Lyme carditis is an extracutaneous manifestation of Lyme disease characterized by episodes of atrioventricular block of varying degrees and additional, less reported cardiomyopathies. The molecular changes associated with the response to Borrelia burgdorferi over the course of infection are poorly understood. Here, we identify broad transcriptomic and proteomic changes in the heart during infection that reveal a profound down-regulation of mitochondrial components. We also describe the long-term functional modulation of macrophages exposed to live bacteria, characterized by an augmented glycolytic output, increased spirochetal binding and internalization, and reduced inflammatory responses. In vitro, glycolysis inhibition reduces the production of tumor necrosis factor (TNF) by memory macrophages, whereas in vivo, it produces the reversion of the memory phenotype, the recovery of tissue mitochondrial components, and decreased inflammation and spirochetal burdens. These results show that B. burgdorferi induces long-term, memory-like responses in macrophages with tissue-wide consequences that are amenable to be manipulated in vivo. ; Supported by grants from the Spanish Ministry of Science, Innovation and Universities (MCIU) co-financed with FEDER funds (SAF2015-65327-R and RTI2018-096494-B-100 to JA; BFU2016-76872-R to EB, AGL2017-86757-R to LA, SAF2017-87301-R to MLMC, SAF2015-64111-R to AP, SAF2015-73549-JIN to HR), Instituto de Salud Carlos III (PIE13/0004 to AP), the Basque Government Department of Health (2015111117 to LA), the Basque Foundation for Innovation and Health Research (BIOEF), through the EiTB Maratoia grant BIO15/CA/016/BS to MLMC, the regional Government of Andalusia co-funded by CEC and FEDER funds (Proyectos de Excelencia P12-CTS-2232) and Fundación Domingo Martínez (to AP). LA is supported by the Ramon y Cajal program (RYC-2013-13666). DB, MMR and TMM are recipients of MCIU FPI fellowships. ACG and AP are recipients of fellowships form the Basque Government. APC is a recipient of a fellowship from the University of the Basque Country. We thank the MCIU for the Severo Ochoa Excellence accreditation (SEV-2016-0644), the Basque Department of Industry, Tourism and Trade (Etortek and Elkartek programs), the Innovation Technology Department of the Bizkaia Province and the CIBERehd network. DB and JA are supported by a grant from the Jesús de Gangoiti Barrera Foundation.
Monomeric C-reactive protein (mCRP), the activated isoform of CRP, induces tissue damage in a range of inflammatory pathologies. Its detection in infarcted human brain tissue and its experimentally proven ability to promote dementia with Alzheimer's disease (AD) traits at 4 weeks after intrahippocampal injection in mice have suggested that it may contribute to the development of AD after cerebrovascular injury. Here, we showed that a single hippocampal administration of mCRP in mice induced memory loss, lasting at least 6 months, along with neurodegenerative changes detected by increased levels of hyperphosphorylated tau protein and a decrease of the neuroplasticity marker Egr1. Furthermore, co-treatment with the monoclonal antibody 8C10 specific for mCRP showed that long-term memory loss and tau pathology were entirely avoided by early blockade of mCRP. Notably, 8C10 mitigated Egr1 decrease in the mouse hippocampus. 8C10 also protected against mCRP-induced inflammatory pathways in a microglial cell line, as shown by the prevention of increased generation of nitric oxide. Additional in vivo and in vitro neuroprotective testing with the anti-inflammatory agent TPPU, an inhibitor of the soluble epoxide hydrolase enzyme, confirmed the predominant involvement of neuroinflammatory processes in the dementia induced by mCRP. Therefore, locally deposited mCRP in the infarcted brain may be a novel biomarker for AD prognosis, and its antibody blockade opens up therapeutic opportunities for reducing post-stroke AD risk. ; This research was funded by the European Competitiveness Operational Programme 2014– 2020, C-Reactive protein therapy for stroke-associated dementia, ID P_37_674, MySMIS code: 103432, contract 51/05.09.2016; Spanish MINECO and European Regional Development Fund, grant number SAF2016-77703; Spanish MCINN, grant number PID2019-106285RB; Catalan Autonomous Government AGAUR, grant number 2017-SGR-106; the CERCA Programme/Generalitat de Catalunya. R.C was supported by a post-doctoral research contract of the Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain.
Air temperature is one of the main factors for describing the weather behaviour in the earth. Since Indonesia is located on and near equator, then monitoring the air temperature is needed to determine either global climate change occurs or not. Climate change can have an impact on biological growth in various fields. For instance, climate change can affect the quality of production and growth of animal and plants. Therefore, air temperature prediction is important to meteorologists and Indonesian government to provide information in many sectors. Various prediction algorithms have been used to predict temperature and produce different accuracy. In this study, the deep learning method with Long Short-Term Memory (LSTM) model is used to predict air temperature. Here, the results show that LSTM model with one layer and Adaptive Moment Estimation (ADAM) optimizer produce accuracy which is 32% of , 0.068 of MAE and 0.99 of RMSE. Moreover, here, ADAM optimizer is found better than Stochastic Gradient Descent (SGD) optimizer.
Abstract Estimates of government expenditure for the next period are very important in the government, for instance for the Ministry of Finance of the Republic of Indonesia, because this can be taken into consideration in making policies regarding how much money the government should bear and whether there is sufficient availability of funds to finance it. As is the case in the health, education and social fields, modeling technology in machine learning is expected to be applied in the financial sector in government, namely in making modeling for spending predictions. In this study, it is proposed the application of Long Short-Term Memory (LSTM) Model for expenditure predictions. Experiments show that LSTM model using three hidden layers and the appropriate hyperparameters can produce Mean Square Error (MSE) performance of 0.2325, Root Mean Square Error (RMSE) of 0.4820, Mean Average Error (MAE) of 0.3292 and Mean Everage Presentage Error (MAPE) of 0.4214. This is better than conventional modeling using the Auto Regressive Integrated Moving Average (ARIMA) as a comparison model. ; Perkiraan pengeluaran belanja pemerintah untuk periode kedepan merupakan hal yang sangat penting di pemerintah dalam hal ini pada Kementerian Keuangan Republik Indonesia, karena hal ini dapat dijadikan bahan pertimbangan dalam mengambil kebijakan terkait berapa nilai uang yang harus ditanggung pemerintah serta apakah ada ketersediaan dana yang cukup dalam membiayai belanja tersebut untuk periode yang akan datang. Seperti halnya pada bidang kesehatan, pendidikan dan sosial, teknologi pemodelan pada Machine Learning diharapkan dapat diterapkan di bidang keuangan pada pemerintahan, yaitu dalam membuat pemodelan untuk prediksi belanja. Pada penelitian ini, diusulkan penerapan model Long Short-Term Memory (LSTM) untuk prediksi belanja. Eksperimen menunjukkan model LSTM dengan menggunakan tiga hidden layers dan hyperparameter yang tepat dapat menghasilkan performa Mean Square Error (MSE) sebesar 0.2325, Root Mean Square Error (RMSE) sebesar 0.4820, Mean Average Error (MAE) sebesar 0.3292 dan Mean Everage Presentage Error (MAPE) sebesar 0.4214. Ini lebih baik dibandingkan pemodelan konvensional menggunakan Auto Regressive Integrated Moving Average (ARIMA) sebagai model pembanding.
The text classification process has been well studied, but there are still many improvements in the classification and feature preparation, which can optimize the performance of classification for specific applications. In the paper we implemented dictionary based approach and long-short term memory approach. In the first approach, dictionaries will be padded based on field's specific input and use automation technology to expand. The second approach, long short term memory used word2vec technique. This will help us in getting a comprehensive pipeline of end-to-end implementations. This is useful for many applications, such as sorting emails which are spam or ham, classifying news as political or sports-related news, etc
In the marine environment, shore-based radars play an important role in military surveillance and sensing. Sea clutter is one of the main factors affecting the performance of shore-based radar. Affected by marine environmental factors and radar parameters, the fluctuation law of sea clutter amplitude is very complicated. In the process of training a sea clutter amplitude prediction model, the traditional method updates the model parameters according to the current input data and the parameters in the current model, and cannot utilize the historical information of sea clutter amplitude. It is only possible to learn the short-term variation characteristics of the sea clutter. In order to learn the long-term variation law of sea clutter, a sea clutter prediction system based on the long short-term memory neural network is proposed. Based on sea clutter data collected by IPIX radar, UHF-band radar and S-band radar, the experimental results show that the mean square error of this prediction system is smaller than the traditional prediction methods. The sea clutter suppression signal is extracted by comparing the predicted sea clutter data with the original sea clutter data. The results show that the proposed sea clutter prediction system has a good effect on sea clutter suppression.
Air pollution levels have risen as an outcome of urban and industrial development in so many developing countries. People and governments all around the world are concerned about air pollution, which has a severe influence on both personal health and long-term global development. As a government, it is responsible for preventing and controlling air pollution, as well as monitoring the pollutant's impacts on human health. There are numerous computer models available, ranging from statistics to artificial intelligence. Pollution levels are still out of control in some parts of the world due to a wide range of sources and factors. Because of accurate estimates of future air pollution, the government can take necessary action. Forecasting air pollution levels based on environmental data is becoming increasingly relevant as people become more worried about global warming and urban sustainability. For replicating the complicated linkages between these variables, advanced Deep Learning (DL) algorithms hold enormous promise. The objective of this work is to provide a high level of accurate solution to the air pollution forecasting problem. Kaggle data will be employed to train a DL model that will forecast air pollution levels.
To protect the environment and achieve the Sustainable Development Goals (SDGs), reducing greenhouse gas emissions has been actively promoted by global governments. Thus, clean energy, such as wind power, has become a very important topic among global governments. However, accurately forecasting wind power output is not a straightforward task. The present study attempts to develop a fuzzy seasonal long short-term memory network (FSLSTM) that includes the fuzzy decomposition method and long short-term memory network (LSTM) to forecast a monthly wind power output dataset. LSTM technology has been successfully applied to forecasting problems, especially time series problems. This study first adopts the fuzzy seasonal index into the fuzzy LSTM model, which effectively extends the traditional LSTM technology. The FSLSTM, LSTM, autoregressive integrated moving average (ARIMA), generalized regression neural network (GRNN), back propagation neural network (BPNN), least square support vector regression (LSSVR), and seasonal autoregressive integrated moving average (SARIMA) models are then used to forecast monthly wind power output datasets in Taiwan. The empirical results indicate that FSLSTM can obtain better performance in terms of forecasting accuracy than the other methods. Therefore, FSLSTM can efficiently provide credible prediction values for Taiwan's wind power output datasets.
The decline and increase in the price of shares of plantation companies is a problem for investors in making decisions to buy or sell shares. Factors influencing the movement of plantation stock prices include CPO commodity price fluctuations, world oil price fluctuations, Rupiah exchange rate fluctuations, government regulations and policies, demands from importing countries, and climate. Forecasting stock prices is expected to help investors to deal with uncertainty in the movement of plantation stock prices. This study applies the Long Short-Term Memory (LSTM) to predict the stock prices of plantation companies using SSMS, LSIP, and SIMP share price data from the period 1 July 2014 - 22 July 2019. Based on the results of the study it was found that the best LSTM model on SSMS shares by using the RMSProp optimizer and 70 hidden neurons produced an RMSE value of 21,328. Then the best LSTM model on LSIP stock by using Adam optimizer and 80 hidden neurons produces an RMSE value of 33,097. Whereas the best LSTM model on SIMP shares using Adamax optimizer and 100 hidden neurons produced an RMSE value of 8,3337.
An earthquake is a tremor felt on the surface of the earth created by the movement of the major pieces of its outer shell. Till now, many attempts have been made to forecast earthquakes, which saw some success, but these attempted models are specific to a region. In this paper, an earthquake occurrence and location prediction model is proposed. After reviewing the literature, long short-term memory (LSTM) is found to be a good option for building the model because of its memory-keeping ability. Using the Keras tuner, the best model was selected from candidate models, which are composed of combinations of various LSTM architectures and dense layers. This selected model used seismic indicators from the earthquake catalog of Bangladesh as features to predict earthquakes of the following month. Attention mechanism was added to the LSTM architecture to improve the model's earthquake occurrence prediction accuracy, which was 74.67%. Additionally, a regression model was built using LSTM and dense layers to predict the earthquake epicenter as a distance from a predefined location, which provided a root mean square error of 1.25. ; Validerad;2021;Nivå 2;2021-04-19 (alebob); Finansiär: Informationand Communication Technology division of the Governmentof the People's Republic of Bangladesh
State-space models are a popular choice in modelling voting intentions and election results by using poll data. The presented multivariate state-space model attempts to go beyond random-walk or Kalman-filter approaches (with comparable performance to simple weighted survey averages) to the problem by introducing a long-short term event memory effect. This effect serves as reasonable explanation to the observation that the voter's share partially tends to reverse to the party's long-term trend after larger short term movements. Any event influencing the voter's share of a party is presumed to have a convex shaped effect decomposable into a short term effect due to e.g. media spreading and a smaller long term effect remaining despite overlay effects of new events and forgetting. This effect is modelled by a mixture of a random walk and two contrasting autoregressive processes. By also taking advantage of the widely observed effect that government parties tend to fall in voter's share, whereas the opposite effect is observed for opposition parties, mid- and long-term predictions of election outcomes can be considerably be improved. The Stan-model is fitted and evaluated on poll data from seven pollsters for the German national elections ("Bundestagswahl") from 1994 to 2017, where low double digits (out-of-sample) improvements in prediction performance can be seen between 3- and 18-months prior elections. By taking into account the pollsters house effects, their poll errors and even more importantly their correlations in poll errors, an appropriate and realistic estimation error can be propagated.
The increasing life expectancy and the alarming growth in the incidence of chronic illness make long term care services in high demand and in dire need of change and innovation. As part of the ANCIEN initiative, which aims to comprise a database of European approaches for dealing with long term care, this document creates an overview of the health systems organized in Romania which target individuals with long term care needs. The method of governance, the people's needs and the available services are presented herein. For the most part, the services provided in this field are covered through the efforts of the family of those in need and are therefore difficult to quantify or analyze. Public services are either insufficient (in terms of quality or accessibility) and the moral stigma associated to using them prevents families from making this choice. However, due to a high demand and a low supply of high quality LTC services, the private market of nursing homes has exploded in the last few years, funded either privately, through NGOs or external donations. The quality and number of available services has greatly improved but the accessibility is still low. At this moment, Romania still does not have an integrated long term care system neither from the legal or the organization of services being offered. There are social and medical services that are run, provided and legislated independently. The current national strategy is to coordinate these services and to create an integrated system with multidisciplinary teams which would include different types of medical specialists and nurses but still maintain and improve the services offered formally or informally as a home based care package.
The implementation of the Covid-19 vaccination carried out by Indonesian government was ignited pros and contras among the public. Certainly, there will be pros and cons about the vaccination from the community. This attituded of pros and cons, which is also called sentiment, can influence people to accept or refuse to be vaccinated. Todays, people express their sentiment in social media in comments, post, or status. One of the methods used to detect sentiment on social media, whether positive or negative, is through a categorisation of text approach. This research provides a deep learning technique for sentiment classification on Twitter that uses Long Short Term Memory (LSTM), for positive, neutral and negative classes. The word2vec word embeddings was used as input, using the pretrained Bahasa Indonesia model from Wikipedia corpus. On the other hand, the topic-based word2vec model was also trained from the Covid-19 vaccination sentiment dataset which collected from Twitter. The data used after balanced is 2564 training data, 778 data validation data, and 400 test data with 1802 neutral data, 1066 negative data, and 566 positive data. The best results from various parameter processes give an F1-Score value of 54% on the test data, with an accuracy of 66%. The result of this research is a model that can classify sentiments with new sentences.
The increasing life expectancy and the alarming growth in the incidence of chronic illness make long term care services in high demand and in dire need of change and innovation. As part of the ANCIEN initiative, which aims to comprise a database of European approaches for dealing with long term care, this document creates an overview of the health systems organized in Romania which target individuals with long term care needs. The method of governance, the people's needs and the available services are presented herein. For the most part, the services provided in this field are covered through the efforts of the family of those in need and are therefore difficult to quantify or analyze. Public services are either insufficient (in terms of quality or accessibility) and the moral stigma associated to using them prevents families from making this choice. However, due to a high demand and a low supply of high quality LTC services, the private market of nursing homes has exploded in the last few years, funded either privately, through NGOs or external donations. The quality and number of available services has greatly improved but the accessibility is still low. At this moment, Romania still does not have an integrated long term care system neither from the legal or the organization of services being offered. There are social and medical services that are run, provided and legislated independently. The current national strategy is to coordinate these services and to create an integrated system with multidisciplinary teams which would include different types of medical specialists and nurses but still maintain and improve the services offered formally or informally as a home based care package.
BACKGROUND: The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function. RESULTS: Here, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-term memory. CONCLUSION: We conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens. ; The work of IF was funded, in part, by the National Science Foundation award DBI-1458359. The work of CSG and AJL was funded, in part, by the National Science Foundation award DBI-1458390 and GBMF 4552 from the Gordon and Betty Moore Foundation. The work of DAH and KAL was funded, in part, by the National Science Foundation award DBI-1458390, National Institutes of Health NIGMS P20 GM113132, and the Cystic Fibrosis Foundation CFRDP STANTO19R0. The work of AP, HY, AR, and MT was funded by BBSRC grants BB/K004131/1, BB/F00964X/1 and BB/M025047/1, Consejo Nacional de Ciencia y Tecnología Paraguay (CONACyT) grants 14-INV-088 and PINV15-315, and NSF Advances in BioInformatics grant 1660648. The work of JC was partially supported by an NIH grant (R01GM093123) and two NSF grants (DBI 1759934 and IIS1763246). ACM acknowledges the support by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy - EXC 2155 "RESIST" - Project ID 39087428. DK acknowledges the support from the National Institutes of Health (R01GM123055) and the National Science Foundation (DMS1614777, CMMI1825941). PB acknowledges the support from the National Institutes of Health (R01GM60595). GB and BZK acknowledge the support from the National Science Foundation (NSF 1458390) and NIH DP1MH110234. FS was funded by the ERC StG 757700 "HYPER-INSIGHT" and by the Spanish Ministry of Science, Innovation and Universities grant BFU2017-89833-P. FS further acknowledges the funding from the Severo Ochoa award to the IRB Barcelona. TS was funded by the Centre of Excellence project "BioProspecting of Adriatic Sea", co-financed by the Croatian Government and the European Regional Development Fund (KK.01.1.1.01.0002). The work of SK was funded by ATT Tieto käyttöön grant and Academy of Finland. JB and HM acknowledge the support of the University of Turku, the Academy of Finland and CSC – IT Center for Science Ltd. TB and SM were funded by the NIH awards UL1 TR002319 and U24 TR002306. The work of CZ and ZW was funded by the National Institutes of Health R15GM120650 to ZW and start-up funding from the University of Miami to ZW. The work of PWR was supported by the National Cancer Institute of the National Institutes of Health under Award Number U01CA198942. PR acknowledges NSF grant DBI-1458477. PT acknowledges the support from Helsinki Institute for Life Sciences. The work of AJM was funded by the Academy of Finland (No. 292589). The work of FZ and WT was funded by the National Natural Science Foundation of China (31671367, 31471245, 91631301) and the National Key Research and Development Program of China (2016YFC1000505, 2017YFC0908402]. CS acknowledges the support by the Italian Ministry of Education, University and Research (MIUR) PRIN 2017 project 2017483NH8. SZ is supported by the National Natural Science Foundation of China (No. 61872094 and No. 61572139) and Shanghai Municipal Science and Technology Major Project (No. 2017SHZDZX01). PLF and RLH were supported by the National Institutes of Health NIH R35-GM128637 and R00-GM097033. JG, DTJ, CW, DC, and RF were supported by the UK Biotechnology and Biological Sciences Research Council (BB/N019431/1, BB/L020505/1, and BB/L002817/1) and Elsevier. The work of YZ and CZ was funded in part by the National Institutes of Health award GM083107, GM116960, and AI134678; the National Science Foundation award DBI1564756; and the Extreme Science and Engineering Discovery Environment (XSEDE) award MCB160101 and MCB160124. The work of BG, VP, RD, NS, and NV was funded by the Ministry of Education, Science and Technological Development of the Republic of Serbia, Project No. 173001. The work of YWL, WHL, and JMC was funded by the Taiwan Ministry of Science and Technology (106-2221-E-004-011-MY2). YWL, WHL, and JMC further acknowledge the support from "the Human Project from Mind, Brain and Learning" of the NCCU Higher Education Sprout Project by the Taiwan Ministry of Education and the National Center for High-performance Computing for computer time and facilities. The work of IK and AB was funded by Montana State University and NSF Advances in Biological Informatics program through grant number 0965768. BR, TG, and JR are supported by the Bavarian Ministry for Education through funding to the TUM. The work of RB, VG, MB, and DCEK was supported by the Simons Foundation, NIH NINDS grant number 1R21NS103831-01 and NSF award number DMR-1420073. CJJ acknowledges the funding from a University of Illinois at Chicago (UIC) Cancer Center award, a UIC College of Liberal Arts and Sciences Faculty Award, and a UIC International Development Award. The work of ML was funded by Yad Hanadiv (grant number 9660 /2019). The work of OL and IN was funded by the National Institute of General Medical Science of the National Institute of Health through GM066099 and GM079656. Research Supporting Plan (PSR) of University of Milan number PSR2018-DIP-010-MFRAS. AWV acknowledges the funding from the BBSRC (CASE studentship BB/M015009/1). CD acknowledges the support from the Swiss National Science Foundation (150654). CO and MJM are supported by the EMBL-European Bioinformatics Institute core funds and the CAFA BBSRC BB/N004876/1. GG is supported by CAFA BBSRC BB/N004876/1. SCET acknowledges funding from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 778247 (IDPfun) and from COST Action BM1405 (NGP-net). SEB was supported by NIH/NIGMS grant R01 GM071749. The work of MLT, JMR, and JMF was supported by the National Human Genome Research Institute of the National of Health, grant numbers U41 HG007234. The work of JMF and JMR was also supported by INB Grant (PT17/0009/0001 - ISCIII-SGEFI / ERDF). VA acknowledges the funding from TUBITAK EEEAG-116E930. RCA acknowledges the funding from KanSil 2016K121540. GV acknowledges the funding from Università degli Studi di Milano - Project "Discovering Patterns in Multi-Dimensional Data" and Project "Machine Learning and Big Data Analysis for Bioinformatics". SZ is supported by the National Natural Science Foundation of China (No. 61872094 and No. 61572139) and Shanghai Municipal Science and Technology Major Project (No. 2017SHZDZX01). RY and SY are supported by the 111 Project (NO. B18015), the key project of Shanghai Science & Technology (No. 16JC1420402), Shanghai Municipal Science and Technology Major Project (No. 2018SHZDZX01), and ZJLab. ST was supported by project Ribes Network POR-FESR 3S4H (No. TOPP-ALFREVE18-01) and PRID/SID of University of Padova (No. TOPP-SID19-01). CZ and ZW were supported by the NIGMS grant R15GM120650 to ZW and start-up funding from the University of Miami to ZW. The work of MK and RH was supported by the funding from King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. URF/1/3454-01-01 and URF/1/3790-01-01. The work of SDM is funded, in part, by NSF award DBI-1458443 ; Sí
