Possibility theory in constraint satisfaction problems: Handling priority, preference and uncertainty
Didier DUBOIS – Hélène FARGIER – Henri PRADE
Institut de Recherche en Informatique de Toulouse (I.R.I.T.) – CNRS
Université Paul Sabatier, 118 route de Narbonne
31062 Toulouse Cedex – France
Tel.: (+33) 61.55.69.42 – Fax: (+33) 61.55.62.39
Email: {dubois, fargier, prade}@irit.fr
Abstract
In classical Constraint Satisfaction Problems (CSPs) knowledge is embedded in a set of hard constraints, each one restricting the possible values of a set of variables. However constraints in real world problems are seldom hard, and CSP's are often idealizations that do not account for the preference among feasible solutions. Moreover some constraints may have priority over others. Lastly, constraints may involve uncertain parameters. This paper advocates the use of fuzzy sets and possibility theory as a realistic approach for the representation of these three aspects. Fuzzy constraints encompass both preference relations among possible instanciations and priorities among constraints. In a Fuzzy Constraint Satisfaction Problem (FCSP), a constraint is satisfied to a degree (rather than satisfied or not satisfied) and the acceptability of a potential solution becomes a gradual notion. Even if the FCSP is partially inconsistent, best instanciations are provided owing to the relaxation of some constraints. Fuzzy constraints are thus flexible. CSP notions of consistency and k-consistency can be extended to this framework and the classical algorithms used in CSP resolution (e.g., tree search and filtering) can be adapted without losing much of their efficiency. Most classical theoretical results remain applicable to FCSPs. In the paper, various types of constraints are modelled in the same framework. The handling of uncertain parameters is carried out in the same setting because possibility theory can account for both preference and uncertainty. The presence of uncertain parameters lead to ill-defined CSPs, where the set of constraints which defines the problem is not precisely known.
Keywords: Constraint satisfaction problem; possibility theory; fuzzy restriction; softness; uncertainty; preference; priority.
1. Introduction
Classical Constraint Satisfaction Problems (CSPs) only consider a set of hard constraints that every solution must satisfy. This rigid representation framework has several drawbacks. First, some problems are over-constrained and have no solutions. A relaxation of the less rigid or important constraints must be performed in order to obtain a solution. Discovering that a problem has no solution may be time-consuming and devising an efficient constraint relaxation method is far from easy. Alternatively, other problems lead to a large set of equally possible solutions, although there often exist preferences among them which remain unexpressed. But, a standard CSP procedure will pick a solution at random. As a matter of fact, in practice, constraints are not always strict and it is desirable to extend the CSP framework in order to accommodate flexible constraints. Devising a framework for representing the flexibility of constraints will avoid artificially unfeasible problems (constraints being self-relaxable), and will avoid the random choice of solutions to loosely constrained problems. By flexible constraints, we mean either (i) soft constraints, which directly express preferences among solutions (i.e., this is a ranking of instantiations which are more or less acceptable for the satisfaction of a soft constraint), or (ii) prioritized constraints, that can be violated if they conflict with more prioritary constraints.
In soft constraints, the flexibility accounts for the possibility of going away from instantiations that satisfy the constraints ideally. Notice that the interest in soft constraints can be traced back to the early CSP litterature; in 1975, Waltz [1] mentioned that he heuristically distinguished between "likely" instanciations of a constraint and "unlikely" ones which are only considered if necessary. Also in computer vision, in 1976 Rosenfeld et al. [2] modelled preference in the detection of convex objects for scene labeling problems, and proposed to use a fuzzy degree of constraint satisfaction. The idea of representing relative preferences by means of weights is also at work in the relaxation labeling process described in 1983 by Hummel and Zucker [3]. More recent works either propose to use fuzzy sets in modeling such constraints [4][5][6][7][8] or to progressively relax the constraints when preferences are in conflict [9].
In prioritized constraints the flexibility lies in the ability to discard constraints involved in inconsistencies, provided that they are not too important. Generally, a weight is associated with each constraint and the request is to minimize the greatest priority levels of the violated constraints [10][11]. More generally, Brewka et al. [12], and Borning et al. [13] identify different forms of constraint relaxation, viewing each constraint as a strict partial order on value assignment and weighting the importance of constraints; in particular, Brewka et al. provide a formal semantics in relation to nonmonotonic reasoning by means of maximal-consistent subsets of constraints.
Freuder [14], Freuder and Wallace [15], and Satoh [16] have devised theoretical foundations for the treatment of flexibility in CSPs. Satoh tries to apply results in nonmonotonic reasoning based on circumscription to the handling of prioritized constraints so as to induce preference relations on the solution set. A similar point of view is adopted by Lang [17] where prioritized constraints are expressed in possibilistic logic (i.e., a logic with weigthed formulas which has a nonmonotonic behaviour in case of partial inconsistency). Taking a dual point of view, Freuder [14] regards a flexible problem as a collection of classical CSPs. A metric can then be defined that evaluates the distance between them. Then, the question is to "find the solutions to the closest solvable problems".
In order to take into account both types of flexibility, a generalization of the CSP framework has been proposed [18], based on Zadeh's possibility theory [19]: the Fuzzy Constraint Satisfaction Problem framework (FCSP). The main point is that both types of flexible constraints are regarded as local criteria that rank-order (partial) instantiations and can be represented by means of fuzzy relations. In a FCSP, constraint satisfaction or violation are no longer an all-or-nothing notion: an instanciation is compatible with a flexible constraint to a degree (belonging to some totally ordered scale). The notion of consistency of a FCSP also becomes a matter of degree. The question is then to combine the satisfaction degrees of the fuzzy constraints in order to determine the total ordering induced over the potential solutions and to choose the best ones. Making a step further, we propose to use this framework also to handle more complex constraints, e.g., nested conditional constraints.
Moreover, the framework offered by possibility theory enables us to represent ill-known parameters, whose precise value is neither accessible nor under our control, under the form of so-called possibility distributions (where the possible values are rank-ordered according to their level of plausibility). Ill-known parameters contrast with decision variables on which a decision-maker has control. This paper shows that constraints whose satisfaction depends on these ill-known parameters can be represented in the setting of possibility theory as well. In the presence of ill-known parameters, robust solutions should be searched for, such that the constraints be satisfied whatever the values of these ill-known parameters. Possibility theory implements this idea in a flexible way. Lastly, ill-known parameters lead to the idea of ill-defined CSPs; by ill-defined CSP we mean a CSP for which we are uncertain about the precise set of constraints which defines it, this uncertainty being due to ill-known factors. This aspect can also be accounted for in our framework.
From an algorithmic point of view, the possibility of extending Waltz' algorithm to fuzzy constraints has been pointed out by Dubois and Prade [20] and by Yager [21]. As we will show, all the classical CSP algorithms (e.g., tree search, AC3, PC2) can easily be adapted to FCSPs. More generally, our framework reveals itself powerful enough to accommodate the definitions of local consistency of a problem (arc-consistency, 3-consistency, k-consistency) —interestingly enough, investigations by the second author [22] indicate that the theoretical results relating levels of local consistency of a CSP to its global consistency [23][24] remain valid in FCSPs.
The next section deals with representation issues concerning flexible constraints. Fuzzy subsets on Cartesian products of domains, i.e., fuzzy relations, are used to model soft and/or prioritized constraints. An illustrative example is provided. The agreement of this representation with the preferential semantics of possibility theory is emphasized. Then the extension (resp.: projection) of fuzzy constraints to larger (resp.: smaller) Cartesian products of domains is recalled as well as the conjunctive or disjunctive combinations of fuzzy relations for representing compound constraints. Finally, this section devoted to representation issues
discusses the modelling of more sophisticated constraints, namely prioritized constraints with safeguard (in order to guarantee the satisfaction of a weaker constraint in case of violation of the prioritized one) and conditional constraints. Then Section 3 formally defines the FCSP framework and compares it to other approaches to flexibility in CSP. This section then presents the essentials of a Branch and Bound algorithm performing the search for the best solutions. Nonmonotonic aspects of FCSPs are also outlined. Different notions of local consistency (arc-consistency, k-consistency) of a FCSP are defined in Section 4; the complexity of extensions of filtering algorithms (e.g., AC3) to the fuzzy set framework is also discussed. Section 5 explains how to handle ill-known parameters pervaded with uncertainty in FCSPs. Before the general conclusion, Section 6 briefly discusses the modelling of ill-defined CSPs in the possibilistic framework. In this setting, ill-defined hard CSPs, where the belonging to the problem of each constraint is (individually) uncertain, are shown to be formally equivalent to FCSPs made of prioritized hard constraints. The relation between ill-defined hard CSPs and CSPs with ill-known parameters is also briefly discussed.
2. Representing Flexible Constraints
A hard constraint C relating a set of decision variables {x1, …, x n} ranging on respective domains D1, …, D n is classically described by an associated relation R: R is the crisp subset of D1 × …×D n that specifies the tuples d = (d1, …, d n) of values which are compatible with C. The set {x1, …, x n} of variables related by R will be denoted by V(R).
2.1. Fuzzy Model of a Soft Constraint
A soft constraint C will be described by means of an associated fuzzy relation R [25], i.e., the fuzzy subset of D1 ×…×D n of values that more or less satisfy C. R is defined by a membership function μR which associates a level of satisfaction μR(d1, …, d n) in a totally ordered set L (with top denoted 1 and bottom denoted 0) to each tuple (d1, …, d n) ! D = D1×… × D n. This membership grade indicates to what extent d = (d1, …, d n) is compatible with (or satisfies) C. Thus, the notion of constraint satisfaction becomes a matter of degree:
μR(d1, …, d n) = 1means(d1, …, d n) totally satisfies C
μR(d1, …, d n) = 0means(d1, …, d n) totally violates C
0 < μR(d1, …, d n) < 1means(d i, …, d n) partially satisfies C
Hard constraints are particular cases of soft constraints, since they involve levels 0 and 1 only.
A soft constraint involving preferences between values is regarded as a local criterion ordering the instantiations of C, preferences levels being represented in the scale L: μR(d1, …, d n) >μR(d'1, …, d'n) means that the first instantiation is preferred to the second one. Interpreting the preference degrees as membership degrees leads to represent a soft constraint by a fuzzy relation.
The assumption of a totally ordered satisfaction scale underlying the above setting may be questioned. The very use of a satisfaction scale instead of just an ordering relation is crucial when it comes to the aggregation of local satisfaction levels. Indeed due to the famous Arrow theorem (e.g., Moulin [26]), it is very difficult to merge several ordering relations that are not commensurate. The satisfaction scale needs not be totally ordered stricly speaking, since a complete lattice will do as well. In the following we assume that L is a totally ordered set, i.e.,
a chain. But the scale of membership grades needs not be numerical, as pointed out years ago
[27]. A qualitative scale makes sense on finite domains. However on continuous domains, as in the case of temporal constraints with continuous time, it is much more natural and simple to
assume that the satisfaction scale is the unit interval; then levels of satisfaction reflect distances to ideal values in the domain.
2.2. Fuzzy Model of a Prioritized Constraint
Fuzzy relations also offer a suitable formalism for the expression of prioritized constraints. When it is possible to a priori exhibit a total preorder over the respective priorities of the constraints, these priorities will be represented by levels in another scale V: a priority degree Pr(C) is attached to each constraint C and indicates to what extent it is imperative that C be satisfied. First consider the case of hard constraints. Pr(C) = 1 means that C is an absolutely imperative constraint while Pr(C) = 0 indicates that it is completely possible to violate C (C has no incidence in the problem). Given two constraints C and C', Pr(C) > Pr(C')means that the satisfaction of C is more necessary than the satisfaction of C'. If C and C' cannot be satisfied simultaneously, solutions compatible with C will be preferred to solutions compatible with C'.
In fact, the scale V can be interpreted as a "violation scale": the greater Pr(C), the less it is possible to violate C. This remark leads us to relate the satisfaction scale L to the violation scale V, considering that there exists an order-reversing bijection from V to L such that L = c(V)= {c(v), v ! V}: c(0)and c(1) are respectively the top element and the bottom element of L, and v " v' in V implies c(v) # c(v') in L. This is one of the basic modeling assumptions in this paper: the c-complement of the level of priority of a constraint is interpreted as the extent to which the constraint can be violated, using the reversed priority scale L = c(V)as a satisfaction scale; L is nothing but V put upside down. Since Pr(C) represents to what extent it is necessary to satisfy C, c(Pr(C)) indicates to what extent it is possible to violate C, i.e., to satisfy its negation. In other words, the constraint C is considered as satisfied at least to degree c(Pr(C)) whatever the considered solution, whether it satisfies C or not. More precisely, the prioritized constraint (C, Pr(C)) is considered as totally satisfied by a tuple if C is satisfied, and satisfied to degree c(Pr(C)) if the tuple violates C. Hence c(V)can be identified to a satisfaction scale as in
the previous section, and a prioritized constraint C may be represented by the fuzzy relation (see Figure 1):
μR (d 1, …, d n ) = c(0) = 1
if (d 1, …, d n ) satisfies C;μR (d 1, …, d n ) = c(Pr (C))
if (d 1, …, d n ) violates C.
μR (d)1, …, d n )
1 – Figure 1. A hard (or crisp) constraint C with priority Pr (C) = $ when c(x) = 1 – x.
Note that when Pr(C) = 1, the characteristic function of C is recovered, while when Pr(C) = 0the constraint C degenerates into the whole domain D.
Conversely, a soft constraint C where preferences are described in terms of a finite number of satisfaction degrees 0 = $0 < $1 <… < $p < 1 in a scale L, can be represented by a finite set of prioritized constraints {C j , 0 # j < p} using the scale L put upside down as a priority scale, via an order-reversing map c:
Pr(C j ) = c($j )defining R j = {(d 1, …, d n ), μR (d 1, …, d n ) " $j+1}, j = 0, p – 1.If moreover it is assumed that c is involutive, that is c(c($)) = $ (this hypothesis is made throughout the whole paper), then it is straightforward to reconstruct the soft constraint C by means of the set of prioritized constraints {(C j , Pr(C j )), 0 # j < p} as shown in Figure 2 where:
μR (d) = min j max(c(Pr(C j )), μR j (d)) for every tuple d = (d 1, …, d n )(1)
$$$$0μR (d)
$d n )
values satisfying C 0, priority 1 – $0 = 1Figure 2. Decomposition of a soft constraint into a family of
prioritized constraints when c(x) = 1 – x.
Finally, a prioritized soft constraint C corresponds to the following fuzzy relation:
μR'(d 1, …, d n ) = max(c(Pr (C)), μR (d 1, …, d n ))(2)
where R is the fuzzy relation describing the preferences of C only. Viewing the soft constraint expressed by R as a family of nested prioritized constraints, the global priority Pr(C) attached to the soft constraint C means that we forget the priorities higher than Pr(C) in the expression of R since
max(c(Pr(C)), μR (d)) = max(c(Pr(C)), min j max(c(Pr(C j )), μR j (d)))
= min j max(c(min(Pr(C), Pr(C j ))), μR j(d)).
To conclude with representation issues, prioritized and soft constraints can be cast in a unique setting that we call "flexible constraints", modelled by fuzzy sets, where flexibility means the capabibility of self-relaxation. This capability is locally imbedded in the description of the constraint, thus avoiding the necessity of a specific constraint relaxation procedure to be
triggered when a set of constraints is found inconsistent. This unification presupposes a strong link between levels of constraint satisfaction, and levels of constraint priority, using a single ordered scale L for both priority and satisfaction and an order-reversing map c that changes one notion into the other. For simplicity, we sometimes use L = [0,1] and c(x) = 1 – x in the following. However all results to be presented remain valid on a qualitative scale.
2.3. Possibility as Preference
The above approach to the joint handling of soft and prioritized constraints is in complete accordance with the basic principles of possibility theory (Zadeh [19], Dubois and Prade [28]). A possibility distribution % is a mapping from a domain D to a linearly ordered scale L ([0,1] in general). Attached to a variable x, a possibility distribution expresses that the value of x is incompletely specified, as soon as & d1' d2, %(d1) > 0 and %(d2) > 0. %(d) = 0 means that it is impossible, or ruled out, that x = d. A normalized possibility distribution % is such that %(d) = 1 for some d, expressing that no conflict on the value of x is present. In the context of constraint satisfaction problems, x is a decision variable, i.e., its value is controllable, and the problem is to select a suitable value for x. The fuzzy set of admissible values for x according to the associated constraint (more generally the fuzzy relation in case x is a vector of elementary variables) can be viewed as a possibility distribution prescribing to what extent a value is judged to be suitable for x according to the constraint. Hence the degree of possibility %(d) is the degree of preference for choosing x = d, with the convention that when %(d) = 0, d is a forbidden value of x, and when %(d) = 1, d is among the values which are definitely preferred, or, more specifically, against which no objection exists. Hence flexible constraints are naturally described by means of possibility distributions.
Given a possibility distribution % attached to a variable x, the occurrence of events of the form x ! A can be assessed by means of possibility and necessity degrees, respectively defined as (see, e.g., [28])
((A) = sup d!A%(d) , N(A) = inf d)A c(%(d))(3)
where c is the order-reversing map on L. They are such that ((A) = c(N(A)), where the overbar denotes complementation, i.e., an event necessarily occurs if its contrary is impossible. ((A) = 1 only means that A is consistent with the constraint represented by % while N(A) = 1 means that the satisfaction, even partial, of the constraint represented by %entails the occurrence of A (i.e., the fuzzy set of solutions which more or less satisfy the constraint represented by % is included in A).
In the case of prioritized constraints, the degree of priority $ of a contraint C is viewed as a degree of necessity of the subset R modelling the constraint, i.e., corresponds to the higher level constraint N(R) "$. The possibility distribution that accounts for the priority level $, as pictured in Figure 1, is the least specific, or equivalently, the largest, the least restrictive possibility distribution %such that N(R) "$ holds, i.e., %(d) is maximal for each d ! D. Indeed N(R) "$ is equivalent to inf d)A c(%(d)) "$ , that is %(d) # c($) for all d )A. The least restrictive soft constraint that respects this condition is %*(d) = 1 if d ! A and c($) otherwise. This is a formal justification of the treatment of prioritized constraints in the previous section.
When R is itself a possibility distribution modelling a soft constraint, whose priority is $, the notion in possibility theory that can account for priority is the necessity of the fuzzy event R,
N(R) = inf d!D%(d) *μR(d) "$(4)
where the arrow * is a multiple-valued implication, that is, a function that is decreasing in the first argument and increasing in the second one; N(R) is the degree of inclusion in R of the fuzzy set with membership function %. When $ = 1, the least specific solution of the above inequality should be % = μR. Indeed "R is fully imperative" is equivalent to the soft constraint R itself. This forces the multiple-valued implication to satisfy %(d) *μR(d) = 1 iff %(d) #μR(d) (that is, when the constraint R is looser than the one described by %). Moreover, if $ = 0, then the least specific solution of the above inequality should again be %(d) = 1, +d (the non-
informative possibility distribution, expressing no constraint). This condition enforces 1 *μR(d) = 0 if μR(d)< 1. Finally, a simplicity requirement, in agreement with the latter, is that %(d) *μR(d) should depend only on (and is decreasing with) %(d) when %(d) > μR(d). Hence %(d) *μR(d) = c(%(d)) if %(d) > μR(d) (where c is the order-reversing mapping considered in Subsection 2.2; often for simplicity c(a) = 1 - a). With this choice we do have the fuzzy model of a soft, prioritized constraint suggested at the end of the previous subsection, i.e.
Pr(C) = $,%(d) = max(c($), μR(d))(5)
when Pr(C) = $ is interpreted as N(R) "$. The fuzzy set of admissible values with respect to a soft constraint with priority $, is thus included to degree $ in the set of values compatible with the same constraint with maximal priority, and thus the satisfaction of this prioritized constraint cannot fall under level c($).
The present semantics of possibility distributions in terms of preference over the possible values of a variable, among which one must choose, contrasts with the alternative semantics in terms of plausibility that a parameter supposedly uncontrollable, or unknown, takes some value. The latter semantics will be envisaged in Section 5.
2.4. Example
A course must involve 7 sessions, namely x lectures, y exercise sessions and z training sessions (C1). There must be about 2 training sessions (C2), i.e., ideally 2, possibly 1 or 3. Dr. B, which gives the exercise part of the course, wants to manage 3 or 4 sessions (C3). Prof A, which gives the lectures, wants to give about 4 lectures (C4), i.e., ideally 4 lectures, possibly 3 or 5). The request of Dr.
B is less important than the one of Prof. A and is itself less important than the imperative constraints C1 and C2. In this example, flexibility is modeled using a five level scale L = ($0 = 0 < $1 = c($3) < $2 = c($2) < $3 = c($1) < $4 = 1), where c is the order-reversing operation. The priorities of C3 and C4 are respectively $2 and
$3 ($2 < $3). The domain of variables x, y and z is the set {0,1,2,3,4,5,6,7}. The following model can be used:
C 1: classical hard constraint
μR 1(x,y,z) = 1 if x + y + z = 7;
μR 1(x, y, z) = 0 otherwise.
C 2: soft constraint (see Figure 3a)
μR 2(z) = 1 if z = 2;
μR 2(x) = $3 if z = 1 or z = 3;
μR 2(z) = 0 otherwise.
C 3: prioritized constraint Pr(C 3) = $2 (see Figure 3b)
μR 3(y) = 1 if y = 3 or y = 4;
μR 3(y) = c($2) = $2 otherwise.
$$$
$$$Figures 3a and 3b. Modeling of C 2 (a) and C 3 (b) by means of fuzzy unary restrictions C 4: soft and prioritized constraint Pr(C 4) = $3 (see Figure 4)
μR 4(x) = 1 if x = 4;
μR 4(x) = max($3, c($3)) = $3 if x = 3 or x = 5;
μR 4(x) = c($3) = $1 otherwise.
$$$x
Figure 4. Modeling of C 4 by means of a fuzzy unary restriction
2.5. Operations on Fuzzy Relations
Flexible constraints are modelled by qualitative fuzzy relations. The usual operations on crisp relations can be easily generalized to fuzzy relations (Zadeh [25]). To do so, we exploit the fact that , being totally ordered, the satisfaction scale L is a complete distributive lattice,where the minimum and the maximum of two elements make sense. The following definitions extend classical set-theoretic notions used in constraint-directed problem-solving:
? A fuzzy relation R' is said to be included into R if and only if (see Figure 5):
+ (d 1, …, d n ) ! D 1 ×… × D n , μR'(d 1, …, d n ) # μR (d 1, …, d n ).
This definition is a generalization of the classical set inclusion. In terms of constraints, C' is tighter that C and C is a relaxation (or a weakening) of C'.
10
d = (d 1, …, d n )μR
μR'
μR"Figure 5. R'- R and R'' R
?The projection of a fuzzy relation R on {x k 1, …, x k n k } - V(R) is a fuzzy relation R .{x k1,…,x kn k } on {x k 1, …, x k n k } such that:
μR .{x k1,…,x kn k }(d k 1, …, d k n k ) = sup {d / d .{x k1,…,x kn k }= (d k1,…,d kn k )} μR (d)
where d.{x k1,…,x kn k}denotes the classical restriction of d = (d1, …, d n) to {x k1, …, x k n k}. This definition is a generalization of the projection of ordinary relations.μ
R.{x k1,…,x kn k}
(d k1, …, d k n k) estimates to what level of satisfaction the instantiation (d k1, …, d k n k) can be extended to an instantiation that satisfies C.
?The cylindrical extension of a fuzzy relation R to {x k1, …, x k n k} /V(R) is a fuzzy relation R0{x k1,…,x kn k} on {x k1, …, x k n k} such that:
μ
R0{x k1,…,x kn k}
(d k1, …, d k n k) = μR((d k1, …, d k n k).V(R))
This definition is a generalization of the cylindrical extension of ordinary relations.
μ
R0{x k1,…,x kn k}
(d k1, …, d k n k) estimates to what extent the instantiation (d k1, …, d k n k) satisfies C.
?The conjunctive combination (or join) of two fuzzy relations R i and R j is a fuzzy relation R i1R j over V(R i)2V(R j)= {x1, …, x k} such that (see Figure 6):
μR i1R j(d1, …, d k) = min(μR i((d1, …, d k).V(R i)),μR j((d1, …, d k).V(R j))).
μR i1R j(d1, …, d k) estimates to what extent (d1, …, d k) satisfies both C i and C j. When V(R i) = V(R j), 1is a generalization of classical set intersection. All properties of the standard intersection (associativity, commutativity, etc.) hold as long as negation is not involved; in particular, there holds (R i 1R j).V(R i)- R i and (R i 1R i) = R i.
1 0
μR j
μR i
μR i1R j
d = (d1, …, d k)
Figure 6. Conjunctive combination of two fuzzy relations R i and R j
Note that the use of the combination rule, allowed by the presence of a unique satisfaction scale, underlies an assumption of commensurability between satisfaction levels pertaining to different constraints: the user who specifies the constraints must describe them by
means of this unique scale L (or by means of the dual scale L T). For instance, in the example of Section 2.4, the satisfaction level $3 of C4 for x !{3,5} is assumed to be equal to the satisfaction level for z !{1,3} and $1 < c(Pr(C3)) < c(Pr(C4)). Although natural and often implicit, this assumption must be emphasized.
?The disjunctive combination of two fuzzy relations R i and R j is a fuzzy relation R i3R j over V(R i)2V(R j)= {x1, …, x k} such that (see Figure 7):
μR i3R j(d1, …, d k) = max(μR i((d1, …, d k).V(R i)),μR j((d1, …, d k).V(R j))).
μR i3R j(d1, …, d k) estimates to what extent (d1, …, d k) satisfies either C i or C j. When V(R i) = V(R j), 3 is a generalization of classical set union. All properties of set union (associativity, commutativity, distributivity over intersection, etc.) hold, if negation is not involved.
1
μR i1R j
μR i
μR j
d = (d1, …, d k)
Figure 7. Disjunctive combination of two fuzzy relations R i and R j
2.6. Prioritized Constraints with Safeguard
The framework of fuzzy constraints offers a convenient tool for representing more sophisticated constraints than the previously encountered ones, for instance prioritized constraints with safeguard, as well as nested conditional constraints as we are going to see. First one may like to express that a constraint C, even with a rather low priority Pr(C) = $, cannot never be completely violated, in the sense that if C is violated, at least a more permissive, minimal, constraint C'is still satisfied. Let R and R'be the fuzzy relations associated with C and C' respectively, with R - R' (C' is more permissive than C, i.e., C' is a relaxation of C). The whole constraint C* corresponding to the pair (C, C') can be viewed as
the conjunction of a prioritized constraint (C) and a weaker but imperative, possibly soft, constraint (C'). This conjunction is represented by the fuzzy relation R*, pictured in Figure 8, and expressed by:
+d !D1×…× D n, μR*(d) = min(max(μR(d), c($)), μR'(d)).(6)
1, …, d n) Figure 8. Representation of a prioritized fuzzy constraint with safeguard
This is a particular case of the decomposition of a soft constraint into prioritized ones when C and C' are hard. Indeed, such constraints express both a requirement with priority $ less than 1 and a weaker requirement with priority 1 and R* is of the form (1):
μR*(d) = min(max(μR(d), c($)), max(μR'(d), c(Pr(C')))) with Pr(C') = 1.
See [29] for the use of such constraints in fuzzy database querying systems. Interestingly enough, R*can be decomposed either as a disjunction or as a conjunction of two fuzzy relations, depending on which fuzzy relation, R or R', the priority weight is combined with. Indeed
μR*(d)= min(max(μR(d), c($)), μR'(d))
= min (max(μR(d), c($)), max(μR(d), μR'(d))) since R - R'
= max(μR(d), min (c($), μR'(d))).
It expresses that satisfying a constraint with safeguard corresponds to either satisfying its stronger form C, or its weaker form C' the satisfaction degree being upper-bounded in this second case by c($).
For instance, a flexible C 5 constraint prescribing: "Prof. A wants to give about four lectures; anyway, he will never accept to give no lecture" is represented by the fuzzy relation R 5* pictured in Figure 9:
$$$x
5'R 5*Figure 9. Modeling of C 5 by fuzzy constraint with safeguard constraint
2.7. Conditional and Hierarchically Organized Constraints
A conditional constraint is a constraint which applies only if another one is satisfied.This notion will be interpreted as follows: A constraint C j conditioned by a hard constraint C i (associated with fuzzy relations R j and R i respectively) is imperative if C i is satisfied and can be dropped otherwise. More generally, the level of satisfaction μR i (d) of a soft conditioning constraint C i by an instance d is viewed as the level of priority of the conditioned constraint C j ,i.e., the greater the level of satisfaction of C i , the greater the priority of C j is. A conditional constraint is then naturally represented by a fuzzy relation R i 4 R j over V(R i )2V(R j ) ={x 1, …, x k } such that:
μR i 4R j (d 1, …, d k ) = max(μR j ((d 1, …, d k ).V(R j )), c(μR i ((d 1, …, d k ).V(R i ))))
R i 4 R j is a prioritized constraint with variable priority: C j has a priority 1 if μR i ((d 1, …,d k ).V(R i )) = 1, i.e., if C i is satisfied, and has a priority 0 (which means that C j can be forgotten), if C i is not satisfied. μR i 4R j (d 1, …, d k ) estimates to what extent d = (d 1, …, d k )satisfies the proposition "if C i is satisfied, then C j must be satisfied too"; the function max(b, 1 – a) is indeed a multiple-valued implication. Note that the conjunction of the two
constraints "C i" and "C j conditioned by C i" is not equivalent to the conjunction "C i and C j" in general, since min(a, max(1 – a, b)) ' min(a,b). The equivalence holds however if C i is a crisp constraint (a = 1 or 0). This is not equivalent when C i is a soft constraint since when C i is not completely satisfied, C j has a priority less than the one of C i.
Let us now show how to represent nested requirements with preferences, such as the ones considered by database authors [30][31], by means of conditional prioritized constraints. Lacroix and Lavency [30] deal with requirements of the form "C1 should be satisfied, and among the solutions to C1 (if any) the ones satisfying C2 are preferred, and among those satisfying both C1 and C2, those satisfying C3 are preferred, and so on", where C1, C2, C3…, are hard constraints. It should be understood in the following way: satisfaying C2 if C1 is not satisfied is of no interest; satisfying C3 if C2 is not satisfied is of no use even if C1 is satisfied. Thus there is a hierarchy between the constraints. For the sake of simplicity, let us consider the case of a compound constraint C made of three nested constraints. Thus, one would like to express that C1 should hold (with priority 1), and that if C1 holds, C2 holds with priority $2, and if C1 and C2hold, C3holds with priority $3 (with $3 < $2 < 1). The constraints C1,C2 and C3 are supposed to restrict the possible values of the same set of variables (the relations are defined on the same referential D1 × …× D n). It is always possible to be in this situation taking the cylindrical extensions of R1, R2 and R3 in V(R1) 2V(R2) 2 V(R3). Using the representation of conditional constraints presented above, this nested conditional constraint may be represented by means of the fuzzy relation R* defined on D1 ×… × D n:
μR*(d)= min(μR1(d),
max[c(μR1(d)), max (μR2(d), c($2))],
max[c[min(μR1(d), μR2(d))], max(μR3(d), c($3))]
= min(μR1(d),
max(μR2(d), c[min(μR1(d), $2)]),
max(μR3(d), c[min(μR1(d), μR2(d), $3)])
In the above expression, it is clear that the priority level of C 2 is min(μR 1(d), $2), i.e.,is $2 if C 1 is completely satisfied and is zero if C 1 is not at all satisfied. Similarly, the priority level of C 3 is actually min(μR 1(d), μR 2(d), $3). Note that it is zero if C 1 is not satisfied even if C 2 is satisfied. It is easy to check that:
μR 1(d) = 1 and μR 2(d) = 1 and μR 3(d) = 1
4 μR *(d) = 1μR 1(d) = 1 and μR 2(d) = 1 and μR 3(d) = 0
4 μR *(d) = c($3)μR 1(d) = 1 and μR 2(d) = 0 and μR 3(d) = 1
4 μR *(d) = c($2) < c($3)μR 1(d) = 1 and μR 2(d) = 0 and μR 3(d) = 0
4 μR *(d) = c($2)μR 1(d) = 04 μR *(d) = 0
Thus, as soon as C 2 is not satisfied, the satisfaction of C 3 or its violation make no difference;in both cases μR *(d) = c($2) < c($3). R * reflects that we are completely satisfied if C 1,C 2 and C 3 are completely satisfied,we are less satisfied if C 1 and C 2 only are satisfied, and we are even less satisfied if only C 1 is satisfied. This is pictured on Figure 10.
C 1
C 2
C 3Figure 10. Levels of satisfaction of a hierarchy of constraints
In the preceding example an unconditioned constraint (C 1) was refined by a hierarchy of conditional prioritized constraints (C 2,C 3). A request looking for candidates such that "if they are not graduated they should have professional experience, and if they have professional
植物细胞工程知识点清单 （一）植物组织培养 1.理论基础（原理）：细胞全能性 2.全能性概念：具有某生物发育所需全部遗传信息的细胞，都具有发育成完整个体的潜能。 3、过程：外植体—脱分化—愈伤组织—再分化—丛芽、不定根—新植株 4、相关概念及实验注意事项 ①外植体：离体植物器官、组织、细胞 ②愈伤组织：高度液泡化，无固定形态的薄壁细胞。全能性高，分化程度低 ③外植体消毒：70%酒精30s—无菌水冲洗—次氯酸钠30min—无菌水冲洗 ④取材：选取形成层部位 ⑤脱分化：23~26o C，避光 ⑥再分化：将愈伤组织转接到分化培养基，光照下培养 ⑦生长素/ 细胞分裂素：比值高—促进生根；比值低—促进发芽 5、植物组织培养概念：在无菌和人工控制条件下，将离体的植物器官，组织，细胞培养在人工配置的培养基上，诱导其产生愈伤组织，丛芽，最终形成完整的植株。 6、地位：是培育转基因植物、植物体细胞杂交培育植物新品种的最后一道工序。 （二）植物体细胞杂交 1、植物体细胞杂交概念：将不同种的植物细胞，在一定条件下融合成杂种细胞，并把杂种细胞培育成新的植物体的过程。 2、过程及注意事项： ①去除细胞壁：酶解法（纤维素酶、果胶酶），获得原生质体 ②原生质体融合方法：物理法（离心、震荡、电刺激）；化学法：聚乙二醇 ③细胞融合成功的标志：杂种细胞再生细胞壁 3、融合结果：获得杂种细胞，进而获得杂种植株。 A细胞+B细胞所得杂种植株遗传物质=A+B 4、成功例子：番茄—马铃薯；烟草—海岛烟草；胡萝卜—羊角芹；白菜—甘蓝 5、优点：克服远缘杂交不亲和障碍 6、局限性：不能按照人的要求表达性状 （三）植物细胞工程应用 1、微型繁殖：可以高效快速地实现种苗的大量繁殖（观赏植物，经济林木，无性繁殖作物） 2、作物脱毒：采用茎尖等分生区组织培养来除去病毒（因为分生区附近的病毒极少或没有） 如：马铃薯；草莓；甘蔗；菠萝、香蕉等经济作物 3、人工种子：以植物组织培养得到的胚状体、不定芽、顶芽和腋芽等为材料，经人工薄膜包装得到的种子。优点：完全保持优良品种的遗传特性，不受季节的限制；方便储藏和运输 4、作物新品种培育：单倍体育种 5、突变体利用：在组织培养中会出现突变体，通过从有用的突变体中选育出新品种（如筛选抗病、抗盐、含高蛋白的突变体） 6、细胞产物的生产：通过能够产生对人们有利的产物的细胞进行组织培养，从而让它们能够产生大量的细胞产物。 如：生产人参皂甙，三七，紫草，银杏等。 （定向诱导愈伤组织细胞分化为产生特定物质的细胞，提纯产物）
细胞工程 主讲人王文星 学前导引 本课程为必修考试课,理论授课32学时，期末考试闭卷 总成绩为100分：出勤+课堂提问占10%, 平时测验占20%,期末试卷占70%. 平时测验1～次,随堂考试,闭卷. 选用教材: 安利国,杨桂文.?细胞工程?第3版，科学出版社，2016 主要参考教材: 李志勇.?细胞工程?第2版，科学出版社，2010 殷红.?细胞工程?第2版，化学工业出版社，2013 第1章细胞工程简介 内容提要 一、定义五、主要研究内容 二、与其它生物工程的关系六、重要应用 三、发展历史七、本章小结 四、研究对象八、思考题 一、细胞工程定义 细胞工程：应用细胞生物学和分子生物学的方法，通过类似于工程学的步骤，在细胞整体水平或细胞器水平上，按照人们的意愿来改变细胞内的遗传物质以获取新型生物或特种细胞产品的一门科学技术。 广义的细胞工程：包括所有的生物组织、器官及细胞离体操作和培养技术，狭义的细胞工程则是指细胞融合和细胞培养技术。 二、细胞工程与其它生物工程的关系 生物化学工程为基因工程、细胞工程、微生物工程、蛋白质工程、酶工程、代谢工程提供产业化技术支持。
基因工程技术为细胞工程提供转基因细胞。 细胞工程技术为微生物工程、酶工程及工程产业化提供充足的 经过遗传改良和性状稳定的微生物、动植物细胞原料。 总结：细胞工程技术是现代生物工程技术各领域连接的桥梁和 纽带；与其它生物工程技术是密切联系，不可分割的有机整体。 三、细胞工程发展历史 细胞工程的理论基础是细胞学说和细胞全能性学说。 在植物学界，100年前，德国学者Haberlandt(1902)发表了着名 的论文《植物细胞离体培养实验》，提出了细胞全能性的观点。 20AD中叶，植物细胞组织培养与细胞的遗传操作相结合，发展 成为植物细胞工程。 20AD60s末兴起的植物单倍体技术是一项在植物育种上用途广 泛的细胞工程技术。 20AD90s以来，虽然基因工程成为生物技术的主流，但是细胞工 程并为失去独立存在价值，它继续在优良苗木繁育、农作物育种和 植物天然药物的开发中起着举足轻重的作用。 在动物学界，1907年美国学者哈里森等人采用盖玻片悬滴培养 蛙胚神经组织，存活数周，而且观察到生长现象，从而开创了动物细 胞培养的先河。 1965年，哈利斯和沃特金斯证明了灭活的病毒在控制的条件下 可以用来诱导动物细胞的融合。至此细胞融合作为一个重要的研究 领域已经引起人们的浓厚兴趣。 20AD70s初，诞生了细胞拆合工程。1972年，Prescott等人首先应用离心技术结合细胞松弛素B分离哺乳类细胞的胞质体获得成功，为研究哺乳类细胞核、质相互关系、细胞质基因的转移开创了新的途径。 近年来，细胞工程取得了迅速发展。如试管植物、试管动物、克隆动物、转基因生物反应器、干细胞等等。其中最具代表性的成就有：1977年，英国采用胚胎工程技术成功培育出世界首例试管婴儿。1997年英国利用成年动物体细胞首次克隆出绵羊“多莉”。2001年英国又宣布成功培育出世界首批转基因猪。2008年：美国科学家利用人胚胎干细胞可以在实验室培育出有携带氧功能的成熟红细胞，这个成果将可能解决个别血型血源紧缺的问题，也可帮助避免输血相关疾病的发生；美国研究人员在患糖尿病的老鼠身上做实验，将普通细胞转化成可分泌胰岛素的胰岛β细胞，减轻了病情。这一研究利用基因重组技术，实现不同种类成体细胞间直接转化，代表再生医学的重大进步。 细胞工程发展历史 2009年，马萨诸塞州总医院(MGH)的研究人员找到一种成功地体外培养肝细胞的方法，培养的肝细胞具有药物毒性筛选功能。研究报告详细介绍了肝细胞如何在高氧条件和无动物血清的条件下生长，并如何快速发挥正常肝脏所具有的功能。 2010年，科学家首次实现将多功能干细胞变成功能性人体肠道组织。 2011年，肿瘤的细胞免疫治疗研究进展：细胞免疫疗法能够靶向肿瘤细胞而不伤及正常组织细胞，并可产生免疫记忆来预防肿瘤复发，有可能成为肿瘤治疗的第四种方法。四、细胞工程的研究对象 细胞或其组成部分和构成的组织、器官等如染色体、细胞核、原生质体、整个细胞、受精卵、胚胎、组织或器官。 五、细胞工程的主要研究内容
专题2细胞工程 一、选择题 1.运用下列各种细胞工程技术培育生物新品种,操作过程中能形成愈伤组织的是()。 ①植物组织培养②植物体细胞杂交③动物细胞培养 ④转基因植物的培育 A.①②③ B.①②④ C.①③④ D.①②③④ 2.生物技术的发展速度很快,已灭绝生物的“复生”将不再是神话。如果世界上最后一只野驴刚死亡,以下较易成功“复生”野驴的方法是()。 A.将野驴的体细胞取出,利用组织培养技术,经脱分化、再分化,培育成新个体 B.将野驴的体细胞两两融合,再经组织培养培育成新个体 C.取出野驴的体细胞核移植到母家驴的去核卵细胞中,经孕育培育成新个体 D.将野驴的基因导入家驴的受精卵中,培育成新个体 3.下列关于细胞工程的叙述,错误的是()。 A.电刺激可诱导植物原生质体融合或动物细胞融合 B.去除植物细胞的细胞壁和将动物组织分散成单个细胞均需酶处理 C.小鼠骨髓瘤细胞和经抗原免疫小鼠的B淋巴细胞融合可制备单克隆抗体 D.某种植物甲、乙两品种的体细胞杂种与甲、乙两品种杂交后代的染色体数目相同 4.用高度分化的植物细胞、组织和器官进行组织培养可以形成愈伤组织,下列叙述错误的是()。 A.该愈伤组织是细胞经过脱分化和分裂形成的 B.该愈伤组织的细胞没有全能性 C.该愈伤组织是由排列疏松的薄壁细胞组成的 D.该愈伤组织可以形成具有生根发芽能力的胚状结构
5.名为“我的皮肤”的生物活性绷带自从在英国诞生后,给皮肤烧伤病人带来了福音。该活性绷带的原理是先采集一些细胞样本,再让其在特殊的膜片上增殖。5~7天后,将膜片敷在患者的伤口上,膜片会将细胞逐渐“释放”到伤口,并促进新生皮肤层生长,达到加速伤口愈合的目的。下列有关叙述中,不正确的是()。 A.“我的皮肤”的获得技术属于动物细胞工程 B.人的皮肤烧伤后会因人体第二道防线的破坏而导致免疫力下降 C.种植在膜片上的细胞样本最好选自患者本人 D.膜片能否把细胞顺利“释放”到伤口,加速患者自身皮肤愈合与细胞膜上的糖蛋白有关 6.既可用于DNA重组技术又可用于细胞融合技术的是()。 A.病毒 B.纤维素酶 C.聚乙二醇 D.质粒 7.培育农作物新品种的过程中,常利用植物组织培养技术。下列叙述正确的是()。 A.培育转基因的外植体得到的新个体属于基因突变个体 B.在植物组织培养过程中用理化因素诱导可获得大量有益突变体 C.单倍体育种中经减数分裂和组织培养两个过程能获得纯合二倍体 D.植物组织培养技术的理论基础是细胞的全能性 8.下列与细胞工程技术相关的表述中,不正确的是()。 A.植物体细胞杂交技术可以克服常规的远缘杂交不亲和障碍 B.动物细胞培养与植物组织培养所用的培养基成分基本相同 C.动物细胞融合与植物原生质体融合的基本原理相同 D.植物组织培养技术的理论基础是细胞的全能性 9.经植物组织培养技术培养出来的植物一般很少有植物病毒危害,其原因在于()。 A.在组织培养过程中经过了脱分化和再分化,进行的是快速分裂和分化 B.人工配制的培养基上,植物病毒根本无法生存
【精编】皮下脂肪瘤介绍及治疗方法（含民间偏方） 1 皮下脂肪瘤皮下脂肪瘤在中医称为痰核。“肉瘤”之名出《干金要方》。多因郁滞伤脾，痰气凝结所致。以皮下肉中生肿块，大如桃、拳，按之稍软，无痛为主要表现的瘤病类疾病。最常见的好发部位为颈，肩，背，臀和乳房是起源于脂肪组织的良性肿瘤，由成熟的脂肪组织所构成。 1 疾病简介皮下脂肪瘤（lipoma）是脂肪组织的良性肿瘤。由成熟的脂肪组织所构成，凡体内有脂肪存在的部位均可发生。脂肪瘤有一层薄的纤维内膜，内有很多纤维索，纵横形成很多间隔，最常见于颈、肩、背、臀和乳房及肢体的皮下组织，面部、头皮、阴囊和阴唇，其次为腹膜后及胃肠壁等处；极少数可出现于原来无脂肪组织的部位。如果肿瘤中纤维组织所占比例较多，则称纤维脂肪瘤。 2 疾病分类根据脂肪瘤的可数目可分为有孤立性脂肪瘤及多发性脂肪瘤二类。此类肿瘤好发于肩、背、臀部、四肢、腰、腹部皮下及大腿内侧，头部发病也常见。位于皮下组织内的脂肪瘤大小不一，大多呈扁圆形或分叶，分界清楚；边界分不清者要提防恶性脂肪瘤的可能。单个称为孤立行型脂肪瘤。两个或两个以上的称为多发性脂肪瘤。
按部位不同可分为皮下脂肪瘤和血管平滑肌脂肪瘤（又称错钩瘤）。根据脂肪瘤发生的部位皮下脂肪瘤为扁平或分叶状、质软，边界清楚的皮下限局性肿物。质软，可推动，表面皮肤正常，发展慢，数目多达数百个，常在皮下。血管平滑肌脂肪瘤错钩瘤多发生于各个器官（肾脏，肝脏较为多见）的毛细血管的平滑肌组织之间的脂肪瘤（又称肾错构瘤，肝错钩瘤）。 3 发病原因皮下脂肪瘤指“脂肪瘤致瘤因子”在患者体细胞内也存在一种致瘤因子，在正常情况下，这种致瘤因子处于一种失活状态（无活性状态），皮下脂肪瘤正常情况下是不会发病，但在各种内外环境的诱因影响作用下，这种脂肪瘤致瘤因子的活性处于活跃状态具有一定的活性，在机体抵抗力下降时，机体内的淋巴细胞、单核吞噬细胞等免疫细胞对致瘤因子的监控能力下降，再加上体内的内环境改变，慢性炎症的刺激、全身脂肪代谢异常的诱因条件下，脂肪瘤致瘤因子活性进一步增强与机体的正常细胞中某些基因片断结合，形成基因异常突变，使正常的脂肪细胞与周围的组织细胞发生一种异常增生现象，导致脂肪组织沉积有关，并向体表或各个内脏器官突出的肿块，称之脂肪瘤。[1] 4 发病机制皮下脂肪瘤是相当常见的皮肤病灶，由正常脂肪细胞集积而成，占软组织良性肿瘤的 80%左右，无明显特殊病因，常发于皮
精编】皮下脂肪瘤介绍及治疗方法（含民间偏方） 1皮下脂肪瘤皮下脂肪瘤在中医称为痰核。“肉瘤”之名出《干金要方》。多因郁滞伤脾，痰气凝结所致。以皮下肉中生肿块，大如桃、拳，按之稍软，无痛为主要表现的瘤病类疾病。最常见的好发部位为颈，肩，背，臀和乳房是起源于脂肪组织的良性肿瘤，由成熟的脂肪组织所构成。 1 疾病简介皮下脂肪瘤（lipoma ）是脂肪组织的良性肿瘤。由成熟的脂肪组织所构成，凡体内有脂肪存在的部位均可发生。脂肪瘤有一层薄的纤维内膜，内有很多纤维索，纵横形成很多间隔，最常见于颈、肩、背、臀和乳房及肢体的皮下组织，面部、头皮、阴囊和阴唇，其次为腹膜后及胃肠壁等处；极少数可出现于原来无脂肪组织的部位。如果肿瘤中纤维组织所占比例较多，则称纤维脂肪瘤。 2疾病分类根据脂肪瘤的可数目可分为有孤立性脂肪瘤及多发性脂肪瘤二类。此类肿瘤好发于肩、背、臀部、四肢、腰、腹部皮下及大腿内侧，头部发病也常见。位于皮下组织内的脂肪瘤大小不一，大多呈扁圆形或分叶， 分界清楚；边界分不清者要提防恶性脂肪瘤的可能。单个称为孤立行型脂肪瘤。两个或两个以上的称为多发性脂肪瘤。 按部位不同可分为皮下脂肪瘤和血管平滑肌脂肪瘤（又称错钩瘤）。 根据脂肪瘤发生的部位皮下脂肪瘤为扁平或分叶状、质软，边界清楚的皮下限局性肿物。质软，可推动，表面皮肤正常，发展慢，数目多达数百个， 常在皮下。血管平滑肌脂肪瘤错钩瘤多发生于各个器官（肾脏，肝脏较为多见）的毛细血管的平滑肌组织之间的脂肪瘤 又称肾错构瘤，肝错钩瘤）。 3发病原因皮下脂肪瘤指“脂肪瘤致瘤因子”在患者体细胞内也存在一种致瘤因子，在正常情况下，这种致瘤因子处于一种失活状态 无活性状态），皮下脂肪瘤正常情况下是不会发病，但在各种内外环境的诱因影响作用下，这种脂肪瘤致瘤因子的活性处于活跃状态具有一定的活性，在机体抵抗力下降时，机体内的淋巴细胞、单核吞噬细胞等免疫细胞对致瘤因子的监控能力下降，再加上体内的内环境改变，慢性炎症的刺激、全身脂肪代谢异常的诱因条件下，脂肪瘤致瘤因子活性进一步增强与机体的正常细胞中某些基因片断结合，形成基因异常突变，使正常的脂肪细胞与周围的组织细胞发生一种异常增生现象，导致脂肪组织沉积有关，并向体表或各个内脏器官突出的肿块，称之脂肪瘤。[1] 4发病机制皮下脂肪瘤是相当常见的皮肤病灶，由正常脂肪细胞集积而
2．1 植物细胞工程 2．1.1 植物细胞工程的基本技术 目标导航 1.以胡萝卜的组织培养为例，简述植物组织培养技术的原理和过程。2.结合教材图2－5，描述植物体细胞杂交技术的原理和过程。 一、细胞工程和细胞的全能性(阅读P32－34) 1．细胞工程的含义 原理和方法细胞生物学和分子生物学 操作水平细胞水平或细胞器水平 目的按照人的意愿来改变细胞内的遗传物质或获得细胞产品 分类植物细胞工程和动物细胞工程 2. (1)定义：具有某种生物全部遗传信息的任何一个细胞，都具有发育成完整生物体的潜能。 (2)植物细胞全能性实现的条件：离体、提供适宜的营养条件和激素。 (3)影响全能性表达的原因：在特定的时间和空间条件下，细胞中的基因会有选择性地表达出各种蛋白质，从而构成生物体的不同组织和器官。 二、植物组织培养(阅读P34－36) 1．理论基础：植物细胞的全能性。 2．前提：离体的植物器官、组织、细胞。 3．条件：无菌和人工控制条件下、人工配制的培养基上、适宜的营养条件。 4．结果：诱导产生愈伤组织、丛芽，最终形成完整植株。 5．胡萝卜的组织培养过程
三、植物体细胞杂交技术(阅读P36－37) 1．概念：将不同种的植物体细胞，在一定条件下融合成杂种细胞，并把杂种细胞培育成新的植物体的技术。 2．过程 (1)去壁：用纤维素酶和果胶酶去除植物细胞壁，获得原生质体。 (2)诱导原生质体融合的方法 ①物理法：离心、振动、电激等。 ②化学法：用聚乙二醇(PEG)诱导。 3．意义：克服了远缘杂交不亲和的障碍。 判断正误： (1)细胞工程是通过分子水平上的操作，按照人的意愿来改变细胞内的遗传物质或获得细胞产品的一门综合科学技术。( ) (2)在理论上，生物的每个细胞都具有发育成完整植株的潜力。( ) (3)细胞全能性的大小依次是：受精卵>生殖细胞>体细胞。( )
植物细胞工程和动物细胞工程默写 1、细胞工程是在或的操作 2、细胞工程按操作对象分为和 3、植物细胞工程通常采用的技术手段是：和 4、植物组织培养的理论基础是： 5、理论上每一个活细胞都应该具有。因为 6、受精卵的全能性最高，受精卵生殖细胞体细胞 7、为什么体内细胞没有表现出全能性，而是分化成为不同的组织、器官 8、植物组织培养的外界条件：， 内在原理是： 9、植物组织培养的过程：经过形成 经由过程形成，最后移栽发育成。 10、是指已分化细胞经诱导，失去其特有的结构和功能而变为未分 化细胞的过程。 11、是指由外植体长出来高度液泡化、无定形状态薄壁细胞组成 的排列疏松无规则的组织。 12、植物体细胞杂交的意义（优势）：。 13、去除细胞壁的常用方法：（纤维素酶、果胶酶等） 14、人工诱导原生质体融合方法：物理法：等； 化学法： 15、融合完成的标志是： 16、植物体细胞杂交过程包括：和。 17、植物体细胞杂交的原理是：和 18、人工种子的特点是：
19、作物脱毒(1)材料: (2)脱毒苗: 20、单倍体育种：(1)方法: (2)优点: ; 21、动物细胞工程常用的技术手段：(基础)、、 、 22、动物细胞培养的原理是：。 23、用处理，一 段时 间后获得单个细胞。 24、细胞贴壁： 25、细胞的接触抑制： 26、原代培养：，培养的第1代细胞与传10代 以内的细胞称为原代细胞培养。 将原代细胞从培养瓶中取出，用处理后配制 成，分装到两个或两个以上的培养瓶中 继续培养，称为 27、目前使用的或冷冻保存的正常细胞通常为 28、细胞株：原代细胞一般传至10代左右细胞生长停滞，大部分细胞衰老死亡，少数细胞存活到40~50代，这种传代细胞为细胞株。 细胞系：细胞株传代至50代后又出现细胞生长停滞状态，只有部分细胞由于遗传物质的改变，使其在培养条件下可以无限制传代，这种传代细胞为细胞系。 细胞株和细胞系的区别：细胞系的遗传物质改变，具有癌细胞的特点，失去接触抑制，容易传代培养。 29、动物细胞培养的条件：1. 2. 3. （培养 液的Ph为）4. 30、细胞所需营养： 等，按种类和所需数量严格配制而成的合成培养基。培养基内还需加入、等天然成分 31、动物细胞培养所需的气体环境：95%的空气和5%的二氧化碳的混合气体。 氧气：；二氧化碳： 32、植物组织培养和动物细胞培养的比较：
生物选修3专题2 细胞工程知识点总结 （一）植物细胞工程 1.理论基础（原理）：细胞全能性 全能性表达的难易程度：受精卵＞生殖细胞＞干细胞＞体细胞；植物细胞＞动物细胞2.植物组织培养技术 （1）过程：离体的植物器官、组织或细胞―→愈伤组织―→试管苗―→植物体 （2）用途：微型繁殖、作物脱毒、制造人工种子、单倍体育种、细 胞产物的工厂化生产。 （3）地位：是培育转基因植物、植物体细胞杂交培育植物新品种的 最后一道工序。 3.植物体细胞杂交技术 （1）过程： （2）诱导融合的方法：物理法包括离心、振动、电刺激等。 化学法一般是用聚乙二醇（PEG）作为诱导剂。 （3）意义：克服了远缘杂交不亲和的障碍。 （二）动物细胞工程 1. 动物细胞培养 （1）概念：动物细胞培养就是从动物机体中取出相关的组织，将它分散成单个细胞，然后放在适宜的培养基中，让这些细胞生长和繁殖。 （2）动物细胞培养的流程：取动物组织块（动物胚胎或幼龄动物的器官或组织）→剪碎→用胰蛋白酶或胶原蛋白酶处理分散成单个细胞→制成细胞悬液→转入培养瓶中进行原代培养→贴满瓶壁的细胞重新用胰蛋白酶或胶原蛋白酶处理分散成单个细胞继续传代培养。
（3）细胞贴壁和接触抑制：悬液中分散的细胞很快就贴附在瓶壁上，称为细胞贴壁。细胞数目不断增多，当贴壁细胞分裂生长到表面相互抑制时，细胞就会停止分裂增殖，这种现象称为细胞的接触抑制。 （4）动物细胞培养需要满足以下条件 ①无菌、无毒的环境：培养液应进行无菌处理。通常还要在培养液中添加一定量的抗生素，以防培养过程中的污染。此外，应定期更换培养液，防止代谢产物积累对细胞自身造成危害。 ②营养：合成培养基成分：糖、氨基酸、促生长因子、无机盐、微量元素等。通常需加入血 清、血浆等天然成分。 ③温度：适宜温度：哺乳动物多是36.5℃＋0.5℃；pH：7.2～7.4。 ④气体环境：95%空气＋5%CO2。O2是细胞代谢所必需的，CO2的主要作用是维持培养液的 pH。 （5）动物细胞培养技术的应用：制备病毒疫苗、制备单克隆抗体、检测有毒物质、培养医 学研究的各种细胞。 2.动物体细胞核移植技术和克隆动物 （1）哺乳动物核移植可以分为胚胎细胞核移植（比较容易）和体细胞核移植（比较难）。（2）选用去核卵(母)细胞的原因：卵(母)细胞比较大，容易操作；卵(母)细胞细胞质多，营养丰富。 （3）体细胞核移植的大致过程是：（右图） 核移植胚胎移植 （4）体细胞核移植技术的应用： ①加速家畜遗传改良进程，促进良畜群繁育；②保护濒危物种，增大存活数量； ③生产珍贵的医用蛋白；④作为异种移植的供体；
细胞工程知识点 植物细胞工程 1.理论基础（原理）： 全能性表达的难易程度：受精卵＞生殖细胞＞干细胞＞体细胞；植物细胞＞动物细胞2.植物组织培养技术 （1）过程：离体的植物器官、组织或细胞―→愈伤组织―→胚状体―→植物体 名称过程形成体特点培养基光 脱分化由外植体脱分化为愈 伤组织生长素与细胞分裂素 的比例 避 光 再分化由愈伤组织分化为有根、芽或有生根发芽的能 力上述比例需 光 营养生长 生殖生长发育成完整的植物体由根、茎、叶、花、果实、 种子组成 自身产生各种激素需 光 （2）用途：微型繁殖、作物脱毒（材料：）、制造人工种子（培养至）、单倍体育种、细胞产物的工厂化生产（培养至）。 （3）地位：是培育转基因植物、植物体细胞杂交培育植物新品种的最后一道工序。 3.植物体细胞杂交技术 （1）过程： 注：杂种细胞的染色体数等于两个杂交细胞的。 （2）原理： （3）诱导融合的方法：物理法包括等。 化学法一般是用作为诱导剂。 （4）意义：。
动物细胞工程 1. 动物细胞培养 （1）概念：动物细胞培养就是从动物机体中取出相关的，将它分散成，然后放在适宜的中，让这些细胞。 （2）动物细胞培养的流程：取动物组织块（的器官或组织）→剪碎→用处理分散成单个细胞→制成→转入培养瓶中进行 →贴满瓶壁的细胞重新用胰蛋白酶或胶原蛋白酶处理分散成单个细胞继续。原代培养（10代以内）：遗传物质 传代培养（10代以后）：遗传物质 （3）细胞贴壁和接触抑制：悬液中分散的细胞很快就，称为（只有一层细胞）。细胞数目不断增多，当贴壁细胞分裂生长到表面时，细胞就 会，这种现象称为。 （4）动物细胞培养需要满足以下条件 ①无菌、无毒的环境：培养液应进行处理。通常还要在培养液中添加一定量 的，以防培养过程中的污染。此外，应， 防止代谢产物积累对细胞自身造成危害。 ②营养：合成培养基成分：糖、氨基酸、、无机盐、等。通常 需加入等天然成分。 ③温度：适宜温度：哺乳动物多是；pH：。 ④气体环境：95% ＋5% 。O2是所必需的，CO2的主要作用 是。 （5）动物细胞培养技术的应用：制备病毒疫苗、、、培养 医学研究的各种细胞。 （
-细胞工程知识点总结 （一）植物细胞工程 1、理论基础（原理）：细胞全能性全能性表达的难易程度：受精卵＞生殖细胞＞干细胞＞体细胞；植物细胞＞动物细胞 2、植物组织培养技术（1）过程：离体的植物器官、组织或细胞―→愈伤组织―→试管苗―→植物体（2）用途：微型繁殖、作物脱毒、制造人工种子、单倍体育种、细胞产物的工厂化生产。（3）地位：是培育转基因植物、植物体细胞杂交培育植物新品种的最后一道工序。 3、植物体细胞杂交技术（1）过程：（2）诱导融合的方法：物理法包括离心、振动、电刺激等。化学法一般是用聚乙二醇（PEG）作为诱导剂。（3）意义：克服了远缘杂交不亲和的障碍。 （二）动物细胞工程 1、动物细胞培养（1）概念：动物细胞培养就是从动物机体中取出相关的组织，将它分散成单个细胞，然后放在适宜的培养基中，让这些细胞生长和繁殖。（2）动物细胞培养的流程：取动物组织块（动物胚胎或幼龄动物的器官或组织）→剪碎→用胰蛋白酶或胶原蛋白酶处理分散成单个细胞→制成细胞悬液→转入培养瓶中进行原代培养→贴满瓶壁的细胞重新用胰蛋白酶或胶原蛋白酶处理分散成单个细胞继续传代培养。（3）细胞贴壁和接触抑
制：悬液中分散的细胞很快就贴附在瓶壁上，称为细胞贴壁。细胞数目不断增多，当贴壁细胞分裂生长到表面相互抑制时，细胞就会停止分裂增殖，这种现象称为细胞的接触抑制。（4）动物细胞培养需要满足以下条件①无菌、无毒的环境：培养液应进行无菌处理。通常还要在培养液中添加一定量的抗生素，以防培养过程中的污染。此外，应定期更换培养液，防止代谢产物积累对细胞自身造成危害。②营养：合成培养基成分：糖、氨基酸、促生长因子、无机盐、微量元素等。通常需加入血清、血浆等天然成分。③温度：适宜温度：哺乳动物多是 36、5℃＋0、5℃；pH： 7、2～ 7、4。④气体环境：95%空气＋5%CO2。O2是细胞代谢所必需的，CO2的主要作用是维持培养液的pH。（5）动物细胞培养技术的应用：制备病毒疫苗、制备单克隆抗体、检测有毒物质、培养医学研究的各种细胞。 2、动物体细胞核移植技术和克隆动物（1）哺乳动物核移植可以分为胚胎细胞核移植（比较容易）和体细胞核移植（比较难）。（2）选用去核卵(母)细胞的原因：卵(母)细胞比较大，容易操作；卵(母)细胞细胞质多，营养丰富。（3）体细胞核移植的大致过程是：（右图）核移植胚胎移植（4）体细胞核移植技术的应用：①加速家畜遗传改良进程，促进良畜群繁育；②保护濒危物种，增大存活数量；③生产珍贵的医用蛋白；④作为异种移
如对您有帮助，可购买打赏，谢谢 眼角长了个小疙瘩怎么办 导语：眼睛长了个小疙瘩是很常见的现象，在这样的情况之下，就好好的进行观察，自己的小疙瘩是什么样的，然后及时的去医院做检查，及时的检查，及 眼睛长了个小疙瘩是很常见的现象，在这样的情况之下，就好好的进行观察，自己的小疙瘩是什么样的，然后及时的去医院做检查，及时的检查，及时的治疗，这是非常不错的，这样就会更好的让自己的身体恢复健康，这样是非常的不错的，那就好好的进行治疗就好了。 应该是麦粒肿的症状，别担心，初起有眼睑痒、痛、胀等不适感觉，之后以疼痛为主，少数病例能自行消退，大多数患者逐渐加重。检查见患处皮肤红肿，触摸有绿豆至黄豆大小结节，并有压痛。如果病变发生在近外眼角处，肿胀和疼痛更加明显，并伴有附近球结膜水肿。部分患者在炎症高峰时伴有恶寒发热、头痛等症状。外麦粒肿3～5日后在皮肤面，内麦粒肿2～3日后在结膜面破溃流脓，炎症随即消退。也有部分麦粒肿既不消散，也不化脓破溃，硬结节长期遗留者。它是一种没有明显疼痛的表面皮肤隆起，主要是眼睑腺体阻塞造成的伴有炎症的话才会自觉的疼痛的症状。小的霰粒肿可以热敷或者理疗按摩疗法，促进消散吸收，较大大的或长时间不能吸收的可以择期手术治疗. 这是脂肪粒，等长时间长了，熟了自己挤就行。先说说脂肪粒的产生（从其他地方转来D，自己还没那么厉害，呵呵 1、体内原因：眼部、面部出现油脂粒大多是由于近期身体内分泌有些失调，致使面部油脂分泌过剩，再加上皮肤没有得到彻底清洁干净，导致毛孔阻塞，很快形成脂肪粒。2、外在因素：眼睛周围是全身最薄的皮肤，约0.07毫米，又没有皮下腺，加上人眼一天频繁眨动，特别容易缺水、干燥、 预防疾病常识分享，对您有帮助可购买打赏
第三章植物组织与细胞培养的基本原理. 一、单选题 1 A进化论 C基因决定论 D细胞多样性 2、植物组织培养的培养基不含 A含 N物质 B碳源生长激素 3、过滤除菌适用对象_ __。 A玻璃器皿 B塑料器皿 C金属器皿 D易被高温破坏的材料如抗生素、激素等试剂 4、将___ 重新转移到成分相同的新鲜培养基上生长的过程称为继代培养。 A外殖体 B愈伤组织 C 生根苗 D 幼苗 5、下列植物细胞全能性最高的是（） A. 形成层细胞 B. 韧皮部细胞 C. 木质部细胞 D. 叶肉细胞 6、植物组织培养过程中的脱分化是指（） A. 植物体的分生组织通过细胞分裂产生新细胞 B. 未成熟的种子经过处理培育出幼苗的过程 C. 植物的器官、组织或细胞，通过离体培养产生愈伤组织的过程 D. 取植物的枝芽培育成一株新植物的过程 三、判断题 1、根尖、茎、芽、嫩叶、种子、花粉均是植物组织培养外殖体的来源。( √ ) 2、一个细胞由分生状态转变为成熟状态的过程，叫脱分化。( × ) 3、所有的植物培养材料都必须高压灭菌锅灭菌121℃、15分钟。( × ) 4、继代培养的最适时间是愈伤组织的生长达到高峰期时。（×） 5、高温处理和低温处理均是控制胚状体同步化的方法（×） 第四章植物离体无性繁殖和脱毒技术 一、选择题（单选） 1. 甘薯种植多年后易积累病毒而导致品种退化。目前生产上采用茎尖分生组织离体培养的方法快速繁殖脱毒的种苗，以保证该品种的品质和产量水平。这种通过分生组织离体培养获得种苗的过程不涉及细胞的 A. 有丝分裂 B. 分化 C. 减数分裂 D. 全能性 2、人工种子是指植物离体培养中产生的胚状体，包裹在含有养分和具有保护功能的物质中，并在适宜条件下能够发芽出苗的颗粒体。下列与人工种子形成过程无关的是（） A．细胞的脱分化和再分化 B．细胞的全能性 C．细胞有丝分裂 D．细胞减数分裂 3. 植物组织培养依据的原理，培养过程的顺序及诱导的植物激素分别是（） ①体细胞全能性②离体植物器官，组织或细胞③根，芽④生长素和细胞分裂素 ⑤生长素和乙烯⑥愈伤组织⑦再分化⑧脱分化⑨植物体 A. ①、②⑧⑥⑦③⑨、④ B. ①、②⑦⑥⑧③⑨、⑤ C. ①、⑥②⑨⑧③⑦、⑤ D. ①、②⑨⑧⑥⑦③、④ 4. 有关细胞全能性的叙述中，不正确的是（） A. 受精卵在自然条件下能够使后代细胞形成完整的个体，全能性最高 B. 卵细胞与受精卵一样，细胞未分化，全能性很高 C. 生物体内细胞由于分化，全能性不能表达 D. 植物细胞离体培养在一定条件下能表现出全能性 5、胚状体是在植物组织培养的哪一阶段上获得的（） A．愈伤组织 B．再分化 C．形成完整植株 D．取离体组织 二、判断题 1、无病毒苗（脱毒苗）就是植物体已没有病毒感染了（×）
细胞工程大纲 一、说明 （一）《细胞工程》的课程性质： 细胞工程学是应用细胞生物学和分子生物学原理与方法，在细胞水平上研究改造生物遗传特性和生物学特性，以获得具有目标性状的细胞系或生物体的有关理论和技术的学科。它是一门现代生物科学理论和工程技术相结合的综合性学科。细胞工程是现代生物技术的重要组成部分之一。同时也是现代生物学研究的重要技术工具。 （二）教材及授课对象： 杨淑慎主编，《细胞工程》，科学出版社，2007 授课对象：本科生物技术专业 （三）《细胞工程》的课程目标： 通过本课程的学习使学生系统掌握该门学科形成与发展，理论与原理，技术与方法等基础知识，结合科研实际以及最新研究动态，使学生对本课程有一个全面的了解；以适应后基因组时代在教学、科研和生产开发各方面对当代生命科学人才知识结构的需求。理论知识方面，重点掌握本学科的基本原理和基本技术即：细胞全能性学说在细胞工程中的指导作用；培养条件下的细胞分化和器官发生的调控；离体培养条件下的遗传与变异特点。掌握不同组织、器官的培养特点和控制方法。了解细胞工程的各类技术在现代生物学与生物技术领域的应用途径与发展潜力。 （四）《细胞工程》课程授课计划（包括学时分配）： 章次内容讲授学时备注一绪论 1 二细胞工程实验室组成及常用仪器设备 2 三细胞工程无菌操作技术 4 四植物细胞工程理论基础 5 五植物离体快繁和脱病毒技术7 六植物原生质体培养 6 七植物花粉和花药培养 6 八植物胚胎培养7 九植物组织培养和次生代谢产物 2
十植物种质资源的离体保存 5 十一动物细胞培养技术 4 十二动物细胞融合技术 4 十三动物干细胞技术 1 十四动物胚胎工程 1 总学时54 （五）教学建议： 它在细胞生物学、遗传学、生理学、生物化学、分子生物学等学科基础上发展起来，本课程以课堂讲授为主，充分发挥学生的学习主动性，引导式教学，展开问题与讨论。灵活采用多媒体教学方式和方法。在教学过程中，围绕细胞工程领域的主要技术原理与方法，将植物组织与细胞培养、动物细胞与组织培养、细胞融合与单克隆抗体、染色体工程、胚胎工程、干细胞工程、细胞拆合与细胞重组及克隆技术和转基因生物与生物反应器等主要技术原理与方法系统地阐述清楚，力求讲深、讲透。 （六）考核要求： 采用期末考核与平时成绩相结合的方式进行考核。按平时占10%（包括学习态度和平时作业等）；期中考试成绩占30%；期终成绩占60%。试卷按照教学大纲的要求，利用试题库对学生进行考试。 二、教学内容 第一部分细胞工程基础 Chapter1 绪论 主要教学目标：全面了解细胞工程与现代生物技术的关系和细胞工程的概念及主要技术领域；了解细胞工程的发展与相关学科的联系；了解细胞工程的发展过程及发展趋势及在现代世界经济中的潜力。 教学方法及教学手段：采用启发式和提问式的教学方式，并将课堂上学生的表现记入学生的平时成绩。 教学重点及难点：生物技术的概念和细胞工程的概念；细胞工程的主要技术领域及其应用。 §1.1细胞工程的定义和基本内容 一、细胞工程的定义
专题2 细胞工程 （一）植物细胞工程 1.理论基础（原理）： 全能性表达的难易程度： 2.植物组织培养技术 （1）过程：离体的植物器官、组织或细胞 ―→愈伤组织 ―→试管苗 ―→植物体 （2）用途： 。 （3）地位：是培育 、 培育植物新品种的最后一道 工序。 3.植物体细胞杂交技术 （1）过程： （2）诱导融合的方法：物理法包括 等。化学法一般是用 作为诱导剂。 （3）意义： （二）动物细胞工程 1. 动物细胞培养 （1）概念：动物细胞培养就是从动物机体中取出相关的 ，将它分散成 ， 然后放在适宜的 中，让这些细胞 。 （2）动物细胞培养的流程：取动物组织块（动物胚胎或幼龄动物的器官或组织）→剪碎→用 处理分散成 →制成 →转入培养瓶中进行 培养→贴满瓶壁的细胞重新用胰蛋白酶或胶原蛋白酶处理分散成单个细胞继续 培养。 （3）细胞贴壁和接触抑制：悬液中分散的细胞很快就 ，称为 。细胞数目不 断增多，当贴壁细胞分裂生长到表面 时，细胞就会 ，这种现象称为 。 （4）动物细胞培养需要满足以下条件 ① ：培养液应进行 处理。通常还要在培养液中添加一 定量的 ，以防培养过程中的污染。此外，应定期更换培养液，防止 。 ② ：合成培养基成分：糖、氨基酸、促生长因子、无机盐、微量元素等。通常需加入 等天然成分。 ③ ：适宜温度：哺乳动物多是 ；pH ： 。 ④ ：95% ＋5% 。O 2是 所必需的，CO 2的主要作用 是 。 （5）动物细胞培养技术的应用： 2.动物体细胞核移植技术和克隆动物 （1）哺乳动物核移植可以分为 核移植（比较容易）和 核移植（比较难）。
核移植 （2）选用去核卵(母)细胞的原因：卵(母)细胞 ； 卵(母)细胞 。 （3）体细胞核移植的大致过程是：（下图） （4）体细胞核移植技术的应用： ① ② ③ ④ ⑤ （5）体细胞核移植技术存在的问题： 克隆动物存在着 问题、表现出 缺陷等。 3.动物细胞融合 （1）动物细胞融合也称 ，是指两个或多个动物细胞结合形成一个细胞的过程。融 合后形成的具有原来 细胞遗传信息的单核细胞，称为 。 （2）动物细胞融合与植物原生质体融合的原理基本相同，诱导动物细胞融合的方法与植物原生质体 融合的方法类似，常用的诱导因素有 等。 （3）动物细胞融合的意义：克服了 ，成为研究 的重要手段。 （4）动物细胞融合与植物体细胞杂交的比较： 4.单克隆抗体 （1）抗体：一个B 淋巴细胞只分泌一种 。从血清中分离出的抗体 。 （2）单克隆抗体的制备过程：
皮下组织有疙瘩是怎么回事 在一般的情况下，大家对身体健康和皮肤护理都特别的主要，尤其是在夏天的时候，皮肤都暴露在外面，接收来自外界的各种刺激，在这样的时候更要做好对皮肤的护理。有时候我，我们会发现在皮肤的下面长出来好多的小疙瘩，这样时候就会让我们感觉到不知道是怎么回事也不知道要怎么治疗。 ★考虑是毛周角化症 毛周角化症俗称“鸡皮肤”，是一种先天遗传病，个人症状 有轻有重。身体缺乏维生素A皮肤干燥，或者天气较为干燥寒冷，毛口收缩的时候腿上汗毛长不出来，症状较明显，夏季则症状较轻。一般不痛不痒，过度清洁时也可能出现瘙痒情况。 ★对策： 毛孔角化症属于先天遗传病，并不能根治，只能通过利用A
酸，或含果酸、乳酸等成分的产品保养皮肤，改善皮肤的角化程度。可去药店咨询医生购买水杨酸类软膏。 ★缺乏维生素A加重小红疙瘩 皮肤下面有小红疙瘩除了与天生体质有关以外，缺乏维生素A引起全身性的皮肤干燥也是加重症状的原因之一。 ★对策 1、洗澡不要用太热的水。过热的水会让皮肤上的皮脂过分流失，这样会加重皮肤干燥。 2、洗澡后抹身体乳。保证皮肤水分充足能有效减轻腿上小红疙瘩，所以可以在每次洗澡后涂上身体乳液。
★可能是闭头粉刺 如果是额头、下巴等部位皮肤下有疙瘩，刚开始是黄白色，不痛不痒，天气炎热脸上油脂分泌旺盛时会有发红，多是闭头粉刺。 ★对策 1、及时清洁皮肤，注意补水，不要使用刺激性护肤品，不要用过于油腻滋润的面霜。 2、多运动促进皮肤排汗，使毛孔中的油脂脏污随着汗水排出，防止油脂堵塞毛孔发炎。 3、多喝水、多吃蔬果，饮食清淡。
4、必要时看医生。 ★可能是脂肪粒 如果皮下的疙瘩是乳白色的，不痛不痒，不受天气、温度等影响，用手不容易挤出，且多分布在眼角周围，考虑是脂肪粒。 ★对策 1、脂肪粒多是皮肤营养过剩引起的，建议不要使用太油腻的护肤品。 2、可取美容院用针挑破，挤出脂肪粒。(别用手挤，小心皮肤感染发炎)