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Grapevine berry quality for winemaking depends on complex and dynamic relationships between the plant and the environment. Winemakers around the world are demanding a better understanding of the factors that influence berry growth and development. In the last decades, an increment in air temperature, CO 2 concentration and dryness occurred in wine-producing regions, affecting the physiology and the biochemistry of grapevines, and by consequence the berry quality. The scientific community mostly agrees in a further raise as a result of climate change during the rest of the century. As a consequence, areas most suitable for viticulture are likely to shift into higher altitudes where mean temperatures are suitable for grape cultivation. High altitude can be defined as the minimum altitude at which the grapevine growth and development are differentially affected. At these high altitudes, the environments are characterized by high thermal amplitudes and great solar radiations, especially ultraviolet-B (UV-B). This review summarizes the environmental contribution of global high altitude-related climatic variables to the grapevine physiology and wine composition, for a better evaluation of the possible establishment of vineyards at high altitude in climate change scenarios.

Nearly 20 y ago, Jared Diamond and Peter Bellwood reviewed the evidence for the associated spread of farming and large language families by the demographic expansions of farmers. Since then, advances in obtaining and analyzing genomic data from modern and ancient populations have transformed our knowledge of human dispersals during the Holocene. Here, we provide an overview of Holocene dispersals in the light of genomic evidence and conclude that they have a complex history. Even when there is a demonstrated connection between a demographic expansion of people, the spread of agriculture, and the spread of a particular language family, the outcome in the results of contact between expanding and resident groups is highly variable. Further research is needed to identify the factors and social circumstances that have influenced this variation and complex history.

Remote Sensing, as a driver for water management decisions, needs further integration with monitoring water quality programs, especially in developing countries. Moreover, usage of remote sensing approaches has not been broadly applied in monitoring routines. Therefore, it is necessary to assess the efficacy of available sensors to complement the often limited field measurements from such programs and build models that support monitoring tasks. Here, we integrate field measurements (2013–2019) from the Mexican national water quality monitoring system (RNMCA) with data from Landsat-8 OLI, Sentinel-3 OLCI, and Sentinel-2 MSI to train an extreme learning machine (ELM), a support vector regression (SVR) and a linear regression (LR) for estimating Chlorophyll-a (Chl-a), Turbidity, Total Suspended Matter (TSM) and Secchi Disk Depth (SDD). Additionally, OLCI Level-2 Products for Chl-a and TSM are compared against the RNMCA data. We observed that OLCI Level-2 Products are poorly correlated with the RNMCA data and it is not feasible to rely only on them to support monitoring operations. However, OLCI atmospherically corrected data is useful to develop accurate models using an ELM, particularly for Turbidity (R2 = 0.7). We conclude that remote sensing is useful to support monitoring systems tasks, and its progressive integration will improve the quality of water quality monitoring programs.

Modeling inland water quality by remote sensing has already demonstrated its capacity to make accurate predictions. However, limitations still exist for applicability in diverse regions, as well as to retrieve non-optically active parameters (nOAC). Models are usually trained only with water samples from individual or local groups of waterbodies, which limits their capacity and accuracy in predicting parameters across diverse regions. This study aims to increase data availability to understand the performance of models trained with heterogeneous databases from both remote sensing and field measurement sources to improve machine learning training. This paper seeks to build a dataset with worldwide lake characteristics using data from water monitoring programs around the world paired with harmonized data of Landsat-8 and Sentinel-2. Additional feature engineering is also examined. The dataset is then used for model training and prediction of water quality at the global scale, time series analysis and water quality maps for lakes in different continents. Additionally, the modeling performance of nOACs are also investigated. The results show that trained models achieve moderately high correlations for SDD, TURB and BOD (R2 = 0.68) but lower performances for TSM and NO3-N (R2 = 0.43). The extreme learning machine (ELM) and the random forest regression (RFR) demonstrate better performance. The results indicate that ML algorithms can process remote sensing data and additional features to model water quality at the global scale and contribute to address the limitations of transferring and retrieving nOAC. However, significant limitations need to be considered, such as calibrated harmonization of water data and atmospheric correction procedures. Moreover, further understanding of the mechanisms that facilitate nOAC prediction is necessary. We highlight the need for international contributions to global water quality datasets capable of providing extensive water data for the improvement of global water monitoring.

Thailand and Laos, located in the center of Mainland Southeast Asia (MSEA), harbor diverse ethnolinguistic groups encompassing all five language families of MSEA: Tai-Kadai (TK), Austroasiatic (AA), Sino-Tibetan (ST), Hmong-Mien (HM), and Austronesian (AN). Previous genetic studies of Thai/Lao populations have focused almost exclusively on uniparental markers and there is a paucity of genome-wide studies. We therefore generated genome-wide SNP data for 33 ethnolinguistic groups, belonging to the five MSEA language families from Thailand and Laos, and analyzed these together with data from modern Asian populations and SEA ancient samples. Overall, we find genetic structure according to language family, albeit with heterogeneity in the AA-, HM-, and ST-speaking groups, and in the hill tribes, that reflects both population interactions and genetic drift. For the TK speaking groups, we find localized genetic structure that is driven by different levels of interaction with other groups in the same geographic region. Several Thai groups exhibit admixture from South Asia, which we date to ∼600–1000 years ago, corresponding to a time of intensive international trade networks that had a major cultural impact on Thailand. An AN group from Southern Thailand shows both South Asian admixture as well as overall affinities with AA-speaking groups in the region, suggesting an impact of cultural diffusion. Overall, we provide the first detailed insights into the genetic profiles of Thai/Lao ethnolinguistic groups, which should be helpful for reconstructing human genetic history in MSEA and selecting populations for participation in ongoing whole genome sequence and biomedical studies.

Previous genome-wide studies have reported South Asian (SA) ancestry in several Mainland Southeast Asian (MSEA) populations; however, additional details concerning population history, in particular the role of sex-specific aspects of the SA admixture in MSEA populations can be addressed with uniparental markers. Here, we generated ∼2.3 mB sequences of the male-specific portions of the Y chromosome (MSY) of a Tai-Kadai (TK)-speaking Southern Thai group (SouthernThai_TK), and complete mitochondrial (mtDNA) genomes of the SouthernThai_TK and an Austronesian (AN)-speaking Southern Thai (SouthernThai_AN) group. We identified new mtDNA haplogroups, e.g. Q3, E1a1a1, B4a1a and M7c1c3 that have not previously reported in Thai populations, but are frequent in Island Southeast Asia and Oceania, suggesting interactions between MSEA and these regions. SA prevalent mtDNA haplogroups were observed at frequencies of ~35–45% in the Southern Thai groups; both of them showed more genetic relatedness to Austroasiatic (AA) speaking Mon than to any other group. For MSY, SouthernThai_TK had ~35% SA prevalent haplogroups and exhibited closer genetic affinity to Central Thais. We also analyzed published data from other MSEA populations and observed SA ancestry in some additional MSEA populations that also reflects sex-biased admixture; in general, most AA- and AN-speaking groups in MSEA were closer to SA than to TK groups based on mtDNA, but the opposite pattern was observed for the MSY. Overall, our results of new genetic lineages and sex-biased admixture from SA to MSEA groups attest to the additional value that uniparental markers can add to studies of genome-wide variation.

Remote-sensing-based machine learning approaches for water quality parameters estimation, Secchi Disk Depth (SDD) and Turbidity, were developed for the Valle de Bravo reservoir in central Mexico. This waterbody is a multipurpose reservoir, which provides drinking water to the metropolitan area of Mexico City. To reveal the water quality status of inland waters in the last decade, evaluation of MERIS imagery is a substantial approach. This study incorporated in-situ collected measurements across the reservoir and remote sensing reflectance data from the Medium Resolution Imaging Spectrometer (MERIS). Machine learning approaches with varying complexities were tested, and the optimal model for SDD and Turbidity was determined. Cross-validation demonstrated that the satellite-based estimates are consistent with the in-situ measurements for both SDD and Turbidity, with R2 values of 0.81 to 0.86 and RMSE of 0.15 m and 0.95 nephelometric turbidity units (NTU). The best model was applied to time series of MERIS images to analyze the spatial and temporal variations of the reservoir’s water quality from 2002 to 2012. Derived analysis revealed yearly patterns caused by dry and rainy seasons and several disruptions were identified. The reservoir varied from trophic to intermittent hypertrophic status, while SDD ranged from 0–1.93 m and Turbidity up to 23.70 NTU. Results suggest the effects of drought events in the years 2006 and 2009 on water quality were correlated with water quality detriment. The water quality displayed slow recovery through 2011–2012. This study demonstrates the usefulness of satellite observations for supporting inland water quality monitoring and water management in this region.

Studies of Native South American genetic diversity have helped to shed light on the peopling and differentiation of the continent, but available data are sparse for the major ecogeographic domains. These include the Pacific Coast, a potential early migration route; the Andes, home to the most expansive complex societies and to one of the most widely spoken indigenous language families of the continent (Quechua); and Amazonia, with its understudied population structure and rich cultural diversity. Here, we explore the genetic structure of 176 individuals from these three domains, genotyped with the Affymetrix Human Origins array. We infer multiple sources of ancestry within the Native American ancestry component; one with clear predominance on the Coast and in the Andes, and at least two distinct substrates in neighboring Amazonia, including a previously undetected ancestry characteristic of northern Ecuador and Colombia. Amazonian populations are also involved in recent gene-flow with each other and across ecogeographic domains, which does not accord with the traditional view of small, isolated groups. Long-distance genetic connections between speakers of the same language family suggest that indigenous languages here were spread not by cultural contact alone. Finally, Native American populations admixed with post-Columbian European and African sources at different times, with few cases of prolonged isolation. With our results we emphasize the importance of including understudied regions of the continent in high-resolution genetic studies, and we illustrate the potential of SNP chip arrays for informative regional-scale analysis.

Background The Chao Lay, also known as sea nomads, include the Austronesian-speaking Moken, Moklen, and Urak Lawoi, who traditionally inhabit the coastal regions and islands of the Andaman Sea in southern Thailand. Their maritime lifestyle has attracted significant interest in their genetic origins and relationships with other sea nomad groups in Island Southeast Asia (ISEA); however, comprehensive genetic data on these communities remain scarce. Here, we generated complete mitochondrial genome sequences from Moken and Moklen groups, along with the Tai-Kadai-speaking southern Thai population and additional Austroasiatic-speaking Maniq samples (hunter-gatherer) from southern Thailand. Results Our findings indicate that the Chao Lay display lower genetic diversity compared to the majority of southern Thai populations. Furthermore, the results suggest the absence of recent maternal expansions among the Chao Lay. Notably, haplogroups D4e1a, E1a1a1a, M21b2, M46a, M50a1, and M71c are predominant among the Chao Lay, underscoring their genetic distinctiveness. Bayesian coalescent age estimates of clades characteristic to Chao Lay for these haplogroups point to the time associated with the Austronesian expansion period. Conclusions The Chao Lay populations were closer to each other than to other groups and exhibited more genetic connections to Mainland Southeast Asian (MSEA) populations than ISEA populations. However, we do not exclude potential origins of the Chao Lay in ISEA or Taiwan, as it is possible that ancestral Chao Lay males incorporated MSEA females into their communities upon arriving in Thailand. Further studies on genome-wide and Y chromosome data would provide more insights into their genetic history.

Abscisic acid (ABA) and gibberellic acid (GA3) are regulators of fruit color and sugar levels, and the application of these hormones is a common practice in commercial vineyards dedicated to the production of table grapes. However, the effects of exogenous ABA and GA3 on wine cultivars remain unclear. We investigated the impact of ABA and GA3 application on Malbec grapevine berries across three developmental stages. We found similar patterns of berry total anthocyanin accumulation induced by both treatments, closely associated with berry H2O2 levels. Quantitative proteomics from berry skins revealed that ABA and GA3 positively modulated antioxidant defense proteins, mitigating H2O2. Consequently, proteins involved in phenylpropanoid biosynthesis were downregulated, leading to decreased anthocyanin content at the almost ripe stage, particularly petunidin-3-G and peonidin-3-G. Additionally, we noted increased levels of the non-anthocyanins E-viniferin and quercetin in the treated berries, which may enhance H2O2 scavenging at the almost ripe stage. Using a linear mixed-effects model, we found statistical significance for fixed effects including the berry H2O2 and sugar contents, demonstrating their roles in anthocyanin accumulation. In conclusion, our findings suggest a common molecular mechanism by which ABA and GA3 influence berry H2O2 content, ultimately impacting anthocyanin dynamics during ripening.