**Acknowledgement**

The authors thank Professors S. E. Donnelly, Drs. N. M. Boag, S. Simpson (Manchester, U.K.) for very critical discussions on electron microscopy, X-ray diffraction and chirality, and for using the High Resolution Electron Microscope (Joel T 3010) and the X-ray diffraction equipments. Professor K. Stadtlander (Iserlohn, Germany) for lending us the Olympus light microscope and the video camera. Financial support from the Biomaterials Project (Bruxelles, Grant BMH4-CT-96-0013), Chemical-Biotechnological Laboratories Inc (Iserlohn) and from Orthomol (Langenfeld, Germany) are gratefully acknowledged.


**4. Conclusions**

In this study, we screened mutant strains with photosensitivity using the negative phototaxis of the *H. pluvialis* in microfluidic device. It was confirmed that the more sensitive the mutant, the higher the astaxanthin productivity. In addition, large-scale cultivation in 100 L photo-bioreactor shows that the strains selected through phototaxis technology can be used in a more scaled-up culture for commercialization. We are planning follow-up studies to screen more cells faster and more efficiently. This screening strategy using the phototaxis of microalgae is advantageous in securement of *H. pluvialis* with improved growth rate and astaxanthin productivity.

**Author Contributions:** Conceptualization, K.H.L. and Y.C.; data curation, K.H.L., Y.C. and J.H.L.; funding acquisition, H.S.K.; methodology, K.H.L., Y.C. and J.H.L.; project administration, C.P., H.Y.Y. and H.S.K.; software, K.H.L., Y.C. and J.H.L.; supervision, C.P., H.Y.Y. and H.S.K.; writing—original draft, K.H.L. and Y.C.; writing—review and editing, C.P., H.Y.Y. and H.S.K. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean Government (NRF-2019R1F1A1058873).

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

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.

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


**7. 11-hydroxysteroid dehydrogenase in the adrenal gland - expression profile under conditions of testosterone withdrawal**

As mentioned above, the enzyme11-HSD catalyzes the interconversion of glucocorticoids to inert metabolites in man and rodents and plays a crucial role in regulating the action of corticosteroids. Inhibition of 11-HSD allows access of cortisol or corticosterone to the mineralcorticoid receptors where they act as mineralcorticoids [148]. Northern blot analyses revealed expression of mRNAs encoding both 11-HSD1 and 11-HSD2 in the whole rat

adrenal gland. *In situ* hybridization of rat adrenal cortex and medulla demonstrated specific localization of 11 HSD1 mRNA predominantly to the cells at the corticomedullary junction, within the inner cortex, suggesting that the oxoreductase enzyme may serve to maintain high medullary glucocorticoid concentrations required for catecholamine biosynthesis. In contrast, 11-HSD2 mRNA was more uniformly distributed in the cortex and was low/absent in the medulla [149, 150]. The expression of 11-HSD2 has been demonstrated in rat adrenal gland by immunohistochemical and molecular analyses and the 11-HSD2 antigen was confined to the zona fasciculata and zona reticularis, but not in the zona glomerulosa or medulla [149-151]. The ubiquitous presence of 11-HSD2 in sodiumtransporting epithelia revealed that mineralcorticosteroid action is facilitated by this enzyme which metabolizes glucocorticoids and allows aldosterone to bind to the nonselective mineralcorticoid receptor [151].

Hydrohysteroid Dehydrogenases – Biological Role and Clinical Importance – Review 133

**Figure 7.** 11-HSD2 immunoreactivity in rat adrenal gland zones. 35 days after EDS (a, b); 7 days after EDS (c, d); 21 days after EDS (e, f). 11-HSD2- immunoreactivity in the zona fasciculata (ZF) and zona

**localization/distribution and relation to Polycistic Ovaries Syndrome and**

Glucocorticoids exert their effects in all parts of the body and they are involved in a number of physiological processes, including female reproduction. The ovary is also affected by the glucocorticoids and it is well known that the reproductive function may be impaired in cases of adrenal hyperactivity. The ovaries express glucocorticoid receptors and one of the prominent glucocorticoids affecting ovarian function is the cortisol [157]. Ovaries lack the necessary enzymes for cortisol synthesis and cortisol is not produced *de novo* [158] but it was delivered by the circulation. The 11β-HSD enzymes play a crucial role in controlling the

reticularis (ZR), and the adipocytes of adrenal capsula adipose (A). Less sensitive were the adrenocorticocytes of zona glomerulosa (ZG). No positive signals in the medulla (M). x 200.

**8. 11β –hydroxysteroid dehydrogenase in the ovary – cellular**

**obesity in women**

Using EDS experimental model in adult rats [152] we found that the dynamic of 11-HSD2 expression correlated with the changes of serum T levels following the exposure after EDS [153]. The lowest 11-HSD2 staining intensity was found 7 days after EDS followed by progressive increase in the immunoreactivity on day 14 and 21 after EDS [152]. Moreover, the restoration of 11-HSD2 activity on day 14 after EDS corresponded with unchanged glandular and serum corticosterone levels in treated rats on day 15 reported by Plecas et al. [154]. Enzymatic assays on tissue homogenates showed extensive conversion of corticosterone to its 11-dehydro product in an NAD+-dependent manner in adrenal gland [151]. Using enzymehistochemistry a strong reduction was found in the activity of NADH2 cytochrome-C-reductase that is involved in NAD+-synthesis as a cofactor in the adrenal gland after EDS treatment of adult rats [155]. Immunohistochemical analysis revealed that the 11-HSD2 expression pattern in adrenal gland of EDS treated rats [152] is very similar to the enzymehistochemical profile of NADH2- cytochrome-C-reductase [155], supporting the view that 11-HSD2 acts as high-affinity NAD+-dependent dehydrogenase in the rat adrenal gland [151]. On the other hand, the increase in the expression of 11β-HSD2 in rat adrenal gland on day 14 after EDS treatment [152] coincided with the appearance of the repopulation of testosterone-producing Leydig cells in the testis [135]. These data suggested a possible role of the gonadal steroids, especially of testosterone, as modulators of the adrenal gland functional activity and they are consistent with previously reported results related to the direct impact of testosterone on the key steps in the adrenal gland steroidogenesis [156]. The above mentioned findings characterized 11β-HSD2 (high-affinity NAD+- dependent unidirectional dehydrogenase) as a potential target of testosterone action in rat adrenal cortex. Our data from EDS experimental model provided new evidence for expression of 11-HSD2 in the adrenal gland under conditions of testosterone withdrawal. The EDS results bring additional elucidation on the functional significance of 11β-HSD system in rat adrenal gland and the regulatory role of testosterone in its activity [152]. Together with our previous studies [135,153], these data suggested the relationship between 11-HSD2 expression in adrenal gland and kinetics of restoration of testosterone production during renewal of testicular adult LCs population after EDS treatment. (Figure 7)

mineralcorticoid receptor [151].

adrenal gland. *In situ* hybridization of rat adrenal cortex and medulla demonstrated specific localization of 11 HSD1 mRNA predominantly to the cells at the corticomedullary junction, within the inner cortex, suggesting that the oxoreductase enzyme may serve to maintain high medullary glucocorticoid concentrations required for catecholamine biosynthesis. In contrast, 11-HSD2 mRNA was more uniformly distributed in the cortex and was low/absent in the medulla [149, 150]. The expression of 11-HSD2 has been demonstrated in rat adrenal gland by immunohistochemical and molecular analyses and the 11-HSD2 antigen was confined to the zona fasciculata and zona reticularis, but not in the zona glomerulosa or medulla [149-151]. The ubiquitous presence of 11-HSD2 in sodiumtransporting epithelia revealed that mineralcorticosteroid action is facilitated by this enzyme which metabolizes glucocorticoids and allows aldosterone to bind to the nonselective

Using EDS experimental model in adult rats [152] we found that the dynamic of 11-HSD2 expression correlated with the changes of serum T levels following the exposure after EDS [153]. The lowest 11-HSD2 staining intensity was found 7 days after EDS followed by progressive increase in the immunoreactivity on day 14 and 21 after EDS [152]. Moreover, the restoration of 11-HSD2 activity on day 14 after EDS corresponded with unchanged glandular and serum corticosterone levels in treated rats on day 15 reported by Plecas et al. [154]. Enzymatic assays on tissue homogenates showed extensive conversion of corticosterone to its 11-dehydro product in an NAD+-dependent manner in adrenal gland [151]. Using enzymehistochemistry a strong reduction was found in the activity of NADH2 cytochrome-C-reductase that is involved in NAD+-synthesis as a cofactor in the adrenal gland after EDS treatment of adult rats [155]. Immunohistochemical analysis revealed that the 11-HSD2 expression pattern in adrenal gland of EDS treated rats [152] is very similar to the enzymehistochemical profile of NADH2- cytochrome-C-reductase [155], supporting the view that 11-HSD2 acts as high-affinity NAD+-dependent dehydrogenase in the rat adrenal gland [151]. On the other hand, the increase in the expression of 11β-HSD2 in rat adrenal gland on day 14 after EDS treatment [152] coincided with the appearance of the repopulation of testosterone-producing Leydig cells in the testis [135]. These data suggested a possible role of the gonadal steroids, especially of testosterone, as modulators of the adrenal gland functional activity and they are consistent with previously reported results related to the direct impact of testosterone on the key steps in the adrenal gland steroidogenesis [156]. The above mentioned findings characterized 11β-HSD2 (high-affinity NAD+- dependent unidirectional dehydrogenase) as a potential target of testosterone action in rat adrenal cortex. Our data from EDS experimental model provided new evidence for expression of 11-HSD2 in the adrenal gland under conditions of testosterone withdrawal. The EDS results bring additional elucidation on the functional significance of 11β-HSD system in rat adrenal gland and the regulatory role of testosterone in its activity [152]. Together with our previous studies [135,153], these data suggested the relationship between 11-HSD2 expression in adrenal gland and kinetics of restoration of testosterone production

during renewal of testicular adult LCs population after EDS treatment. (Figure 7)

**Figure 7.** 11-HSD2 immunoreactivity in rat adrenal gland zones. 35 days after EDS (a, b); 7 days after EDS (c, d); 21 days after EDS (e, f). 11-HSD2- immunoreactivity in the zona fasciculata (ZF) and zona reticularis (ZR), and the adipocytes of adrenal capsula adipose (A). Less sensitive were the adrenocorticocytes of zona glomerulosa (ZG). No positive signals in the medulla (M). x 200.


**2.2. A default mode of brain function**

"Whilst part of what we perceive comes through our senses from the object before us, another part (and it may be the larger part) always comes out of our own head." William James (1890)

In classical studies of brain function, the main accepted model is based in task-evoked responses. In general, the used experiments encourage a reflexive view of how the brain works, ignoring that brain functions may be mainly intrinsic, connecting by themselves information and processing it to respond to environmental demands. By carefully analyzing the allocation of the brain's energy resources, Raichel [4] argues that the essence of brain function is indeed mainly intrinsic and components of signal transduction and metabolic pathways are all in a continuous state of flux.

Consider this functional aspect of human brain, described in Raichel's research [3, 4]:

*"In the mid-1990s we noticed quite by accident that, surprisingly, certain brain regions experienced a decreased level of activity from the baseline resting state when subjects carried out some task. These areas—in particular, a section of the medial parietal cortex (a region near the middle of the brain involved with remembering personal events in one's life, among other things)—registered this drop when other areas were engaged in carrying out a defied task such as reading aloud. Befuddled, we labeled the area showing the most depression MMPA, for "medial mystery parietal area. This cuing among the visual and auditory parts of the cortex, for instance—probably ensures that all regions of the brain are ready to react in concert to stimuli. Further analyses indicated that performing a particular task increases the brain's energy consumption by less than 5 percent of the underlying baseline activity. A large fraction of the overall activity—from 60 to 80 percent of all energy used by the brain—occurs in circuits unrelated to any external event."*

According to [3, 4], the human brain has a default mode of function controlled by a default mode network (DMN) which serves as a master organizer of its dark energy. The DMN is thought to behave like an orchestra conductor, issuing timing signals, much as a conductor waves a baton, to coordinate activity among different brain regions. This orchestrated way of doing things is described in a neat story in [4] where during a quite beach afternoon a placid tourist does daydreaming watching nowhere. In his lap rests a magazine that he's been reading for a while, suddenly a weird looking insect lands in its naked leg, firing a cascade of stimulus. The point is that during the following chains of events, where the human tries to get rid of a potential danger, the brain in fact consumes less energy during daydreaming. Raichel found that the default mode network burns energy and maintains the control of the whole body, while many other powerful neural processors (vision, sense of touch, etc.) return to the borderline of activity and keep on burning energy, ready to actuate.

The lesson about this biological brain story is that to survive in a complex physical world, our robots and robot controllers should have a safe default mode that keeps itself in charge, burns energy, preserves the mechanical structure in a safe condition, and is ready to evocate other behaviors under stimuli.


*3.3. Unsupervised Machine Learning Models*

Unsupervised machine learning is a major part of machine learning. It can study the intrinsic relationship of datasets without data labels when dealing with practical problems. The main applications of unsupervised learning are: segmenting a dataset by some shared attributes; detecting exceptions that are not suitable for any group; and simplifying datasets by aggregating variables with similar properties. Among these, the objective of this study is to explore the susceptibility of submarine landslides. As a consequence, an important unsupervised machine learning class named clustering, which contains k-means, spectral, and hierarchical clustering, was selected as the study method. Each clustering method was built after parameter selection with internal validation measures: the Calinski–Harabasz index [22], silhouette index [23], and Davies–Bouldin index [24]. The more precise the clustering result, the higher the Calinski–Harabasz score and silhouette index. The lower

the Davies–Bouldin index, the more accurate the clustering result. The clustering results' performances were validated with external validation measures; for instance, liquefaction distribution, hydrodynamic action, slope angle, etc.

#### 3.3.1. k-Means

The k-means clustering algorithm is a typical unsupervised machine learning model that is widely used for the clustering analysis [25] of non-labeled data. The advantage of k-means is that it is easy to implement and visualize the result. The number of clusters is the only parameter that needs to be specified beforehand. To build a k-means model:

$$dist(\mathbf{x}, \boldsymbol{\mu}) = \sqrt{\sum\_{i=1}^{n} (\mathbf{x}\_i - \boldsymbol{\mu}\_i)^2}$$

where *x* is the sample point; *μ* is the center of mass of the cluster; *n* is the number of features in each sample point; and *i* is each feature of the constituent point *x*.

(d) Summarize the total distances of all clusters:

$$\begin{array}{c} \text{Clustering } \text{ Sum } \ of \ \text{Square(CSS)} = \sum\_{j=0}^{m} \sum\_{i=0}^{n} \left( \boldsymbol{x}\_{i} - \boldsymbol{\mu}\_{i} \right)^{2} \\ \text{Total } \ \text{Clustering } \ \text{Sum } \ of \ \text{Square} = \sum\_{l=1}^{k} \text{CSS}\_{l} \end{array}$$

where *m* is the number of samples in a cluster and *j* is the number of each sample.

#### 3.3.2. Spectral Clustering

Spectral clustering is another unsupervised machine learning model, which clusters through the characteristic vector of the Laplacian matrix of sample data. Spectral clustering maps data from a high-dimensional space to low-dimensional space, and then, uses other clustering algorithms to cluster in a low-dimensional space. Compared with k-means, spectral clustering uses a dimension reduction algorithm, which is more suitable for highdimensional data processing and more effective for sparse data processing. Spectral clustering outputs clusters *A*1, *A*2, ... , *An* by inputting *n* sample points *X* = {*x*1, *x*2, ..., *xn*} and the number of clusters k. In this model, the kernel function parameter and cluster number are the influential parameters. The specific steps are below:

(a) Calculate the similarity matrix *W* of *n* ∗ *n*, which includes the minimum proximity method, k-proximity method, and full-connection method. The full-connection method used in this study is as described:

$$s\_{ij} = s(\mathfrak{x}\_{i\prime}\mathfrak{x}\_{j}) = \sum\_{i=1, j=1}^{n} \exp\frac{-\left\|\mathfrak{x}\_{i} - \mathfrak{x}\_{j}\right\|^{2}}{2\sigma}$$

where *sij* = the similarity matrix and *σ* = kernel function parameter, which controls the neighborhood width of the sample point.

(b) Calculation matrix *D*:

$$d\_i = \sum\_{j=1}^{n} w\_{ij}$$

where *D* is the *n* ∗ *n* diagonal matrix formed with *di*.

#### 3.3.3. Hierarchical Clustering

Hierarchical clustering is another unsupervised algorithm that is based on hierarchical methods. When using hierarchical clustering, each object is regarded as a cluster, and then, the clusters are merged step by step according to some rules so that the number of cluster classes is reached. The advantages are: the similarity of distance and rule is easy to define and is limited, and the hierarchical relationship of classes can be found and can be clustered into other shapes. Meanwhile, the disadvantages are: the computational complexity is too high, a singular value can also have a great influence, and the algorithm is likely to cluster into chains. To build a hierarchical clustering model:

The effective parameters are cluster number, linkage, and affinity, of which linkage contains Ward, average, and complete; and affinity contains Euclidean, Manhattan, and cosine. Ward can only be combined with Euclidean when averaged, which provides the best performance but a large computation, and can be combined with Manhattan and cosine. We should study the parameters and validate the result before building the final hierarchical clustering model.


**4. Inducing the chaos theory to explain behavioral phenomena**

Generally, many organizations use ICT to improve their competitive advantage so that this could transform their organizational efficiency, productivity, and effectiveness. From another point of view, they intend to use ICT to change their social and corporate environments [39]. However, if they cannot manage their ICT correctly, they are shadowed by the adverse effects due to their low use of it [40]. This paper recalls the implementation of a new ICT system that consisted of

complex and collaborative relationships. This implementation led to stress for the users as they could not cope with their organization's demand that they use the new ICT system. Brod [35] introduced technostress as an illness resulting from a person's inability to adapt to new computer technology. It is typified by over-identification or computer anxiety. Ragu-Nathan et al. [11] described technostress as a problem because the users could not overcome the difficulties with the new ICT system, or they could not become familiar with the new system. Technostress can affect the individual's orientation regarding the time he/she spends doing something, his/her communication mode, and his/her interpersonal relationships as well as his/her job outcomes, i.e., performance or satisfaction.

To explain this phenomenon, researchers into information systems conduct studies in various disciplines, including psychology, sociology, philosophy, and organizational studies. These disciplines explain the stress phenomenon as a source of contextual paradigms, and previous researchers often used the person-environment fit model to describe technostress [5, 39, 40]. This theory stated that when the relationship between people and their environment is beyond the equilibrium condition, it will create stress [41], i.e., technostress. This theory also portraits technostress as a linear system, while the interaction between humans and technology (i.e. computers) is problematic for the development of information systems.

This paper argues that ICT users have specific conditions with which they can interpret and understand the environmental conditions through their capabilities. ICT users' power triggers them to find various and complex responses. Thus, chaos can be an ally or a desired quality when integrated into an organizational system, especially when the ICT users try to innovate and develop [4]. This theory showed that the users' chaotic cognition triggers the relationship of their stressed transactions. ICT users, furthermore, must have strategies to deal with the chaos. Coping is a thing that individuals do, which sometimes allows them to solve problems and adapt to changes.

The inducement of the chaos theory in explaining the ICT users' behavior is not deniable. The authors demonstrate the chaotic behavior from two sides, which are complex interactions and the collaboration of the ICT system's elements [5–18], and both defacement [22] and iconoclastic methods [23]. These two sides affect ICT users' behavior when they have to face the technostress's creators. By these means, these sides influence the ICT users' performances and satisfaction when they are in a chaotic situation. Although the ICT users could mitigate this chaos, they may choose to face it, depending on how mature their personalities are. In other words, the ICT users have to cope with the complicated uncertainty or technostress creators by relying upon their personalities and emotions to overcome the chaotic problems.

This paper supports the undeniable inducement of the chaos theory to explain the ICT users' mitigation of the harmful effects of technostress. It argues that the technostress's creators at first settled on the ICT users' cognitive states. In other words, the ICT users got their experiential values, which are enjoyment, escapism, visual appeal, social affiliation, and entertainment, when they faced situations with technostress. From the perspective of learning, the authors propose that chaos theory relates to the ICT users' learning processes [63]. We take into account that chaotic mitigation affects the ICT users and may prevent them from dealing with the technostress efficiently and effectively. We recommend that information systems or applications must be developed with consideration given to facilitating the ICT users' experiential values. It means that the information systems and applications make the ICT users increase their enjoyment, entertainment, social affiliation and visual appeal as well as decreasing their escapism. The authors argue that technostress for ICT users would otherwise have occurred.


*4.2. Chemicals and Reagents*

Ech A was received as a gift from G.B. Elyakov, Pacific Institute of Bioorganic Chemistry, Vladivostok, Russia. PPI, SP600125, SB203580, LY294002, and CAL-101 were purchased from Selleck Chemicals (Houston, TX, USA). NAC was obtained from Sigma-Aldrich (St. Louis, MO, USA).

#### *4.3. Immunophenotypic Analysis by Flow Cytometry*

PBMCs, PB-CD34+ cells, and BM-CD34+ cells were tested for cell surface antigen expression by immunofluorescence and flow cytometric analysis. Cells were harvested and rinsed with PBS, following which CD34-PE (Miltenyi Biotec, #130-113-179, 1:50), CD38-FITC (Miltenyi Biotec, #130-113-426, 1:50), CD45RA-APC (Miltenyi Biotec, #130-117-742, 1:50), and 7-AAD (eBioscience, Waltham, MA, USA, 3 μL/105 cells) were added. Thereafter, cells were incubated at 4 ◦C for 10 min in the dark. Finally, the stained cells were analyzed using a BD AccuriTM C6 (BD Biosciences).


*Article* **The Impact of Proximity to Wet Markets and Supermarkets on Household Dietary Diversity in Nanjing City, China**

**Taiyang Zhong 1,\*, Zhenzhong Si 2, Jonathan Crush 2, Zhiying Xu 1, Xianjin Huang 1,\*, Steffanie Scott 3, Shuangshuang Tang 1 and Xiang Zhang 1**

Received: 21 March 2018; Accepted: 11 April 2018; Published: 8 May 2018

**Abstract:** This study investigated the influence of the proximity to wet markets and supermarkets on urban household dietary diversity in Nanjing. Based on the data collected through a citywide survey in 2015 and the map data of wet markets and supermarkets, the Poisson regression model was deployed to examine the correlations between geographical proximity to supermarkets and wet markets and household dietary diversity. The result shows that the coefficients for the distance to the nearest wet market are not statistically significant. Although the coefficients for the distance to nearest supermarket are statistically significant, they were too minor to reach a practical importance. We argue, however, that the insignificant correlations reflect exactly the high physical accessibility to food outlets and the extensive spatially dense food supply network constituted by wet markets, supermarkets and small food stores in Nanjing, due in part to the food infrastructure development planning in Nanjing that has ensured relatively equal and convenient access to wet markets or supermarkets for all households. Our findings are verified by the survey data that more than 90% of households purchased fresh food items within their neighborhoods or in walking distance. In addition to the densely distributed food outlets, various other factors contributed to the non-significant influence of the distance to the nearest wet market and supermarket, in particular, the numerous small food stores within or close to residential communities, the prevalence of three-generation extended household structure and the high household income.

**Keywords:** proximity to food outlets; dietary diversity; food access; food security; food environment; food geographies

#### **1. Introduction**

The relationship between household dietary diversity and access to food markets was investigated in several studies. Some suggest that an increase in distance to food markets may decrease dietary diversity and increase food insecurity [1–3]. Restricted access to supermarkets, in particular, can reduce healthy food consumption [4]. In contrast, other studies maintain that there is no causal linkage between access to food markets and dietary diversity. A study in Michigan, USA, for example, found that physical distance to food outlets providing healthy food did not significantly influence fruit and vegetable consumption [5]. Another study suggests that it is the price of food in supermarkets, rather

than the physical distance to market, that most influences the consumption of fruits and vegetables [6]. This implies that the direct cost of food is a much more important factor than indirect factors such as physical distance and travel cost.

Another group of researchers suggest that the potential impact of distance on dietary diversity is mediated by other factors. Even when supermarkets are physically present in low-income urban areas, for example, this does not necessarily improve dietary diversity since they tend to carry a less healthy and diverse range of foods [7]. A study in the US found that an increase in the distance to a supermarket decreases the odds of fruit and vegetable consumption in metropolitan areas but has no impact in non-metropolitan areas [4]. An analysis of data from 21 different African countries found that distance to the nearest road (and therefore transaction costs for food purchase) had a significantly negative impact on fruit and vegetable consumption, but no significant effect on animal source food consumption [8]. The impact of improved locational access to food markets also tends to vary with household income with low-income households benefitting more than wealthier groups [9]. Thus, while distance to food outlets does seem to be an important variable in household food consumption, a consensus has yet to be reached on its influence on household food security [10].

There is a widespread assumption that the one-stop shopping associated with supermarkets is more convenient than multi-stop shopping and therefore more attractive to consumers. However, in practice, food shopping practices are complex and there are interactions and integration between different market outlets [11]. Despite the proliferation of large supermarkets and hypermarkets, the multi-stop shopping model still prevails in much of Asia [12]. Chinese consumers value the freshness of food and prefer to buy small amounts of fresh vegetables on a daily basis rather than storing vegetables for a longer period [13]. The main advantages of wet markets over supermarkets is the freshness and affordability of food, regardless of supermarket penetration [13,14]. Food purchasing is also shaped by the practice of shopping for different foods at different outlets (i.e., cross-platform shopping); for instance, buying perishable food in traditional wet markets and processed food in supermarkets. Multi-stop shopping at different forms of retail outlet means that dietary diversity and household food security cannot be seen as the outcome of distance to a single food purchasing location.

Previous studies have focused on the impact of proximity to supermarkets on food security and have neglected the influence of proximity to wet markets. Moreover, most studies of food security in China have focused on national or regional-level food supply with few studies paying attention to household-level food security in urban areas. Quantitative analysis of the relationship between physical access to food outlets and household dietary diversity of China is absent. To bridge this gap, this study examines the relationship between proximity to wet markets and supermarkets and urban household dietary diversity.

#### **2. Wet Markets and Supermarkets in Nanjing**

Despite the proliferation of supermarket chains since the 1990s, wet markets remain the most prevalent food outlet in urban China. They specialize principally in fresh vegetables, fruit, livestock products, aquatic products (such as live fish and shrimp), poultry products, and staple foods (such as rice and other grains and flours). The Chinese government launched a program in the early 2000s, known as *nong gai chao* in Chinese, to convert wet markets into supermarkets in many large cities [15,16]. However, this project failed in many cities including in Nanjing [13] and wet markets remain dominant in fresh food retailing [13,17]. In the city of Dalian, in northeast China, wet markets are the main fresh food source for almost half (49%) of urban households [18]. In Shanghai, they are the source of fresh meat and vegetables for 76% and 59% of households, respectively [19]. Wet markets carry a variety of fresh foods at low cost, providing a price advantage over supermarkets [13].

The study area is Nanjing, the capital city of Jiangsu province located in East China. It has a population of 8.33 million by the end of 2017 [20]. In Nanjing, wet markets have conventionally been the dominant outlet for fresh, unprocessed food. There were 351 wet markets in Nanjing in 2015, equating to about one wet market per 19,100 people on average (excluding rural households) or one

per 23,464 people (including rural households). The overall density of wet markets is one per 2.1 km2. In contrast, there are 63 chain supermarkets in Nanjing, operated by eight companies. The major chains include two Chinese chains, Suguo (38 supermarkets) and BHG (8), and two foreign-owned chains, Carrefour (5) and Wal-Mart (5). Figure 1 shows the location of wet markets and supermarkets across Nanjing's 11 districts.

**Figure 1.** Location of Wet Markets and Supermarkets in Nanjing City. Source: Data from BaiduMap (map.baidu.com).

Nanjing's upgraded wet markets are somewhat different from traditional Chinese wet markets. Traditionally, wet markets were housed in temporary sheds or in the open air. Most wet markets in Nanjing are now housed in permanent buildings and stalls selling meat are usually equipped with refrigeration facilities [13]. Nanjing has had no open air wet market or wet markets in temporary sheds since the end of 2014 [21]. The space of a wet market is usually divided into small stalls which are rented and operated by private individual food vendors.

Wet markets in Nanjing fall under a two-tier management system. The first tier is the Nanjing Municipal Government which owns the city's wet markets and regulates their distribution [22]. The municipal government has supervised the establishment of wet markets to ensure a spatially even distribution by enacting several specific regulations. We elaborate on this policy context in Section 5.1. The second management tier means that wet markets are operated and managed either by state-owned or private companies or offices (hereafter, the management body). The management body is selected by the district-level governments and is responsible for the safety and sanitation of the wet market, stall lease management, facility maintenance and food safety monitoring [22]. The vendors renting the

stalls in wet markets buy food from wholesale markets, distribution centers or other sources and pay a stall rent and fee to the management body.

Supermarkets are another important food source for households in Nanjing. Table 1 shows the distribution of wet markets and supermarkets in each of the 11 districts. It demonstrates that, with only 63 supermarkets selling vegetables and fruit, the number of supermarkets is much smaller than the number of wet markets in every district. Unlike wet markets, there is no statutory requirement for supermarket development by the size of population of an area. The variation of population per wet market across the districts is much smaller than that of supermarket.

**Table 1.** Wet Markets, Supermarkets and Population in Nanjing.

Source: Population data from [23].

#### **3. Methodology**

#### *3.1. Household Dietary Diversity*

The household data used in this paper are extracted from the urban household baseline food security survey in Nanjing conducted in July 2015, and funded by the Hungry City Partnership. The total sample size was 1210 households, randomly selected from 972 urban communities in all 11 districts of Nanjing. The survey was conducted by undergraduate and graduate student enumerators from Nanjing University using digital surveys on android tablets. The data were then uploaded and synthesized on the online Ona database. Household dietary diversity was measured by the Household Dietary Diversity Score (HDDS) [24]. HDDS is a widely used indicator developed by the Food and Nutrition Technical Assistance (FANTA) project [25]. Various studies have proved its relevance and significance in measuring per capita energy consumption. We recognize the critiques on the indicator's accuracy to measure the quality of food access [26], yet it is still a valid indicator to reflect the diversity of household food intake. Food items consumed in the 24 h prior to the survey were grouped into the following 12 food groups: (a) cereals; (b) roots and tubers; (c) vegetables; (d) fruit; (e) meat, poultry and offal; (f) eggs; (g) fish and seafood; (h) pulses, legumes and nuts; (i) milk and milk products; (j) oil and fats; (k) sugar and honey; and (l) other foods. The HDDS is calculated from the number of food groups eaten from and ranges in value from 0 to 12, where the higher the score the greater the diversity in the household diet.

Tables 2 and 3 show the statistical summary of the HDDS in Nanjing and household food consumption by food groups. The dietary diversity of Nanjing households is relatively high with a mean HDDS of 7.83. Some 60% of households scored between 7 and 12 (i.e., eating foodstuffs from between 7 and 12 of the food groups). Over 80% had a score of 6 or more and only 17% had a score of 5 or less. By way of comparison, the mean HDDS of other cities in the Hungry Cities Partnership project was significantly lower (Table 3).

There were notable differences in the frequency of consumption of different food groups (Table 4). Cereals (including wheat, rice and other grains) ranked first with about 98% of households consuming cereals. The vegetable and fruit groups ranked second and third, respectively, with percentages of about 97% and 80%. The roots and tubers group ranked lowest with a proportion of about 34%, slightly lower than fish and seafood at 37%.

**Table 2.** Frequency Distribution of HDDS in Nanjing.

**Table 3.** Comparison of HDDS Scores in HCP Cities.

**Table 4.** Frequency Distribution of Consumption of Food Groups.

Wet markets and supermarkets are the two most frequently used food sources in Nanjing [27]. Almost 93% and 87% of households buy food from wet markets and supermarkets, respectively (Table 5). However, there is a notable difference between the purchasing frequencies at wet markets and supermarkets. About 70% of households use wet markets at least five days a week, while the number for supermarkets is only about 17%.

**Table 5.** Frequency of Patronage of Wet Markets and Supermarkets.

#### *3.2. Network Distance to Food Markets*

Household locations were collected by the enumerators using android tablets with built-in GPS, with a positioning accuracy of 15 m. The location data of wet markets and supermarkets were calculated from the BaiduMap (map.baidu.com)—the most widely used online map service in China. Other outlets, such as small stores and mobile vendors, were not included in the analysis because of the logistical difficulty of plotting their GPS locations. Because the GPS in tablets is based on the WGS84 coordinate system but the BaiduMap uses the BD09 coordinate system where obfuscation were added based the WGS84 coordinate system [28], the GPS coordinates of the households' location were converted into BD09 coordinates before analysis.

The network distance from households to wet markets and supermarkets was calculated using Route Matrix API v2.0 Beta of BaiduMap which is an API (Application Programming Interface) providing the service for map distance calculation, developed by BaiduMap [29], where the travel distance between origins and destinations are calculated by summing the distance between multiple points based on road network [30]. The Route Matrix API v2.0 Beta provides three transport modes for network distance calculation: walking, by car and by bicycle. This study chose the pedestrian mode because walking and bicycling are the two principal transport modes for food shopping and, of these, walking is the most important. The survey found that about 90% of households bought their primary food within walking distance of their homes more than five times a week. The percentage of respondents who bought fresh vegetable, fruit and pork within walking distance were 93% (*N* = 988), 92% (*N* = 974) and 92% (*N* = 957), respectively. Another survey conducted in Nanjing in 2012 found that 61%, 20% and 17% of elderly Nanjing residents went shopping by walking, bicycle (including electric bicycle), and public transportation, respectively [31]. The figures for young adults were 40%, 37%, and 11% for young adults. We then calculated the walking distance from each household residence to the nearest wet market and the nearest supermarket.

#### *3.3. Dependent and Independent Variables*

Table 6 presents the definitions, expected signs, and summary statistics of variables used in this paper. The HDDS was used as the dependent variable. The primary factors seen as potentially influencing household dietary diversity were as follows:

#### *3.4. Regression Model*

A Poisson model was used in this study to investigate the influence of physical access to food stores on household dietary diversity. The value of the dependent variable *HDDS* varies from 1 to 12, which is a count variable. The value of *HDDS* is assumed to have a Poisson distribution with expectation *μ*, for independent variables *Xi*, the Poisson regression model for expected counts can be specified as an exponential function [38]. For the dependent variable *HDDS*, the Poisson regression model is as follows:

$$\mu\_i = E(HDDS\_i | X\_i) = \exp(\beta\_0 + \beta\_i X\_i)^2$$

where *HDDS* is the HDDS of household *i*, *Xi* refers to the vector of independent variables, and *β*0 and *βi* are the constant and the coefficient vector for independent variables, respectively. The alternative log–linear model can be written as:

$$\ln(\mu\_i) = \exp(\beta\_0 + \beta\_i X\_i)$$

**Table**

Both the variable *HHS* and the dummy variable set including *HHS2*–*HHS9* are used to reflect the size of a household, in terms of continuous and discrete numbers, respectively. Because of the one-child policy enforced in China between 1979 and 2015, nuclear households generally have a small household size. Thus, it is not reasonable for the model to include both the variable *HHS* and the dummy variable for household size. Households with more than 4 or 5 persons are also usually extended households. The independent variable *HHS* representing household size therefore reflects almost the same information as the dummy variable *EXTD* when the value of variable *HHS* is more than 4 or 5 persons, which makes it inappropriate to include both the variable *HHS* and the dummy variable *EXTD* in the analysis. Therefore, this study considered three different models including different sets of independent variables reflecting household size (variable *HHS* or dummy variables *HHS2*–*HHS9*) and household type (dummy variable *EXTD*) (see Model I, Model II and Model III in Table 7).

**Table 7.** Estimated Results of Poisson Model for Household Dietary Diversity.

Note: \* denotes significant at 1%-level, \*\* significant at 5%-level, and \*\*\* significant at 10%-level.

To investigate the relationship between dietary diversity and proximity to the nearest supermarket or wet market, the variable *DTSM* and *DTWM* in Model I, Model II and Model III were replaced by the variable *DTNM*. As a result, Model IV, Model V and Model VI were generated and calculated, i.e., Model IV was built from the Model I by replacing the variable *DTSM* and *DTWM* with variable *DTNM*, and the same holds for Model V and Model VI. For the estimated results for Model IV, Model V and Model VI, see Table 7.

#### **4. Models of Dietary Diversity**

The models with different sets of independent variables are presented in Table 7. The correlation coefficient between the variable *DTWM* and *DTSM* is 0.2038 (significant at 1% level, *N* = 1180), the collinearity diagnostics results indicated that the issue of multicollinearity can be ignored. To mitigate or avoid the possible problems due to a relatively large number of dummy variables being used, three groups of models with different number of dummy variables were estimated, and the three groups included Model I and Model IV, Model III and Model VI, and Model II and Model V, respectively. Model III includes those variables reflecting household structure and excluding those reflecting household size. Models I and II include those variables reflecting household size and excludes those reflecting household structure. Model I uses the variable *HHS* to measure household size rather than the set of dummy variables *HHS2–HHS9*, while Model II used the set of dummy variable *HHS2–HHS9* rather than the variable *HHS.* The three models perform satisfactorily in terms of goodness of fit. All six models are significant at the 1%-level. The signs for all the explanatory variables are consistent with expectations.

Table 6 presents the value of AIC and BIC. Smaller AIC and BIC indicate better models [38]. Model I has the smallest values of both AIC and BIC, suggesting that it is statistically superior to the other five models. As there are no major differences in AIC and BIC in the six models, the estimated results of the other five models are also worthy of being analyzed as they include different variables from Model I.

The results of this analysis indicate that physical access to wet markets is not a predictor of household dietary diversity in Nanjing. The signs of the estimated coefficients for the variable *DTWM* are consistent with expectation, but the estimated coefficients are statistically insignificant, which suggests that the distance to the nearest wet market is not a determinant of HDDS. However, the suppression effect caused by a "third variable" (*X*2, suppressor) could render the relationship between independent variable (*X*1) and dependent variable (*Y*) insignificant [39], smaller, or of opposite sign [40,41]. Households that are farthest from wet markets could have decreased odds of buying food from wet markets but increased probability of buying food from small food stores, so that purchase of food from small food stores could be a suppressor (the "third variable" *X*2). Thus, a new variable *SFSA* was generated, which refers to whether households buy food in small food stores. Following the testing procedure developed by Wen and Ye [42], the possible mediation and suppression effects of the variable *SFSA* were tested. The results indicate that there are no mediation or suppression effects for the variables *DTWM* and *SFSA*. This confirms that distance to wet markets is not a predictor or determinant of urban household dietary diversity in Nanjing.

The estimation results also suggest that physical access to supermarkets has a limited influence on household dietary diversity. The estimated coefficients of the variable *DTSM* of Models I, II and III are all statistically significant at 5% level and the signs of the estimated coefficients are consistent with expectations. However, all the coefficients of the variable *DTSM* in Models I, II and III are quite small (Table 7). The factor by which the expected count changes can be calculated as Exp(*β*) for a unit change in the explanatory variable, keeping other independent variables constant [43]. According to the estimated coefficients in Models I, II and III, for a unit increase of 100 m in the variable *DTSM* (distance to the nearest supermarket), the expected value of a household's HDDS decreases by a factor of 0.9994 or 0.1 percent, which is a very small magnitude of change. Even for an increase of 10 units (1000 m) in the variable *DTSM*, the expected value of a household's HDDS decreases by a factor of only 0.9934, or less than 1%. The test results also indicate that there is no mediation and suppression effect for the variable *DTSM* and variable *SFSA*. Therefore, the influence of the proximity to a supermarket on HDDS is also nearly negligible, regardless of the statistical significance of the estimated coefficients.

The estimation coefficients for the variable *DTNM* also indicate that proximity to the nearest wet market or supermarket is not a predictor of household dietary diversity. The signs of the estimated coefficients for the variable *DTNM* are consistent with expectations, but the estimated coefficients for the variable *DTNM* of Model IV, Model V and Model VI are statistically insignificant. The test of the mediation and suppression effects indicates that there are no effects for the variable *DTNM* and variable *SFSA* which suggests that physical access to wet markets or supermarkets is not a determinant of household dietary diversity. Thus, information regarding the estimated coefficients of the variable *DTWM*, *DTSM* and *DTNM* indicates that proximity to wet markets and supermarkets is not a predictor or determinant of urban household dietary diversity. In other words, the difference in the distance to wet markets or supermarkets makes no difference to urban household dietary diversity in Nanjing.

#### **5. Implications for Dietary Diversity**

#### *5.1. Wet Market Planning Policies*

The insignificant statistical correlation between the distance to the nearest market and household dietary diversity in Nanjing does not necessarily mean that proximity to food outlets is not important for residents' access to diverse food items. It is therefore important to understand the underlying reasons for the insignificant correlation. The most important reason is that the food infrastructure development planning in Nanjing has led to relatively equal and convenient access to wet markets or supermarkets for all households. This relates to the "mayor responsible for vegetable basket" system launched by the Chinese central government in 1988.

The system makes mayors responsible for promoting the production of and securing the supply of non-grain food [44]. The mandatory system has ensured an extensive food supply network in Nanjing, and is the foundation for the high level of physical accessibility to food. Accessibility was further enhanced by the Development Plan for Vegetable Basket Project (2008–2012) issued by the Nanjing Municipal Government in 2008, which specifies that the construction of wet markets should be strengthened [45].

Food infrastructure, and particularly the development of wet markets, has been a requirement for the development of new residential communities in Nanjing since the early 2000s. In 2003, the Nanjing Municipal Government issued regulations on wet market planning and construction, which specified that each newly-developed residential community with a construction area over 50,000 square meters should construct a new wet market with an area no less than 1000 square meters [46,47]. In 2004, the Commodity Network Plan of Nanjing City planned to have a wet market with a service radius of 500–1000 m for every 30,000 residents [48]. In 2011, the Nanjing Municipal Government updated these standards and required a wet market with an area of no less than 2000 square meters and a service radius of 500 m for every 25,000 residents; and a wet market with an area no less than 1500 square meters for each town with a population larger than 20,000 [49]. According to the Plan of Commercial Network in Nanjing (2015–2030) for Public Consultation, more than 200 new wet markets will be established in Nanjing by 2030 [50]. Besides these food infrastructure planning policies, the Nanjing Municipal Government has implemented the policy of "fresh produce zones" in supermarkets. In 2011, Nanjing Municipal Government issued a policy document which required that no less than 20% of existing supermarkets' area and 30% for newly-opened supermarkets should be used for fresh produce retail [49].

The implementation of these policies regarding food market development and planning means that there is relatively easy access to wet markets and supermarkets in Nanjing. About 26%, 56%, 74% and 80% of the interviewed households had a network distance to the nearest wet market or supermarket of less than 0.5 km, 1.0 km, 1.5 km and 2.0 km, respectively (Table 8). Assuming a median walking speed for an adult of 4.5 km/h or 1.25 m/s [51], and a 15-min walk as the commonly accepted walking time in food studies [36,52], then anything up to about 1.1 km is an acceptable walking distance.

About 58% of the surveyed households' walking distance to the nearest wet market or supermarket was less than 1.1 km (Table 8). The average distance to the nearest wet market or supermarket was 1.2 km for those households that reported buying vegetables, fruits and meat from wet markets or supermarkets. Cycling is also a popular transportation mode in Nanjing. An average speed by bicycle of 6.05 km/h [53] would mean about 1.5 km for a 15-min or 2.0 km for a 20-min ride by bicycle. About 74% and 80% of the surveyed households had a cycling distance to the nearest wet market or supermarket of less than 1.5 km and 2.0 km, respectively.

**Table 8.** Distance from Households to the Nearest Wet Markets or Supermarkets.

#### *5.2. Offsetting Effect of Small Food Stores*

Another factor that contributes to the high level of physical access to food in Nanjing could be the many small food stores, including small shops and *xiao mai bu* (family run small stores, which are often a window on the wall facing the main street, selling processed food, condiments, cigarettes and other small commercial goods), located within or near residential communities. The survey in 2015 shows that 35% of surveyed households buy food from small food stores and that 26% do so at least five days a week or once a week. Unfortunately, the massive number of small food stores in Nanjing make it nearly impossible to comprehensively geocode them. However, we should not ignore the important role of small food stores in household food accessibility.

There probably has been an offsetting effect of small food stores in ensuring food diversity for households who live relatively far away from wet markets and supermarkets. A study in New Orleans found that other types of stores did offset the relative lack of supermarkets for snack foods but not fresh produce [54]. The offsetting effect could also be true in Nanjing. As small food stores are close to residential communities, they could contribute to household dietary diversity in relatively underserved areas. This is a reasonable conclusion given that individual small food stores provide more than seven of the types of food included in the HDDS indicator. Additionally, the common clustering of small food stores further enhances the diversity of their supply. Unlike small stores in the US where food is more expensive compared to supermarkets and large grocery stores [36], supermarkets in China have no price advantage over wet markets [13]. The primary reason is that the labor cost and food waste of supermarkets is higher than that of wet markets, and, in most cases, wholesale markets are the main supplier for both supermarkets and wet markets, although some supermarkets have their own source of fresh produce or suppliers [13]. As the small shops can also obtain vegetables directly from peri-urban small-scale producers at lower costs than that from wholesale markets, wet markets have no price advantage over small-scale stores [13].

#### *5.3. Local Food Purchasing Behavior*

The high level of physical accessibility to food outlets in Nanjing is mirrored in the high proportion of households buying food in their neighborhood or within walking distance. According to the Hungry Cities Food Purchases Matrix used in the survey [55], more than 90% of households said they normally buy most fresh food items within their neighborhoods or within walking distance (Table 9). Specifically, 92–93% of households buy their fresh vegetables, fruit and pork in their neighborhoods or within walking distance. A slightly lower percentage buy fresh animal products in their neighborhoods or within walking distance: 89% for eggs, 88% for fresh shellfish, 86% for fresh lamb and 73% for milk. Table 9 shows that more than 90% of surveyed households bought their main food items in their neighborhoods or within walking distance. In contrast, only 58% of households were within easy walking distance (up to 1.1 km) of their nearest wet market or supermarket. The difference between 90% and 58% is 32%, which is offset by the presence of small food stores. This is further evidence that small-scale food stores contribute to access to food within neighborhoods, in addition to their offsetting effects where households are relatively far away from wet markets and supermarkets.

**Table 9.** Location of Food Outlets Where Fresh Food Items Normally Purchased.

Note: "Within" refers to within walking distance, "beyond" refers to beyond walking distance.

#### *5.4. Household Demographic Factors and Dietary Diversity*

This study also examined the impacts of other factors (including household size, structure and income) on dietary diversity. The estimation results indicate that, unlike distances to food outlets, household size, structure, and income all significantly influence household dietary diversity (Table 7). Those coefficients of variables in Model I and V are statistically significant and consistent with expectations, including the variables *HHS*, *HHS2*–*HHS6*, *EXTD*, *HHIM* and *HHIH*. The coefficients for the variable *HHS* in Models I and V were 0.0307 and 0.0306, respectively. For an increase in household size by one, a household's mean HDDS increases by a factor of 1.03% or by 3.10%. This is also a small change considering that the mean HDDS is 7.83. The coefficients for variables *HHS2*, *HHS3*, *HHS4*, *HHS5* and *HHS6* are statistically significant in Models I and V. There are similar coefficient values for variables *HHS2*, *HHS3*, *HHS4*, *HHS5* and *HHS6* in Models I and V. The coefficients for variable *HHS7*, *HHS8* and *HHS9* are not statistically significant in both models. This indicates that those households with 2–6 members have a higher HDDS than one-person households. Compared with the reference category of households with one person, multi-person households have an expected HDDS value increase of 17% (2 persons), 20% (3), 21% (4), 31% (5) and 23% (6) (based on the estimated coefficients in Model I). The variable *HHS5* has the highest coefficient among variables *HHS2*–*HHS6*. Due to the one-child policy, a five-person household usually means a household with one child, parents and grandparents (which is also categorized as an extended household). The coefficients for the variable *EXTD* were 0.0825 and 0.0845 in Model III and Model VI, respectively. Being an extended household increases the value of HDDS by about 9% (8.6% and 8.8% for Model III and Model VI, respectively). This indicates that household size and household structure have a moderate impact on household dietary diversity.

Extended households are relatively common in Nanjing, making up just over one quarter of all the surveyed households (households were sampled in the daytime when it was more likely that retired people were home, but employed people were not). The relatively high percentage of extended households diminishes the sensitivity of household dietary diversity to physical access to wet markets and supermarkets. Another study has indicated that household structure plays an important role in Chinese family-based food consumption [35]. Dual-career families (where both husband and wife work) are common in China. This means that it is the grandparents in extended households who buy the food and do most of the cooking and other domestic work [35]. In addition to extended households with three generations living in one dwelling, it is also common for grandparents to live in different dwellings within a short distance from the household of their adult children and grandchild and are commonly involved in the food purchasing and preparation for their children's household [35]. As retired grandparents in extended households have more flexibility in terms of time and food purchase location, they are less sensitive to the shopping distance than young family members who devote most of their time to work. As a result, support from grandparents could make

the HDDS of extended households and some nuclear households less sensitive to the distance to wet markets and supermarkets than households without the support of grandparents.

The estimation results also suggest that some characteristics of household heads are predictors of household dietary diversity. The coefficients for the variable *HHE* and *HHM* are significantly negative. Being a household with an unmarried household head decreases the expected HDDS by 18%, compared with other households.  However, the coefficients for the variable *HHA* (household head age) are not statistically significant, and neither are the coefficients for the variable *HHG* (household head gender) except in Model V. This is consistent with previous studies about household dietary diversity in China [1].

#### *5.5. Household Income and Dietary Diversity*

The significant positive coefficients for the variables reflecting household income and housing type (*HHIM*, *HHIH* and *HOUSE*) indicate that income is an important determinant of urban household dietary diversity. An increase in household income contributes to an increase in dietary diversity. Middle- and high-income households have a higher HDDS than low-income households. Being a middle-income household increases the expected HDDS by about 13% compared to a low-income household (mean of 13.1%, 11.4%, 13%, 13.9%, 12.2% and 13.9% for Model I, Model II, Model III, Model IV, Model V and Model VI, respectively). Being a high-income household increases the value of HDDS by about 11%.

In Nanjing, three-quarters of households live in apartments, with only a small proportion (2.4%) of wealthier households living in houses. The significant positive coefficients of the variable *HOUSE* suggest that households living in houses have higher dietary diversity. This is reflected in the 21% increase of HDDS of households living in houses, compared to low-income households. Other studies indicate that an increase in household income increases a household's economic access to food [56].

Increased income could contribute to dietary diversity by improving a household's transport facilities and food-preserving facilities. Electric bicycles, for example, are a faster and more expensive vehicle than traditional bicycles (priced about 10 times higher). The speed limit of an electric bicycle is 20 km/h, which means that the travel distance of ten minutes by electric bicycle is about 3 km. Our spatial analysis found that more than 90% of households had a network distance to the nearest wet market or supermarket of less than 3 km. The high-level of food accessibility is further enhanced by the increasing popularity of private cars in Nanjing. On average, there were 59.7 electric bicycles and 40.4 private cars per 100 urban households in 2015 [23]. In 2012, 10% of young adults and 1% of the elderly in Nanjing shopped for food by car [31]. The prevalence of refrigerators may also contribute to dietary diversity. In 2016, there were 102.4 and 109.5 refrigerators per 100 urban and rural households, respectively [23].

#### *5.6. Urban Agriculture and Dietary Diversity*

Although the estimated coefficients for the variable *CROPPING* are positive and the signs are consistent with expectation, the coefficients are statistically insignificant. This indicates that whether households grow their own food or not does not significantly influence dietary diversity. This is simply because urban farming in Nanjing has very limited access to land and thus is unable to produce a significant quantity of food. Moreover, even in the peri-urban or rural areas, the variety of produce is constrained by the size of farms and seasonality, which does not contribute to household dietary diversity.

#### **6. Conclusions**

This paper shows that, in contrast to studies in other contexts where proximity to food stores is one of the determinants of household dietary diversity [1,57], the distance from the household home to the nearest wet market or supermarket has no significant impact on household dietary diversity in Nanjing. The coefficients for the distance to the nearest wet market are not statistically significant. The coefficients for distance to the nearest supermarket are of statistical significance but no economic significance or practical significance, as the very small coefficients indicate that distance to the nearest supermarket has no noticeable impact on household dietary diversity. However, these results do not necessarily indicate that the distance to food outlets is not important for household dietary diversity in other contexts. Indeed, the high level of food accessibility, thanks to the spatially dense food supply network in Nanjing, diminishes the correlation between distance and dietary diversity. Small food stores, together with wet markets and supermarkets, have created a favorable food environment in terms of physical access to food, which in turn leads to a non-significant relationship between the proximity to wet markets or supermarkets and household dietary diversity. The spatial distribution of wet markets, supermarkets and small-scale food stores constitute a favorable food environment in term of geographic access to food, which results in a relatively equal geographical access to food outlets. Such access decouples any linkage between the proximity to wet markets or supermarkets and household dietary diversity.

The study also found that various factors contribute to the non-significant influence of distance to the nearest wet market and supermarket. These include relatively high accessibility to food outlets, the prevalence of three-generation extended household structure, and household income. Extended households with three generations are less sensitive to the distance to wet markets and supermarkets because the grandparents who conduct most food purchasing and cooking in the households are more flexible in terms of time and food purchase location. In addition, higher household income and better transport and the popularity of refrigerators all contribute to the insignificance of the proximity to wet markets or supermarkets in determining urban household dietary diversity.

The implications of this study for food system planning in terms of urban land use governance are twofold. First, it is important to achieve high access to food by allowing and encouraging mixed land use for food outlets within or close to residential communities. Most wet markets and supermarkets in Nanjing are located close to residential communities, and small food stores are even located within residential communities. The policies that encourage mixed land use for food outlets have greatly enhanced residents' physical accessibility to food outlets. This is not only the situation in Nanjing but also common in other Chinese cities. Second, it is important to include wet markets in urban infrastructure planning systems, and making the construction of wet markets a requirement for the development plan of new residential areas can be an effective tool to improve and secure physical access to food outlets.

**Acknowledgments:** This research was funded by the Hungry Cities Partnership project supported by the Social Sciences and Humanities Research Council of Canada and the International Development Research Centre through the International Partnerships for Sustainable Societies (IPaSS) Program.

**Author Contributions:** Taiyang Zhong, Zhenzhong Si, Jonathan Crush and Xianjin Huang designed the research. Taiyang Zhong, Zhenzhong Si, Jonathan Crush, Zhiying Xu, Xianjin Huang, Steffanie Scott, Shuangshuang Tang and Xiang Zhang wrote the paper.

**Conflicts of Interest:** The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.

#### **References**

© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).


**Remodelling of Skeletal Muscle Extracellular Matrix: Effect of Unloading and Reloading**

Eva-Maria Riso, Priit Kaasik and Teet Seene

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/62295

#### **Abstract**

This chapter summarizes studies that examine remodelling of extracellular matrix (ECM) and role of regulatory factors of ECM during unloading and reloading. Hypokinesia has a catabolic effect on both the contractile apparatus and ECM of the skeletal muscle, causing the formation of muscle atrophy, the decrease of the synthesis of contractile proteins and disturbance of the collagen metabolism. The metabolism of fibrillar and non-fibrillar collagens in ECM plays a crucial role in exercise and sport, influencing the strength development through transmission of contractile force in skeletal muscle. The impair‐ ment of motor activity and muscle strength is accompanied by the muscle atrophy. The muscle atrophy caused by inactivity and recovery from atrophy demonstrates the plasticity of muscle. Muscle mass and volume increase in a relatively short time, but the recovery of strength takes much longer and is related with the regeneration of the muscle structures. The recovery period of the contractile apparatus and ECM structures is different in slow-and fast-twitch skeletal muscle.

Although the muscle tissue's response to inactivity is more pronounced than the response of ECM, important changes occur in the connective tissue structures during unloading, causing the impairment of the functional characteristics of the skeletal muscle.

**Keywords:** ECM, regulatory factors of ECM, unloading, reloading, functional charac‐ teristics of skeletal muscle


**2. Anatomy and etiopathogenesis**

with functional disability (**Figure 1A,B**).In puppies, the surgical correction must be performed between 1 and 3 months of age, and not later than 3 months [2]. At this age, the skeletal deformities can be reversed after alignment of the limb. Patellar luxation may occur at birth, during growth or at a later stage. The pathophysiology of congenital luxation remains a topic of discussion [3–5]; a consequence of complex skeletal abnormalities that alter the limb alignment is considered [5]. The condition may be unilateral or bilateral and can be asymptomatic, and most cases are medial [3, 5]. Occasionally, the luxation can occurin both directions in the same joint [3], which is a surgical challenge. It is possible to find medial patellar luxation in one joint and lateral luxation in another joint in the same dog. Some cases exhibit patellar subluxation. Although much less frequently than in dogs, patellar dislocation also occurs in

**Figure 1.** (A) Abnormal position associated with bilateral grade IV medial patellar luxation in puppies 60 and (B) 90 days old; (C) ventrodorsal radiograph, showing bilateral medial luxation of the patella, and angular deformities in a 90-day-old poodle; (D) ventrodorsal radiograph, showing bilateral lateral luxation of the patella in a 9-month-old dog

The incidence of severe articular lesions found during routine surgeries in small-, medium- or large-breed dogs presenting with patellar luxation is high. In some cases, the patella injury is so serious that correction is not possible or prudent. In these instances, a better treatment option may be prosthetic replacement. Patellectomy is suggested as a treatment alternative in some severe cases [5, 6], but the removal of the patella does not correct the skeletal changes [6], calling

According to the literature [5], surgery might not be necessary in cases without clinical manifestations or when lameness is mild; however, even under such circumstances, the joint

Patellar luxation is a degenerative condition, and surgical treatment should be performed as early as possible, while clinical signs are mild or even before they appear. The aims of this

breed Sharpei; (E) abnormal stance associated with bilateral grade IV lateral patellar.

this therapeutic option into question.

damage is irreversible.

cats [3].

160 Canine Medicine - Recent Topics and Advanced Research

The patella is an oval-shaped sesamoid bone that is located in the quadriceps tendon [7, 8] and connected to the fabella by a thin band of loose connective tissue known as the medial and lateral patellofemoral ligament [3, 7, 8]. The tendon located between the distal aspect of the patella and the tuberosity of the tibia is known as the patellar ligament [3]. The patella acts as a lever arm favouring the extension of the quadriceps [3], and the patellofemoral joint increases the mechanical efficiency of the quadriceps mechanism [8]. The correct alignment of the quadriceps femoris, patella, trochlea, patellar ligament and tibial tuberosity prevents patellar luxation or subluxation [8]. An adequate supply of articular cartilage depends on a normal joint between femoral trochlea and patella. Patellar luxation causes articular cartilage degeneration [3].

The pathogenesis of developmental patellar luxation remains speculative and controversial [3, 5, 9]. Based on the thesis by Putnam [10], abnormal femoral and neck angles of inclination and anteversion are proposed to influence development and eventual malformation of the pelvic limb. However, this has not been proven [11].

The III and IV degrees of luxation are associated with severe angular and rotational deformities of the femur and tibia [8], which are more pronounced in medial luxation; the limb has an Sshaped conformation (**Figure 1B, C**). Some cases are difficult to treat, with poorer prognosis. Corrective osteotomy must be considered in these cases to restore the normal alignment of the quadriceps complex [5, 6]. It is sometimes necessary to also remove up to 1.0 cm of bone extension to return the patella to the femoral trochlea. In these cases, patella tibial tuberosity alignment is achieved but paw rotation remains. Therefore, early intervention should be considered in cases of patellar luxation independent of the existence of clinical signs.

Clinical evidence shows that intermittent patellar luxation, contrary to the logical assumption, produces varying degrees of injury to the articular surfaces of the patella and the femoral condyle, causing degeneration of articular cartilage. This may also result in a flattening of the femoral condyle and consequently facilitate dislocation [8]. The position of the patella relative to the femoral trochlea, more proximal, is considered by some authors [8, 13, 14] to be an important factor in canine patellar luxation.

Although seldom discussed in the literature, subluxation also occurs and is observed in clinical practice. The affected dogs are typically adults presenting with pain and lameness due to the wear on the patella [8]. This condition is difficult to diagnose, especially in heavy English Bulldogs; a careful physical examination is necessary. Other findings, including cruciate ligament rupture and even the presence of intra-articular loose bodies and flap formation, can be seen in cases of chronic patellar dislocation.

Medial patellar dislocation is responsible for approximately 80% [5] of cases, with bilateral involvement in up to 50–65% [5, 14]. Lameness is one of the presenting clinical signs; however, it is subjective; lameness is seen in other conditions that affect the hind limb, which should be included in the differential diagnosis [6, 15]. Medial luxation is considered characteristic of small breeds, but it has been reported with increasing frequency in large and giant breeds [5].

Although many dogs present at 6–12 months of age, some animals with mild lameness present later, with rupture of the cranial cruciate ligament or pain caused by osteoarthritis [8]. Often, the patellar luxation is not considered to be an important joint disorder until it becomes severe. In puppies, the situation is even more critical. Post-operative studies show that it is possible to correct the limb deformities and achieve complete reversal of skeletal changes if the surgery is performed before 60 days of age [2].

Lateral luxation also occurs in small dogs; the skeletal deformities are the reverse of those seen with medial luxation (**Figure 1D, E**), demonstrating an increased angle of anteversion, coxa valga, medial torsion of the distal femur, lateral condylar dysplasia, lateral rotation of the tibia and external rotation of the paw [16, 17]. The articular cartilage injuries in both types of luxation result in osteoarthritis [16, 18], which is usually mild-to-moderate and unrelated to the degree of luxation or lameness [19]. Lateral luxation of the patella may cause injury at the origin of the long digital extensor tendon, leading to rupture of the tendon in severe cases.


*2.1. Laser Setup and Material*

Figure 1a shows the experimental laser setup schematically. It consists of a pulsed Yb:YAG disk laser source (TruMicro 5050 of Trumpf GmbH, Ditzingen, Germany) emitting a laser beam with a wavelength of 1030 nm, maximum pulse frequency of 400 kHz, pulse energies up to 125 μJ and a fixed pulse duration of 6.7 ps. The fluence profile of the focal laser spot is nearly Gaussian (*M*2 < 1.3). The polarization of the laser beam exiting the laser head is linear. Besides exposing the material to linear laser polarization, also a quarter wave plate was included in the setup to achieve circular polarization, which may lead to triangular shaped LIPSS textures. The beam was focused on the surface of the samples, using a telecentric F*θ* lens (Ronar of Linos GmbH, Göttingen, Germany) with a focal length of 80 mm, resulting in a focal spot with an *e*−2-diameter of *d* = 33.6 ± 1.6 μm (see Section 2.2.1).

The samples consists of polished CoCrMo discs with a diameter of 25 mm and a thickness of 3 mm. The surface roughness (*R*a = 0.003 ± 0.0003 μm, Rq = 0.004 ± 0.0004 μm) of the discs was measured with an atomic force microscope (NX10, Park Systems Corp., Suwon, South Korea). manufacturer's headquarters. The beam was scanned over the substrate using a galvoscanner (intelliSCAN14 of ScanLab GmbH, Puchheim, Germany) at normal incidence in air, see Figure 1b. Different shapes and sizes of LIPSS were produced by adjusting the type of polarization (linear or circular) and by adjusting the laser peak fluence (*F*0) and the number of overscans of the laser spot over the surface (*N*OS). The scan velocity of the laser spot (*v*), the laser pulse frequency (*f*F) and the spatial pitch between laser pulses on the surface (Δ*x*, Δ*y*) were kept constant in this study at *v* = 2 m/s, *f*F = 1000 Hz and Δ*x* = Δ*y* = 5 μm, respectively, see Figure 1b. This yields a geometrical pulse-to-pulse overlap (OL) in both, *x*- and *y*-direction, of *OL* = 1 − *v*/(*d* · *f*F) ≈ 0.85. All samples were cleaned in an ultrasonic bath with ethanol for 20 min and dried in ambient air after laser treatment. Table 1 shows the chemical composition of the samples.

**Table 1.** Cobalt–Chrome–Molybdenum (CoCrMo) alloy composition in weight percent, the composition is balanced (Bal.) with Cobalt.

**Figure 1.** Schematic representations of the laser setup and the scanning trajectory of the laser spot. (**a**) Schematic representation of the laser setup; *λ*/2: half-wave plate; BSC: beam splitter cube; BD: beam dump; *λ*/4: quarter-wave plate (optional). (**b**) Scanning trajectory of the laser spot; *f*F: pulse frequency; *v*: scan velocity; *d*: beam diameter; *OL*: geometrical pulse-to-pulse overlap; *N*OS: number of overscans; Δ*x*, Δ*y*: geometrical pitch between subsequent laser pulses in *x*- and *y*-direction.

#### *2.2. Analysis Tools*

#### 2.2.1. Laser Beam Characterization

The laser power was measured using an thermopile power sensor (PM30 of Coherent, Santa Clara, CA, USA) with a measurement uncertainty of ±1%, connected to a readout unit (FieldMaxII-TOP of Coherent, Santa Clara, CA, USA). The focal spot diameter 33.6 ± 1.6 μm (*e*−2) was measured using a laser beam characterization device (MicroSpotMonitor of Primes GmbH, Pfungstadt, Germany).

## 2.2.2. Surface Morphology Dimensions

Laser-induced surface structures were analyzed using a scanning electron microscope (JSM-7200F, Jeol, Tokyo, Japan). From SEM micrographs, the periodicity of LIPSS areas were analyzed with the help of a 2D fast Fourier transform (FFT) algorithm using a MATLAB [19] script. Details of the script are reported in our earlier work [20].

The roughness of the surface textures was analyzed by means of an atomic force microscopy (NX10, Park Systems Corp., Suwon, South Korea) in true non-contact™ mode using a non-contact cantilever (PPP-NCHR, 125 × 30 × 4 μm, Tip < 10 nm, Park Systems Corp., Suwon, South Korea). The roughness parameters that were extracted from these measurements are used to characterize the surface by means of root mean square surface area roughness (*R*q), average surface area roughness (*R*a), maximum peak height (*R*p), maximum valley depth (*R*v), skewness (*R*sk), kurtosis (*R*ku) and the ratio between the real surface area and the projected area (*σ*).