coverage: Melbourne, Victoria, AustraliaThe Argus was a major Australian metropolitan daily, published in Melbourne from 1846 to 1957. It is the primary resource for data on 19th century Australia and is widely recognised as the general Australian newspaper of record for this period. In size, range of news, accuracy and objectivity of reporting and its literary content, the Argus is without peer. Australia lacks a continuous index to any of its quality newspapers. Researchers in Australia spend many hours searching for material that an index can guide them to in seconds. Researchers in the United Kingdom and the United States can consult indexes to The Times and the New York Times that cover the whole period of their publication. This is the service that the Argus Index will provide to researchers on Australia. The index will provide all Australians with access to their history which is buried in the pages of the paper. Through the identification of dates of significant events, the index will also serve as a guide to the contents of other newspapers. The years 1846 to 1859 were indexed by the late J. A. Feely who was Chief Librarian at the Melbourne Public library (now the State Library of Victoria). The Argus itself produced an index to the paper for the years 1910 to 1949. For a long time the desirability of filling the fifty-year gap between 1859 and 1910 had been recognised. In 1983 the History and Heritage committee of Victoria's 150th Anniversary Board took up the idea and the Argus Index Project was born.The funding provided by the Anniversary Board was only enough to launch the project. It was sustained by a wide variety of funding bodies, but never with enough to allow rapid progress to be made. It took fifteen years for the first decade of indexing (1860-69) to be completed. In 2001 the project was recognised as worthy of support by the Australian Research Council (ARC) under its infrastructure program. This has allowed the project to advance much more rapidly. The first grant from the ARC allowed the indexing of the second decade (1870-79) to be completed in a little over two years. The funding provided in 2003 will allow for the completion of the third decade (1880-89) by 2008. We hope that with the ARC's continued support to have the project complete by 2010. By that time, the newspaper itself may have been digitised so that users of the index will have online access to the articles that the index has identified. Under the ARC research infrastructure scheme, universities and other public bodies across the country are encouraged to work co-operatively on projects. Our project, based at La Trobe University, has been supported by the University of Melbourne (where Stuart Macintyre is chief investigator). Monash University (Marian Quartly) Curtin University of Technology (Richard Nile), Australian National University (Ann McGrath) Griffith University (Patrick Buckridge) and at the National Library (Warwick Cathro). The National Library is a central player. It has been responsible for refashioning the index so that it can be delivered online. The decade of the 1870s is the first to be made available in this form. As further decades are completed they will be made available online, (as is now the case with the 1880s), as will the 1860s which so far has been issued only in hard copy. The project is greatly indebted for the efforts of the library staff and in particular Judith Pearce and Bronwyn Lee.The provision of more generous funding allowed the project to hire more staff but it continues to rely on the volunteer work of those who read and record the contents of the paper. The manager of the reading program is Diana Phoenix whose outstanding work is also provided to the project free of charge. In 2004 she received an Arts Portfolio Leadership Award for her outstanding volunteer contribution to the State Library of Victoria. The State Library has been the place where much of the work has been done and through Alannah Kelly has been a consistent supporter of the project. More recently through Shane Carmody it has officially become one of the ARC partners. Geraldine Suter has been the chief indexer almost from the beginning. The high standard of her work needs no endorsement; it is evident in the quality, consistency, and accessibility of the index. All those involved in the project have seen its value and it could not have survived and prospered without their dedication. A former Vice Chancellor at La Trobe University, Professor Michael Osborne, was a consistent supporter and kept the project alive when it hit hard times. (John Hirst, Editor-in-Chief.).The Argus Index website was hosted by the National Library of Australia from 2005 until 2012. Prior to the decommissioning of the website the indexing data was reformatted into a set of PDF publications which recreate the "look and feel" of the earlier print indexes and which include links to digitised Argus pages in the Trove system.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

ARGUW Argus Capital Corp. Warrant

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

ARGU Argus Capital Corp. Class A Common Stock

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Argus is a prominent source of pricing evaluations and business insights extensively utilized in the energy and commodity sectors, specifically for physical supply agreements and the settlement and clearing of financial derivatives. Argus pricing is also employed as a benchmark in swaps markets, for mark-to-market valuations, project financing, taxation, royalties, and risk management. Argus provides comprehensive services globally and continuously develops new assessments to mirror evolving market dynamics and trends. Covered assets encompass Energy, Oil, Refined Products, Power, Gas, Generation fuels, Petrochemicals, Transport, and Metals.

Browse Argus Propane Far East Index vs. Japan C&F Naphtha (Platts) Futures (3NA) market data. Get instant pricing estimates and make batch downloads of binary, CSV, and JSON flat files.

The CME Group Market Data Platform (MDP) 3.0 disseminates event-based bid, ask, trade, and statistical data for CME Group markets and also provides recovery and support services for market data processing. MDP 3.0 includes the introduction of Simple Binary Encoding (SBE) and Event Driven Messaging to the CME Group Market Data Platform. Simple Binary Encoding (SBE) is based on simple primitive encoding, and is optimized for low bandwidth, low latency, and direct data access. Since March 2017, MDP 3.0 has changed from providing aggregated depth at every price level (like CME's legacy FAST feed) to providing full granularity of every order event for every instrument's direct book. MDP 3.0 is the sole data feed for all instruments traded on CME Globex, including futures, options, spreads and combinations. Note: We classify exchange-traded spreads between futures outrights as futures, and option combinations as options.

Origin: Directly captured at Aurora DC3 with an FPGA-based network card and hardware timestamping. Synchronized to UTC with PTP

Supported data encodings: DBN, CSV, JSON Learn more

Supported market data schemas: MBO, MBP-1, MBP-10, TBBO, Trades, OHLCV-1s, OHLCV-1m, OHLCV-1h, OHLCV-1d, Definition, Statistics Learn more

Resolution: Immediate publication, nanosecond-resolution timestamps

Explore historical ownership and registration records by performing a reverse Whois lookup for the email address argus-i@rambler.ru..

The sample collection information about four C. argus populations and three A. argus populations obtained from different geographical locations. The primers were designed using Primer 5.0 (Zhai et al., 2008). According to the full-length mitochondrial sequence of C. argus published in GenBank (accession number NC 015191.1), the upstream primers for the amplification of D-loop region sequences were 5¢-GCCTCTTTCCTTTACTTCTC-3¢, and the downstream primers were 5¢-GGGTGTATTGAGCCTGATA-3¢.The PCR products were bidirectionally sequenced by Sangon Biotech Co., Ltd. The D-loop sequences were separately aligned and trimmed to equal lengths using the MEGA 5.2 (Tamura et al., 2011) and ClustalX 2.1 (Thompson et al., 1997) software. Genetic diversity parameters were estimated using DnaSP 5.0 (Rozas et al., 2003) software. A Neighbor-joining (NJ) phylogenetic tree and the genetic distance among all the populations (the average genetic distances based on Kimura’s two-parameter model), and A UPGMA (Unweighted Pair Group Method with Arithmetic Mean) tree and the genetic distance among all the populations (the average genetic distances based on Tajima-Nei model) were constructed and computed using MEGA 5.2 software. Arlequin 3.5(Excoffier and Lischer, 2010) was used to calculate the nucleotide composition statistics, molecular analysis of variance (AMOVA), and genetic differentiation index (F-statistics, FST). Network 4.6 (Polzin and Daneshmand, 2003) was used to construct a haploid Reduced media-type (MJ) Network graph.

The steam coal price index is a measure of the price of steam coal in the global market. This article provides an overview of the steam coal price index, its importance in the energy sector, and the commonly used indices such as the Global Coal Newcastle Index (GCNC), Miller Argus Index (MAI), and Richards Bay Index (RB Index). It also highlights the role of these indices in tracking price trends, making investment decisions, and understanding market dynamics in the coal industry.

Discover the significance of the API (Argus/Platts) coal index in international markets, focusing on key benchmarks like API2 and API4, which are crucial for transparency and reliable pricing in the coal industry. Learn how these indices aid producers, consumers, and traders in making informed decisions amidst changing market dynamics and regulatory scrutiny.

The Newcastle Coal Index is a widely used benchmark for coal prices in the Asia-Pacific region. It is published daily and calculated by global commodity price reporting agency Argus Media. This article explains how the index is used, its importance in the coal market, and its reflection of market sentiment towards coal as an energy source.

The evolution of body size within and among species is predicted to be influenced by multifarious environmental factors. However, the specific drivers of body size variation have remained difficult to understand because of the wide range of proximate factors that covary with ectotherm body sizes across populations with varying local environmental conditions. Here, we used female Eremias argus lizards collected from different populations across their wide range in China and constructed linear mixed models to assess how climatic conditions and/or available resources at different altitudes shape the geographical patterns of lizard body size across altitude. Lizard populations showed significant differences in body size across altitudes. Furthermore, we found that climatic and seasonal changes along the altitudinal gradient also explained variations in body size among populations. Specifically, body size decreased with colder and drier environmental conditions at high altitudes, reversing Bergmann’s rule. Limited resources at high altitudes, measured by the low vegetative index, may also constrain body size. Therefore, our study demonstrates that multifarious environmental factors could strongly influence the intraspecific variation in organisms’ body size. Methods We collected 432 female E. argus lizards between 2011 through 2021 from field locations across China varying in altitude and environmental conditions. We also used the Raster package in R to extract environmental variables for each population of lizards in our study.

Urea rose to 397.50 USD/T on June 27, 2025, up 2.58% from the previous day. Over the past month, Urea's price has fallen 12.40%, but it is still 27.20% higher than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. This dataset includes a chart with historical data for Urea.

Results of the evaluation questionnaires.

In both cultured and wild ponds, nocardiosis is a chronic, systemic disease with a long survival time from natural infection to death in fish Our previous results showed that N. seriolae colonized visceral tissues and caused the formation of granulomas within the snakehead tissues at 3–4 d post-challenge under artificial challenge conditions (data not published). Therefore, we believe that sampling at 96 h (4 days) post-challenge might be more advantageous for analyzing mechanisms of the host–N. seriolae interaction in snakeheads. .

Despite the significance of n-3 long-chain polyunsaturated fatty acid (n-3 LC-PUFA) in fish oil in promoting the maturation of female broodstocks, the detailed mechanism of the effect on the expression of hepatic reproduction-related genes is still unclear. In this study, transcriptome sequencing was used to analyze the effect of the higher dietary n-3 LC-PUFA level on gene expression in the liver of adult females spotted scat. Two-year-old female spotted scat (average weight, 242.83 ± 50.90 g) were fed with diets containing 8% fish oil (FO) or 8% soybean oil (SO) for 40 days. The fatty acid profile in the serum, liver, and ovary was analyzed, and high proportions of n-3 LC-PUFA were observed in the FO group. The final average fish body weight and gonadosomatic index were similar between the FO and SO groups. The serum vitellogenin (Vtg) and hepatosomatic index (HSI) of the FO group were significantly higher and lower than that of the SO group, respectively. Comparatively, the liver transcriptome analysis showed 497 upregulated and 267 downregulated genes in the FO group. Among them, the expression levels of three estrogen-regulated genes (i.e., Vtga, Vtgb, and Zp4) were significantly higher in the FO than in the SO group. This expression pattern could be related to the upregulation of Hsd17b7 (the key gene for the synthesis of liver steroid hormone) and the downregulation of the Hsp90 (the estrogen receptor chaperone). The expression levels of Foxo1a and Lep, which are involved in the lipid metabolism, decreased significantly in the FO group, which may be related to the lower level of HSI in the FO group. The genes related to liver LC-PUFA absorption and transport, Fabp2 and Mfsd2ab, were significantly upregulated in the FO group, indicating that fish actively adapt to high-fish-oil diets. In brief, high-fish-oil diets can influence the expression of genes related to liver n-3 LC-PUFA metabolism and reproduction, inhibit the accumulation of liver fat, and promote the liver health and gonad development. This study will contribute to clarifying the mechanism of dietary n-3 LC-PUFA on promoting reproductive development in teleost fish.

Validity measures of visual assessment of infection.