Cell cycle-dependent regulation of extra-adrenal glucocorticoid synthesis in murine intestinal epithelial cells

Glucocorticoids are anti-inflammatory steroids with important applications in the treatment of inflammatory diseases. Endogenous glucocorticoids are mainly produced by the adrenal glands, although there is increasing evidence for extra-adrenal sources. Recent findings show that intestinal crypt cells produce glucocorticoids, which contribute to the maintenance of intestinal immune homeostasis. Intestinal glucocorticoid synthesis is critically regulated by the transcription factor liver receptor homologue-1 (LRH-1). As expression of steroidogenic enzymes and LRH-1 is restricted to the proliferating cells of the crypts, we aimed to investigate the role of the cell cycle in the regulation of LRH-1 activity and intestinal glucocorticoid synthesis. We here show that either pharmacological or molecular modulation of cell cycle progression significantly inhibited expression of steroidogenic enzymes and synthesis of glucocorticoids in intestinal epithelial cells. Synchronization of intestinal epithelial cells in the cell cycle revealed that expression of steroidogenic enzymes is preferentially induced at the G(1)/S stage. Differentiation of immature intestinal epithelial cells to mature nonproliferating cells also resulted in reduced expression of steroidogenic enzymes. This cell cycle-related effect on intestinal steroidogenesis was found to be mediated through the regulation of LRH-1 transcriptional activity. This mechanism may restrict intestinal glucocorticoid synthesis to the proliferating cells of the crypts.

USING PROBABILISTIC GRAPHICAL MODELS TO DRAW INFERENCES IN SENSOR NETWORKS WITH TRACKING APPLICATIONS

Sensor networks have been an active research area in the past decade due to the variety of their applications. Many research studies have been conducted to solve the problems underlying the middleware services of sensor networks, such as self-deployment, self-localization, and synchronization. With the provided middleware services, sensor networks have grown into a mature technology to be used as a detection and surveillance paradigm for many real-world applications. The individual sensors are small in size. Thus, they can be deployed in areas with limited space to make unobstructed measurements in locations where the traditional centralized systems would have trouble to reach. However, there are a few physical limitations to sensor networks, which can prevent sensors from performing at their maximum potential. Individual sensors have limited power supply, the wireless band can get very cluttered when multiple sensors try to transmit at the same time. Furthermore, the individual sensors have limited communication range, so the network may not have a 1-hop communication topology and routing can be a problem in many cases. Carefully designed algorithms can alleviate the physical limitations of sensor networks, and allow them to be utilized to their full potential. Graphical models are an intuitive choice for designing sensor network algorithms. This thesis focuses on a classic application in sensor networks, detecting and tracking of targets. It develops feasible inference techniques for sensor networks using statistical graphical model inference, binary sensor detection, events isolation and dynamic clustering. The main strategy is to use only binary data for rough global inferences, and then dynamically form small scale clusters around the target for detailed computations. This framework is then extended to network topology manipulation, so that the framework developed can be applied to tracking in different network topology settings. Finally the system was tested in both simulation and real-world environments. The simulations were performed on various network topologies, from regularly distributed networks to randomly distributed networks. The results show that the algorithm performs well in randomly distributed networks, and hence requires minimum deployment effort. The experiments were carried out in both corridor and open space settings. A in-home falling detection system was simulated with real-world settings, it was setup with 30 bumblebee radars and 30 ultrasonic sensors driven by TI EZ430-RF2500 boards scanning a typical 800 sqft apartment. Bumblebee radars are calibrated to detect the falling of human body, and the two-tier tracking algorithm is used on the ultrasonic sensors to track the location of the elderly people.

Trial-to-trial size variability of motor-evoked potentials. A study using the triple stimulation technique

Motor-evoked potentials (MEPs) vary in size from one stimulus to the next. The objective of this study was to determine the cause and source of trial-to-trial MEP size variability. In two experiments involving 10 and 14 subjects, the variability of MEPs to cortical stimulation (cortical-MEPs) in abductor digiti minimi (ADM) and abductor hallucis (AH) was compared to those responses obtained using the triple stimulation technique (cortical-TST). The TST eliminates the effects of motor neuron (MN) response desynchronization and of repetitive MN discharges. Submaximal stimuli were used in both techniques. In six subjects, cortical-MEP variability was compared to that of brainstem-MEP and brainstem-TST. Variability was greater for MEPs than that for TST responses, by approximately one-third. The variability was the same for cortical- and brainstem-MEPs and was similar in ADM and AH. Variability concerned at least 10-15% of the MN pool innervating the target muscle. With the stimulation parameters used, repetitive MN discharges did not influence variability. For submaximal stimuli, approximately two-third of the observed MEP size variability is caused by the variable number of recruited alpha-MNs and approximately one-third by changing synchronization of MN discharges. The source of variability is most likely localized at the spinal segmental level.

BOLD correlates of EEG alpha phase-locking and the fMRI default mode network

Phase locking or synchronization of brain areas is a key concept of information processing in the brain. Synchronous oscillations have been observed and investigated extensively in EEG during the past decades. EEG oscillations occur over a wide frequency range. In EEG, a prominent type of oscillations is alpha-band activity, present typically when a subject is awake, but at rest with closed eyes. The spectral power of alpha rhythms has recently been investigated in simultaneous EEG/fMRI recordings, establishing a wide-range cortico-thalamic network. However, spectral power and synchronization are different measures and little is known about the correlations between BOLD effects and EEG synchronization. Interestingly, the fMRI BOLD signal also displays synchronous oscillations across different brain regions. These oscillations delineate so-called resting state networks (RSNs) that resemble the correlation patterns of simultaneous EEG/fMRI recordings. However, the nature of these BOLD oscillations and their relations to EEG activity is still poorly understood. One hypothesis is that the subunits constituting a specific RSN may be coordinated by different EEG rhythms. In this study we report on evidence for this hypothesis. The BOLD correlates of global EEG synchronization (GFS) in the alpha frequency band are located in brain areas involved in specific RSNs, e.g. the 'default mode network'. Furthermore, our results confirm the hypothesis that specific RSNs are organized by long-range synchronization at least in the alpha frequency band. Finally, we could localize specific areas where the GFS BOLD correlates and the associated RSN overlap. Thus, we claim that not only the spectral dynamics of EEG are important, but also their spatio-temporal organization.

Parameter-free extraction of the functional network topology from intracranial EEG recordings: a time-resolved study of graph properties in focal onset seizures

Rationale: Focal onset epileptic seizures are due to abnormal interactions between distributed brain areas. By estimating the cross-correlation matrix of multi-site intra-cerebral EEG recordings (iEEG), one can quantify these interactions. To assess the topology of the underlying functional network, the binary connectivity matrix has to be derived from the cross-correlation matrix by use of a threshold. Classically, a unique threshold is used that constrains the topology [1]. Our method aims to set the threshold in a data-driven way by separating genuine from random cross-correlation. We compare our approach to the fixed threshold method and study the dynamics of the functional topology. Methods: We investigate the iEEG of patients suffering from focal onset seizures who underwent evaluation for the possibility of surgery. The equal-time cross-correlation matrices are evaluated using a sliding time window. We then compare 3 approaches assessing the corresponding binary networks. For each time window: * Our parameter-free method derives from the cross-correlation strength matrix (CCS)[2]. It aims at disentangling genuine from random correlations (due to finite length and varying frequency content of the signals). In practice, a threshold is evaluated for each pair of channels independently, in a data-driven way. * The fixed mean degree (FMD) uses a unique threshold on the whole connectivity matrix so as to ensure a user defined mean degree. * The varying mean degree (VMD) uses the mean degree of the CCS network to set a unique threshold for the entire connectivity matrix. * Finally, the connectivity (c), connectedness (given by k, the number of disconnected sub-networks), mean global and local efficiencies (Eg, El, resp.) are computed from FMD, CCS, VMD, and their corresponding random and lattice networks. Results: Compared to FMD and VMD, CCS networks present: *topologies that are different in terms of c, k, Eg and El. *from the pre-ictal to the ictal and then post-ictal period, topological features time courses that are more stable within a period, and more contrasted from one period to the next. For CCS, pre-ictal connectivity is low, increases to a high level during the seizure, then decreases at offset. k shows a ‘‘U-curve’’ underlining the synchronization of all electrodes during the seizure. Eg and El time courses fluctuate between the corresponding random and lattice networks values in a reproducible manner. Conclusions: The definition of a data-driven threshold provides new insights into the topology of the epileptic functional networks.

Method of collaborative detection of autonomous transport vehicles based on laser rangefinder data

To master changing performance demands, autonomous transport vehicles are deployed to make inhouse material flow applications more flexible. The socalled cellular transport system consists of a multitude of small scale transport vehicles which shall be able to form a swarm. Therefore the vehicles need to detect each other, exchange information amongst each other and sense their environment. By provision of peripherally acquired information of other transport entities, more convenient decisions can be made in terms of navigation and collision avoidance. This paper is a contribution to collective utilization of sensor data in the swarm of cellular transport vehicles.

Application of Time-Delay Estimation to Mixed Reality Multisensor Tracking

Spatial tracking is one of the most challenging and important parts of Mixed Reality environments. Many applications, especially in the domain of Augmented Reality, rely on the fusion of several tracking systems in order to optimize the overall performance. While the topic of spatial tracking sensor fusion has already seen considerable interest, most results only deal with the integration of carefully arranged setups as opposed to dynamic sensor fusion setups. A crucial prerequisite for correct sensor fusion is the temporal alignment of the tracking data from several sensors. Tracking sensors are typically encountered in Mixed Reality applications, are generally not synchronized. We present a general method to calibrate the temporal offset between different sensors by the Time Delay Estimation method which can be used to perform on-line temporal calibration. By applying Time Delay Estimation on the tracking data, we show that the temporal offset between generic Mixed Reality spatial tracking sensors can be calibrated. To show the correctness and the feasibility of this approach, we have examined different variations of our method and evaluated various combinations of tracking sensors. We furthermore integrated this time synchronization method into our UBITRACK Mixed Reality tracking framework to provide facilities for calibration and real-time data alignment.

Decreased functional connectivity of EEG theta-frequency activity in first-episode, neuroleptic-naïve patients with schizophrenia: preliminary results

We explored and refined the hypothesis that during a first episode of acute schizophrenia a disorganization of brain functioning is present. A novel EEG measure was introduced, Global Field Synchronization (GFS), that estimates functional connectivity of brain processes in different EEG frequency bands. The measure was applied to EEG's from 11 never-treated, first-episode, young patients with an acute, positive, schizophrenic symptomatology and from 19 controls, residing in Bern, Switzerland. In comparison to age- and sex- matched controls, patients had significantly decreased GFS in the theta EEG frequency band, indicating a loosened functional connectivity of processes in this frequency. The result was confirmed in an independent, comparable patient group from Osaka, Japan (9 patients and 9 controls), thus making a total of 20 analyzed patients. Previous EEG research in healthy, awake subjects indicated a positive correlation of theta activity with memory functions. Thus, our result suggests a loss of mutual interdependence of memory functions in patients with acute schizophrenia, which agrees well with previous reports of working memory dysfunction in schizophrenia.

Jackson County Heifer Development Project Summary

The heifer development project was a five year project conducted on the site of the former Jackson County Farm north of Andrew, Iowa, for four years and on an area producer’s farm for the fifth year. Heifers arrived around December 1 each year and the average number of heifers each year was 43 with a low of 37 and high of 47. After a 30+ day warm-up period the heifers were put on a 112-day test from early January to late April. They were fed a shelled corn and legume-grass hay ration consisting of between 13% and 14% crude protein and a range of .44 to .58 megacal/pound of NEg over the five years. During the 112-day test heifers gained 1.86, 1.78, 1.5, 1.63 and 2.2 pounds per day, respectively, for years 1992 through 1996. The actual average breeding weight was less than the target weight in three years by 5, 12 and 22 pounds and exceeded the target weight in two year by 17 and 28 pounds. Estrus synchronization used a combination of MGA feeding and Lutalyse injection. Heifers were heat detected and bred 12 hours later for a three-day period. On the fourth day, all heifers not bred were mass inseminated. Heifers then ran with the cleanup bull for 58 days. The average synchronization response rate during the project was 79%. The overall pregnancy rates based on September pregnancy averaged 92%. The five year average total cost per head for heifer development was $286.18 or about $.85 per day. Feed and pasture costs averaged 61% of the total costs.

Strategies for Artificial Insemination of Cattle at Synchronized Ovulation or Synchronized Estrus

Two synchronization protocols in lactating dairy and beef cows and in dairy heifers were tested for efficacy of breeding by artificial insemination (AI) with or without estrus detection. Controls received three prostaglandin F2a (PGF2a) injections 14 days apart before AI at observed estrus. Pregnancy rates were compared with animals on the Ovsynch protocol that combined gonadotropin releasing hormone (GnRH) and PGF2a treatments with a timed AI 16 to 20 hours after the second GnRH injection. The pregnancy rates were similar at synchronized ovulation to fixed-time AI in lactating cows, but not effective in heifers because of the lack of synchronization.

Comparison of Various Methods of Estrus Detection in Synchronized Virgin Beef Heifers

Methods of heat detection were compared in the Mid- Crest Area Cattle Evaluation Program (MACEP) heifer development program in the 1998-breeding season. A total of 189 heifers from thirteen consignors entered the program on November 10, 1997. These heifers were condition scored, hip height measured, weighed, disposition scored, booster vaccinated, and treated for parasites at the time of arrival. Determination of the heifer’s mature weight was made and a target of 65% of their mature weight at breeding was established. The ration was designed to meet this goal. The heifers were kept in a dry lot until all heifers were AI bred once. The heifers were periodically weighed and condition scored to monitor their gains and the ration was adjusted as needed. The estrus synchronization program consisted of an oral progesterone analog for 14 days; 17 days after completion of the progesterone analog treatment a single injection of prostaglandin was given and the heifers were then estrus detected. Two concurrent methods of estrus detection were utilized: 1) Ovatec â electronic breeding probe (probe), 2) HeatWatchâ estrus detection system (HW), and 3) a combination of probe and HW. Probe readings were obtained each 12 hours and the heifers were continuously monitored for estrus activity using the HW system. The probe was used as the primary estrus detection method and the HW system was used as a back-up system. Those heifers that did not demonstrate any estrus signs prior to 96 hours post prostaglandin treatment were mass inseminated at 96 hours. Post AI breeding, 151 of the heifers were placed on pasture and ran with clean-up bulls for 60 days. The remaining heifers left the program after the AI breeding was completed. Pregnancy to the AI breeding was determined by ultrasound on June 29, 1998. Results from using both probe and HW were 60% pregnant by AI, probe alone was 32% pregnant by AI, and HW alone was 27% pregnant by AI. The result of mass insemination was 20% pregnant by AI.

Jackson County Heifer Development Project Update

The heifer development project took place the past four years on the site of the former Jackson County Farm north of Andrew, Iowa. Heifers arrived around December 1 with 38 heifers delivered for 1992, 44 for 1993, 46 for 1994, and 47 for 1995. After a 30+ day warm-up period, the heifers were put on a 112-day test from early January to late April. They were fed a shelled corn and legume-grass hay ration consisting of between 13% and 14% crude protein and .48, .58, .44, and .54 megacal/pound of NEg respectively for the years 1992 - 1995. During the 112-day test heifers gained 1.86, 1.78, 1.5, and 1.63 pounds per day respectively for years 1992 through 1995. The 1995 heifers averaged 853 pounds at breeding (22 pounds under target weight). This compares with previous years in which the breeding weight was less than target weight in two years by 5 and 12 pounds and exceeded the target weight in one year by 17 pounds. Estrus synchronization used a combination of MGA feeding and Lutalyse injection. Heifers were heatdetected and bred 12 hours later for a three-day period. On the fourth day, all heifers not bred were mass inseminated. Heifers then ran with the cleanup bull for 58 days. The synchronization response rate in 1995 was 83%, which compares with the three year previous average of 77%. The overall pregnancy rates based on September pregnancy exams were 94.6% in 1992, 93% in 1993, 91% in 1994, and 91.5% in 1995. Development costs for the 326 days in 1995 totaled $269.14 per heifer. This compares with the average of $286. 92 for the three previous years. The four-year average total cost per head for heifer development was $282.48, or about $.84 per day. Feed and pasture costs represented 58% of the total costs, or $.49 per day.

Conception Rate Using the Select-Synch Protocol in Combination with a Lower Dose Progesterone-Releasing Intravaginal Insert (1.38 g) in Swiss Dairy Cows

Background: Synchronization programs have become standard in the dairy industry. In Switzerland, these programs are used but newly. The objective of this study was A) to estimate the pregnancy rate after a Select-Synch protocol in- cluding a low dosage of progesterone in CIDR (1.38 g). As a second step B) this pregnancy rate should be compared to cows from another Swiss study that used a Select-Synch protocol with the 1.9 g insert (Rudolph et al., 2011). Methods: A) 196 cows were included in the study. Cows received a CIDR 1.38 g and 2.5 ml of buserelin i.m. on d 0. On d 7, the CIDR insert was removed and 5 ml of dinoprost was administered i.m. On d 0 a milk sample for progesterone analysis was taken. Pregnancy was determined at or more than 35 days after artificial insemination. B) The 1.38 g group and the 1.9 g group were compared as to cow and farm factors, number of preceding AI’s, gynecological and uterine pretreat- ment and treatment itself. A forward selection procedure was used (test result considered significant if p-value  0.05). Results: A) The pregnancy rate, using the Select-Synch protocol with the CIDR 1.38 g was 44.4%. B) The CIDR 1.9 g Select-Synch group revealed a pregnancy rate of 50.4% (Rudolph et al., 2011). Significant differences between the groups were not found. Conclusion: The 1.38 g CIDR-Select-Synch protocol may be recommended for multiparous dairy cows. The pregnancy rate compared to the 1.9 g CIDR-Select-Synch protocol was 8% lower, but this difference was not significant.

SIGNALING MECHANISMS THAT CONTROL GAP JUNCTIONAL COUPLING BETWEEN RETINAL NEURONS

Gap junctions between neurons form the structural substrate for electrical synapses. Connexin 36 (Cx36, and its non-mammalian ortholog connexin 35) is the major neuronal gap junction protein in the central nervous system (CNS), and contributes to several important neuronal functions including neuronal synchronization, signal averaging, network oscillations, and motor learning. Connexin 36 is strongly expressed in the retina, where it is an obligatory component of the high-sensitivity rod photoreceptor pathway. A fundamental requirement of the retina is to adapt to broadly varying inputs in order to maintain a dynamic range of signaling output. Modulation of the strength of electrical coupling between networks of retinal neurons, including the Cx36-coupled AII amacrine cell in the primary rod circuit, is a hallmark of retinal luminance adaptation. However, very little is known about the mechanisms regulating dynamic modulation of Cx36-mediated coupling. The primary goal of this work was to understand how cellular signaling mechanisms regulate coupling through Cx36 gap junctions. We began by developing and characterizing phospho-specific antibodies against key regulatory phosphorylation sites on Cx36. Using these tools we showed that phosphorylation of Cx35 in fish models varies with light adaptation state, and is modulated by acute changes in background illumination. We next turned our focus to the well-studied and readily identifiable AII amacrine cell in mammalian retina. Using this model we showed that increased phosphorylation of Cx36 is directly related to increased coupling through these gap junctions, and that the dopamine-stimulated uncoupling of the AII network is mediated by dephosphorylation of Cx36 via protein kinase A-stimulated protein phosphatase 2A activity. We then showed that increased phosphorylation of Cx36 on the AII amacrine network is driven by depolarization of presynaptic ON-type bipolar cells as well as background light increments. This increase in phosphorylation is mediated by activation of extrasynaptic NMDA receptors associated with Cx36 gap junctions on AII amacrine cells and by Ca2+-calmodulin-dependent protein kinase II activation. Finally, these studies indicated that coupling is regulated locally at individual gap junction plaques. This work provides a framework for future study of regulation of Cx36-mediated coupling, in which increased phosphorylation of Cx36 indicates increased neuronal coupling.

Direct linking of Greenland and Antarctic ice cores at the Toba eruption (74 ka BP)

The Toba eruption that occurred some 74 ka ago in Sumatra, Indonesia, is among the largest volcanic events on Earth over the last 2 million years. Tephra from this eruption has been spread over vast areas in Asia, where it constitutes a major time marker close to the Marine Isotope Stage 4/5 boundary. As yet, no tephra associated with Toba has been identified in Greenland or Antarctic ice cores. Based on new accurate dating of Toba tephra and on accurately dated European stalagmites, the Toba event is known to occur between the onsets of Greenland interstadials (GI) 19 and 20. Furthermore, the existing linking of Greenland and Antarctic ice cores by gas records and by the bipolar seesaw hypothesis suggests that the Antarctic counterpart is situated between Antarctic Isotope Maxima (AIM) 19 and 20. In this work we suggest a direct synchronization of Greenland (NGRIP) and Antarctic (EDML) ice cores at the Toba eruption based on matching of a pattern of bipolar volcanic spikes. Annual layer counting between volcanic spikes in both cores allows for a unique match. We first demonstrate this bipolar matching technique at the already synchronized Laschamp geomagnetic excursion (41 ka BP) before we apply it to the suggested Toba interval. The Toba synchronization pattern covers some 2000 yr in GI-20 and AIM-19/20 and includes nine acidity peaks that are recognized in both ice cores. The suggested bipolar Toba synchronization has decadal precision. It thus allows a determination of the exact phasing of inter-hemispheric climate in a time interval of poorly constrained ice core records, and it allows for a discussion of the climatic impact of the Toba eruption in a global perspective. The bipolar linking gives no support for a long-term global cooling caused by the Toba eruption as Antarctica experiences a major warming shortly after the event. Furthermore, our bipolar match provides a way to place palaeo-environmental records other than ice cores into a precise climatic context.