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The rate of daily return of a stock is one of the important indicators for investors to anticipate benefits or losses from historical data. This paper will focus on the stock of Amazon, which is a popular choice for stock traders and contains data from August 25, 2017, to August 24, 2022. By using regression models such as a simple linear regression model and an autoregressive integrated moving average (ARIMA), the recent daily return value is predicted based on data during these 5 years. The simple linear regression can show the trend of stock price and the predicted response rate of daily return using the linear model. Furthermore, ARIMA is a more advanced time series model to provide a more accurate rate of daily return with confidence intervals. The predicted trend and rate of daily return are useful for investors to make decisions to buy or sell a stock recently. The trend can tell investors whether the stock price would go up and the daily return can indicate how many benefits can they earn if they choose to invest in this stock.

Accurate estimates of snow water equivalent (SWE) based on remote sensing have been elusive, particularly in mountain areas. However, there now appears to be some potential for direct satellite-based SWE observations along ground tracks that only cover a portion of a spatial domain (e.g., watershed). Fortunately, spatiotemporally continuous meteorological and surface variables could be leveraged to infer SWE in the gaps between satellite ground tracks. Here, we evaluate statistical and machine learning (ML) approaches to performing track-to-area (TTA) transformations of SWE observations in California's upper Tuolumne River watershed using synthetic data. The synthetic SWE measurements are designed to mimic a potential future P-band Signals of Opportunity (P-SoOP) satellite mission with a (along-track) spatial resolution of about 500 m. We construct relationships between multiple meteorological and surface variables and synthetic SWE observations along observation tracks, and we then extend these relationships to unobserved areas between ground tracks to estimate SWE over the entire watershed. Domain-wide, SWE inferred on 1 April using two synthetic satellite tracks (∼4.5 % basin coverage) led to percent errors of basin-averaged SWE (PEBAS) of 24.5 %, 4.5 % and 6.3 % in an extremely dry water year (WY2015), a normal water year (WY2008) and an extraordinarily wet water year (WY2017), respectively. Assuming a 10 d overpass interval, percent errors of basin-averaged SWE during both snow accumulation and snowmelt seasons were mostly less than 10 %. We employ a feature sensitivity analysis to overcome the black-box nature of ML methods and increase the explainability of the ML results. Our feature sensitivity analysis shows that precipitation is the dominant variable controlling the TTA SWE estimation, followed by net long-wave radiation (NetLong). We find that a modest increase in the accuracy of SWE estimation occurs when more than two ground tracks are leveraged. The accuracy of 1 April SWE estimation is only modestly improved for track repeats more often than about 15 d.

The registration system reform with information disclosure as the core is a major institutional innovation in China's capital market in recent years. Compared with the "government-led" approval system, the pilot registration-based review system for the Science and Technology Innovation Board is characterized by "market-led, government-supervised”. In this context, how to better play the role of intermediaries and realize the benign interaction of "gatekeeper" supervision to protect the interests of investors is an important issue. An empirical analysis is made on IPO approval and IPO time used using the measurement of accountancy firm expertise model. The results of both models show that accountancy firm expertise have no significant impact on IPO approval and IPO time used, by contrast, ROA and have significant impact on IPO approval and IPO time used, that is to say, it depends more on the financial situation of the enterprise to influence the IPO approval and IPO time used of the enterprise.

The antimicrobial resistance crisis requires the introduction of novel antibiotics. The use of conventional broad-spectrum compounds selects for resistance in off-target pathogens and harms the microbiome. This is especially true for Mycobacterium tuberculosis, where treatment requires a 6-month course of antibiotics. Here we show that a novel antimicrobial from Photorhabdus noenieputensis, which we named evybactin, is a potent and selective antibiotic acting against M. tuberculosis. Evybactin targets DNA gyrase and binds to a site overlapping with synthetic thiophene poisons. Given the conserved nature of DNA gyrase, the observed selectivity against M. tuberculosis is puzzling. We found that evybactin is smuggled into the cell by a promiscuous transporter of hydrophilic compounds, BacA. Evybactin is the first, but likely not the only, antimicrobial compound found to employ this unusual mechanism of selectivity.

The natriuretic peptide system (NPS) and renin-angiotensin-aldosterone system (RAAS) function oppositely at multiple levels. While it has long been suspected that angiotensin II (ANGII) may directly suppress NPS activity, no clear evidence to date supports this notion. This study was designed to systematically investigate ANGII–NPS interaction in humans, in vivo, and in vitro. Circulating atrial, b-type, and c-type natriuretic peptides (ANP, BNP, CNP), cyclic guanosine monophosphate (cGMP), and ANGII were simultaneously investigated in 128 human subjects. Prompted hypothesis was validated in vivo to determine the influence of ANGII on ANP actions. The underlying mechanisms were further explored via in vitro approaches. In humans, ANGII demonstrated an inverse relationship with ANP, BNP, and cGMP. In regression models predicting cGMP, adding ANGII levels and the interaction term between ANGII and natriuretic peptides increased the predictive accuracy of the base models constructed with either ANP or BNP, but not CNP. Importantly, stratified correlation analysis further revealed a positive association between cGMP and ANP or BNP only in subjects with low, but not high, ANGII levels. In rats, co-infusion of ANGII even at a physiological dose attenuated cGMP generation mediated by ANP infusion. In vitro, we found the suppressive effect of ANGII on ANP-stimulated cGMP requires the presence of ANGII type-1 (AT1) receptor and mechanistically involves protein kinase C (PKC), as this suppression can be substantially rescued by either valsartan (AT1 blocker) or Go6983 (PKC inhibitor). Using surface plasmon resonance (SPR), we showed ANGII has low binding affinity to the guanylyl cyclase A (GC-A) receptor compared to ANP or BNP. Our study reveals ANGII is a natural suppressor for the cGMP-generating action of GC-A via AT1/PKC dependent manner and highlights the importance of dual-targeting RAAS and NPS in maximizing beneficial properties of natriuretic peptides in cardiovascular protection.