Genetic Screens Identify Host Factors for SARS-CoV-2 and Common Cold Coronaviruses.

The Coronaviridae are a family of viruses that cause disease in humans ranging from mild respiratory infection to potentially lethal acute respiratory distress syndrome. Finding host factors common to multiple coronaviruses could facilitate the development of therapies to combat current and future coronavirus pandemics. Here, we conducted genome-wide CRISPR screens in cells infected by SARS-CoV-2 as well as two seasonally circulating common cold coronaviruses, OC43 and 229E. This approach correctly identified the distinct viral entry factors ACE2 (for SARS-CoV-2), aminopeptidase N (for 229E), and glycosaminoglycans (for OC43). Additionally, we identified phosphatidylinositol phosphate biosynthesis and cholesterol homeostasis as critical host pathways supporting infection by all three coronaviruses. By contrast, the lysosomal protein TMEM106B appeared unique to SARS-CoV-2 infection. Pharmacological inhibition of phosphatidylinositol kinases and cholesterol homeostasis reduced replication of all three coronaviruses. These findings offer important insights for the understanding of the coronavirus life cycle and the development of host-directed therapies.

Pulsatility damping in the microcirculation: Basic pattern and modulating factors.

Blood flow pulsatility is an important determinant of macro- and microvascular physiology. Pulsatility is damped largely in the microcirculation, but the characteristics of this damping and the factors that regulate it have not been fully elucidated yet. Applying computational approaches to real microvascular network geometry, we examined the pattern of pulsatility damping and the role of potential damping factors, including pulse frequency, vascular viscous resistance, vascular compliance, viscoelastic behavior of the vessel wall, and wave propagation and reflection. To this end, three full rat mesenteric vascular networks were reconstructed from intravital microscopic recordings, a one-dimensional (1D) model was used to reproduce pulsatile properties within the network, and potential damping factors were examined by sensitivity analysis. Results demonstrate that blood flow pulsatility is predominantly damped at the arteriolar side and remains at a low level at the venular side. Damping was sensitive to pulse frequency, vascular viscous resistance and vascular compliance, whereas viscoelasticity of the vessel wall or wave propagation and reflection contributed little to pulsatility damping. The present results contribute to our understanding of mechanical forces and their regulation in the microcirculation.

Epileptic Seizure Detection Using Geometric Features Extracted from SODP Shape of EEG Signals and AsyLnCPSO-GA.

Epilepsy is a neurological disorder that is characterized by transient and unexpected electrical disturbance of the brain. Seizure detection by electroencephalogram (EEG) is associated with the primary interest of the evaluation and auxiliary diagnosis of epileptic patients. The aim of this study is to establish a hybrid model with improved particle swarm optimization (PSO) and a genetic algorithm (GA) to determine the optimal combination of features for epileptic seizure detection. First, the second-order difference plot (SODP) method was applied, and ten geometric features of epileptic EEG signals were derived in each frequency band (δ, θ, α and ß), forming a high-dimensional feature vector. Secondly, an optimization algorithm, AsyLnCPSO-GA, combining a modified PSO with asynchronous learning factor (AsyLnCPSO) and the genetic algorithm (GA) was proposed for feature selection. Finally, the feature combinations were fed to a naïve Bayesian classifier for epileptic seizure and seizure-free identification. The method proposed in this paper achieved 95.35% classification accuracy with a tenfold cross-validation strategy when the interfrequency bands were crossed, serving as an effective method for epilepsy detection, which could help clinicians to expeditiously diagnose epilepsy based on SODP analysis and an optimization algorithm for feature selection.

Physiological magnesium concentrations increase fidelity of diverse reverse transcriptases from HIV-1, HIV-2, and foamy virus, but not MuLV or AMV.

Reverse transcriptases (RTs) are typically assayed using optimized Mg2+ concentrations (~5-10 mM) several-fold higher than physiological cellular free Mg2+ (~0.5 mM). Recent analyses demonstrated that HIV-1, but not Moloney murine leukaemia (MuLV) or avain myeloblastosis (AMV) virus RTs has higher fidelity in low Mg2+. In the current report, lacZα-based α-complementation assays were used to measure the fidelity of several RTs including HIV-1 (subtype B and A/E), several drug-resistant HIV-1 derivatives, HIV-2, and prototype foamy virus (PFV), all which showed higher fidelity using physiological Mg2+, while MuLV and AMV RTs demonstrated equivalent fidelity in low and high Mg2+. In 0.5 mM Mg2+, all RTs demonstrated approximately equal fidelity, except for PFV which showed higher fidelity. A Next Generation Sequencing (NGS) approach that used barcoding to determine mutation profiles was used to examine the types of mutations made by HIV-1 RT (type B) in low (0.5 mM) and high (6 mM) Mg2+ on a lacZα template. Unlike α-complementation assays which are dependent on LacZα activity, the NGS assay scores mutations at all positions and of every type. Consistent with α-complementation assays, a ~four-fold increase in mutations was observed in high Mg2+. These findings help explain why HIV-1 RT displays lower fidelity in vitro (with high Mg2+ concentrations) than other RTs (e.g. MuLV and AMV), yet cellular fidelity for these viruses is comparable. Establishing in vitro conditions that accurately represent RT's activity in cells is pivotal to determining the contribution of RT and other factors to the mutation profile observed with HIV-1.

Cometabolic degradation of 1,4-dioxane by a tetrahydrofuran-growing Arthrobacter sp. WN18.

1,4-Dioxane (dioxane), an emerging groundwater contaminant, is frequently detected in landfill leachates with its structural analog, tetrahydrofuran (THF). Along with undesirable leakage of landfill leachates, dioxane and THF inevitably percolate into groundwater leading to a broader region of contamination. Cometabolic bioremediation is an effective approach to manage commingled THF and dioxane pollution. In this study, a newly isolated bacterium Arthrobacter sp. WN18 is able to co-oxidize dioxane with THF as the primary substrate. Meanwhile, the THF-induced thmADBC gene cluster was responsible for the dioxane degradation rate indicating THF monooxygenase is the essential enzyme that initializing α-hydroxylation of THF and dioxane. Further, γ-butyrolactone and HEAA were characterized as the key metabolites of THF and dioxane, respectively. In addition, WN18 can tolerate the inhibition of trichloroethylene (5.0 mg/L) as a representative of co-existing leachate constituent, and sustain its activity at various pH (5-11), temperatures (15-42 °C), and salinities (up to 4%, as NaCl wt). Like other Arthrobacter species, WN18 also exhibited the capability of fixing nitrogen. All this evidence indicates the feasibility and advantage of WN18 as a thmADBC-catalyzed inoculator to bioremediate co-contamination of THF and dioxane.

Tunable ultra-flat optical-comb-enabled, reconfigurable, and efficient coherent channelized receiver.

In this Letter, based on two advanced tunable ultra-flat optical frequency comb generators (T-FOCGs), a coherent channelized receiver with high channelized efficiency and reconfigurability is proposed. In the T-FOCG, the number of 1 dB comb lines increases with the gain, but the optical power of these 1 dB comb lines has almost the constant variance. In the proposed scheme, one optical carrier can support four sub-channels. Meanwhile, the number and bandwidth of sub-channels, as well as the bandwidth and center frequency of an original broadband signal, are all tunable. In this Letter, we verify the feasibility of the coherent channelized receiver by channelizing a 4 GHz signal with a 20 GHz center frequency into four 1 GHz sub-channels, and the reconfigurability is demonstrated by channelizing a 10 GHz signal with frequencies from 18 to 28 GHz into five 2 GHz sub-channels. Moreover, the error-vector magnitude curves of the directly received and the channelized quadrature amplitude modulation (QAM) signal at different amounts of beat noise are compared.

Frequency-dependent response in cortical network with periodic electrical stimulation.

Electrical stimulation can shape oscillations in brain activity. However, the mechanism of how periodic electrical stimulation modulates brain oscillations by time-delayed neural networks is poorly understood at present. To address this question, we investigate the effects of periodic stimulations on the oscillations generated via a time-delayed neural network. We specifically study the effect of unipolar and asymmetric bidirectional pulse stimulations by altering amplitude and frequency in a systematic manner. Our findings suggest that electrical stimulations play a central role in altering oscillations in the time-delayed neural network and that these alterations are strongly dependent on the stimulus frequency. We observe that the time-delayed neural network responds differently as the stimulation frequency is altered, as manifested by changes in resonance, entrainment, non-linear oscillation, or oscillation suppression. The results also indicate that the network presents similar response activities with increasing stimulus frequency under different excitation-inhibition ratios. Collectively, our findings pave the way for exploring the potential mechanism underlying the frequency-dependent modulation of network activity via electrical stimulations and provide new insights into possible electrical stimulation therapies to the neurological and psychological disorders in clinical practice.

Investigation into the diversity in the fractal dimensions of arterioles and venules in a microvascular network - A quantitative analysis.

Fractal dimension is a robust fractal parameter for estimating the morphology of vascular networks. It reflects the property of vascular networks that may vary and thus, differentiate between individual networks and/or identify physiological and pathological conditions. As such, fractal dimension differs also between arteriolar and venular compartments, yet the underlying reason is so far unclear. In order to understand the mechanisms behind these differences, we quantitatively analyzed the impacts of vessel attributes on the fractal dimension. Fractal dimension and vessel attributes given by vessel density (VD), vessel length density (VL), and diameter index (DI=VD/VL) were analyzed in three microvascular networks of the rat mesentery, which were reconstructed from experimental data. The results show that differences in diameter between arterioles and venules are primarily responsible for arterio-venous differences in fractal dimension. Moreover, multiple linear regression analysis demonstrates that the sensitivity of the variation of fractal dimension to vessel length and diameter varies with the type of the vessels. While the change of vessel length contributes 57.8 ±â€¯3.4% to the variation of arteriolar dimension, vessel diameter contributes 63.9 ±â€¯4.8% to the variation of venular dimension. The present study provides an explanation for the different fractal dimension and dimension variation in arteriolar and venular compartments. It highlights the importance of estimating the fractal dimensions of arterioles and venules separately, which will enhance the ability of feature extraction by fractal analysis in physiological and clinical application.

Lysine acetylation regulates the function of the global anaerobic transcription factor FnrL in Rhodobacter sphaeroides.

The metabolism of the purple non-sulfur bacterium Rhodobacter sphaeroides is versatile and it can grow under various conditions. Here, we report evidence that the anaerobic photosynthetic metabolism of R. sphaeroides is regulated by protein lysine acetylation. Using a proteomic approach, 59 acetylated peptides were detected. Among them is the global anaerobic transcription factor FnrL, which regulates the biosynthetic pathway of tetrapyrroles and synthesis of the photosynthetic apparatus. Lysine 223 of FnrL was identified as acetylated. We show that all three lysines in the DNA binding domain (K223, K213 and K175) of FnrL can be acetylated by acetyl-phosphate in vitro. A bacterial deacetylase homolog, RsCobB can deacetylate FnrL in vitro. The transcription of genes downstream of FnrL decreased when the DNA binding domain of FnrL was acetylated, as revealed by chromatin immunoprecipitation and acetylation-mimicking mutagenesis. An increasing number of acetylated lysines resulted in a further decrease in DNA binding ability. These results demonstrate that the lysine acetylation can fine tune the function of the oxygen-sensitive FnrL; thus, it might regulate anaerobic photosynthetic metabolism of R. sphaeroides.

Pulse wave velocity in the microcirculation reflects both vascular compliance and resistance: Insights from computational approaches.

OBJECTIVE: PWV is the speed of pulse wave propagation through the circulatory system. mPWV emerges as a novel indicator of hypertension, yet it remains unclear how different vascular properties affect mPWV. We aim to identify the biomechanical determinants of mPWV. METHODS: A 1D model was used to simulate PWV in a rat mesenteric microvascular network and, for comparison, in a human macrovascular arterial network. Sensitivity analysis was performed to assess the relationship between PWV and vascular compliance and resistance. RESULTS: The 1D model enabled adequate simulation of PWV in both micro- and macrovascular networks. Simulated arterial PWV changed as a function of vascular compliance but not resistance, in that arterial PWV varied at a rate of 0.30 m/s and -6.18 × 10-3  m/s per 10% increase in vascular compliance and resistance, respectively. In contrast, mPWV depended on both vascular compliance and resistance, as it varied at a rate of 2.79 and -2.64 cm/s per 10% increase in the respective parameters. CONCLUSIONS: The present study identifies vascular compliance and resistance in microvascular networks as critical determinants of mPWV. We anticipate that mPWV can be utilized as an effective indicator for the assessment of microvascular biomechanical properties.

Modeling of pulsatile flow-dependent nitric oxide regulation in a realistic microvascular network.

Hemodynamic pulsatility has been reported to regulate microcirculatory function. To quantitatively assess the impact of flow pulsatility on the microvasculature, a mathematical model was first developed to simulate the regulation of NO production by pulsatile flow in the microcirculation. Shear stress and pressure pulsatility were selected as regulators of endothelial NO production and NO-dependent vessel dilation as feedback to control microvascular hemodynamics. The model was then applied to a real microvascular network of the rat mesentery consisting of 546 microvessels. As compared to steady flow conditions, pulsatile flow increased the average NO concentration in arterioles from 256.8±93.1nM to 274.8±101.1nM (P<0.001), with a corresponding increase in vessel dilation by approximately 7% from 27.5±10.6% to 29.4±11.4% (P<0.001). In contrast, NO concentration and vessel size showed a far lesser increase (about 1.7%) in venules under pulsatile flow as compared to steady flow conditions. Network perfusion and flow heterogeneity were improved under pulsatile flow conditions, and vasodilation within the network was more sensitive to heart rate changes than pulse pressure amplitude. The proposed model simulates the role of flow pulsatility in the regulation of a complex microvascular network in terms of NO concentration and hemodynamics under varied physiological conditions.

[Optimization of the parameters of microcirculatory structural adaptation model based on improved quantum-behaved particle swarm optimization algorithm].

The vessels in the microcirculation keep adjusting their structure to meet the functional requirements of the different tissues. A previously developed theoretical model can reproduce the process of vascular structural adaptation to help the study of the microcirculatory physiology. However, until now, such model lacks the appropriate methods for its parameter settings with subsequent limitation of further applications. This study proposed an improved quantum-behaved particle swarm optimization (QPSO) algorithm for setting the parameter values in this model. The optimization was performed on a real mesenteric microvascular network of rat. The results showed that the improved QPSO was superior to the standard particle swarm optimization, the standard QPSO and the previously reported Downhill algorithm. We conclude that the improved QPSO leads to a better agreement between mathematical simulation and animal experiment, rendering the model more reliable in future physiological studies.

[Surgery for pediatric intractable epilepsy due to posterior quadrantic cortical dysplasia].

OBJECTIVE: To investigate the clinical features and surgical strategy for pediatric intractable epilepsy due to posterior quadrantic cortical dysplasia and to assess the surgical outcomes. METHODS: The clinical features and preoperative evaluation results of 14 children with intractable epilepsy due to posterior quadrantic cortical dysplasia were retrospectively analyzed. The localization values of video-electroencephalography and intraoperative monitoring and the indications, advantages and disadvantages of temporoparietooccipital disconnection were evaluated. RESULTS: The 14 children had different seizure types, of which spasm was the most common one. The lesions of cortical dysplasia involved the central cerebral region in 2 cases. After temporoparietooccipital disconnection in 14 patients, 13 cases were seizure-free; only one case still had seizures, but the frequency dropped by more than 50%. CONCLUSIONS: Temporoparietooccipital disconnection is a safe and effective surgical procedure for children with intractable epilepsy due to posterior quadrantic cortical dysplasia.

Extraction of Mg(OH)2 from Mg silicate minerals with NaOH assisted with H2O: implications for CO2 capture from exhaust flue gas.

The utilisation of Mg(OH)2 to capture exhaust CO2 has been hindered by the limited availability of brucite, the Mg(OH)2 mineral in natural deposits. Our previous study demonstrated that Mg(OH)2 can be obtained from dunite, an ultramafic rock composed of Mg silicate minerals, in highly concentrated NaOH aqueous systems. However, the large quantity of NaOH consumed was considered an obstacle for the implementation of the technology. In the present study, Mg(OH)2 was extracted from dunite reacted in solid systems with NaOH assisted with H2O. The consumption of NaOH was reduced by 97% with respect to the NaOH aqueous systems, maintaining a comparable yield of Mg(OH)2 extraction, i.e. 64.8-66%. The capture of CO2 from a CO2-N2 gas mixture was tested at ambient conditions using a Mg(OH)2 aqueous slurry. Mg(OH)2 almost fully dissolved and reacted with dissolved CO2 by forming Mg(HCO3)2 which remained in equilibrium storing the CO2 in the aqueous solution. The CO2 balance of the process was assessed from the emissions derived from the power consumption for NaOH production and Mg(OH)2 extraction together with the CO2 captured by Mg(OH)2 derived from dunite. The process resulted as carbon neutral when dunite is reacted at 250 °C for durations of 1 and 3 hours and CO2 is captured as Mg(HCO3)2.

Multiple feature extraction and classification of electroencephalograph signal for Alzheimers' with spectrum and bispectrum.

In this paper, we have combined experimental neurophysiologic recording and statistical analysis to investigate the nonlinear characteristic and the cognitive function of the brain. Spectrum and bispectrum analyses are proposed to extract multiple effective features of electroencephalograph (EEG) signals from Alzheimer's disease (AD) patients and further applied to distinguish AD patients from the normal controls. Spectral analysis based on autoregressive Burg method is first used to quantify the power distribution of EEG series in the frequency domain. Compared to the control group, the relative power spectral density of AD group is significantly higher in the theta frequency band, while lower in the alpha frequency bands. In addition, median frequency of spectrum is decreased, and spectral entropy ratio of these two frequency bands undergoes drastic changes at the P3 electrode in the central-parietal brain region, implying that the electrophysiological behavior in AD brain is much slower and less irregular. In order to explore the nonlinear high order information, bispectral analysis which measures the complexity of phase-coupling is further applied to P3 electrode in the whole frequency band. It is demonstrated that less bispectral peaks appear and the amplitudes of peaks fall, suggesting a decrease of non-Gaussianity and nonlinearity of EEG in ADs. Notably, the application of this method to five brain regions shows higher concentration of the weighted center of bispectrum and lower complexity reflecting phase-coupling by bispectral entropy. Based on spectrum and bispectrum analyses, six efficient features are extracted and then applied to discriminate AD from the normal in the five brain regions. The classification results indicate that all these features could differentiate AD patients from the normal controls with a maximum accuracy of 90.2%. Particularly, different brain regions are sensitive to different features. Moreover, the optimal combination of features obtained by discriminant analysis may improve the classification accuracy. These results demonstrate the great promise for scape EEG spectral and bispectral features as a potential effective method for detection of AD, which may facilitate our understanding of the pathological mechanism of the disease.

Complexity extraction of electroencephalograms in Alzheimer's disease with weighted-permutation entropy.

In this paper, weighted-permutation entropy (WPE) is applied to investigating the complexity abnormalities of Alzheimer's disease (AD) by analyzing 16-channel electroencephalograph (EEG) signals from 14 severe AD patients and 14 age-matched normal subjects. The WPE values are estimated in the delta, the theta, the alpha, and the beta sub-bands for each channel with an overlapped sliding window. WPE is modified from the permutation entropy (PE), which has been recently suggested as a measurement to extract the complexity of the EEG signals. The advantage of WPE over PE is verified by both the model simulated and the experimental EEG signals. Although the results show that both the average PE and WPE of AD patients are decreased in contrast with the normal group in these four sub-bands, especially in the theta band, WPE can exhibit a better performance in distinguishing the AD patients from the normal controls by the more significant differences in the four sub-bands, which may be attributed to the brain dysfunction. Thus, it suggests that WPE may become a probable useful tool to detect brain dysfunction in AD and it seems to be promising to disclose the abnormalities of brain activity for other neural disease.

Characterization of complexity in the electroencephalograph activity of Alzheimer's disease based on fuzzy entropy.

In this paper, experimental neurophysiologic recording and statistical analysis are combined to investigate the nonlinear characteristic and the cognitive function of the brain. Fuzzy approximate entropy and fuzzy sample entropy are applied to characterize the model-based simulated series and electroencephalograph (EEG) series of Alzheimer's disease (AD). The effectiveness and advantages of these two kinds of fuzzy entropy are first verified through the simulated EEG series generated by the alpha rhythm model, including stronger relative consistency and robustness. Furthermore, in order to detect the abnormality of irregularity and chaotic behavior in the AD brain, the complexity features based on these two fuzzy entropies are extracted in the delta, theta, alpha, and beta bands. It is demonstrated that, due to the introduction of fuzzy set theory, the fuzzy entropies could better distinguish EEG signals of AD from that of the normal than the approximate entropy and sample entropy. Moreover, the entropy values of AD are significantly decreased in the alpha band, particularly in the temporal brain region, such as electrode T3 and T4. In addition, fuzzy sample entropy could achieve higher group differences in different brain regions and higher average classification accuracy of 88.1% by support vector machine classifier. The obtained results prove that fuzzy sample entropy may be a powerful tool to characterize the complexity abnormalities of AD, which could be helpful in further understanding of the disease.

A one-dimensional mathematical model for studying the pulsatile flow in microvascular networks.

Techniques that model microvascular hemodynamics have been developed for decades. While the physiological significance of pressure pulsatility is acknowledged, most of the microcirculatory models use steady flow approaches. To theoretically study the extent and transmission of pulsatility in microcirculation, dynamic models need to be developed. In this paper, we present a one-dimensional model to describe the dynamic behavior of microvascular blood flow. The model is applied to a microvascular network from a rat mesentery. Intravital microscopy was used to record the morphology and flow velocities in individual vessel segments, and boundaries are defined according to the experimental data. The system of governing equations constituting the model is solved numerically using the discontinuous Galerkin method. An implicit integration scheme is adopted to increase computing efficiency. The model allows the simulation of the dynamic properties of blood flow in microcirculatory networks, including the pressure pulsatility (quantified by a pulsatility index) and pulse wave velocity (PWV). From the main input arteriole to the main output venule, the pulsatility index decreases by 66.7%. PWV obtained along arterioles declines with decreasing diameters, with mean values of 77.16, 25.31, and 8.30 cm/s for diameters of 26.84, 17.46, and 13.33 µm, respectively. These results suggest that the 1D model developed is able to simulate the characteristics of pressure pulsatility and wave propagation in complex microvascular networks.

Decreased coherence and functional connectivity of electroencephalograph in Alzheimer's disease.

In this paper, we investigate the abnormalities of electroencephalograph (EEG) signals in the Alzheimer's disease (AD) by analyzing 16-scalp electrodes EEG signals and make a comparison with the normal controls. Coherence is introduced to measure the pair-wise normalized linear synchrony and functional correlations between two EEG signals in different frequency domains, and graph analysis is further used to investigate the influence of AD on the functional connectivity of human brain. Data analysis results show that, compared with the control group, the pair-wise coherence of AD group is significantly decreased, especially for the theta and alpha frequency bands in the frontal and parieto-occipital regions. Furthermore, functional connectivity among different brain regions is reconstructed based on EEG, which exhibit obvious small-world properties. Graph analysis demonstrates that the local functional connections between regions for AD decrease. In addition, it is found that small-world properties of AD networks are largely weakened, by calculating its average path lengths, clustering coefficients, global efficiency, local efficiency, and small-worldness. The obtained results show that both pair-wise coherence and functional network can be taken as effective measures to distinguish AD patients from the normal, which may benefit our understanding of the disease.

A combined method to estimate parameters of the thalamocortical model from a heavily noise-corrupted time series of action potential.

A combined method composing of the unscented Kalman filter (UKF) and the synchronization-based method is proposed for estimating electrophysiological variables and parameters of a thalamocortical (TC) neuron model, which is commonly used for studying Parkinson's disease for its relay role of connecting the basal ganglia and the cortex. In this work, we take into account the condition when only the time series of action potential with heavy noise are available. Numerical results demonstrate that not only this method can estimate model parameters from the extracted time series of action potential successfully but also the effect of its estimation is much better than the only use of the UKF or synchronization-based method, with a higher accuracy and a better robustness against noise, especially under the severe noise conditions. Considering the rather important role of TC neuron in the normal and pathological brain functions, the exploration of the method to estimate the critical parameters could have important implications for the study of its nonlinear dynamics and further treatment of Parkinson's disease.