ALBERT

Description: The relationships between monthly recorded ground deposition rates (GDRs) and the spatiotemporal characteristics of dust concentrations in southwest Iran were investigated. A simulation by the Weather Research and Forecasting Model coupled with the Chemistry modeling system (WRF-Chem) was conducted for dust deposition during 2014–2015. The monthly dust deposition values observed at 10 different gauge sites (G01–G10) were mapped to show the seasonal and spatial variations in dust episodes at each location. An analysis of the dust deposition samples, however, confirmed that the region along the deposition sites is exposed to the highest monthly dust load, which has a mean value of 2.4 mg cm−2. In addition, the study area is subjected to seasonally varying deposition, which follows the trend: spring 〉 summer 〉 winter 〉 fall. The modeling results further demonstrate that the increase in dust emissions is followed by a windward convergence over the region (particularly in the spring and summer). Based on the maximum likelihood classification of land use land cover, the modeling results are consistent with observation data at gauge sites for three scenarios [S.I, S.II, and S.III]. The WRF model, in contrast with the corresponding observation data, reveals that the rate factor decreases from the southern [S.III—G08, G09, and G10] through [S.II—G04, G05, G06, and G07] to the northern points [S.I—G01, G02, and G03]. A narrower gap between the modeling results and GDRs is indicated if there is an increase in the number of dust particles moving to lower altitudes or an increase in the dust resident time at high altitudes. The quality of the model forecast is altered by the deposition rate and is sensitive to land surface properties and interactions among land and climate patterns.

Description: Recent technological advances have seen increasing numbers of complex structures from diverse pore-forming toxins (PFT). The ClyA family of α-PFTs comprises a broad variety of assemblies including single-, two- and three-component toxin systems. With crystal structures available for soluble subunits of all major groups in this extended protein family, efforts now focus on obtaining molecular insights into physiological pore formation. This review provides an up-to-date discussion on common and divergent structural and functional traits that distinguish the various ClyA family PFTs. Open questions of this research topic are outlined and discussed.

Description: Upon acylation of the proteasome by the β-lactone inhibitor salinosporamide A (SalA), tetrahydrofuran formation occurs by intramolecular alkylation of the incipient alkoxide onto the choroethyl sidechain and irreversibly blocks the active site. Our previously described synthetic approach to SalA, utilizing a bioinspired, late-stage, aldol-β-lactonization strategy to construct the bicyclic β-lactone core, enabled synthesis of (–)-homosalinosporamide A (homoSalA). This homolog was targeted to determine whether an intramolecular tetrahydropyran is formed in a similar manner to SalA. Herein, we report the X-ray structure of the yeast 20S proteasome:homoSalA-complex which reveals that tetrahydropyran ring formation does not occur despite comparable potency at the chymotrypsin-like active site in a luminogenic enzyme assay. Thus, the natural product derivative homoSalA blocks the proteasome by a covalent reversible mode of action, opening the door for further fine-tuning of proteasome inhibition.

Description: India’s largest freshwater ecosystem of the Kolleru Lake has experienced severe threats by land-use changes, including the construction of illegal fishponds around the lake area over the past five decades. Despite efforts to protect and restore the lake and its riparian zones, environmental pressures have increased over time. The present study provides a synthesis of human activities through major land-use changes around Kolleru Lake both before and after restoration measures. For this purpose, archives of all Landsat imageries from the last three decades were used to detect land cover changes. Using the Google Earth Engine cloud platform, three different land-use scenarios were classified for the year before restoration (1999), for 2008 immediately after the restoration, and for 2018, i.e., the current situation of the lake one decade afterward. Additionally, the NDVI (Normalized Difference Vegetation Index) and NDWI (Normalized Difference Water Index) indices were used to identify land cover dynamics. The results show that the restoration was successful; consequently, after a decade, the lake was transformed into the previous state of restoration (i.e., 1999 situation). In 1999, 29.7% of the Kolleru Lake ecosystem was occupied by fishponds, and, after a decade of sustainable restoration, 27.7% of the area was fishponds, almost reaching the extent of the 1999 situation. On the one hand, aquaculture is one of the most promising sources of income, but there is also limited awareness of its negative environmental impacts among local residents. On the other hand, political commitment to protect the lake is weak, and integrated approaches considering all stakeholders are lacking. Nevertheless, alterations of land and water use, increasing nutrient concentrations, and sediment inputs from the lake basin have reached a level at which they threaten the biodiversity and functionality of India’s largest wetland ecosystem to the degree that immediate action is necessary to prevent irreversible degradation.

Description: The analysis of various characteristics and trends of precipitation is an essential task to improve the utilization of water resources. Lake Issyk-Kul basin is an upper alpine catchment, which is more susceptible to the effects of climate variability, and identifying rainfall variations has vital importance for water resource planning and management in the lake basin. The well-known approaches linear regression, Şen’s slope, Spearman’s rho, and Mann-Kendall trend tests are applied frequently to try to identify trend variations, especially in rainfall, in most literature around the world. Recently, a newly developed method of Şen-innovative trend analysis (ITA) provides some advantages of visual-graphical illustrations and the identification of trends, which is one of the main focuses in this article. This study obtained the monthly precipitation data (between 1951 and 2012) from three meteorological stations (Balykchy, Cholpon-Ata, and Kyzyl-Suu) surrounding the Lake Issyk-Kul, and investigated the trends of precipitation variability by applying the ITA method. For comparison purposes, the traditional Mann–Kendall trend test also used the same time series. The main results of this study include the following. (1) According to the Mann-Kendall trend test, the precipitation of all months at the Balykchy station showed a positive trend (except in January (Zc = −0.784) and July (Zc = 0.079)). At the Cholpon-Ata and Kyzyl-Suu stations, monthly precipitation (with the same month of multiple years averaged) indicated a decreasing trend in January, June, August, and November. At the monthly scale, significant increasing trends (Zc 〉 Z0.10 = 1.645) were detected in February and October for three stations. (2) The ITA method indicated that the rising trends were seen in 16 out of 36 months at the three stations, while six months showed decreasing patterns for “high” monthly precipitation. According to the “low” monthly precipitations, 14 months had an increasing trend, and four months showed a decreasing trend. Through the application of the ITA method (January, March, and August at Balykchy; December at Cholpon-Ata; and July and December at Kyzyl-Suu), there were some significant increasing trends, but the Mann-Kendall test found no significant trends. The significant trend occupies 19.4% in the Mann-Kendall test and 36.1% in the ITA method, which indicates that the ITA method displays more positive significant trends than Mann–Kendall Zc. (3) Compared with the classical Mann-Kendall trend results, the ITA method has some advantages. This approach allows more detailed interpretations about trend detection, which has benefits for identifying hidden variation trends of precipitation and the graphical illustration of the trend variability of extreme events, such as “high” and “low” values of monthly precipitation. In contrast, these cannot be discovered by applying traditional methods.

Description: Dust and atmospheric particles have been described in southwestern Iran primarily in terms of load, concentration and transport. The passive deposition, however, has been discussed inadequately. Therefore, the relationships between different climate zones in southwestern Iran and dust deposition rates were quantified between 2014 and 2017 using both space- (second modern-era retrospective analysis for research and applications, version 2 reanalysis model) and ground-based (eolian ground deposition rate) tools. In addition, the surface meteorological records, including the wind patterns favoring the occurrence of dust events, were examined. A hot desert climate (BWh), hot semi-arid climate (BSh), and temperate hot and dry summer climate (Csa) were identified as the three dominant climate regions in the study area, exhibiting the highest average dust deposition rates. In this study, correlations between the most relevant climate patterns and deposition rate weather parameters were found to describe a region’s deposition rate when a dust event occurred. Based on these results, the BSh and Csa regions were found to be associated with the seasonal cycle of dust events in March, April, and May, revealing that in the long run meteorological conditions were responsible for the varying dust deposition rates. Relatively, precipitation and temperature were the two major factors influencing dust deposition rates, not wind speed. Moreover, the peak seasonal deposition rates in the spring and summer were 8.40 t km−2 month−1, 6.06 t km−2 month−1, and 3.30 t km−2 month−1 for the BWh, BSh, and Csa climate regions, respectively. However, each of these climate types was directly related to the specific quantity of the dust deposition rates. Overall, the highest dust deposition rates were detected over the years studied were 100.80 t km−2 year−1, 79.27 t km−2 year−1, and 39.60 t km−2 year−1 for BWh, BSh, and Csa, respectively.