Enantio- and Diastereodivergent Cyclopropanation of Allenes by Directed Evolution of an Iridium-Containing Cytochrome.

Alkylidene cyclopropanes (ACPs) are valuable synthetic intermediates because of their constrained structure and opportunities for further diversification. Although routes to ACPs are known, preparations of ACPs with control of both the configuration of the cyclopropyl (R vs S) group and the geometry of the alkene (E vs Z) are unknown. We describe enzymatic cyclopropanation of allenes with ethyl diazoacetate (EDA) catalyzed by an iridium-containing cytochrome (Ir(Me)-CYP119) that controls both stereochemical elements. Two mutants of Ir(Me)-CYP119 identified by 6-codon (6c, VILAFG) saturation mutagenesis catalyze the formation of (E)-ACPs with -93% to >99% ee and >99:1 E/Z ratio with just three rounds of 96 mutants. By four additional rounds of mutagenesis, an enzyme variant was identified that forms (Z)-ACPs with up to 94% ee and a 28:72 E/Z ratio. Computational studies show that the orientation of the carbene unit dictated by the mutated positions accounts for the stereoselectivity.

Interrater reliability of interictal EEG waveforms in Lennox-Gastaut Syndrome.

OBJECTIVE: Identification of EEG waveforms is critical for diagnosing Lennox-Gastaut Syndrome (LGS) but is complicated by the progressive nature of the disease. Here, we assess the interrater reliability (IRR) among pediatric epileptologists for classifying EEG waveforms associated with LGS. METHODS: A novel automated algorithm was used to objectively identify epochs of EEG with transient high power, which were termed events of interest (EOIs). The algorithm was applied to EEG from 20 LGS subjects and 20 healthy controls during NREM sleep, and 1350 EOIs were identified. Three raters independently reviewed the EOIs within isolated 15-second EEG segments in a randomized, blinded fashion. For each EOI, the raters assigned a waveform label (spike and slow wave, generalized paroxysmal fast activity, seizure, spindle, vertex, muscle, artifact, nothing, or other) and indicated the perceived subject type (LGS or control). RESULTS: Labeling of subject type had 85% accuracy across all EOIs and an IRR of κ =0.790, suggesting that brief segments of EEG containing high-power waveforms can be reliably classified as pathological or normal. Waveform labels were less consistent, with κ =0.558, and the results were highly variable for different categories of waveforms. Label mismatches typically occurred when one reviewer selected "nothing," suggesting that reviewers had different thresholds for applying named labels. SIGNIFICANCE: Classification of EEG waveforms associated with LGS has weak IRR, due in part to varying thresholds applied during visual review. Computational methods to objectively define EEG biomarkers of LGS may improve IRR and aid clinical decision-making.

Chemo- and enantioselective intramolecular silver-catalyzed aziridinations of carbamimidates.

Transition metal-catalyzed asymmetric nitrene transfer is a powerful method to generate enantioenriched amines found in natural products and bioactive molecules. A highly chemo- and enantioselective intramolecular silver-catalysed aziridination of 2,2,2-trichloroethoxysulfonyl (Tces)-protected carbamimidates gives [4.1.0]-bicyclic aziridines in good yields and up to 99% ee.

Evolution of Cortical Functional Networks in Healthy Infants.

During normal childhood development, functional brain networks evolve over time in parallel with changes in neuronal oscillations. Previous studies have demonstrated differences in network topology with age, particularly in neonates and in cohorts spanning from birth to early adulthood. Here, we evaluate the developmental changes in EEG functional connectivity with a specific focus on the first 2 years of life. Functional connectivity networks (FCNs) were calculated from the EEGs of 240 healthy infants aged 0-2 years during wakefulness and sleep using a cross-correlation-based measure and the weighted phase lag index. Topological features were assessed via network strength, global clustering coefficient, characteristic path length, and small world measures. We found that cross-correlation FCNs maintained a consistent small-world structure, and the connection strengths increased after the first 3 months of infancy. The strongest connections in these networks were consistently located in the frontal and occipital regions across age groups. In the delta and theta bands, weighted phase lag index networks decreased in strength after the first 3 months in both wakefulness and sleep, and a similar result was found in the alpha and beta bands during wakefulness. However, in the alpha band during sleep, FCNs exhibited a significant increase in strength with age, particularly in the 21-24 months age group. During this period, a majority of the strongest connections in the networks were located in frontocentral regions, and a qualitatively similar distribution was seen in the beta band during sleep for subjects older than 3 months. Graph theory analysis suggested a small world structure for weighted phase lag index networks, but to a lesser degree than those calculated using cross-correlation. In general, graph theory metrics showed little change over time, with no significant differences between age groups for the clustering coefficient (wakefulness and sleep), characteristics path length (sleep), and small world measure (sleep). These results suggest that infant FCNs evolve during the first 2 years with more significant changes to network strength than features of the network structure. This study quantifies normal brain networks during infant development and can serve as a baseline for future investigations in health and neurological disease.

Ptychographic sensor for large-scale lensless microbial monitoring with high spatiotemporal resolution.

Traditional microbial detection methods often rely on the overall property of microbial cultures and cannot resolve individual growth event at high spatiotemporal resolution. As a result, they require bacteria to grow to confluence and then interpret the results. Here, we demonstrate the application of an integrated ptychographic sensor for lensless cytometric analysis of microbial cultures over a large scale and with high spatiotemporal resolution. The reported device can be placed within a regular incubator or used as a standalone incubating unit for long-term microbial monitoring. For longitudinal study where massive data are acquired at sequential time points, we report a new temporal-similarity constraint to increase the temporal resolution of ptychographic reconstruction by 7-fold. With this strategy, the reported device achieves a centimeter-scale field of view, a half-pitch spatial resolution of 488 nm, and a temporal resolution of 15-s intervals. For the first time, we report the direct observation of bacterial growth in a 15-s interval by tracking the phase wraps of the recovered images, with high phase sensitivity like that in interferometric measurements. We also characterize cell growth via longitudinal dry mass measurement and perform rapid bacterial detection at low concentrations. For drug-screening application, we demonstrate proof-of-concept antibiotic susceptibility testing and perform single-cell analysis of antibiotic-induced filamentation. The combination of high phase sensitivity, high spatiotemporal resolution, and large field of view is unique among existing microscopy techniques. As a quantitative and miniaturized platform, it can improve studies with microorganisms and other biospecimens at resource-limited settings.

Scalp EEG markers of normal infant development using visual and computational approaches.

The infant brain is rapidly developing, and these changes are reflected in scalp electroencephalography (EEG) features, including power spectrum and sleep spindle characteristics. These biomarkers not only mirror infant development, but they are also altered by conditions such as epilepsy, autism, developmental delay, and trisomy 21. Prior studies of early development were generally limited by small cohort sizes, lack of a specific focus on infancy (0-2 years), and exclusive use of visual marking for sleep spindles. Therefore, we measured the EEG power spectrum and sleep spindles in 240 infants ranging from 0-24 months. To rigorously assess these metrics, we used both clinical visual assessment and computational techniques, including automated sleep spindle detection. We found that the peak frequency and power of the posterior dominant rhythm (PDR) increased with age, and a corresponding peak occurred in the EEG power spectra. Based on both clinical and computational measures, spindle duration decreased with age, and spindle synchrony increased with age. Our novel metric of spindle asymmetry suggested that peak spindle asymmetry occurs at 6-9 months of age.Clinical Relevance- Here we provide a robust characterization of the development of EEG brain rhythms during infancy. This can be used as a basis of comparison for studies of infant neurological disease, including epilepsy, autism, developmental delay, and trisomy 21.

Optofluidic ptychography on a chip.

We report the implementation of a fully on-chip, lensless microscopy technique termed optofluidic ptychography. This imaging modality complements the miniaturization provided by microfluidics and allows the integration of ptychographic microscopy into various lab-on-a-chip devices. In our prototype, we place a microfluidic channel on the top surface of a coverslip and coat the bottom surface with a scattering layer. The channel and the coated coverslip substrate are then placed on top of an image sensor for diffraction data acquisition. Similar to the operation of a flow cytometer, the device utilizes microfluidic flow to deliver specimens across the channel. The diffracted light from the flowing objects is modulated by the scattering layer and recorded by the image sensor for ptychographic reconstruction, where high-resolution quantitative complex images are recovered from the diffraction measurements. By using an image sensor with a 1.85 µm pixel size, our device can resolve the 550 nm linewidth on the resolution target. We validate the device by imaging different types of biospecimens, including C. elegans, yeast cells, paramecium, and closterium sp. We also demonstrate a high-resolution ptychographic reconstruction at a video framerate of 30 frames per second. The reported technique can address a wide range of biomedical needs and engenders new ptychographic imaging innovations in a flow cytometer configuration.

High-throughput lensless whole slide imaging via continuous height-varying modulation of a tilted sensor.

We report a new, to the best of our knowledge, lensless microscopy configuration by integrating the concepts of transverse translational ptychography and defocus multi-height phase retrieval. In this approach, we place a tilted image sensor under the specimen for introducing linearly increasing phase modulation along one lateral direction. Similar to the operation of ptychography, we laterally translate the specimen and acquire the diffraction images for reconstruction. Since the axial distance between the specimen and the sensor varies at different lateral positions, laterally translating the specimen effectively introduces defocus multi-height measurements while eliminating axial scanning. Lateral translation further introduces sub-pixel shift for pixel super-resolution imaging and naturally expands the field of view for rapid whole slide imaging. We show that the equivalent height variation can be precisely estimated from the lateral shift of the specimen, thereby addressing the challenge of precise axial positioning in conventional multi-height phase retrieval. Using a sensor with 1.67 µm pixel size, our low-cost and field-portable prototype can resolve the 690 nm linewidth on the resolution target. We show that a whole slide image of a blood smear with a 120mm2 field of view can be acquired in 18 s. We also demonstrate accurate automatic white blood cell counting from the recovered image. The reported approach may provide a turnkey solution for addressing point-of-care and telemedicine-related challenges.

An Effortful Approach to Social Affiliation in Schizophrenia: Preliminary Evidence of Increased Theta and Alpha Connectivity during a Live Social Interaction.

People with schizophrenia often experience a profound lack of motivation for social affiliation-a facet of negative symptoms that detrimentally impairs functioning. However, the mechanisms underlying social affiliative deficits remain poorly understood, particularly under realistic social contexts. Here, we investigated subjective reports and electroencephalography (EEG) functional connectivity in schizophrenia during a live social interaction. Individuals with schizophrenia (n = 16) and healthy controls (n = 29) completed a face-to-face interaction with a confederate while having EEG recorded. Participants were randomly assigned to either a Closeness condition designed to elicit feelings of closeness through self-disclosure or a Small-Talk condition with minimal disclosure. Compared to controls, patients reported lower positive emotional experiences and feelings of closeness across conditions, but they showed comparably greater subjective affiliative responses for the Closeness (vs. Small-Talk) condition. Additionally, patients in the Closeness (vs. Small-Talk) condition displayed a global increase in connectivity in theta and alpha frequency bands that was not observed for controls. Importantly, greater theta and alpha connectivity was associated with greater subjective affiliative responding, greater negative symptoms, and lower disorganized symptoms in patients. Collectively, findings indicate that patients, because of pronounced negative symptoms, utilized a less efficient, top-down mediated strategy to process social affiliation.

Computational characteristics of interictal EEG as objective markers of epileptic spasms.

OBJECTIVE: Favorable neurodevelopmental outcomes in epileptic spasms (ES) are tied to early diagnosis and prompt treatment, but uncertainty in the identification of the disease can delay this process. Therefore, we investigated five categories of computational electroencephalographic (EEG) measures as markers of ES. METHODS: We measured 1) amplitude, 2) power spectra, 3) Shannon entropy and permutation entropy, 4) long-range temporal correlations, via detrended fluctuation analysis (DFA) and 5) functional connectivity using cross-correlation and phase lag index (PLI). EEG data were analyzed from ES patients (n = 40 patients) and healthy controls (n = 20 subjects), with multiple blinded measurements during wakefulness and sleep for each patient. RESULTS: In ES patients, EEG amplitude was significantly higher in all electrodes when compared to controls. Shannon and permutation entropy were lower in ES patients than control subjects. The DFA intercept values in ES patients were significantly higher than control subjects, while DFA exponent values were not significantly different between the groups. EEG functional connectivity networks in ES patients were significantly stronger than controls when based on both cross-correlation and PLI. Significance for all statistical tests was p < 0.05, adjusted for multiple comparisons using the Benjamini-Hochberg procedure as appropriate. Finally, using logistic regression, a multi-attribute classifier was derived that accurately distinguished cases from controls (area under curve of 0.96). CONCLUSIONS: Computational EEG features successfully distinguish ES patients from controls in a large, blinded study. SIGNIFICANCE: These objective EEG markers, in combination with other clinical factors, may speed the diagnosis and treatment of the disease, thereby improving long-term outcomes.

Schizotypy dimensions are associated with altered resting state alpha connectivity.

The disconnection hypothesis of schizophrenia says that symptoms are explained by dysfunctional connections across a wide range of brain networks. Despite some support for this hypothesis, there have been mixed findings. One reason for these may be the multidimensional nature of schizophrenia symptoms. In order to clarify the relationship between symptoms and brain networks, the current study included individuals at risk for schizophrenia-spectrum disorders who either report extreme levels of positive schizotypy traits (perceptual aberrations and magical ideation, or "PerMag"; n = 23), or an extreme negative schizotypy trait (social anhedonia, or "SocAnh"; n = 19), as well as a control group (n = 18). Resting-state alpha electroencephalography was collected, and functional networks for each subject were measured using the phase-lag index to calculate the connectivity between channel pairs based on the symmetry of instantaneous phase differences over time. Furthermore, graph theory measures were introduced to identify network features exclusive to schizotypy groups. We found that the PerMag group exhibited a smaller difference in node strength and clustering coefficient in frontal/occipital and central/occipital regional comparisons compared to controls, suggesting a more widespread network. The SocAnh group exhibited a larger difference in degree in the central/occipital regional comparison relative to controls, suggesting a localized occipital focus in the connectivity network. Regional differences in functional connectivity suggest that different schizotypy dimensions are manifested at the network level by different forms of disconnections. Taken together, these findings lend further support to the disconnection hypothesis and suggest that altered connectivity networks may serve as a potential biomarker for schizophrenia risk.

Manipulating visual perspective for obsessional imagery and its impact on obsessive-compulsive symptoms in an analogue sample.

Visual perspective may have an important role in the phenomenology of intrusive images relevant to psychological disorders such as obsessive-compulsive disorder (OCD). The aim of the current study was to examine the subjective and behavioural effects of manipulating visual perspective, to either field or observer, on intrusive images related to doubting and contamination concerns. One hundred and twelve undergraduate participants with high levels OCD symptoms were asked to identify and imagine an intrusive image related to either doubting or contamination concerns. We then randomly assigned them to re-visualise their image from either a field (first-person) or observer (third-person) visual perspective. Participants shifted towards using an observer perspective demonstrated a greater decrease on ratings of subjective measures of image-related distress, prospective likelihood of the image occurring, and urges to suppress the image, relative to those shifted to a field perspective. In addition, those in the observer perspective evidenced a greater decrease on behavioural indices relevant to OCD, such as reduced frequency of the intrusive image and decreased efforts to neutralise the image. We discuss implications for imagery in OCD.

Depressive Symptoms and the Link with Academic Performance among Rural Taiwanese Children.

Previous studies reflect a high prevalence of depressive symptoms among Taiwanese adolescents (ages 13-18), but there is an absence of literature related to the risk of depression of children in Taiwan (ages 6-12), particularly among potentially vulnerable subgroups. To provide insight into the distribution of depressive symptoms among children in rural Taiwan and measure the correlation between academic performance, we conducted a survey of 1655 randomly selected fourth and fifth-grade students at 92 sample schools in four relatively low-income counties or municipalities. Using the Center for Epidemiological Studies-Depression Scale (CES-D) we assessed the prevalence of depressive symptoms in this sample, in addition to collecting other data, such as performance on a standardized math test as well as information on a number of individual and household characteristics. We demonstrate that the share of children with clinically significant symptoms is high: 38% of the students were at risk of general depression (depression score ≥ 16) and 8% of the students were at risk of major depression (depression score > 28). The results of the multivariate regression and heterogeneous analysis suggest that poor academic performance is closely associated with a high prevalence of depressive symptoms. Among low-performing students, certain groups were disproportionately affected, including girls and students whose parents have migrated away for work. Results also suggest that, overall, students who had a parent who was an immigrant from another country were at greater risk of depression. These findings highlight the need for greater resource allocation toward mental health services for elementary school students in rural Taiwan, particularly for at-risk groups.

Effect of interictal epileptiform discharges on EEG-based functional connectivity networks.

OBJECTIVE: Functional connectivity networks (FCNs) based on interictal electroencephalography (EEG) can identify pathological brain networks associated with epilepsy. FCNs are altered by interictal epileptiform discharges (IEDs), but it is unknown whether this is due to the morphology of the IED or the underlying pathological activity. Therefore, we characterized the impact of IEDs on the FCN through simulations and EEG analysis. METHODS: We introduced simulated IEDs to sleep EEG recordings of eight healthy controls and analyzed the effect of IED amplitude and rate on the FCN. We then generated FCNs based on epochs with and without IEDs and compared them to the analogous FCNs from eight subjects with infantile spasms (IS), based on 1340 visually marked IEDs. Differences in network structure and strength were assessed. RESULTS: IEDs in IS subjects caused increased connectivity strength but no change in network structure. In controls, simulated IEDs with physiological amplitudes and rates did not alter network strength or structure. CONCLUSIONS: Increases in connectivity strength in IS subjects are not artifacts caused by the interictal spike waveform and may be related to the underlying pathophysiology of IS. SIGNIFICANCE: Dynamic changes in EEG-based FCNs during IEDs may be valuable for identification of pathological networks associated with epilepsy.

Semi-supervised protein subcellular localization.

BACKGROUND: Protein subcellular localization is concerned with predicting the location of a protein within a cell using computational method. The location information can indicate key functionalities of proteins. Accurate predictions of subcellular localizations of protein can aid the prediction of protein function and genome annotation, as well as the identification of drug targets. Computational methods based on machine learning, such as support vector machine approaches, have already been widely used in the prediction of protein subcellular localization. However, a major drawback of these machine learning-based approaches is that a large amount of data should be labeled in order to let the prediction system learn a classifier of good generalization ability. However, in real world cases, it is laborious, expensive and time-consuming to experimentally determine the subcellular localization of a protein and prepare instances of labeled data. RESULTS: In this paper, we present an approach based on a new learning framework, semi-supervised learning, which can use much fewer labeled instances to construct a high quality prediction model. We construct an initial classifier using a small set of labeled examples first, and then use unlabeled instances to refine the classifier for future predictions. CONCLUSION: Experimental results show that our methods can effectively reduce the workload for labeling data using the unlabeled data. Our method is shown to enhance the state-of-the-art prediction results of SVM classifiers by more than 10%.