Perception of causality in schizophrenia spectrum disorder

Patients with schizophrenia spectrum disorders often maintain deviating views on cause-effect relationships, especially when positive and disorganization symptoms are manifest. Altered perceived causality is prominent in delusional ideation, in ideas of reference, and in the mentalizing ability (theory of mind [ToM]) of patients. Perceiving causal relationships may be understood either as higher order cognitive reasoning or as low-level information processing. In the present study, perception of causality was investigated as a low-level, preattentional capability similar to gestalt-like perceptual organization. Thirty-one patients (24 men and 7 women with mean age 27.7 years) and the same number of healthy control subjects matched to patients with respect to age and sex were tested. A visual paradigm was used in which 2 identical discs move, from opposite sides of a monitor, steadily toward and then past one another. Their coincidence generates an ambiguous, bistable percept (discs either "stream through" or "bounce off" one another). The bouncing perception, ie, perceived causality, is enhanced when auditory stimuli are presented at the time of coincidence. Psychopathology was measured using the Positive and Negative Syndrome Scale. It was found that positive symptoms were strongly associated with increased perceived causality and disorganization with attenuated perceived causality. Patients in general were not significantly different from controls, but symptom subgroups showed specifically altered perceived causality. Perceived causality as a basic preattentional process may contribute to higher order cognitive alterations and ToM deficiencies. It is suggested that cognitive remediation therapy should address both increased and reduced perception of causality.

Real-time quantitative broad-range PCR assay for detection of the 16S rRNA gene followed by sequencing for species identification

Here we determined the analytical sensitivities of broad-range real-time PCR-based assays employing one of three different genomic DNA extraction protocols in combination with one of three different primer pairs targeting the 16S rRNA gene to detect a panel of 22 bacterial species. DNA extraction protocol III, using lysozyme, lysostaphin, and proteinase K, followed by PCR with the primer pair Bak11W/Bak2, giving amplicons of 796 bp in length, showed the best overall sensitivity, detecting DNA of 82% of the strains investigated at concentrations of < or =10(2) CFU in water per reaction. DNA extraction protocols I and II, using less enzyme treatment, combined with other primer pairs giving shorter amplicons of 466 bp and 342 or 346 bp, respectively, were slightly more sensitive for the detection of gram-negative but less sensitive for the detection of gram-positive bacteria. The obstacle of detecting background DNA in blood samples spiked with bacteria was circumvented by introducing a broad-range hybridization probe, and this preserved the minimal detection limits observed in samples devoid of blood. Finally, sequencing of the amplicons generated using the primer pair Bak11W/Bak2 allowed species identification of the detected bacterial DNA. Thus, broad-spectrum PCR targeting the 16S rRNA gene in the quantitative real-time format can achieve an analytical sensitivity of 1 to 10 CFU per reaction in water, avoid detection of background DNA with the introduction of a broad-range probe, and generate amplicons that allow species identification of the detected bacterial DNA by sequencing. These prerequisites are important for its application to blood-containing patient samples.

On Time Series Analysis of Public Health and Biomedical Data

A time series is a sequence of observations made over time. Examples in public health include daily ozone concentrations, weekly admissions to an emergency department or annual expenditures on health care in the United States. Time series models are used to describe the dependence of the response at each time on predictor variables including covariates and possibly previous values in the series. Time series methods are necessary to account for the correlation among repeated responses over time. This paper gives an overview of time series ideas and methods used in public health research.

Compressed sensing based cyclic feature spectrum sensing for cognitive radios

Spectrum sensing is currently one of the most challenging design problems in cognitive radio. A robust spectrum sensing technique is important in allowing implementation of a practical dynamic spectrum access in noisy and interference uncertain environments. In addition, it is desired to minimize the sensing time, while meeting the stringent cognitive radio application requirements. To cope with this challenge, cyclic spectrum sensing techniques have been proposed. However, such techniques require very high sampling rates in the wideband regime and thus are costly in hardware implementation and power consumption. In this thesis the concept of compressed sensing is applied to circumvent this problem by utilizing the sparsity of the two-dimensional cyclic spectrum. Compressive sampling is used to reduce the sampling rate and a recovery method is developed for re- constructing the sparse cyclic spectrum from the compressed samples. The reconstruction solution used, exploits the sparsity structure in the two-dimensional cyclic spectrum do-main which is different from conventional compressed sensing techniques for vector-form sparse signals. The entire wideband cyclic spectrum is reconstructed from sub-Nyquist-rate samples for simultaneous detection of multiple signal sources. After the cyclic spectrum recovery two methods are proposed to make spectral occupancy decisions from the recovered cyclic spectrum: a band-by-band multi-cycle detector which works for all modulation schemes, and a fast and simple thresholding method that works for Binary Phase Shift Keying (BPSK) signals only. In addition a method for recovering the power spectrum of stationary signals is developed as a special case. Simulation results demonstrate that the proposed spectrum sensing algorithms can significantly reduce sampling rate without sacrifcing performance. The robustness of the algorithms to the noise uncertainty of the wireless channel is also shown.

Time-dependent interactions of the two porphyrinic compounds chlorin e6 and mono-L-aspartyl-chlorin e6 with phospholipid vesicles probed by NMR spectroscopy

The distribution processes of chlorin e6 (CE) and monoaspartyl-chlorin e6 (MACE) between the outer and inner phospholipid monolayers of 1,2-dioleoyl-phosphatidylcholine (DOPC) vesicles were monitored by 1H NMR spectroscopy through analysis of chemical shifts and line widths of the DOPC vesicle resonances. Chlorin adsorption to the outer vesicle monolayer induced changes in the DOPC 1H NMR spectrum. Most pronounced was a split of the N-methyl choline resonance, allowing for separate analysis of inner and outer vesicle layers. Transbilayer distribution of the chlorin compounds was indicated by time-dependent characteristic spectral changes of the DOPC resonances. Kinetic parameters for the flip-flop processes, that is, half-lives and rate constants, were obtained from the experimental data points. In comparison to CE, MACE transbilayer movement was significantly reduced, with MACE remaining more or less attached to the outer membrane layer. The distribution coefficients for CE and MACE between the vesicular and aqueous phase were determined. Both CE and MACE exhibited a high affinity for the vesicular phase. For CE, a positive correlation was found between transfer rate and increasing molar ratio CE/DOPC. Enhanced membrane rigidity induced by increasing amounts of cholesterol into the model membrane was accompanied by a decrease of CE flip-flop rates across the membrane. The present study shows that the movement of porphyrins across membranes can efficiently be investigated by 1H NMR spectroscopy and that small changes in porphyrin structure can have large effects on membrane kinetics.

Sleepiness and vigilance tests

Objective assessments of subjective complaints such as sleepiness, tiredness or fatigue using sleepiness and vigilance tests aim to identify its causes and to judge the fitness to drive or to work of the affected person. "Vigilance" comprises wakefulness, alertness and attention and is therefore not merely reciprocal to sleepiness. Since it is a complex phenomenon with several dimensions it is unlikely to be appropriately assessed by one single "vigilance test". One important dimension of vigilance discussed here is wakefulness with its counterpart of overt sleep and the whole spectrum of various levels in between. The transit zone between full wakefulness and overt sleep is mainly characterised by the subjective complaint of sleepiness, which cannot be measured directly. Only the consequences of reduced wakefulness such as a shortened sleep latency, slowed cognitive function and prolonged reaction time can be measured objectively. It is, therefore, more promising to combine a battery of subjective and objective tests to answer a specific question in order to achieve the most appropriate description for a given clinical or medicolegal situation. However even then we must keep in mind that many other important aspects of fitness to drive / fitness to work such as neurological, psychiatric and neuropsychological functions including risk taking behaviour are not covered by vigilance tests. A comprehensive, multidisciplinary approach is essential in such situations.

Climate spectrum estimation in the presence of timescale errors

We introduce an algorithm (called REDFITmc2) for spectrum estimation in the presence of timescale errors. It is based on the Lomb-Scargle periodogram for unevenly spaced time series, in combination with the Welch's Overlapped Segment Averaging procedure, bootstrap bias correction and persistence estimation. The timescale errors are modelled parametrically and included in the simulations for determining (1) the upper levels of the spectrum of the red-noise AR(1) alternative and (2) the uncertainty of the frequency of a spectral peak. Application of REDFITmc2 to ice core and stalagmite records of palaeoclimate allowed a more realistic evaluation of spectral peaks than when ignoring this source of uncertainty. The results support qualitatively the intuition that stronger effects on the spectrum estimate (decreased detectability and increased frequency uncertainty) occur for higher frequencies. The surplus information brought by algorithm REDFITmc2 is that those effects are quantified. Regarding timescale construction, not only the fixpoints, dating errors and the functional form of the age-depth model play a role. Also the joint distribution of all time points (serial correlation, stratigraphic order) determines spectrum estimation.

Detecting offsets in GPS time series: first results from the Detection of Offsets in GPS Experiment

The accuracy of Global Positioning System (GPS) time series is degraded by the presence of offsets. To assess the effectiveness of methods that detect and remove these offsets, we designed and managed the Detection of Offsets in GPS Experiment. We simulated time series that mimicked realistic GPS data consisting of a velocity component, offsets, white and flicker noises (1/f spectrum noises) composed in an additive model. The data set was made available to the GPS analysis community without revealing the offsets, and several groups conducted blind tests with a range of detection approaches. The results show that, at present, manual methods (where offsets are hand picked) almost always give better results than automated or semi‒automated methods (two automated methods give quite similar velocity bias as the best manual solutions). For instance, the fifth percentile range (5% to 95%) in velocity bias for automated approaches is equal to 4.2 mm/year (most commonly ±0.4 mm/yr from the truth), whereas it is equal to 1.8 mm/yr for the manual solutions (most commonly 0.2 mm/yr from the truth). The magnitude of offsets detectable by manual solutions is smaller than for automated solutions, with the smallest detectable offset for the best manual and automatic solutions equal to 5 mm and 8 mm, respectively. Assuming the simulated time series noise levels are representative of real GPS time series, robust geophysical interpretation of individual site velocities lower than 0.2–0.4 mm/yr is therefore certainly not robust, although a limit of nearer 1 mm/yr would be a more conservative choice. Further work to improve offset detection in GPS coordinates time series is required before we can routinely interpret sub‒mm/yr velocities for single GPS stations.

When the systems fail to communicate. Cerebral resting state networks in schizophrenia spectrum disorders.

There have been numerous attempts to reveal the neurobiological basis of schizophrenia spectrum disorders. Results however, remain as heterogeneous as the schizophrenia spectrum disorders itself. Therefore, one aim of this thesis was to divide patients affected by this disorder into subgroups in order to homogenize the results of future studies. In a first study it is suggested that psychopathological rating scales should focus on symptoms-clusters that may have a common neurophysiological background. The here presented Bern Psychopathology Scale (BPS) proposes that alterations in three wellknown brain systems (motor, language, and affective) are largely leading to the communication failures observable on a behavioral level, but also - as repeatedly hypothesized - to dysconnectivity within and between brain systems in schizophrenia spectrum disorders. The external validity of the motor domain in the BPS was tested against the objective measure of 24 hours wrist actigraphy, in a second study. The subjective, the quantitative, as well as the global rating of the degree of motor disorders in this patient group showed significant correlations to the acquired motor activity. This result confirmed in a first step the practicability of the motor domain of the BPS, but needs further validation regarding pathological brain alterations. Finally, in a third study (independent from the two other studies), two cerebral Resting State Networks frequently altered in schizophrenia were investigated for the first time using simultaneous EEG/fMRI: The well-known default mode network and the left working memory network. Besides the changes in these fMRI-based networks, there are well-documented findings that patients exhibit alterations in EEG spectra compared to healthy controls. However, only through the multimodal approach it was possible to discover that patients with schizophrenia spectrum disorders have a slower driving frequency of the Resting State Networks compared to the matched healthy controls. Such a dysfunctional coupling between neuronal frequency and functional brain organization could explain in a uni- or multifactorial way (dysfunctional cross-frequency coupling, maturational effects, vigilance fluctuations, task-related suppression), how the typical psychotic symptoms might occur. To conclude, the major contributions presented in this thesis were on one hand the development of a psychopathology rating scale that is based on the assumption of dysfunctional brain networks, as well as the new evidence of a dysfunctional triggering frequency of Resting State Networks from the simultaneous EEG/fMRI study in patients affected by a schizophrenia spectrum disorder.

Expanding the Mutation Spectrum Affecting αIIbβ3 Integrin in Glanzmann Thrombasthenia: Screening of the ITGA2B and ITGB3 Genes in a Large International Cohort.

We report the largest international study on Glanzmann thrombasthenia (GT), an inherited bleeding disorder where defects of the ITGA2B and ITGB3 genes cause quantitative or qualitative defects of the αIIbβ3 integrin, a key mediator of platelet aggregation. Sequencing of the coding regions and splice sites of both genes in members of 76 affected families identified 78 genetic variants (55 novel) suspected to cause GT. Four large deletions or duplications were found by quantitative real-time PCR. Families with mutations in either gene were indistinguishable in terms of bleeding severity that varied even among siblings. Families were grouped into type I and the rarer type II or variant forms with residual αIIbβ3 expression. Variant forms helped identify genes encoding proteins mediating integrin activation. Splicing defects and stop codons were common for both ITGA2B and ITGB3 and essentially led to a reduced or absent αIIbβ3 expression; included was a heterozygous c.1440-13_c.1440-1del in intron 14 of ITGA2B causing exon skipping in 7 unrelated families. Molecular modeling revealed how many missense mutations induced subtle changes in αIIb and β3 domain structure across both subunits thereby interfering with integrin maturation and/or function. Our study extends knowledge of Glanzmann thrombasthenia and the pathophysiology of an integrin. This article is protected by copyright. All rights reserved.

Real-Time RT-PCR for the Detection of Lyssavirus Species

The causative agents of rabies are single-stranded, negative-sense RNA viruses in the genus Lyssavirus of Rhabdoviridae, consisting of twelve classified and three as yet unclassified species including classical rabies virus (RABV). Highly neurotropic RABV causes rapidly progressive encephalomyelitis with nearly invariable fatal outcome. Rapid and reliable diagnosis of rabies is highly relevant for public and veterinary health. Due to growing variety of the genus Lyssavirus observed, the development of suitable molecular assays for diagnosis and differentiation is challenging. This work focused on the establishment of a suitable real-time RT-PCR technique for rabies diagnosis as a complement to fluorescent antibody test and rabies tissue culture infection test as gold standard for diagnosis and confirmation. The real-time RT-PCR was adapted with the goal to detect the whole spectrum of lyssavirus species, for nine of which synthesized DNA fragments were used. For the detection of species, seven probes were developed. Serial dilutions of the rabies virus strain CVS-11 showed a 100-fold higher sensitivity of real-time PCR compared to heminested RT-PCR. Using a panel of thirty-one lyssaviruses representing four species, the suitability of the protocol could be shown. Phylogenetic analysis of the sequences obtained by heminested PCR allowed correct classification of all viruses used.

Real-Time Localization Using Software Defined Radio

Service providers make use of cost-effective wireless solutions to identify, localize, and possibly track users using their carried MDs to support added services, such as geo-advertisement, security, and management. Indoor and outdoor hotspot areas play a significant role for such services. However, GPS does not work in many of these areas. To solve this problem, service providers leverage available indoor radio technologies, such as WiFi, GSM, and LTE, to identify and localize users. We focus our research on passive services provided by third parties, which are responsible for (i) data acquisition and (ii) processing, and network-based services, where (i) and (ii) are done inside the serving network. For better understanding of parameters that affect indoor localization, we investigate several factors that affect indoor signal propagation for both Bluetooth and WiFi technologies. For GSM-based passive services, we developed first a data acquisition module: a GSM receiver that can overhear GSM uplink messages transmitted by MDs while being invisible. A set of optimizations were made for the receiver components to support wideband capturing of the GSM spectrum while operating in real-time. Processing the wide-spectrum of the GSM is possible using a proposed distributed processing approach over an IP network. Then, to overcome the lack of information about tracked devices’ radio settings, we developed two novel localization algorithms that rely on proximity-based solutions to estimate in real environments devices’ locations. Given the challenging indoor environment on radio signals, such as NLOS reception and multipath propagation, we developed an original algorithm to detect and remove contaminated radio signals before being fed to the localization algorithm. To improve the localization algorithm, we extended our work with a hybrid based approach that uses both WiFi and GSM interfaces to localize users. For network-based services, we used a software implementation of a LTE base station to develop our algorithms, which characterize the indoor environment before applying the localization algorithm. Experiments were conducted without any special hardware, any prior knowledge of the indoor layout or any offline calibration of the system.

Real-time localization using software defined radio

Service providers make use of cost-effective wireless solutions to identify, localize, and possibly track users using their carried MDs to support added services, such as geo-advertisement, security, and management. Indoor and outdoor hotspot areas play a significant role for such services. However, GPS does not work in many of these areas. To solve this problem, service providers leverage available indoor radio technologies, such as WiFi, GSM, and LTE, to identify and localize users. We focus our research on passive services provided by third parties, which are responsible for (i) data acquisition and (ii) processing, and network-based services, where (i) and (ii) are done inside the serving network. For better understanding of parameters that affect indoor localization, we investigate several factors that affect indoor signal propagation for both Bluetooth and WiFi technologies. For GSM-based passive services, we developed first a data acquisition module: a GSM receiver that can overhear GSM uplink messages transmitted by MDs while being invisible. A set of optimizations were made for the receiver components to support wideband capturing of the GSM spectrum while operating in real-time. Processing the wide-spectrum of the GSM is possible using a proposed distributed processing approach over an IP network. Then, to overcome the lack of information about tracked devices’ radio settings, we developed two novel localization algorithms that rely on proximity-based solutions to estimate in real environments devices’ locations. Given the challenging indoor environment on radio signals, such as NLOS reception and multipath propagation, we developed an original algorithm to detect and remove contaminated radio signals before being fed to the localization algorithm. To improve the localization algorithm, we extended our work with a hybrid based approach that uses both WiFi and GSM interfaces to localize users. For network-based services, we used a software implementation of a LTE base station to develop our algorithms, which characterize the indoor environment before applying the localization algorithm. Experiments were conducted without any special hardware, any prior knowledge of the indoor layout or any offline calibration of the system.

The spectrum of the non-rapid eye movement sleep electroencephalogram following total sleep deprivation is trait-like

The sleep electroencephalogram (EEG) spectrum is unique to an individual and stable across multiple baseline recordings. The aim of this study was to examine whether the sleep EEG spectrum exhibits the same stable characteristics after acute total sleep deprivation. Polysomnography (PSG) was recorded in 20 healthy adults across consecutive sleep periods. Three nights of baseline sleep [12 h time in bed (TIB)] following 12 h of wakefulness were interleaved with three nights of recovery sleep (12 h TIB) following 36 h of sustained wakefulness. Spectral analysis of the non-rapid eye movement (NREM) sleep EEG (C3LM derivation) was used to calculate power in 0.25 Hz frequency bins between 0.75 and 16.0 Hz. Intraclass correlation coefficients (ICCs) were calculated to assess stable individual differences for baseline and recovery night spectra separately and combined. ICCs were high across all frequencies for baseline and recovery and for baseline and recovery combined. These results show that the spectrum of the NREM sleep EEG is substantially different among individuals, highly stable within individuals and robust to an experimental challenge (i.e. sleep deprivation) known to have considerable impact on the NREM sleep EEG. These findings indicate that the NREM sleep EEG represents a trait.

The growing spectrum of antibody-associated inflammatory brain diseases in children.

OBJECTIVE To describe the clinical spectrum, diagnostic evaluation, current management, and neurologic outcome of pediatric antibody-associated inflammatory brain diseases (AB-associated IBrainD). METHODS We performed a single-center retrospective cohort study of consecutive patients aged ≤18 years diagnosed with an AB-associated IBrainD at The Hospital for Sick Children, Toronto, Ontario, Canada, between January 2005 and June 2013. Standardized clinical data, laboratory test results, neuroimaging features, and treatment regimens were captured. RESULTS Of 169 children (93 female, 55%) diagnosed with an IBrainD, 16 (10%) had an AB-associated IBrainD. Median age at presentation was 13.3 years (range 3.1-17.9); 11 (69%) were female. Nine patients (56%) had anti-NMDA receptor encephalitis, 4 (25%) had aquaporin-4 autoimmunity, 2 (13%) had Hashimoto encephalitis, and 1 (6%) had anti-glutamic acid decarboxylase 65 (GAD65) encephalitis. The key presenting features in children with anti-NMDA receptor encephalitis, Hashimoto encephalopathy, and anti-GAD65 encephalitis included encephalopathy, behavioral symptoms, and seizures; patients with aquaporin-4 autoimmunity showed characteristic focal neurologic deficits. Six patients (38%) required intensive care unit admission at presentation. Median time from symptom onset to diagnosis was 55 days (range 6-358). All but 1 patient received immunosuppressive therapy. One child with anti-NMDA receptor encephalitis died due to multiorgan failure. At last follow-up, after a median follow-up time of 1.7 years (range 0.8-3.7), 27% of the children had function-limiting neurologic sequelae. CONCLUSIONS Children with AB-associated IBrainD represent an increasing subgroup among IBrainD; 1 in 4 children has function-limiting residual neurologic deficits. Awareness of the different clinical patterns is important in order to facilitate timely diagnosis and initiate immunosuppressive treatment.