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Abstract Microglial reactivity is a pathological hallmark in many neurodegenerative diseases. During stimulation, microglia undergo complex morphological changes, including loss of their characteristic ramified morphology, which is routinely used to detect and quantify inflammation in the brain. However, the underlying molecular mechanisms and the relation between microglial morphology and their pathophysiological function are unknown. Here, proteomic profiling of lipopolysaccharide (LPS)-reactive microglia identifies microtubule remodeling pathways as an early factor that drives the morphological change and subsequently controls cytokine responses. We find that LPS-reactive microglia reorganize their microtubules to form a stable and centrosomally-anchored array to facilitate efficient cytokine trafficking and release. We identify cyclin-dependent kinase 1 (Cdk-1) as a critical upstream regulator of microtubule remodeling and morphological change in-vitro and in-situ. Cdk-1 inhibition also rescues tau and amyloid fibril-induced morphology changes. These results demonstrate a critical role for microtubule dynamics and reorganization in microglial reactivity and modulating cytokine-mediated inflammatory responses.

Tumor progression locus 2 (TPL2) (MAP3K8) is a central signaling node in the inflammatory response of peripheral immune cells. We find that TPL2 kinase activity modulates microglial cytokine release and is required for microglia-mediated neuron death in vitro. In acute in vivo neuroinflammation settings, TPL2 kinase activity regulates microglia activation states and brain cytokine levels. In a tauopathy model of chronic neurodegeneration, loss of TPL2 kinase activity reduces neuroinflammation and rescues synapse loss, brain volume loss, and behavioral deficits. Single-cell RNA sequencing analysis indicates that protection in the tauopathy model was associated with reductions in activated microglia subpopulations as well as infiltrating peripheral immune cells. Overall, using various models, we find that TPL2 kinase activity can promote multiple harmful consequences of microglial activation in the brain including cytokine release, iNOS (inducible nitric oxide synthase) induction, astrocyte activation, and immune cell infiltration. Consequently, inhibiting TPL2 kinase activity could represent a potential therapeutic strategy in neurodegenerative conditions.

BackgroundEarly recognition of severely injured patients in prehospital settings is of paramount importance for timely treatment and transportation of patients to further treatment facilities. The dispatching accuracy has seldom been addressed in previous studies. ObjectiveIn this study, we aimed to build a machine learning–based model through text mining of emergency calls for the automated identification of severely injured patients after a road accident. MethodsAudio recordings of road accidents in Taipei City, Taiwan, in 2018 were obtained and randomly sampled. Data on call transfers or non-Mandarin speeches were excluded. To predict cases of severe trauma identified on-site by emergency medical technicians, all included cases were evaluated by both humans (6 dispatchers) and a machine learning model, that is, a prehospital-activated major trauma (PAMT) model. The PAMT model was developed using term frequency–inverse document frequency, rule-based classification, and a Bernoulli naïve Bayes classifier. Repeated random subsampling cross-validation was applied to evaluate the robustness of the model. The prediction performance of dispatchers and the PAMT model, in severe cases, was compared. Performance was indicated by sensitivity, specificity, positive predictive value, negative predictive value, and accuracy. ResultsAlthough the mean sensitivity and negative predictive value obtained by the PAMT model were higher than those of dispatchers, they obtained higher mean specificity, positive predictive value, and accuracy. The mean accuracy of the PAMT model, from certainty level 0 (lowest certainty) to level 6 (highest certainty), was higher except for levels 5 and 6. The overall performances of the dispatchers and the PAMT model were similar; however, the PAMT model had higher accuracy in cases where the dispatchers were less certain of their judgments. ConclusionsA machine learning–based model, called the PAMT model, was developed to predict severe road accident trauma. The results of our study suggest that the accuracy of the PAMT model is not superior to that of the participating dispatchers; however, it may assist dispatchers when they lack confidence while making a judgment.

The rapid development of AIOT-related technologies has revolutionized various industries. The advantage of such real-time sensing, low costs, small sizes, and easy deployment makes extensive use of wireless sensor networks in various fields. However, due to the wireless transmission of data, and limited built-in power supply, controlling energy consumption and making the application of the sensor network more efficient is still an urgent problem to be solved in practice. In this study, we construct this problem as a tree structure wireless sensor network mathematical model, which mainly considers the QoS and fairness requirements. This study determines the probability of sensor activity, transmission distance, and transmission of the packet size, and thereby minimizes energy consumption. The Lagrangian Relaxation method is used to find the optimal solution with the lowest energy consumption while maintaining the network’s transmission efficiency. The experimental results confirm that the decision-making speed and energy consumption can be effectively improved.

A stock forecasting and trading system is a complex information system because a stock trading system needs to be analyzed and modeled using data science, machine learning, and artificial intelligence. Previous time series models have been widely used to forecast stock prices, but due to several shortcomings, these models cannot apply all available information to make a forecast. The relationship between stock prices and related factors is nonlinear and involves nonstationary fluctuations, and accurately forecasting stock prices is not an easy task. Therefore, this study used support vector machines (linear and radial basis functions), gene expression programming, multilayer perceptron regression, and generalized regression neural networks to calculate the importance of indicators. We then integrated the five indicator selection methods to find the key indicators. Next, we used long short-term memory (LSTM) and gated recurrent units (GRU) to build time series models for forecasting stock prices and compare them with the listing models. To evaluate the effectiveness of the proposed model, we collected six different stock market data from 2011 to 2019 to evaluate their forecast performance based on RMSE and MAPE metrics. It is worth mentioning that this study proposes two trading policies to evaluate trading profits and compare them with the listing methods, and their profits are pretty good to investors. After the experiments, the proposed time series model (GRU/LSTM combined with the selected key indicators) exhibits better forecast ability in fluctuating and non-fluctuating environments than the listing models, thus presenting an effective reference for stakeholders.

Wi-Fi was developed to support software-defined networks that are crucial for achieving high quality of service for next generation wireless networks. In this software-defined networking technology, network resources are utilized to reduce the effects of several influential factors such as the limited number of nonoverlapping channels, co-channel interference, rapidly changing distribution of customers, and access point (AP) failure. These factors cause irregular network traffic and service unavailability in some users. In this paper, we address the problems causing the AP failure and interference. The studied problems were modeled as linear and nonlinear mathematical programming problems. The Lagrangian relaxation approach, which is a type of divide-and-conquer method, was used to solve the load distribution problems near optimally. A self-repairing heuristic multiple-level load-balancing traffic adjustment was proposed to manage the problems pertaining to the AP failure. The delay difference metric, which is defined as the difference between tolerable delay and transmission delay, was used to evaluate the difference between the upper bound and lower bound of the delay. Moreover, the metric was used to estimate the improvement ratio between the existing methods and the proposed method. Modifiable transmission power ranges (which involve the cell breathing method) and association managements are adjusted to minimize the narrow gap and improve the self-repairing performance. Thus, resources are appropriately allocated to users, and adequate QoS is attained.

Rapid industrialization and urban development are the main causes of air pollution, leading to daily air quality and health problems. To find significant pollutants and forecast their concentrations, in this study, we used a hybrid methodology, including integrated variable selection, autoregressive distributed lag, and deleted multiple collinear variables to reduce variables, and then applied six intelligent time series models to forecast the concentrations of the top three pollution sources. We collected two air quality datasets from traffic and industrial monitoring stations and weather data to analyze and compare their results. The results show that a random forest based on selected key variables has better classification metrics (accuracy, AUC, recall, precision, and F1). After deleting the collinearity of the independent variables and adding the lag periods using the autoregressive distributed lag model, the intelligent time-series support vector regression was found to have better forecasting performance (RMSE and MAE). Finally, the research results could be used as a reference by all relevant stakeholders and help respond to poor air quality.

Accurately forecasting housing prices will enable investors to attain profits, and it can provide information to stakeholders that housing prices in the community are falling, stabilizing, or rising. Previous studies on housing price forecasting mostly used hedonic pricing and weighted regression methods, which led to the lack of consideration of the nonlinear relationship model and its explanatory power. Furthermore, the attribute data of housing price forecasts are a heterogeneous study, and they are difficult to forecast accurately. Therefore, this study proposes an intelligent homogeneous model based on an enhanced weighted kernel self-organizing map (EW-KSOM) for forecasting house prices; that is, this study proposes an EW-KSOM algorithm to cluster the collected data and then applies random forest, extra tree, multilayer perception, and support vector regression to forecast the house prices of full, district, and apartment complex data. In the experimental comparison, we compare the performance of the proposed enhanced weighted kernel self-organizing map with the listing clustering methods. The results show that the best forecast algorithm is the combined EW-KSOM and random forest under the root mean square error and root-relative square error, and the proposed method can effectively improve the forecast capability of housing prices and understand the influencing factors of housing prices in full and important districts. Furthermore, we obtain that the top five key factors influencing house prices are transferred land area, house age, building transfer total area, population percentage, and the total number of floors. Lastly, the research results can provide references for investors and related organizations.

The outbreak of COVID-19 around the world has caused great damage to the global economy. The tourism industry is among the worst-hit industries. How to focus on visitors who are most helpful to the tourism industry and develop sustainable strategy of operation is a very important question for after the epidemic is over. This study applied two-stage data envelopment analysis (DEA) and principal component analysis (PCA) to investigate past statistics from the Tourism Bureau and explore the shopping patterns of tourists who travel to Taiwan. The focus will be on tourists from major countries such as China, Japan, and Southeast Asian countries. According to the analysis of tourists from different countries, the money spent by tourists from different countries is concentrated on different items, and there are subitems that they particularly like to purchase. For the analysis of the purpose of coming to Taiwan, some tourism areas worth developing (such as medical treatment and leisure) are also presented in the research results. Based on these results, and according to the sustainable development goals, specific recommendations for the sustainability strategy of operation are made as a reference for the government and relevant industries. This research also broadens the scope of application of DEA and points out a different direction for future research.

Public transportation systems are an effective way to reduce traffic congestion, air pollution, and energy consumption. Today, smartcard technology is used to shorten the time spent boarding/exiting buses and other types of public transportation; however, this does not alleviate all traffic congestion problems. Accurate forecasting of passenger flow can prevent serious bus congestion and improve the service quality of the transportation system. To the best of the current authors’ knowledge, fewer studies have used smartcard data to forecast bus passenger flow than on other types of public transportation, and few studies have used time-series lag periods as forecast variables. Therefore, this study used smartcard data from the bus system to identify important variables that affect passenger flow. These data were combined with other influential variables to establish an integrated-weight time-series forecast model. For different time data, we applied four intelligent forecast methods and different lag periods to analyze the forecasting ability of different daily data series. To enhance the forecast ability, we used the forecast data from the top three of the 80 combined forecast models and adapted their weights to improve the forecast results. After experiments and comparisons, the results show that the proposed model can improve passenger flow forecasting based on three bus routes with three different series of time data in terms of root-mean-square error (RMSE) and mean absolute percentage error (MAPE). In addition, the lag period was found to significantly affect the forecast results, and our results show that the proposed model is more effective than other individual intelligent forecast models.

Parvalbumin-expressing interneurons (PVs) in the dentate gyrus provide activity-dependent regulation of adult neurogenesis as well as maintain inhibitory control of mature neurons. In mature neurons, PVs evoke GABAA postsynaptic currents (GPSCs) with fast rise and decay phases that allow precise control of spike timing, yet synaptic currents with fast kinetics do not appear in adult-born neurons until several weeks after cell birth. Here we used mouse hippocampal slices to address how PVs signal to newborn neurons prior to the appearance of fast GPSCs. Whereas PV-evoked currents in mature neurons exhibit hallmark fast rise and decay phases, newborn neurons display slow GPSCs with characteristics of spillover signaling. We also unmasked slow spillover currents in mature neurons in the absence of fast GPSCs. Our results suggest that PVs mediate slow spillover signaling in addition to conventional fast synaptic signaling, and that spillover transmission mediates activity-dependent regulation of early events in adult neurogenesis.

Platinum (Pt) is widely used as an activator in direct methanol fuel cells (DMFCs). However, the development of Pt catalyst is hindered due to its high cost and CO poisoning. A multi-metallic catalyst is a promising catalyst for fuel cells. We develop a simple and rapid method to synthesize PtNiCo/rGO nanocomposites (NCs). The PtNiCo/rGO NCs catalyst was obtained by microwave-assisted synthesis of graphene oxide (GO) with Pt, Ni, and Co precursors in ethylene glycol (EG) solution after heating for 20 min. The Pt-Ni-Co nanoparticles showed a narrow particle size distribution and were uniformly dispersed on the reduced graphene oxide without agglomeration. Compared with PtNiCo catalyst, PtNiCo/rGO NCs have superior electrocatalytic properties, including a large electrochemical active surface area (ECSA), the high catalytic activity of methanol, excellent anti-toxic properties, and high electrochemical stability. The ECSA can be up to 87.41 m2/g at a scan rate of 50 mV/s. They also have the lowest oxidation potential of CO. These excellent electrochemical performances are attributed to the uniform dispersion of PtNiCo nanoparticles, good conductivity, stability, and large specific surface area of the rGO carrier. The synthesized PtNiCo/rGO nanoparticles have an average size of 17.03 ± 1.93 nm. We also investigated the effect of catalyst material size on electrocatalytic performance, and the results indicate that PtNiCo/rGO NC catalysts can replace anode catalyst materials in fuel cell applications in the future.

Since Charnes, Cooper, and Rhodes introduced data envelopment analysis (DEA) in 1978, later called the DEA-CCR model, many studies applied this technique to different fields. Based on the original CCR model, many modified DEA models were developed by researchers. Since 1999, Seiford and Zhu presented a two-stage DEA model. Later, these models were widely used in many studies. However, the relationship between the efficiency scores that are obtained from the original CCR model and the two-stage DEA model remains unknown. To fill this gap, this study proposed a theoretical relationship between the efficiency scores that are calculated from the two-stage DEA model and those that are obtained from the original CCR model. How the sets of nonsymmetrical weights affected the efficiency scores were also investigated. Theorems regarding the relationship were developed, and then the model was utilized to evaluate the two-stage efficiency scores of the insurance companies (non-life) and bank branches. The results show that using a two-stage DEA model can get more information about operational efficiency than the traditional CCR model does. The findings from this study about the two-stage DEA technique can provide significant reasons for using this model to evaluate performance efficiency.

Many different time-series methods have been widely used in forecast stock prices for earning a profit. However, there are still some problems in the previous time series models. To overcome the problems, this paper proposes a hybrid time-series model based on a feature selection method for forecasting the leading industry stock prices. In the proposed model, stepwise regression is first adopted, and multivariate adaptive regression splines and kernel ridge regression are then used to select the key features. Second, this study constructs the forecasting model by a genetic algorithm to optimize the parameters of support vector regression. To evaluate the forecasting performance of the proposed models, this study collects five leading enterprise datasets in different industries from 2003 to 2012. The collected stock prices are employed to verify the proposed model under accuracy. The results show that proposed model is better accuracy than the other listed models, and provide persuasive investment guidance to investors.

Fluctuations in the growth rate of a bacterial culture during unbalanced growth are generally considered undesirable in quantitative studies of bacterial physiology. Under well-controlled experimental conditions, however, these fluctuations are not random but instead reflect the interplay between intra-cellular networks underlying bacterial growth and the growth environment. Therefore, these fluctuations could be considered quantitative phenotypes of the bacteria under a specific growth condition. Here, we present a method to identify "phenotypic signatures" by time-frequency analysis of unbalanced growth curves measured with high temporal resolution. The signatures are then applied to differentiate amongst different bacterial strains or the same strain under different growth conditions, and to identify the essential architecture of the gene network underlying the observed growth dynamics. Our method has implications for both basic understanding of bacterial physiology and for the classification of bacterial strains.

Photoswitchable neurotransmitter receptors are powerful tools for precise manipulation of neural signaling. However, their applications for slow or longlasting biological events are constrained by fast thermal relaxation of cis-azobenzene. We address this issue by modifying the ortho positions of azobenzene used in the tethered ligand. In cultured cells and intact brain tissue, conjugating inhibitory neurotransmitter receptors with one of the derivatives, dMPC1, allows bidirectional receptor control with 380 and 500 nm light. Moreover, the receptors can be locked in either an active or an inactive state in darkness after a brief pulse of light. This strategy thus enables both rapid and sustained manipulation of neurotransmission, allowing optogenetic interrogation of neural functions over a broad range of time scales. [ABSTRACT FROM AUTHOR]

In the CNS, excitatory amino acid transporters (EAATs) localized to neurons and glia terminate the actions of synaptically released glutamate. Whereas glial transporters are primarily responsible for maintaining low ambient levels of extracellular glutamate, neuronal transporters have additional roles in shaping excitatory synaptic transmission. Here we test the hypothesis that the expression level of the Purkinje cell (PC)-specific transporter, EAAT4, near parallel fiber (PF) release sites controls the extrasynaptic glutamate concentration transient following synaptic stimulation. Expression of EAAT4 follows a parasagittal banding pattern that allows us to compare regions of high and low EAAT4-expressing PCs. Using EAAT4 promoter-driven eGFP reporter mice together with pharmacology and genetic deletion,weshow that the level of neuronal transporter expression influences extrasynaptic transmission from PFs to adjacent Bergmann glia (BG). Surprisingly, a twofold difference in functional EAAT4 levels is sufficient to alter signaling to BG, although EAAT4 may only be responsible for removing a fraction of released glutamate. These results demonstrate that physiological regulation of neuronal transporter expression can alter extrasynaptic neuroglial signaling. [ABSTRACT FROM AUTHOR]

This study presents a novel one-step preparation of functionalized multi-walled carbon nanotubes (MWCNTs) by free-radical modification. MA-POA, i.e. maleic anhydride (MA) grafted on molecular weight 400 and 2000 poly(oxyalkylene)amines (POA400 and POA2000), was attached onto the MWCNTs, forming MWCNTs/MA-POA400 and MWCNTs/MA-POA2000. The functionalized MWCNTs, especially MWCNTs/MA-POA2000, exhibited higher solubility than the pristine MWCNTs in organic solvents and were well dispersion in a vinyl ester matrix. Furthermore, this study also investigated mechanical, electrical and single fuel cell properties of functionalized MWCNT nanocomposite bipolar plates for use in polymer electrolyte membrane fuel cells (PEMFCs). The flexural strength of the nanocomposite bipolar plates containing 2 wt% MWCNTs/MA-POA2000 was increased from 28 MPa to 48.33MPa. In addition, the in-plane and through-plane electrical conductivities and half-cell resistance of the nanocomposite bipolar plates were 780%, 168% and 253%, respectivel,y higher than those of the original composite bipolar plates with the addition of only a small quantity (1 wt%) of MWCNTs/MA-POA2000. The maximum current density and power density of the single cell tests of the nanocomposite bipolar plate with 1 wt% MWCNTs/MA-POA2000 were enhanced from 1.03 to 1.32 A cm−2 and from 0.392 to 0.580 W cm−2, respectively. The overall performance confirms the MWCNTs/MA-POA2000 nanocomposite bipolar plates prepared in this study are suitable for PEMFC application. [ABSTRACT FROM AUTHOR]

Serum- and glucocorticoid-inducible kinase 1 (SGK1) has been shown to play an important role in spatial memory formation, but the molecular mechanism underlying this effect of SGK1 was not known. zif268 is an immediate early gene that is induced by water maze learning. To investigate the role of SGK1 in the regulation of zif268 expression, the dominant negative mutant of SGK1, SGK1 S422A, was infused to the hippocampal CA1 area of rats, and was found to decrease significantly the mRNA level of zif268 in both naïve animals and trained animals. SGK1 was also found to phosphorylate serum response factor (SRF) at Ser73, Ser75, and Ser99, and phosphorylate CREB1 at Ser133. Inhibition of SGK1 phosphorylation sites on SRF and CREB1 with alanine substitution significantly diminished SGK1-enhanced zif268 expression in the promoter-luciferase assay. SGK1 also phosphorylates Elk-1 and SGK1 phosphorylation of Elk-1 decreased the transcriptional activity of Elk-1. But SGK1 phosphorylation of Elk-1 did not affect SGK1-enhanced zif268 expression. Moreover, the phosphorylation of SGK1 was increased in rat CA1 area after water maze learning, accompanied by increased phosphorylation of SRF at Ser99 and increased phosphorylation of CREB1 at Ser133. All these effects were antagonized by SGK1 S422A transfection. These results together suggest that SGK1 enhances zif268 expression through the mediation of SRF and CREB1, and these signaling pathways are associated with spatial memory formation in rats. [ABSTRACT FROM AUTHOR]

Background: Nucleic acid hybridization, a fundamental technique in molecular biology, can be modified into very effective and sensitive methods for detecting particular targets mixed with millions of non-target sequences. Therefore, avoiding cross-hybridization is the most crucial issue for developing diagnostic methods based on hybridization. Results: To develop a probe with a high discriminating power, this study constructed a web service, the Unique Probe Selector (UPS), for customized probe design. The UPS service integrates a probe design mechanism and a scoring system for evaluating the performance of probe annealing and the uniqueness of a probe in a user-defined genetic background. Starting from an intuitive web interface, the UPS accepts a query with single or multiple sequences in fasta format. The best probe(s) for each sequence can be downloaded from result pages in a fasta or .csv format with a summary of probe characteristics. The option "Unique probe within group" selects the most unique probe for each target sequence with low probability to hybridize to the other sequences in the same submitted query. The option "Unique probe in the specific organism" devises probes for each submitted sequence to identify its target among selected genetic backgrounds based on Unigene. Conclusion: The UPS evaluates probe-to-target hybridization under a user-defined condition in silico to ensure high-performance hybridization and minimizes the possibility of non-specific reactions. UPS has been applied to design human arrays for gene expression studies and to develop several small arrays of gene families that were inferred as molecular signatures of cancer typing/ staging or pathogen signatures. Notably, UPS is freely accessible at http://array.iis.sinica.edu.tw/ups/. [ABSTRACT FROM AUTHOR]

A mathematical model is presented for studying the effects of interactions among team members on the process of replacing members of a team in an organization. The model provides the ability to control the number of members that interact with each individual on the team. Through the use of analysis and computer simulations, it is shown how the amount of interaction affects the tradeoff between the expected performance and the number of replacements and interviews needed to find a good team using various replacement policies. New managerial insights into this process--such as the fact that it is not necessarily optimal to replace the worst-performing team members--are provided. [ABSTRACT FROM AUTHOR]