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The progress of the global effort to achieve Sustainable Development Goals (SDGs) is increasingly impeded by the degradation of critical and fundamental resources such as water, food, energy, and ecosystem services. The Mid-Latitude Region (MLR) of the world is at the forefront of confronting these challenges due to rapid population growth, increasing poverty, and drought and climate change that are exacerbating the transition of semi-arid landscapes to deserts. While scientific studies are accumulating around the water-food-energy-ecosystem nexus, efforts to simulate how the linkages among the elements relate to SDGs are lacking in the MLR. We attempt to review and analyze existing literature about how water-food-energy-ecosystems operate, interact, and relate to SDGs. We identified 37 relationships and ascertained the nature of their interactions, of which 12 are significant and have direct bearings on the SDGs. The findings show that most studies and approaches that address the nexus challenges in the MLR exist in silos. In addition, there is a lack of a scientific approach to quantify how the nexus operates and relates to SDGs. For instance, past studies show that deforestation for agriculture could increase food security. However, there is a weak focus on trade-offs (e.g., loss of ecosystem services due to deforestation). Deforestation is also shown to have a negative relationship with the quantity and quality of water (SDG6) as well as the functionality of an ecosystem (SDG15). Furthermore, the review has indicated a negative relationship between irrigated agriculture and water and a positive relationship with food. This directly implies that water and food issues must be addressed in tandem and not separately if we are to achieve SDGs 2 and 6. The review supports the idea that water, food, energy, and ecosystem services cannot be managed separately, and that future approaches must focus on integrating and optimizing the connections among them to ensure sustainable development. Key words: water-food-energy-ecosystem nexus, Mid-Latitude Region, Sustainable Development Goals, relationship, scoping review, 1. Introduction Progress towards Sustainable Development Goals (SDGs) is impeded by excessive consumption and production that degrade critical natural resources such as water, food, energy, and ecosystem services. Environmental degradation [...]

Abnormalities in climate in recent years have been attributed to climate change. In particular, an increase in the frequency of droughts has started to influence the way we live. There is an urgent need to study and monitor droughts to prevent severe damage. Drought monitoring research is conducted using the meteorological drought index, a compilation of weather data. Although weather data are highly dependent on climatic zones, existing studies have not considered the drought index by climatic zones. This study aims to identify the spatial and climatic distribution of meteorological drought index studies and provide a well-matched combination of the drought index and climatic classification through a meta-analytic review. Data were used in the form of research papers covering the Palmer Drought Severity Index (PDSI), Standardized Precipitation Index (SPI), and Standardized Precipitation Evapotranspiration Index (SPEI) published from 2011 to 2021. The trends showed that SPEI has become a leading index for the study of drought monitoring among the three drought indices since 2015. Additionally, Asia was the focus of much of this drought research due to climate events in China. Each continent and climate classification was assigned to a different dominant drought index. For example, PDSI was commonly used in North America and continental climates, SPI in Africa, Europe, South America, and tropical and dry climates, and SPEI in Asia and temperate climates. This evaluates countries, in which drought monitoring studies are rare, using a meteorological index based on the same climatic zones. Monitoring and predicting drought patterns would improve the basic needs of humankind. Key words: meta-analysis, meteorological drought index, drought review, SPEI, SPI, PDSI Les anomalies climatiques des dernieres annees ont ete attributes aux changements climatiques. En particulier, l'augmentation de la frequence des secheresses a commence a influencer notre mode de vie. Il y a un besoin urgent d'etudier et de surveiller les secheresses afin de prevenir les dommages importants. La recherche sur la surveillance de la secheresse est realisee a l'aide de l'indice de secheresse meteorologique, une compilation de donnees meteorologiques. Bien que les donnees meteorologiques dependent fortement des zones climatiques, les etudes existantes n'ont pas considere l'indice de secheresse par zones climatiques. Cette etude vise a identifier la distribution spatiale et climatique des etudes sur l'indice de secheresse meteorologique et a fournir une combinaison bien assortie de l'indice de secheresse et de la classification climatique par le biais d'une revue meta-analytique. Les donnees ont ete utilisees sous la forme d'articles de recherche couvrant l'indice de severite de la secheresse de Palmer (PDSI - Palmer Drought Severity Index), l'indice normalise de precipitation (INP) et l'indice normalise precipitations moins evapotranspiration (INPE) publies de 2011 a 2021. Les tendances ont montre que l'INPE est devenu un indice de premier plan pour l'etude de la surveillance de la secheresse parmi les trois indices de secheresse depuis 2015. En outre, l'Asie a ete au centre d'une grande partie de cette recherche sur la secheresse en raison des evenements climatiques en Chine. Chaque continent et classification climatique a ete assigne a un indice de secheresse dominant different. Par exemple, le PDSI etait couramment utilise en Amerique du Nord et dans les climats continentaux, l'INP en Afrique, en Europe, en Amerique du Sud et dans les climats tropicaux et secs, et l'INPE en Asie et dans les climats temperes. Les pays, dans lesquels les etudes de surveillance de la secheresse sont rares, sont ainsi evalues a l'aide d'un indice meteorologique base sur les memes zones climatiques. La surveillance et la prevision des patrons de secheresse permettraient d'ameliorer les besoins fondamentaux de l'humanite. [Traduit par la Redaction] Mots-cles: meta-analyse, indice meteorologique de secheresse, synthese sur la secheresse, SPEI, INP, INPE, 1. Introduction Climate change has led to dramatic transitions in our Earth's ecosystems. This has resulted in an abnormal climate, in which the present and upcoming climate cycles are changing [...]

Forest fires have devastating effects on extensive forest areas, compromising vital ecological services such as air purification, water conservation, and recreational opportunities, thus posing a significant socioeconomic threat. Furthermore, the risk of forest fires is steadily increasing due to climate change. The most effective method for mitigating forest fire risk is proactive prevention before forest fires can occur by identifying high-risk areas based on land surface conditions. This study aimed to develop a machine learning-based forest fire diagnostic model designed for Republic of Korea, considering both satellite-derived land surface data and anthropogenic factors. For the remote sensing data, VTCI (Vegetation Temperature Condition Index) was used to reflect the land surface dryness. In addition, fire activity maps for buildings, roads and cropland were used to consider the influence of human activities. The forest fire diagnostic model yielded an accuracy of 0.89, demonstrating its effectiveness in predicting forest fire risk. To validate the effectiveness of the model, 92 short-term forest fire risk forecast maps were generated from March to May 2023 with real-time data on forest fire occurrences collected for verification. The results showed that 73% of forest fires were accurately classified within high-risk zones, confirming the operational accuracy of the model. Through the forest fire diagnostic model, we have presented the impact relationships of meteorological, topographical, and environmental data, as well as the dryness index based on satellite images and anthropogenic factors, on forest fire occurrence. Additionally, we have demonstrated the potential uses of surface condition data. [ABSTRACT FROM AUTHOR]

Forest spatial information is regularly established and managed as basic data for national forest planning and forest policy establishment. Among them, the grade of vegetation conservation shall be investigated and evaluated according to the value of vegetation conservation. As the collection of field data over large or remote areas is difficult, unmanned aerial vehicles (UAVs) are increasingly being used for this purpose. Consequently, there is a need for research on UAV-monitoring and three-dimensional (3D) image generation techniques. In this study, a new method that can efficiently collect and analyze UAV spatial data to survey and assess forests was developed. Both UAV-based and LiDAR imaging methods were evaluated in conjunction with the ground control point measurement method for forest surveys. In addition, by fusing the field survey database of each target site and the UAV optical and LiDAR images, the Gongju, Samcheok, and Seogwipo regions were analyzed based on deep learning. The kappa value showed 0.59, 0.47, and 0.78 accuracy for each of the sites in terms of vegetation type (artificial or natural), and 0.68, 0.53, and 0.62 accuracy in terms of vegetation layer structure. The results of comparative analysis with ecological natural maps by establishing vegetation conservation levels show that about 83.9% of the areas are consistent. The findings verified the applicability of this UAV-based approach for the construction of geospatial information on forests. The proposed method can be useful for improving the efficiency of the Vegetation Conservation Classification system and for conducting high-resolution monitoring in forests worldwide. [ABSTRACT FROM AUTHOR]

Dominant intermediate Charcot-Marie-Tooth disease type C (DI-CMTC) is a dominantly inherited neuropathy that has been classified primarily based on motor conduction velocity tests but is now known to involve axonal and demyelination features. DI-CMTC is linked to tyrosyl-tRNA synthetase (YARS)-associated neuropathies, which are caused by E196K and G41R missense mutations and a single de novo deletion (153-156delVKQV). It is well-established that these YARS mutations induce neuronal dysfunction, morphological symptoms involving axonal degeneration, and impaired motor performance. The present study is the first to describe a novel mouse model of YARS-mutation-induced neuropathy involving a neuron-specific promoter with a deleted mitochondrial targeting sequence that inhibits the expression of YARS protein in the mitochondria. An adenovirus vector system and in vivo techniques were utilized to express YARS fusion proteins with a Flag-tag in the spinal cord, peripheral axons, and dorsal root ganglia. Following transfection of YARS-expressing viruses, the distributions of wild-type (WT) YARS and E196K mutant proteins were compared in all expressed regions; G41R was not expressed. The proportion of Flag/green fluorescent protein (GFP) double-positive signaling in the E196K mutant-type mice did not significantly differ from that of WT mice in dorsal root ganglion neurons. All adenovirus genes, and even the empty vector without the YARS gene, exhibited GFP-positive signaling in the ventral horn of the spinal cord because GFP in an adenovirus vector is driven by a cytomegalovirus promoter. The present study demonstrated that anatomical differences in tissue can lead to dissimilar expressions of YARS genes. Thus, use of this novel animal model will provide data regarding distributional defects between mutant and WT genes in neurons, the DI-CMTC phenotype, and potential treatment approaches for this disease., Competing Interests: Conflicts of interest: None declared.