The power spectrum of the membrane voltage driven by a single nACh receptor.

Being the primary mean of translating the chemical signal into the electric signal in neuromuscular junction, the nACh receptor is the first kind of ligand-gated channel that its kinetic structure has been known clearly. The next problem is to know quantitatively how it works in a living system. In this paper, we construct a piece-wise deterministic process to mimic the membrane voltage fluctuation driven by a single nACh receptor. The power spectrum of the voltage fluctuation is solved analytically, which reveals the 1/ω4-type noise.

Low Correlation Interference OFDM-NLFM Waveform Design for MIMO Radar Based on Alternating Optimization.

The OFDM chirp signal is suitable for MIMO radar applications due to its large time-bandwidth product, constant time-domain, and almost constant frequency-domain modulus. Particularly, by introducing the time-frequency structure of the non-linear frequency modulation (NLFM) signal into the design of an OFDM chirp waveform, a new OFDM-NLFM waveform with low peak auto-correlation sidelobe ratio (PASR) and peak cross-correlation ratio (PCCR) is obtained. IN-OFDM is the OFDM-NLFM waveform set currently with the lowest PASR and PCCR. Here we construct the optimization model of the OFDM-NLFM waveform set with the objective function being the maximum of the PASR and PCCR. Further, this paper proposes an OFDM-NLFM waveform set design algorithm inspired by alternating optimization. We implement the proposed algorithm by the alternate execution of two sub-algorithms. First, we keep both the sub-chirp sequence code matrix and sub-chirp rate plus and minus (PM) code matrix unchanged and use the particle swarm optimization (PSO) algorithm to obtain the optimal parameters of the NLFM signal's time-frequency structure (NLFM parameters). Next, we keep current optimal NLFM parameters unchanged, and optimize the sub-chirp sequence code matrix and sub-chirp rate PM code matrix using the block coordinate descent (BCD) algorithm. The above two sub-algorithms are alternately executed until the objective function converges to the optimal solution. The results show that the PASR and PCCR of the obtained OFDM-NLFM waveform set are about 5 dB lower than that of the IN-OFDM.

Forecasting the dynamics of cumulative COVID-19 cases (confirmed, recovered and deaths) for top-16 countries using statistical machine learning models: Auto-Regressive Integrated Moving Average (ARIMA) and Seasonal Auto-Regressive Integrated Moving Average (SARIMA).

Most countries are reopening or considering lifting the stringent prevention policies such as lockdowns, consequently, daily coronavirus disease (COVID-19) cases (confirmed, recovered and deaths) are increasing significantly. As of July 25th, there are 16.5 million global cumulative confirmed cases, 9.4 million cumulative recovered cases and 0.65 million deaths. There is a tremendous necessity of supervising and estimating future COVID-19 cases to control the spread and help countries prepare their healthcare systems. In this study, time-series models - Auto-Regressive Integrated Moving Average (ARIMA) and Seasonal Auto-Regressive Integrated Moving Average (SARIMA) are used to forecast the epidemiological trends of the COVID-19 pandemic for top-16 countries where 70%-80% of global cumulative cases are located. Initial combinations of the model parameters were selected using the auto-ARIMA model followed by finding the optimized model parameters based on the best fit between the predictions and test data. Analytical tools Auto-Correlation function (ACF), Partial Auto-Correlation Function (PACF), Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) were used to assess the reliability of the models. Evaluation metrics Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Percent Error (MAPE) were used as criteria for selecting the best model. A case study was presented where the statistical methodology was discussed in detail for model selection and the procedure for forecasting the COVID-19 cases of the USA. Best model parameters of ARIMA and SARIMA for each country are selected manually and the optimized parameters are then used to forecast the COVID-19 cases. Forecasted trends for confirmed and recovered cases showed an exponential rise for countries such as the United States, Brazil, South Africa, Colombia, Bangladesh, India, Mexico and Pakistan. Similarly, trends for cumulative deaths showed an exponential rise for countries Brazil, South Africa, Chile, Colombia, Bangladesh, India, Mexico, Iran, Peru, and Russia. SARIMA model predictions are more realistic than that of the ARIMA model predictions confirming the existence of seasonality in COVID-19 data. The results of this study not only shed light on the future trends of the COVID-19 outbreak in top-16 countries but also guide these countries to prepare their health care policies for the ongoing pandemic. The data used in this work is obtained from publicly available John Hopkins University's COVID-19 database.

Various Auto-Correlation Functions of m-Bit Random Numbers Generated from Chaotic Binary Sequences.

This paper discusses the auto-correlation functions of m-bit random numbers obtained from m chaotic binary sequences generated by one-dimensional nonlinear maps. First, we provide the theoretical auto-correlation function of an m-bit sequence obtained by m binary sequences that are assumed to be uncorrelated to each other. The auto-correlation function is expressed by a simple form using the auto-correlation functions of the binary sequences. This implies that the auto-correlation properties of the m-bit sequences can be easily controlled by the auto-correlation functions of the original binary sequences. In numerical experiments using a computer, we generated m-bit random sequences using some chaotic binary sequences with prescribed auto-correlations generated by one-dimensional chaotic maps. The numerical experiments show that the numerical auto-correlation values are almost equal to the corresponding theoretical ones, and we can generate m-bit sequences with a variety of auto-correlation properties. Furthermore, we also show that the distributions of the generated m-bit sequences are uniform if all of the original binary sequences are balanced (i.e., the probability of 1 (or 0) is equal to 1/2) and independent of one another.

Detection of the Vibration Signal from Human Vocal Folds Using a 94-GHz Millimeter-Wave Radar.

The detection of the vibration signal from human vocal folds provides essential information for studying human phonation and diagnosing voice disorders. Doppler radar technology has enabled the noncontact measurement of the human-vocal-fold vibration. However, existing systems must be placed in close proximity to the human throat and detailed information may be lost because of the low operating frequency. In this paper, a long-distance detection method, involving the use of a 94-GHz millimeter-wave radar sensor, is proposed for detecting the vibration signals from human vocal folds. An algorithm that combines empirical mode decomposition (EMD) and the auto-correlation function (ACF) method is proposed for detecting the signal. First, the EMD method is employed to suppress the noise of the radar-detected signal. Further, the ratio of the energy and entropy is used to detect voice activity in the radar-detected signal, following which, a short-time ACF is employed to extract the vibration signal of the human vocal folds from the processed signal. For validating the method and assessing the performance of the radar system, a vibration measurement sensor and microphone system are additionally employed for comparison. The experimental results obtained from the spectrograms, the vibration frequency of the vocal folds, and coherence analysis demonstrate that the proposed method can effectively detect the vibration of human vocal folds from a long detection distance.

Generalized spot auto-focusing method with a high-definition auto-correlation function in transmission electron microscopy.

The spot auto-focusing (AF) method with a unique high-definition auto-correlation function (HD-ACF) proposed in the previous paper is improved and is now applicable to general specimens at a wide range of magnifications. According to the definition where the AF is defocused to obtain the highest resolution, the proposed method achieves the sharpest HD-ACF profile in the AF spot image. The relationship where the sharpest HD-ACF profile gives the highest resolution is theoretically explained, and practical AF examples for different specimens and magnifications are experimentally demonstrated. Specimens include a yeast cell thin section at 10-k magnification, a standard grating replica used as a ruler at 50-k, a crystal lattice of graphitized carbon at 400-k and a 60°-tilted thin section (yeast cell) at 10-k. Different procedures are prepared to actively identify the defocus position that gives the sharpest HD-ACF profile. Every AF result demonstrates the highest-resolution image.

Article numbers as a leading indicator of publication time / Números de artigos como um indicador importante do tempo de publicação

Aims: in health professions education (HPE), the use of statistics is commonly associated with somewhat larger samples, whereas smaller samples or single subjects (i.e., N = 1) are usually labelled as needing some kind of 'qualitative' approach. However, statistical methods can be very useful in small samples and for individual subjects as well, especially where we have time series of repeated measurements of the same outcome variable(s) of interest. The aim of this article is twofold: to demonstrate an example of a cross-correlation function for single subjects in a HPE context and to suggest a few settings in HPE where this cross-correlation function can be of use. Method: the example uses data from a recent Open Access publication on among others article numbers and publication time in a number of major HPE journals to examine the relation between the number of articles published and median publication time over time in the zero-cost Open-Source statistical program R version 4.0.5. Results: as to be expected, the number of articles published appears somewhat of a leading indicator of publication time: both number of articles in year 'y' and number of articles in year 'y minus 1' correlate > 0.6 with median publication time in year 'y', while correlations of other time differences (e.g., number of articles in year 'y minus 2' and median publication time in year 'y', or median publication time in year 'y' and number of articles in year 'y plus 1') are substantially smaller. Conclusion: in line with recent literature, this article demonstrates that the cross-correlation function can be used in the context of small samples and single subjects. While the example focusses on article numbers and publication times, it can equally be applied in for example studying relations between knowledge, skills and attitude in individuals, or relations between behaviors of individuals working in pairs or small groups.

Introdução: na educação de profissionais de saúde, o uso de estatísticas é associado comumente a amostras um pouco maiores, enquanto as amostras menores ou assuntos únicos (ou seja, N = 1) são geralmente rotulados como precisando de algum tipo de abordagem "qualitativa". No entanto, os métodos estatísticos podem ser muito úteis em pequenas amostras e para sujeitos individuais, especialmente quando temos séries temporais de medições repetidas da(s) mesma(s) variável(is) de desfecho de interesse. O objetivo deste artigo é demonstrar um exemplo de uma função de correlação cruzada para sujeitos individuais em um contexto de educação de profissionais de saúde e sugerir algumas configurações em que essa função pode ser útil. Método: o exemplo usa dados de uma publicação recente de acesso aberto sobre, entre outros, números de artigos e tempo de publicação em vários dos principais periódicos da educação de profissionais de saúde para examinar a relação entre o número de artigos publicados e o tempo médio de publicação ao longo do tempo, no programa R versão 4.0.5, programa estatístico de código aberto de custo zero. Resultados: o número de artigos publicados parece ser um indicador importante do tempo de publicação: tanto o número de artigos no ano "y" quanto o número de artigos no ano "y menos 1" se correlacionam > 0,6 com o tempo médio de publicação no ano "y", enquanto as correlações de outras diferenças de tempo são substancialmente menores, como, por exemplo, número de artigos no ano " y menos 2" e tempo médio de publicação no ano " y", ou tempo médio de publicação no ano "y" e número de artigos no ano "y mais 1"). Conclusão: de acordo com a literatura recente, este artigo demonstra que a função de correlação cruzada pode ser usada no contexto de pequenas amostras e indivíduos únicos. Embora o exemplo se concentre em números de artigos e tempos de publicação, pode igualmente ser aplicado, por exemplo, no estudo de relações entre conhecimento, habilidades e atitudes em indivíduos, ou relações entre comportamentos de indivíduos que trabalham em pares ou pequenos grupos.

The impact of the lateral geniculate nucleus and corticogeniculate interactions on efficient coding and higher-order visual object processing.

Principles of efficient coding suggest that the peripheral units of any sensory processing system are designed for efficient coding. The function of the lateral geniculate nucleus (LGN) as an early stage in the visual system is not well understood. Some findings indicate that similar to the retina that decorrelates input signals spatially, the LGN tends to perform a temporal decorrelation. There is evidence suggesting that corticogeniculate connections may account for this decorrelation in the LGN. In this study, we propose a computational model based on biological evidence reported by Wang et al. (2006), who demonstrated that the influence pattern of V1 feedback is phase-reversed. The output of our model shows how corticogeniculate connections decorrelate LGN responses and make an efficient representation. We evaluated our model using criteria that have previously been tested on LGN neurons through cell recording experiments, including sparseness, entropy, power spectra, and information transfer. We also considered the role of the LGN in higher-order visual object processing, comparing the categorization performance of human subjects with a cortical object recognition model in the presence and absence of our LGN input-stage model. Our results show that the new model that considers the role of the LGN, more closely follows the categorization performance of human subjects.

AmylPepPred: Amyloidogenic Peptide Prediction tool.

UNLABELLED: We present an efficient computational architecture designed using supervised machine learning model to predict amyloid fibril forming protein segments, named AmylPepPred. The proposed prediction model is based on bio-physio-chemical properties of primary sequences and auto-correlation function of their amino acid indices. AmylPepPred provides a user friendly web interface for the researchers to easily observe the fibril forming and non-fibril forming hexmers in a given protein sequence. We expect that this stratagem will be highly encouraging in discovering fibril forming regions in proteins thereby benefit in finding therapeutic agents that specifically aim these sequences for the inhibition and cure of amyloid illnesses. AVAILABILITY: AmylPepPred is available freely for academic use at www.zoommicro.in/amylpeppred.