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A visionary and interdisciplinary scientist who brought a fearless passion to everything she did, inspiring all those around her.
Alma Dal Co tragically passed away on 14 November 2022 at the age of 33, doing what she loved most — spearfishing near the Italian island of Pantelleria. Alma was a visionary scientist at the beginning of what was promising to become a stellar career. As a physicist turned biologist, Alma wanted to unravel how complexity emerges from simplicity. Despite her young age, she had already made an important impact on the field by showing how the activities of microbial communities emerge from interactions between individual cells. Alma was a warm and caring friend, and a committed and inspiring mentor. She pursued science with fearless passion, creativity, vision and dedication.Alma Dal Co in 2016 in Joshua Tree National Park, California. Photograph by Simon van Vliet.Alma had an exceptionally sharp and creative mind, and an insatiable curiosity. She kept exploring new directions, working on everything from gene-regulatory circuits to microbial communities, to developmental processes. She was the embodiment of a true interdisciplinary scientist, combining state-of-the-art experiments with advanced computational approaches. The unifying theme of her work was to understand how interactions between individuals (be it fish, microorganisms or pancreatic cells) give rise to complex behaviour at higher levels of organization. She strived to derive simple, quantitative rules to explain the complexity that we see around us. Alma believed that science is a team effort: she was generous with her time, and always happy to discuss ideas and share resources. No matter where she went, she quickly connected with people, built formal and informal networks, and fostered collaborations and friendships.Alma was born in Turin and grew up in Venice, in Italy. Her true home, however, was Pantelleria, an Italian island in the Mediterranean Sea off the coast of Sicily. Alma spent her summers in the sea from an early age, developing a deep and lasting bond with it. The sea was not only a place to recharge, but also a source of inspiration: Alma became fascinated by the intricate behaviours of octopuses and schools of fish, creating a lasting sense of wonder about the natural world. Alma’s primary education focused on the humanities, but most of all music. In 2002, she was accepted to the conservatorium in Venice to study the piano. However, her love for the natural world remained and in 2007 she started studying physics in Padua. In 2011, she finished her BSc in physics and a year later her education at the conservatorium. Both a career in music and in science were an option, but Alma chose science and moved to Turin to study the physics of complex systems. Music always remained important in her life, and she played the piano whenever she could.Alma’s transition to biology started in Turin in the laboratory of Michelle Caselle, where she used mathematical models to study gene regulatory networks. She discovered how the regulation of gene expression can reduce stochastic fluctuations and provide robustness to the expression of an organism’s phenotype (A. Dal Co et al. Nucleic Acids Res. 45, 1069–1078; 2017). In 2014, she exchanged the blackboard for the wet lab, and moved to Zurich, Switzerland, to start her PhD with Martin Ackermann at ETH and the aquatic research institute Eawag. Despite the struggles of having to learn hands-on biology without formal training, she was not deterred from pursuing a highly challenging project.Alma developed an innovative approach to gain a mechanistic understanding of how metabolic interactions between individual microbial cells determine the dynamics of spatially structured communities. She quantified the growth of single cells in a synthetic microbial community and developed computational tools to infer their interaction network. She showed that cells in these communities live in a small world: they only interact with few neighbours (A. Dal Co et al. Nat. Ecol. Evol. 4, 366–375; 2020). This short interaction range limits the growth of mutually dependent microorganisms, thereby counteracting the evolution of metabolic specialization. Moreover, Alma developed a mathematical framework to quantitatively predict the dynamics of microbial communities from the molecular properties of the underlying intercellular interactions (S. van Vliet et al. PLoS Comput. Biol. 18, e1009877; 2022). Together, these works have made an important contribution to our understanding of how microbial communities function, and they have inspired numerous follow-up projects, both by Alma herself (for example, A. Dal Co et al. Phil. Trans. R. Soc. B 374, 20190080; 2019) and by others in the field (for example, J. van Gestel et al. Nat. Commun. 12, 2324; 2021).Alma finished her PhD in 2019, winning the ETH medal for an outstanding thesis. She then moved to Harvard to study developmental processes, together with Michael Brenner. She quickly developed a large network of collaborators and designed an innovative project to study pancreatic islet formation. However, COVID-19-related laboratory restrictions brought an early end to these plans, and Alma instead developed a novel computational framework that can be applied to both animal tissues and microbial communities to study how local cell–cell interactions can create spatial structure at the scale of multicellular systems.In September 2021, Alma started an assistant professorship at the University of Lausanne. At the age of 32, she was one of youngest professors ever appointed there. Thanks to her leadership, she quickly assembled a highly interdisciplinary, collaborative and cohesive team of talented young scientists. The group’s research was as varied as Alma’s interests. A major theme was to gain a quantitative understanding of how cell–cell interactions affect the function and structure of microbial communities and other multicellular systems. Her group combines state-of-the art experimental tools such as optogenetics, microfluidics and single-cell imaging, with computational approaches and mathematical modelling to study the dynamics of a wide range of model systems.During her very short career as an assistant professor, Alma was a core member of the Swiss National Research Program on microbiome research (https://nccr-microbiomes.ch); was awarded two major grants; established a large network of collaborators; and was invited to present her work at numerous international meetings. Most importantly, Alma fostered a strong sense of community, both in her group and beyond — creating an open, inclusive and interactive space to discuss science and life.Interacting with Alma was never dull: her passion and energy were infectious and her curiosity and openness a source of inspiration. She always kept you on your toes with her constant stream of pointed questions. But most of all, her easy laugh and positive energy made working with her an extraordinarily joyous experience.With Alma the world has lost a visionary scientist. We are deeply saddened that we will never see what other discoveries she would have made. However, it offers some conciliation to see how profoundly Alma has impacted the people around her, leaving a lasting impression even on those she only briefly met. Her vision, spirit and leadership have profoundly changed many around her and will continue to be a source of inspiration for many years to come. More

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Research areaThe YREB covers Shanghai, Jiangsu, Zhejiang, Anhui, Jiangxi, Hubei, Hunan, Chongqing, Sichuan, Guizhou, and Yunnan. It includes the Yangtze River Delta urban agglomerations (YRDUA), Yangtze River midstream urban agglomeration (YRMUA), and Chengdu-Chongqing urban agglomeration (CCUA). With a regional area of 2.05 million km2, the YREB runs through the eastern, central and western regions in China32. In 2019, the total GDP of YREB is 45.8 trillion yuan, accounting for 46.2% of the national GDP. The YREB plays a pivotal strategic support and leading role in the overall situation of stable economic growth in China33. At the same time, the contradiction between the shortage of land resources and economic growth in the YREB is very prominent. Therefore, this paper selects 107 cities in YREB as the research sample. The specific geographic locations are shown in Fig. 2. This article uses ARCGIS 10.2 version to draw the map. The URL link is http://demo.domain.com:6080/arcgis/services.Figure 2The geographic location of the YREB in China.Full size imageResearch methodsGlobal Malmquist–Luenberger indexUGLUE refers to the effective utilization degree of land elements under certain input of other elements. The green utilization of urban land mainly comes from three aspects: first, improve the utilization intensity of the existing actual input land, that is, increase the input intensity of other elements of the unit land area. Second, reduce the input of land in the production process to avoid excessive waste of land. Third, promote the optimal allocation of land elements among production units. Technical efficiency refers to the maximum degree that all factor inputs need to expand or shrink in equal proportion when all production units reach the production frontier. However, for production units with high technical efficiency, the factor allocation structure may not be reasonable. The land factors may still have the problem of under-input or over-input, resulting in the reduction of UGLUE.Pastor and Lovell34 proposed a global index, which uses all the inspection periods of each decision-making unit as a benchmark to construct the production frontier. According to the current benchmark construction period t, the production possibility set reference set is defined as follows:$$P_{C}^{t} (x^{t} ) = left{ {left. {(y^{t} ,b^{t} )} right|x^{t} {kern 1pt} can{kern 1pt} , produce{kern 1pt} , b^{t} ,y^{t} } right}$$
(1)
The global benchmark is defined as: (P_{G} = P_{C}^{1} , cup ,P_{C}^{2} , cup , cdots ,P_{C}^{t}), The subscripts C and G represent the current benchmark and the global benchmark respectively. The ML index of decision-making unit i is calculated based on the current reference benchmark:$$ML^{S} (x^{t} ,y^{t} ,b^{t} ,x^{t + 1} ,y^{t + 1} ,b^{t + 1} ) = frac{{1 + D_{C}^{S} (x^{t} ,y^{t} ,b^{t} )}}{{1 + D_{C}^{S} (x^{t + 1} ,y^{t + 1} ,b^{t + 1} )}}$$
(2)
Among them, the superscript S indicates two adjacent periods, t period and t + 1 period. The subscript C indicates the current benchmark, which is a simplified directional distance function. (ML^{s} > 1), indicates that the productivity increases. (ML^{s} < 1), indicates that the productivity decreases.According to Hofmann et al.35, the GMLI is defined as follows:$$GMLI^{t,t + 1} (x^{t} ,y^{t} ,b^{t} ,x^{t + 1} ,y^{t + 1} ,b^{t + 1} ) = frac{{1 + D_{G}^{T} (x^{t} ,y^{t} ,b^{t} )}}{{1 + D_{G}^{T} (x^{t + 1} ,y^{t + 1} ,b^{t + 1} )}}$$ (3) Among them, (D_{G}^{T} (x,y,b) = max left{ {alpha |(y - alpha y,b - alpha b) in P_{G} (x)} right}). (GMLI^{t,t + 1} > 1) indicates that the productivity has increased. (GMLI^{t,t + 1} < 1) indicates that the productivity decreases. The GMLI is further broken down as follows:$$begin{aligned} & GMLI^{t,t + 1} (x^{t} ,y^{t} ,b^{t} ,x^{t + 1} ,y^{t + 1} ,b^{t + 1} ) = frac{{1 + D_{G}^{T} (x^{t} ,y^{t} ,b^{t} )}}{{1 + D_{G}^{T} (x^{t + 1} ,y^{t + 1} ,b^{t + 1} )}} \ & quad = frac{{1 + D_{G}^{t} (x^{t} ,y^{t} ,b^{t} )}}{{1 + D_{G}^{t + 1} (x^{t + 1} ,y^{t + 1} ,b^{t + 1} )}} times left[ {frac{{(1 + D_{G}^{T} (x^{t} ,y^{t} ,b^{t} ))/(1 + D_{C}^{T} (x^{t} ,y^{t} ,b^{t} ))}}{{(1 + D_{G}^{T} (x^{t + 1} ,y^{t + 1} ,b^{t + 1} ))/(1 + D_{C}^{T} (x^{t + 1} ,y^{t + 1} ,b^{t + 1} ))}}} right] \ & quad = frac{{TE^{t + 1} }}{{TE^{t} }} times left( {frac{{BPG_{t + 1}^{t + 1} }}{{BPG_{t}^{t + 1} }}} right) = EC_{t}^{t + 1} times BPC_{t}^{t + 1} \ end{aligned}$$ (4) Among them, TE is the change of technological progress. EC is the change of technological efficiency. The change of technological progress reflects the change of the highest technical level. The improvement of the highest technical level often requires the introduction and innovation of advanced technology, which often requires a large amount of investment. The change of technical efficiency reflects the gap with the highest technical level. Narrowing the gap with the highest technical level often requires improvements in internal management and governance structures. (BPG_{t}^{t + 1}) is the “best practitioner gap” between the current period and overall technological frontier. (BPC_{t}^{t + 1}) measures the changes in the “best practitioner gap” between two periods (technological changes). (BPC_{t}^{t + 1} , > , 1 ,) indicates technological progress. (BPC_{t}^{t + 1} < 1) indicates technology regress.Econometric techniques of industrial agglomeration on UGLUEIn recent years, many scholars used the traditional SPM for empirical analysis, which is a basic measurement model suitable for panel data. Therefore, this article firstly uses the traditional SPM to analyze the impact of industrial agglomeration on UGLUE. The formula is:$$begin{aligned} ln UGLUE_{it} & = alpha_{0} + alpha_{1} ln RZI_{it} + alpha_{2} ln RZI_{it} *ln RZI_{it} + alpha_{3} ln RDI_{it} + alpha_{4} ln EC_{it} \ & quad + alpha_{5} ln GDP_{it} + alpha_{6} ln TEC_{it} + alpha_{7} ln ROAD_{it} + alpha_{8} ln GOV_{it} + varepsilon_{it} \ end{aligned}$$ (5) Among them, ε is the disturbance term. i represents the city, i in this paper involves 107 cities in YREB. t represents the time, and the range of t in this paper is from 2007 to 2016. UGLUE is the explained variable, which represents the UGLUE. RZI and RDI are explanatory variables, representing industrial specialization agglomeration and industrial diversification agglomeration. EC is the industrial structure. GDP is the level of economic development. TEC is the level of technology. ROAD is the level of infrastructure. GOV is the degree of government intervention. (alpha_{1}) to (alpha_{8}) is the coefficient of each variable.Formula (5) assumes that the UGLUE changes with the changes of various influencing factors in the current period. That is, there is no time lag effect. But in reality, land use often has a time lag effect. The previous level has a non-negligible impact on the current results. Therefore, this paper selects the dynamic panel model for empirical analysis. However, there is often a two-way causal relationship between industrial agglomeration and UGLUE, which may cause endogenous bias. For example, cities with higher UGLUE levels tend to have higher levels of economic development, which promotes industrial agglomeration in this city. Therefore, this paper adopts the method of system GMM for regression analysis of dynamic panel model. Compared with mixed OLS, system GMM can make full use of sample information, select appropriate lag terms as instrumental variables36. It can effectively solve the endogeneity problem between industrial agglomeration and UGLUE. Based on the above analysis, this paper introduces the first-order lag term of UGLUE on the basis of formula (5). The DPM is as follows:$$begin{aligned} ln UGLUE_{it} & = beta_{0} + tau ln UGLUE_{i(t - 1)} + beta_{1} ln RZI_{it} + beta_{2} ln RZI_{it} times ln RZI_{it} + beta_{3} ln RDI_{it} \ & quad + beta_{4} ln EC_{it} + beta_{5} ln GDP_{it} + beta_{6} ln TEC_{it} + beta_{7} ln ROAD_{it} + beta_{8} ln GOV_{it} + varepsilon_{{{text{it}}}} \ end{aligned}$$ (6) Among them, (tau) is the first-order lag coefficient of UGLUE, reflecting the time lag effect of UGLUE.Variable descriptionExplained variableThe GMLI is used to measure the UGLUE of 107 cities in YREB. According to existing research37, the following core evaluation index of UGLUE are selected (see Table 1). Regarding input indicators, we mainly choose land element input M, labor element input L, and capital element input K as input indicators. Regarding output indicators, we choose the added value of the secondary and tertiary industries in the municipal area as the expected output, and use the GDP deflator to convert it into a comparable value. At the same time, pollution indicators such as industrial wastewater emissions, industrial sulfur dioxide emissions, and industrial smoke (dust) emissions are selected as undesired output. Since the GMLI reflects the growth rate of UGLUE, this paper assumes that the GMLI in 2006 is 1, and then multiplies the calculated GMLI year by year to obtain the development level of UGLUE in each city from 2007 to 2016.Table 1 Input and output index.Full size tableExplanatory variablesIndustrial specialization index ZI is usually used to measure the specialization level of urban industries. The specialization index is represented by the share of the employment of the industry in the total employment of the city:$$ZI_{i} = Max_{j} (S_{ij} )$$ (7) Nextly, we use the relative specialization index to make a horizontal comparison of the specialization level between different cities:$$RZI_{i} = Max(S_{ij} /S_{j} )$$ (8) The most common measure of the level of industrial diversification is the Herfindahl–Hirschman Index (HHI). For city i, the HHI is the sum of the square sum of employment shares of all industries in the city. The diversification index is the reciprocal of the HHI:$$DZ_{i} = frac{1}{{sumlimits_{j} {S_{ij}^{2} } }}$$ (9) The expression of relative diversification index is as follows:$$RDI_{i} = {1 mathord{left/ {vphantom {1 {sumlimits_{j} {left| {S_{ij} - S_{j} } right|} }}} right. kern-0pt} {sumlimits_{j} {left| {S_{ij} - S_{j} } right|} }}$$ (10) Among them, Sij is the employment proportion of j industry in city i, and Sj is the proportion of the total employment of the national j industry. The greater value of RZI and RDI, the higher level of industrial specialization and diversification.Control variablesRegarding control variables, we choose the following variables as control variables.Industrial structure (EC): The continuous optimization of industrial structure promotes the improvement of UGLUE through three aspects: saving land, increasing land income and promoting the optimal allocation of land resources. This paper selects the added value of the tertiary industry as a percentage of GDP (take the logarithm) to express.Technological level (TEC): The higher the technological innovation level of a city is, the more it promotes the use of input elements and the transformation of innovation results, thereby improving the UGLUE. This paper selects the proportion of science and technology expenditure to fiscal expenditure (take the logarithm) to represent.Economic development level (GDP): The continuous economic development promote the rational allocation of various production factors and increase the level of urban land output, thereby improving the UGLUE. This paper selects GDP per capita (take the logarithm) to express.Road infrastructure level (ROAD): The continuous improvement of infrastructure reduces transportation costs and transaction costs, and promotes communication externalities between producers, consumers, and between producers and consumers. This paper selects the average road area per capita (take the logarithm) to express.Government behavior (GOV): Fiscal expenditure is an important means for the government to carry out macro-control. Appropriate fiscal expenditure makes up for market shortages, improves factor flow and resource allocation efficiency, and realizes positive economic externalities. This paper selects the proportion of fiscal expenditure to GDP (take the logarithm) to express. We can see the meaning of specific variables from Table 2.Table 2 The descriptive statistics of variables.Full size tableData sourceThe object of this thesis is the 107 cities in YREB from 2007 to 2016. The urban construction land area data comes from the "China Urban Construction Statistical Yearbook", and the rest of the index data all come from the "China City Statistical Yearbook". The URL link is https://www.cnki.net/. 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