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Polyvinylidene fluoride (PVDF) support membranes containing varies functionalized multi-walled carbon nanotubes (F-MWCNTs) were prepared via phase inversion. Polyamide (PA) films were formed upon the support membranes by interfacial polymerization. The characteristics (include wettability, pore size, surface roughness, and porosity) of PVDF support membranes were improved by embedding F-MWCNTs into the polymer solution. PA films formed on the different support layers exhibited different morphology, separation performance, and formation mechanism. The results revealed that, as two key parameters, pore size and surface wettability showed dominant influence on the properties of the selective layer. Herein, we proposed a speculative conceptual model to explain the synergistic effect of pore size and surface wettability of F-MWCNTs/PVDF support membrane on interfacial polymerization mechanism. [ABSTRACT FROM AUTHOR]

This study presents a multiscale assessment of the springtime U.S. Mesoscale Convective Systems (MCSs) in the NOAA Geophysical Fluid Dynamics Laboratory (GFDL)'s Atmosphere Model version 4 (AM4). In AM4, MCSs exhibit lower intensity but longer duration, producing more precipitation compared to observation. The overall MCS activity demonstrates a "location bias" with its peak shifting from the Southern Great Plains to the Midwest in AM4, causing an eastward shift in associated precipitation. However, the dry bias of MCS precipitation over the Great Plains due to this shift is compensated by additional precipitation from amplified extratropical cyclone activities. Further analysis reveals that AM4 effectively reproduces the spatiotemporal distribution and relative frequency contribution of large-scale forcing patterns driving MCS genesis. The MCS location bias emerges under all forms of large-scale forcing patterns and is further attributed to local dynamic and thermodynamic factors including weaker surface lows, eastward-shifted fronts, and suppressed low-level jets (LLJs). Here we argue that the MCS location bias results from AM4 biases in both synoptic-mesoscale anomalies (i.e., fronts and LLJs) and seasonal mean circulations. The lack of two-way air-sea interaction in AM4 creates a hemispheric-scale sea level pressure bias, which is ultimately responsible for a seasonal mean northerly bias in lower-tropospheric winds and the subsequent weakening of LLJs. The existence of such biases in prescribed sea surface temperature (SST) experiments implies the need for extra caution when utilizing extended-range forecasts for MCSs over the continental U.S. [ABSTRACT FROM AUTHOR]

This paper reports on the development of a resource-efficient FPGA-based neural network regression model for potential applications in the future hardware muon trigger system of the ATLAS experiment at the Large Hadron Collider (LHC). Effective real-time selection of muon candidates is the cornerstone of the ATLAS physics programme. With the planned ATLAS upgrades for the High Luminosity LHC, an entirely new FPGA-based hardware muon trigger system will be installed that will process full muon detector data within a 10 μ s latency window. The large FPGA devices planned for this upgrade should have sufficient spare resources to allow deployment of machine learning methods for improving identification of muon candidates and searching for new exotic particles. Our neural network regression model promises to improve rejection of the dominant source of background trigger events in the central detector region, which are due to muon candidates with low transverse momenta. This model was implemented in FPGA using 157 digital signal processors and about 5000 lookup tables. The simulated network latency and deadtime are 122 and 25 ns, respectively, when implemented in the FPGA device using a 320 MHz clock frequency. Two other FPGA implementations were also developed to study the impact of design choices on resource utilisation and latency. The performance parameters of our FPGA implementation are well within the requirements of the future muon trigger system, therefore opening a possibility for deploying machine learning methods for future data taking by the ATLAS experiment. [ABSTRACT FROM AUTHOR]

Microalgae have attracted significant attention for biofuel production and transcription factors that can effectively regulate lipid accumulation in higher plants. In this study we expressed the transcription factor LEAFY COTYLEDON 2 from Arabidopsis thaliana in Chlorella sorokiniana. The relative electron transport rate (rel.ETR) of C. sorokiniana-AtLEC2 improved significantly under high-intensity light. As a result, the total lipid content and main fatty acid composition (C16, C18) increased by more than onefold. Differentially expressed proteins were analyzed using a label-free strategy to elucidate the regulation mechanism of AtLEC2. The results showed upregulation of TAG biosynthesis (G3PDH) and photosynthesis-related proteins (e.g., ATP synthase F0, Photosystem II, Photosystem I P700, cytochrome b6, cytochrome b6f, chlorophyll a-b binding protein, Photosystem I assembly protein). Furthermore, AtLEC2 expression in C. sorokiniana led to fixed carbon flow in photosynthesis to TAG through G3PDH upregulation. The current study will provide a new direction for the cultivation of high-yield oil algae and will reveal the mechanism of regulation of AtLEC2 on TAG accumulation in C. sorokiniana. [ABSTRACT FROM AUTHOR]

Objectives: To investigate the correlation between the extent of excessive trabeculation assessed by fractal dimension (FD) and myocardial contractility assessed by cardiac MRI feature tracking in patients with left ventricular noncompaction (LVNC) and normal left ventricular ejection fraction (LVEF). Methods: Forty-one LVNC patients with normal LVEF (≥ 50%) and 41 healthy controls were retrospectively included. All patients fulfilled three available diagnostic criteria on MRI. Cardiac MRI feature tracking was performed on cine images to determine left ventricular (LV) peak strains in three directions: global radial strain (GRS), global circumferential strain (GCS), and global longitudinal strain (GLS). The complexity of excessive trabeculation was quantified by fractal analysis on short-axis cine stacks. Results: Compared with controls, patients with LVNC had impaired GRS, GCS, and GLS (all p < 0.05). The global, maximal, and regional FD values of the LVNC population were all significantly higher than those of the controls (all p < 0.05). Global FD was positively correlated with the end-diastolic volume index, end-systolic volume index, and stroke volume index (r = 0.483, 0.505, and 0.335, respectively, all p < 0.05), but negatively correlated with GRS and GCS (r = − 0.458 and 0.508, respectively, both p < 0.001). Moreover, apical FD was also weakly associated with LVEF and GLS (r = − 0.249 and 0.252, respectively, both p < 0.05). Conclusion: In patients with LVNC, LV systolic dysfunction was detected early by cardiac MRI feature tracking despite the presence of normal LVEF and was associated with excessive trabecular complexity assessed by FD. Key Points: • Left ventricular global strain was already impaired in patients with extremely prominent excessive trabeculation but normal left ventricular ejection fraction. • An increased fractal dimension was associated with impaired deformation in left ventricular noncompaction. [ABSTRACT FROM AUTHOR]