Zonda is a strong, warm, very dry wind associated with adiabatic compression upon descending the eastern slopes of the Andes Cordillera in western-central Argentina. This research seeks, first, to validate the skill of a statistical forecast of zonda based on the behavior of the vertical structure of the atmosphere and, second, to describe the climatology of the vertical profile leeward of the Andes. The forecast was built for May-August 1974/1983, and was verified against a series of cases recorded in the Mendoza Aero and San Juan Aero weather stations for May-August 2005/2014. It made use of the Stepwise Discriminant Analysis (SDA) and rawinsonde data from Mendoza Aero as predictors, with the following input variables: surface pressure, temperature, dew point, and the zonal and meridional components of the wind on surface and of the fixed levels up to 200 hPa. The variables selected as predictors by the SDA were: surface pressure, dew point depression at 850 hPa, meridional wind component at 850 hPa, and zonal wind component at 400 hPa. Climatology of the vertical profile of the atmosphere leeward of the Andes was built from daily rawinsonde data from Mendoza Aero for May-August 1974/1983. Zonda markedly influences the atmospheric structure leeward of the Andes in western-central Argentina. Its maximum impact occurs at 850 to 800 hPa, with significant heating and decrease of humidity. Validation of the prediction program considered deterministic and probabilistic forecasts. Contingency tables show that probability of zonda occurrence in the plains is generally overestimated, and false alarm cases are far more frequent than surprise events. The main contribution of this paper is precisely the validation of the prediction model, which ensures forecasters one more tool to improve zonda forecasting; this, in turn, will aid decision-makers when taking steps to ameliorate zonda wind impact.
Trends of national and sub-national burden attributed to kidney dysfunction risk factor in Iran: 1990-2019

Background Kidney dysfunction is a risk factor for cardiovascular disease and chronic kidney disease. Herein, we aimed to describe the attributable burden of kidney dysfunction at the national and sub-national levels in Iran. Methods The Global Burden of Disease (GBD) 2019 data were extracted on the deaths, disability-adjusted life years (DALYs), years of life lost, and years lived with disability attributed to the risk factor of kidney dysfunction by age and sex at the national and provincial levels from 1990-2019. Also, risk exposure was reported by summary exposure value (SEV) with a range of 0 to 100. The estimated values were based on a comparative risk assessment framework. Results In 2019, the age-standardized death rate and age-standardized DALYs rate attributable to kidney dysfunction were 58.2 (95% uncertainty interval of 48.8-68.1) and 1127.2 (981.1-1282.7) per 100,000 population in Iran, respectively. Also, the Sistan and Baluchistan province (1729.3 [1478.3-2006.4]) and the province of Tehran (681.9 [571.4-809.8]) had the greatest and lowest age-standardized DALYs rates, respectively. Nationally, SEVs increased from 22.8 to 26.2. The age-standardized burden attributable to kidney dysfunction had a positive association with age advancement. The attributable age-standardized deaths and DALYs rates in all socio-demographic index regions decreased from 1990-2019. Also, the highest and lowest attributable age-standardized DALYs rates of kidney dysfunction came from ischemic heart disease and peripheral artery disease in 2019, respectively. Conclusion Although the attributed age-standardized DALYs and death rates decreased from 1990-2019, risk exposure increased and remains a crucial risk factor in Iran. Therefore, policymakers should consider preparing a preventive program that takes into account different levels of prevention of kidney dysfunction.
Advances in IoT Security: Vulnerabilities, Enabled Criminal Services, Attacks, and Countermeasures

Although the Internet of Things (IoT) incorporates millions of heterogeneous devices to provide advanced intelligent services and has greatly impacted our lives over time, it has a huge blind spot since its design favors connectivity over security. Myriad efforts have been made to secure it, but it is still one of the most lucrative and often an easy target for attackers. IoT devices remain at higher risk of attack due to their intrinsic properties which include but are not limited to extreme heterogeneity, mostly plug-and-play nature, computational limitations, improper patch management, unnecessary open ports, default or no security credentials, and extensive use of reusable open-source software. To address these security concerns we need to thoroughly understand IoT devices’ vulnerabilities, associated attacks, and how criminal services can abuse these devices. In this article, we present recent advances in IoT security vulnerabilities, criminal services by empirically identifying major vulnerable IoT devices and cyber attacks exploiting them by cyber criminals. Additionally, we present mapping of vulnerabilities, criminal services, attacks, and potential solutions against such vulnerabilities and attacks. We have also presented different approaches in a tabular form for side-by-side comparison.
Remora Optimization Algorithm with Enhanced Randomness for Large-Scale Measurement Field Deployment Technology

In the large-scale measurement field, deployment planning usually uses the Monte Carlo method for simulation analysis, which has high algorithm complexity. At the same time, traditional station planning is inefficient and unable to calculate overall accessibility due to the occlusion of tooling. To solve this problem, in this study, we first introduced a Poisson-like randomness strategy and an enhanced randomness strategy to improve the remora optimization algorithm (ROA), i.e., the PROA. Simultaneously, its convergence speed and robustness were verified in different dimensions using the CEC benchmark function. The convergence speed of 67.5–74% of the results is better than the ROA, and the robustness results of 66.67–75% are better than those of the ROA. Second, a deployment model was established for the large-scale measurement field to obtain the maximum visible area of the target to be measured. Finally, the PROA was used as the optimizer to solve optimal deployment planning; the performance of the PROA was verified by simulation analysis. In the case of six stations, the maximum visible area of the PROA reaches 83.02%, which is 18.07% higher than that of the ROA. Compared with the traditional method, this model shortens the deployment time and calculates the overall accessibility, which is of practical significance for improving assembly efficiency in large-size measurement field environments.
A Framework of Travel Mode Identification Fusing Deep Learning and Map-Matching Algorithm

The ubiquity of mobile phone signaling data (MPSD) allows us to study travel mode identification (TMI) of a larger scale of population in cities than GPS data and travel survey. Existing studies suffers from deficient data cleaning, ignoring low spatial accuracy of data and lack of the common datasets labeled with travel modes. This study proposes a novel TMI framework with MPSD and pseudo MPSD by designing hidden markov model (HMM)-based map-matching algorithm and bi-directional long short-term memory (Bi-LSTM)-based deep neural network (DNN). In this framework, conventional data cleaning method is modified to eliminate outliers in raw MPSD to improve data quality. In addition, HMM-based map-matching algorithm is designed to estimate the actual position of travelers from MPSD for spatial-temporal accuracy improvement. Specifically, pseudo MPSD is reconstructed by multi-source data aiming at overcoming the facts lacking of labeled MPSD. Both MPSD and labeled pseudo MPSD are processed to features, where the latter is used to train model based on Bi-LSTM, and the former is input to the trained model for TMI application validation. To evaluate the framework performance, a case study is carried out. Experimental results show that our framework outperforms baseline models on the same datasets. Furthermore, the accuracy improvement of designed approaches is demonstrated by a series of algorithm comparisons. The framework has good generality and flexibility, and can be extended to other cities for TMI.
Warm molecular gas in the envelope and outflow of IRAS 12496-7650 (DK Chamaeleontis)

Aims. To obtain insight into the physical structure of the warm gas in the inner envelope of protostars and the interaction with the outflow. Methods. Sub-millimeter observations of 12 CO, 13 CO and/or C 18 O in J = 3-2, J = 4-3 and J = 7-6 were obtained with the APEX Telescope towards IRAS 12496-7650, an intermediate mass young stellar object. The data are compared to ISO-LWS observations of CO J = 14-13 up to J = 19-18 lines to test the different proposed origins of the CO lines. Results. The outflow is prominently detected in the 3-2 and 4-3 lines, but not seen at similar velocities in the 7-6 line, constraining the temperature in the high-velocity (>5 km s -1 from line center) gas to less than 50 K, much lower than inferred from the analysis of the ISO-LWS data. In addition, no isothermal gas model can reproduce the emission in both the 7-6 and the higher-J ISO-LWS lines. The 7-6 line probably originates in the inner (<250 AU) region of the envelope at ∼150 K. Detailed radiative transfer calculations suggest that the ISO-LWS lines are excited by a different mechanism, possibly related to the larger-scale outflow. All possible mechanisms on scales smaller than 8" are excluded. High-resolution continuum as well as high-J 12 CO and isotopic line mapping are needed to better constrain the structure of the warm gas in the inner envelope and the interaction with the outflow.
Smart teaching abilities of junior high school teachers in Thailand

Abstract Smart teaching is an essential skill for teachers to effectively transform their classrooms into active learning environments. However, teachers need to have specific abilities to apply the smart teaching approach which comprised of pedagogy, technology, and classroom management. This research was conducted based on the Khon Kaen University Smart Learning Project which has been implementing in junior high schools in Thailand to enhance students’ competencies in active learning environments for five years. The research aimed to study the smart teaching abilities of teachers in the project which composed of: (1) creating the student’s expected learning outcomes, (2) course contents management, (3) application of teaching technique and technology, and (4) organization of learning activities in a digital learning environment. A survey method was done using questionnaires sent to teachers of grades 7–9 who were teaching in three subjects; science, mathematics and English language, of which 1,226 were responded. Data was analyzed by using descriptive statistics. The results of smart teaching abilities of teachers based on their backgrounds: teaching subjects (science, mathematics, and English language), teaching classes (grades 7, 8, and 9), and teaching experiences (1–10 years, 11–20 years, and >20 years) were presented. It was found that overall, mathematics teachers have higher smart teaching abilities than those of science and English teachers. Teachers of grade 7 and teachers with 1–10 years of teaching experiences were found to have higher smart teaching abilities that the others in their dependent variables. This research provides an understanding of the situation which will be useful for the teachers’ skills development. In addition, the information is useful for junior high school teachers and administrators to use as a guideline for enhancing smart teaching ability in the context of new learning environments.
Entorhinal Cortex Functional Connectivity during Item Long-Term Memory and the Role of Sex

A growing body of literature shows there are sex differences in the patterns of brain activity during long-term memory. However, there is a paucity of evidence on sex differences in functional brain connectivity. We previously identified sex differences in the patterns of connections with the hippocampus, a medial temporal lobe (MTL) subregion, during spatial long-term memory. The perirhinal/entorhinal cortex, another MTL subregion, plays a critical role in item memory. In the current functional magnetic resonance imaging (fMRI) study, we investigated perirhinal/entorhinal functional connectivity and the role of sex during item memory. During the study phase, abstract shapes were presented to the left or right of fixation. During the test phase, abstract shapes were presented at fixation, and the participants classified each item as previously “old” or “new”. An entorhinal region of interest (ROI) was identified by contrasting item memory hits and misses. This ROI was connected to regions generally associated with visual memory, including the right inferior frontal gyrus (IFG) and visual-processing regions (the bilateral V1, bilateral cuneus, and left lingual gyrus). Males produced greater connectivity than females with the right IFG/insula and the right V1/bilateral cuneus. Broadly, these results contribute to a growing body of literature supporting sex differences in the brain.
Consideration of spectroscopic measurements with broadband Fourier domain mode-locked laser with two semiconductor optical amplifiers at ∼1550  nm

Abstract. In this study, an experimental system was developed using a broadband wavelength-swept laser for fast and broadband spectroscopic measurements at ∼1550 nm. The broadband wavelength-swept laser employed Fourier domain mode-locking (FDML) to realize a high-speed sweep rate of 50.7 kHz. FDML lasers at ∼1550 nm experience a problem of the sweep bandwidth being limited by the amplification wavelength range of the semiconductor optical amplifier (SOA). To realize a sweep bandwidth >100 nm, the proposed broadband FDML laser incorporated SOAs with different amplification wavelength ranges in parallel. Consequently, it expanded the sweep bandwidth to 120 nm at a center wavelength of 1544 nm. The experimental system employed the broadband FDML laser and introduced reference and compensation optics. These optics can compensate for the effects of fluctuations in optical output intensity and wavelength shift in the laser to improve measurement stability. Moreover, the experimental system demonstrated fast transmission spectrum measurements with a wavelength range of 1500 to 1580 nm.