**13. Conclusions and recommendations**

**Name of local diet as consumed/ snacks/drinks**

82 Antioxidant-Antidiabetic Agents and Human Health

Boiled yam with fish stew

Cassava flour with vegetable soup

Yam flour with vegetables stew

Boiled beans and boiled plantain with beef/fish stew

Fermented cassava/*C. gigantea* blend

> Fermented maize/ *C. gigantea*

*Fermented sorghum/C. gigantea*

**Description of diet**

Boiled yam (*D. rotundata*) consumed with stew prepared fish, vegetable oil, fresh tomatoes, red pepper and vegetables

Cooked, thick paste made from *Manihot esculenta* root flourconsumed with traditional soup prepared with 'soup thickener' fresh vegetables, beef/fish, vegetable oil, fresh tomatoes and red pepper

Cooked, thick paste made from *D. rotundata tuber* flour consumed with traditional soup prepared with 'soup thickener' fresh vegetables, beef/fish, vegetable oil, fresh tomatoes and red pepper

Boiled cowpea (*V. unguiculata*) and boiled *Musa parasidiaca* consumed with traditional soup prepared with 'soup thickener' fresh vegetables, beef/fish, vegetable oil, fresh tomatoes and red pepper

*Candida tropicalis* fermented *Manihot esculenta* root flour blended with *Cola gigantea* seed flour and other basic ingredients for rat study to provide 10% protein and 20g total dietary fibre/ 1000kcal

*Candida tropicalis* fermented *Zea mays* seed flour blended with *Cola gigantea* seed flour and other basic ingredients for rat study to provide 10% protein and 20g total dietary fibre/1000kcal

*Candida tropicalis* fermented *Sorghum bicolor* seed flour blended with *Cola gigantea* seed flour and other basic ingredients for rat study to provide 10%

**Type of assay AM= animal model**

*In vivo*

*In vivo*

*In vivo*

*In vivo*

*In vivo* (AM) Dietary fibre,

*In vivo* (AM) Dietary fibre,

*In vivo* (AM) Dietary fibre,

**Potential active ingredients**

(human) Dietary fibre Anti-hyperglycaemic;

(human) Dietary fibre Anti-hyperglycaemic;

(human) Dietary fibre Anti-hyperglycaemic;

(human) Dietary fibre Anti-hyperglycaemic;

**Anti-diabetic property**

anti-hyperlipidaemic [129]

anti-hyperlipidaemic [129]

anti-hyperlipidaemic [129]

anti-hyperlipidaemic [129]

protein Anti-hyperglycaemia [130]

protein Anti-hyperglycaemia [130]

protein Anti-hyperglycaemia [130]

**Ref.**

Diet therapy, undisputedly, has long been recognized as key tool in the clinical management of diabetes. The challenge centres on how to translate the existing scientific evidence into practical terms for individual and household application in the management of diabetes mellitus. Relatively, a few clinical studies were published in Nigeria. Widely used parameters for diabetes-related clinical studies in Nigeria are post-prandial blood glucose response, antioxidant activity, lipid profiles and GI of diets. Most of the studies conducted on single foods were on dietary fibre. Much more indepth research is needed on phytochemical constituents of the rich national flora as regards phytochemical index and diabetes control. This is because health benefits of these indigenous plants are of public health importance. Based on recent documentation, Nigeria is rich in varieties of plant foods. Only a few varieties or cultivars were captured relative to diverse nature of Nigeria. It has generated a database for future investi‐ gations. Relevant research outputs on the plant foods would encourage diversification and increase consumption to maintain dietary adequacy and optimize their health benefits/ therapeutic potentials.

Much of the *in vivo* studies on antidiabetic effects of indigenous diets were conducted on human subjects. Levels of incorporation of fruits and vegetables into the traditional recipes are inadequate. The recipes used for traditional diets vary from one region/ethnic group to another in Nigeria. The variations in recipes for these diets might influence their nutrient concentrations and effects on blood glucose level. It is imperative to fill this gap using FBDG. This guideline proposed by FAO/WHO/UNU [76] has not been embraced and applied widely to enhance their health benefits in communities despite its advantages over the traditional and orthodox medicine. Rat study model to simulate this approach using high dietary fibre plantbased diet showed that traditional mixed diet as consumed can attenuate post-prandial blood glucose concentration in diabetic adult rats. There is research need to focus on wider varieties of traditional diets as consumed in Nigeria.

The goal for optimizing the use of indigenous plants with anti-diabetic potentials to control and prevention type 2-diabetes rests on conducting controlled clinical trials using food-based approach as the last step to establish their efficacy in humans. Evidently, these diets adequate in macronutrients, micronutrients and phytochemicals can retard and prevent diabetic complications. Optimum utilization of these indigenous anti-diabetic plant foods in Nigeria and their diversification would enhance management of diabetes and improve health of people in the communities.

INRA Programme on Natural Resources Survey, Nairobi Kenya Series No. B5 (c);

Anti-Diabetic Effects of Nigerian Indigenous Plant Foods/Diets

http://dx.doi.org/10.5772/57240

85

[10] Maziya-Dixon, B., Akinyele, I. O., Oguntona, E. B., Sanusi, R. A. and Harris, E. Niger‐

[11] Nicholas, K. Diabetes education and psychological care. *Practical Diabetes Internation‐*

[12] Federal Ministry of Health. National Expert Committee on Non-Communicable Dis‐ eases in Nigeria: Report of a national survey. Federal Ministry of Health, Lagos, Ni‐

[13] International Diabetes Federation (IDF) *Diabetes Atlas,* 2nd ed.. Brussels, IDF; 2003.

[14] Hoet, J. J. The role of foetal and infant growth and nutrition in the causality of diabe‐ tes and cardiovascular disease in later life. Sub-Committee on Nutrition News 1992;

[15] American Diabetes Association. Nutrition recommendations and principles for peo‐ ple with diabetes. (Position statement). *Diabetes Care* 1999; 22 Supplement 1.

[16] Bennet, B. C. Plant as foods. Economy Botany. UNESCO-Encyclopedia of Life Sup‐

[17] Cleave, T. L. The neglect of natural principles in current medical practice. *Journal of*

[18] Burkitt, D. P., Walter, A. R. P & Painter, N. S. Dietary fibre and disease. *Journal of*

[19] Olowokudejo, J. D., Kadiri, A. B. & Travih, V. A. An ethnobotanical survey of herbal markets, and medical plants in Lagos state of Nigeria. *Ethnobotanical leaflets* 2008; 12:

[20] Odugbemi, T. & Ayoola, A. Medicinal plants from Nigeria: an overview. In: Odugbe‐ mi, T. (ed.). *A text book of medicinal plants from Nigeria*. Lagos, Nigeria: University of

[21] Fred-Jaiyesimi, A. A. & Ajibesin, K. K. Ethnobotanical survey of toxic plants and plants parts in Ogun state, Nigeria. *International Journal of Green Pharmacy* 2012; 6 (3):

[22] Chan, C., Ngoh, G. & Yusoff, R. A brief review of antidiabetic plants: Global distribu‐ tion, active ingredients, extraction techniques and mechanisms. *Pharmacognosy Re‐*

[9] Europa. Nigeria In: *Africa South of the Sahara*, 27th Edition; 1998. p780 – 806.

ia food consumption survey 2001 – 2003. Ibadan, Nigeria. IITA; 2004

1993.

*al* 1996; 13: 383- 355.

geria;1992

14: 10 - 13.

851-865.

174-179.

port Systems (EOLSS); 2009.

Lagos press; 2008. p 9-17.

*view*s 2012; 6 (11): 22-28

*the Royal Naval Medical Service* 1956; 42: 55-82

*American Medical Association* 1974; 299: 1068-1074.


**5. Nosocomial infections by VRE**

Nosocomial infections by Enterococci are Urinary tract infection, endocarditis, bacteremia, catheter related infections, wound infections, intra- abdominal and pelvic infections and recently even oral infections have been reported.


*2.1.4. Equipment*

This consists of 908 kg (2000 lb) or 68 kg (150 lb) containers, weigh scales, chlorinator, injectors, switchover modules, vacuum lines, booster pumps, solution lines, diffusers, and a flow meter. Safety requirements are passive ventilation, mechanical ventilation, warning alarms and devices, showers, panic hardware for doors, and eye washes. A separate air tight room for chlorination equipment is mandatory. For disinfection process, 100% of chlorine gas is available [4].


**10. The role of farm management**

Modern animal production is mainly based on economic principles; neither animal health and welfare nor ecological issues are taken much into consideration. However, the high morbidity and mortality in dairy production associated with poor welfare conditions necessarily questions modern industrial farming practices. In the past, politicians, agronomists and animal scientists have appeared to assume an ability in the markets and in science to drive technological changes which will enable the economic-environment system to both satisfy increasing global food demands and simultaneously solve problems of animal health and welfare. These key assumptions, however, have turned out to belong to the category of wishful thinking, lacking as they are in any profound evidence. They generally neglect, on different scales, the biological basics, particularly the complexity of physiological processes as well as the ambivalent nature of productivity and the resultant tradeoffs. Whether processes are beneficial or non-beneficial very much depends on the context in which they take place and the level at which the situation in question is being analyzed. The same is also true for the possible options of balancing the trade-offs between economic interests and animal health and welfare in a costeffective manner. Thus, the frequent attempts to formulate one-size-fits-all general recommendations for a successful implementation of measures are often misleading and contradict the actual context-specific nature of biological processes and the subsequent need for context-specific solutions at all levels of dairy farming.

#### **10.1 Lack in orientation**

For the farmers, it is often very important to know where they stand in relation to other farms. Data from a representative number of farms could be used to create a scale ranging from very low to very high prevalence of Pds per farm unit thus giving farm management an idea and orientation as to whether the individual farm belongs to the category of farms with a low, a middling or a high level of health problems. However, as long as data on production diseases is not sufficiently solid, it has little practical value for farmers and they can basically disregard it. Thus, a diagnostic procedure is essential for the assessment of the prevalence of Pds as well as for the identification and implementation of measures appropriate for the farm specific situation and for the need to balance partly contradicting goals. Both the rate of productivity and the prevalence of production diseases on dairy farms emerge from very complex processes. Focusing on single aspects without taking into account both the context and the conflicts between achieving productivity and the development of production diseases does not allow any truly valid statements and can be said to be overly narrow.

In contrast to zoonotic and epizootic diseases, productions diseases are not yet a matter of public concern. Although accompanied by pain, suffering, distress and longer persistent harm, they are not even fully recognized as a severe animal welfare issue. Being primarily treated as an internal affair of the farm, Pds are not regulated by legislation but left in the hands of the farmers and their individual readiness to act. Generally, personal economic concerns are a greater deciding factor than concern for the health of animals. Indeed, it is often said that farm

**67**

odd individual and occur regularly rather than occasionally.

*Nutrition and Health-Management in Dairy Production DOI: http://dx.doi.org/10.5772/intechopen.89447*

and the envisaged target figures.

**10.2 Need for profound data**

management is interested in reducing Pds solely due to economic reasons. Indeed, there is no doubt that Pds can cause severe economic losses. However, there is still a lack in valid data about the degree of failure costs, and particularly on the effort in terms of labor time and investments required to reduce the prevalence of production diseases in the farm specific context [4]. To reduce production diseases in a cost-effective way and to maintain production at a competitive level is not only highly contextual and thus requires appropriate farm-specific measures but also relies on a function of margin utility. This applies to the use of resources e.g. high-quality feed, labor time, investments to reduce production diseases as well as to the intensification occurring in the increased use of inputs. Additionally, the conditions outside the system boundaries (in terms of the price of products sold, availability and price of resources needed to reduce production diseases) have to be considered. Cost–benefit relationships not only depend on the status quo for both productivity and production diseases but also on the gap between the status quo

Optimization of the relationship between productivity and the prevalence of Pds to the benefit of both the farmer's income and the health and welfare of farm animals requires access to reliable farm-specific data. Thus, a major question is how to increase the availability of valid data and how to create an overview that can support decisions of management regarding an efficient allocation of available resources. Records of milk performance at an individual level – either through daily milk yield measurements or official milk recording – are valuable tools, and not only for performance monitoring. They also reflect the individual requirements of the dairy cows in the course of lactation and are thus essential for implementing a target-oriented nutrient supply. Often this data may be considered unnecessarily costly or time-consuming for flat rate concentrate feeding or TMR systems. Lactation curves plotted for individuals or groups of animals provide a very graphic illustration of performance. Since it is always one of the first things to be affected by the diet, milk components are an essential element for monitoring the impacts of energy and nutrient supply on dairy cows.  Furthermore, feed intake is a critical factor in providing the right degree of nutrition. Given the wide impact, dietary problems and imbalances can have on productivity, monitoring specific aspects of dairy health and fertility can be very valuable in feeding management. However, appropriate techniques for the assessment of feed intake at the individual level are not yet fully developed for use in farm practice. One sophisticated and cost-effective technology which is available but seldom implemented on dairy farms is measuring equipment which can continuously determine the body weight development. More often in use is the tool of Body Condition Scoring which provides information about the measure of a cow's energy balance. However, one disadvantage of this tool is that long temporal delays can occur in receiving the information on discrepancies and thus in subsequent responses to the information. The time delay between cows receiving too little energy in their feed rations and any resulting pregnancy rate problems often go unrecognized until it is too late to do much about them. This is compounded by the fact that fertility is not given sufficient prominence within rationing programs although fertility, like lameness, is now recognized as being caused by a number of factors, including inadequate nutrition. Furthermore, disorders like acidosis, ketosis, displaced abomasum and fatty liver are clear signs of dietary problems, especially if they affect a number of animals rather than just the

*Nutrition and Health-Management in Dairy Production DOI: http://dx.doi.org/10.5772/intechopen.89447*

*Livestock Health and Farming*

**10.1 Lack in orientation**

and can be said to be overly narrow.

not able to correctly interpret data regarding the negative energy balance of the individual cows and thus cannot know which animals are at a higher or a lower risk, which animals are able to cope with the NEB and which ones are showing disorders

Modern animal production is mainly based on economic principles; neither animal health and welfare nor ecological issues are taken much into consideration. However, the high morbidity and mortality in dairy production associated with poor welfare conditions necessarily questions modern industrial farming practices. In the past, politicians, agronomists and animal scientists have appeared to assume an ability in the markets and in science to drive technological changes which will enable the economic-environment system to both satisfy increasing global food demands and simultaneously solve problems of animal health and welfare. These key assumptions, however, have turned out to belong to the category of wishful thinking, lacking as they are in any profound evidence. They generally neglect, on different scales, the biological basics, particularly the complexity of physiological processes as well as the ambivalent nature of productivity and the resultant tradeoffs. Whether processes are beneficial or non-beneficial very much depends on the context in which they take place and the level at which the situation in question is being analyzed. The same is also true for the possible options of balancing the trade-offs between economic interests and animal health and welfare in a costeffective manner. Thus, the frequent attempts to formulate one-size-fits-all general recommendations for a successful implementation of measures are often misleading and contradict the actual context-specific nature of biological processes and the subsequent need for context-specific solutions at all levels of dairy farming.

For the farmers, it is often very important to know where they stand in relation to other farms. Data from a representative number of farms could be used to create a scale ranging from very low to very high prevalence of Pds per farm unit thus giving farm management an idea and orientation as to whether the individual farm belongs to the category of farms with a low, a middling or a high level of health problems. However, as long as data on production diseases is not sufficiently solid, it has little practical value for farmers and they can basically disregard it. Thus, a diagnostic procedure is essential for the assessment of the prevalence of Pds as well as for the identification and implementation of measures appropriate for the farm specific situation and for the need to balance partly contradicting goals. Both the rate of productivity and the prevalence of production diseases on dairy farms emerge from very complex processes. Focusing on single aspects without taking into account both the context and the conflicts between achieving productivity and the development of production diseases does not allow any truly valid statements

In contrast to zoonotic and epizootic diseases, productions diseases are not yet a matter of public concern. Although accompanied by pain, suffering, distress and longer persistent harm, they are not even fully recognized as a severe animal welfare issue. Being primarily treated as an internal affair of the farm, Pds are not regulated by legislation but left in the hands of the farmers and their individual readiness to act. Generally, personal economic concerns are a greater deciding factor than concern for the health of animals. Indeed, it is often said that farm

as a sign of adaption stress due to whatever reasons.

**10. The role of farm management**

**66**

management is interested in reducing Pds solely due to economic reasons. Indeed, there is no doubt that Pds can cause severe economic losses. However, there is still a lack in valid data about the degree of failure costs, and particularly on the effort in terms of labor time and investments required to reduce the prevalence of production diseases in the farm specific context [4]. To reduce production diseases in a cost-effective way and to maintain production at a competitive level is not only highly contextual and thus requires appropriate farm-specific measures but also relies on a function of margin utility. This applies to the use of resources e.g. high-quality feed, labor time, investments to reduce production diseases as well as to the intensification occurring in the increased use of inputs. Additionally, the conditions outside the system boundaries (in terms of the price of products sold, availability and price of resources needed to reduce production diseases) have to be considered. Cost–benefit relationships not only depend on the status quo for both productivity and production diseases but also on the gap between the status quo and the envisaged target figures.

#### **10.2 Need for profound data**

Optimization of the relationship between productivity and the prevalence of Pds to the benefit of both the farmer's income and the health and welfare of farm animals requires access to reliable farm-specific data. Thus, a major question is how to increase the availability of valid data and how to create an overview that can support decisions of management regarding an efficient allocation of available resources. Records of milk performance at an individual level – either through daily milk yield measurements or official milk recording – are valuable tools, and not only for performance monitoring. They also reflect the individual requirements of the dairy cows in the course of lactation and are thus essential for implementing a target-oriented nutrient supply. Often this data may be considered unnecessarily costly or time-consuming for flat rate concentrate feeding or TMR systems. Lactation curves plotted for individuals or groups of animals provide a very graphic illustration of performance. Since it is always one of the first things to be affected by the diet, milk components are an essential element for monitoring the impacts of energy and nutrient supply on dairy cows.  Furthermore, feed intake is a critical factor in providing the right degree of nutrition. Given the wide impact, dietary problems and imbalances can have on productivity, monitoring specific aspects of dairy health and fertility can be very valuable in feeding management. However, appropriate techniques for the assessment of feed intake at the individual level are not yet fully developed for use in farm practice. One sophisticated and cost-effective technology which is available but seldom implemented on dairy farms is measuring equipment which can continuously determine the body weight development. More often in use is the tool of Body Condition Scoring which provides information about the measure of a cow's energy balance. However, one disadvantage of this tool is that long temporal delays can occur in receiving the information on discrepancies and thus in subsequent responses to the information. The time delay between cows receiving too little energy in their feed rations and any resulting pregnancy rate problems often go unrecognized until it is too late to do much about them. This is compounded by the fact that fertility is not given sufficient prominence within rationing programs although fertility, like lameness, is now recognized as being caused by a number of factors, including inadequate nutrition. Furthermore, disorders like acidosis, ketosis, displaced abomasum and fatty liver are clear signs of dietary problems, especially if they affect a number of animals rather than just the odd individual and occur regularly rather than occasionally.

### **10.3 Dealing with complexity**

Due to the fact that being confronted with the complexity of production diseases for a long time, farm practice is unswervingly in search of simple approaches to deal with unintended side-effects. This contradicts with the major challenge, namely variation, in dairy farming: variation in the energy and nutrient supply of the individual cow, variation in the utilization and partitioning of energy and nutrients and the differences in the requirements needed for the essential tasks of regulation and immune defense, resulting in a large variation in the gaps between supply and demand. Without feeding control measures, it is left to the animals to cope with the occurring discrepancies. In this respect, an apparent "survival of the fittest" selection occurs at the farm level. However, it is not necessarily a selection between farm animals based on comparable initial and boundary conditions but on a highly variable situation in terms of performance level and additional demands by fluctuating internal and external stressors. Consequently, the selection might occur to a high degree by pure chance.

The prevalence of production diseases on farms indicates the degree of shortcomings of farm management at two levels. Firstly, regarding the degree of a demand-oriented supply with energy and nutrients according to the individual requirements, and secondly, regarding protection against stressors such as pathogens, crowding effects or heat stress which lead to a need for additional resources. Production diseases are always context specific. The context is characterized by the specific farm conditions, the individual cow situation and the interactions between both. Dairy farms vary widely when it comes to the living conditions of the animals. Thus, health problems require a diagnostic procedure at the farm level. This diagnosis needs to include the most relevant influencing factors involved in the multifactorial processes as well as estimations about the most effective and efficient strategies in the farm-specific context [3, 67, 68].

#### **10.4 Assuming responsibility**

An important prerequisite for any improvements, however, is related to the need to assume responsibility not only for the results of efforts to increase productivity but also for the negative side effects of the production processes. The farm management designs the living conditions of farm animals and organizes the allocation of resources but it is also, to some degree, responsible for the resource partitioning processes within an organism. This applies to breeding for a high number of lactocytes in the mammary gland, which is responsible for a prioritized skim of glucose from the body pool, and to sucking away every last drop of milk via milking. Metabolic disorders indicate an imbalanced trade-off between the original goal of sustaining the offspring via milk and the goal of self-maintenance of the dam. Farmers are challenged to reduce the degree of trade-offs by adapting the breeding practices to the quality of available nutrients and, at least, temporarily, decreasing the amount of milk extracted during milking, perhaps also the frequency of milking. To increase milk production, it is not uncommon in intensive dairy systems to increase milking frequency to three times daily. Reducing milking frequency is much less common. In doing so, it is in fact possible to improve the overall energy balance of cows during early lactation with once-daily milking [69]. Furthermore, this procedure can entail an improvement in the metabolic profile [70] and immune function [71] of dairy cows. In contrast, Soberon et al. [72] reported that cows subjected to an increased milking frequency are 1.4 times more likely than the control cows to be classified as sub-clinically ketotic. Depending on the stage of

**69**

*Nutrition and Health-Management in Dairy Production DOI: http://dx.doi.org/10.5772/intechopen.89447*

production per cow.

**10.5 Unfair competition**

lactation, breed, and parity, the reduction in milk yield losses in the course of shortterm alterations to milking frequency in early lactation varies considerably and can amount up to 22% [73]. This figure has been revealed as an average milk loss across 30 different international short-term studies. While it is comprehensible that short-term alterations to milking frequency may provide a tool to better manage the metabolism and energy balance of cows during early lactation [74], many farmers fear that the losses in milk yield could be too high. All the more is it necessary not to go for a general strategy but to develop a farm specific strategy that suits the situation of the individual cows, thereby also considering the fears of farmers. The aim should be to throttle the withdrawal of milk by adapting the quantity to the estimation of risk for the individual cow. This practice would be suitable under certain infrastructural farm conditions, including the availability of valid data on dairy systems where an emphasis is placed on animal health rather than on milk

Finally, the crucial question is, at which rate of disturbances and Pds should an intervention by farm management take place. Currently this is determined solely by individual farm management, often to the detriment of the animals. Farm management, however, would be well-advised to show an interest in reducing the prevalence of production diseases. A high prevalence of production diseases represents a low health performance by farm management. Farms with a high prevalence of Pds are not only disregarding their obligation to prevent suffering of the animals but are also delivering inferior products to the market. Thus, low levels of Pds should be seen as a significant production goal which carry as much weight as productivity goals. However, setting low disease levels as a production goal will only occur when farmers realize that they can gain an advantage over competitors who have higher levels of Pds. On the other hand, farms behave unfairly when they cause, and/or basically ignore, a high level of Pds and related welfare problems and therefore produce an inferior level of product and process quality while simultaneously achieving the same market prices as those who invest time, money and effort in product and process improvements. Moreover, farm associations, like those of organic agriculture, should be more concerned about unsatisfactory health performances amongst member farms and doing more to raise the lack of concern shown by their consumer clientele as this defies general consumer expectations and any efforts to justify the premium prices [75]. Whether it is intrinsically motivated or forced by economic reasons or the demands of retailers to improve the current unsatisfactory situation regarding the prevalence of production diseases, farm management needs to know how and where to direct its efforts. Benchmarking would offer an appropriate methodological approach to deal with the issue of unfair competition and also with the uncertainties in the assessment of Pd data as these methodological uncertainties affect all farms, if not exactly to the same degree. Benchmarking allows target figures to be deduced from the average levels obtained from assessing a sufficient number of comparable farms or from an estimate of the optimum balance between productivity and disease related loss and failure costs. While farmers are generally hesitant in their readiness to extend control to others, farm management lacks orientation regarding its own position in relation to other farms and regarding the target figures it should aim for in the future as long as benchmarking is not established. The lack of benchmarking for Pd values in relation to the product quantity of products from animal origin can be seen as one of the main barriers in the fight to reduce nutritional disorders and related Pds in dairy production.

#### *Nutrition and Health-Management in Dairy Production DOI: http://dx.doi.org/10.5772/intechopen.89447*

lactation, breed, and parity, the reduction in milk yield losses in the course of shortterm alterations to milking frequency in early lactation varies considerably and can amount up to 22% [73]. This figure has been revealed as an average milk loss across 30 different international short-term studies. While it is comprehensible that short-term alterations to milking frequency may provide a tool to better manage the metabolism and energy balance of cows during early lactation [74], many farmers fear that the losses in milk yield could be too high. All the more is it necessary not to go for a general strategy but to develop a farm specific strategy that suits the situation of the individual cows, thereby also considering the fears of farmers. The aim should be to throttle the withdrawal of milk by adapting the quantity to the estimation of risk for the individual cow. This practice would be suitable under certain infrastructural farm conditions, including the availability of valid data on dairy systems where an emphasis is placed on animal health rather than on milk production per cow.

### **10.5 Unfair competition**

*Livestock Health and Farming*

**10.3 Dealing with complexity**

high degree by pure chance.

in the farm-specific context [3, 67, 68].

**10.4 Assuming responsibility**

Due to the fact that being confronted with the complexity of production diseases for a long time, farm practice is unswervingly in search of simple approaches to deal with unintended side-effects. This contradicts with the major challenge, namely variation, in dairy farming: variation in the energy and nutrient supply of the individual cow, variation in the utilization and partitioning of energy and nutrients and the differences in the requirements needed for the essential tasks of regulation and immune defense, resulting in a large variation in the gaps between supply and demand. Without feeding control measures, it is left to the animals to cope with the occurring discrepancies. In this respect, an apparent "survival of the fittest" selection occurs at the farm level. However, it is not necessarily a selection between farm animals based on comparable initial and boundary conditions but on a highly variable situation in terms of performance level and additional demands by fluctuating internal and external stressors. Consequently, the selection might occur to a

The prevalence of production diseases on farms indicates the degree of shortcomings of farm management at two levels. Firstly, regarding the degree of a demand-oriented supply with energy and nutrients according to the individual requirements, and secondly, regarding protection against stressors such as pathogens, crowding effects or heat stress which lead to a need for additional resources. Production diseases are always context specific. The context is characterized by the specific farm conditions, the individual cow situation and the interactions between both. Dairy farms vary widely when it comes to the living conditions of the animals. Thus, health problems require a diagnostic procedure at the farm level. This diagnosis needs to include the most relevant influencing factors involved in the multifactorial processes as well as estimations about the most effective and efficient strategies

An important prerequisite for any improvements, however, is related to the need to assume responsibility not only for the results of efforts to increase productivity but also for the negative side effects of the production processes. The farm management designs the living conditions of farm animals and organizes the allocation of resources but it is also, to some degree, responsible for the resource partitioning processes within an organism. This applies to breeding for a high number of lactocytes in the mammary gland, which is responsible for a prioritized skim of glucose from the body pool, and to sucking away every last drop of milk via milking. Metabolic disorders indicate an imbalanced trade-off between the original goal of sustaining the offspring via milk and the goal of self-maintenance of the dam. Farmers are challenged to reduce the degree of trade-offs by adapting the breeding practices to the quality of available nutrients and, at least, temporarily, decreasing the amount of milk extracted during milking, perhaps also the frequency of milking. To increase milk production, it is not uncommon in intensive dairy systems to increase milking frequency to three times daily. Reducing milking frequency is much less common. In doing so, it is in fact possible to improve the overall energy balance of cows during early lactation with once-daily milking [69]. Furthermore, this procedure can entail an improvement in the metabolic profile [70] and immune function [71] of dairy cows. In contrast, Soberon et al. [72] reported that cows subjected to an increased milking frequency are 1.4 times more likely than the control cows to be classified as sub-clinically ketotic. Depending on the stage of

**68**

Finally, the crucial question is, at which rate of disturbances and Pds should an intervention by farm management take place. Currently this is determined solely by individual farm management, often to the detriment of the animals. Farm management, however, would be well-advised to show an interest in reducing the prevalence of production diseases. A high prevalence of production diseases represents a low health performance by farm management. Farms with a high prevalence of Pds are not only disregarding their obligation to prevent suffering of the animals but are also delivering inferior products to the market. Thus, low levels of Pds should be seen as a significant production goal which carry as much weight as productivity goals. However, setting low disease levels as a production goal will only occur when farmers realize that they can gain an advantage over competitors who have higher levels of Pds. On the other hand, farms behave unfairly when they cause, and/or basically ignore, a high level of Pds and related welfare problems and therefore produce an inferior level of product and process quality while simultaneously achieving the same market prices as those who invest time, money and effort in product and process improvements. Moreover, farm associations, like those of organic agriculture, should be more concerned about unsatisfactory health performances amongst member farms and doing more to raise the lack of concern shown by their consumer clientele as this defies general consumer expectations and any efforts to justify the premium prices [75]. Whether it is intrinsically motivated or forced by economic reasons or the demands of retailers to improve the current unsatisfactory situation regarding the prevalence of production diseases, farm management needs to know how and where to direct its efforts. Benchmarking would offer an appropriate methodological approach to deal with the issue of unfair competition and also with the uncertainties in the assessment of Pd data as these methodological uncertainties affect all farms, if not exactly to the same degree. Benchmarking allows target figures to be deduced from the average levels obtained from assessing a sufficient number of comparable farms or from an estimate of the optimum balance between productivity and disease related loss and failure costs. While farmers are generally hesitant in their readiness to extend control to others, farm management lacks orientation regarding its own position in relation to other farms and regarding the target figures it should aim for in the future as long as benchmarking is not established. The lack of benchmarking for Pd values in relation to the product quantity of products from animal origin can be seen as one of the main barriers in the fight to reduce nutritional disorders and related Pds in dairy production.


**Shaoxia Guo 1,2, Guilong Liu 3,4,\*, Tong Han 5, Ziyang Zhang 1,2 and Yuan Liu 1,2,\***

Received: 11 February 2019; Accepted: 22 February 2019; Published: 27 February 2019

**Abstract:** Owing to the outstanding catalytic performance for higher alcohol synthesis, Ga-Co catalysts have attracted much attention. In view of their unsatisfactory stability and alcohol selectivity, herein, K-modulated Co nanoparticles trapped in La-Ga-O catalysts were prepared by the reduction of La1−xKxCo0.65Ga0.35O3 perovskite precursor. Benefiting from the atomic dispersion of all the elements in the precursor, during the reduction of La1−xKxCo0.65Ga0.35O3, Co nanoparticles could be confined into the K-modified La-Ga-O composite oxides, and the confinement of La-Ga-O could improve the anti-sintering performance of Co nanoparticles. In addition, the addition of K modulated parts of La-Ga-O into La2O3, which ameliorated the anti-carbon deposition performance. Finally, the addition of K increased the dispersion of cobalt and provided more electron donors to metallic Co, resulting in a high activity and superior selectivity to higher alcohols. Benefiting from the above characteristics, the catalyst possesses excellent activity, good selectivity, and superior stability.

**Keywords:** perovskite-type oxide (PTO); cobalt; gallium; potassium; higher alcohols; syngas


**3. Evaluation of implant-soft tissue response**

The soft tissue interface especially the structure of collagen fibre bundles received more attention over the past 10 years with studies that include animal models such as dogs [10–12, 14] and monkeys [23, 24] and human [25, 26] used to explore the structure and dimension of soft tissue-implant interface. Recently, Chai and co-workers [18] have ventured upon the use of three-dimensional oral mucosal models by using the tissue engineering technology to investigate the nature of the peri-implant biological seal.

#### **3.1. Implant-soft tissue interface models**

The advantages and disadvantages of each implant-soft tissue interface study models are described in the next section. These models were developed in order to enhance our under‐ standing of the soft tissue response on various materials with different surface topography and to establish best methods to evaluate the biological seal of peri-implant tissue. Generally, an in vitro study using monolayer cell culture model is conducted to assess the cytotoxicity of the cells and quickly observe cell activities and behaviour towards new dental implant materials. Histomorphometric analysis of en bloc tissue consisting of both soft tissue and implant body is the best method to demonstrate the presence of epithelial and connective tissue attachment at the soft tissue-implant interface. Yet, due to limited opportunity to obtain histological section from human, animal models were developed.

#### *3.1.1. In vitro studies*

Presently, in vitro testing is performed as a prerequisite to in vivo evaluation. However, the in vitro techniques do not reflect the clinical situation and the progress in our understanding of extra- and intracellular processes that occur in connective tissue attachment. Thus, the data cannot be extrapolated into clinical applications. Nevertheless, the study involving monolayer cells is by far the most popular and easy-to-conduct study although more sensitive in vitro evaluations are now available. The cell shape, activities and response can be evaluated morphometrically via immunocytochemical staining [27], or by analysis using scanning electron [28, 29] or fluorescent [30, 31] miscroscopies. Additionally, the gene and protein expressions for cell adhesion and attachment can also be carried out [27, 32, 33]. Most studies used primary human gingival [29, 32, 34] and periodontal [35] fibroblasts as a cell model, which are cultured directly onto the dental implant materials surface. Keratinocytes are also frequently used [27, 36, 37]. Compared to fibroblasts, keratinocytes by far is most difficult to culture. Cochran et al. [35] compared the behaviour of periodontal and gingival fibroblast as well as keratinocytes towards the titanium with different surface textures. They found that human fibroblast and epithelial cell attachment and proliferation are significantly affected by surface characteristics of titanium. Of three cell types, gingival fibroblasts appeared to attach best, followed by periodontal ligament fibroblasts and epithelial cells. Both types of fibroblasts grow and proliferate well on both rough and smooth titanium surfaces compared to epithelial cells once they are attached to the surface [35]. Other study found a significant decrease in the number of gingival fibroblasts on rough titanium (Ti) surfaces compared with smooth polished Ti surfaces [30, 34]. On the other hand, Oates et al. [32] found that the fibroblasts adhesion and attachment are enhanced in rougher surface than smooth surface, in contrast to other findings [33] where focal adhesion kinases were immunogold labelled. In a different study using ceramic, fibroblasts attached more on the milled ceramic and appeared to follow the direction of the fine irregularities on the surface [38]. Nevertheless, most common finding of those studies is that cells were oriented in a parallel order along the grooves of the machined surface but arranged randomly when in contact with a rough surface. Hence, the in vitro models appear to be able to provide an insight and could be used to guide specific cell attachment or specific material with surface characteristics for in vivo models. Animal models are the most common in vivo models carried out compared to human studies.

### *3.1.2. Animal models*

**3.1. Implant-soft tissue interface models**

44 Dental Implantology and Biomaterial

*3.1.1. In vitro studies*

histological section from human, animal models were developed.

The advantages and disadvantages of each implant-soft tissue interface study models are described in the next section. These models were developed in order to enhance our under‐ standing of the soft tissue response on various materials with different surface topography and to establish best methods to evaluate the biological seal of peri-implant tissue. Generally, an in vitro study using monolayer cell culture model is conducted to assess the cytotoxicity of the cells and quickly observe cell activities and behaviour towards new dental implant materials. Histomorphometric analysis of en bloc tissue consisting of both soft tissue and implant body is the best method to demonstrate the presence of epithelial and connective tissue attachment at the soft tissue-implant interface. Yet, due to limited opportunity to obtain

Presently, in vitro testing is performed as a prerequisite to in vivo evaluation. However, the in vitro techniques do not reflect the clinical situation and the progress in our understanding of extra- and intracellular processes that occur in connective tissue attachment. Thus, the data cannot be extrapolated into clinical applications. Nevertheless, the study involving monolayer cells is by far the most popular and easy-to-conduct study although more sensitive in vitro evaluations are now available. The cell shape, activities and response can be evaluated morphometrically via immunocytochemical staining [27], or by analysis using scanning electron [28, 29] or fluorescent [30, 31] miscroscopies. Additionally, the gene and protein expressions for cell adhesion and attachment can also be carried out [27, 32, 33]. Most studies used primary human gingival [29, 32, 34] and periodontal [35] fibroblasts as a cell model, which are cultured directly onto the dental implant materials surface. Keratinocytes are also frequently used [27, 36, 37]. Compared to fibroblasts, keratinocytes by far is most difficult to culture. Cochran et al. [35] compared the behaviour of periodontal and gingival fibroblast as well as keratinocytes towards the titanium with different surface textures. They found that human fibroblast and epithelial cell attachment and proliferation are significantly affected by surface characteristics of titanium. Of three cell types, gingival fibroblasts appeared to attach best, followed by periodontal ligament fibroblasts and epithelial cells. Both types of fibroblasts grow and proliferate well on both rough and smooth titanium surfaces compared to epithelial cells once they are attached to the surface [35]. Other study found a significant decrease in the number of gingival fibroblasts on rough titanium (Ti) surfaces compared with smooth polished Ti surfaces [30, 34]. On the other hand, Oates et al. [32] found that the fibroblasts adhesion and attachment are enhanced in rougher surface than smooth surface, in contrast to other findings [33] where focal adhesion kinases were immunogold labelled. In a different study using ceramic, fibroblasts attached more on the milled ceramic and appeared to follow the direction of the fine irregularities on the surface [38]. Nevertheless, most common finding of those studies is that cells were oriented in a parallel order along the grooves of the machined surface but arranged randomly when in contact with a rough surface. Hence, the in vitro models appear to be able to provide an insight and could be used to guide specific cell attachment or

Studies using animals as in vivo models for evaluation of soft tissue response around dental implant have been extensively conducted and are well documented. In animal models, the histological section of peri-implant tissue was made possible, which becomes the gold standard for the implant-soft tissue interface analysis. While dogs models such as the beagle [8, 14] being the most common animal of choice, monkeys [23, 39] and minipigs [40] were also used to demonstrate the presence of epithelial and connective tissue attachment around transmucosal region of dental implants histologically.

The experiments in animals demonstrated that the dimension of the mucosal attachment to implants was similar to the gingival attachment at teeth and was composed of an epithelial portion about 1.5–2 mm long and a cell-rich connective tissue portion close to the implant that was about 1–1.5 mm high [10]. Animal models were also used to evaluate the soft tissue response towards different abutment materials. Abrahamsson et al. [11] investigated the influence of abutment material on the location and the quality of the attachment that occurred between the peri-implant mucosa and the implant. They found no proper attachment formed at the abutment level made of gold alloy and porcelain when compared to those made of titanium and ceramic. In addition, similar finding was noted by Welander et al. [41] when titanium, zirconia and Au/Pt alloy were used. The tissue around abutment made from titanium and zirconia was stable; meanwhile, an apical migration of epithelium was noted on Au/Pt alloy. In another study, Abrahamsson et al. [42] demonstrated that the soft tissue attachment that formed at implants made of commercially pure titanium (c.p. titanium) was not influenced by the roughness of the titanium surface.

Among many, dogs have been the most common animal of choice. This is possibly due to easy access with regard to clinical examinations and oral hygiene procedures of the dogs. It must be noted that non-human primates bear more resemblence to human anatomy and histology than any other animal, thus may offer a higher degree of relevance to human. Nevertheless, the results from animal experiments should always be carefully interpreted since the healing response and immuno-reaction in animals might not be similar to human, so the data might not be comparable. A given sequence of soft tissue integration to implants in a dog may not correspond exactly to an expected outcome in humans. The differences in tissue response during healing between human-human subjects may sometimes become more pronounced between different human to human subjects than between animals and humans. Moreover, the healing response in animals is also less predictable compared to human. In the light of evidence-based dentistry, the result from animal studies should be interpreted cautiously. Additionally, animal studies are also bound to ethical considerations, where study design and calculation of sample size of animals in experiments are to be carried out with caution. Essentially, to have more clinical validity, human randomized control trials should be carried out to obtain more information on the peri-implant tissues.

#### *3.1.3. Human studies*

The composition of the connective tissue interface towards implants was studied in both animal experiments and human biopsy materials. While human studies are very limited due to ethical issues, the evidence of epithelial and connective tissue attachment around periimplant regions are obtained mostly from failed implant [43], autopsy[44, 45] or clinical studies [1, 46], where the presence of connective tissue attachment on these studies is still difficult to demonstrate. Most of the human studies that have been carried out were clinical studies in which the traditional periodontal parameters were used for monitoring the soft tissue responses around dental implants intra-orally. According to clinical studies that involve the marginal bone levels, we can conclude that bone level is stable as it implies that the soft tissue integration has not migrated apically [1, 46, 47]. Liljenberg et al. [48] in their study of soft tissue biopsies of edentulous ridge mucosa and peri-implant mucosa revealed that the composition of both tissues were nearly identical in terms of collagen, cells and vascular structures. The peri-implant mucosa harboured a junctional epithelium that contained significantly enhanced numbers of different inflammatory cells infiltration. On the other hand, Piatelli et al. [44] found that there was no inflammatory infiltrate in epithelium or connective tissue in human autopsy biopsies of titanium dental implants. It is also interesting to note that the collagen fibres in the coronal part were parallel to implant surface while in the apical region the fibres were in a perpendicular fashion was found. Additionally, Glauser et al. [49] used both hard and soft tissue biopsies of mini titanium implants with different surface characteristics to demonstrate the establishment of junctional epithelium attachment to the implant surfaces. They noted that collagen fibres and the fibroblasts were oriented parallel to the implant surface. The oxidized and acid-etched implants revealed less epithelial downgrowth and longer connective tissue than machined implants [49]. As for different types of materials, Vigolo et al. [46] assessed the peri-implant mucosa around abutments made of gold alloy and titanium and found no difference between the two types of abutments with regard to peri-implant marginal bone level and soft tissue parameters. Meanwhile, Nevins et al. [26] using en bloc biopsy demonstrated intimate contact of junctional epithelium cells to implant surface and connective tissue with functionally oriented collagen fibres running towards the implant surface designed with Laser-Lok microchannels. Nonetheless, it is unethical to remove implant in order to attain en bloc tissue for histological analyses in human, and data from autopsy did not necessarily represent the ultrastructural nature of the peri-implant interface. In addition, not all animal experiments can be replicated in human samples due to cost and ethical considerations. For this reason, the investigation of peri-implant interface for improvement of connective tissue attachment is rather difficult to conduct in human. Thus, the need of development of different models for histological analyses may be essential.

#### *3.1.4. Three-dimensional oral tissue engineering*

As the opportunity to undertake human studies is limited, many studies that evaluated the peri-implant interface were carried out using animal models. With advances in knowledge on tissue regeneration, tissue-engineered oral mucosal equivalents (three-dimensional oral mucosal model, 3D OMM) have been developed for clinical applications and also for con‐ ducting in vitro studies on biocompatibility, mucosal irritation, disease and other basic oral biological phenomena such as for grafting of oral mucosal defects [50, 51]. The 3D OMM consists of both epithelium and connective tissue layers, grown in the laboratory using collagen membrane as the scaffold. Therefore, evaluation of cell-cell interaction between epithelium, connective tissue and implant surface using 3D OMM is possible and could become an alternative method to study the nature of peri-implant interface. The use of 3D OMM will permit histological preparation and histomorphometric analysis of the interface. With the modification of culture technique, Chai et al. [18] have constructed 3D OMM and have demonstrated the presence of peri-implant tissue with features that mimicked those seen in vivo when tested with titanium. Chai and co-workers [19] further developed the 3D OMM and succeeded in obtaining formed peri-implant-like-epithelium (PILE) on the polished, ma‐ chined, sand-blasted and TiUnite titanium surfaces. Using the 3D OMM, ultrastructural investigation of the soft tissue-implant interface with transmission electron microscopy (TEM) is also possible. It is also interesting to note that the presence of hemidesmosome-like structure as an epithelial attachment to the material surface is shown using this model (**Figure 4**). Moreover, the biological seal of peri-implant tissue can also be demonstrated quantitatively with 3D OMM [52, 53]. This can be carried out via assessment of penetrative behaviour of radioisotope material through the 3D OMM model [52]. Alternatively, the biological seal of peri-implant can also be assessed through the measurement of degree formed by pocket or non-pocket epithelial attachment at the oral mucosal model-material interface [18, 53]. Although only limited study is available on the use of 3D OMM for evaluating the peri-implant interface, this model appears to have a more promising prospect than the monolayer cell culture model. This model is a useful method to evaluate the soft tissue response prior to investigation with an animal model.

*3.1.3. Human studies*

46 Dental Implantology and Biomaterial

histological analyses may be essential.

*3.1.4. Three-dimensional oral tissue engineering*

The composition of the connective tissue interface towards implants was studied in both animal experiments and human biopsy materials. While human studies are very limited due to ethical issues, the evidence of epithelial and connective tissue attachment around periimplant regions are obtained mostly from failed implant [43], autopsy[44, 45] or clinical studies [1, 46], where the presence of connective tissue attachment on these studies is still difficult to demonstrate. Most of the human studies that have been carried out were clinical studies in which the traditional periodontal parameters were used for monitoring the soft tissue responses around dental implants intra-orally. According to clinical studies that involve the marginal bone levels, we can conclude that bone level is stable as it implies that the soft tissue integration has not migrated apically [1, 46, 47]. Liljenberg et al. [48] in their study of soft tissue biopsies of edentulous ridge mucosa and peri-implant mucosa revealed that the composition of both tissues were nearly identical in terms of collagen, cells and vascular structures. The peri-implant mucosa harboured a junctional epithelium that contained significantly enhanced numbers of different inflammatory cells infiltration. On the other hand, Piatelli et al. [44] found that there was no inflammatory infiltrate in epithelium or connective tissue in human autopsy biopsies of titanium dental implants. It is also interesting to note that the collagen fibres in the coronal part were parallel to implant surface while in the apical region the fibres were in a perpendicular fashion was found. Additionally, Glauser et al. [49] used both hard and soft tissue biopsies of mini titanium implants with different surface characteristics to demonstrate the establishment of junctional epithelium attachment to the implant surfaces. They noted that collagen fibres and the fibroblasts were oriented parallel to the implant surface. The oxidized and acid-etched implants revealed less epithelial downgrowth and longer connective tissue than machined implants [49]. As for different types of materials, Vigolo et al. [46] assessed the peri-implant mucosa around abutments made of gold alloy and titanium and found no difference between the two types of abutments with regard to peri-implant marginal bone level and soft tissue parameters. Meanwhile, Nevins et al. [26] using en bloc biopsy demonstrated intimate contact of junctional epithelium cells to implant surface and connective tissue with functionally oriented collagen fibres running towards the implant surface designed with Laser-Lok microchannels. Nonetheless, it is unethical to remove implant in order to attain en bloc tissue for histological analyses in human, and data from autopsy did not necessarily represent the ultrastructural nature of the peri-implant interface. In addition, not all animal experiments can be replicated in human samples due to cost and ethical considerations. For this reason, the investigation of peri-implant interface for improvement of connective tissue attachment is rather difficult to conduct in human. Thus, the need of development of different models for

As the opportunity to undertake human studies is limited, many studies that evaluated the peri-implant interface were carried out using animal models. With advances in knowledge on tissue regeneration, tissue-engineered oral mucosal equivalents (three-dimensional oral mucosal model, 3D OMM) have been developed for clinical applications and also for con‐

**Figure 4.** Hemidesmosome-like structures (black arrows) formed from 3D OMM and specimens (Ti). P = polished and M = machined surfaces. (Reproduced with permission from [19]).

#### **3.2. Analyses of the soft tissue-implant interface**

The soft-tissue implant interface can be investigated through histomorphometric and histo‐ logic analyses. Of both, the preparation for latter analysis is very difficult to carry out especially if the implant is attached to the tissue. The histological studies also allow identification of specific protein markers expressed by any of the tissue or cells in response to dental implants. The histological sections can then be analysed under different types of microscopies. Among the known microscopic analyses for assessing the peri-implant interface are light microscopy (LM), scanning electron microscopy (SEM), confocal laser scanning microscopy (CLSM), focus ion beam (FIB) and transmission electron microscopy (TEM). Similar to SEM, CLSM allows assessment of peri-implant interface and cell-cell interaction without the need of histological processing as for light microscopy. With these two, direct visualization of implant-soft tissue interface is possible with appropriate preparation for each microscopy. The tissue or specimens can also be fluorescently labelled for the identification of adhesion molecules or cells and examined under CLSM [43]. While the use of 3D oral mucosal model with implant material intact may allow direct examination of the connective tissue attachment, the method to prepare the specimen still remains challenging and technically demanding. More studies in term of optimization of certain promising technique such as FIB for TEM analysis must be explored in order to obtain the ultrastructural nature of the implant-soft tissue interface.


*2.7. Analysis Related Cis-Elements in the Candidate DlWRKY Genes*

To analyze the potential function of *DlWRKY* genes in response to various responses, the *cis*-elements in the promoter region of the *DlWRKY* genes were further analyzed. Among these 55 genes, 54 genes could perform *cis*-elements analysis except *DlWRKY*45, which only contain 270 promoter bases. All the *DlWRKY* genes shared the light-responsive boxes and stress-responsive boxes in their promoter. Hormone-related *cis*-elements, such as AuxRR-core, TCA-element, CGTCA-motif, GARE-motif, P-box, and ERE (Ethylene-responsive element), existed in the promoter of all *DlWRKY* genes except *DlWRKY*11, *DlWRKY*41, and *DlWRKY*52. Additionally, circadian-related *cis*-elements were found in the promoter of 39 *DlWRKY* genes and Meristem-related *cis*-elements were only presented in the promoter of 20 *DlWRKY* genes (Figure 7, Tables S5 and S6).

*Int. J. Mol. Sci.* **2018**, *19*, x FOR PEER REVIEW 11 of 20

**Figure 6.** The expression patterns of the selected *DlWRKY* genes under various hormonal and abiotic stresses. The x-axis indicates various treatments and the y-axis indicates the relative expression level. Error bars were obtained from three independent biological replicates. Values with the same letter were not significantly different when assessed using Duncan's multiple range test (*p*  < 0.05, *n* = 3). SA represents salicylic acid, JA represents jasmonic acid, HS represents heat stress, CS represents cold stress, DS represents drought stress, and SS represents salinity stress. **Figure 6.** The expression patterns of the selected *DlWRKY* genes under various hormonal and abiotic stresses. The x-axis indicates various treatments and the y-axis indicates the relative expression level. Error bars were obtained from three independent biological replicates. Values with the same letter were not significantly different when assessed using Duncan's multiple range test (*p* < 0.05, *n* = 3). SA represents salicylic acid, JA represents jasmonic acid, HS represents heat stress, CS represents cold stress, DS represents drought stress, and SS represents salinity stress.

*Int. J. Mol. Sci.* **2018**, *19*, x FOR PEER REVIEW 12 of 20

**Figure 7.** The predicted *cis*-elements in the promoter of the *DlWRKY* genes. The 1.5 kb sequences of 55 *DlWRKY* genes were analyzed with the PlantCARE software. **Figure 7.** The predicted *cis*-elements in the promoter of the *DlWRKY* genes. The 1.5 kb sequences of 55 *DlWRKY* genes were analyzed with the PlantCARE software.

#### **3. Discussion 3. Discussion**

The WRKY proteins, an important transcription factor superfamily which is involved in plant development and stress responses, have been widely detected in various organisms from single-celled green algae to monocots and dicots [15]. Recently, the successful genome sequencing of longan makes it possible to analyze WRKY TFs at the whole-genome level [37]. The present study is the first to identify and characterize WRKY proteins from whole-genome sequences of The WRKY proteins, an important transcription factor superfamily which is involved in plant development and stress responses, have been widely detected in various organisms from single-celled green algae to monocots and dicots [15]. Recently, the successful genome sequencing of longan makes it possible to analyze WRKY TFs at the whole-genome level [37]. The present study is the first to identify and characterize WRKY proteins from whole-genome sequences of longan.

longan. In this study, we identified 59 candidate WRKY genes in the longan genome (471.88 Mb) using the HMM and Blastn search methods. These genes included 58 *DlWRKYs*, which were also found by Lin et al. [37], and one gene *Dlo\_*022548.1 (*DlWRKY*36) found in our study. Finally, after the WRKY domain scanning and sequence alignment, 55 *DlWRKY* genes were determined in the longan genome (Table 1). The number of *WRKY* genes in longan was similar to those found in grape (59 *VvWRKYs*), whose genome size is 487 Mb, which is similar to that of the longan genome [29]. However, the size of the WRKY family in longan is smaller than that in *A. thaliana* (72), *Oryza sativa* ssp. *Indica* (102), and the common bean (88), although their genome sizes are similar (*O*. *sativa* ssp. *Indica*, 466 Mb; common bean, 587 Mb) or even smaller (*A. thaliana*, 119 Mb) than the longan genome size (Table S7) [28,38,39]. Therefore, the number of WRKY family members is not necessarily correlated with the genome size. Previous studies showed that the only group I *WRKYs* are present in green algae and all *WRKY* genes originated from the group I C-terminal WRKY domains, whereas group II members were evolved in the common ancestor of land plants, and Group III members emerged in the common ancestor of seed plants [15]. In addition, as a newly defined and the most dynamic group with many duplication events, the differences in the number of *WRKY* genes in Group III are the primary cause of the sizes of *WRKY* gene families [40]. In the present study, the differences in the number of *WRKY* genes between longan and *Arabidopsis* In this study, we identified 59 candidate WRKY genes in the longan genome (471.88 Mb) using the HMM and Blastn search methods. These genes included 58 *DlWRKYs*, which were also found by Lin et al. [37], and one gene *Dlo\_*022548.1 (*DlWRKY*36) found in our study. Finally, after the WRKY domain scanning and sequence alignment, 55 *DlWRKY* genes were determined in the longan genome (Table 1). The number of *WRKY* genes in longan was similar to those found in grape (59 *VvWRKYs*), whose genome size is 487 Mb, which is similar to that of the longan genome [29]. However, the size of the WRKY family in longan is smaller than that in *A. thaliana* (72), *Oryza sativa* ssp. *Indica* (102), and the common bean (88), although their genome sizes are similar (*O*. *sativa* ssp. *Indica*, 466 Mb; common bean, 587 Mb) or even smaller (*A. thaliana*, 119 Mb) than the longan genome size (Table S7) [28,38,39]. Therefore, the number of WRKY family members is not necessarily correlated with the genome size. Previous studies showed that the only group I *WRKYs* are present in green algae and all *WRKY* genes originated from the group I C-terminal WRKY domains, whereas group II members were evolved in the common ancestor of land plants, and Group III members emerged in the common ancestor of seed plants [15]. In addition, as a newly defined and the most dynamic group with many duplication events, the differences in the number of *WRKY* genes in Group III are the primary cause of the sizes of *WRKY* gene families [40]. In the present study, the differences in the number of *WRKY* genes between longan and *Arabidopsis* mainly existed in groups IIc and III, indicating that the group IIc and III *WRKY* genes may play important roles in the functional evolution of *DlWRKYs*.

mainly existed in groups IIc and III, indicating that the group IIc and III *WRKY* genes may play important roles in the functional evolution of *DlWRKYs*. According to the classification scheme for the WRKY family of Eulgem et al. [41], the DlWRKY proteins were divided into three distinct clusters: groups I, II, and III. Group II proteins were further divided into five distinct groups: a–e (Figure 1 and Table 1). In addition, subgroup IIc contained the largest number of WRKY proteins. These results were consistent with the results observed in other species [28,29,42–44]. The WRKY motif was fairly conserved in longan WRKY proteins, and three variants of this motif were observed. All the DlWRKYs, except DlWRKY19 and DlWRKY47, possessed WRKYGQK. DlWRKY19, which belonged to subgroup IIc, possessed WRKYGKK. DlWRKY19, which belonged to subgroup III, possessed WKKYRQK. In the common According to the classification scheme for the WRKY family of Eulgem et al. [41], the DlWRKY proteins were divided into three distinct clusters: groups I, II, and III. Group II proteins were further divided into five distinct groups: a–e (Figure 1 and Table 1). In addition, subgroup IIc contained the largest number of WRKY proteins. These results were consistent with the results observed in other species [28,29,42–44]. The WRKY motif was fairly conserved in longan WRKY proteins, and three variants of this motif were observed. All the DlWRKYs, except DlWRKY19 and DlWRKY47, possessed WRKYGQK. DlWRKY19, which belonged to subgroup IIc, possessed WRKYGKK. DlWRKY19, which belonged to subgroup III, possessed WKKYRQK. In the common bean, the variants WRKYGKK, WRKYGEK, WKKYEDK, and WKKYCEDK are mainly observed in subgroup IIc [28]; in mulberry,

bean, the variants WRKYGKK, WRKYGEK, WKKYEDK, and WKKYCEDK are mainly observed in

WRKYGKK is detected in subgroup IIb [27]. Moreover, in rice, nine variants, most of which belong to groups III and IIc, are observed [45]. Previous studies showed that these variations of the WRKYGQK motif might change the DNA binding specificities of downstream target genes, and WRKY genes with the variations of the WRKYGQK motif may recognize binding sequences other than the W-box element ((C/T)TGAC(C/T)) [15]. Hence, the result suggested that DlWRKY19 and DlWRKY47 may possess different binding specificities and functions from those of other DlWRKY proteins.

WRKY family genes play important roles in diverse plant development and shown a tissue-specific expression in many plant species [15,40]. For example, *AtWRKY*75 exerts a negative effect on root hair development [46]. *SUSIBA*2 [47] and *MINISEED*3 [48] play roles in the regulation of seed development. In grape, nearly half of the 59 *VvWRKY* genes show no significant organ/tissue-related differences in expression, and some clear spatial differences are noted [29]. In mulberry, 13 *WRKY* genes exhibit the highest expression in the *Morus notabilis* root tissue. A maximum of 25 *WRKYs* show the highest expression in the bark tissue, and 10 WRKY genes display the highest expression in other stages [27]. In the present study, the expression profiles of 55 longan *WRKY* genes in nine longan tissues were ascertained by RNA-seq analysis (Figure 4). The results demonstrated variation in the expression pattern of *DlWRKY* genes. In total, 25 *DlWRKY* genes (*DlWRKY*1, 2, 3, 5, 6, 8, 9, 13, 14, 23, 24, 28, 30, 32, 35, 37, 38, 39, 44, 49, 50, 52, 53, and 54) were highly expressed in at least six longan tissues. As highly expressed genes usually play important roles in plant development [44], we concluded that the 25 highly expressed *DlWRKY* genes might be important regulatory factors in longan development. It was found that group I and group IId *WRKY* genes are ancestral to other *WRKY* genes in plants or algae and are more likely to be constitutively expressed in different tissues [15,40]. For instance, most of the highly expressed *SiWRKY* genes belonged to group I and IId [40]. Consistent with these studies, in the present study, most of the members of groups I (9 of 11) and IId (4 of 6) were the highly expressed gene. In contrast, 12 *DlWRKY* genes were expressed at low levels in all tested tissues and these minimally expressed *DlWRKY* genes were distributed in almost all the *WRKY* gene subgroups except for IId. Meanwhile, six *DlWRKY* genes were preferential accumulation in no more than three tissues, implying that these genes might play crucial roles during the development of specific organs. Additionally, these specifically or minimally expressed *DlWRKY* genes could be induced under environment stimuli. For example, *DlWRKY*10, 22, 41, and 47 were not detected in leaves under normal conditions, but they were induced by different abiotic stresses (Figure 6). Similar results were also found in other studies [15,40,49].

Perpetual flowering is a crucial trait for fruit trees as it enlarges the production period [50]. To date, the genetic control of PF has been deciphered in several model plants. For example, In *Arabidopsis*, the PF trait is controlled by *PERPETUAL FLOWERING 1* (*PEP1*), an orthologue of the FLC floral repressor [51]. In the diploid strawberry and rose, the PF trait is due to a mutation in the orthologue of the *TERMINAL FLOWER* 1 (*TFL*1) floral repressor [50,52]. Recent studies showed that the PF trait of some cultivated strawberries is genetically controlled by the major *FaPFRU* locus, which is non-orthologous to *TFL*1 [53,54]. However, the multi-year delay in the onset of flowering and the long juvenile phase hampers the research of PF traits in perennials, such as longan. Although WRKY TFs regulate various plant developments, only a few data are available on whether WRKY TFs are involved in the flowering time regulation. Meanwhile, as a kind of TF, WRKY genes regulated plant flowering by being directly active or inhibiting the downstream target gene. For example, promoter sequences of *FT*, *LFY*, and *AP1* harbor W-boxes (TTTGACT/C); *AtWRKY*71 affects the flowering time of plants by directly regulating these genes [16]. In our study, all the 55 *DlWRKY* genes were constructively expressed in the three test flower induction process of the "SX" longan, while 18 *DlWRKY* genes showed a specific expression in the "SJ" longan (Figure 5a). This result indicated that these 18 *DlWRKY* genes may specifically be involved in the flower induction of "SJ". In summary, we proposed that these 18 *DlWRKY* genes may participate in the forming of the longan PF habit, which further studies are required to verify the function of these genes.

WRKY genes play crucial roles in the response to abiotic and biotic stress-induced defense signaling pathways [15]. Numerous studies have demonstrated that WRKY genes are expressed strongly and rapidly in response to particular abiotic stresses [15,22,29,40,52]. Consistent with these previous studies, our study showed that 44 *DlWRKY* genes (80%) showed up- or down-regulated expression in at least one tested treatment (Figure 6 and Figure S1), thereby highlighting the extensive involvement of *WRKY* genes in environmental adaptation. SA, JA, and Eth play important roles in biotic and abiotic stresses [55]. Many *WRKYs*, such as *AtWRKY*28, *AtWRKY*46, *AtWRKY*70, and *AtWRKY*54, play an important role in SA- and JA-dependent defense signaling pathways [53,56,57]. In the present study, 27 and 18 *DlWRKY* genes were up- or down-regulated by SA and MeJA treatment, respectively. For example, *DlWRKY*25, the orthologue of *AtWRKY*70 and *AtWRKY*54, was regulated by the SA and JA treatments. *AtWRKY*25 and *AtWRKY*33 regulate plant adaptation to salinity stress through an interaction with their upstream or downstream target genes [58]; their orthologue *DlWRKY*8 in longan was regulated by SA, JA, heat, drought, and salinity. In grape [29], *VvWRKY*42 and its orthologue *DlWRKY*11 in our study were up-regulated by salt treatment. Furthermore, we observed same orthologous genes with different expression patterns under stress treatment. *DlWRKY*44 was down-regulated under drought, and its orthologous gene *VvWRKY*35 was up-regulated under this stress treatment. *DlWRKY*19 showed no significant differential expression in response to salinity, and its orthologous gene *VvWRKY*25 was up-regulated [29]. We speculate that these orthologous genes may be involved in the different signaling pathways in different species. Additionally, only one gene (*DlWRKY*52) was significantly highly expressed under all abiotic stresses. These results indicated that the different *DlWRKYs* played different roles in regulating stress response and that further investigation of the functions of these *DlWRKY* genes is necessary. Differential responses of several *WRKYs* are regulated by the presence of *cis*-elements in their promoter region [27,40,49]. For example, *Morus*013217, which contains three LTREs in its promoter regions showed a strong response to cold stress [27]. Similar results were also found in our study. For instance, four HSEs were found in the promoter regions of *DlWRKY*2, which showed a strong response to heat stress. *DlWRKY*36, *DlWRKY*46, and *DlWRKY*48 showed responsiveness to SA treatment and their expressions were all up-regulated, and more than two TCA-elements were found in their promoters. While the *DlWRKY*11 and *DlWRKY*52 hormone-related *cis*-elements existed in their promoter, they showed no response to the SA or MeJA treatments (Figure 6 and Table S6). Thus, these *cis*-elements could provide more evidence of the DlWRKY genes in response to different stresses or hormonal signaling.


*4.4. Approximants*

Figure 9a shows fitted approximants for the transfer functions for FEFLOW outputs for experiments 7 and 8 (1D). The exponential functions with a time delay were reasonable approximations for both the perched situation and the unperched situation. For the latter, the approximant underestimated the later stages.

**Figure 9.** (**a**) Fitted approximants for 1D FEFLOW (num-erical) transfer functions for modelling experiments (7) and (8). The approximants are respectively 1 − exp(−0.11 × (t − 0.8)) for t > 2; 1 − exp(−0.32 × (t − 3.5)) for t > 4. (**b**) Superposition of a succession of fitted approximants (a) to reductions of irrigation accession from 350 to 200, 200 to 150 (5 years later), 150 to 100 (5 years later) and 100 to 50 (5 years later) and compared to FEFLOW (Num-erical) output for 2D modelling Experiment 14-1.

Figure 9b showed the superposition of these approximants for a series of successive reductions of irrigation accession from 350 to 200 to 150 to 100 to 50 mm/year at time intervals of 5 years. The superposition of the approximants overestimated recharge during the later stage, but still provided a reasonable approximation for application to groundwater modelling.


**4. SIAM dataflow**

This section outlines the default dataflow of the SIAM chiplet-based IMC architecture. The computation dataflow is illustrated in **Figure 13**. Before starting the inference task, the weights are obtained from the DRAM and allocated to the IMC chiplets according to the partition and mapping engine described in Section III-C. There are two possible scenarios based on this: either no layer is spread across several chiplets, or a layer is distributed across multiple chiplets. Let us consider the case where layer N of the DNN is assigned to the first chiplet, as depicted in **Figure 13(a)**. During computation, the entire layer is processed within one chiplet, generating the computed output activations for layer N. The global accumulator and buffer are not utilized during this process and are turned off. When the computation is done, the output activations are sent to the chiplets that execute layer N + 1. If two chiplets are required to map the weights for layer N + 1, the NoP transfers the output activation from layer

### **Figure 13.**

*Computation dataflow within the chiplet-based IMC architecture in SIAM. Two cases arise: (a) no layer is partitioned across two or more chiplets and (b) a layer is partitioned across two or more chiplets.*

N to both chiplets, as shown in **Figure 13(a)**. **Figure 13(b)** presents the computation flow for layer N + 1, where both chiplets execute computations in parallel. The mapping ensures that an equal number of weights are assigned to each chiplet, avoiding any workload imbalances. After the computation is completed, the generated partial sums are aggregated using the global accumulator and buffer. Then, the accumulated outputs from layer N + 1 are transferred to the chiplets that hold the weights for layer N + 2. This process is repeated until all layers are processed and the final output is generated. The algorithmic implementation of the dataflow used in the SIAM IMC chiplet architecture is explained in Algorithm 4.


**4. Conclusions**

This work presents the results obtained from a study of the effects of both: SiC disper‐ sion in the metallic matrix and thermal treatment on the tribological characteristics (hardness, wear resistance, and coefficient of friction) of electrodeposited Ni-P-SiC coatings.

Our results show that the dispersion of SiC particles in the metallic matrix improves coating tribological properties such as hardness and wear resistance while diminishing the friction coefficient.

The best results were obtained when a current density of 0.042 A/cm2 was used in an electrolytic bath containing 0.02 g mL-1 of SiC. Such conditions produce a metallic matrix with a concentration of 0.52 at.% SiC particles that in turn increase the Ni-P hardness from 430 HV (for the case when SiC particles are absent) to 600 HV. Despite the observed enhancement in hardness, such values are still below those exhibited by hard Cr coatings (1020 HV).

The XRD results indicated that the Ni-P-SiC composites were amorphous in nature. Thermal treatment between 400 °C and 500 °C changed the microstructure of the Ni-P-SiC matrix from amorphous to crystalline. Thermally treating the Ni-P-SiC coatings at temperatures ≥500 °C transformed the amorphous alloy into a continuous Ni3P layer containing isolat‐ ed Ni crystals.

An increased hardness and a decreased wear coefficient were observed after heat treat‐ ment for 60 min at 500 °C because of the formation of a Ni3P phase.

We also observed that the wear volume was inversely proportional to the microhardness of the deposits. As a result, the Ni-P-SiC coatings that were thermally treated at 500 °C possessed the greatest wear resistance; this resistance was superior even to that of hard Cr coatings.


**2.2.2 Insulin**

Insulin is involved in the upregulation of megalin, which in turn may be associated with increased endocytosis and metabolism of glomerular-filtered proteins, leading to intracellular metabolic overload. Cross-talk between insulin-insulin receptor substrate/phosphoinositide 3-kinase and angiotensin II-ERK1/2 signaling pathways was previously observed in cultured PTCs (Hosojima et al. 2009), and the balance between the actions and interactions of angiotensin II and insulin may also be important in regulating the expression of megalin *in vivo*. Hyperinsulinemia associated with metabolic syndrome is thought to stimulate insulin signaling in PTCs, while impaired insulin signaling (so-called insulin resistance) is suggested to occur in kidney cells in type 2 diabetic models; moreover, the intrarenal activation of angiotensin II appears to be associated with the development of insulin resistance (Tiwari et al. 2007). Recently, megalin was shown to be downregulated by the lipopolysaccharide-tumor necrosis factorα-ERK1/2 signaling pathway that may also impair insulin signaling in diabetes (Takeyama et al. 2011). In addition, positive control of megalin expression has been demonstrated by peroxisome proliferator-activated receptor isoforms peroxisome proliferator-activated receptor (PPAR)α and PPARγ and their insulin-sensitizing agonists (Cabezas et al. 2011).


**1. Introduction**

During drilling, the pipe is said to be stuck when the drill string cannot rotate or move. Pipe stuck is happened due to many reasons like inadequate hole cleaning, fractured and faulted formation, and junk inside the hole. There are two mechanisms of pipe sticking; mechanical sticking and differential sticking. Both mechanisms are affected by fluid design and mud cake [1]. Mechanical pipe sticking occurs due to many drilling obstructions like inclination of the well or key seat difficulty, wherefore high attention, and continuous measurement are required to prevent any changes in

bore deflection [1, 2]. There are many reasons for pipes being stuck such as the inefficiency of lifting the excavated rock fragments, swelling of the shale layers, or the drilling column being lowered in narrow areas. Mechanical sticking happens when the pipe is in motion and sticks due to insufficient cutting slip velocity, poor mud properties, and Poor hole-cleaning. The friction between the tube and wall,) as the well wall generates a groove (which causes inflexibility. Well demolition or rock pieces accumulation inside the mud due to bad clay properties of viscosity also leads to bad sticking and high difficulty of pulling or lowering the pipe during connection [3, 4]. Due to rock interaction, the nature of induced drill strings will be non-smooth. The desirable response of the system is to operate in equilibrium, with a constant rotation, but permanent vibrations or permanent sticking can occur due to large perturbation in the system. Borehole instability is a problem associated with shear failure at the wellbore walls. When the mud pressure is not enough for backing the wellbore, in this case, the peripheral stresses around the wellbore will exceed the rock strength and mechanical sticking will occur. In vertical and deviated wells, the significant wellbore instability problem is a stuck pipe which causes the non-productive time for the majority field's development [5].

The problems and challenges that result in the possibility of blockage of the drilling pipes are included a decrease in productivity, loss of time, and additional operations that lead to the cost increase [6]. All operations will temporarily stop during the nonproductive time while drilling. In addition to financial losses and lost time, there will be a possibility leading to weakening the economics of a project [7]. The mitigation of stick-slip problems via analysis is a very important step in controlling the effective parameters. The parameter combinations that are affected by the rough nature of rock interaction should be determined previously to suppress and control the stick-slip during the drilling process [8]. The main optimization drilling factors are concerned with the rate of penetration, intelligent drilling, wellbore design, and increasing drill tool life. At this point, drilling technologies start using some special analysis trajectories for implementing directional wells in hard rock, and deep well. However, these trajectories may involve some types of complex drilling and will lead to a high-cost increase [9]. Moreover, drilling performance can be limited and effect by many parameters like shocks and drill string vibrations. The mechanical sticking due to the interactions between the borehole wall, drill string, and mud cake during the drill string stopping needs to be predicted to avoid the risks. Drilling problems like loss of circulation, torque, drag, and cuttings transport can be predicted and solved by using the general scheme by utilization of effective drilling data [10]. The crucial task to preventing a stuck pipe is the early identification of the problem. Spotting oil around the pipe section is the more usual way to reduce the pressure and free the stuck pipe [11].

A theoretical evaluation method is a measurement tool used to investigate and control a drilling string. Stuck pipe occurrence is an undesirable event and leads to string failure. Consequently, the analysis and calculation of the drilling conditions are very essential. Pipe sticking that happened in some deviated wells is considered a significant drilling problem. Simulation and analysis are accurate tools for predicting and avoiding severe collapse or failure and decreasing uncertainties of nonproductive time [12]. Predicting method like Artificial Neural Network (ANN) is the most powerful tools to avoid stuck pipe risks and can be employed for modifying the drilling variables. It's applicable whenever the relationships between parameters are complicated, and the prediction accuracy may exceed 90% [13]. The Genetic algorithm optimization-based artificial intelligence (AI) is used as an efficient predictive

*Modeling and Analysis Techniques for Solving Mechanical Pipe Sticking Problems… DOI: http://dx.doi.org/10.5772/intechopen.107307*

analytics technique for drilling pipe stuck. The key contribution was to mitigate the pipe stuck through automate pipe stuck classification by using ML algorithms. During a drilling operation, the most expensive problem that can occur is the pipe stick. Complications of this problem can account for approximately half of the total drilling cost. A genetic algorithm (GA) can optimize the drilling parameters and be used to mitigate the risk of drilling pipe stuck [14]. There are some important procedures and considerations that should follow for the best results in pipe releasing. It involves that the pipe should be free in a short time after sticking. The procedure of freeing the pipe is shown in **Figure 1** [15]. Sufficient hole cleaning is one of the essential factors of a drilling operation's success. Inadequate hole cleaning and wellbore instability are the main factors that caused the mechanical sticking. Non-sufficient hole cleaning is one of the crucial causes of non-successful drilling operations, especially in deviated wells when the inclination angle is between 30 and 60 degrees.

In this chapter, the main causes of mechanical stuck pipe problems have been explained. The finite element software (ANSYS/2019 R3), has been adopted for this analysis to find some possible solutions for mitigating these effects on the drilling process. The findings of this study present the outcome analysis of mechanical stuck pipe which is summarized by dynamic analysis.

**Figure 1.** *The procedure of freeing the pipe [15].*


**2. Experimental**

#### **2.1 Reagents**

All the reagents that were used were of analytical grade. The water used for the preparation of solutions in the experiment was deionized. The solutions to be evaluated were prepared by dissolution of salts of nickel (Ni(NO3)2 6H2O), cobalt (Co(NO3)2⋅6H2O), and copper (Cu(NO3)2) in concentrations from 0 to 1,000 ppm.

#### **2.2 Preparation of the adsorbent**

Brushite natural (nDCPD) was obtained from bovine bone, which was washed with hot water several times to remove tissue debris, and then it was dried at 353 K for 24 h. Next, the bones were crushed and sieved to obtain a particle size of 150 mesh (104 μm). Then, the powder obtained was treated with HCl and NaOH solution, 10−2 M, respectively, using a ratio of 30% w/v. Finally nDCPD was stored until its use [1].

### **2.3 Characterization of natural brushite**

The X-ray diffraction patterns (XRD) were obtained in a Rigaku diffractometer (Ultima IV). The Fourier transform infrared studies of the samples were performed in an IR 100 Analyzer spectrophotometer (PerkinElmer), in a range of 400–4000 cm−1 . The scanning electron microscopy (SEM) images were obtained in a JOEL equipment (6510-Plus).


*Edited by Philippe E. Spiess*

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