Coherence Correlation Research Articles

Disruption of hippocampal rhythms via optogenetic stimulation during the critical period for memory development impairs spatial cognition

Objective . Investigate whether the disruption of normal hippocampal rhythms via optogenetic stimulation of the medial septum (MS) during late postnatal development has enduring effects on the allocentric memory of rat pups. Methods . Hippocampal theta oscillations were non-endogenously regulated in rat pups using blue light (465-nm) pan-neuronal optogenetic activation of the MS from postnatal day (P)21–25 using random sinewave frequencies ranging from 0.5 to 110 Hz. Non-stimulated and yellow light (590-nm) stimulated rats were used as controls, and local field potentials from the hippocampus and MS were recorded unilaterally during stimulations. In early adulthood, spatial cognition was assessed using the active avoidance task. Rats were perfused and tissue containing the MS and hippocampus was harvested and sectioned to assess cell density. Power spectrum density of both the MS and hippocampus; coherence, and voltage correlation analysis between both structures were used to compare baseline to the disruptive stimulation. Results . During late postnatal development, non-selective optogenetic activation of the MS tightly regulated hippocampal oscillations. Disruptive stimulation increased power but reduced coherence and voltage correlations; and resulted in impaired spatial learning compared with controls. Impairment of spatial cognition was not attributed to MS or hippocampal cell loss. Conclusions. These results demonstrate that hippocampal oscillations can be precisely regulated with non-selective optogenetic stimulation of the MS in weanling rats. A disruptive hippocampal stimulation protocol during the critical period of memory development results in long-standing spatial cognitive deficits that is not attributed to cell loss. Significance . Disruptive activity during postnatal development, such as occurs during early life seizures, has complex effects on developing neural circuits relevant to cognition. By mimicking disruption of MS-hippocampal entrainment in an isolated fashion, it may be possible to pinpoint mechanisms through which ELS may affect cognition.

Alumina abrasive grains mediated grinding induced glass surface and fractal analysis of SiO2 glass

Fabrication of optical components such as passive optics (mirrors, prisms, lenses) or active optics (polarizer’s, laser gain media, adaptive optics) starts from a bulk material and reaches to the required size and shape. To achieve the required specification of the component, a series of process has to be followed from grinding to polishing stage. Initially, in grinding stage, material removal is faster, with more surface damage and less geometric control. During polishing stage, material removal is slower, with no surface damage but with greater geometric control. Hence, to achieve good surface quality, the component or work piece has to be controlled from grinding stage. We present an advanced surface and fractal analysis study on fused silica samples processed with different grit sizes such as 3, 5, 12, and 25 μm aluminium oxide (Al2O3) abrasives. The maximum sub surface damage (SSD) with respect to size of the abrasive grain ranged between 30 μm to 5 μm in empirical module. In theoretical module it varied from 45 μm to 5.4 μm for fused silica glass, mediated with loose abrasive aluminium oxide powder of 25 μm and 3 μm respectively. An experimental investigation is reported on the increasing effect of Al2O3 grit sizes on the surface topography such as average roughness (Ra), which varied from 57 nm to 847 nm for 3 μm and 25 μm Al2O3 abrasives respectively. Three-dimensional image analysis was captured through Phase Shift Interferometry (PSI) and Coherence Correlation Interferometer (CCI) technique. Field Emission Scanning Electron Microscope (FESEM) technique was utilized to characterize the Al2O3 powders and the processed fused silica samples. Later, the images have been analysed using two-dimensional multifractal detrended fluctuation analysis (2D-MFDFA) to understand and confirm the multiple fractal nature on fused silica samples caused by varying grit size of Al2O3 abrasives.

Rain Streak Removal via Dual Graph Convolutional Network

Deep convolutional neural networks (CNNs) have become dominant in the single image de-raining area. However, most deep CNNs-based de-raining methods are designed by stacking vanilla convolutional layers, which can only be used to model local relations. Therefore, long-range contextual information is rarely considered for this specific task. To address the above problem, we propose a simple yet effective dual graph convolutional network (GCN) for single image rain removal. Specifically, we design two graphs to perform global relational modeling and reasoning. The first GCN is used to explore global spatial relations among pixels in feature maps, while the second GCN models the global relations across the channels. Compared to standard convolutional operations, the proposed two graphs enable the network to extract representations from new dimensions. To achieve the image rain removal, we further embed these two graphs and multi-scale dilated convolution into a symmetrically skip-connected network architecture. Therefore, our dual graph convolutional network is able to well handle complex and spatially long rain streaks by exploring multiple representations, e.g., multi-scale local feature, global spatial coherence and cross-channel correlation. Meanwhile, our model is easy to implement, end-to-end trainable and computationally efficient. Extensive experiments on synthetic and real data demonstrate that our method achieves significant improvements over the recent state-of-the-art methods.

Light echos and coherent autocorrelations in a black hole spacetime

The Event Horizon Telescope recently produced the first images of a black hole. These images were synthesized by measuring the coherent correlation function of the complex electric field measured at telescopes located across the Earth. This correlation function corresponds to the Fourier transform of the image under the assumption that the source emits spatially incoherent radiation. However, black holes differ from standard astrophysical objects: in the absence of absorption and scattering, an observer sees a series of increasingly demagnified echos of each emitting location. These echos correspond to rays that orbit the black hole one or more times before reaching the observer. This multi-path propagation introduces spatial and temporal correlations into the electric field that encode properties of the black hole, irrespective of intrinsic variability. We explore the coherent temporal autocorrelation function measured at a single telescope. Specifically, we study the simplified toy problem of scalar field correlation functions ⟨Ψ(t)Ψ(0)⟩ sourced by fluctuating matter located near a Schwarzschild black hole. We find that the correlation function is peaked at times equal to integer multiples of the photon orbit period; the corresponding power spectral density vanishes like λ/r g where r g = GM/c 2 is the gravitational radius of the black hole and λ is the wavelength of radiation observed. For supermassive black holes observed at millimeter wavelengths, the power in echos is suppressed relative to direct emission by ∼10−13 λ mm/M 6, where λ mm = λ/(1 mm) and M 6 = M/(106 M ⊙). Consequently, detecting multi-path propagation near a black hole using the coherent electric field autocorrelation is infeasible with current technology.

Secondary Iron Mineral Detection via Hyperspectral Unmixing Analysis with Sentinel-2 Imagery

The exposition of minerals to oxygen as well as non-treated tailings in mining activities alter the balance of the ecosystem, specifically leading to the generation of acidic solutions in the presence of sulfidic minerals. Several secondary iron minerals are precipitated in these settings that can be detected via remote sensing applications. The purpose of this research is to investigate the capacity of hyperspectral analysis to determine the abundance of Acid Mine Drainage (AMD)-indicator secondary iron minerals in mine sites with the guidance of ground truth information. To this end, we focus on an abandoned coal mine site in Turkey to detect secondary iron minerals associated with AMD via multispectral Sentinel-2 imagery, in accordance with the laboratory analysis of field-collected samples through X-Ray Diffraction (XRD), Inductive Coupled Plasma (ICP), and ASD spectral analysis. In relation with the conducted laboratory XRD and ICP results, the proposed methodology first reveals the iron-induced absorption feature located between 700 and 900 nm on field-collected ASD spectra and reference USGS spectra through a baseline method, namely parabola fitting method. The subsequent remote sensing analysis then applies hyperspectral unmixing to Sentinel-2 imagery and identifies the spectral endmember indicating iron-absorption behavior by computing its spectral angle distance to reference spectra. The experiments reveal that while the iron absorption characteristics are not apparent in pixel spectra, the utilized unmixing methodology enables capturing of those features at sub-pixel level on the resulting endmembers. Second, the comparison between the calculated abundances with unmixing and iron levels obtained with ground based ICP analysis indicate coherent correlation values. Finally, among the utilized unmixing methods, the performance of SISAL is found better than MVSA with the resulting correlation values of 0.76 and 0.63, respectively, while also returning closer endmembers to the reference iron spectra. The performed research demonstrates the potential of hyperspectral applications on Sentinel-2 data to uncover the sub-pixel iron-induced spectral features in the visible region, proving compatible results between the spectral and laboratory analysis.

Relationship between first-order coherence and the maximum violation of the three-setting linear steering inequality for a two-qubit system

Coherence and nonlocal correlations are two fundamental features of quantum mechanics and the relations between them have received much attention recently. While the relations of coherence to Bell nonlocality and to entanglement have been established, an interesting question left open is how to quantitatively characterize the relation of coherence to steering, which is one kind of nonlocal correlation lying between Bell nonlocality and entanglement. In this paper, we address this question by considering the first-order coherence and the three-setting linear steering inequality for a two-qubit system. Interestingly, we find that the first-order coherence for both subsystems has a complementary relation with the maximum violation of the three-setting linear steering inequality. A weighted sum of their squares keeps invariant under unitary operations, although each of them changes in general. This suggests that the maximum violation of the three-setting linear steering inequality can be taken as a measure of hidden coherence unveiled by unitary operations.

Quantitative Optical Coherence Tomography Angiography (OCTA) Parameters in a Black Diabetic Population and Correlations with Systemic Diseases.

This is a cross-sectional, prospective study of a population of black diabetic participants without diabetic retinopathy aimed to investigate optical coherence tomography angiography (OCTA) characteristics and correlations with systemic diseases in this population. These parameters could serve as novel biomarkers for microvascular complications; especially in black populations which are more vulnerable to diabetic microvascular complications. Linear mixed models were used to obtain OCTA mean values ± standard deviation and analyze statistical correlations to systemic diseases. Variables showing significance on univariate mixed model analysis were further analyzed with multivariate mixed models. 92 eyes of 52 black adult subjects were included. After multivariate analysis; signal strength intensity (SSI) and heart disease had statistical correlations to superficial capillary plexus vessel density in our population. SSI and smoking status had statistical correlations to deep capillary plexus vessel density in a univariate analysis that persisted in part of the imaging subset in a multivariate analysis. Hyperlipidemia; hypertension; smoking status and pack-years; diabetes duration; creatinine; glomerular filtration rate; total cholesterol; hemoglobin A1C; and albumin-to-creatinine ratio were not significantly associated with any OCTA measurement in multivariate analysis. Our findings suggest that OCTA measures may serve as valuable biomarkers to track systemic vascular functioning in diabetes mellitus in black patients.

Successive Graph Convolutional Network for Image De-raining

Deep convolutional neural networks (CNNs) have shown their advantages in the single image de-raining task. However, most existing CNNs-based methods utilize only local spatial information without considering long-range contextual information. In this paper, we propose a graph convolutional networks (GCNs)-based model to solve the above problem. We specifically design two graphs to extract representations from new dimensions. The first graph models the global spatial relationship between pixels in the feature, while the second graph models the interrelationship across the channels. By integrating conventional CNNs and our GCNs into a single framework, the proposed method is able to explore comprehensive feature representations from three aspects, i.e., local spatial patterns, global spatial coherence and channel correlation. To better exploit the explored rich feature representations, we further introduce a simple yet effective recurrent operations to perform the de-raining process in a successive manner. Benefiting from the rich information exploration and exploitation, our method achieves state-of-the-art results on both synthetic and real-world data sets.

Quantum resource covariance

The developments of special relativity and quantum mechanics marked the beginning of the modern physics age. The former has taught us that while space and time are frame dependent notions, there is a quantity -- the space-time interval -- whose value all inertial observers agree upon. This reveals, so to speak, a genuine "fact" of the universe, a relativistic invariant. On the other hand, since the dawn of quantum mechanics, there is no consensus on what the theory is all about. The situation is admittedly subtler: quantum theory is grounded on a complex vector space and the very notions of observer and reference frame are controversial. Here we construct a theoretical framework within which a given combination of quantum resources is shown to be a Galilean invariant. To this end, we postulate a principle of relational symmetry between "the observer" and "the observed" and employ the notion of quantum reference frame. Unitary transformations then follow that allow us to perceive the physical resources seen from the viewpoint of any quantum system. Interestingly, we find that one needs more than quantum coherence and quantum correlations to prove quantum resources covariance. Finally, we show that the notion of physical reality implied by quantum mechanics is not absolute.

Damage detection using SAR coherence statistical analysis, application to Beirut, Lebanon

Early well-coordinated response during unexpected catastrophes can define the near future of the stricken regions. Beirut city, Lebanon, was one of the unfortunate regions to endure the horrific ordeal of an unexpected explosion that caused thousands of human casualties, billions of dollars’ worth of property damage, and destroyed its main maritime entry point. In this paper, we identify damaged regions and classify their severity using a simple and robust SAR correlation technique. We employ phase coherence and amplitude correlation of a SAR stack to estimate pixels’ damage probability using hypothesis testing. We use a spatial phase filter applied in the frequency domain to improve the estimated coherence by removing the spatial decorrelation component of the total estimated coherence. Using this filter improved the coherence of nearly 44.2% of pixels identified with coherence less than 0.25 in our study area. The estimated damaged regions are presented and compared against a damage map issued by Advanced Rapid Imaging and Analysis (ARIA) which shows an average agreement of 68.3%. Also, a fine agreement was observed when compared to optical satellite images.

Pairwise quantum correlations in four-level quantum dot systems

In this paper we assume quantum dots can be assimilated to Fermi Hubbard sites when the Coulomb interaction between electrons is higher compared to their tunneling. The study of pairwise entanglement in a small size array of quantum dots allows to model each pair as a quadrit-quadrit system (4 × 4 mixed state) instead of the more common and simplistic approach of describing it in quantum information as a qubit-qubit system. We study the effect of Coulomb interaction and temperature on pairwise entanglement as well as on quantum coherence and total correlations. The crucial results of this study are that entanglement resists better the increase in temperature when the Coulomb interaction is stronger. Moreover, we successfully explain the behavior of these correlations in terms of the energy spectrum, namely the ground state degeneracy and the state energy difference.

Relationship of the Extent of Antarctic and Arctic Ice with Temperature Changes, 1979\u20132020

Quantitative estimates of the relationship between interannual variations in the extent of Antarctic and Arctic sea ice and changes in the surface air temperature in the Northern and Southern hemispheres are obtained using satellite, ground-based, and reanalysis data for the past four decades (1980–2019). It is shown that the previously noted general increase in the extent of Antarctic sea ice observed until recent years from satellite data (available only since the late 1970s) over the background global warming and a rapid decrease in the extent of Arctic sea ice is associated with a regional decrease in the surface temperature at Antarctic latitudes from the end of the 1970s. This is a result of regional manifestation of natural climate variations with periods of up to several decades against the background of global secular warming with a relatively weak temperature trend over the ocean in the Southern Hemisphere. Since 2016, a sharp decrease in the extent of Antarctic sea ice in the Southern Ocean has been observed. The results of the correlation and cross-wavelet analysis indicate significant coherence and negative correlation with the surface temperature of the extent of sea ice in recent decades, not only in the Arctic, but also in the Antarctic.

Mining Subsidence Monitoring With Modified Time-Series SAR Interferometry Method Based on the Multi-Level Processing Strategy

The supply of coal is related to the stability of social development in China. Coal mining faces are generally located under farmland and wasteland to avoid destroying the livelihood and property of residents, which seriously affects the selection of permanent scatterer (PS) with time-series interferometric synthetic aperture radar (TS-InSAR) method. The advanced TS-InSAR (ATS-InSAR) method combining PS and distributed scatterer (DS) monitoring modules is a useful tool to increase measurement points. However, the number of DS and the accuracy of measurement are different with different thresholds of temporal coherence. On the basis of ATS-InSAR, we propose a modified method applying the multi-level processing strategy to obtain more reliable deformation information in this paper. 16 sentinel-1A images are used to monitor mining subsidence in Peixian, China. The results of three different methods are cross-verified. Meanwhile, reliable DS pixels are identified by using the thresholding of both temporal coherence and Pearson correlation coefficient through the hierarchical processing. The results show that the deformation of the modified method reveals a large subsidence with the maximum rate of -563 mm/yr. The number of measurement points selected by the modified method is about 6.6 times that of the TS-InSAR method, and 1.3 times that of the ATS-InSAR method. The modified strategy can extract a great number of reliable pixels and reduce error propagation to ensure measurement accuracy. This research offers information to relevant departments for risk management purpose.

The impact of major macroeconomic variables on foreign direct investment in Nigeria: evidence from a wavelet coherence technique

Utilizing Nigeria as a case study, the investigators explored the linkages between FDI inflows and some selected macroeconomic indicators (exports, gross capital formation, trade openness, inflation, and economic growth) utilizing yearly data spanning between 1981 and 2018. The study used ARDL technique to capture the linkages between FDI inflows and its determining indicators. Furthermore, the wavelet coherence techniques was used. The main novelty of wavelet coherence is that it can obtain information on dynamic correlation and/or causality between economic variables at different frequencies and different time periods. Additionally, the FMOLS and the DOLS are employed as a robustness check to the ARDL long-run estimation. The findings from the ARDL long-run estimate reveal that exports and trade openness exert positive impact on FDI inflows. The findings from the FMOLS and DOLS backed ARDL results. Furthermore, the results of the wavelet coherence-based causality and wavelet correlation techniques further provide supportive evidence to the ARDL technique. To the authors' knowledge, no previous studies have used the wavelet coherence and wavelet correlation techniques to explore these dynamics. Based on these findings, policy directions were initiated.

Change of Geometric Accuracy of Structural Steels after Carburizing in Gas

Nowadays, carburizing at low pressure or vacuum carburizing is often used, which is a modification of carburizing in gas, while carburization is performed at pressures well below the level of atmospheric pressure (101 kPa). The advantage of this process is that no internal oxidation occurs; carburizing has a higher rate and uniformity of carburization, good reproducibility of the process and lower deformation. Carburizing is mainly used for parts subjected to abrasion (eg gears). However, even with this process, the geometric accuracy and surface texture of the parts change. The changes of geometric accuracy of ground samples from carbon steel C45, low-alloy Mn-Cr carburizing steel 16MnCr5 and medium-alloyed Ni-Cr carburizing steel 14NiCr14, which were vacuum-carburized in a CO/CH4 atmosphere, were evaluated in this work. Geometric accuracy was evaluated by measurement using a 3D coordinate measuring machine, surface texture using a coherent correlation interferometer. After the carburizing process, changes in the dimensions of the samples were found in all steels. For C45 steel, the sample dimensions were reduced by 0.028 mm ± 0.004 mm, the surface roughness expressed by the parameter Ra decreased by 0.28 µm, which is 20%. In case of carburizing steels there was an increase in the dimensions of the samples, especially in the case of 14NiCr14 steel and a smaller difference in surface roughness. The same parameters of the carburizing process lead to different behaviour of carburized parts in different steels, in terms of changes in dimensions and surface texture.

Exploiting ontology information in fuzzy SVM social media profile classification

Nowadays, social media like Twitter, Facebook, blogs, and LinkedIn are considered the most used sources of information, while at the same time being the most visited and most used sources of disinformation. These can have a negative impact on several areas and on our minds, hence on our behavior. It is obvious that this disinformation is closely related to the profiles of the authors of this information. The purpose of author profiling is to analyze the texts published by the authors in order to determine their profile category. A wide range of methods for selecting statistical characteristics and machine learning has been studied in recent years in order to automatically classify this information. However, these main methods of selecting statistical characteristics and machine learning used for this purpose have not proven their great performance in the processing of data from social networks. The main contribution of this article consists in integrating the semantic component, which has not been taken into account in the main approaches studied in the literature, as additional functionalities enabling the identification of relevant information. Our hypothesis is that the concepts and the relationships between these concepts tend to have a more coherent correlation with relevant and irrelevant information, and can therefore increase the discriminating power of classifiers. The semantic approach proposed revolves around an ontology combined with the linear SVM classifier and then with the fuzzy SVM classifier. The experimental study carried out, on the different collections of Twitter profiles. On our approach and on the main approaches to the literature that we have studied, as well as the analysis of the results obtained. The results we have clearly show the limits of these studied approaches and confirm the performance of our approach, as well as the efficiency of the integration of the semantic component in the categorization of Twitter profiles.

Unreasonable human disturbance shifts the positive effect of climate change on tree-ring growth of Malus sieversii in the origin area of world cultivated apples

The wild apple (M. sieversii) in Xinjiang is the ancestor of the world cultivated apple, and its death and decline have aggravated the degradation of wild fruit forests in the Tianshan Mountains. However, the reasons of degradation of the wild fruit forest is not clear. Therefore, we hypothesized that human disturbance shifts the positive effects of climate change on tree-ring growth of wild apple. We tested this hypothesis by answering two scientific questions. (1) Does climate change have a significant impact on tree-ring growth of wild apple trees? (2) Has unreasonable human disturbance altered the effect of climate change on tree-ring growth? We chose wild apples at different degenerate levels to contrastively analyze the effects of climate change on the tree-ring growth of M. sieversii using analysis of wavelet coherence and Pearson correlation, and quantifying the effect of climate changes on tree-ring of M. sieversii with periodic superimposed trend models. We confirmed the effects of human disturbance on tree-ring of wild apple using mixed linear models. The result showed climate change contributed to a 50.9% increase in the tree-ring chronology of wild apple in non-degraded areas after 2008. However, unreasonable human disturbance resulted in a significant decrease (P < 0.01) to tree-ring chronology of wild apple in degenerate areas. We determined that unreasonable human disturbance altered the positive effects of climate change on tree-ring growth of wild apple. Therefore, we concluded that the main reason for degradation of wild fruit forests was unreasonable human disturbance. Moreover, the effect of pest control measures on promoting tree-ring growth was not significant in the recent short term, suggesting that pest control and management of wild fruit forest should be further strengthened. Our research comprehensively considered the effects of both climate change, anthropogenic activities, pests and diseases on wild apple in a wild fruit forest, and confirmed the main reason for degeneration of wild fruit forests. These results not only determined the influence of two key factors on the growth of M. sieversii, but also provided a scientific basis for the ecological conservation of wild apple trees in the future.

Non-Markovianity leading to coherence revivals in an open quantum system

Coherence and quantum correlations have been identified as fundamental resources for quantum information tasks. As recently shown, these resources can be interconverted. In multipartite systems, entanglement represents a prominent case among quantum correlations, one which can be activated from coherence. All this makes coherence a key resource for securing the operational advantage of quantum technologies. When dealing with open systems, decoherence hinders full exploitation of quantum resources. Here, we present a protocol that allows reaching the maximal achievable amount of coherence in an open quantum system. By implementing our protocol, or suitable variants of it, coherence losses might be fully compensated, thereby leading to coherence revivals. We provide an experimental proof of principle of our protocol through its implementation with an all-optical setup.

Disruption of hippocampal rhythms via optogenetic stimulation during the critical period for memory development impairs spatial cognition

BackgroundHippocampal oscillations play a critical role in the ontogeny of allocentric memory in rodents. During the critical period for memory development, hippocampal theta is the driving force behind the temporal coordination of neuronal ensembles underpinning spatial memory. While known that hippocampal oscillations are necessary for normal spatial cognition, whether disrupted hippocampal oscillatory activity during the critical period impairs long-term spatial memory is unknown. Here we investigated whether disruption of normal hippocampal rhythms during the critical period have enduring effects on allocentric memory in rodents. Objective/hypothesisWe hypothesized that disruption of hippocampal oscillations via artificial regulation of the medial septum during the critical period for memory development results in long-standing deficits in spatial cognition. MethodsAfter demonstrating that pan-neuronal medial septum (MS) optogenetic stimulation (465 nm activated) regulated hippocampal oscillations in weanling rats we used a random pattern of stimulation frequencies to disrupt hippocampal theta rhythms for either 1Hr or 5hr a day between postnatal (P) days 21–25. Non-stimulated and yellow light-stimulated (590 nm) rats served as controls. At P50-60 all rats were tested for spatial cognition in the active avoidance task. Rats were then sacrificed, and the MS and hippocampus assessed for cell loss. Power spectrum density of the MS and hippocampus, coherences and voltage correlations between MS and hippocampus were evaluated at baseline for a range of stimulation frequencies from 0.5 to 110 Hz and during disruptive hippocampal stimulation. Unpaired t-tests and ANOVA were used to compare oscillatory parameters, behavior and cell density in all animals. ResultsNon-selective optogenetic stimulation of the MS in P21 rats resulted in precise regulation of hippocampal oscillations with 1:1 entrainment between stimulation frequency (0.5–110 Hz) and hippocampal local field potentials. Across bandwidths MS stimulation increased power, coherence and voltage correlation at all frequencies whereas the disruptive stimulation increased power and reduced coherence and voltage correlations with most statistical measures highly significant (p < 0.001, following correction for false detection). Rats receiving disruptive hippocampal stimulation during the critical period for memory development for either 1Hr or 5hr had marked impairment in spatial learning as measured in active avoidance test compared to non-stimulated or yellow light-control rats (p < 0.001). No cell loss was measured between the blue-stimulated and non-stimulated or yellow light-stimulated controls in either the MS or hippocampus. ConclusionThe results demonstrated that robust regulation of hippocampal oscillations can be achieved with non-selective optogenetic stimulation of the MS in rat pups. A disruptive hippocampal stimulation protocol, which markedly increases power and reduces coherence and voltage correlations between the MS and hippocampus during the critical period of memory development, results in long-standing spatial cognitive deficits. This spatial cognitive impairment is not a result of optogenetic stimulation-induced cell loss.

Four-dimensional structure and sub-seasonal regulation of the Indian summer monsoon multi-decadal mode

A better understanding of the drivers and teleconnection mechanisms responsible for the multi decadal mode (MDM) of variability of the Indian summer monsoon rainfall (ISMR) with major socio-economic impacts in the region through clustering of large-scale floods or droughts is key to improving the poor simulation of ISMR MDM by most climate models. Here, using the longest instrumental record of ISMR available (1813–2006) and longest atmospheric and oceanic re-analyses, the global four dimensional (space–time) structures of atmospheric and oceanic fields of the multi-decadal mode of ISMR and sub-seasonal evolution of the teleconnection mechanism are brought out, essential for understanding underlying drivers but lacking so far. The relationships between the spatial structure of winds, Sea Surface Temperature (SST) and thermocline depth with the ISMR MDM indicate that the tropical ocean over the Indo-Pacific domain is passive responding primarily to the surface winds associated with the mode. A close association between the Atlantic Meridional Overturning Circulation (AMOC), north Atlantic (NA) SST, NA sea surface salinity (SSS) and the ISMR MDM indicate a slow oceanic pathway linking NA SST and the ISMR. In addition to strong correlation (~ 0.9) between global spatial patterns of JJAS SST associated with the MDMs of ISMR, NA SST and AMOC, strong temporal coherence (correlations ~ 0.9) between them is suggestive of regulation of the ISMR MDM (T ~ 65-years) by the NA SST associated with the Atlantic Multidecadal Oscillation (AMO) through a ‘fast’ atmospheric bridge. On a seasonal time scale, the atmospheric bridge manifests in the form of a stationary Rossby wave train generated by an anticyclonic (cyclonic) barotropic vorticity located above positive (negative) SST anomaly over NA in two phases of the AMO. That the AMO SST is the driver of the ISMR MDM is further supported when we unravel the sub-seasonal face of the teleconnection between the two. We show that phase locking of active (break) spells with annual cycle during positive (negative) phases of the ISMR MDM are forced by a similar phase locking of barotropic anticyclonic (cyclonic) vorticity over the NA SST with the annual cycle through the generation of a quasi-stationary Rossby wave train with an anticyclonic (cyclonic) vorticity at upper level over the Indian region with the NA columnar vorticity leading Indian monsoon rainfall by about a week. Our findings provide a basis for enhanced predictability of tropical climate through slow modulation by extra-tropical SST.