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The rapid emergence of multi-drug resistant Gram-negative pathogens has driven the introduction of novel β-lactam combination agents (BLCs) to the antibiotic market: ceftolozane-tazobactam, ceftazidime-avibactam, meropenem-vaborbactam, imipenem-relebactam, cefiderocol, and sulbactam-durlobactam. These agents are equipped with innovative mechanisms that confer broad Gram-negative activity, notably against certain challenging carbapenemases. While their introduction offers a beacon of hope, clinical microbiology laboratories must navigate the complexities of susceptibility testing for these agents due to their diverse activity profiles against specific β-lactamases and the possibility of acquired resistance mechanisms in some bacterial isolates. This review explores the complexities of these novel antimicrobial agents detailing the intricacies of their application, providing guidance on the nuances of susceptibility testing, interpretation, and result reporting in clinical microbiology laboratories. [ABSTRACT FROM AUTHOR]

A good understanding of the processes of land surface temperature (LST) change is important for assessing regional climate change. In the present study, we obtained the MODIS MOD11A2 LST products over the Yangtze River Delta (YRD) from 2001 to 2020. In order to comprehensively assess the spatial and temporal variability of LST in the YRD region over the past two decades, the Theil–Sen Median trend analysis and Mann–Kendall test, BFAST01 trend decomposition, and landscape pattern analysis were used in this study. We show that the rate of linear change in LST in the YRD ranges from −0.019 °C/month to 0.046 °C/month. The BFAST01 trend decomposition identifies more details of LST change and monotonic increases, reversal increase, and interruption increase are the main warming trends. The distribution of the different trend types shows strong aggregation with high spatial heterogeneity. The LST breakpoints are mainly located in the northern and southern YRD, which frequently occurred during 2010–2013. Of the various land types, breakpoints occur most frequently in cropland and high NDVI (0.5–0.7) areas, and the intensity of most of them is within 2 °C. In addition, much stronger warming occurs in urban areas than in other land types. Our study provides a better understanding of the dynamics of LST in the YRD region over the past 20 years and highlights that breakpoints cannot be circumvented in regional temperature assessment. [ABSTRACT FROM AUTHOR]

Global institutional changes (GICs), having influenced energy prices, led to a steady upward trend in carbon prices on the EU ETS. The aim of the article is to assess the changes in the relationship between carbon prices and energy prices under GICs. The Bai–Perron tests for structural breaks identified two dates as the breakpoint, 21 April 2016 and 21 September 2020. We test the hypothesis that powerful external factors (GIC) are changing the trend pattern of the carbon price time series. New pricing rules of the carbon price are being formed after the breakpoint. We use daily observations from 4 January 2010 to 1 September 2022. We use GARCH models with multiple stationary time series to discover a relationship energy price with the carbon price before and after the break points. We found that three models for two breakpoints better describe the relationship between carbon prices and energy prices than two models for one breakpoint, much less one model for the entire period. We find that the carbon price depends on energy prices, especially on the price of oil, in a statistically significant way, but the gas price is not statistically significant after 21 September 2020. [ABSTRACT FROM AUTHOR]

The scientific judgement of the structural abrupt transition characteristics of the carbon market price is an important means to comprehensively analyze its fluctuation law and effectively prevent carbon market risks. However, the existing methods for identifying structural changes of the carbon market based on carbon price data mostly regard the carbon price series as a deterministic time series and pay less attention to the uncertainty implied by the carbon price series. We propose a framework for identifying abrupt transitions in the carbon market from the perspective of a complex network by considering the influence of random factors on the carbon price series, expressing the carbon price series as a sequence of probability density functions, using the distribution of probability density to reveal the uncertainty information implied by carbon price series and constructing a recurrence network of carbon price probability density. Based on the community structure, the break index and statistical test method are defined. The simulation verifies the effectiveness and superiority of the method compared with traditional methods. An empirical analysis uses the carbon price data of the European Union carbon market and seven pilot carbon markets in China. The results show many abrupt transitions in the carbon price series of the two markets, whose occurrence period is closely related to major events. [ABSTRACT FROM AUTHOR]

Changing trends in ecosystem productivity can be quantified using satellite observations of Normalized Difference Vegetation Index (NDVI). However, the estimation of trends from NDVI time series differs substantially depending on analyzed satellite dataset, the corresponding spatiotemporal resolution, and the applied statistical method. Here we compare the performance of a wide range of trend estimation methods and demonstrate that performance decreases with increasing inter-annual variability in the NDVI time series. Trend slope estimates based on annual aggregated time series or based on a seasonal-trend model show better performances than methods that remove the seasonal cycle of the time series. A breakpoint detection analysis reveals that an overestimation of breakpoints in NDVI trends can result in wrong or even opposite trend estimates. Based on our results, we give practical recommendations for the application of trend methods on long-term NDVI time series. Particularly, we apply and compare different methods on NDVI time series in Alaska, where both greening and browning trends have been previously observed. Here, the multi-method uncertainty of NDVI trends is quantified through the application of the different trend estimation methods. Our results indicate that greening NDVI trends in Alaska are more spatially and temporally prevalent than browning trends. We also show that detected breakpoints in NDVI trends tend to coincide with large fires. Overall, our analyses demonstrate that seasonal trend methods need to be improved against inter-annual variability to quantify changing trends in ecosystem productivity with higher accuracy. [ABSTRACT FROM AUTHOR]

This study aims to investigate how alternations of the land surface temperature (LST) affects the normalized difference vegetation index (NDVI) in Paphos forest, Cyprus, using Landsat-5 and Landsat-8 imagery for the time periods 1993–2000 and 2013–2018, respectively. A total of 262 Landsat images were processed to compute the mean monthly NDVI and LST values and create a time series. Using the Cook's distance, the effect of missing values in the analysis of the time series were examined. Results from the cross-correlation and cross-variograms, decomposition model, and the BFAST algorithm were compared to produce reliable conclusions on forest changes and satellite, meteorological, and environmental data were combined to interpret the changes that occurred inside the forest. The decomposition analysis showed a decrease of 2.7% in the LST for the period 1993–2000 and an increase of 4.6% in the LST during the period 2013–2018. The NDVI trend is negatively correlated to the LST trend for both time periods. An increase in the LST trend was identified in November 1998 as well as in the NDVI trend in October 1994 and May 2014 that was caused by favorable climatic conditions. An increase in the NDVI trend from May 2014 to December 2015 may be related to reduced pityocampa attacks. An abrupt decrease was detected in December 2015 that was probably caused by the locust invasion that occurred in the island earlier that year. A positive correlation appears for LST and NDVI variables for time lags 4, 5, 6, 7, and 8 months. Overall, it was shown that LST and NDVI analysis is very promising for identifying potential forest decline. [ABSTRACT FROM AUTHOR]

The in vitro antimicrobial susceptibility of 29 strains of the major periodontal pathogen Porphyromonas gingivalis and three P. gulae (as an ancestor) to nine antibiotics (amoxicillin, amoxicillin/clavulanate, clindamycin, metronidazole, moxifloxacin, doxycycline, azithromycin, imipenem, and cefoxitin) was evaluated by E-testing of minimal inhibitory concentration (MIC) according to international standards. The results were compared with 16 international studies reporting MICs from 1993 until recently. In addition, 77 currently available P. gingivalis genomes were screened for antimicrobial resistance genes. E-testing revealed a 100% sensitivity of P. gingivalis and P. gulae to all antibiotics. This was independent of the isolation year (1970 until 2021) or region, including rural areas in Indonesia and Africa. Regarding studies worldwide (675 strains), several method varieties regarding medium, McFarland inoculation standards (0.5–2) and incubation time (48–168 h) were used for MIC-testing. Overall, no resistances have been reported for amoxicillin + clavulanate, cefoxitin, and imipenem. Few strains showed intermediate susceptibility or resistance to amoxicillin and metronidazole, with the latter needing both confirmation and attention. The only antibiotics which might fail in the treatment of P. gingivalis-associated mixed anaerobic infections are clindamycin, macrolides, and tetracyclines, corresponding to the resistance genes erm(B), erm(F), and tet(Q) detected in our study here, as well as fluoroquinolones. Periodical antibiotic susceptibility testing is necessary to determine the efficacy of antimicrobial agents and to optimize antibiotic stewardship. [ABSTRACT FROM AUTHOR]

This study explored the long-term trends and breakpoints of vegetation, rainfall, and temperature in Taiwan from overall and regional perspectives in terms of vertical differences from 1982 to 2012. With time-series Advanced Very-High-Resolution Radiometer (AVHRR) normalized difference vegetation index (NDVI) data and Taiwan Climate Change Estimate and Information Platform (TCCIP) gridded monthly climatic data, their vertical dynamics were investigated by employing the Breaks for Additive Seasonal and Trend (BFAST) algorithm, Pearson's correlation analysis, and the Durbin–Watson test. The vertical differences in NDVI values presented three breakpoints and a consistent trend from positive (1982 to 1989) to negative at varied rates, and then gradually increased after 2000. In addition, a positive rainfall trend was discovered. Average and maximum temperature had similar increasing trends, while minimum temperature showed variations, especially at higher altitudes. In terms of regional variations, the vegetation growth was stable in the north but worse in the central region. Higher elevations revealed larger variations in the NDVI and temperature datasets. NDVI, along with average and minimum temperature, showed their largest changes earlier in higher altitude areas. Specifically, the increasing minimum temperature direction was more prominent in the mid-to-high-altitude areas in the eastern and central regions. Seasonal variations were observed for each region. The difference between the dry and wet seasons is becoming larger, with the smallest difference in the northern region and the largest difference in the southern region. Taiwan's NDVI and climatic factors have a significant negative correlation (p < 0.05), but the maximum and minimum temperatures have significant positive effects at low altitudes below 500 m. The northern and central regions reveal similar responses, while the south and east display different feedbacks. The results illuminate climate change evidence from assessment of the long-term dynamics of vegetation and climatic factors, providing valuable references for establishing correspondent climate-adaptive strategies in Taiwan. [ABSTRACT FROM AUTHOR]

In this study, the spatiotemporal behavior of vegetation cover in the Kurdistan province of Iran was analyzed for the first time by TIMESAT and Breaks for Additive Season and Trend (BFAST) algorithms. They were applied on Normalized Vegetation Index (NDVI) time series from 2000 to 2016 derived from Moderate Resolution Imaging Spectroradiometer (MODIS) observations. The TIMESAT software package was used to estimate the seasonal parameters of NDVI and their relation to land covers. BFAST was applied for identifying abrupt changes (breakpoints) of NDVI and their magnitudes. The results from TIMESAT and BFAST were first reported separately, and then interpreted together. TMESAT outcomes showed that the lowest and highest amplitudes of NDVI during the whole time period happened in 2008 and 2010. The spatial distribution of the number of breakpoints showed different behaviors in the west and east of the study area, and the breakpoint frequency confirmed the extreme NDVI amplitudes in 2008 and 2010 found by TIMESAT. For the first time in Iran, a correlation analysis between accumulated precipitations and maximum NDVIs (from one to seven months before the NDVI maximum) was conducted. The results showed that precipitation one month before had a higher correlation with the maximum NDVIs in the region. Overall, the results describe the NDVI behavior in terms of greenness, lifetime, abrupt changes for the different land covers, and across the years, suggesting how the northwest and west of the study area can be more susceptible to drought conditions. [ABSTRACT FROM AUTHOR]

In the last century, the growing evidence that global fisheries are depleting natural resources much faster than they can recover has led to negative processes, like overfishing, being addressed with increasingly complex models and thus mitigating or regulating actions that aim to protect stocks. Said negative processes contain two components: (i) they can diminish the reproductive potential of fish stocks, called over exploitation by recruitment, and (ii) the effect of early capture prevents the full realization of the growth potential, called overfishing by growth. In this article, the structure of common sardine sizes is analyzed. Due to the precise moment in which pre-recruits are incorporated into the exploited phase of the stock is unknown, the estimation of a recruitment size is a hard problem. This problem is addressed by modeling the mean size via a stochastic process, applying models of structural change. A time series (2001–2015) was analyzed on a weekly time scale based on the size structure of the pelagic fishery landings in the central-southern zone of Chile ( 32 ∘ 10 ′ – 40 ∘ 14 ′ LS), from the V. to the XIV. Region. Specifically, the evolution of sizes according to macro-zones was studied for the conglomerates identified in two sub-zones, the V.–VIII. and IX.–XIV. Regions. In this context, the reference size for juveniles to cautiously allow the recruitment process of the common sardine from the central-southern zone of Chile was estimated, and the behavior of these sizes was spatially analyzed. Finally, a statistical inferential criterion was established that confirms the mean size of juveniles with a certain margin of error, which allows nonetheless later on to define a fraction that could be protected to avoid overfishing by growth. [ABSTRACT FROM AUTHOR]