A Novel Established Pyroptosis-participant Genes Signature for Predicting the Prognosis of Idiopathic Pulmonary Fibrosis

Idiopathic pulmonary fibrosis (IPF), caused by a strange reason and characterized by a bad clinical prognosis, is an advanced, long-time, and irreversible interstitial lung disease (ILD). In recent years, it has been confirmed that a pyroptosis is a phlogistic form of programmed cellular demise. At the same time, the expression of the pyroptosis-participant gene (PPG) in IPF and their pertinence behind prognosis remains indistinct. In this research, we identified 17 pyroptosis regulators that were distinguishingly expressed entre IPF and controls. The ground on these distinguishingly expressed genes (DEGs), entire IPF conditions could be uncoupled into two subtypes. The prognostic significance of respective PPG for surviving was assessed to find a polygenes signature doing with a Gene Expression Omnibus (GEO) pack (GSE28042). Via putting into use the least absolute shrinkage and selection operator (LASSO) Cox regression approach, a 6-gene signature was structured and ranged entire IPF sicks in GSE28042 into a shallow-threat or high-threat group. IPF sicks in the shallow-threat group revealed meaningfully upper survival chances than those in the high-threat group (pvalue < 0.001). Exploiting the mid threat-score from GSE28042, IPF sicks from another GEO cohort(GSE70866-GPL17077) were separated into two threat subgroups, and the shallow-threat set had augmented overall survival (OS) time ( P = 0.0018). United the clinical characteristics, the threat-score was a sole element for forecasting the overall survival of IPF sicks. Function enrichment analyses bespoke that modification of morphology or physiology of other organisms, killing of cellular of other organisms, and disruption of cellular of other organisms biological processes were increased in the high-threat group. GSEA(Gene Set Enrichment Analysis) showed that cancer- and autoimmune disease-participant “KEGG” gene sets were highly enriched in the high−threat phenotype. In general, PPGs play a crucial role in IPF and can be done to forecast the prognosis of IPFs, and our consequences suggest that the high-threat group of IPF may be linked with the response of other organisms and autoimmunity as well.

Quantitative Precipitation Forecasting Using an Improved Probability-Matching Method and Its Application to a Typhoon Event

This present study aims to explore how forecasters can quickly make accurate predictions by using various high-resolution model forecasts. Based on three high temporal-spatial resolution (3 km, hourly) numerical weather prediction models (CMA-MESO, CMA-GD, CMA-SH3) from the China Meteorological Administration (CMA), the hourly precipitation characteristics of three model within 24 h from March to September 2020 are discussed and integrated into a single, hourly, deterministic quantitative precipitation forecast (QPF) by making use of an improved weighted moving average probability-matching method (WPM). The results are as follows: (1) In non-rainstorm forecasts, CMA-MESO and CMA-GD have similar forecast abilities. However, in rainstorm forecasts, CMA-MESO has a notable advantage over the other two models. Thus, CMA-MESO is selected as a critical factor when participating in sensitivity experiments. (2) Compared with the traditional equal-weight probability-matching method (PM), the WPM improves the different grade QPF because it can effectively reduce rainfall pattern bias by making use of the weighted moving average (WMA). Additionally, the WPM threat score in rainstorm forecast similarly improved from 0.051 to 0.056, with a 9.8% increase relative to the PM. (3) The sensitivity experiments show that an optimal rainfall intensity score (WPM-best) can further improve the QPF and overcome all single models in both rainstorm and non-rainstorm forecasts, and the WPM-best has a rainstorm threat score skill of 0.062, with an increase of 21.6% compared with the PM. The performance of the WPM-best will be better if the precipitation intensity is stronger and the valid forecast periods is longer. It should be noted that there is no need to select models before using the WPM-best method, because WPM-best can give a very low weight to the less-skillful model in a more objective way. (4) The improved WPM method is also applied to investigate the heavy-rainfall case induced by typhoon Mekkhala (2020), where the improved WPM technique significantly improves rainstorm forecasting ability compared with a single model.

Evaluation of ECMWF Precipitation Predictions in China during 2015–2018

Precipitation forecasts from the ECMWF model from March to September during 2015–2018 were evaluated using observed precipitation at 2411 stations from the China Meteorological Administration. To eliminate the influence of varying climatology in different regions in China, the Stable Equitable Error in Probability Space method was used to obtain criteria for 3-h and 6-h accumulated precipitation at each station and classified precipitation into light, medium, and heavy precipitation. The model was evaluated for these categories using categorical and continuous methods. The threat score and the equitable threat score showed that the model’s forecasts of rainfall were generally more accurate at shorter lead times, and the best performance occurred in the middle and lower reaches of the Yangtze River Basin. The miss ratio for heavy precipitation was higher in the northern region than in the southern region, while heavy precipitation false alarms were more frequent in the southwestern China. Overall, the miss ratio and false alarm ratio for heavy precipitation were highest in northern China and western China, respectively. For light and medium precipitation, the model performed best in the middle and lower reaches of the Yangtze River Basin. The model predicted too much light and medium precipitation, but too little heavy precipitation. Heavy precipitation was generally underestimated over all of China, especially in the western region of China, South China, and the Yungui Plateau. Heavy precipitation was systematically underestimated because of the resolution and the related parametrization of convection.

“If You Know the Enemy and Know Yourself”: Addressing the Problem of Biological Invasions in Ports Through a New NIS Invasion Threat Score, Routine Monitoring, and Preventive Action Plans

Invasive alien species (IAS) are currently considered one of the greatest threats to global marine ecosystems. Thus, ships and maritime activity have been identified as the main factors responsible for the vast majority of accidental species translocations around the world, implying that prevention should be the core of environmental port policies. Preventive port strategies should include analyzing risks based on traffic origins and volumes, revising port policies for inspections, estimating probabilities of non-indigenous species (NIS) appearance, monitoring routine species within ports, and finally implementing management plans and focused actions. Here, we conducted a comprehensive NIS prediction analysis for the port of Gijon (northern Spain), one of the largest ports in the south Bay of Biscay, as a case study that can be extrapolated to other international seaports. An extensive bibliographic search (1953–2020) was conducted and we identified 380 species that have been transported through hull fouling and ballast water around the world. We evaluated their likelihood of arriving (from 14 years of traffic data) and becoming established (from habitat suitability and demonstrated impacts and invasion ability) within the Gijon port, creating a new NIS Invasion Threat Score (NIS-ITS). This new index could help to identify target species that are likely invaders for early detection and prevention policies within the port. The results showed that 15 NIS had &gt;90% likelihood of becoming a biological invasion problem in Gijon Port. At the same time, we reported morphological and genetic analysis of biota found in two successive annual monitoring surveys of Gijon port and ships (n = 612 individuals) revealing 18 NIS, including 6 of the NIS predicted from high NIS-ITS. Actually, 80% (12 NIS) of those potentially most dangerous species (NIS-ITS &gt; 90%) have already been detected in the Bay of Biscay area. We propose the use of this new tool for a risk-reduction strategy in ports, based on accurate predictions that help in promoting specific early detection tests and specific monitoring for NIS that have a high chance of establishment. All international seaports can adopt this strategy to address the problem of biological invasions and become “blueports” in line with EU policy.

A Comparative Study of Weather Forecasting Simulation Models through Sensors for Accurate Monsoon Predictions in the Indian Ocean

When Monsoon depressions form over the seas, the Moderate Resolution Imaging Spectroradiometer (MODIS) provides humidity and high-horizontal resolution temperature details about the depressions. These high-resolution satellite data related to temperature and humidity can improve the poor predicting rate of depressions [1]. Using three-dimensional variational data assimilation (3DVAR) and with the help of humidity profiles along with MODIS temperature. We can achieve an advanced prospect of detection and a larger value of (ETS) equitable threat score observed over 48 hours collected precipitation with respect to the control run. The 3DVAR assimilation of Doppler Weather Radar wind data associated with Indian Meteorological Department (IMD) surface data and upper air data helped in the improvements in the simulation of strong gradients associated with horizontal wind speed ,higher warm core temperature , high vertical velocity & better precipitation and spatial distribution.[2]. The effect of Spectral sensor microwave imager (SSM/I), humidity profiles, use of Advanced TIROS Vertical Sounder (ATOVS) temperature and total precipitable water (TPW) helped in improving the ‘‘forecast impact’’ parameters of ‘‘bias score’’ and ‘‘equitable threat score’’ with respect to the assimilation of satellite observation[3] . In this paper we have discussed a comparative study of different proposed techniques to analyze its effects in improving the low prediction rates of depressions.

Information gain-based modular fuzzy neural network to forecast rainstorms

This study considers large-scale heavy rainfall as a forecast object based on the European central numerical forecast model product and uses a nonlinear fuzzy neural network (FNN) intelligent calculation method to establish a short-term forecast model of rainstorms. The information gain method is introduced to the predictor processing of the forecast model. Then the characteristics of many rainstorm predictors are calculated and screened on the basis of feature weight, information is condensed, some non-correlated forecast information variables are extracted, and the network structure of the forecast model is optimized. The modeled samples are determined and reconstructed by setting thresholds, and the modular forecast models of heavy rainfall and weak rainfall are established. The actual forecast results of the 24 h experimental prediction of the independent samples of large-scale rainstorms in Guangxi in 2012–2016 showed that the information gain-based modular FNN rainstorm forecasting model has higher prediction accuracy and a more stable forecasting effect. The various types of scores of 24 h of rainstorm (≧50 mm) at 89 weather stations in Guangxi from 2012 to 2016 are: threat score (TS) is 0.368, ETS: equal threat score (E) is 0.141, hit rate (POD) is 0.296, empty report rate (FAR) is 0.559, forecast bias (B) is 0.671, and HSS skill score (H) is 0.247. Further comparison and analysis of the European Centre for Medium-Range Weather Forecasts (ECMWF) numerical forecasting model forecast results indicated that the new model performed nonlinear intelligence calculated interpretation modeling on ECMWF numerical forecasting model products, and forecasting accuracy is improved to a certain extent compared with that of the original model. Forecasting techniques are positive and have good release effects, thereby improving the rain forecasting ability of ECMWF to a certain extent and providing a better reference value for business forecasters.

PENENTUAN STRATEGI PEMASARAN DALAM MEMPERTAHANKAN EKSISTENSI DAN MENGADAPI PERSAINGAN (Studi Kasus Pada Gemilang Art Glass Di Modo)

Appropriate marketing strategies are needed to increase sales and achieve the goals of a company. This research was conducted to determine marketing strategies using SWOT analysis so that a company is able to maintain its existence and face competition and what is the most appropriate strategy that can be applied to a company. The research was conducted at Gemilang Art Glass Company in Modo. From the results of the SWOT analysis showed that the strength (strength) 3.66, and weakness (weakness) 0.76, opportunity (opportunity) 2.86, and the threat (threatr) 1.28. From the SWOT diagram above can be seen from the difference in strength (strength) and weakness (weakness) score higher strength (strength) with a difference (+) 2.9, while the difference in the opportunity (opportunity) and threat (threat) score value is higher the opportunity (opportunity) with a difference (+) 1.58. So that it clearly shows that Gemilang Art Glass has been dealing with the right path by continuing to carry out aggressive strategies to increase sales. Based on the above results it can be concluded that an aggressive strategy that can be done is the S-O strategy. The strategy includes improving quality and service so that consumers feel satisfied and comfortable and attract new customers, increase company capacity by utilizing the ability of teamwork and company experience to reach potential markets, provide information about new products to consumers by using internet technology, and by treating employees well and establish a sense of brotherhood will make employees always professional and disciplined in carrying out the work.

Antarctic cloud detection and classification from far and mid infrared downwelling radiance spectra: performances optimization and results

&lt;p&gt;This work aims at determining the best performing mid and far-infrared (MIR and FIR) joint spectral interval to identify and classify clouds in the Antarctic region by mean of a machine learning algorithm.&lt;/p&gt;&lt;p&gt;About 1700 spectral-resolved radiances, collected during 2013 by the ground based Radiation Explorer in the Far InfraRed-Prototype for Applications and Development, REFIR-PAD (Palchetti et al., 2015) at Dome C, Antarctic Plateau, are selected in coincidence with the co-located with backscatter and depolarization profiles derived from a tropospheric lidar system (Ricaud et al., 2017) to pre-classify clear sky, ice clouds, or mixed phase clouds.&lt;/p&gt;&lt;p&gt;A machine learning cloud identification and classification algorithm named CIC (Maestri et al., 2019), trained with a pre-selected set of REFIR-PAD spectra, is applied to this dataset by assuming that no other information than the spectrum itself is known.&lt;/p&gt;&lt;p&gt;The CIC algorithm is applied by considering different spectral intervals, in order to maximize the classification results for each class (clear sky, ice clouds, mixed phase clouds). A CIC &quot;threat score&quot; is defined as the classification true positives divided by the sum of true positives, false positives, and false negatives. The maximization of the threat score is used to assess the algorithm performances that span from 58% to 96% in accordance with the selected interval. The best performing spectral range is the 380-1000 cm&lt;sup&gt;-1&lt;/sup&gt;. The result, besides suggesting the importance of a proper algorithm calibration in accordance with the used sensor, highlights the fundamental role of the FIR part of the spectrum.&lt;/p&gt;&lt;p&gt;The calibrated CIC algorithm is then applied to a larger REFIR-PAD dataset of about 90000 spectra collected from 2012 to 2015. Some results of the full dataset cloud classification are also presented.&lt;/p&gt;&lt;p&gt;The present work contributes to the preparatory studies for the Far-infrared Outgoing Radiation Understanding and Monitoring (FORUM) mission that has recently been selected as ESA&amp;#8217;s 9&lt;sup&gt;th&lt;/sup&gt; Earth Explorer mission, scheduled for launch in 2026.&amp;#160;&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;References:&lt;/p&gt;&lt;p&gt;&lt;span&gt;Maestri, T., Cossich, W., and Sbrolli, I., 2019: Cloud identification and classification from high spectral resolution data in the far infrared and mid-infrared, Atmos. Meas. Tech., 12, pp. 3521 - 3540&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;Palchetti, L., Bianchini, G., Di Natale, G., and Del Guasta, M., 2015: Far infrared radiative properties of water vapor and clouds in Antarctica. Bull. Amer. Meteor. Soc., 96, 1505&amp;#8211;1518, doi: http://dx.doi.org/10.1175/BAMS-D-13-00286.1.&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;Ricaud, P., Bazile, E., del Guasta, M., Lanconelli, C., Grigioni, P., and Mahjoub, A., 2017: Genesis of diamond dust, ice fog and thick cloud episodes observed and modelled above Dome C, Antarctica, Atmos. Chem. Phys., 17, 5221&amp;#8211;5237, https://doi.org/10.5194/acp-17-5221-2017.&lt;/span&gt;&lt;/p&gt;

Development and Evaluation of a WRF-Based Mesoscale Numerical Weather Prediction System in Northwestern China

Based on the U.S. Weather Research and Forecasting (WRF) numerical model, this study has developed the Northwest Mesoscale Numerical Prediction Service and Experimental System (NW-MNPS). Surface and sounding data assimilation has been introduced for this system. Effects of model vertical layers and land-use data replacement have been assessed. A year-long forecast validation and analysis have been performed. The following results have been obtained: (1) Data assimilation can improve the performance of regional numerical forecasting. (2) Compared to simulations with 40 vertical layers, simulations with 55 vertical layers are more accurate. The average absolute error and root-mean-square error of the 48 h surface element forecast decrease. The analysis of threat score (TS) and equitable threat score (ETS) shows that there are higher TS and ETS values for various precipitation intense levels, in particular for heavy rainfall when comparing a 55-vertical-layer test with a 40-vertical-layer test. (3) Updating the database to include vegetation coverage can more accurately reflect actual surface conditions. The updated land-use data reduce prediction errors in all domains of the NW-MNPS.

Global Precipitation Forecasts by Merging Extrapolation-Based Nowcast and Numerical Weather Prediction with Locally Optimized Weights

Over the past few decades, precipitation forecasts by numerical weather prediction (NWP) models have been remarkably improved. Yet, precipitation nowcasting based on spatiotemporal extrapolation tends to provide a better precipitation forecast at shorter lead times with much less computation. Therefore, merging the precipitation forecasts from the NWP and extrapolation systems would be a viable approach to quantitative precipitation forecast (QPF). Although the optimal weights between the NWP and extrapolation systems are usually defined as a global constant, the weights would vary in space, particularly for global QPF. This study proposes a method to find the optimal weights at each location using the local threat score (LTS), a spatially localized version of the threat score. We test the locally optimal weighting with a global NWP system composed of the local ensemble transform Kalman filter and the Nonhydrostatic Icosahedral Atmospheric Model (NICAM-LETKF). For the extrapolation system, the RIKEN’s global precipitation nowcasting system called GSMaP_RNC is used. GSMaP_RNC extrapolates precipitation patterns from the Japan Aerospace Exploration Agency (JAXA)’s Global Satellite Mapping of Precipitation (GSMaP). The benefit of merging in global precipitation forecast lasts longer compared to regional precipitation forecast. The results show that the locally optimal weighting is beneficial.