Measuring Tax Complexity

This paper critically examines a number of issues relating to the measurement of tax complexity. It starts with an analysis of the concept of tax complexity, distinguishing tax design complexity and operational complexity. It considers the consequences/costs of complexity, and then examines the rationale for measuring complexity. Finally it applies the analysis to an examination of an index of complexity developed by the UK Office of Tax Simplification (OTS).

Identification of case complexity and increased health care utilization in patients with rheumatoid arthritis.

OBJECTIVES: To document biopsychosocial profiles of patients with rheumatoid arthritis (RA) by means of the INTERMED and to correlate the results with conventional methods of disease assessment and health care utilization. METHODS: Patients with RA (n = 75) were evaluated with the INTERMED, an instrument for assessing case complexity and care needs. Based on their INTERMED scores, patients were compared with regard to severity of illness, functional status, and health care utilization. RESULTS: In cluster analysis, a 2-cluster solution emerged, with about half of the patients characterized as complex. Complex patients scoring especially high in the psychosocial domain of the INTERMED were disabled significantly more often and took more psychotropic drugs. Although the 2 patient groups did not differ in severity of illness and functional status, complex patients rated their illness as more severe on subjective measures and on most items of the Medical Outcomes Study Short Form 36. Complex patients showed increased health care utilization despite a similar biologic profile. CONCLUSIONS: The INTERMED identified complex patients with increased health care utilization, provided meaningful and comprehensive patient information, and proved to be easy to implement and advantageous compared with conventional methods of disease assessment. Intervention studies will have to demonstrate whether management strategies based on INTERMED profiles can improve treatment response and outcome of complex patients.

Effects of sodium arsenite on neurite outgrowth and glutamate AMPA receptor expression in mouse cortical neurons.

There has been broad concern that arsenic in the environment exerts neurotoxicity. To determine the mechanism by which arsenic disrupts neuronal development, primary cultured neurons obtained from the cerebral cortex of mouse embryos were exposed to sodium arsenite (NaAsO2) at concentrations between 0 and 2μM from days 2 to 4 in vitro and cell survival, neurite outgrowth and expression of glutamate AMPA receptor subunits were assessed at day 4 in vitro. Cell survival was significantly decreased by exposure to 2μM NaAsO2, whereas 0.5μM NaAsO2 increased cell survival instead. The assessment of neurite outgrowth showed that total neurite length was significantly suppressed by 1μM and 2μM NaAsO2, indicating that the lower concentration of NaAsO2 impairs neuritogenesis before inducing cell death. Immunoblot analysis of AMPA receptor subunit expression showed that the protein level of GluA1, a specific subunit of the AMPA receptor, was significantly decreased by 1μM and 2μM NaAsO2. When immunocytochemistry was used to confirm this effect by staining for GluA1 expression in neuropeptide Y neurons, most of which contain GluA1, GluA1 expression in neuropeptide Y neurons was found to be significantly suppressed by 1μM and 2μM NaAsO2 but to be increased at the concentration of 0.5μM. Finally, to determine whether neurons could be rescued from the NaAsO2-induced impairment of neuritogenesis by compensatory overexpression of GluA1, we used primary cultures of neurons transfected with a plasmid vector to overexpress either GluA1 or GluA2, and the results showed that GluA1/2 overexpression protected against the deleterious effects of NaAsO2 on neurite outgrowth. These results suggest that the NaAsO2 concentration inducing neurite suppression is lower than the concentration that induces cell death and is the same as the concentration that suppresses GluA1 expression. Consequently, the suppression of GluA1 expression by NaAsO2 seems at least partly responsible for neurite suppression induced by NaAsO2.

Transitions in social complexity along elevational gradients reveal a combined impact of season length and development time on social evolution.

Eusociality is taxonomically rare, yet associated with great ecological success. Surprisingly, studies of environmental conditions favouring eusociality are often contradictory. Harsh conditions associated with increasing altitude and latitude seem to favour increased sociality in bumblebees and ants, but the reverse pattern is found in halictid bees and polistine wasps. Here, we compare the life histories and distributions of populations of 176 species of Hymenoptera from the Swiss Alps. We show that differences in altitudinal distributions and development times among social forms can explain these contrasting patterns: highly social taxa develop more quickly than intermediate social taxa, and are thus able to complete the reproductive cycle in shorter seasons at higher elevations. This dual impact of altitude and development time on sociality illustrates that ecological constraints can elicit dynamic shifts in behaviour, and helps explain the complex distribution of sociality across ecological gradients.

In vivo neuropathology of cortical changes in elderly persons with schizophrenia.

BACKGROUND: Elderly schizophrenia patients frequently develop cognitive impairment of unclear etiology. Magnetic resonance imaging (MRI) studies revealed brain structural abnormalities, but the pattern of cortical gray matter (GM) volume and its relationship with cognitive and behavioral symptoms are unknown. METHODS: Magnetic resonance scans were taken from elderly schizophrenia patients (n = 20, age 67 +/- 6 SD, Mini-Mental State Examination [MMSE] 23 +/- 4), Alzheimer's disease (AD) patients (n = 20, age 73 +/- 9, MMSE 22 +/- 4), and healthy elders (n = 20, age 73 +/- 8, MMSE 29 +/- 1). Patients were assessed with a comprehensive neuropsychological and behavioral battery. Cortical pattern matching and a region-of-interest analysis, based on Brodmann areas (BAs), were used to map three-dimensional (3-D) profiles of differences in patterns of gray matter volume among groups. RESULTS: Schizophrenia patients had 10% and 11% lower total left and right GM volume than healthy elders (p < .001) and 7% and 5% more than AD patients (p = .06 and ns). Regions that had both significantly less gray matter than control subjects and gray matter volume as low as AD mapped to the cingulate gyrus and orbitofrontal cortex (BA 30, 23, 24, 32, 25, 11). The strongest correlate of gray matter volume in elderly schizophrenia patients, although nonsignificant, was the positive symptom subscale of the Positive and Negative Syndrome Scale, mapping to the right anterior cingulate area (r = .42, p = .06). CONCLUSIONS: The orbitofrontal/cingulate region had low gray matter volume in elderly schizophrenia patients. Neither cognitive impairment nor psychiatric symptoms were significantly associated with structural differences, even if positive symptoms tended to be associated with increased gray matter volume in this area.

Lactate modulates the activity of primary cortical neurons through a receptor-mediated pathway.

Lactate is increasingly described as an energy substrate of the brain. Beside this still debated metabolic role, lactate may have other effects on brain cells. Here, we describe lactate as a neuromodulator, able to influence the activity of cortical neurons. Neuronal excitability of mouse primary neurons was monitored by calcium imaging. When applied in conjunction with glucose, lactate induced a decrease in the spontaneous calcium spiking frequency of neurons. The effect was reversible and concentration dependent (IC50 ∼4.2 mM). To test whether lactate effects are dependent on energy metabolism, we applied the closely related substrate pyruvate (5 mM) or switched to different glucose concentrations (0.5 or 10 mM). None of these conditions reproduced the effect of lactate. Recently, a Gi protein-coupled receptor for lactate called HCA1 has been introduced. To test if this receptor is implicated in the observed lactate sensitivity, we incubated cells with pertussis toxin (PTX) an inhibitor of Gi-protein. PTX prevented the decrease of neuronal activity by L-lactate. Moreover 3,5-dyhydroxybenzoic acid, a specific agonist of the HCA1 receptor, mimicked the action of lactate. This study indicates that lactate operates a negative feedback on neuronal activity by a receptor-mediated mechanism, independent from its intracellular metabolism.

GWAS of human bitter taste perception identifies new loci and reveals additional complexity of bitter taste genetics.

Human perception of bitterness displays pronounced interindividual variation. This phenotypic variation is mirrored by equally pronounced genetic variation in the family of bitter taste receptor genes. To better understand the effects of common genetic variations on human bitter taste perception, we conducted a genome-wide association study on a discovery panel of 504 subjects and a validation panel of 104 subjects from the general population of São Paulo in Brazil. Correction for general taste-sensitivity allowed us to identify a SNP in the cluster of bitter taste receptors on chr12 (10.88- 11.24 Mb, build 36.1) significantly associated (best SNP: rs2708377, P = 5.31 × 10(-13), r(2) = 8.9%, β = -0.12, s.e. = 0.016) with the perceived bitterness of caffeine. This association overlaps with-but is statistically distinct from-the previously identified SNP rs10772420 influencing the perception of quinine bitterness that falls in the same bitter taste cluster. We replicated this association to quinine perception (P = 4.97 × 10(-37), r(2) = 23.2%, β = 0.25, s.e. = 0.020) and additionally found the effect of this genetic locus to be concentration specific with a strong impact on the perception of low, but no impact on the perception of high concentrations of quinine. Our study, thus, furthers our understanding of the complex genetic architecture of bitter taste perception.

Analyzing complexity in foreign language monologic oral production in a CLIL context

The study tested three analytic tools applied in SLA research (T-unit, AS-unit and Idea-unit) against FL learner monologic oral data. The objective was to analyse their effectiveness for the assessment of complexity of learners' academic production in English. The data were learners' individual productions gathered during the implementation of a CLIL teaching sequence on Natural Sciences in a Catalan state secondary school. The analysis showed that only AS-unit was easily applicable and highly effective in segmenting the data and taking complexity measures

Sleep EEG changes after middle cerebral artery infarcts in mice: different effects of striatal and cortical lesions.

STUDY OBJECTIVES: Hemispheric stroke in humans is associated with sleep-wake disturbances and sleep electroencephalogram (EEG) changes. The correlation between these changes and stroke extent remains unclear. In the absence of experimental data, we assessed sleep EEG changes after focal cerebral ischemia of different extensions in mice. DESIGN: Following electrode implantation and baseline sleep-wake EEG recordings, mice were submitted to sham surgery (control group), 30 minutes of intraluminal middle cerebral artery (MCA) occlusion (striatal stroke), or distal MCA electrocoagulation (cortical stroke). One and 12 days after stroke, sleep-wake EEG recordings were repeated. The EEG recorded from the healthy hemisphere was analyzed visually and automatically (fast Fourier analysis) according to established criteria. MEASUREMENTS AND RESULTS: Striatal stroke induced an increase in non-rapid eye movement (NREM) sleep and a reduction of rapid eye movement sleep. These changes were detectable both during the light and the dark phase at day 1 and persisted until day 12 after stroke. Cortical stroke induced a less-marked increase in NREM sleep, which was present only at day 1 and during the dark phase. In cortical stroke, the increase in NREM sleep was associated in the wake EEG power spectra, with an increase in the theta and a reduction in the beta activity. CONCLUSION: Cortical and striatal stroke lead to different sleep-wake EEG changes in mice, which probably reflect variable effects on sleep-promoting and wakefulness-maintaining neuronal networks.

Electro-cortical correlates of motor inhibition : a comparison between selective and non-selective stop tasks

In this study, we compared a selective stop task (transition from a bimanual in-phase to a unimanual index fingers' tapping), with a non-selective stop task (stopping a bimanual in-phase tapping at all), and with a switching task (transition from in-phase to anti-phase bimanual tapping). The aim was twofold: 1) to identify the electro-cortical correlates of selective and non-selective inhibition processes and 2) to investigate which type of inhibition - selective or not - is required when switching between two bimanual motor patterns. The results revealed that all tasks led to enhanced activation (alpha power) of the left sensorimotor and posterior regions which seems to reflect an overall effort to stop the preferred bimanual in-phase tendency. Each task implied specific functional connectivity reorganizations (beta coherence) between cerebral motor areas, probably reflecting engagement in a new unimanual or bimanual movement.

What do we gain from simplicity versus complexity in species distribution models?

Species distribution models (SDMs) are widely used to explain and predict species ranges and environmental niches. They are most commonly constructed by inferring species' occurrence-environment relationships using statistical and machine-learning methods. The variety of methods that can be used to construct SDMs (e.g. generalized linear/additive models, tree-based models, maximum entropy, etc.), and the variety of ways that such models can be implemented, permits substantial flexibility in SDM complexity. Building models with an appropriate amount of complexity for the study objectives is critical for robust inference. We characterize complexity as the shape of the inferred occurrence-environment relationships and the number of parameters used to describe them, and search for insights into whether additional complexity is informative or superfluous. By building 'under fit' models, having insufficient flexibility to describe observed occurrence-environment relationships, we risk misunderstanding the factors shaping species distributions. By building 'over fit' models, with excessive flexibility, we risk inadvertently ascribing pattern to noise or building opaque models. However, model selection can be challenging, especially when comparing models constructed under different modeling approaches. Here we argue for a more pragmatic approach: researchers should constrain the complexity of their models based on study objective, attributes of the data, and an understanding of how these interact with the underlying biological processes. We discuss guidelines for balancing under fitting with over fitting and consequently how complexity affects decisions made during model building. Although some generalities are possible, our discussion reflects differences in opinions that favor simpler versus more complex models. We conclude that combining insights from both simple and complex SDM building approaches best advances our knowledge of current and future species ranges.

Central and Cortical Gray Mater Segmentation of Magnetic Resonance Images of the Fetal Brain

Motivation. The study of human brain development in itsearly stage is today possible thanks to in vivo fetalmagnetic resonance imaging (MRI) techniques. Aquantitative analysis of fetal cortical surfacerepresents a new approach which can be used as a markerof the cerebral maturation (as gyration) and also forstudying central nervous system pathologies [1]. However,this quantitative approach is a major challenge forseveral reasons. First, movement of the fetus inside theamniotic cavity requires very fast MRI sequences tominimize motion artifacts, resulting in a poor spatialresolution and/or lower SNR. Second, due to the ongoingmyelination and cortical maturation, the appearance ofthe developing brain differs very much from thehomogenous tissue types found in adults. Third, due tolow resolution, fetal MR images considerably suffer ofpartial volume (PV) effect, sometimes in large areas.Today extensive efforts are made to deal with thereconstruction of high resolution 3D fetal volumes[2,3,4] to cope with intra-volume motion and low SNR.However, few studies exist related to the automatedsegmentation of MR fetal imaging. [5] and [6] work on thesegmentation of specific areas of the fetal brain such asposterior fossa, brainstem or germinal matrix. Firstattempt for automated brain tissue segmentation has beenpresented in [7] and in our previous work [8]. Bothmethods apply the Expectation-Maximization Markov RandomField (EM-MRF) framework but contrary to [7] we do notneed from any anatomical atlas prior. Data set &Methods. Prenatal MR imaging was performed with a 1-Tsystem (GE Medical Systems, Milwaukee) using single shotfast spin echo (ssFSE) sequences (TR 7000 ms, TE 180 ms,FOV 40 x 40 cm, slice thickness 5.4mm, in plane spatialresolution 1.09mm). Each fetus has 6 axial volumes(around 15 slices per volume), each of them acquired inabout 1 min. Each volume is shifted by 1 mm with respectto the previous one. Gestational age (GA) ranges from 29to 32 weeks. Mother is under sedation. Each volume ismanually segmented to extract fetal brain fromsurrounding maternal tissues. Then, in-homogeneityintensity correction is performed using [9] and linearintensity normalization is performed to have intensityvalues that range from 0 to 255. Note that due tointra-tissue variability of developing brain someintensity variability still remains. For each fetus, ahigh spatial resolution image of isotropic voxel size of1.09 mm is created applying [2] and using B-splines forthe scattered data interpolation [10] (see Fig. 1). Then,basal ganglia (BS) segmentation is performed on thissuper reconstructed volume. Active contour framework witha Level Set (LS) implementation is used. Our LS follows aslightly different formulation from well-known Chan-Vese[11] formulation. In our case, the LS evolves forcing themean of the inside of the curve to be the mean intensityof basal ganglia. Moreover, we add local spatial priorthrough a probabilistic map created by fitting anellipsoid onto the basal ganglia region. Some userinteraction is needed to set the mean intensity of BG(green dots in Fig. 2) and the initial fitting points forthe probabilistic prior map (blue points in Fig. 2). Oncebasal ganglia are removed from the image, brain tissuesegmentation is performed as described in [8]. Results.The case study presented here has 29 weeks of GA. Thehigh resolution reconstructed volume is presented in Fig.1. The steps of BG segmentation are shown in Fig. 2.Overlap in comparison with manual segmentation isquantified by the Dice similarity index (DSI) equal to0.829 (values above 0.7 are considered a very goodagreement). Such BG segmentation has been applied on 3other subjects ranging for 29 to 32 GA and the DSI hasbeen of 0.856, 0.794 and 0.785. Our segmentation of theinner (red and blue contours) and outer cortical surface(green contour) is presented in Fig. 3. Finally, torefine the results we include our WM segmentation in theFreesurfer software [12] and some manual corrections toobtain Fig.4. Discussion. Precise cortical surfaceextraction of fetal brain is needed for quantitativestudies of early human brain development. Our workcombines the well known statistical classificationframework with the active contour segmentation forcentral gray mater extraction. A main advantage of thepresented procedure for fetal brain surface extraction isthat we do not include any spatial prior coming fromanatomical atlases. The results presented here arepreliminary but promising. Our efforts are now in testingsuch approach on a wider range of gestational ages thatwe will include in the final version of this work andstudying as well its generalization to different scannersand different type of MRI sequences. References. [1]Guibaud, Prenatal Diagnosis 29(4) (2009). [2] Rousseau,Acad. Rad. 13(9), 2006, [3] Jiang, IEEE TMI 2007. [4]Warfield IADB, MICCAI 2009. [5] Claude, IEEE Trans. Bio.Eng. 51(4) (2004). [6] Habas, MICCAI (Pt. 1) 2008. [7]Bertelsen, ISMRM 2009 [8] Bach Cuadra, IADB, MICCAI 2009.[9] Styner, IEEE TMI 19(39 (2000). [10] Lee, IEEE Trans.Visual. And Comp. Graph. 3(3), 1997, [11] Chan, IEEETrans. Img. Proc, 10(2), 2001 [12] Freesurfer,http://surfer.nmr.mgh.harvard.edu.

Strong isomorphism reductions in complexity theory

We give the first systematic study of strong isomorphism reductions, a notion of reduction more appropriate than polynomial time reduction when, for example, comparing the computational complexity of the isomorphim problem for different classes of structures. We show that the partial ordering of its degrees is quite rich. We analyze its relationship to a further type of reduction between classes of structures based on purely comparing for every n the number of nonisomorphic structures of cardinality at most n in both classes. Furthermore, in a more general setting we address the question of the existence of a maximal element in the partial ordering of the degrees.