Mycobacterium abscessus (Mab) is an opportunistic pathogen afflicting individuals with underlying lung disease such as Cystic Fibrosis (CF) or immunodeficiencies. Current treatment strategies for Mab infections are limited by its inherent antibiotic resistance and limited drug access to Mab in its in vivo niches resulting in poor cure rates of 30-50%. Mab\'s ability to survive within macrophages, granulomas and the mucus laden airways of the CF lung requires adaptation via transcriptional remodeling to counteract stresses like hypoxia, increased levels of nitrate, nitrite, and reactive nitrogen intermediates. Mycobacterium tuberculosis (Mtb) is known to coordinate hypoxic adaptation via induction of respiratory nitrate assimilation through the nitrate reductase narGHJI. Mab, on the other hand, does not encode a respiratory nitrate reductase. In addition, our recent study of the transcriptional responses of Mab to hypoxia revealed marked down-regulation of a locus containing putative nitrate assimilation genes, including the orphan response regulator nnaR (nitrate/nitrite assimilation regulator). These putative nitrate assimilation genes, narK3 (nitrate/nitrite transporter), nirBD (nitrite reductase), nnaR, and sirB (ferrochelatase) are arranged contiguously while nasN (assimilatory nitrate reductase identified in this work) is encoded in a different locus. Absence of a respiratory nitrate reductase in Mab and down-regulation of nitrogen metabolism genes in hypoxia suggest interplay between hypoxia adaptation and nitrate assimilation are distinct from what was previously documented in Mtb. The mechanisms used by Mab to fine-tune the transcriptional regulation of nitrogen metabolism in the context of stresses e.g. hypoxia, particularly the role of NnaR, remain poorly understood. To evaluate the role of NnaR in nitrate metabolism we constructed a Mab nnaR knockout strain (MabΔnnaR ) and complement (MabΔnnaR+C ) to investigate transcriptional regulation and phenotypes. qRT-PCR revealed NnaR is necessary for regulating nitrate and nitrite reductases along with a putative nitrate transporter. Loss of NnaR compromised the ability of Mab to assimilate nitrate or nitrite as sole nitrogen sources highlighting its necessity. This work provides the first insights into the role of Mab NnaR setting a foundation for future work investigating NnaR\'s contribution to pathogenesis.

BACKGROUND: Prediction models have gained immense importance in various fields for decision-making purposes. In the context of tennis, relying solely on the probability of winning a single match may not be sufficient for predicting a player\'s future performance or ranking. The performance of a tennis player is influenced by the timing of their matches throughout the year, necessitating the incorporation of time as a crucial factor. This study aims to focus on prediction models for performance indicators that can assist both tennis players and sports analysts in forecasting player standings in future matches.
METHODS: To predict player performance, this study employs a dynamic technique that analyzes the structure of performance using both linear and nonlinear time series models. A novel approach has been taken, comparing the performance of the non-linear Neural Network Auto-Regressive (NNAR) model with conventional stochastic linear and nonlinear models such as Auto-Regressive Integrated Moving Average (ARIMA), Exponential Smoothing (ETS), and TBATS (Trigonometric Seasonal Decomposition Time Series).
RESULTS: The study finds that the NNAR model outperforms all other competing models based on lower values of Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). This superiority in performance metrics suggests that the NNAR model is the most appropriate approach for predicting player performance in tennis. Additionally, the prediction results obtained from the NNAR model demonstrate narrow 95% Confidence Intervals, indicating higher accuracy and reliability in the forecasts.
CONCLUSIONS: In conclusion, this study highlights the significance of incorporating time as a factor when predicting player performance in tennis. It emphasizes the potential benefits of using the NNAR model for forecasting future player standings in matches. The findings suggest that the NNAR model is a recommended approach compared to conventional models like ARIMA, ETS, and TBATS. By considering time as a crucial factor and employing the NNAR model, both tennis players and sports analysts can make more accurate predictions about player performance.

Forecasts are valuable to countries to make informed business decisions and develop data-driven strategies. The production of pulses is an integral part of agricultural diversification initiatives because it offers promising economic opportunities to reduce rural poverty and unemployment in developing countries. Pulses are the cheapest source of protein needed for human health. India\'s pulses production guidelines must be based on accurate and best forecast models. Comparing classical statistical and machine learning models based on different scientific data series is the subject of high-level research today. This study focused on the forecasting behaviour of pulses production for India, Karnataka, Madhya Pradesh, Maharashtra, Rajasthan and Uttar Pradesh. The data series was split into a training dataset (1950-2014) and a testing dataset (2015-2019) for model building and validation purposes, respectively. ARIMA, NNAR and hybrid models were used and compared on training and validation datasets based on goodness of fit (RMSE, MAE and MASE). This research demonstrates that due to the diverse agricultural conditions across different provinces in India, there is no single model that can accurately predict pulse production in all regions. This study\'s highest accuracy model is ARIMA. ARIMA outperforms NNAR, a machine learning model. Pulse production in India, Rajasthan, and Madhya Pradesh will expand by 26.11%, 12.62%, and 0.51% from 2020 to 2030, whereas it would decline by - 6.5%, - 6.21%, and - 6.76 per cent in Karnataka, Maharashtra, and Uttar Pradesh, respectively. The current forecast results could allow policymakers to develop more aggressive food security and sustainability plans and better Indian pulses production policies in the future.

Cancer remains a leading cause of worldwide mortality and is a growing, multifaceted global burden. As a result, cancer prevention and cancer mortality reduction are counted among the most pressing public health issues of the twenty-first century. In turn, accurate projections of cancer incidence and mortality rates are paramount for robust policymaking, aimed at creating efficient and inclusive public health systems and also for establishing a baseline to assess the impact of newly introduced public health measures. Within the European Union (EU), Romania consistently reports higher mortality from all types of cancer than the EU average, caused by an inefficient and underfinanced public health system and lower economic development that in turn have created the phenomenon of \"oncotourism\". This paper aims to develop novel cancer incidence/cancer mortality models based on historical links between incidence and mortality occurrence as reflected in official statistics and population web-search habits. Subsequently, it employs estimates of the web query index to produce forecasts of cancer incidence and mortality rates in Romania. Various statistical and machine-learning models-the autoregressive integrated moving average model (ARIMA), the Exponential Smoothing State Space Model with Box-Cox Transformation, ARMA Errors, Trend, and Seasonal Components (TBATS), and a feed-forward neural network nonlinear autoregression model, or NNAR-are estimated through automated algorithms to assess in-sample fit and out-of-sample forecasting accuracy for web-query volume data. Forecasts are produced with the overperforming model in the out-of-sample context (i.e., NNAR) and fed into the novel incidence/mortality models. Results indicate a continuation of the increasing trends in cancer incidence and mortality in Romania by 2026, with projected levels for the age-standardized total cancer incidence of 313.8 and the age-standardized mortality rate of 233.8 representing an increase of 2%, and, respectively, 3% relative to the 2019 levels. Research findings thus indicate that, under the no-change hypothesis, cancer will remain a significant burden in Romania and highlight the need and urgency to improve the status quo in the Romanian public health system.

The coronavirus disease (COVID-19) is a severe, ongoing, novel pandemic that emerged in Wuhan, China, in December 2019. As of January 21, 2021, the virus had infected approximately 100 million people, causing over 2 million deaths. This article analyzed several time series forecasting methods to predict the spread of COVID-19 during the pandemic\'s second wave in Italy (the period after October 13, 2020). The autoregressive moving average (ARIMA) model, innovations state space models for exponential smoothing (ETS), the neural network autoregression (NNAR) model, the trigonometric exponential smoothing state space model with Box-Cox transformation, ARMA errors, and trend and seasonal components (TBATS), and all of their feasible hybrid combinations were employed to forecast the number of patients hospitalized with mild symptoms and the number of patients hospitalized in the intensive care units (ICU). The data for the period February 21, 2020-October 13, 2020 were extracted from the website of the Italian Ministry of Health ( www.salute.gov.it ). The results showed that (i) hybrid models were better at capturing the linear, nonlinear, and seasonal pandemic patterns, significantly outperforming the respective single models for both time series, and (ii) the numbers of COVID-19-related hospitalizations of patients with mild symptoms and in the ICU were projected to increase rapidly from October 2020 to mid-November 2020. According to the estimations, the necessary ordinary and intensive care beds were expected to double in 10 days and to triple in approximately 20 days. These predictions were consistent with the observed trend, demonstrating that hybrid models may facilitate public health authorities\' decision-making, especially in the short-term.