In this short article, we overview a concept of electronic toroidal multipoles, and their ordering with associated physical properties in non-magnetic and magnetic materials. The toroidal multipoles are introduced as microscopic electronic variables in view of symmetry and connection to Dirac theory. They are classified according to crystallographic and magnetic point groups, which allows us to discuss various possible cross correlations in a transparent and unified manner. The representative examples of toroidal orders and related phenomena, and the mutual relationship between these orders are given, with focusing on monopoles and dipoles. The concept of toroidal multipoles would promote future studies toward observations and identifications of unknown electronic phases and their related physical phenomena. .

This study investigated the sensitivity and specificity of identifying heart failure with reduced ejection fraction (HFrEF) from measurements of the intensity and timing of arterial pulse waves. Previously validated methods combining ultrafast B-mode ultrasound, plane-wave transmission, singular value decomposition (SVD), and speckle tracking were used to characterize the compression and decompression (\"S\" and \"D\") waves occurring in early and late systole, respectively, in the carotid arteries of outpatients with left ventricular ejection fraction (LVEF) < 40%, determined by echocardiography, and signs and symptoms of heart failure, or with LVEF ≥ 50% and no signs or symptoms of heart failure. On average, the HFrEF group had significantly reduced S-wave intensity and energy, a greater interval between the R wave of the ECG and the S wave, a reduced interval between the S and D waves, and an increase in the S-wave shift (SWS), a novel metric that characterizes the shift in timing of the S wave away from the R wave of the ECG and toward the D wave (all P < 0.01). Receiver operating characteristics (ROCs) were used to quantify for the first time how well wave metrics classified individual participants. S-wave intensity and energy gave areas under the ROC of 0.76-0.83, the ECG-S-wave interval gave 0.85-0.88, and the S-wave shift gave 0.88-0.92. Hence the methods, which are simple to use and do not require complex interpretation, provide sensitive and specific identification of HFrEF. If similar results were obtained in primary care, they could form the basis of techniques for heart failure screening.NEW & NOTEWORTHY We show that heart failure with reduced ejection fraction can be detected with excellent sensitivity and specificity in individual patients by using B-mode ultrasound to detect altered pulse wave intensity and timing in the carotid artery.

We introduce McDAPS, an interactive software for assessing autonomic imbalance from non-invasive multi-channel physiological recordings. McDAPS provides a graphical user interface for data visualization, beat-to-beat processing and interactive analyses. The software extracts beat-to-beat RR interval systolic blood pressure, diastolic blood pressure, the pulse amplitude of photoplethysmogram and the pulse-to-pulse interval. The analysis modules include stationary and time-varying power spectral analyses, moving-correlation analysis and univariate analyses. Analyses can also be performed in batch mode if multiple datasets have to be processed in the same way. The program exports results in standard CSV format. McDAPS runs in MATLAB, and is supported on MS Windows and MAC OS systems. The MATLAB source code is available at https://github.com/thuptimd/McDAPS.git.

Intrinsically disordered proteins (IDPs) are significantly enriched in proline residues, which can populate specific local secondary structural elements called PPII helices, characterized by small packing densities. Proline is often thought to promote disorder, but it can participate in specific π·CH interactions with aromatic side chains resulting in reduced conformational flexibilities of the polypeptide. Differential local motional dynamics are relevant for the stabilization of preformed structural elements and can serve as nucleation sites for the establishment of long-range interactions. NMR experiments to probe the dynamics of proline ring systems would thus be highly desirable. Here we present a pulse scheme based on 13C detection to quantify dipole-dipole cross-correlated relaxation (CCR) rates at methylene CH2 groups in proline residues. Applying 13C-CON detection strategy provides exquisite spectral resolution allowing applications also to high molecular weight IDPs even in conditions approaching the physiological ones. The pulse scheme is illustrated with an application to the 220 amino acids long protein Osteopontin, an extracellular cytokine involved in inflammation and cancer progression, and a construct in which three proline-aromatic sequence patches have been mutated.

Stock markets are generally perceived as a barometer of the economy and respond to international monetary policies even before economic activities. Many central banks have turned to unconventional policy measures in response to various financial crises such as the global financial crisis of 2007-2009 or the recent crisis caused by COVID-19. To examine the cross-correlation of overall international monetary policies with stock markets, we employ the daily shadow short rate (SSR), which has the advantage of allowing comparison across unconventional and conventional regimes. The analysis is made through a multifractal context using multifractal detrended cross correlation analysis (MF-DXA), considering daily data from 1st January 2000 to 31st March 2022 and country specific SSR and the stock markets of eight developed economies. The main empirical findings are the following: (i) all the country specific pairs of SSR with stock markets have significant multifractal characteristics (ii) the pairs of NZ-SSR/NZX50, US-SSR/DJIA, and CN-SSR/S&P TSX have the highest multifractal patterns while EU-SSR/Euro-area Index has the lowest multifractal patterns (iii) Australian and New Zealand stock markets exhibit anti-persistent cross-correlation with SSR while the remainder have persistent cross-correlation in their multifractality. Lastly, the findings of this study have several important implications for central banks and stock market participants.

Annexin A1 (A1) has been shown to form a tetrameric complex (A1t) with S100A11 which is implicated in calcium homeostasis and EGFR pathways. In this work, a full-length model of the A1t was generated for the first time. Multiple molecular dynamics simulations were performed on the complete A1t model for several hundred nanoseconds each to assess the structure and dynamics of A1t. These simulations yielded three structures for the A1 N-terminus (ND) which were identified via principal component analysis. The orientations and interactions of the first 11 A1-ND residues for all three structures were conserved, and their binding modes were strikingly similar to those of the Annexin A2 N-terminus in the Annexin A2-p11 tetramer. In this study, we provided detailed atomistic information for the A1t. Strong interactions were identified within the A1t between the A1-ND and both S100A11 monomers. Residues M3, V4, S5, E6, L8, K9, W12, E15, and E18 of A1 were the strongest interactions between A1 and the S100A11 dimer. The different conformations of the A1t were attributed to the interaction between W12 of the A1-ND with M63 of S100A11 which caused a kink in the A1-ND. Cross-correlation analysis revealed strong correlated motion throughout the A1t. Strong positive correlation was observed between the ND and S100A11 in all simulations regardless of conformation. This work suggests that the stable binding of the first 11 residues of A1-ND to S100A11 is potentially a theme for Annexin-S100 complexes and that the flexibility of the A1-ND allows for multiple conformations of the A1t.Communicated by Ramaswamy H. Sarma.

UNASSIGNED: Functional Near-Infrared Spectroscopy is an optical brain monitoring technique which uses NIRS to perform functional neuroimaging. It uses near-infrared light for measuring brain activity and to estimate the cortical hemodynamic activity in the brain due to motor activity. Functional NIRS measures the changes in oxygen levels in oxygenated and deoxygenated hemoglobin by optical absorption. One of the main challenges in the analysis of fNIRS signals is the signal degradation due to the interference from noise and artifacts from multiple sources.
UNASSIGNED: In this context, this research aims to analyze the connectivity between different regions of the brain using graph theory and hence the geometrical association of brain networks in terms of functional parameters. In this study, the impact of two noise removal processes (CBSI and TDDR), along with two types of correlation fNIRS such as Pearson\'s Correlation (PC), and Cross Correlation (CC) and various whole-brain network architectures on the reproducibility of graph measurements for individual participants has been carefully examined for different densities ranging from 5% to 50%.The graph measures\' repeatability at the individual level was studied using the test-retest variability (TRT).
UNASSIGNED: The test-retest variability for global measurements in binary networks was substantially large at low densities, regardless of the noise removal method or the kind of correlation. Very low test -reset values are observed for weighted networks and great reproducibility for measures of the entire graph. When comparing the test-retest values for various methods, the kind of correlation, the absolute value of the correlation, and the weight calculation method on the raw correlation value all had significant major effects.
UNASSIGNED: Based on a weighted network with the absolute cross correlation functioning as the weight, this study revealed that normalized global graph measurements were reliable. The node definition techniques that were utilized to remove noise were not essential for the normalized graph measures to be reproducible.

Fluorescence correlation spectroscopy (FCS) is a widely used method for measuring molecular diffusion and chemical kinetics. However, when a mixture of fluorescent species is taken into account, the conventional FCS method has limitations in extracting autocorrelations for different species and cross correlations between different species. Recently developed fluorescence lifetime correlation spectroscopy (FLCS) based on time-tagged time-resolved (TTTR) photon recording, which can record the global and micro arrival time for each individual photon, has been used to discriminate different species according to fluorescence lifetime. Here, based on two-dimensional lifetime decay maps constructed from TTTR photon stream, we have developed a quantitative lifetime-deconvolution FCS model (LDFCS) to extract precise chemical rates for chemical conversions in multi-species systems. The key point of LDFCS model is separation of different species according to the global distribution of fluorescence lifetime and then deconvolution of autocorrelations and cross-correlations from the two-dimensional lifetime decay maps constructed by the micro arrival times of photon pairs at each delay time.

Recent developments in high-density neurophysiological tools now make it possible to record from hundreds of single neurons within local, highly interconnected neural networks. Among the many advantages of such recordings is that they dramatically increase the quantity of identifiable, functional interactions between neurons thereby providing an unprecedented view of local circuits. Using high-density, Neuropixels recordings from single neocortical columns of primary visual cortex in nonhuman primates, we identified 1000s of functionally interacting neuronal pairs using established crosscorrelation approaches. Our results reveal clear and systematic variations in the synchrony and strength of functional interactions within single cortical columns. Despite neurons residing within the same column, both measures of interactions depended heavily on the vertical distance separating neuronal pairs, as well as on the similarity of stimulus tuning. In addition, we leveraged the statistical power afforded by the large numbers of functionally interacting pairs to categorize interactions between neurons based on their crosscorrelation functions. These analyses identified distinct, putative classes of functional interactions within the full population. These classes of functional interactions were corroborated by their unique distributions across defined laminar compartments and were consistent with known properties of V1 cortical circuitry, such as the lead-lag relationship between simple and complex cells. Our results provide a clear proof-of-principle for the use of high-density neurophysiological recordings to assess circuit-level interactions within local neuronal networks.

Adult primary visual cortex features well demonstrated orientation selectivities. However, the imposition of a non-preferred stimulus for many minutes (adaptation) or the application of an antidepressant drug, such as ketamine, shifts the peak of the tuning curve, assigning a novel selectivity to a neuron. The effect of ketamine on V1 neural circuitry is not yet ascertained. The present investigation explores (in control, post-adaptation, and following local ketamine application) the modification of orientation selectivities and its outcome on functional relationships between neurons in mouse and cat. Two main results are revealed. Electrophysiological neuronal responses of monocular stimulation show that in cells exhibiting large orientation shifts after adaptation, ketamine facilitates the cell\'s recovery. Whereas in units displaying small shifts following adaptation, the drug increases the magnitude of orientation shifts. In addition, pair-wise cross correlogram analyses show modifications of functional relationships between neurons revealing updated micro-circuits as a consequence of ketamine application. We report in cat but not in mouse, that ketamine significantly increases the connectivity rate, their strengths, and an enhancement of neuronal synchrony.