*3.1. Spatial-Temporal Evolution of Production Pattern of Soybean*

Changes in the soybean sown area, production, and yield over the past 71 years in China are shown in Figure 2. The soybean yield increased from 614 kg/ha in 1949 to 1937 kg/ha in 2019 at an average annual growth rate of 3.08%. The increase in soybean yield is attributed to improved cultivars, increased application of fertilizers, improved cultural practices, better pest/weed control, and the rapid adoption of technologies by producers [2]. China's soybean production and yield changes remained consistent and exhibited a fluctuating upwards trend over the past 71 years. Soybean total production increased from 5.11 million tons (Mt) in 1949 to 18.08 Mt in 2019, at an average annual growth rate of 3.63%. The soybean sown area grew from 8.32 million hectares (Mha) in 1949 to 9.33 Mha in 2019, but the sown average annual growth rate reached only 0.17%, with dramatic fluctuations. Affected by high-quality and low-priced imported soybeans, the soybean sown area in China rapidly decreased from 9.48 Mha in 2001 to 6.83 Mha in 2015, reaching almost the lowest value in 71 years. To control the rapid decline in the soybean sown area, the Chinese government started pilot projects involving soybean target price subsidies in the NECP and Inner Mongolia in 2014; began to promote the corn–soybean rotation system in the NECP in 2016; and launched a soybean revitalization plan in the NECP, HHHP, and Southwest China in 2019. These policies have increased the willingness of soybean farmers to amplify the soybean sown area, facilitating an expansion in the area [31].

The coefficients of variation of soybean production, sown area, and yield in China from 1949 to 2019 are listed in Table 1. Among the coefficients of variation of the soybean sown area, production, and yield, the soybean sown area was the biggest in 1949, 2009, and 2019; the soybean yield was the biggest in 1959; and soybean production was the biggest in 1969, 1979, 1989 and 1999. This indicates that the spatial differences in the soybean yield, production and sown area in China from 1949 to 2019 experienced a process dominated by an area-yield-production-production-production-production-area-area pattern. The soybean yield rapidly increased amid improved soybean varieties, increased application of chemical fertilizers, and enhanced mechanization levels. Under the cumulated influence of the dual factors of soybean yield and sown area, the spatial differences in soybean

cultivation in China experienced a process dominated by production from 1969 to 1999, and the coefficient of variation of soybean production continued to boost. With what can be achieved with the existing level of scientific and technological development, the increase in soybean yield was limited, and the national level relatively remained constant. Under the influence of national soybean support policies, the sown area in the main soybeanproducing region has increased rapidly, and the differences among the various provinces of China are significant [32]. Therefore, among the variation coefficients of the soybean sown area, production, and yield, the spatial difference in the soybean sown area was the greatest in 2009 and 2019.

**Figure 2.** Changes in the soybean sown area, production, and yield in China from 1949 to 2019.

**Table 1.** Coefficient of variation of soybean production, sown area, and yield in China from 1949 to 2019.

China's soybean production increased by a net amount of 12.97 Mt from 1949 to 2019, with significant variation across the nine agricultural zones (Figure 3). Soybean production in the NASR, SBSR, and NECP showed rapid growth, with average annual growth rates of 33.54%, 8.23%, and 5.38%, respectively. Soybean production in the YGP and MLYP slowly increased to 0.8 and 2.6 Mt, respectively, and production remained stable. Soybean production in the SC began to nosedive after a gradual increase from 0.09 Mt in 1949 to 0.4 Mt in 1999, decreasing to 0.19 Mt in 2019. Soybean production for the LP, HHHP, and QTP fluctuated at approximately 0.5 Mt, 2 Mt, and 1000 t, respectively.

**Figure 3.** Changes in soybean production in the nine agricultural zones of China from 1949 to 2019.

The soybean sown area in China slowly increased by 1.01 Mha from 1949 to 2019, with significant variation across the nine agricultural zones (Figure 4). The soybean sown area in the NASR, SBSR, NECP, and YGP exhibited positive growth, increasing by 1.13, 0.27, 2.52, and 0.22 Mha, respectively. The NECP remained the main soybean-producing area in China, and the soybean sown area significantly increased with the proportion of the soybean sown area to the total land area of China increasing from 26.28% to 50.45%. At the same time, the soybean sown area in the NASR rapidly expanded at an average annual growth rate of 10.83%. Moreover, the soybean sown area in the YGP underwent a slowly fluctuating upwards trend with an annual growth rate of 1.27%. On the contrary, the soybean sown area in the MLYP, SC, LP, and HHHP exhibited a downward trend, decreasing by 0.1, 0.08, 0.19, and 2.76 Mha, respectively. The soybean sown area of the HHHP dropped at a higher rate, with an average annual growth rate of −1.15%, and the proportion of soybean sown areas in China declined from 41.33% to 7.27%. This downturn can mainly be attributed to the lesser benefits, and more notably, to the extending return gaps than those of corn production [10].

**Figure 4.** *Cont*.

**Figure 4.** Changes in the soybean sown area in the nine agricultural zones of China from 1949 to 2019.

#### *3.2. Spatial-Temporal Evolution of the Comparative Advantages of Soybean Production and Its Spatial Agglomeration Characteristics*

In general, the spatial differentiation characteristics of the comparative advantages of soybean planting efficiency in China from 1949 to 2019 can be divided into three stages: in the first three decades after the founding of new China, provinces with comparative advantages in soybean planting efficiency were concentrated in northern China, mainly distributed in the NECP, NASR, and HHHP (Figure 5a–d). In 1979, the soybean yield in the NECP and HHHP reached as high as 1086.88 and 1072.82 kg/ha, respectively. From 1979 to 1999, the comparative advantages of soybean planting efficiency in Henan, Jiangsu, Zhejiang, Anhui, Fujian, Hubei, and Sichuan experienced a process from scratch, and provinces with comparative advantages in soybean planting efficiency continued to agglomerate in the MLYP and SBSR (Figure 5d–f). The soybean yield in the MLYP and SBSR increased by 107% and 54.45%, respectively, from 1032.57 and 1099.48 kg/ha, respectively, in 1979 to 2141.81 and 1698.11 kg/ha in 1999, respectively. Since 1999, the comparative advantages of soybean planting efficiency have gradually extended across China. All provinces in China, except Jilin, Beijing, Tianjin, Ningxia, Shanxi, Henan, and Anhui achieved comparative advantages in soybean planting efficiency in 2009 (Figure 5g). Only five Chinese provinces, namely, Hubei, Guizhou, Guangxi, Jilin, and Anhui did not achieve comparative advantages in soybean planting efficiency in 2019 (Figure 5h). The soybean yield in the SC and HHHP regions was high, reaching 2794.12 and 2566.37 kg/ha, respectively, reaching levels much higher than the national average of 1937.41 kg/ha.

Provinces with comparative advantages in soybean planting scales in China, from 1949 to 2019, were mainly located in the NECP, HHHP, and MLYP regions (Figure 6). The spatial evolution of the comparative advantages in soybean planting scales can be divided into three stages. From 1949 to 1989, comparative advantages in soybean planting scales in China exhibited the spatial characteristics of steady expansion, spreading from seven provinces in 1949 to nine provinces in 1989 (Figure 6a–e). Heilongjiang, Jilin, and Liaoning in the NECP and Henan in the HHHP have consistently maintained their comparative advantages. The proportion of the soybean sown area in the NECP grew from 26.28% to 39.24% with a net growth increase of 0.98 Mha. From 1989 to 1999, the spatial distribution of the comparative advantages in soybean planting scales in China remained relatively stable (Figure 6e,f). Provinces with comparative advantages in soybean planting scales in 1999 include the NECP, Inner Mongolia, Tianjin, Shanxi, Anhui, and Guangxi. From 1999 to 2019, comparative advantages in soybean planting scales in China contributed to a spatial contraction and stabilization (Figure 6f–h). Liaoning, Tianjin, Shanxi, Guangxi, and Jilin lost their comparative advantages in soybean planting scales. Only four provinces exhibited comparative advantages in soybean planting scales in 2019, i.e., Inner Mongolia, Heilongjiang, Anhui, and Zhejiang, and their spatial distribution tended to remain stable.

**Figure 5.** Spatial distribution of the comparative advantages in soybean planting efficiency in China from 1949 to 2019.

**Figure 6.** Spatial distribution of comparative advantages in soybean planting scales in China from 1949 to 2019.

To explore the spatial pattern differentiation characteristics of the comparative advantage of soybean cultivation, we used Geoda software to calculate the Global Moran's index. The first step was to create a weight file, the second step was to set the weight file, and the Rook spatial adjacency weight matrix with common boundaries was selected. The third step was to calculate the Global Moran's I index of soybean planting efficiency and scale. The Global Moran's I index and related indicators for the comparative advantages of soybean planting efficiency and scale over the eight periods are shown in Table 2. The Global Moran's I index of soybean planting efficiency in 1949, 1959, 1989, 1999, and 2009 was higher than 0 and satisfies the requirements of the significance level test, indicating that the comparative advantages of soybean planting efficiency during these five periods revealed certain clustering characteristics in terms of spatial distribution. Similarly, the comparative advantages of soybean planting scales for these eight periods were spatially clustered. The Global Moran's I index and Z values underwent an increase-decreaseincrease-decrease process, and the comparative advantages of the soybean planting scale saw a spatial agglomeration strengthening-weakening-strengthening-weakening process.

**Table 2.** Change in the Global Moran's I index of comparative advantages in soybean planting efficiency and scale in China from 1949 to 2019.

Note: \*\* and \*\*\* indicate that the Global Moran's I index is significant at the 5% and 1% levels, respectively.

According to the spatial distribution map of the comparative advantages of soybean planting efficiency (Figure 7), the high–high spatial aggregation areas in 1949 occurred in the NECP, LP, NASR, and HHHP, i.e., Heilongjiang, Jilin, Liaoning, Inner Mongolia, Ningxia, Gansu, Shaanxi, Shanxi, and Hebei. The low–low spatial aggregation areas included Yunnan and Guizhou in the YGP and Hunan (Figure 7a). The spatial distribution of low–low aggregation areas in 1959 is consistent with that in 1949, while the high–high aggregation areas included the NECP and Inner Mongolia (Figure 7b). In 1989, Xinjiang was a high–high aggregation area, while low–low areas shifted from the YGP to the MLYP, including Yunnan, Hubei, and Hunan (Figure 7c). In 1999, the soybean planting efficiency cluster exhibited aggregation with high–high aggregation areas remaining in Xinjiang, low– low aggregation areas remaining in Hunan, and Beijing becoming a high–low aggregation area (Figure 7d). In 2009, Fujian was a high–high aggregation area, and Tibet was a low–high aggregation area (Figure 7e).

According to the spatial distribution map of the comparative advantages of the soybean planting scale (Figure 8), the spatial distribution of the comparative advantages of the soybean planting scale from 1949 to 1979 was relatively similar, with the high–high aggregation areas mainly occurring in Heilongjiang and Jilin; low–high aggregation areas remaining in Inner Mongolia; low–low aggregation areas, which were largely located in Xinjiang, Sichuan, Guizhou, and Hunan (Figure 8a–d). In 1989, the high–high aggregation areas included Heilongjiang, Jilin, and Inner Mongolia while Tibet became a high–low aggregation area and only Guizhou was a low–low aggregation area (Figure 8e). High–high aggregation areas in 1999 were the same as those in 1989, while the low–low aggregation areas included Xinjiang, Tibet, and Sichuan (Figure 8f). Aggregation areas in 2009 and 2019 remained consistent, with Heilongjiang and Jilin identified as high–high aggregation areas and Xinjiang was identified as a low–low aggregation area (Figure 8g,h), indicating that

the aggregation areas of the comparative advantages of soybean planting scales basically formed a highly stable state in recent years.

**Figure 7.** Local indicator of spatial association (LISA) aggregation map of the comparative advantages of soybean planting efficiency in China.

**Figure 8.** LISA aggregation map of the comparative advantages of soybean planting scale in China.

#### **4. Discussion**

The soybean yield is the main contributor to soybean production. From 1949 to 2019, soybean production in China was mainly affected by soybean yields; the contribution of the soybean yield was as high as 95.21%, and the contribution of the soybean sown area was only 4.79% (Table 3). However, there were differences in the dominant contributors to soybean production in different periods. In 1959–1979 and 1999–2009, the soybean sown area was the dominant contributor to soybean production; in 1949–1959, 1979–1999, and 2009–2019, the soybean yield was the dominant contributor to soybean production. According to USDA data, soybean yields in the United States and Brazil reached 3190 kg/ha and 3480 kg/ha, respectively, in 2019, however, China's soybean yield was only 1937 kg/ha in 2019. In the future, China needs to further improve soybean breeding technology, the quality of soybean fields, and farmland infrastructure to boost soybean yields and continue to narrow the yield gap between China and major soybean-producing countries [12].

**Table 3.** Contributions of soybean sown area and yield to soybean production from 1949 to 2019.

The patterns of soybean production are affected by multiple factors, such as natural resource endowments, economic development levels, soybean imports, and national policies. Soybean crops are highly adaptable to the natural environment, however natural conditions, such as climate, temperature, and soil conditions have a profound impact on the yield and quality of soybeans [33]. The NECP and HHHP present obvious comprehensive advantages in soybean production. The rapid development of the economy, the optimization of the dietary structure of urban and rural residents, and the increasing demand for meat, eggs, and milk have led to an acceleration in the consumption of feed grains, and such consumption has had a positive effect on soybean production [1]. Soybean yield in China is far lower than the levels in the United States and Brazil. Amid rising prices of agricultural production factors, domestic soybeans have no price advantage in the international market; the enthusiasm of farmers to plant soybean crops has dimmed; and the soybean planting area has been superseded by crops yielding more income, such as corn and rice [6]. In general, the soybean sown area in China is negatively related to soybean imports. In addition, national policies also affect patterns of soybean production. China has formulated a series of policies, including a soybean revitalization plan, however, it focuses on the NECP, HHHP, and southwestern China, resulting in an accumulation of soybean production space in these areas [34]. Therefore, the influencing factors of patterns of soybean production vary in different periods.

Grain imports have caused a deterioration of ecological environments, both for importing and exporting countries. To promote global sustainable development, grain importing countries can alleviate domestic grain shortages as well as exporting countries' resourcing and environmental problems by optimizing the spatial distribution of domestic grain to increase production and reduce resource and environmental consumption. This paper only qualitatively analyzed the influencing factors of patterns of soybean production, and quantitative analysis should be conducted in the future. In addition, county-scale soybean production data should be obtained to conduct more refined research in the future.

#### **5. Conclusions and Policy Implications**

#### *5.1. Conclusions*

The soybean sown area has not varied considerably, however, spatial patterns of soybean planting underwent tremendous changes from 1949 to 2019. The NECP is the main soybean-producing area, and its main position has strengthened. The NASR, SBSR, MLYP, and YGP regions have become new growth poles in terms of total soybean production, and soybean production in the HHHP region has decreased.

The spatial-temporal differentiation characteristics of the comparative advantages of soybean planting efficiency over the past 71 years can be divided into three stages: those concentrated in northern China from 1949 to 1979, those concentrated in the MLYP region from 1979 to 1999, and those spread across China from 1999 to 2019. The spatial-temporal differentiation characteristics of the comparative advantages in soybean planting scale over the past 71 years can also be divided into three stages: steady expansion from 1949 to 1989, relatively stable spatial distribution from 1989 to 1999, and spatial contraction and stabilization from 1999 to 2019.

The spatial distribution of the comparative advantages in soybean planting efficiency in 1949, 1959, 1989, 1999, and 2009 exhibited the characteristics of agglomeration. From 1949 to 2019, comparative advantages in soybean planting scales showed characteristics of spatial agglomeration, which involved a dynamic strengthening-decreasing-strengtheningdecreasing change process. Currently, Heilongjiang and Jilin, in the NECP region, are high–high aggregation areas with comparative advantages in their soybean planting scales, and Xinjiang is a low–low aggregation area.

This paper illustrates the spatial-temporal evolution of the patterns of soybean production and the spatial-temporal evolution of the comparative advantages in soybean production and its spatial agglomeration characteristics. The results can guide China in formulating a food security strategy. Optimizing the spatial layout of soybean planting according to the comparative advantages of soybean planting efficiency; determining key areas for soybean planting subsidies based on the comparative advantage of the soybean planting scale; considering the spatial distribution of China's soybean industry chain according to the agglomeration characteristics of the comparative advantage in soybean planting scale, thus, alleviating the national soybean shortage problem. This paper can also guide China in formulating grain security strategies in the context of the COVID-19 pandemic, major power games, and extreme climate disasters.

### *5.2. Policy Implications*

The comparative advantages of soybean planting efficiency in SC and MLYP should be fully exploited. Li et al. [35] calculated the rate of cultivated land abandonment in mountainous counties in China from 2014 to 2015, which reached 14.32%, with abandonment rates reaching 34.03% in Jiangxi; 20–30% in Sichuan, Zhejiang, and Hunan; and 10–20% in Fujian, Guangdong, and Hainan. More than 30% of all plots have been abandoned in southwestern China since 1992 [36], 10.45% of all cropland in the Guizhou-Guangxi karst mountain area has been abandoned since 2001 [37], and 5.35% of all croplands have been abandoned in Sichuan Province [38]. Cropland abandonment totalled approximately 43.12 Mha and mainly occurred in the Northern China Plain and Sichuan Plain during the 1990–2010 period [39]. Zhang et al. [40] reported that counties with cropland abandonment from 1992 to 2017 were concentrated in SC. Provinces with high rates of cropland abandonment in China are high-value areas in terms of soybean yield and achieve comparative advantages in soybean planting efficiency. However, the current agricultural planting structure ignores the remarkable potential to achieve increased soybean production on abandoned cropland in SC and MLYP. Therefore, it is necessary to fully utilize the abandoned croplands in SC and MLYP to grow soybeans, obtain regional comparative advantages in soybean planting efficiency, expand the soybean sowing area, and develop the MLYP as a new growth pole for soybean production, centered in Anhui and Jiangsu; thereby increasing soybean production and self-sufficiency in China.

Grain–soybean rotation should be promoted in the HHHP and NECP. The HHHP was historically the main soybean-producing region in China. From 1949 to 1979, Shandong and Henan exhibited comparative advantages in soybean planting scales. However, with the increasing sown area for wheat and corn, the proportion of the soybean sown area in the HHHP region decreased from 41.33% to 7.27%, while soybean production fell from 34.99% to 9.62% in China between 1949 and 2019. The HHHP has no longer exhibited comparative advantages in scale since 1999 but remains a high-value area in terms of soybean yield and exhibits comparative advantages in soybean planting efficiency, with water resources being the main restraint on food production in the region [41,42]. Within the context of fallow cropland, the sown area of water-consuming crops, such as wheat, should be trimmed down and replaced with soybean or corn–soybean rotation cultivation land, in accordance with local conditions [43–45], to gradually restore the main soybean production area, reduce groundwater consumption in the region and fully utilize the comparative advantages in soybean planting efficiency in the HHHP region. The black soil layer in the Northeast Plain has been skimmed for more than 71 years since reclamation [46], and the organic matter content has decreased by 4–7% below the levels at the beginning of reclamation. With the rapid expansion of rice cultivation [29], water scarcity has also alarmingly emerged, posing a serious threat to food security in China [47]. From 2009–2019, Heilongjiang and Liaoning in the NECP region exhibited comparative advantages in soybean planting efficiency. In the future, corn–soybean rotation in the NECP should be vigorously promoted to exploit the comparative efficiency of regional soybean cultivation and the role of soybean crops in nitrogen fixation. Simultaneously, the application of cropland quality improvement projects in the region should be encouraged to progressively improve soil fertility and increase the supply of high-quality soybean crops.

The soybean planting subsidy system should be reinforced. To become a World Trade Organization (WTO) member, China reduced tariffs on imported soybeans from 130% to 3% in 1995 [48]. Since domestic soybeans did not have a price advantage over foreign high-quality and inexpensive genetically modified soybeans, the domestic soybean market has been continuously occupied by imported soybeans [49]. To this end, the *No. 1 Central Document* of 2014 reported that a pilot project of soybean target price subsidies in the NECP and Inner Mongolia was launched. However, with the rise in the price of soybean production factors and the high cost of domestic soybean production, the actual profits of soybean planting lowered from 435.44 yuan in 2012 to 157.46 yuan in 2018. When considering labour and land costs, the net profit of soybean planting in 2018 reached −192.04 yuan. In addition, soybean production suffers negative impacts from extreme climate hazards and crop pests [50,51]. In 2019, the soybean planting area in Heilongjiang, Inner Mongolia, and Anhui accounted for 57.17% of the total area of China, and soybean production accounted for 61.01% of the total production in China. In addition, Heilongjiang and Inner Mongolia exhibited comparative advantages in soybean planting efficiency and scale. Therefore, it is necessary to focus on increasing subsidies for soybean planting in the region, increasing subsidies for agricultural insurance premiums, and increasing the amount of insurance compensation to enhance incomes; therefore, encouraging farmers to plant soybean crops and stabilize the planting area and production of the main soybeanproducing region in China.

A complete soybean industry chain should be created in areas with comparative advantages in soybean planting scale abilities. From 1949 to 2019, the center of soybean production moved 335.89 km along the direction of 89.59◦ northeast, and the center of gravity of the soybean sown area moved 467.21 km along the direction of 77.41◦ northeast. At present, Heilongjiang and Jilin form a high–high cluster area with comparative advantages in the soybean planting scale, and it is necessary to fully utilize these advantages to implement major joint soybean research, accelerate the application of biotechnology to soybean breeding, and upgrade the breeding capacity of soybean seeds. Moderate-scale operation activities of soybean crops in the region should be accelerated with a focus on cultivating and supporting a number of soybean planting cooperatives, large producers, family farms, and other new business entities to promote large-scale operations. A soybean industry chain integrating soybean breeding, production, processing, and marketing should be established in the NECP region, Inner Mongolia, and Anhui to raise the market competitiveness of Chinese soybeans.

**Author Contributions:** Conceptualization, W.C., X.K., B.Z. and L.W.; methodology, W.C.; software, W.C.; investigation, X.K., L.W. and W.C.; data curation, W.C.; writing—original draft preparation, W.C.; writing—review and editing, W.C., X.K., L.K. and Y.L.; visualization, W.C. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was supported by the Major Program of the National Social Science Foundation of China (19ZDA096) and the National Natural Science Foundation of China (Grant Nos. 41771561,<PHONE_NUMBER>6, and 42171267).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** All participation was voluntary and verbal consent was obtained from all subjects involved in the study.

**Data Availability Statement:** Publicly available sources of the data used in this study are described in the article; for other data used, please contact the corresponding author on reasonable grounds.

**Acknowledgments:** The authors appreciate the insightful and constructive comments of the anonymous reviewers.

**Conflicts of Interest:** The authors declare no conflict of interest.


*Wound Infection: Commercially Available AMPs and Perspectives*

In this section, we mention studies that analyse the management of MRSA wound infections in mouse models with commercially available antimicrobial peptides such as teicoplanin, daptomycin, and dalbavancin.

Ghiselli et al. (2006) wanted to compare the efficacy of topical vs. systemic teicoplanin for the treatment of wound infection with *Staphylococcus aureus* in a mouse model. Data analysis showed that strong inhibition of bacterial growth was achieved in all groups treated with intraperitoneal teicoplanin. However, the highest inhibition of bacterial growth was obtained in the group that received teicoplanin-soaked Allevyn and intraperitoneal teicoplanin. Histological examination showed that each treatment modality was able to reduce the delay in wound repair, but the best results were obtained with teicoplaninsoaked Allevyn, with and without intraperitoneal teicoplanin, associated with a wound remodelling similar to that of not-infected mice (assessing microvessel density, VEGF expression, and granulation tissue formation in tissue sections) [53].

Daptomycin is a bactericidal lipopeptide antibiotic widely used to treat systemic infections caused by Gram-positive cocci [54]. In a study conducted by Simonetti et al. (2017), a mouse model was used to study the in vivo efficacy of daptomycin in the treatment of burn wound infections caused by *S. aureus* and evaluate the wound healing process through morphological and immunohistochemical analysis, compared to teicoplanin. The highest inhibition of infection in terms of bacterial load was achieved in the group that received daptomycin (2.0 × 103 ± 0.4 × 103 CFU/g), which also showed better overall healing with epithelialisation and significantly higher collagen scores than the other groups, and these findings were also confirmed by immunohistochemical data on EGFR and FGF-2. The results of this in vivo study in an animal model showed that daptomycin demonstrated stronger antimicrobial activity than teicoplanin [55]. Moreover, daptomycin, in a previous study, showed synergy in its effect against MRSA when combined with other antibiotics such as tigecycline [56].

Dalbavancin is a semisynthetic lipoglycopeptide active against Gram-positive bacteria and has been approved for the treatment of acute bacterial skin and skin structure infections (ABSSSI) [57]. In a 2020 study conducted by Simonetti O. et al., the aim was to determine the effect of dalbavancin administration on wound healing compared to that of vancomycin and to elucidate if and how EGFR, MMP-1, MMP-9, and VEGF could be involved in its therapeutic mechanisms. A mouse model of MRSA skin infection was established, and mice were treated daily with vancomycin or weekly with dalbavancin at days 1 and 8. Both dalbavancin and vancomycin were effective in reducing the bacterial load (8.71 × 105 ± 9.02 × 105 CFU/mL vs. 8.04 × 106 ± 7.96 × 106 CFU/mL, respectively). The wounds treated with dalbavancin showed well-organised granulation tissue with numerous blood vessels, although slightly less than that in the uninfected group. The immunohistochemical staining also showed elevated EGFR and VEGF expression in both treated groups (higher in dalbavancin-treated mice), decreased MMP-1 and MMP-9 levels in uninfected tissue, and in both treated tissues compared with untreated infected wounds. This study revealed faster healing with dalbavancin treatment than might be associated with higher EGFR and VEGF levels, with the lowest values of MMP-9 and MMP-1 expression [58].

#### **5. AMPs and** *Enterococcus faecalis* **Infection**

Enterococci are responsible for multiple nosocomial infections, and they have a high frequency of multidrug infections. The majority of enterococcal infections are caused by *Enterococcus faecalis,* which is often resistant to multiple antibiotics. Thus, it is very important to search for new antimicrobial compounds such as AMPs [59].

Giacometti et al. (2004) evaluated the in vitro activity of temporin A against *E. faecalis* nosocomial isolates, including vancomycin-resistant strains, and its in vitro activity combined with antibiotics. Temporin A demonstrated potent antibacterial activity against *E. faecalis*, including vancomycin-resistant strains, in vitro, especially when combined with amoxicillin/clavulanic acid and imipenem. In conclusion, this peptide could be used in the future as an adjuvant in the therapy for enterococcal infections [60].

Cirioni et al. conducted an experimental study to evaluate both the in vitro and in vivo interaction between the Laur-CKK-NH2 lipopeptide and daptomycin using two *Enterococcus faecalis* strains with different patterns of susceptibilities. The in vitro experiments showed that the Laur-CKK-NH2 dimer is able to prevent the emergence of daptomycin resistance. Additionally, for in vivo studies using a mouse model of enterococcal sepsis, the Laur-CKK-NH2 dimer and daptomycin exhibited the highest efficacy in measuring lethality and bacteremia [61].

#### **6. AMPs and Gram-Negative Bacteria**

Infections sustained by multi-drug-resistant (MDR) Gram-negative bacteria represent one of the most important therapeutic challenges, considering that their resistance to antibiotics is expanding from extended-spectrum beta-lactamases and carbapenemases [62] to the *mcr* gene, which is responsible for colistin resistance [63]. This is why new molecules need to be evaluated in order to overcome AMRs. AMPs can also be a valuable aid in the treatment of Gram-negative infections.


*Article* **Ethnicities in Post-Communist Romania: Spatial Dynamics, Fractionalisation, and Polarisation at the NUTS-3 Level**

**Marina-Alexandra Rotaru, Remus Cre¸tan \* and Ana-Neli Ianăs¸**

Department of Geography, West University of Timi¸soara, Bulevardul Vasile Parvan no 4, 300223 Timi¸soara, Romania<EMAIL_ADDRESS>(M.-A.R<EMAIL_ADDRESS>(A.-N.I.)

**\*** Correspondence<EMAIL_ADDRESS>
**Abstract:** Scholars have shown a special interest in discovering and studying the role of ethnic diversity and spatiality at the country and region levels. This study contributes to the theoretical debate on the spatial dynamics of ethnicities, with the aim of (1) determining the evolution of ethnic dynamics in post-communist Romania and (2) applying ethnic fractionalisation and polarisation indices. The study uses a mixed methods approach based on a descriptive statistics analysis and applies the fractionalisation and polarisation indices to Romania's NUTS-3 (i.e., county) level. The findings suggest that the ethnic spatial dynamics in post-communist Romania have shown a decrease in all ethnicities due to migration and low birth rates, with the exception of the Roma ethnicity, for whom the trend is increasing. Additionally, polarisation and fractionalisation indices have different evolutionary manifestations depending on the dynamics of the ethnic groups present in certain geographical areas. Although neither of the two analysed indices has witnessed profound change at the spatial level, these small changes in spatial and short-term ethnic diversity can help us advance knowledge about co-existence in ethnically diverse societies. Higher values of the two indices are obvious in several counties where ethnic Hungarians cohabitate with Romanians and other ethnicities. This discovery can inform policy-makers to implement more policies for the further peaceful co-existence of Hungarians, Romanians, and other ethnic groups in Transylvania and other western counties in Romania. Furthermore, as the population growth trend for the Roma ethnicity is upward, Romania has to implement proper policies and build better government infrastructure to counter social inequality against the Roma people. This will help curb potential conflicts between the Roma and other ethnic groups at the local level. Finally, as most ethnicities decreased in number in post-communist times, further attention needs to be paid to the erosion of ethnic diversity in Romania because this could have a negative impact on economic development, social trust, and democracy.

**Keywords:** Romania; ethnicities; fractionalisation; polarisation; NUTS-3 units


**2.1 Introduction**

In this first section, we describe the main characteristics of the corona micro-discharge formation and development as a function of several operating parameters such as the geometry of electrodes or the shape and magnitude of applied high voltage. Then, based on Boltzmann kinetic theory, we describe the strongly coupled electrical, hydrodynamics and chemical phenomena that take place in a compressible gas crossed by micro-discharges.