Toddlers default to canonical surface-to-meaning mapping when learning verbs

This work was supported by grants from the French Agence Nationale de la Recherche (ANR-2010-BLAN-1901) and from French Fondation de France to Anne Christophe, from the National Institute of Child Health and Human Development (HD054448) to Cynthia Fisher, Fondation Fyssen and Ecole de Neurosciences de Paris to Alex Cristia, and a PhD fellowship from the Direction Générale de l'Armement (DGA, France) supported by the PhD program FdV (Frontières du Vivant) to Isabelle Dautriche. We thank Isabelle Brunet for the recruitment, Michel Dutat for the technical support, and Hernan Anllo for his puppet mastery skill. We are grateful to the families that participated in this study. We also thank two anonymous reviewers for their comments on an earlier draft of this manuscript

Neural Signal to Violations of Abstract Rules Using Speech-Like Stimuli.

As the evidence of predictive processes playing a role in a wide variety of cognitive domains increases, the brain as a predictive machine becomes a central idea in neuroscience. In auditory processing, a considerable amount of progress has been made using variations of the Oddball design, but most of the existing work seems restricted to predictions based on physical features or conditional rules linking successive stimuli. To characterize the predictive capacity of the brain to abstract rules, we present here two experiments that use speech-like stimuli to overcome limitations and avoid common confounds. Pseudowords were presented in isolation, intermixed with infrequent deviants that contained unexpected phoneme sequences. As hypothesized, the occurrence of unexpected sequences of phonemes reliably elicited an early prediction error signal. These prediction error signals do not seemed to be modulated by attentional manipulations due to different task instructions, suggesting that the predictions are deployed even when the task at hand does not volitionally involve error detection. In contrast, the amount of syllables congruent with a standard pseudoword presented before the point of deviance exerted a strong modulation. Prediction error's amplitude doubled when two congruent syllables were presented instead of one, despite keeping local transitional probabilities constant. This suggests that auditory predictions can be built integrating information beyond the immediate past. In sum, the results presented here further contribute to the understanding of the predictive capabilities of the human auditory system when facing complex stimuli and abstract rules

A Comparison between the Compass Fundus Perimeter and the Humphrey Field Analyzer

Purpose: To evaluate relative diagnostic precision and test–retest variability of 2 devices, the Compass (CMP, CenterVue, Padova, Italy) fundus perimeter and the Humphrey Field Analyzer (HFA, Zeiss, Dublin, CA), in detecting glaucomatous optic neuropathy (GON). Design: Multicenter, cross-sectional, case-control study. Participants: We sequentially enrolled 499 patients with glaucoma and 444 normal subjects to analyze relative precision. A separate group of 44 patients with glaucoma and 54 normal subjects was analyzed to assess test–retest variability. Methods: One eye of recruited subjects was tested with the index tests: HFA (Swedish interactive thresholding algorithm [SITA] standard strategy) and CMP (Zippy Estimation by Sequential Testing [ZEST] strategy), 24-2 grid. The reference test for GON was specialist evaluation of fundus photographs or OCT, independent of the visual field (VF). For both devices, linear regression was used to calculate the sensitivity decrease with age in the normal group to compute pointwise total deviation (TD) values and mean deviation (MD). We derived 5% and 1% pointwise normative limits. The MD and the total number of TD values below 5% (TD 5%) or 1% (TD 1%) limits per field were used as classifiers. Main Outcome Measures: We used partial receiver operating characteristic (pROC) curves and partial area under the curve (pAUC) to compare the diagnostic precision of the devices. Pointwise mean absolute deviation and Bland–Altman plots for the mean sensitivity (MS) were computed to assess test–retest variability. Results: Retinal sensitivity was generally lower with CMP, with an average mean difference of 1.85±0.06 decibels (dB) (mean ± standard error, P < 0.001) in healthy subjects and 1.46±0.05 dB (mean ± standard error, P < 0.001) in patients with glaucoma. Both devices showed similar discriminative power. The MD metric had marginally better discrimination with CMP (pAUC difference ± standard error, 0.019±0.009, P = 0.035). The 95% limits of agreement for the MS were reduced by 13% in CMP compared with HFA in participants with glaucoma and by 49% in normal participants. Mean absolute deviation was similar, with no significant differences. Conclusions: Relative diagnostic precision of the 2 devices is equivalent. Test–retest variability of MS for CMP was better than for HFA

Detecting glaucoma from multi-modal data using probabilistic deep learning

Objective: To assess the accuracy of probabilistic deep learning models to discriminate normal eyes and eyes with glaucoma from fundus photographs and visual fields. Design: Algorithm development for discriminating normal and glaucoma eyes using data from multicenter, cross-sectional, case-control study. Subjects and participants: Fundus photograph and visual field data from 1,655 eyes of 929 normal and glaucoma subjects to develop and test deep learning models and an independent group of 196 eyes of 98 normal and glaucoma patients to validate deep learning models. Main outcome measures: Accuracy and area under the receiver-operating characteristic curve (AUC). Methods: Fundus photographs and OCT images were carefully examined by clinicians to identify glaucomatous optic neuropathy (GON). When GON was detected by the reader, the finding was further evaluated by another clinician. Three probabilistic deep convolutional neural network (CNN) models were developed using 1,655 fundus photographs, 1,655 visual fields, and 1,655 pairs of fundus photographs and visual fields collected from Compass instruments. Deep learning models were trained and tested using 80% of fundus photographs and visual fields for training set and 20% of the data for testing set. Models were further validated using an independent validation dataset. The performance of the probabilistic deep learning model was compared with that of the corresponding deterministic CNN model. Results: The AUC of the deep learning model in detecting glaucoma from fundus photographs, visual fields, and combined modalities using development dataset were 0.90 (95% confidence interval: 0.89–0.92), 0.89 (0.88–0.91), and 0.94 (0.92–0.96), respectively. The AUC of the deep learning model in detecting glaucoma from fundus photographs, visual fields, and both modalities using the independent validation dataset were 0.94 (0.92–0.95), 0.98 (0.98–0.99), and 0.98 (0.98–0.99), respectively. The AUC of the deep learning model in detecting glaucoma from fundus photographs, visual fields, and both modalities using an early glaucoma subset were 0.90 (0.88,0.91), 0.74 (0.73,0.75), 0.91 (0.89,0.93), respectively. Eyes that were misclassified had significantly higher uncertainty in likelihood of diagnosis compared to eyes that were classified correctly. The uncertainty level of the correctly classified eyes is much lower in the combined model compared to the model based on visual fields only. The AUCs of the deterministic CNN model using fundus images, visual field, and combined modalities based on the development dataset were 0.87 (0.85,0.90), 0.88 (0.84,0.91), and 0.91 (0.89,0.94), and the AUCs based on the independent validation dataset were 0.91 (0.89,0.93), 0.97 (0.95,0.99), and 0.97 (0.96,0.99), respectively, while the AUCs based on an early glaucoma subset were 0.88 (0.86,0.91), 0.75 (0.73,0.77), and 0.92 (0.89,0.95), respectively. Conclusion and relevance: Probabilistic deep learning models can detect glaucoma from multi-modal data with high accuracy. Our findings suggest that models based on combined visual field and fundus photograph modalities detects glaucoma with higher accuracy. While probabilistic and deterministic CNN models provided similar performance, probabilistic models generate certainty level of the outcome thus providing another level of confidence in decision making

A Plasmodium membrane receptor platform integrates cues for egress and invasion in blood forms and activation of transmission stages

Critical events in the life cycle of malaria-causing parasites depend on cyclic guanosine monophosphate homeostasis by guanylyl cyclases (GCs) and phosphodiesterases, including merozoite egress or invasion of erythrocytes and gametocyte activation. These processes rely on a single GCalpha, but in the absence of known signaling receptors, how this pathway integrates distinct triggers is unknown. We show that temperature-dependent epistatic interactions between phosphodiesterases counterbalance GCalpha basal activity preventing gametocyte activation before mosquito blood feed. GCalpha interacts with two multipass membrane cofactors in schizonts and gametocytes: UGO (unique GC organizer) and SLF (signaling linking factor). While SLF regulates GCalpha basal activity, UGO is essential for GCalpha up-regulation in response to natural signals inducing merozoite egress and gametocyte activation. This work identifies a GC membrane receptor platform that senses signals triggering processes specific to an intracellular parasitic lifestyle, including host cell egress and invasion to ensure intraerythrocytic amplification and transmission to mosquitoes

Plasmodium P-Type Cyclin CYC3 Modulates Endomitotic Growth during Oocyst Development in Mosquitoes

Cell-cycle progression and cell division in eukaryotes are governed in part by the cyclin family and their regulation of cyclin-dependent kinases (CDKs). Cyclins are very well characterised in model systems such as yeast and human cells, but surprisingly little is known about their number and role in Plasmodium, the unicellular protozoan parasite that causes malaria. Malaria parasite cell division and proliferation differs from that of many eukaryotes. During its life cycle it undergoes two types of mitosis: endomitosis in asexual stages and an extremely rapid mitotic process during male gametogenesis. Both schizogony (producing merozoites) in host liver and red blood cells, and sporogony (producing sporozoites) in the mosquito vector, are endomitotic with repeated nuclear replication, without chromosome condensation, before cell division. The role of specific cyclins during Plasmodium cell proliferation was unknown. We show here that the Plasmodium genome contains only three cyclin genes, representing an unusual repertoire of cyclin classes. Expression and reverse genetic analyses of the single Plant (P)-type cyclin, CYC3, in the rodent malaria parasite, Plasmodium berghei, revealed a cytoplasmic and nuclear location of the GFP-tagged protein throughout the lifecycle. Deletion of cyc3 resulted in defects in size, number and growth of oocysts, with abnormalities in budding and sporozoite formation. Furthermore, global transcript analysis of the cyc3-deleted and wild type parasites at gametocyte and ookinete stages identified differentially expressed genes required for signalling, invasion and oocyst development. Collectively these data suggest that cyc3 modulates oocyst endomitotic development in Plasmodium berghei

Plasmodium P-type cyclin CYC3 modulates endomitotic growth during oocyst development in mosquitoes

Cell-cycle progression and cell division in eukaryotes are governed in part by the cyclin family and their regulation of cyclin-dependent kinases (CDKs). Cyclins are very well characterised in model systems such as yeast and human cells, but surprisingly little is known about their number and role in Plasmodium, the unicellular protozoan parasite that causes malaria. Malaria parasite cell division and proliferation differs from that of many eukaryotes. During its life cycle it undergoes two types of mitosis: endomitosis in asexual stages and an extremely rapid mitotic process during male gametogenesis. Both schizogony (producing merozoites) in host liver and red blood cells, and sporogony (producing sporozoites) in the mosquito vector, are endomitotic with repeated nuclear replication, without chromosome condensation, before cell division. The role of specific cyclins during Plasmodium cell proliferation was unknown. We show here that the Plasmodium genome contains only three cyclin genes, representing an unusual repertoire of cyclin classes. Expression and reverse genetic analyses of the single Plant (P)-type cyclin, CYC3, in the rodent malaria parasite, Plasmodium berghei, revealed a cytoplasmic and nuclear location of the GFP-tagged protein throughout the lifecycle. Deletion of cyc3 resulted in defects in size, number and growth of oocysts, with abnormalities in budding and sporozoite formation. Furthermore, global transcript analysis of the cyc3-deleted and wild type parasites at gametocyte and ookinete stages identified differentially expressed genes required for signalling, invasion and oocyst development. Collectively these data suggest that cyc3 modulates oocyst endomitotic development in Plasmodium berghei

Do additional testing locations improve the detection of macular perimetric defects in glaucoma?

PURPOSE: To evaluate the ability of additional central testing locations to improve detection of macular visual field (VF) defects in glaucoma. DESIGN: Prospective cross-sectional study. PARTICIPANTS: Four hundred forty healthy people and 499 patients with glaucomatous optic neuropathy (GON) were tested with a fundus tracked perimeter (CMP; CenterVue) using a 24-2 grid with 12 additional macular locations (24-2+). METHODS: Glaucomatous optic neuropathy was identified based on expert evaluation of optic nerve head photographs and OCT scans, independently of the VF. We defined macular defects as locations with measurements outside the 5% and 2% normative limits on total deviation (TD) and pattern deviation (PD) maps within the VF central 10°. Classification was based on the total number of affected macular locations (overall detection) or the largest number of affected macular locations connected in a contiguous cluster (cluster detection). Criteria based on the number of locations and cluster size were used to obtain equivalent specificity between the 24-2 grid and the 24-2+ grids, calculated using false detections in the healthy cohort. Partial areas under the receiver operating characteristic curve (pAUCs) were also compared at specificities of 95% or more. MAIN OUTCOME MEASURES: Matched specificity comparison of the ability to detect glaucomatous macular defects between the 24-2 and 24-2+ grids. RESULTS: At matched specificity, cluster detection identified more macular defects with the 24-2+ grid compared with the 24-2 grid. For example, the mean increase in percentage of detection was 8% (95% confidence interval, 5%-11%) and 10% (95% confidence interval [CI], 7%-13%) for 5% TD and PD maps, respectively, and 5% (95% CI, 2%-7%) and 6% (95% CI, 4%-8%) for the 2% TD and PD maps, respectively. Good agreement was found between the 2 grids. The improvement measured by pAUCs was also significant but generally small. The percentage of eyes with macular defects ranged from about 30% to 50%. Test time for the 24-2+ grid was longer (21% increase) for both cohorts. Between 74% and 98% of defects missed by the 24-2 grid had at least 1 location with sensitivity of < 20 dB. CONCLUSIONS: Visual field examinations with additional macular locations can improve the detection of macular defects in GON modestly without loss of specificity when appropriate criteria are selected