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Bartram D.J.,Fort Dodge Animal Health | Heasman L.,Westpoint Veterinary Group | Batten C.A.,Institute for Animal Health | Oura C.A.L.,Institute for Animal Health | And 3 more authors.
Cattle Practice | Year: 2010

Groups of cattle and sheep that had received a primary course of vaccination with an inactivated bluetongue virus serotype 8 (BTV-8) vaccine were booster vaccinated 6 or 12 months later with the homologous vaccine or an alternative inactivated BTV-8 vaccine. The neutralising antibody responses in these animals were compared. Antibody titres to the alternative vaccine were significantly higher than to the homologous vaccine (P=0.008) in cattle and there was no significant difference between the antibody responses to alternative and homologous vaccines in sheep (P=0.973). These data indicate that cattle and sheep primed with one inactivated BTV-8 vaccine may be effectively boostered with an alternative commercial inactivated BTV-8 vaccine. Source


Bartram D.J.,Fort Dodge Animal Health | Heasman L.,Westpoint Veterinary Group | Batten C.A.,Institute for Animal Health | Oura C.A.L.,Institute for Animal Health | And 3 more authors.
Veterinary Journal | Year: 2011

Cattle and sheep that had received a primary course of vaccination with an inactivated bluetongue virus serotype 8 (BTV-8) vaccine were booster vaccinated 6 or 12. months later with the homologous vaccine or an alternative inactivated BTV-8 vaccine and neutralising antibody responses were determined. Antibody titres to the alternative vaccine were significantly higher than to the homologous vaccine (P= 0.013) in cattle. There was no significant difference between the antibody responses to alternative and homologous vaccines in sheep. These data indicate that cattle and sheep primed with one inactivated BTV-8 vaccine may be effectively boosted with an alternative commercial inactivated BTV-8 vaccine. © 2010 Elsevier Ltd. Source


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Site: http://www.nature.com/nature/current_issue/

This study is based on data from 33 mice (both males and females, age >postnatal day (P) 60). Ten VGAT-ChR2-EYFP mice (Jackson Laboratory, JAX 014548) and nine PV-ires-cre44 crossed to Rosa26-LSL-ReaChR, red-shifted channelrhodopsin reporter mice (JAX 24846)45, were used for photoinhibition behaviour experiments. A subset of these mice (five VGAT-ChR2-EYFP mice, seven PV × ReaChR mice) was used for simultaneous electrophysiology and behaviour. Seven mice (six VGAT-ChR2-EYFP, one PV × ReaChR mice) were used for the callosotomy experiment. Two Tlx_PL56-cre (MMRRC 036547)46 crossed to Ai32 (Rosa26-ChR2 reporter mice, JAX 012569)47 mice were used for photoactivation experiment. Two untrained VGAT-ChR2-EYFP mice and two untrained PV × ReaChR mice were used to characterize the photoinhibition in ALM. One Tlx_PL56-cre mouse was used for anatomical characterization of the ALM axonal projection pattern. All procedures were in accordance with protocols approved by the Janelia Institutional Animal Care and Use Committee. Mice were housed in a 12 h:12 h reverse light:dark cycle and tested during the dark phase. On days not tested, mice received 1 ml of water. On other days, mice were tested in experimental sessions lasting 1–2 h, in which they received all their water (range, 0.5–2 ml). If mice did not maintain a stable body weight, they received supplementary water48. All surgical procedures were carried out aseptically under 1–2% isofluorane anaesthesia. Buprenorphine HCl (0.1 mg kg−1, intraperitoneal injection; Bedford Laboratories) was used for postoperative analgesia. Ketoprofen (5 mg kg−1, subcutaneous injection; Fort Dodge Animal Health) was used at the time of surgery and postoperatively to reduce inflammation. After the surgery, mice were allowed to recover for at least 3 days with free access to water before water restriction. The experiments were not randomized. The investigators were not blinded to allocation during experiments and outcome assessment. Mice were prepared for photoinhibition and electrophysiology with a clear-skull cap and a headpost3. The scalp and periosteum over the dorsal surface of the skull were removed. A layer of cyanoacrylate adhesive (Krazy glue, Elmer’s Products Inc.) was directly applied to the intact skull. A custom made headpost48 was placed on the skull with its anterior edge aligned with the suture lambda (approximately over cerebellum) and cemented in place with clear dental acrylic (Lang Dental Jet Repair Acrylic; 1223-clear). A thin layer of clear dental acrylic was applied over the cyanoacrylate adhesive covering the entire exposed skull, followed by a thin layer of clear nail polish (Electron Microscopy Sciences, 72180). The behavioural task and training have been described3, 48. The stimulus was a metal pin (0.9 mm in diameter), presented at one of two possible positions (Fig. 1a). The two pole positions were 4.29 mm apart along the anterior-posterior axis (approximately 40° of whisking angle) and were constant across sessions. The posterior pole position was 5 mm from the whisker pad. A two-spout lickport (4.5 mm between spouts) was used to record answer licks and deliver water rewards. At the beginning of each trial, the vertical pole moved into reach of the whiskers (0.2 s travel time), where it remained for 1 s, after which it was retracted (retraction time 0.2 s). The sample epoch is defined as the time between the pole movement onset to 0.1 s after the pole retraction onset (sample epoch, 1.3 s, Fig. 1a). Mice touched the object at both pole positions, typically with a different set of whiskers. The delay epoch (durations, 1.2–1.7 s) followed the sample epoch. An auditory ‘go’ cue indicated the end of the delay epoch (pure tone, 3.4 kHz, 0.1 s duration). Licking early during the trial was punished by a loud alarm sound (siren buzzer, 0.05 s duration), followed by a brief timeout (1–1.2 s). Licking the correct lickport after the go cue led to a liquid reward (3 μl). Licking the incorrect lickport triggered a timeout (2–5 s). Trials in which mice did not lick within a 1.5-s window after the go cue were rare and typically occurred at the end of a session. Light from a 473 nm laser (Laser Quantum, Gem 473) or a 594 nm laser (Cobolt Inc., Colbolt Mambo 100) was controlled by an acousto-optical modulator (AOM; Quanta Tech) and a shutter (Vincent Associates). Photoinhibition of ALM was performed through the clear-skull cap implant by directing the laser over the skull (beam diameter: 400 μm at 4σ). The light transmission through the intact skull is 50%3. Photoinhibition was deployed on 25% of the behavioural trials during behavioural testing. To prevent the mice from distinguishing photoinhibition trials from control trials using visual cues, a ‘masking flash’ (40 1-ms pulses at 10 Hz) was delivered using 470 nm or 591 nm LEDs (Luxeon Star) near the eyes of the mice. The masking flash began as the pole started to move and continued through the end of photoinhibition. For silencing we stimulated cortical GABAergic neurons in VGAT-ChR2-EYFP mice, or parvalbumin-positive interneurons in PV-ires-cre mice crossed to reporter mice expressing ReaChR45. The two methods resulted in similar photoinhibition (Extended Data Fig. 2). The photoinhibition silenced 90% of spikes (Extended Data Fig. 2b) in a cortical area of 1 mm radius (at half-max) through all cortical layers3 (Extended Data Fig. 2d). To minimize rebound excitation after photoinhibition offset, we linearly ramped down the laser power (100 or 200 ms). This photostimulus was empirically determined3 to produce robust photoinhibition with minimal rebound (Extended Data Fig. 2c). The duration of the delay epoch varied to accommodate different photoinhibition conditions. In the unilateral photoinhibition experiment (Fig. 1, Extended Data Fig. 2a), a fixed 1.3-s delay epoch was used. We used a 40-Hz photostimulus with a sinusoidal temporal profile (1.5 mW average power) and a 100-ms linear ramp. We photoinhibited for 0.5 s, including the 100 ms ramp, during different task epochs (Fig. 1a). Photostimuli ended 1.6 s (sample), 0.8 s (early delay), 0.3 s (late delay), or 0 s (before cue) before the go cue. We also tested unilateral photoinhibition of longer durations in separate experiments (1.3 s including a 100-ms ramp, Extended Data Fig. 3). To accommodate the longer photoinhibition, we randomly varied the duration of the delay epoch from 1.2 s to 1.7 s in 0.1-s increments. This resulted in photoinhibition that terminated at different times before the go cue. In the bilateral photoinhibition experiment (Fig. 2), a fixed 1.7-s delay epoch was used to allow more time for neuronal activity to recovery after photoinhibition. We photoinhibited for 0.8 s, including a 200-ms ramp during offset, either at the beginning of the sample epoch or at the beginning of the delay epoch. To photoinhibit single cortical locations (Fig. 2a, 1 laser spot), we used the 40-Hz sinusoidal photostimulus (1.5 mW average power). To photoinhibit multiple cortical locations (Fig. 2a, multiple laser spots), we used a constant photostimulus and a scanning galvo (GVSM002, Thorlabs), which stepped the laser beam sequentially through the photoinhibition sites at the rate of 1 step per 5 ms (step time: <0.2 ms; dwell time:>4.8 ms; measured using a photodiode). Peak power was adjusted depending on the number of cortical locations to achieve 1.5 mW average power per location. The photoinhibition during scanning was similar to the standard condition (Extended Data Fig. 2). To estimate the proportion of ALM silenced by photoinhibition, we estimated the boundaries of ALM using photoinhibition behavioural data from3. ALM was defined as the area where photoinhibition over the entire delay epoch produced significant behavioural effects. ALM boundaries (Fig. 1b, grey area) were derived by deconvolving the area producing significant behavioural effects with the point-spread function of the photoinhibition method3 (Extended Data Fig. 2d). At 80% activity reduction, photoinhibition with 1 laser spot covered 58% of ALM in one hemisphere (Fig. 1b). For photoactivation we stimulated layer 5 intratelencephalic neurons in Tlx_PL56-cre mice46 crossed to reporter mice expressing ChR2 (Ai32)30. The delay epoch was 1.3 s long. The photostimulus was a 20-Hz sinusoid (0.53 mW average power) applied during different task epochs (Extended Data Fig. 5b). Photoactivation was deployed on 40% of the behavioural trials during electrophysiology. A small craniotomy (diameter, 1 mm) was made over left ALM (centred on 2.5 mm anterior, 1.5 mm lateral) one day before the recording session3. Extracellular spikes were recorded using NeuroNexus silicon probes (A4x8-5 mm-100-200-177). The 32 channel voltage signals were multiplexed, digitized by a PCI6133 board at 312.5 kHz (National instrument) at 14 bit, demultiplexed (sampling at 19531.25 Hz) and stored for offline analysis. Three to seven recordings were made from each craniotomy. To minimize brain movement, a drop of silicone gel (3-4680, Dow Corning) was applied over the craniotomy after the electrode was in the tissue. The tissue was allowed to settle for several minutes before the recording started. During electrophysiology, photoinhibition was deployed on 40% of the trials to obtain a larger number of trials per condition. Three photoinhibition conditions were tested during each recording session. In the unilateral photoinhibition experiment (Fig. 1, Extended Data Fig. 2a), photoinhibition during sample, early delay, and late delay epoch were tested. In the bilateral photoinhibition experiment (Fig. 2, Extended Data Fig. 2a), photoinhibition of left ALM (ipsilateral, 1 laser spot), right ALM (contralateral, 1 laser spot), and both hemispheres (4 laser spot) were tested. In separate experiments (Extended Data Figs 2a and 7), ipsilateral photoinhibition with 4 laser spots, contralateral photoinhibition with 4 laser spots, and bilateral photoinhibition with 1 laser spot were tested. The placement of the corpus callosum cut was determined based on ALM axonal projection patterns. AAV2/1-CAG-EGFP (Addgene, plasmid 28014) was injected into one hemisphere of ALM (Extended Data Fig. 10c). The injection coordinate was 2.5 mm anterior to bregma and 1.5 mm lateral to the midline. The injection was made through the thinned skull using a custom volumetric injection system. Glass pipettes (Drummond) were pulled and bevelled to a sharp tip (outer diameter of 30 μm). Pipettes were back-filled with mineral oil and front-loaded with viral suspension immediately before injection. 50-nl volumes were injected 500 and 800 μm deep. Two weeks after injection, mice were perfused and their brains were sectioned (50 μm) and processed using standard fluorescent immunohistochemical techniques. Confocal images were acquired on a Zeiss microscope, a 10 × objective and a Hamamatsu Orca Flash 4 camera46. ALM axons extend caudally from the injection site. Corpus callosum axons separate from pyramidal tract and corticothalamic axons approximately 1.2 mm anterior to bregma. ALM corpus callosum axons were confined to the anterior regions of corpus callosum and were densest around 1 mm from bregma (Extended Data Fig. 10c). Corpus callosum axon bisection was made through an elongated craniotomy either over the left (3 mice) or right (4 mice) hemisphere. A 3.5-mm-deep cut was made using a micro knife (Fine Science Tools, 10318-14) mounted on a micromanipulator (Sutter Instrument). The cut was 0.5 mm from the midline and was at a slight angle to avoid the pyramidal tract and corticothalamic axons (Extended Data Fig. 10d). The cut extended from 1.5 mm anterior to bregma to 1 mm posterior. Care was taken to avoid damaging the superior sagittal sinus. In the same surgery, a second craniotomy was made over left ALM for electrophysiology. Approximately 17 h after the surgery mice were tested in behavioural experiments (Fig. 5, Extended Data Fig. 10). Mice were tested in daily recording sessions for 5–7 days after the callosotomy. Mice were perfused immediately after the last recording session and the brains were processed for histology (Extended Data Fig. 10d). In a subset of the mice, brain sections were stained for GFAP (mouse; Sigma G3893, 1:2,000 dilution) (Extended Data Fig. 10d). Performance was computed as the fraction of correct reports, excluding lick-early trials (Figs 1, 2, 3, 4, 5). Chance performance was 50%. We also separately computed the performance for lick-right and lick-left trials (Figs 3, 4 and Extended Data Figs 3, 6, 9). Behavioural effects of photoinhibition were quantified by comparing the performance under photoinhibition with control performance using two-tailed t-test (Figs 1, 2, 5 and Extended Data Fig. 3). The extracellular recording traces were band-pass filtered (300–6 kHz). Events that exceeded an amplitude threshold (4 s.d. of the background) were subjected to manual spike sorting to extract single units3. 1,012 single units were recorded during behaviour across 58 recording sessions (20 sessions of unilateral experiments, Fig. 1; 38 sessions of bilateral experiments, Fig. 2, Extended Data Fig. 7). Spike widths were computed as the trough-to-peak interval in the mean spike waveform. Units with spike width <0.35 ms were defined as fast-spiking neurons (72 out of 1,012) and units with spike widths >0.45 ms as putative pyramidal neurons (890 out of 1,012). Units with intermediate values (0.35–0.45 ms, 50 out of 1,012) were excluded. This classification was previously verified by optogenetic tagging of GABAergic neurons3. We concentrated our analyses on the putative pyramidal neurons. Neurons were tested for significant trial-type selectivity during the sample, delay, or response epochs, using the spike counts from the lick-left and lick-right trials (two-tailed t-test, P < 0.05). Neurons that significantly differentiated trial types during any one of the trial epochs were deemed ‘selective’ (634 out of 890). To compute selectivity (Figs 1, 2, 5 and Extended Data Fig. 1), we first determined each neuron’s preferred trial type using spike counts from a subset of the trials (10 trials), selectivity is calculated as the spike rate difference between the trial types on the remaining data. Standard errors of the mean were obtained by bootstrap across neurons. To quantify the effect of photoinhibition on individual ALM neuron spike rates (Figs 1, 2, 5 and Extended Data Figs 5, 7), we used a two-tailed t-test on spike counts binned in 400-ms windows (control versus photoinhibition). Spike counts from lick-right trials and lick-left trials were pooled. Spike rates were tested at different times during the task (in 50-ms time steps) and significance was reported for P < 0.01. Quantification of the effects of perturbations on movement selectivity was complicated by the fact that ALM selectivity is coupled to upcoming movements. Grouping trials by the final movement (for example, using only correct lick-right trials) to compute selectivity would miss the trials in which photoinhibition caused the mice to switch future movements, thus underestimating the effects of photoinhibition on selectivity. We therefore used all trials (correct and incorrect) to compute selectivity when quantifying selectivity changes caused by photoinhibition (Figs 1, 2, 5 and Extended Data Figs 5, 7). Selectivity change was the selectivity difference between control and photoinhibition trials. To quantify the recovery time course of selectivity after photoinhibition, we looked for the first time bin when selectivity on photoinhibition trials reached 80% of the control selectivity (Figs 1g, 2e, 5e and Extended Data Figs 5, 7, green lines). Standard errors of the mean were obtained by bootstrap across neurons. To analyse the relationship between ALM population activity and upcoming movements, we restricted analysis to the recording sessions from the bilateral photoinhibition experiments (Fig. 2) with >5 neurons recorded simultaneously for >5 trials per condition (16 out of 38 sessions, Figs 3, 4 and Extended Data Figs 6, 8, 9). For a population of n neurons, we found an n × 1 vector, in the n dimensional activity space that maximally separated the response vectors in lick-right trials and lick-left trials, we term this vector the coding direction (CD). Average spike counts were computed in a 400-ms window in 10-ms steps. For each movement direction (lick right and lick left, correct trials only) we computed the average spike counts and , n × 1 response vectors that described the population response at that time. During the sample and delay epochs the direction of the difference in the mean response vectors, , was stable (correlation of w values between late sample epoch versus late delay epoch, 0.61 ± 0.05; Extended Data Fig. 9b). We averaged the w values from the sample and delay epochs to obtain the coding direction (CD). Because our estimate of the covariance was noisy, the CD gave better discrimination than the linear discrimant vector (CD divided by the within-group covariance). The projection along the CD captured 65.6 ± 5.1% of the population selectivity for lick-left and lick-right trials over the sample and delay epochs (root mean square (r.m.s.), of the spike rate difference between lick-right trials and lick-left trials), and 36.4 ± 6.3% of the total variance in ALM task-related activity (Extended Data Fig. 8a). Activity variance was quantified as the r.m.s. of the baseline subtracted activity over the sample and delay epoch. To project the ALM population activity along the CD we used independent control and perturbation trials from the trials used to compute the CD. For each trial we computed the spike counts for each neuron, x (n × 1), at each time point. The projected trajectories in Figs 3, 5 and Extended Data Figs 6, 7, 8, 9 were obtained as CDTx. Both correct and incorrect trials were used to compute the projected trajectries, grouped by the instructed movements. To quantify the separation between trajectories on lick-right and lick-left trials, we computed ROC values using CDTx at the end of the delay epoch for each session. To average trajectories across multiple behavioural sessions (Figs 3, 5 and Extended Data Figs 7, 8, 9), we first offset the trajectories for a particularly session by subtracting the mean CDTx across all trials and time points in that session. This removed fluctuations in mean activity from session to session. The offsets were computed using the independent control trials that were used to calculate the CD. Standard errors of the mean were obtained by bootstrapping individual sessions. To predict upcoming movements using ALM responses projected onto the CD (Figs 3, 4 and Extended Data Figs 8b, 9), we used the response vector x from the last time bin before the go cue (last 400 ms of the delay epoch). For each session, we computed a decision boundary (DB) to best separate the projected responses, CDTx, from lick-right and lick-left trials: σ2 is the variance of the projected responses CDTx across multiple lick-right or lick-left trials. Both the CD and decision boundary were computed using independent control trials and separate control and photoinhibition trials were used to predict performance. Data from multiple sessions were pooled in Figs 3, 4 and Extended Data Fig. 9. We decomposed ALM activity into three forms of dynamics (Fig. 3 and Extended Data Fig. 8). The modes were computed using a subset of control trials (correct trials only) and ipsilateral perturbation trials. The projections in the figures are for independent control trials and perturbation trials. The projection along the CD (mode 1) captured the movement selectivity in activity. The persistent mode (mode 2) was the difference in the mean response vectors between ipsilateral perturbed and unperturbed lick-right trials at the go cue. Mode 3 was the mean response vectors between ipsilateral perturbed and unperturbed lick-left trials at the go cue, further rotated using Gram–Schmidt process to be orthogonal to mode 2. We did not orthogonalize the CD mode and persistent mode, so that any potential selectivity common to these modes was not removed. There was a small overlap between mode 1 and modes 2–3 (the activity variance and selectivity shared by modes 1–3 are quantified in Extended Data Fig. 8a). Modes 2 and 3 describe the vast majority of the persistent changes in activity after ipsilateral perturbations. Two additional modes (4 and 5) captured the remaining activity variance. We first found eigenvectors of the population activity matrix using singular value decomposition. The data for the singular value decomposition (SVD) was an n × t matrix, consisting of the baseline-subtracted PSTHs for n neurons, with the lick-right and lick-left trials concatenated together (t time bins). The first two eigenvectors (n × 1) were rotated using the Gram–Schmidt process to be orthogonal to modes 1–3, yielding modes 4 and 5. Modes 1–5 together explained 98.5 ± 0.5% of the total variance of task-related activity and 95.8 ± 1.2% of population selectivity over the sample and delay epochs. To predict upcoming movements using the projected responses on persistent mode and ramping mode (Fig. 3), we computed decision boundaries on the projected responses using the same procedures as for the CD mode. Model code can be found at https://github.com/kpdaie/LiDaie. We constructed neural networks that have the ability to produce slow ramps of preparatory activity (Fig. 6a) when receiving transient or constant input, similar to a subset of ALM neurons. Our models include a phenomenological attractor model (Fig. 6b), explicit integrators (Fig. 6c and Extended Data Fig. 1f, g), and recurrent neural networks (RNNs) trained to produce ramping output (Fig. 6d and Extended Data Fig. 2h, i). We compared the responses of the models and ALM to transient silencing. All networks were simulated for two seconds. Photoinhibition was simulated by holding the activity of half of the neurons in each network at zero for times 0.2 s < t < 1.0 s. Activity of the ith neuron r (t) was governed by the equation: The cellular time constant, τ, the connectivity matrix, W, and the synaptic nonlinearity, f(r), differed across the models. N is the number of neurons, is a tonic and non-selective input, and is Gaussian random noise. In all simulations networks received either transient (0.05 s < t < 0.1 s) or persistent (0.1 s < t < 1.9 s) sensory inputs I (t). The network was simulated with N = 100, τ = 100 ms, and linear synapses, . The connectivity matrix was constructed so that all eigenvalues except for one were equal to zero. The non-zero eigenvalue was set to 0.99, producing feedback so that the activity of the network decays with time constant (ref. 35). The input was either persistent (Extended Data Fig. 1f, left) or transient (Extended Data Fig. 1f, right), in which case the output from the integrator was cascaded into a second identical network to produce ramping activity. Silencing was simulated by holding the activity of a randomly-selected population of 50 neurons at zero for times 0.2 s < t < 1.0 s. Corrective feedback37 was incorporated into an integrator network to confer robustness against perturbations. The model consists of a pair of excitatory and inhibitory neurons. Corrective feedback was achieved by a mismatch in the time constants for excitatory and inhibitory connections, which generates negative derivative feedback. The network exhibits robustness against random perturbations that equally affect the excitatory and inhibitory neurons, but is not robust against asymmetric activation of inhibitory neurons (for example, photoinhibition). The function f(r) is linear f(r) = s where the auxiliary variable s is determined by the equation: The synaptic time constant determines how quickly the post-synaptic currents respond to changes in presynaptic activity. The synaptic time constants were: inhibitory synapses, 10 ms; excitatory to inhibitory neurons, 25 ms; excitatory to excitatory neurons, 100 ms. The network received a task-selective persistent input. Photoinhibition was simulated by injecting large currents into the inhibitory neuron and disallowing negative spike rates, which results in silencing of the excitatory neuron. We used FORCE22 training to minimize the difference between the network readout (z(t)) and a ramping waveform (Extended Data Fig. 1h). z(t) is a linear combination of the activity of each neuron with weights determined by the vector w (that is, ). Tuning of z(t) was accomplished by simulating the activity of an initially randomly connected recurrent neural network (RNN) for 2 seconds (time step, 1 ms) and adjusting W every 2 ms during the simulation. This process was repeated 30 times. The initial connectivity matrix was chosen to be sparse with a connection probability p = 0.1. Non-zero connections were chosen from a Gaussian random distribution. The variance in connection strength was . 1.5 is a gain factor which is sufficiently strong to produce chaotic activity49. In addition, we used τ = 200 ms, f(r) = tanh(r), N = 400 and transient input. Photoinhibition was simulated by transiently clamping the activity of a randomly-selected population of 200 (that is, N/2) neurons to zero. The network received either persistent (Extended Data Fig. 1h, left) or transient (Extended Data Fig. 1h, right) sensory input. For persistent input the network behaved similar to an integrator exhibiting a recovery of selectivity, albeit at an offset level upon removal of photoinhibition. RRNs were trained with FORCE as described above and further stabilized (tamed chaos)23. The algorithm was designed to stabilize selected trajectories in chaotic networks via a recursive retuning of recurrent connection strengths based on a recursive least-squares rule50. To minimize the number of synapses that required tuning, the FORCE network was made sparse by eliminating weak connections that were smaller than an arbitrary threshold and using linear regression to adjust the remaining weights to maintain the dynamics. Elimination of weak synapses reduced the time needed to train the network. Twenty iterations of the tamed chaos algorithm were then run with weights being adjusted every 10 ms. Perturbations were applied as described for the FORCE trained network above. This training resulted in a modest increase in the robustness of the network. Two identical two-neuron unilateral attractor modules were constructed so that each neuron excited itself with weight 0.5235 and inhibited the other neuron in the same module with −0.5235. Each neuron was reciprocally connected with one partner from the other module with strength 0.3. τ = 100 ms and , where . Transient input I (t) (amplitude, 0.1) was provided to either the right-preferring (blue, Fig. 6b) or left-preferring (red, Fig. 6b) neurons, depending on the trial type. All neurons received a tonic input T (t) with amplitude 0.5 and noise with variance 0.01. Two modules with inter-module connections were tuned to produce robustness against unilateral photoinhibition. Each module consisted of four neurons (numbers refer to neuronal indices, with reference to the connection matrix W): Right preferring integrator neurons (1, 5) and left preferring integrator neurons (2, 6). Integration was produced by positive feedback achieved through mutual inhibition between left and right preferring neurons with strength −1 (ref. 15); these integrating pairs are represented schematically by the circles labelled ∫ in Fig. 6c. The modules are connected through the recovery neurons (3, 7; ‘R’ in Fig. 6c) and gating inhibitory neurons (4, 8; ‘G’ in Fig. 6c). The input I (t) was persistent with amplitude 0.04 to the right-preferring neuron and −0.04 to the left-preferring neuron during lick-right trials. The signs of the inputs were flipped for lick-left trials. In addition, each integrator neuron received tonic input T (t) with amplitude 40.0 to produce baseline activity at 20.0. The recovery (Fig. 6c) and gating inhibitory (Fig. 6c) neurons together produce robustness. They receive positive input from the right-preferring neuron and negative input from the left-preferring neuron. After removal of photoinhibition, the recovery neuron restores the activity of the contralateral integrator neurons. This restorative connection has strength 0.5. To avoid excessive coupling between modules during normal function the recovery neuron is strongly inhibited by the gating neuron with strength −6.0. The full connectivity matrix is shown below. For example, element W is the connection from the recovery neuron in module 2 (neuron 7) onto the right preferring neuron of module 1 (neuron 1). The time constant of this network was τ = 10 ms. Synapses in this network were linear, but activity was restricted to be positive.  = 0 in this network. To generate a modular RNN we started, as above (see Trained RNN, FORCE learning), with a randomly connected RNN with N = 400. We then classified 200 neurons as module 1 and the other 200 as module 2. FORCE training was performed as described above, but we first tuned only the intra-modular connections so that each module could produce its own ramping output. Next, inter-modular connections were trained in the presence of transient photoinhibition (described above) of module 1, so that the output of module 1 would recover upon removal of photoinhibition and the output of module 2 would be minimally affected by the photoinhibition. This process was then repeated for photoinhibition of module 2. In this network T (t) = 0 and  = 0.


Billi M.,Fort Dodge Animal Health
Veterinary Journal | Year: 2011

An epidemiological survey for canine parvovirus (CPV) and canine coronavirus (CCoV) infections was conducted in Western Europe. A total of 156 faecal samples were collected from dogs with diarrhoea in Spain (n=47), Italy (n= 39), France (n= 26), Germany (n= 21), the United Kingdom (n= 8), Belgium (n= 10), and the Netherlands (n= 5). Using molecular assays for virus detection and characterisation, CPV and CCoV were found to be widespread in European dog populations, either alone or in mixed infections. In agreement with previous reports, the original type CPV-2 was shown not to circulate in European dogs. The recently identified virus variant CPV-2c was predominant in Italy and Germany and present at high rates in Spain and France but was not detected in the UK or Belgium. Except for the UK, CCoV genotype I was identified in all European countries involved in the survey, albeit at a lower prevalence rates than CCoV genotype II. © 2009 Elsevier Ltd. Source


Ghidu V.P.,Temple University | Ilies M.A.,Temple University | Cullen T.,Fort Dodge Animal Health | Pollet R.,Fort Dodge Animal Health | Abou-Gharbia M.,Temple University
Bioorganic and Medicinal Chemistry Letters | Year: 2011

CL285032 is an anxiolytic compound currently under investigation as a possible treatment for canine noise phobia associated anxiety. A robust scale-up and manufacturing process is essential for the development and marketability of the drug. The current synthetic route, although reliable, requires seven steps and has a low overall yield (18%), leaving opportunity for improvement. We are presenting an efficient alternative approach toward the synthesis of CL285032 and the results thereof. © 2010 Elsevier Ltd. All rights reserved. Source

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