However, the Ser704Cys (S704C) variant does not affect Wnt signaling but does inhibit neuronal migration via a cytoskeleton signaling pathway. These results provide insight into the normal function of DISC1 genetic variation, and suggest that brain structural and functional phenotypes associated selleck chemical with DISC1 variants may arise due to their disruptive effects on specific signaling pathways during brain development. To discover different human DISC1

variants, we performed deep-sequencing of all DISC1 exons (exome sequencing) on 717 individuals (166 bipolar disorder, 203 schizophrenia, and 369 control individuals). We initially identified common and rare DISC1 variants within this sample set and subsequently genotyped additional healthy individuals and patients as part of a larger study (∼16,500 individuals). From the identified variants, we selected a subset of rare and

common variants to determine their impact on canonical Wnt signaling (Table 1 and data not shown). We focused on the common variants R264Q, L607F, BMS-387032 nmr and S704C specifically since they have been associated with brain structural and psychiatric behavioral phenotypes. We also tested the function of different rare DISC1 variants that were closer to the N terminus since this is one of the regions of DISC1 that binds to GSK3β (data not shown) (Mao et al., 2009). We specifically focused on assaying the function of one rare variant for all experiments, A83V, in order to test its ability to bind GSK3β and modulate Wnt signaling and neural progenitor proliferation. Furthermore, although our initial DISC1 exome sequencing suggested some common and rare variants might be enriched in SZ or BPD populations, larger genotyping efforts revealed there was no statistically significant bias for variants associated with SZ or BPD. To determine if DISC1 variants regulate Wnt

signaling, we performed in vitro assays to measure Wnt-induced T Cell Factor/Lymphoid Enhancer Factor (TCF/LEF) transcriptional activity. We have previously reported that overexpressing mouse wild-type (WT)-mDISC1 can significantly Sodium butyrate potentiate 16 hr Wnt-induction of TCF/LEF activity in HEK293 cell lines (Mao et al., 2009). Using this result as our reference, we tested whether the human DISC1 variants also potentiated TCF/LEF activity similarly to WT-human DISC1 (WT-DISC1). Interestingly, we found that the majority of the rare DISC1 variants, including A83V, and the common R264Q and L607F variants unable to potentiate TCF/LEF activity compared with WT-DISC1, as they performed no different than GFP controls (Figure 1A, data not shown). However, the S704C common polymorphism was able to significantly potentiate activity, suggesting that it does not display loss of function activity. It is possible these results could arise due to differences in expression levels of the different hDISC1 variants.

(2004)

that simultaneously acquires FAIR, BOLD, and VASO signals. The EPI module was the same for the FAIR and simultaneous BOLD/CBF/VASO sequence and was a single-shot EPI with a BW of 132 kHz and a slice thickness of 3 mm. For the Selleckchem ISRIB simultaneous BOLD/CBF/VASO sequence, data were typically acquired at a resolution of 0.75 × 0.75 mm2, with a FOV of 64 × 32 mm2 and a matrix size of 86 × 43. The TI was 925 ms for the VASO-echo and 1,300 ms for the FAIR-echo at a TR of 3,000 ms. The TI for the VASO-echo was determined based on the inversion of blood in veins in the calcarine sulcus. The echo times were 8.4, 8.4, and 30.5 ms for the VASO-echo, FAIR-echo, and BOLD-echo, respectively. Hyperbolic secant pulses were used for inversion. For high-resolution FAIR (n = 6), a single slice was acquired oriented perpendicular to the cortical surface with a FOV of 64 × 24 mm2 or 64 × 16 mm2, a matrix of 128 × 48 or 128 × 32 (resolution, 0.5 × 0.5 × 3 mm3), a TI of 1,400 ms, and a TR of 4,500 ms. The shortest possible TE was used ranging from 8.6 to 11.6 ms depending on the matrix and FOV. To determine whether flow in large vessels affects the CBF profiles, a diffusion-weighted SE FAIR-EPI was used Selleck Venetoclax as a control.

Its sequence parameters were the same as for the GE-based high-resolution FAIR, except that the TE was 26.4 ms and the b factor was 20. Data were analyzed using custom-written routines in MatLab (The MathWorks). Activation maps were generated using t tests. MycoClean Mycoplasma Removal Kit No smoothing was applied in the analyses (the exception is Figure 1, where data were smoothed for display purposes). For measuring the VASO-CBV signal, only the nonselective inversion was used, reducing the number of images per scan to 64. To determine the mean percent functional signal change in the regions with positive and negative BOLD, ROIs for the positive and negative BOLD were drawn in the operculum of V1, based on the high-resolution raw (i.e., not thresholded for significant activation) BOLD percentage change activation maps. The same ROIs were used to calculate

functional CBV changes. For calculation of functional CBF, ROIs were drawn based on the unthresholded CBF percentage change maps after verifying the locations of the ROIs in the BOLD scans. Functional activation as a function of cortical depth was analyzed by calculating the profiles perpendicular to the cortex (see the Supplemental Experimental Procedures for a detailed description of the analysis procedures and the factors affecting the laminar resolution). The areas over which the profiles were calculated were defined based on the extent of the negative BOLD activation, which amounted to a distance of 7–8 mm along the cortex for each slice and hemisphere. Two to three slices were used for BOLD and CBV profiles. The same coordinates were used to calculate BOLD and functional CBV profiles.

In a complementary, shRNA-independent approach to assess the role of LRRTM4 in synapse development, we treated hippocampal neurons with excess LRRTM4-Fc to FRAX597 solubility dmso competitively disrupt the trans-synaptic interaction of LRRTM4 with presynaptic receptors.

Neurons were treated for 6 days and the density of VGlut1/PSD-95-positive puncta in Prox1-positive cells was analyzed at DIV14. LRRTM4-Fc treatment reduced excitatory synapse density by 40% compared to cells treated with Fc control protein, similar to treatment with LRRTM2-Fc ( Figure 5O). These results are in agreement with the effects of LRRTM4 knockdown, supporting a role of LRRTM4 in regulating excitatory synapse development. LRRTM2 and LRRTM4 share a similar synaptogenic activity in hippocampal neurons, but LRRTM4 is distinct from LRRTM2 in that it has two

presynaptic binding partners, neurexin and glypican. To begin assessing the role of these two LRRTM4 receptors in synapse development, we tested whether excess GPC4-Fc or Nrx1β(-S4)-Fc could block excitatory synapse formation in Prox1-positive neurons. In agreement with previous results (Chih et al., 2006), 6-day treatment with Nrx1β(-S4)-Fc caused a reduction in excitatory synapse density in DIV14 hippocampal neurons (Figures S6A and S6B). However, GPC4-Fc did not affect excitatory synapse density at this time point nor did 3-day treatment with GPC4-Fc in immature neurons see more (Figures

S6A–S6D). Possibly, neurexin can compensate when the glypican-LRRTM4 interaction is blocked. Alternatively, since LRRTM4-Fc decreases excitatory synapse density (Figure 5O), but GPC4-Fc does not (Figure S6), it could be that GPI-anchored glypican is only part of a functional presynaptic LRRTM4 receptor and requires a yet unidentified transmembrane signaling coreceptor. Such signaling might be required for the development of synaptic contacts between neurons. We next analyzed whether excess HS could interfere with LRRTM4-mediated synapse formation onto heterologous cells. HEK293T cells already expressing myc-LRRTM2 or myc-LRRTM4 were cocultured with DIV7 hippocampal neurons for 12 hr in the presence of 0.5 mg/ml HS. Exogenous HS did not affect LRRTM2-mediated presynaptic differentiation but abolished the synaptogenic activity of LRRTM4 (Figures 6A–6D). To test whether LRRTM4-mediated presynaptic differentiation requires endogenous HS, we treated DIV7 neurons with heparinase III (2 hr, 1 U/ml), washed and cocultured them for an additional 8 hr with 293T cells expressing myc-LRRTM. Staining with the 3G10 HS stub antibody confirmed the efficiency of hepIII treatment in hippocampal neurons (data not shown). Enzymatic removal of HS did not affect LRRTM2’s ability to instruct presynaptic differentiation (Figures 6E and 6F) but strongly reduced LRRTM4’s synaptogenic activity (Figures 6G and 6H).

We suggest that nElavl regulates the protein interacting partners of this critical enzyme by maintaining a balance between the isoforms selleck screening library of the Gls gene. Taken together, we establish nElavl proteins as regulators of neuron-specific AS, determine an nElavl-RNA map associated with alternative splicing and uncover a new nElavl-regulated biological pathway, namely the glutamate synthesis pathway. By investigating other nElavl targets our data set also offers the possibility of identifying other interesting functions of these neuronal proteins. A 17.7 kb targeting

vector (see Supplemental Experimental Procedures) was selected in SV-129 ES cells, transferred into the germline of SV129/FVB mice, and the ACNF targeting cassette auto-excised in the male germ cells. All animal studies in this work were in accordance with the Code of Practice for the Housing and Care of Animals Used in Scientific Procedures, and was approved by the Rockefeller University Comparative Biosciences Center. Western Selleck Anti-cancer Compound Library blots were performed using 50 μg of cortex extract per lane. A pan anti-nElavl antibody (α-nElavl; paraneoplastic Hu antibody; RU IRB approved protocol 0148; patient

code NA-0018, a 63-year-old with small cell lung cancer and Hu encephalomyelopathy who had a pan-sensory neuropathy expired from prolonged status epilepticus) was used for IF. nElavl-RNA complexes in brain tissue were UV crosslinked and immunoprecipitated using specific human antisera. Isolated RNA molecules were reverse-transcribed, PCR amplified and sequenced on an Illumina GAIIx at the Rockefeller University Genomics Resource Center (see Supplemental Information). Three and one-half micrograms of total RNA from Elavl3−/−;Elavl4−/− and littermate WT P0 mice cortical tissue was reverse transcribed

and sense target DNA was prepared as described in “GeneChip Whole-Transcript next (WT) Sense Target Labeling Assay” protocol from Affymetrix. Labeled Target DNA was hybridized to GeneChip Mouse Exon 1.0 ST Array and to custom made Exon Junction Array (Affymetrix) at the Rockefeller University Genomics Resource Center. RT-PCR was used to validate alternative splicing changes as described (Licatalosi et al., 2008; Ule et al., 2005b). P0 cortex was dissected and immediately frozen in −80°C. RNA was isolated using Trizol plus RNA purification kit (Invitrogen). RNA was reverse transcribed using superscript III reverse transcriptase (Invitrogen). Abundance of RNA isoforms were determined by semiquantitative RT-PCR or where indicated by quantitative PCR, respectively. The number of PCR cycles used was in the linear range of product amplification. Rabbit anti-glutaminase antibody was courtesy of Norman Curthoys, Colorado State University. Cortex was dissected out at P0 and immediately frozen at −80°C.

We performed additional analyses to check for possible biases imposed by thresholding (>6× standard deviation of the baseline): First, we computed input across a 3 × 3 grid around the soma (Figures S6D–S6F). Second, we generated a mask by averaging the responses across cells within a group. The mask was defined by significant responses (>5× standard deviation). The mask was then used to compute input from the original maps (Figures S6G–S6I). Third, we also computed the mean pixel value over the entire map without thresholding (data not shown). These three analysis methods yielded consistent results. Since the time between stimulus and the beginning of the baseline period for the next trial was

fairly short (300 ms), we corrected for bleedthrough across trials (baseline drift). Because the grid size Veliparib for stimulation was always larger than the dendritic

arbors of the recorded cells (for example, Figure 3B), we estimated the baseline drift from the traces far outside the cell’s dendritic arbor (these traces were “blanks” that could not have contained true responses; they thus represent pure baseline drift). We then subtracted the baseline AZD6738 manufacturer drift from the mean value of all other traces. Paired comparisons used the nonparametric Wilcoxon signed-rank test (Figure 6 and Figure 7, S6, S7, and S9). This work was funded by the Howard Hughes Medical Institute. much We thank Gordon Shepherd for advice and extensive discussions; Asaf Keller for advice on electrical microstimulation in vM1; Tim O’Connor for programming; Brenda Shields, Amy Hu, Alma Arnold, and Kevin McGowan for technical support; Takashi Sato and Haining Zhong for help with experiments and analysis; Stefanie Kaech Petrie for help with the blind retrograde beads counting; and Diego Gutnisky and Zengcai Guo for comments on the manuscript. “
“The olfactory bulb is the first processing center of information about odorants. In mammals, the olfactory system is the

only sensory system in which peripheral information is sent directly to the cortex, bypassing the sensory thalamus. Therefore, it has been proposed that the bulb combines the function of peripheral sensory system and the thalamus (Kay and Sherman, 2007). Consistent with this proposal, several studies have demonstrated that activity in the olfactory bulb reflects not only sensory information but also the animal’s internal state (Adrian, 1950 and Rinberg et al., 2006) and task-dependent variables (Doucette and Restrepo, 2008, Fuentes et al., 2008 and Kay and Laurent, 1999). The relative simplicity of the anatomy of the olfactory bulb and the combination of both sensory- and state-dependent activity in a single network make it an attractive model for the study of principles of sensory information processing. The surface of the olfactory bulb is covered by ≈≈ 2000 glomeruli.

While the idea that language affects thought and conscious experience (Whorf, 1956) was out of favor Trametinib supplier for a while,

it has reemerged as an important principle in recent times (Lakoff, 1987 and Lucy, 1997). One way that language is important is that it allows the semantic categorization of experience, including emotional experience. For example, there are more than 30 words in English for gradations of fear (fear, panic, anxiety, worry, trepidation, consternation, etc.) (Marks, 1987). The human brain may be able to categorize emotional states in broad strokes without language but it is unlikely that specific emotions (fear, anger, sadness, joy) could come about without words. Accordingly, lacking language and emotion words, an animal brain cannot partition emotional experience in this way. In short, the language of emotion likely contributes to the experiences one has in emotional situations (Schachter, 1975, Johnson-Laird and Oatley, 1989, Scherer, 1984 and Reisenzein, 1995). Indeed, different cultures and their languages express emotions differently (Kitayama and Markus, 1994, Wierzbicka, 1994 and Averill, 1980). The dimensional theory of emotion views emotion words as markers in a multidimensional semantic space of feelings (Russell, 1980 and Russell and Barrett, 1999). The dimensional theory is incompatible with a basic emotions view, since the latter argues that feelings

associated with basic emotions are due to hard-wired circuits, but is compatible Bosutinib with the survival circuit view, which posits indirect and nonobligatory, as opposed to

casual, links between survival circuits and feelings. But the impact of language goes far beyond simple semantics and labeling. We use syntactic processes to evaluate the logical truth of propositional statements. While not all human thought involves propositional statements and logic, syntactic processing provides the human brain and mind with unique features and advantages. Through syntax, the human mind can simulate who will do what to whom in a social situation instantaneously rather than having to learn by trial and Methisazone error. So what then might a bat or a rat experience without the kind of cerebral hardware that is characteristic of the human brain? Some have proposed that in addition to full blown feelings that humans talk about, more basic, less differentiated feelings (crude states of positive or negative valence, or maybe even somewhat finer categories based on memory of feelings from the past in similar situations) may exist in other animals. Such states have been called core affects (Panksepp, 1998 and Panksepp, 2005; Damasio, 1994 and Damasio, 1999; Barrett et al., 2007 and Russell, 2003). While we cannot ask other animals about their feelings, studies of humans can begin to unravel how such states are experienced.

E.R., unpublished data). This somewhat broader pattern of expression of Prdm8 relative to Bhlhb5 suggests that Prdm8 may have additional partners to which it can couple, and one attractive candidate in this regard is Bhlhb4: loss of function studies have

revealed that Bhlhb4 is required for the survival of rod bipolar cells and, furthermore, that this factor is expressed, like Prdm8, in the embryonic diencephalon and DRG (Bramblett et al., 2002 and Bramblett et al., 2004). Because Prdm8 contains a SET domain that is characteristic of histone methyltransferases, it is possible that it may directly mediate repression of target genes by methylating target gene-associated histones. Consistent find more with this idea, Prdm8 has been shown to methylate histone H3K9 in vitro (Eom et al., 2009), a modification associated with transcriptional repression. Likewise, the tumor suppressor Prdm2 and the meiotic recombination determinant Prdm9 also show intrinsic histone methyltransferase activity (Hayashi et al., 2005 and Kim et al., 2003). However, several other Prdm family members, including Prdm1,

Prdm5, and Prdm6, appear to mediate repression indirectly by recruiting the histone methyltransferase, G9A (Davis et al., 2006, Duan et al., 2005 and Gyory et al., 2004). Thus, MDV3100 solubility dmso it is not yet clear whether Prdm8 functions directly or indirectly to mediate transcriptional repression. In either case, however, Prdm8 appears to be required for Adenylyl cyclase the repression of Bhlhb5 target genes. A curious aspect of the Bhlhb5/Prdm8 repressor complex is that, while each requires the other to repress target gene expression, we do not observe a perfect coincidence the expression of Bhlhb5 and Prdm8. Indeed, in many cases, the expression of these two factors appears to be somewhat reciprocal—neurons with highest levels of Bhlhb5 tend to have low levels of Prdm8, and vice versa. This disparity in expression level implies that Bhlhb5 and Prdm8 do not always

exist as part of a  functional repressor complex, and furthermore suggests that the expression of these factors is very tightly controlled, possibly to limit the degree and/or the duration of gene repression mediated by Bhlhb5/Prdm8. In keeping with this idea, we find that the Bhlhb5/Prmd8 repressor appears to curb its own activity by restricting the expression of Prdm8, which is upregulated in Bhlhb5 knockout mice. These observations suggest that Bhlhb5 and Prdm8 are part of a complex regulatory network that needs to be precisely coordinated for proper development. One of the consequences of disrupting the function of the Bhlhb5/Prdm8 repressor complex is that Cdh11 is aberrantly overexpressed, and our findings suggest that this misexpression has detrimental consequences for neural circuit development.

Thus, learning releases an inhibitory constraint

on the ability of MBNs to respond to the learned odor. The changes in ability to learn about odors by altering the expression of Rdl in the MBs occurs for both aversive and appetitive conditioning, consistent with the possibility that the influence of inhibitory input is through the CS rather than the US pathway (Liu et al., 2009). Olfactory Veliparib in vivo learning may therefore increase the response properties of the MBNs to the learned odor by reducing the inhibition. A similar strategy for learning may occur during auditory learning in vertebrates. The vertebrate auditory system, with cortical auditory neurons turned to respond to an optimal tone frequency, offers a unique system for exploring how tone learning alters the frequency receptive fields for primary auditory neurons (Weinberger, 2004). Froemke et al. (2007) reported that pairing the presentation of pure tones with electrical stimulation of the nucleus basalis, which provides cholinergic modulation to the cortex and acts as a substitute

US, alters the receptive fields of cortical neurons toward the frequency of tone presented. The mechanism underlying this plasticity in frequency receptive fields is a rapid (within 20 s) reduction in the inhibitory drive on these neurons with a subsequent increase in their excitability by the tone paired with cholinergic release. The net effect of pairing is to enhance selleck inhibitor the excitability of cortical neurons by the learned tone. One report offers experimental support for learning-induced plasticity in the dopaminergic neurons (DA) that are thought to innervate the MBNs (Figure 1B).

MTMR9 Riemensperger et al. (2005) expressed a calcium reporter in the DA neurons and imaged the DA fibers that innervate the MB lobes in flies before and after olfactory conditioning. Surprisingly, they observed calcium responses in these neurons when odors were presented to the flies, even though the DA neurons at the time were hypothesized to be part of the US pathway and not the CS pathway. Although there is no increase in the magnitude of the calcium responses of the DA neurons to the trained odor after conditioning, the data indicate that the duration of the calcium response may be prolonged. Multiple training trials were used to generate this plasticity, with the training-induced increase in calcium response forming by 15 min after the first pairing of odor and shock. This suggests that training alters the response properties of these neurons to the learned odor. More recent studies indicate that the DA neurons are anatomically and functionally heterogeneous (Mao and Davis, 2009). The DA neurons reside in different clusters in the brain. One cluster with 12 DA neurons (PPL1) innervates distinct zones of the MB lobes (Figure 1B).

11, p =

0.059, whole-brain FWE corrected). Next, we examined the LFPC’s involvement in the three tasks involving explicit decisions (Precommitment, Choice, and Opt-Out). We extracted E7080 manufacturer parameter estimates from our ROI in LFPC based on a previous study (−34, 56, −8; Boorman et al., 2009) for LL decisions in the three decision tasks and conducted a repeated-measures ANOVA to compare LFPC activation across tasks (Figure 4C). This analysis demonstrated a significant main effect of task on LFPC activity (F(3,17) = 5.573, p = 0.008). Pairwise post hoc comparisons revealed that LFPC activation was significantly greater during precommitment choices than during LL choices in the Opt-Out task (t(19) = 3.83, p = 0.003, Bonferroni corrected). The LFPC mean parameter estimate for precommitment choices was also greater than that for LL choices in the Choice task, but the difference did not survive correction for multiple comparisons, mirroring our behavioral self-control findings (compare Figure 4C with Figure 2C). We note that the Choice task, like the Precommitment task, also involves the opportunity to make a binding choice for LL; our results therefore support the notion that the LFPC is sensitive to the opportunity to make binding choices for large, but delayed, rewards. For comparison, we also investigated whether regions involved in willpower (DLPFC, IFG, and PPC) were sensitive to opportunities

to precommit. We extracted parameter estimates from these regions (using RO4929097 ROI coordinates from previous studies; Table S8) during LL choices in the three decision tasks and subjected them to a repeated-measures ANOVA. None of these regions were sensitive to opportunities to precommit (Figure S1); the effect of task was not significant for DLPFC (F(3,17) = 1.676, p = 0.215), IFG (F(3,17) = 1.209, p = 0.322), or PPC (F(3,17) = 0.924, p = 0.415). Thus, DLPFC, IFG, and PPC showed activation

patterns consistent with their role in self-control more generally but were not sensitive to opportunities to precommit. Finally, we subjected the parameter estimates from LFPC, DLPFC, IFG, and PPC for the three decision tasks to a repeated-measures ANOVA with region and task as within-subjects of factors. Parameter estimates were z transformed to control for differences in mean parameter estimates across regions. This analysis revealed a significant interaction between region and task (F(6,114) = 3.989, p = 0.001), confirming our above observations that the LFPC was differentially activated across decision tasks, but the regions engaged during willpower (DLPFC, IFG, and PPC) were not. We next investigated the possibility that LFPC implements decisions to precommit by controlling activity in the DLPFC, in line with theories positing that the LFPC sits at the top of a cognitive control hierarchy from which it orchestrates different courses of actions represented in DLPFC (Tsujimoto et al.

We show that the main determinant of discrimination is the distance between ePN activity patterns. Experimental manipulations of this distance have graded and predictable behavioral consequences. iPN inhibition enhances the contrast between closely related odors by imposing a high-pass filter on ePN synapses in the LH that stretches the distances between overlapping odor representations. We considered rate code representations of odors in the ∼50 glomerular channels that constitute the front end of the fly olfactory system. Odors were denoted by vectors of ∼50 components, which indicated the mean spike frequencies in each glomerular channel.

Choosing experimental odors with characterized Cabozantinib ORN response spectra (Hallem and Carlson, 2006 and Hallem et al., 2004) allowed us to assign numerical values to TSA HDAC nmr 24 of these ∼50 components. We termed these 24 components the ORN activity vector (Figure 1A). The corresponding ePN activity vectors were calculated by applying a saturating transformation to each ORN activity vector component plus an inhibitory scaling factor (m) that reflects the activation of GABAergic antennal lobe interneurons

and alters the slope of the transformation as a function of total ORN activity ( Olsen et al., 2010) ( Figure 1A). Different glomeruli vary somewhat in their sensitivity to inhibition, but our calculations of ePN firing rates assumed a uniform scaling factor of m = 10.63 ( Luo et al., 2010 and Olsen et al., 2010). Varying m in the physiologically plausible range of 5 to 15 ( Luo et al., 2010 and Olsen et al., 2010) had little impact on our conclusions ( Figure S1 available online). Because glomerular connectivity between ORNs and ePNs is 1:1

( Jefferis et al., 2001 and Stocker et al., 1990), ePN activity vectors also have ∼50 components, one for the average spike frequency of each class of ePN. We could assign numerical values to 24 of these components by selecting odors with known ORN response spectra ( Figure 1A). ePN activity vectors were used to define two types of pairwise distance between odor representations (Kreher et al., 2008). The Euclidean distance is the length of the line segment Oxalosuccinic acid connecting the tips of two activity vectors in 24-dimensional space, reflecting the distribution of firing rates across the ePN population. Cosine distance measures the angle between two activity vectors. Large cosine distances indicate that the vectors are nearly orthogonal (suggesting little overlap of the corresponding neural activity patterns), whereas small distances indicate that the vectors are nearly parallel, and the activity patterns are similar in structure but not necessarily in magnitude. The main difference between the two metrics is that Euclidean distance is sensitive to scale (i.e.