Dissociating Valuation and Saliency Signals during
Cerebral Cortex January2011;21:95--102
doi:10.1093/cercor/bhq065
Advance Access publication May5,2010
Dissociating Valuation and Saliency Signals during Decision-Making
Ab Litt1,Hilke Plassmann2,3,Baba Shiv1and Antonio Rangel4,5
1Graduate School of Business,Stanford University,CA94305,USA,2INSEAD,77300,France,3INSERM,E cole Normale Supe rieure, 75654,France,4Computation and Neural Systems,California Institute of Technology,CA91125,USA and5Humanities and Social Sciences,California Institute of Technology,CA91125,USA
Address correspondence to Antonio Rangel,California Institute of Technology,121Baxter Hall,Pasadena,CA91125,USA.E-mail:rangel @https://www.wendangku.net/doc/5b6247671.html,.
There is a growing consensus that the brain computes value and saliency-like signals at the time of decision-making.Value signals are essential for making choices.Saliency signals are related to motivation,attention,and arousal.Unfortunately,an unequivocal characterization of the areas involved in these2distinct sets of processes is made dif?cult by the fact that,in most experiments, both types of signals are highly correlated.We dissociated value and saliency signals using a novel human functional magnetic resonance imaging decision-making task.Activity in the medial orbitofrontal,rostral anterior cingulate,and posterior cingulate cortices was modulated by value but not saliency.The opposite was true for dorsal anterior cingulate,supplementary motor area, insula,and the precentral and fusiform gyri.Only the ventral striatum and the cuneus were modulated by both value and saliency.
Keywords:attention,decision-making,motor preparation,saliency, valuation
Introduction
There is a growing consensus in behavioral neuroscience that the brain makes simple decisions by assigning values to the different stimuli under consideration and then comparing those values to make a choice(Montague and Berns2002; Rangel et al.2008).This has motivated much interest in locating the neural substrates of value computations at the time of choice.Multiple studies have investigated this question using human functional magnetic resonance imaging(fMRI)and monkey and rat electrophysiology(Wallis and Miller2003; Padoa-Schioppa and Assad2006;Kable and Glimcher2007; Plassmann et al.2007;Tom et al.2007;Hare et al.2008)and have found that activity in areas such as the medial orbitofrontal cortex(mOFC),anterior cingulate cortex (ACC),and ventral striatum(VStr)correlate with behavioral measures of stimulus value at the time of choice.These results have been widely interpreted as evidence that these areas are involved in the valuation stage of the decision-making process.
Unfortunately,identifying neural activity associated with value signals is dif?cult because in many experimental paradigms value and saliency signals are highly correlated:the higher valued items also attract more attention,engage higher levels of motor preparation,and lead to higher levels of emotional arousal(Maunsell2004;Roesch and Olson2004).As a result,without further controls,one cannot conclude that a correlation between neural activity and value implies that this activity is truly involved in value coding for the purposes of decision-making.It is important to emphasize that this potential confound is not a mere theoretical curiosity,since activity correlated with value has been found in areas traditionally associated with visual processing such as V1 (Serences2008),areas involved in motor preparation such as the supplementary motor area(SMA)(Wunderlich et al.2009), and areas involved in visual attention such as lateral intraparietal cortex(Platt and Glimcher1999).Some studies have attempted to control for these types of confounds(e.g., Plassmann et al.2007),but the existing controls have not been able to rule out this confound in all the areas that have been shown associated with valuation at the time of decision-making in human fMRI studies.
Here,we present the results of a novel human fMRI decision-making task designed to dissociate value and saliency signals at the time of choice,thus addressing this problem.Value signals provide a measure of the desirability of the stimuli,which is given by the expected amount of reward that they generate if consumed(Montague and Berns2002;Glimcher et al.2005; Rangel et al.2008).Value signals are positive for appetitive stimuli and negative for aversive stimuli.In contrast,saliency signals provide a measure of the importance of the stimulus, which plays an important role in allocating attentional, motivational,and other computational processes in the brain. Saliency signals are larger for stimuli that are likely to have a larger impact in the organism,such as highly appetitive or highly aversive consumption items.
The basic idea of the experiment is simple.Subjects are shown appetitive and aversive foods,spanning a range from ‘‘strongly disliked’’to‘‘strongly liked,’’and are asked to indicate through a button press whether or not they want to eat them at the end of the experiment.The presence of both appetitive and aversive stimuli of varying degrees of preference allows us to separate value from nonvalue signals:Whereas an area encoding for value should exhibit monotonically increasing activation from the very aversive to the very appetitive stimuli, an area associated with salience should exhibit a stronger response to strongly liked and strongly disliked items than to ‘‘weakly liked’’and‘‘weakly disliked’’items.
Several studies have provided evidence for a dissociation between these2types of signals at the time of stimulus consumption(Anderson et al.2003;Small et al.2003),or in Pavlovian paradigms in which no decisions are made(Jensen et al. 2007;Cooper and Knutson2008;Matsumoto and Hikosaka2009). However,only2animal studies to date have attempted to control for this important confound during decisions(Roesch and Olson 2004;Lin and Nicolelis2008).Roesch and Olson(2004)collected recordings from neurons in the macaque orbitofrontal cortex (OFC)and premotor cortex in a simple binary decision paradigm. They found value signals in OFC and motivational attentional-arousal signals in premotor cortex.Lin and Nicolelis(2008)found similar motivational attentional-arousal signals in rat basal
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forebrain neurons.However,a whole brain search aimed at dissociating both types of signals in humans at the time of decision-making has not been carried out to date,and it is unknown if areas such as the cingulate cortex are associated with value or saliency processes.
Two aspects of the study are worth highlighting from the outset.First,while our experiment allows us to dissociate value signals from those that are associated with motor preparation, attention or arousal,it does not allow us to dissociate between areas involved in the latter set of processes.Nevertheless,given the importance for behavioral neuroscience of characterizing the neural substrates of stimulus valuation,distinguishing areas that have the properties of value signals from those that are associated with alternative correlated computations is crucial for correctly interpreting both existing and future results. Second,our results show that many previous studies were correct in interpreting activity in areas such as the mOFC,ACC, and posterior cingulate cortex as value signals,since their activity correlates with value but not saliency measures.We emphasize that such ex post con?rmation of existing results does not detract from the importance of carrying out this experiment,since ruling out this important confound would have been impossible without actually carrying out the necessary controls.
Materials and Methods
Subjects
Twenty subjects participated in the experiment(16males,ages 19--52years).All subjects were right-handed,healthy,had normal or corrected-to-normal vision,had no history of psychiatric diagnoses, neurological or metabolic illnesses,and were not taking medications that interfere with the performance of fMRI.Subjects also reported not having a history of eating disorders and were screened for liking some of the foods described below and disliking others.Subjects were told that the goal of the experiment was to study food preferences and gave written consent before participating.The review board of the California Institute of Technology(Pasadena,CA)approved the study.Subjects received$35for their participation.
Stimuli
Subjects made decisions on60different food items.The set of food items was selected based on prior behavioral pilot data to span positive and negative preferences for most subjects.Thirty items were selected from a set of pictures that was rated as appetitive by most subjects. These included sweet and salty snack foods such as potato chips and candy bars.Thirty additional items were selected from a set of pictures that was rated as aversive by the majority of subjects.Examples include various types of canned meat such as liverwurst and various types of baby food.The foods were presented to the subjects as color pictures (72dpi)using video goggles.
Task
Subjects performed2tasks:a liking-rating task prior to the fMRI session and a food choice task during the scanning session.
During the liking-rating task,subjects were asked to provide ratings (–2=NOT AT ALL to2=VERY MUCH)for each of the60food items that they would encounter during the scanning task.The ratings were anchored to the question‘‘How much would you like to eat this item at the end of the experiment?’’
The food choice task is described in Figure1A.Subjects were instructed not to eat immediately before arriving for the experiment and to have eaten no more than a light meal in the4preceding hours. In each trial,subjects were asked to make a binding decision about whether or not they wanted to eat the current food item at the end of the experiment.The decisions were binding because subjects knew that at the end of the experiment,a trial would be selected at random and that their response on that trial would be implemented.Thus,they would have to eat the food item shown in that trial if they said‘‘Yes,’’and they would not be allowed to eat it if they said‘‘No.’’On each trial, they were presented with a picture of an item and had up to2s to enter one of4responses:‘‘Strong No,’’‘‘No,’’‘‘Yes,’’or‘‘Strong Yes.’’Note that this feature of the design allowed us to measure the choice and the strength of preference simultaneously.Furthermore,the4responses were used to de?ne value and saliency measures as follows.The value signal takes values from–2(=Strong No)to+2(=Strong Yes).The saliency signal takes a value of1(=No,Yes)or2(=Strong No,Strong Yes).Each of the60items was shown4times in the scanning task, twice per session in2consecutive sessions.
Trial ordering was fully randomized within and across subjects,with pseudorandomized intertrial blank-screen intervals to ensure identical full-task time across subjects.To avoid activation artifacts caused by the assignment of responses to buttons,the mapping of responses to buttons was counterbalanced(in the left-to-right directions)across subjects.
fMRI Data Acquisition
The functional imaging was conducted in a Siemens3.0-T Trio MRI scanner.We acquired gradient echo T2*-weighted echo-planar images (EPIs)with blood oxygen level--dependent(BOLD)contrast.To optimize functional sensitivity in the OFC,we used a tilted acquisition in an oblique orientation of30°to the anterior commissure--posterior commissure line(Deichmann et al.2003).We also used an8-channel phased array coil that yields a40%signal increase in signal in the mOFC over a standard head coil.Each volume comprised of44axial slices.A total of700volumes(2sessions,%16-min each)were collected in an interleaved-ascending manner.The imaging parameters were as follows:echo time,30ms;?eld of view,192mm;in-plane resolution and slice thickness,3mm;repetition time,2.75s.Whole-brain high-resolution T1-weighted structural scans(13131mm)were acquired from every subject.
fMRI Data Preprocessing
The T1-weighted structural scans for each subject were coregistered with their mean EPI and averaged together to permit anatomical localization of the functional activations at the group level.
Image Figure1.(A)Experimental design.(B)Reaction times by value(as measured by the subjects’responses).Error bars indicate±1standard error.
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analysis was performed using SPM5(Wellcome Department of Imaging Neuroscience,Institute of Neurology,London,UK).Temporal nor-malization was applied to the scans with a time of acquisition of1.9375 referenced to the last volume.To correct for subject motion,the images were realigned to the last volume,spatially normalized to a standard T*2template with a resampled voxel size of3mm and spatially smoothed using a Gaussian kernel with a full-width at half-maximum of 8mm.Intensity normalization and high-pass temporal?ltering(using a?lter width of128s)were also applied to the data.
General Linear Model
We estimated a mixed-effects general linear model of the BOLD activity in the following3steps.
First,for each subject,we estimated a general linear model with AR(1)and the following independent variables for each of the 2sessions:
(R1)Indicator variable for item presentation during nonmissed decision trials,
(R2)Indicator variable for item presentation parametrically modu-lated by the value signal(coding:–2=‘‘Strong No,’’–1=‘‘No,’’+1=‘‘Yes,’’+2=‘‘Strong Yes’’),
(R3)Indicator variable for item presentation parametrically modu-lated by the saliency signal(coding:+1=‘‘Yes’’/‘‘No,’’+2=‘‘Strong Yes’’/‘‘Strong No’’),
(R4)Indicator variable for item presentation during missed decision trials,and
(R5--R11)Six movement regressors and session constants. Regressors R1--R3were modeled using boxcar functions with durations equal to the subject’s response time in that trial.R4was modeled using a boxcar function with a duration of2s.Each of the regressors of interest(R1--R4)was convolved with a canonical hemodynamic response function.
Second,we calculated the following?rst-level single-subject con-trasts:1)regressor R2versus baseline and2)regressor R3versus baseline.
Finally,for each of these?rst-level contrasts,we estimated a second-level mixed-effects analysis by computing a1-sample t-test on the single-subject contrast coef?cients.All?gures and tables report results at a level of P<0.001uncorrected with an extent threshold of 5contiguous voxels.Anatomical localizations were carried out by overlaying the t-maps on a normalized structural image averaged across subjects,with reference to an anatomical atlas(Duvernoy1999). Post Hoc ROI Analyses1
In order to measure the strength of the signals encoded in the regions identi?ed by the whole-brain analysis,we carried out an independent region of interest(ROI)analysis.This allowed us to test,for example,if areas in which activity correlated with value also exhibited activity correlated with saliency and vice versa.The following procedure was used to compute the effect size plots shown in Figures2B,3B,and4B. First,we extracted an estimate of the particular regressor of interest (i.e.,the estimated‘‘beta’’value)for each subject i from a voxel that was identi?ed using the GLM estimates from all other subjects except for i. In particular,for each subject i,we identi?ed a peak voxel for the contrast of interest by selecting the voxel within the anatomical area of interest that exhibited peak activity for that contrast in a mixed-effects analysis that included all subjects except for i.Second,the set of extracted beta values(one for each subject)were then averaged,and2-sided t-tests were used to test the signi?cance of the regressor of interest.For the effect size plot in Figure4B,we used the peak voxels from the associated reported conjunction analysis(but the procedure was identical otherwise).
Post Hoc ROI Analyses2
In order to provide further veri?cation that the BOLD responses varied with the behavioral choices as suggested by the previous analyses,we estimated an additional GLM model with AR(1)(all omitted details are as in the main GLM):(R1)Indicator variable for item presentation receiving a‘‘Strong No’’response,
(R2)Indicator variable for item presentation receiving a‘‘No’’response,
(R3)Indicator variable for item presentation receiving a‘‘Yes’’response,
(R4)Indicator variable for item presentation receiving a‘‘Strong Yes’’response,
(R5)Indicator variable for item presentation during missed decision trials,and
(R6--R12)Movement regressors and session constants.
We then extracted beta values for regressors R1--R4using the same procedure described above,which is necessary to guarantee that the
ROI analysis is independent from the whole-brain analysis.The resulting effect size plots are reported in Figures2C,3C,and4C.
Results
The basic idea of the experiment is simple(Fig.1A).In every trial,subjects were shown a picture of either an appetitive(e.g., potato chips and candy bars)or an aversive food(e.g.,canned meats and baby foods)and had to decide if they wanted to eat
that food.At the end of the experiment,one of the trials was selected at random and the decision made by the subject on
that trial was implemented.Importantly,subjects made their choices using a4-point scale(‘‘Strong No,’’‘‘No,’’‘‘Yes,’’Strong Yes’’).
The behavioral response allowed us to de?ne2signals of interest.First,the value of an item was given by the magnitude
of the response:–2=‘‘Strong No,’’–1=‘‘No,’’+1=‘‘Yes,’’and+2=
‘‘Strong Yes.’’Second,we de?ne a‘‘saliency signal’’given by the absolute value of this response coding:+1=‘‘Yes’’/‘‘No’’and
+2=‘‘Strong Yes’’/‘‘Strong No.’’The term saliency is meant to capture the variety of psychological processes(such as attention,motor preparation,and arousal)that might be activated more strongly for highly liked or disliked items,than
for weakly liked or disliked ones.
Behavioral Results
Figure1B shows that the saliency of the stimulus had an effect
on response times:strong responses(high-saliency)were signi?cantly faster than weak responses(low-saliency;t77=
–4.22,P<0.0001).Positive responses,regardless of strength, were also signi?cantly faster than negative responding(t77=
–2.98,P=0.004).
Brain Activity Correlated with Value
Activity in the mOFC,rostral anterior cingulate cortex(rACC),
and dorsal posterior cingulate cortex(dPCC)was positively correlated with value(Fig.2A;for a complete list of activations,
see Table1).No areas exhibited negative correlation with value
at our omnibus threshold of P<0.001uncorrected.
We carried out2independent post hoc effect size analyses
in these ROIs(for details,see Materials and Methods)because previous studies have argued that these areas are associated
with value computation at the time of choice(Wallis and Miller 2003;Padoa-Schioppa and Assad2006;Kable and Glimcher 2007;Plassmann et al.2007;Tom et al.2007;Hare et al.2008).
An effect size analysis,depicted in Figure2B,showed that activity in these areas was correlated with value but not with saliency.Note also that value signals increased monotonically
with the behavioral responses.To verify that this is the case,we independently estimated the average BOLD response by
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behavioral choice.As shown in Figure 2C ,activity in the mOFC,rACC,and dPCC increased monotonically with the positivity of the choice,consistent with value coding.
Brain Activity Correlated with Saliency
Activity in several areas,including the dorsal anterior cingulate cortex (dACC),SMA,precentral gyrus,posterior insula,and fusiform gyrus (Fig.3A ;for a complete list of activations,see Table 2)correlated positively with the saliency measure but not with value.No areas exhibited negative correlation with saliency at our omnibus threshold of P <0.001uncorrected.An independent post hoc effect size analysis showed that activity in these areas did not correlate with value (Fig.3B ).
Saliency signals should exhibit a U -shape with regard to the behavioral responses (i.e.,activation should be larger for the
‘‘Strong Yes’’and ‘‘Strong No’’responses than the non-Strong responses).To verify that this was the case,we independently estimated the average BOLD response by behavioral choice.As shown in Figure 3C ,activity in all these areas exhibited the required pattern.Note,also,that there were no signi?cant activation differences between trials with ‘‘Strong Yes’’and ‘‘Strong No’’responses or between trials with ‘‘Yes’’and ‘‘No’’responses,which shows that the saliency measure used in the study is highly correlated with the computations performed in these areas.
Brain Activity Correlated with Both Value and Saliency Only the cuneus and VStr exhibited activation that correlated positively with both the value and saliency signals.Figure 4A shows the result of a conjunction analysis identifying areas of the VStr in which activity was associated with both types of signals (Table 3).Independent post hoc analyses of the effect sizes and responses by behavioral choice using the previous methods led to the same conclusion (Fig.4B --C ).Discussion
The results in this paper provide a clear dissociation between areas involved with valuation at the time of choice and saliency-like signals that might be associated with attention,motor preparation,or arousal.Activity in the mOFC,rACC,and dPCC correlated with value but not with saliency signals.In contrast,activity in the dACC,SMA,precentral gyrus,posterior insula,and fusiform gyrus correlated with saliency but not with value.Only activity in the VStr and the cuneus correlated with both.Our results have implications for several areas of neurosci-ence.First,a growing number of studies has found that activity in the mOFC and rACC is correlated at the time of choice with behavioral measures of the value of stimuli in a wide variety of tasks (Wallis and Miller 2003;Padoa-Schioppa and Assad 2006;Kable and Glimcher 2007;Plassmann et al.2007;Tom et al.2007;Hare et al.2008).This has been widely interpreted as evidence that these areas might be involved in the
assignment
Figure 2.(A )Regions in which activity was correlated with value included the mOFC (à6,24,à21),rACC (à139,3),and dPCC (à3,à33,39).(B )Effect size plots for these 3areas showing that activity correlated with value but not with saliency.Note that all effect size plots were constructed using a procedure that ensures independence from the procedure used to identify the ROIs.Signi?cance levels for t -tests:**P \0.01,***P \0.001.(C )Effect size plots for these 3areas as a function of the behavioral response.
Table 1
Regions in which activity during the decision period was correlated with value MNI-coordinate (x ,y ,z )Number of voxels Region of activation
Side
BA
T
9,à69,27232Precuneus R 7m 6.31à6,à69,30151Precuneus
L 7m 5.95à39,à60,929Middle temporal lobe,subgyral L 5.83à6,à90,à341Cuneus/lingual gyrus L 17 5.650,39,3193Rostral anterior cingulate L 32,24 5.456,42,0268Rostral anterior cingulate R 32,24 5.409,à81,18103Cuneus
R 17 4.9824,39,5425Superior frontal gyrus R 8 4.8639,à60,à4517Cerebellar tonsil
R 4.69à3,à33,39127Dorsal posterior cingulate
L 31 4.58à6,24,à2149Medial rectal/frontal gyrus,mOFC L 11 4.61à48,à57,4563Supramarginal gyrus L 40 4.5842,à63,5128Angular gyrus
R 7 4.45à51,à6,à2412Inferior temporal gyrus L 20 4.179,à39,36104Dorsal posterior cingulate R 31 3.956,24,à1843Medial frontal gyrus,mOFC R 11
3.92à6,6,à915VStr L 3.86à9,9,à1215VStr
L 3.84à21,à18,à18
5
Parahippocampal gyrus L
3.60
Note:Height threshold:T 53.5794,P 50.001(uncorrected).Extent threshold:k 55voxels.BA 5Brodmann area.
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Figure3.(A)Regions in which activity was correlated with the saliency measure included the dACC(à3,0,42),SMA(9,à12,60),precentral gyrus(36,à18,57),posterior insula(à33,à21,15),and fusiform gyrus(30,à60,à18).(B)Effect size plots for these5areas showing that activity correlated with saliency but not value.Signi?cance level for t-tests:***P\0.001.(C)Effect size plots for these areas as a function of the behavioral response.
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of values to stimuli during decision-making.Our data,together with the ?ndings by Roesch and Olson (2004),suggest that this conclusion is justi?ed even though previous studies did not carry out the controls necessary to rule out the involvement of these regions in attentional,motivational,or arousal processes.
Second,our results contribute to the literature seeking to dissociate value and saliency signals.Several previous studies have presented evidence for and against such dissociations at the time of stimulus consumption (Anderson et al.2003;Small et al.2003),or in Pavlovian paradigms in which no decisions are made (Jensen et al.2007;Cooper and Knutson 2008;Matsumoto and Hikosaka 2009).Although the computations made by the brain in these tasks might be very different from those made during decision-making,it is interesting to note some common results.Cooper and Knutson (2008)found that the VStr also correlates with both saliency and valence during the anticipation of probabilistic rewards.In a related study,Jensen et al.(2007)found both positive and negative pre-diction error signals in a similar region of VStr (although see Seymour et al.2007for results dissociating gain and loss encoding in the striatum).Small et al.(2003)found valence signals in OFC and anterior insula and saliency signals in the amygdala,cerebellum,pons,and middle insula during a gusta-tion task.Anderson et al.(2003)found valence signals in response to odors in the OFC and saliency signals in amygdala.Thus,some of the ?ndings from these alternative paradigms parallel the ones obtained here,which suggests that some common valuation and saliency-type processes might be activated at the time of decision and during consumption and reward anticipation.
Closer to our study,Roesch and Olson (2004)recorded from neurons in the macaque OFC and premotor cortex in a simple binary decision paradigm in which values and saliency were also orthogonalized.Consistent with our ?ndings,they found valuation signals in OFC and motivational--attentional--arousal signals in premotor cortex.Lin and Nicolelis (2008)recorded from rat basal forebrain neurons in a go/no-go task.They found that activity in this area at the time of decision was modulated by the saliency of the stimulus not by its value.However,since they only had 2stimuli (one positive and one negative),it is hard to fully interpret the nature of the signals identi?ed in this area.The results in this paper extend these ?ndings to humans and provide evidence for the dissociation of saliency and value signals during choice.
Third,our results provide new insights into the role of the striatum in decision making.Several previous studies have argued that this area is involved in the computation of value signals (Kable and Glimcher 2007;Knutson et al.2007;Tom et al.2007).Others have argued that it might be involved in saliency and the deployment of motor responses (Horvitz 2000;Tricomi et al.2004;Zink et al.2004).Our results show that common regions of the striatum are involved in both value and saliency computations at the time of decision-making.This suggests that the striatum might be a critical area where the value signals necessary to make choices come together with the motor signals necessary to implement them.
Fourth,a comparison of our results with the literature on risk coding also provides some novel insights about the role of the anterior insula in valuation and decision-making.Recent studies (Preuschoff et al.2006,2008)have shown activity in the anterior insula correlated with the amount of risk that individuals faced on a Pavlovian reward task with stochastic payoffs.Note that the risk signal in this task closely resembles a saliency signal,since it is high for stimuli with very high or very low probability of reward,and close to zero for stimuli with an average probability of reward.Together with our ?ndings,this suggests that the anterior insula might be involved
Table 3
Conjunction analysis showing regions in which activity during the decision period was correlated with both valence and saliency MNI-coordinate (x ,y ,z)Number of voxels Region of activation Side BA T 12,à84,362Cuneus R 17 5.02à6,12,à125VStr L 4.54à9,à90,337Cuneus L 17
4.049,12,à15
7
VStr
R
3.74
Note:Height threshold:T 53.5794,P 50.001(uncorrected).Extent threshold:k 55voxels.BA 5Brodmann area.
Table 2
Regions in which activity during the decision period was correlated with saliency MNI-coordinate (x ,y ,z )Number of voxels Region of activation
Side
BA
T
30,à60,à18505Fusiform gyrus (O4)R 378.31à30,à54,à21649Fusiform gyrus (O4)L 378.2336,à18,57438Precentral gyrus R 47.18à54,0,3656Precentral gyrus L 4,6 6.17à30,à27,57275Precentral gyrus L 4 6.04à33,à21,1525Insula,posterior
L 13 5.6112,à63,à5161Inferior semilunar lobule R 5.389,à81,18203Cuneus R 17 5.299,à12,60172SMA
R 6 4.9527,à54,à5113Cerebellar tonsil R 4.91à36,9,à1815Temporal pole
L 38 4.890,à3,4568Dorsal anterior cingulate R 32#,24# 4.67à3,0,4242Dorsal anterior cingulate L 32#,24#
4.49à12,à81,0133Lingual gyrus L 17 4.29à3,à12,6084SMA L 6
4.11à9,9,à146VStr
L 4.0045,à18,2115Insula,posterior R 13
3.8712,11,à15
11
VStr R
3.79
Note:Height threshold:T 53.5794,P 50.001(uncorrected).Extent threshold:k 55voxels.BA 5Brodmann
area.
Figure 4.(A )Region of VStr (à9,9,à12)in which activity was correlated with both value and saliency.(B )Effect size plots this area showing that activity correlated with both value and saliency.***P \0.001.(C )Effect size plots for this area as a function of the behavioral response.
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in identifying stimuli with extreme values for a wide class of stimuli and in a wide range of valuation-related tasks.
A few methodological and conceptual aspects deserve further discussion.First,by de?ning the saliency signal to be equal to the absolute value of the behavioral response,the study implicitly assumed that strongly disliked and strongly liked items induce attentional,motor preparation,or arousal responses of equal strength.Theoretically,this is equivalent to assuming that all these processes correlated with the‘‘magni-tude’’of the value,regardless of its sign.While the previous literature does not offer a guide about whether or not this is a valid assumption,it is worth pointing out that the results in Figures2C,3C,and4C are consistent with the notion of saliency employed here.
Second,the study does not assume that value is the only driver of attention,motor preparation,or arousal.For example,stimulus familiarity can in?uence attention,previous experience making decisions with a stimuli is known to in?uence the level of motor preparation,and visceral states can have strong effects on overall levels of arousal.The only assumption that this study makes is that these processes might also be in?uenced to some extent by a stimulus’value, which is consistent with the reaction time data shown in Figure1B.
Third,a limitation of the study is that it cannot distinguish between different attentional,motor preparation,and arousal signals.However,it is important to emphasize that this is the ?rst human neuroimaging study that is able to systematically rule out these confounds for areas,such as ventromedial prefrontal cortex and rACC,that have been traditionally associated with valuation.It is also possible to speculate about their respective roles based on the previous literature.The dACC,precentral gyrus,and SMA have been associated with the preparation and execution of motor responses(Bush et al. 2002;Rushworth et al.2004)and thus might be a critical part of the motivational system.The insula has been shown to encode bodily states and thus is likely to be associated with arousal(Craig2002).Finally,the fusiform gyrus has been shown to respond selectively to certain types of stimuli and thus might be involved in the deployment of attention (Vulleumier2005).
More generally,the results presented here show the importance of including both appetitive and aversive stimuli in decision-making studies whenever possible.Since a large number of areas correlate with value when only appetitive or aversive stimuli are included,it is easy to misinterpret as valuation areas regions that are actually associated with attention,motor preparation,or arousal processing.
Funding
Moore Foundation to A.R.
Notes
Con?ict of Interest:None declared.
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脐带血造血干细胞库管理办法(试行)
脐带血造血干细胞库管理办法（试行） 第一章总则 第一条为合理利用我国脐带血造血干细胞资源，促进脐带血造血干细胞移植高新技术的发展，确保脐带血 造血干细胞应用的安全性和有效性，特制定本管理办法。 第二条脐带血造血干细胞库是指以人体造血干细胞移植为目的，具有采集、处理、保存和提供造血干细胞 的能力，并具有相当研究实力的特殊血站。 任何单位和个人不得以营利为目的进行脐带血采供活动。 第三条本办法所指脐带血为与孕妇和新生儿血容量和血循环无关的，由新生儿脐带扎断后的远端所采集的 胎盘血。 第四条对脐带血造血干细胞库实行全国统一规划，统一布局，统一标准，统一规范和统一管理制度。 第二章设置审批 第五条国务院卫生行政部门根据我国人口分布、卫生资源、临床造血干细胞移植需要等实际情况，制订我 国脐带血造血干细胞库设置的总体布局和发展规划。 第六条脐带血造血干细胞库的设置必须经国务院卫生行政部门批准。 第七条国务院卫生行政部门成立由有关方面专家组成的脐带血造血干细胞库专家委员会（以下简称专家委
员会），负责对脐带血造血干细胞库设置的申请、验收和考评提出论证意见。专家委员会负责制订脐带血 造血干细胞库建设、操作、运行等技术标准。 第八条脐带血造血干细胞库设置的申请者除符合国家规划和布局要求，具备设置一般血站基本条件之外， 还需具备下列条件： （一）具有基本的血液学研究基础和造血干细胞研究能力； （二）具有符合储存不低于1 万份脐带血的高清洁度的空间和冷冻设备的设计规划； （三）具有血细胞生物学、HLA 配型、相关病原体检测、遗传学和冷冻生物学、专供脐带血处理等符合GMP、 GLP 标准的实验室、资料保存室； （四）具有流式细胞仪、程控冷冻仪、PCR 仪和细胞冷冻及相关检测及计算机网络管理等仪器设备； （五）具有独立开展实验血液学、免疫学、造血细胞培养、检测、HLA 配型、病原体检测、冷冻生物学、 管理、质量控制和监测、仪器操作、资料保管和共享等方面的技术、管理和服务人员； （六）具有安全可靠的脐带血来源保证； （七）具备多渠道筹集建设资金运转经费的能力。 第九条设置脐带血造血干细胞库应向所在地省级卫生行政部门提交设置可行性研究报告，内容包括：
基于单片机的自动存包系统设计
基于单片机的自动存包系统设计 摘要 近年来，随着生活水平的提高，人们对于社会消费品的质量和数量的要求也在逐渐增加。为了更好的为广大顾客服务，在一些商场、影院、超市等公共场合通常设置有自动存包柜，本次便是针对这一现象进行设计。 本文详细介绍了国内自动存包控制系统的发展现状，发展中所面临的问题。并详细介绍了本系统采用的AT89S52单片机做控制器，可以同时管理四个存包柜。柜门锁是由继电器控制，当顾客需要存包的时候，可以自行到存包柜前按“开门”键，需要顾客向光学指纹识别系统输入个指纹，然后通过继电器进行开门（用亮灯表示），顾客即可存包，并需将柜门关上。当顾客需要取包时，要将只要将之前输入的指纹放置于指纹识别器前方，指纹识别器采集到指纹信息输出相应的高低电平信号传给单片机，系统比较密码一致后，发出开箱信号至继电器将柜门打开，顾客即可将包取出。它具有功能实用、操作简便、安全可靠、抗干扰性强等特点。 关键词：自动存包柜,单片机,指纹识别器
李少鹏：基于单片机的自动存包系统设计 Based on single chip microcomputer automatic package design Abstract In recent years, with the improvement of living standards, people for social consumer goo ds quality and quantity requirements are to increase gradually. In order to better service for the g eneral customers, in some stores, movie theaters, supermarkets public Settings are to be put auto matically usually bag ark, it is functional practical, simple operation, safe and reliable, anti-jamm ing strong sexual characteristics. Domestic deposit automatic control system are introduced in detail in this paper the development of the status quo, problems faced in the development of. And introduces in detail the system adopts single chip microcomputer controller, can simultaneously manage multiple pack ark. Cupboard door lock controlled by relay, when customers need to save package, will be allowed to save package before the ark according to the "open" button, need customer to the system input fingerprint, and then through the relay to open the door (with lighting), customers can save package, and the cupboard door must be closed. When customers need to pick up package, as long as before the input fingerprint should be placed on the fingerprint recognizer, fingerprint recognizer collecting to the fingerprint information and output the corresponding high and low level signal to the microcontroller, the system is password consistent, signal out of the box to the relay Key words: Automatic Storage Bag, Microcontroller, Fingerprint recognizer。
基于全局对比度的显著性区域检测
基于全局对比度的显著性区域检测 Ming-Ming Cheng1Guo-Xin Zhang1 Niloy J. Mitra2 Xiaolei Huang3Shi-Min Hu1 1TNList, Tsinghua University 2 KAUST 3 Lehigh University 摘要 视觉显著性的可靠估计能够实现即便没有先验知识也可以对图像适当的处理，因此在许多计算机视觉任务中留有一个重要的步骤，这些任务包括图像分割、目标识别和自适应压缩。我们提出一种基于区域对比度的视觉显著性区域检测算法，同时能够对全局对比度差异和空间一致性做出评估。该算法简易、高效并且产出满分辨率的显著图。当采用最大的公开数据集进行评估时，我们的算法比已存的显著性检测方法更优越，具有更高的分辨率和更好的召回率。我们还演示了显著图是如何可以被用来创建用于后续图像处理的高质量分割面具。 1 引言 人们经常毫不费力地判断图像区域的重要性，并且把注意力集中在重要的部分。由于通过显著性区域可以优化分配图像分析和综合计算机资源，所以计算机检测图像的显著性区域存在着重要意义。提取显著图被广泛用在许多计算机视觉应用中，包括对兴趣目标物体图像分割[13, 18]、目标识别[25]、图像的自适应压缩[6]、内容感知图像缩放[28, 33,30, 9]和图像检索[4]等。 显著性源于视觉的独特性、不可预测性、稀缺性以及奇异性，而且它经常被归因于图像属性的变化，比如颜色、梯度、边缘和边界等。视觉显著性是通过包括认知心理学[26, 29]、神经生物学[8, 22]和计算机视觉[17, 2]在内的多学科研究出来的，与我们感知和处理视觉刺激密切相关。人类注意力理论假设人类视力系统仅仅详细处理了部分图像，同时保持其他的图像基本未处理。由Treisman和Gelade [27]，Koch和Ullman [19]进行的早期工作，以及随后由Itti，Wolfe等人 提出的注意力理论提议将视觉注意力分为两个阶段：快速的、下意识的、自底向 上的、数据驱动显著性提取；慢速的、任务依赖的、自顶向下的、目标驱动显著
显著性目标检测中的视觉特征及融合
第34卷第8期2017年8月 计算机应用与软件 Computer Applications and Software VoL34 No.8 Aug.2017 显著性目标检测中的视觉特征及融合 袁小艳u王安志1潘刚2王明辉1 \四川大学计算机学院四川成都610064) 2 (四川文理学院智能制造学院四川达州635000) 摘要显著性目标检测，在包括图像/视频分割、目标识别等在内的许多计算机视觉问题中是极为重要的一 步，有着十分广泛的应用前景。从显著性检测模型过去近10年的发展历程可以清楚看到，多数检测方法是采用 视觉特征来检测的，视觉特征决定了显著性检测模型的性能和效果。各类显著性检测模型的根本差异之一就是 所选用的视觉特征不同。首次较为全面地回顾和总结常用的颜色、纹理、背景等视觉特征，对它们进行了分类、比较和分析。先从各种颜色特征中挑选较好的特征进行融合，然后将颜色特征与其他特征进行比较，并从中选择较 优的特征进行融合。在具有挑战性的公开数据集ESSCD、DUT-0M0N上进行了实验，从P R曲线、F-M easure方法、M A E绝对误差三个方面进行了定量比较，检测出的综合效果优于其他算法。通过对不同视觉特征的比较和 融合，表明颜色、纹理、边框连接性、Objectness这四种特征在显著性目标检测中是非常有效的。 关键词显著性检测视觉特征特征融合显著图 中图分类号TP301.6 文献标识码 A DOI：10. 3969/j. issn. 1000-386x. 2017.08. 038 VISUAL FEATURE AND FUSION OF SALIENCY OBJECT DETECTION Yuan Xiaoyan1,2Wang Anzhi1Pan Gang2Wang Minghui1 1 (College o f Computer Science，Sichuan University，Chengdu 610064，Sichuan，China) 2 {School o f Intelligent M anufacturing, Sichuan University o f A rts and Science, Dazhou 635000, Sichuan, China) Abstract The saliency object detection is a very important step in many computer vision problems, including video image segmentation, target recognition, and has a very broad application prospect. Over the past 10 years of development of the apparent test model, it can be clearly seen that most of the detection methods are detected by using visual features, and the visual characteristics determine the performance and effectiveness of the significance test model. One of the fundamental differences between the various saliency detection models is the chosen of visual features. We reviewed and summarized the common visual features for the first time, such as color, texture and background. We classified them, compared and analyzed them. Firstly, we selected the better features from all kinds of color features to fuse, and then compared the color features with other characteristics, and chosen the best features to fuse. On the challenging open datasets ESSCD and DUT-OMON, the quantitative comparison was made from three aspects：PR curve, F-measure method and MAE mean error, and the comprehensive effect was better than other algorithms. By comparing and merging different visual features, it is shown that the four characteristics of color, texture, border connectivity and Objectness are very effective in the saliency object detection. Keywords Saliency detection Visual feature Feature fusion Saliency map 收稿日期:2017-01-10。国家重点研究与发展计划项目（2016丫?80700802,2016丫?80800600);国家海洋局海洋遥感工程技术 研究中心创新青年项目（2015001)。袁小艳，讲师，主研领域:计算机视觉，机器学习，个性化服务。王安志，讲师。潘刚，讲师。王 明辉，教授。
自动存包柜的设计与仿真
自动存包柜的设计与仿真 摘要 本课题是基于单片机的自动存包柜设计。自动存包柜是新一代的存包柜，具有功能实用、操作简单、管理方便、安全可靠等特点，能够更好的服务于不同市场的广大群众，使用者可以根据简明清晰的操作说明自行完成存包取包工作。本系统由MCS-51单片机构成核心控制系统，整个系统由主控部分、键盘显示控制部分、执行部分三部分组成，通过随机密码的产生和核对完成自动存包取包过程。本设计中各元器件便于安装且操作简单，能基本实现存包取包功能。 关键词：自动存包柜；单片机；随机密码
Design and Simulation of Automatic Lockers ABSTRACT This topic is microcontroller-based automatic lockers.Automatic lockers is a new generation of lockers, with a practical, simple operation, easy management, safe and reliable, able to better serve the broad masses of the different markets, users are based on a clear and concise instructions to complete the deposit bags to take the package. The system consists of MCS-51 microcontroller core control system, the entire system from the main section, the keyboard display control part of the implementation of some of the three-part composition, random password generation and check completed automatically save the package to take the package process. Various components of this design is easy to install and easy to operate, can basically save the package to take package function. Key words :Automatic lockers; microcontroller; random password
显著性区域检测matlab_code_20170412
1.Main_program %====test1 clear close all clc %img_in,输入的待处理的图像 %n，图像分块的patch的大小 %img_rgb:缩放后的rgb图 %img_lab：rgb2lab后的图 %h，w，图像高宽 %mex_store,存储矩阵 img=imread('test1.jpg'); % img=imread('E:\例程\matlab\显著性分割\假设已知均值\test picture\0010.tif'); max_side=120; yu_value=0.8; img_lab = pre_rgb2lab(img,max_side); [ img_scale_1,img_scale_2,img_scale_3,img_scale_4 ] = get4Scale( img_lab ); [ DistanceValue_scale_1_t1,DistanceValue_scale_1_exp,DistanceValue_scale_1_t1_rang,DistanceValue_scale_1_exp_rang] = distanceValueMap_search_onescale_2( img_scale_1,max_side ); [ DistanceValue_scale_2_t1,DistanceValue_scale_2_exp,DistanceValue_scale_2_t1_rang,DistanceValue_scale_2_exp_rang] = distanceValueMap_search_onescale_2( img_scale_2,max_side ); [ DistanceValue_scale_3_t1,DistanceValue_scale_3_exp,DistanceValue_scale_3_t1_rang,DistanceValue_scale_3_exp_rang] = distanceValueMap_search_onescale_2( img_scale_3,max_side ); [ DistanceValue_scale_4_t1,DistanceValue_scale_4_exp,DistanceValue_scale_4_t1_rang,DistanceValue_scale_4_exp_rang] = distanceValueMap_search_onescale_2( img_scale_4,max_side ); value_C_1=Center_weight( DistanceValue_scale_1_exp_rang,yu_value ); value_C_2=Center_weight( DistanceValue_scale_2_exp_rang,yu_value ); value_C_3=Center_weight( DistanceValue_scale_3_exp_rang,yu_value ); value_C_4=Center_weight( DistanceValue_scale_4_exp_rang,yu_value ); [h,w]=size(value_C_1); value_C_1_resize=value_C_1; value_C_2_resize=imresize(value_C_2,[h,w]); value_C_3_resize=imresize(value_C_3,[h,w]); value_C_4_resize=imresize(value_C_4,[h,w]); value_C_sum=(value_C_1_resize+value_C_2_resize+value_C_3_resize+value_C_4_resize)/4; figure, imshow(value_C_sum); figure, imshow(value_C_1_resize); figure, imshow(value_C_2_resize); figure, imshow(value_C_3_resize); figure, imshow(value_C_4_resize); 8888888888888888888888888888888888888888888888888888888888888888888888888888
卫生部办公厅关于印发《脐带血造血干细胞治疗技术管理规范(试行)
卫生部办公厅关于印发《脐带血造血干细胞治疗技术管理规 范(试行)》的通知 【法规类别】采供血机构和血液管理 【发文字号】卫办医政发[2009]189号 【失效依据】国家卫生计生委办公厅关于印发造血干细胞移植技术管理规范(2017年版)等15个“限制临床应用”医疗技术管理规范和质量控制指标的通知 【发布部门】卫生部(已撤销) 【发布日期】2009.11.13 【实施日期】2009.11.13 【时效性】失效 【效力级别】部门规范性文件 卫生部办公厅关于印发《脐带血造血干细胞治疗技术管理规范（试行）》的通知 （卫办医政发〔2009〕189号） 各省、自治区、直辖市卫生厅局，新疆生产建设兵团卫生局： 为贯彻落实《医疗技术临床应用管理办法》，做好脐带血造血干细胞治疗技术审核和临床应用管理，保障医疗质量和医疗安全，我部组织制定了《脐带血造血干细胞治疗技术管理规范（试行）》。现印发给你们，请遵照执行。 二〇〇九年十一月十三日
脐带血造血干细胞 治疗技术管理规范（试行） 为规范脐带血造血干细胞治疗技术的临床应用，保证医疗质量和医疗安全，制定本规范。本规范为技术审核机构对医疗机构申请临床应用脐带血造血干细胞治疗技术进行技术审核的依据，是医疗机构及其医师开展脐带血造血干细胞治疗技术的最低要求。 本治疗技术管理规范适用于脐带血造血干细胞移植技术。 一、医疗机构基本要求 （一）开展脐带血造血干细胞治疗技术的医疗机构应当与其功能、任务相适应，有合法脐带血造血干细胞来源。 （二）三级综合医院、血液病医院或儿童医院，具有卫生行政部门核准登记的血液内科或儿科专业诊疗科目。 1.三级综合医院血液内科开展成人脐带血造血干细胞治疗技术的，还应当具备以下条件： （1）近3年内独立开展脐带血造血干细胞和（或）同种异基因造血干细胞移植15例以上。 （2）有4张床位以上的百级层流病房，配备病人呼叫系统、心电监护仪、电动吸引器、供氧设施。 （3）开展儿童脐带血造血干细胞治疗技术的，还应至少有1名具有副主任医师以上专业技术职务任职资格的儿科医师。 2.三级综合医院儿科开展儿童脐带血造血干细胞治疗技术的，还应当具备以下条件：
图像显著性目标检测算法研究
图像显著性目标检测算法研究 随着移动电子设备的不断升级与应用,使用图像来记录或表达信息已成为一种常态。我们要想快速地在海量图像中提取出有价值的信息,那么需要模拟人类视觉系统在机器视觉系统进行计算机视觉热点问题的研究。 图像显著性目标检测对图像中最引人注意且最能表征图像内容的部分进行检测。在图像显著性目标检测任务中,传统的方法一般利用纹理、颜色等低层级视觉信息自下向上地进行数据驱动式检测。 对于含有单一目标或高对比度的自然场景图像,可以从多个角度去挖掘其显著性信息,如先验知识、误差重构等。然而,对于那些具有挑战性的自然场景图像,如复杂的背景、低对比度等,传统的方法通常会检测失败。 基于深度卷积神经网络的算法利用高层级语义信息结合上下文充分挖掘潜在的细节,相较于传统的方法已取得了更优越的显著性检测性能。本文对于图像显著性检测任务存在的主要问题提出了相应的解决方法。 本文的主要贡献如下:为充分挖掘图像多种显著性信息,并使其能够达到优势互补效果,本文提出了一种有效的模型,即融合先验信息和重构信息的显著性目标检测模型。重构过程包括密度重构策略与稀疏重构策略。 密度重构其优势在于能够更准确地定位存在于图像边缘的显著性物体。而稀疏重构更具鲁棒性,能够更有效地抑制复杂背景。 先验过程包含背景先验策略与中心先验策略,通过先验信息可更均匀地突出图像中的显著性目标。最后,把重构过程与先验过程生成的显著特征做非线性融合操作。 实验结果充分说明了该模型的高效性能与优越性能。针对图像中存在多个显
著性目标或者检测到的显著性目标存在边界模糊问题,本文提出了一种基于多层级连续特征细化的深度显著性目标检测模型。 该模型包括三个阶段:多层级连续特征提取、分层边界细化和显著性特征融合。首先,在多个层级上连续提取和编码高级语义特征,该过程充分挖掘了全局空间信息和不同层级的细节信息。 然后,通过反卷积操作对多层级特征做边界细化处理。分层边界细化后,把不同层级的显著特征做融合操作得到结果显著图。 在具有挑战性的多个基准数据集上使用综合评价指标进行性能测试,实验结果表明该方法具有优越的显著性检测性能。对于低对比度或者小目标等问题,本文提出一种新颖模型,即通道层级特征响应模型。 该模型包含三个部分:通道式粗特征提取,层级通道特征细化和层级特征图融合。该方法基于挤压激励残差网络,依据卷积特征通道之间的相关性进行建模。 首先,输入图像通过通道式粗特征提取过程生成空间信息丢失较多的粗糙特征图。然后,从高层级到低层级逐步细化通道特征,充分挖掘潜在的通道相关性细节信息。 接着,对多层级特征做融合操作得到结果显著图。在含有复杂场景的多个基准数据集上与其它先进算法进行比较,实验结果证明该算法具有较高的计算效率和卓越的显著性检测性能。
卫生部关于印发《脐带血造血干细胞库设置管理规范(试行)》的通知
卫生部关于印发《脐带血造血干细胞库设置管理规范(试行)》的通知 发文机关：卫生部(已撤销) 发布日期： 2001.01.09 生效日期： 2001.02.01 时效性：现行有效 文号：卫医发(2001)10号 各省、自治区、直辖市卫生厅局： 为贯彻实施《脐带血造血干细胞库管理办法（试行）》，保证脐带血临床使用的安全、有效，我部制定了《脐带血造血干细胞库设计管理规范（试行）》。现印发给你们，请遵照执行。 附件：《脐带血造血干细胞库设置管理规范（试行）》 二○○一年一月九日 附件： 脐带血造血干细胞库设置管理规范（试行） 脐带血造血干细胞库的设置管理必须符合本规范的规定。 一、机构设置 （一）脐带血造血干细胞库（以下简称脐带血库）实行主任负责制。 （二）部门设置 脐带血库设置业务科室至少应涵盖以下功能：脐带血采运、处理、细胞培养、组织配型、微生物、深低温冻存及融化、脐带血档案资料及独立的质量管理部分。 二、人员要求
（一）脐带血库主任应具有医学高级职称。脐带血库可设副主任，应具有临床医学或生物学中、高级职称。 （二）各部门负责人员要求 1．负责脐带血采运的人员应具有医学中专以上学历，2年以上医护工作经验，经专业培训并考核合格者。 2．负责细胞培养、组织配型、微生物、深低温冻存及融化、质量保证的人员应具有医学或相关学科本科以上学历，4年以上专业工作经历，并具有丰富的相关专业技术经验和较高的业务指导水平。 3．负责档案资料的人员应具相关专业中专以上学历，具有计算机基础知识和一定的医学知识，熟悉脐带血库的生产全过程。 4．负责其它业务工作的人员应具有相关专业大学以上学历，熟悉相关业务，具有2年以上相关专业工作经验。 （三）各部门工作人员任职条件 1．脐带血采集人员为经过严格专业培训的护士或助产士职称以上卫生专业技术人员并经考核合格者。 2．脐带血处理技术人员为医学、生物学专业大专以上学历，经培训并考核合格者。 3．脐带血冻存技术人员为大专以上学历、经培训并考核合格者。 4．脐带血库实验室技术人员为相关专业大专以上学历，经培训并考核合格者。 三、建筑和设施 （一）脐带血库建筑选址应保证周围无污染源。 （二）脐带血库建筑设施应符合国家有关规定，总体结构与装修要符合抗震、消防、安全、合理、坚固的要求。 （三）脐带血库要布局合理，建筑面积应达到至少能够储存一万份脐带血的空间；并具有脐带血处理洁净室、深低温冻存室、组织配型室、细菌检测室、病毒检测室、造血干/祖细胞检测室、流式细胞仪室、档案资料室、收/发血室、消毒室等专业房。 （四）业务工作区域应与行政区域分开。
自动寄存柜
“程序设计（Ⅱ）”综合编程实验报告 （2011-2012学年第2学期） 实验项目名称：自动寄存柜 一、实验内容与要求 某超市门口的自动寄存柜有n个寄存箱，并且有一个投币控制器，顾客想要寄存小件物品时，只要在投币控制器投入1个1元的硬币，如果此时有空闲的箱子，寄存柜就会自动打开一个空的箱子，并且打印输出一张小小的密码纸条；如果没有空闲的箱子，则提示“本柜已满”。当顾客离开超市时，用密码纸条上指定的数字密码依次输入到开箱控制器，则顾客所存包的箱子门就自动打开，顾客取走物品后，关上门。 输入数据时，可先输入寄存箱总数n，再由用户选择是“投硬币”还是“输密码”。 如果选择“投硬币”，则只有硬币值是1才开箱。如果有空闲的箱子，则输出箱子编号及密码（4位数字）；如果无空闲的箱子，则提示：“本柜已满”。 如果选择“输密码”，若输入的密码与某一箱子密码相符，则显示打开的箱子编号，否则输出提示：“密码错误”。 请编写开箱控制程序实现上述过程。 输入输出示例 寄存箱总数:10 1.投硬币 2.输密码 0.退出请选择: 1 投币值:1 寄存箱编号:1 密码:9342 1.投硬币 2.输密码 0.退出请选择: 2 输入密码:9342 1号寄存箱已打开 1.投硬币 2.输密码 0.退出请选择:0 结束
二、系统设计 1、解题思路 （1）先输入寄存箱数量，并判断数量是否正确。 （2）先输出"1.投硬币 2.输密码 0.退出请选择:"，再输入数字选择是“投硬币”还是“输密码”。。 （3）若“投硬币”，则只有硬币值是1才开箱，并判断是否有空闲的箱子。若有则输出箱子编号及密码。 （4）若选择“输密码”，则判断输入的密码是否正确。 2、数据结构描述 3、程序框架结构 4、关键算法描述 （1）输入寄存箱数量 srand((int)time(0)); printf("寄存柜数量:"); scanf("%d",&num); printf("\n"); while(num<=0){ printf("寄存柜数量错误, 请重新输入\n\n"); printf("寄存柜数量:"); scanf("%d",&num); printf("\n"); } （2）判断是否有空闲的箱子。若有则输出箱子编号及密码。 if(x==1){ printf("投币值:"); scanf("%d",&coin); printf("\n"); if(coin==1){ if(count