Light-gated specific oxidase-like activity of a self-assembled Pt(II) nanozyme for environmental remediation.

Artificial enzyme equivalents, also known as nanozymes, are a practical tool for environmental remediation when compared to their natural counterparts due to their high operational stability, efficiency, and cost-effectiveness. Specific oxidase mimicking nanozymes are well suited to degrade toxic chemicals from industrial waste such as phenols and azo dyes. Therefore, photocatalytic nanozymes using visible/sunlight would provide a viable strategy for sustainable environmental remediation. Herein, we introduce an aggregation-induced emissive Pt(II) complex, which self-assembles in water providing NanoPtA nanotapes. These structures exhibit a specific oxidase-like nanozyme activity driven by light. The NanoPtA structure assists in the photogeneration of singlet oxygen in water via a triplet excited 3MMLCT state, leading to a specific oxidase-like activity instead of a peroxidase-like activity. The self-assembled nanozyme showed great stability under harsh environmental conditions and exhibited photo-induced specific oxidase-mimetic activity, which was considerably more efficient than the natural enzyme or other specific nanozymes. We demonstrated efficient NanoPtA-induced photocatalytic degradation of various phenolic compounds and azo dyes within 5-10 minutes of light irradiation. Notably, the system operates under sunlight and exhibits reusability over twenty cycles of catalytic reactions. Another fascinating aspect of NanoPtA is the unaltered catalytic performance for more than 75 days, providing a robust enzyme-equivalent for practical sustainable environmental remediation.

An aggregation induced emission active bis-heteroleptic ruthenium(II) complex for luminescence light-up detection of pyrophosphate ions.

A red emissive ruthenium(II) complex 1[PF6]2 of an amino ethanol substituted 1,10-phenanthroline-based ligand (L1) has been developed and characterized by spectroscopic analysis and single-crystal X-ray diffraction. Complex 1 shows an aggregation-induced emission (AIE) enhancement and forms nano-aggregates in the poor solvent water and highly dense polyethylene glycol (PEG) media. The possible reason behind the AIE properties may be the rigidity gained through weak supramolecular interactions between neighbouring phenanthroline ligands and PF6- counterions. The AIE properties were supported by UV-vis and photoluminescence (PL) spectroscopy and dynamic light scattering (DLS) studies to substantiate the formation of nano-aggregates and to understand the morphology of the aggregated particles, scanning electron microscopy (SEM) and transmission electron microscopy (TEM) studies were performed. Compound 1[PF6]2 was highly selective towards pyrophosphate ions (PPi) over other phosphates such as ATP, ADP, AMP and H2PO4- ions and other competitive anions in the PL spectroscopic channel in acetonitrile. The PL titrations of 1[PF6]2 with PPi in CH3CN furnished the association constant Ka = 1.08 × 104 M-1 and the detection limit was calculated as low as 1.54 µM. The PPi detection has been established through the unique H-bonding interaction, supported by 1H NMR titration. Finally, the cytotoxicity study and bioimaging were carried out for biological application. The complex shows very low cytotoxicity and good biocompatibility and is suitable for intracellular PPi imaging.

Temporally Controlled Multienzyme Catalysis Using a Dissipative Supramolecular Nanozyme.

The development of superior functional enzyme mimics (nanozymes) is essential for practical applications, including point-of-care diagnostics, biotechnological applications, biofuels, and environmental remediation. Nanozymes with the ability to control their catalytic activity in response to external fuels offer functionally valuable platforms mimicking nonequilibrium systems in nature. Herein, we fabricated a supramolecular coordination bonding-based dynamic vesicle that exhibits multienzymatic activity. The supramolecular nanozyme shows effective laccase-like catalytic activity with a KM value better than the native enzyme and higher stability in harsh conditions. Besides, the nanostructure demonstrates an efficient peroxidase-like activity with NADH peroxidase-like properties. Generation of luminescence from luminol and oxidation of dopamine are efficiently catalyzed by the nanozyme with high sensitivity, which is useful for point-of-care detections. Notably, the active nanozyme exhibits dynamic laccase-mimetic activity in response to pH variation, which has never been explored before. While a neutral/high pH leads to the self-assembly, a low pH disintegrates the assembled nanostructures and consequently turns off the nanozyme activity. Altogether, the self-assembled Cu2+-based vesicular nanostructure presents a pH-fueled dissipative system demonstrating effective temporally controlled multienzymatic activity.

Cyclometalated iridium(III) complex of a 1,2,3-triazole-based ligand for highly selective sensing of pyrophosphate ion.

A new cyclometalated Ir(III) complex of a methylene-bridged benzimidazole-substituted 1,2,3-triazole methanol ligand has been synthesized for the photoluminescent detection of pyrophosphate (H2P2O72-) anions. The solution structure of 1[PF6] was fully characterized by 1D (1H, 13C) and 2D (1H-1H COSY, 1H-13C HSQC, and 1H-13C HMBC) NMR spectroscopy, and ESI-HRMS. The 1[PF6] acted as a highly selective luminescent sensor for H2P2O72- in CH3CN over other competitive ions, including H2PO4-, ATP, ADP and AMP. The PL titration of 1[PF6] with H2P2O72- in CH3CN furnished the association constant Ka = 8.6 × 107 M-1 and a low detection limit of ∼127 nM. The structure of the analyte interacting ligand renders the Ir(III) complex-based probe highly selective for H2P2O72- ions. The PL enhancement with H2P2O72- is due to the hydrogen bonding interaction of H2P2O72- with the triazole C-H, imidazole N-H, methylene hydrogen and hydroxyl groups of the ligand that has been supported by 1H NMR titration. Further, the PL enhancement of 1·H2P2O72- adducts was supported by triplet-state TDDFT calculations. In 1·H2P2O72-, the 3MLCT-3MC energy gap is increased, and the 1·H2P2O72- emits efficiently from the 3MLCT and 3ILCT excited states. Finally, a cytotoxicity study and live-cell imaging were performed. The probe showed low cytotoxicity against HeLa cells and was suitable for intracellular pyrophosphate imaging.

Effect of Modulating fMRI Time-Series on Fluid Ability and Fluid Intelligence for Healthy Humans.

This paper investigates the effect of filtering (or modulating) the functional magnetic resonance imaging (fMRI) time-series on intelligence metrics predicted using dynamic functional connectivity (dFC). Thirteen brain regions that have highest correlation with intelligence are selected and their corresponding time-series are filtered. Using filtered time-series, the modified intelligence metrics are predicted. This experiment investigates whether modulating the time-series of one or two regions of the brain will increase or decrease the fluid ability and fluid intelligence among healthy humans. Two sets of experiments are performed. In the first case, each of the thirteen regions is separately filtered using four different digital filters with passbands: i) 0 - 0.25π, ii) 0.25π - 0.5π, iii) 0.5π - 0.75π, and iv) 0.75π - π, respectively. In the second case, two of the thirteen regions are filtered simultaneously using a low-pass filter of passband 0 - 0.25π. In both cases, the predicted intelligence declined for 45-65% of the subjects after filtering in comparison with the ground truths. In the first case, the low-pass filtering process had the highest predicted intelligence among the four filters. In the second case, it was noticed that the filtering of two regions simultaneously resulted in a higher prediction of intelligence for over 80% of the subjects compared to low-pass filtering of a single region.

Ruthenium(II) Polypyridine Complex-Based Aggregation-Induced Emission Luminogen for Rapid and Selective Detection of Phosgene in Solution and in the Gas Phase.

A bis-heteroleptic ruthenium(II) complex, Ru-1, of 4,7-bis(2-aminoethylamino)-1,10-phenanthroline for selective "turn-on" detection of highly toxic chemical warfare agent phosgene is presented. Probe Ru-1 exhibits aggregation-induced emission (AIE), and the restricted intramolecular motion is responsible for the AIE activity. In a CHCl3/CH3CN [95:5 (v/v)] solvent mixture, a unique self-assembled vesicular structure was formed after aggregation, which was supported by transmission electron microscopy, field emission scanning electron microscopy, and atmoic force microscopy studies. Probe Ru-1 showed a rapid and highly selective luminescence turn-on response for phosgene over other competitive chemical warfare agents with a low detection limit (13.9 nM) in CH3CN. The 2-aminoethylamino groups in Ru-1 act as a reacting site for nucleophilic addition to the carbonyl center of phosgene and undergo intramolecular cyclization. The final product of the phosgene-mediated reaction, Ru-1-Phos, contains 2-imidazolidinone groups, which has been confirmed by electrospray ionization mass spectometry and 1H nuclear magnetic resonance (NMR) spectroscopy. 1H NMR titration of Ru-1 with phosgene supported the reaction mechanism and also pointed to the simultaneous reaction of phosgene at two 2-aminoethylamino sites. For the first time, the crystal structure of the phosgene reaction product, Ru-1-Phos, containing the cyclized 2-imidazolidinone group was confirmed by single-crystal X-ray diffraction, which indubitably validates the reaction mechanism. Triplet state time-dependent density functional theory calculations showed that the weak luminescence of Ru-1 was mostly due to the population of the non-emissive 3MC state. The cyclization reaction with phosgene and the corresponding 2-imidazolidinone product formation populated the emissive 3MLCT state in Ru-1-Phos and is the key reason for the enhanced luminescence. Furthermore, a low-cost portable test paper strip has been fabricated with Ru-1 for the real-time selective monitoring of phosgene gas at the nanomolar level.

Ruthenium(II) Complex-Based G-quadruplex DNA Selective Luminescent 'Light-up' Probe for RNase H Activity Detection.

A bis-heteroleptic ruthenium(II) complex, 1[PF6 ]2 of benzothiazole amide substituted 2,2'-bipyridine ligand (bmbbipy) has been synthesized for the selective detection of G-quadruplex (GQ) DNA and luminescence-assay-based RNase H activity monitoring. Compound 1[PF6 ]2 exhibited aggregation-caused quenching (ACQ) in water. Aggregate formation was supported by DLS, UV-vis, and 1 H NMR spectroscopy results, and the morphology of aggregated particles was witnessed by SEM and TEM. 1[PF6 ]2 acted as an efficient GQ DNA-selective luminescent light-up probe over single-stranded and double-stranded DNA. The competency of 1[PF6 ]2 for selective GQ structure detection was established by PL and CD spectroscopy. For 1[PF6 ]2 , the PL light-up is exclusively due to the rigidification of the benzothiazole amide side arm in the presence of GQ-DNA. The interaction between the probe and GQ-DNA was analyzed by molecular docking analysis. The GQ structure detection capability of 1[PF6 ]2 was further applied in the luminescent 'off-on' RNase H activity detection. The assay utilized an RNA:DNA hybrid, obtained from 22AG2-RNA and 22AG2-DNA sequences. RNase H solely hydrolyzed the RNA of the RNA:DNA duplex and released G-rich 22AG2-DNA, which was detected via the PL enhancement of 1[PF6 ]2 . The selectivity of RNase H activity detection over various other restriction enzymes was also demonstrated.

Classification of Adolescent Major Depressive Disorder Via Static and Dynamic Connectivity.

This paper introduces an approach for classifying adolescents suffering from MDD using resting-state fMRI. Accurate diagnosis of MDD involves interviews with adolescent patients and their parents, symptom rating scales based on Diagnostic and Statistical Manual of Mental Disorders (DSM), behavioral observation as well as the experience of a clinician. Discovering predictive biomarkers for diagnosing MDD patients using functional magnetic resonance imaging (fMRI) scans can assist the clinicians in their diagnostic assessments. This paper investigates various static and dynamic connectivity measures extracted from resting-state fMRI for assisting with MDD diagnosis. First, absolute Pearson correlation matrices from 85 brain regions are computed and they are used to calculate static features for predicting MDD. A predictive sub-network extracted using sub-graph entropy classifies adolescent MDD vs. typical healthy controls with high accuracy, sensitivity and specificity. Next, approaches utilizing dynamic connectivity are employed to extract tensor based, independent component based and principal component based subject specific attributes. Finally, features from static and dynamic approaches are combined to create a feature vector for classification. A leave-one-out cross-validation method is used for the final predictor performance. Out of 49 adolescents with MDD and 33 matched healthy controls, a support vector machine (SVM) classifier using a radial basis function (RBF) kernel using differential sub-graph entropy combined with dynamic connectivity features classifies MDD vs. healthy controls with an accuracy of 0.82 for leave-one-out cross-validation. This classifier has specificity and sensitivity of 0.79 and 0.84, respectively.

Predicting Biological Gender and Intelligence From fMRI via Dynamic Functional Connectivity.

OBJECTIVE: This paper explores the predictive capability of dynamic functional connectivity extracted from functional magnetic resonance imaging (fMRI) of the human brain, in contrast to static connectivity used in past research. METHODS: Several state-of-the-art features extracted from static functional connectivity of the brain are employed to predict biological gender and intelligence using publicly available Human Connectome Project (HCP) database. Next, a novel tensor parallel factor (PARAFAC) decomposition model is proposed to decompose sequence of dynamic connectivity matrices into common connectivity components that are orthonormal to each other, common time-courses, and corresponding distinct subject-wise weights. The subject-wise loading of the components are employed to predict biological gender and intelligence using a random forest classifier (respectively, regressor) using 5-fold cross-validation. RESULTS: The results demonstrate that dynamic functional connectivity can indeed classify biological gender with a high accuracy (0.94, where male identification accuracy was 0.87 and female identification accuracy was 0.97). It can also predict intelligence with less normalized mean square error (0.139 for fluid intelligence and 0.031 for fluid ability metrics) compared with other functional connectivity measures (the nearest mean square error were 0.147 and 0.037 for fluid intelligence and fluid ability metrics, respectively, using static connectivity approaches). CONCLUSION: Our work is an important milestone for the understanding of non-stationary behavior of hemodynamic blood-oxygen level dependent (BOLD) signal in brain and how they are associated with biological gender and intelligence. SIGNIFICANCE: The paper demonstrates that dynamic behavior of brain can contribute substantially towards forming a fingerprint of biological gender and intelligence.

Unveiling the Metabolic Mystery of Fragility Hip Fracture in Indian Patients: A Histomorphometric and Biochemical Correlation.

BACKGROUND: High prevalence of hypovitaminosis D is being reported in Indian patients with fragility hip fracture. The gold standard to diagnose osteoporosis and osteomalacia is bone histomorphometry. There is no study evaluating histopathological histomorphometry in Indian fragility hip fracture patients. The purpose of the study was to evaluate fragility hip fracture patients for histopathological osteomalacia and osteoporosis by histomorphometry and to correlate histopathological findings with biochemical hypovitaminosis D. MATERIALS AND METHODS: A total of 55 patients of fragility hip fractures recruited for prospective cross-sectional study. During definitive fracture fixation of these fragility hip fractures, bone biopsy taken from neck region of femur by a novel approach for histomorphometry. Histomorphometric analysis was based on three indices, namely osteoid seam width, osteoblast surface, and osteoid surface. We also analysed blood bone biochemistry and correlated with bone histomorphometry. RESULTS: In fragility hip fracture patients, the prevalence of histomorphometric osteoporosis and osteomalacia were very low (only 9.4% had osteoporosis and none had osteomalacia) however in blood bone biochemistry, we found high prevalence (85.5%) of hypovitaminosis D. We also noted significant changes when correlated bone histomorphometry with different blood bone biochemistry. CONCLUSION: Indian patients with fragility hip fracture were found to have high prevalence of biochemical hypovitaminosis D but unlike western literature, there was low prevalence of histomorphometric osteoporosis with no evidence of histomorphometric osteomalacia. Correct knowledge about metabolic status of fragility hip fracture is required to improve outcome, decrease complications and to optimise cost of the treatment.

Sub-graph entropy based network approaches for classifying adolescent obsessive-compulsive disorder from resting-state functional MRI.

This paper presents a novel approach for classifying obsessive-compulsive disorder (OCD) in adolescents from resting-state fMRI data. Currently, the state-of-the-art for diagnosing OCD in youth involves interviews with adolescent patients and their parents by an experienced clinician, symptom rating scales based on Diagnostic and Statistical Manual of Mental Disorders (DSM), and behavioral observation. Discovering signal processing and network-based biomarkers from functional magnetic resonance imaging (fMRI) scans of patients has the potential to assist clinicians in their diagnostic assessments of adolescents suffering from OCD. This paper investigates the clinical diagnostic utility of a set of univariate, bivariate and multivariate features extracted from resting-state fMRI using an information-theoretic approach in 15 adolescents with OCD and 13 matched healthy controls. Results indicate that an information-theoretic approach based on sub-graph entropy is capable of classifying OCD vs. healthy subjects with high accuracy. Mean time-series were extracted from 85 brain regions and were used to calculate Shannon wavelet entropy, Pearson correlation matrix, network features and sub-graph entropy. In addition, two special cases of sub-graph entropy, namely node and edge entropy, were investigated to identify important brain regions and edges from OCD patients. A leave-one-out cross-validation method was used for the final predictor performance. The proposed methodology using differential sub-graph (edge) entropy achieved an accuracy of 0.89 with specificity 1 and sensitivity 0.80 using leave-one-out cross-validation with in-fold feature ranking and selection. The high classification accuracy indicates the predictive power of the sub-network as well as edge entropy metric.

Highly Selective Detection of Hypochlorous Acid by a Bis-heteroleptic Ru(II) Complex of Pyridyl-1,2,3-triazole Ligand via C(sp2)-H Hydroxylation.

A Ru(II) complex (Ru-1) of a substituted pyridyl-1,2,3-triazole ligand (BtPT) for highly selective "light-up" detection of hypochlorous acid is presented. An unusual anti-Markovnikov HOCl addition to the C═C bond of 1,2,3-triazole and a highly specific C(sp2)-H hydroxylation over epoxidation made Ru-1 a highly selective luminescent HOCl probe. The abnormal regio- and stereoselective HOCl addition and subsequent hydroxylation mechanism in detail is supported by the combination of ESI-MS, 1H/13C NMR spectroscopy, and 1H NMR titration. The hydroxylation at the C5 center in 1,2,3-triazole increases the electron density and makes BtPT a better σ-donor as well as π-donor, which in turn increases the 3MC-3MLCT energy gap and inhibits the nonradiative decay from the excited state of Ru-1 and is the key reason for luminescence light-up. Most importantly, the exogenous and endogenous HOCl imaging in the living HEK293T cells is also demonstrated. The probe showed low cytotoxicity and efficiently permeated the cell membrane. The cell-imaging experiments revealed rapid staining of the extranuclear region of HEK293T cells which clearly indicates the presence of cytoplasmic HOCl. The endogenous HOCl generation and imaging, stimulated by lipopolysaccharides (LPS) and paraquat in the HEK293T cells, is also demonstrated.

Ranking Regions, Edges and Classifying Tasks in Functional Brain Graphs by Sub-Graph Entropy.

This paper considers analysis of human brain networks or graphs constructed from time-series collected from functional magnetic resonance imaging (fMRI). In the network of time-series, the nodes describe the regions and the edge weights correspond to the absolute values of correlation coefficients of the time-series of the two nodes associated with the edges. The paper introduces a novel information-theoretic metric, referred as sub-graph entropy, to measure uncertainty associated with a sub-graph. Nodes and edges constitute two special cases of sub-graph structures. Node and edge entropies are used in this paper to rank regions and edges in a functional brain network. The paper analyzes task-fMRI data collected from 475 subjects in the Human Connectome Project (HCP) study for gambling and emotion tasks. The proposed approach is used to rank regions and edges associated with these tasks. The differential node (edge) entropy metric is defined as the difference of the node (edge) entropy corresponding to two different networks belonging to two different classes. Differential entropy of nodes and edges are used to rank top regions and edges associated with the two classes of data. Using top node and edge entropy features separately, two-class classifiers are designed using support vector machine (SVM) with radial basis function (RBF) kernel and leave-one-out method to classify time-series for emotion task vs. no-task, gambling task vs. no-task and emotion task vs. gambling task. Using node entropies, the SVM classifier achieves classification accuracies of 0.96, 0.97 and 0.98, respectively. Using edge entropies, the classifier achieves classification accuracies of 0.91, 0.96 and 0.94, respectively.

Organoiridium(III) Complexes as Luminescence Color Switching Probes for Selective Detection of Nerve Agent Simulant in Solution and Vapor Phase.

In this work, cationic organoiridium(III) complex based photoluminescent (PL) probes have been developed to selectively detect the chemical warfare nerve agent mimic, diethyl chlorophosphate(DCP) at nanomolar range by distinct bright green to orange-red luminescence color switching (on-off-on) in solution as well as in the vapor phase. Interference of other chemical warfare agents (CWAs) and their mimics was not observed either by PL spectroscopy or with the naked-eye in solution and gas phase. The detection was attained via a simultaneous nucleophilic attack of two -OH groups of the 4,7-dihydroxy-1,10-phenanthroline ligand with DCP by forming bulkier phosphotriester. The detailed reaction mechanism was established through extensive 1H NMR titration, 31P NMR, and ESI-MS analysis. Finally, a test paper strip and solid poly(ethylene oxide) (PEO) film with iridium(III) complex 1[PF6] were fabricated for the vapor-phase detection of DCP. The solution and vapor-phase detection properties of these luminescent Ir(III) complexes can offer a worthy approach into the design of new metal complex based PL switching probes for chemical warfare agents.

Classification of Major Depressive Disorder from Resting-State fMRI.

Major Depressive Disorder (MDD) is a very serious mental illness that can affect the daily lives of patients. Accurate diagnosis of this disorder is necessary for planning individualized treatment. However, diagnosing MDD requires the clinicians to personally interview the subjects and rate the symptoms based on Diagnostic and Statistical Manual of Mental Disorders (DSM), which can be very time consuming. Discovering quantifiable signals and biomarkers associated with MDD using functional magnetic resonance imaging (fMRI) scans of patients have the potential to assist the clinicians in their assessment. This paper explores the use of resting-state functional connectivity and network features to classify MDD vs. healthy subjects. For each subject, mean time-series are extracted from 85 brain regions and they are decomposed to 4-frequency bands. Mean time-series for each of the frequency bands are utilized to compute the Pearson correlation and network characteristics. Features are selected separately from correlation and network characteristics using Minimum Redundancy Maximum Relevance (mRMR) to create the final classifier. The proposed scheme achieves 79% accuracy (65 out of 82 subjects classified correctly) with 86% sensitivity (42 out of 49 MDD subjects identified correctly) and 70% specificity (23 out of 33 controls identified correctly) using leave-one-out classification with in-fold feature selection. Pearson correlation had the highest discrimination in band 0.015-0.03 Hz and network based features had the highest discrimination in band 0.03-0.06 Hz for distinguishing MDD vs. healthy subjects.

Predicting Male vs. Female from Task-fMRI Brain Connectivity.

A number of behavioral and cognitive functions of brain differ between male and female. Occurrences of psychiatric disorders, e.g., attention deficit hyperactivity disorder, autism, depression and schizophrenia also vary from male to female. Understanding the unique cognitive expressions in gender-specific brain functions may lead to insights into the risks and associated responses for a certain external simulation or medications. Previously resting-state functional magnetic resonance imaging (r-fMRI) has been used extensively to understand gender differences using functional network connectivity analysis. However, how the brain functional network changes during a cognitive task for different genders is relatively unknown. This paper makes use of a large data set to test whether task-fMRI functional connectivity can be utilized to predict male vs. female. In addition, it also identifies functional connectivity features that are most predictive of gender. The cognitive task-fMRI data consisting 475 healthy controls is taken from the Human Connectome Project (HCP) database. Pearson correlation coefficients are extracted using mean time-series from anatomical brain regions. Partial least squares (PLS) regression with feature selection on the correlation coefficients achieves a classification accuracy of 0.88 for classifying male vs. female using emotion task data. In addition it is found that inter hemispheric connectivity is most important for predicting gender from task-fMRI.

A cationic organoiridium(iii) complex-based AIEgen for selective light-up detection of rRNA and nucleolar staining.

A green emissive cationic organoiridium(iii) complex, 2[PF6], with a benzimidazole-substituted 1,2,3-triazole-pyridine (BiPT) ligand has been synthesized for target-specific cellular imaging and selective detection of ribosomal RNA (rRNA) over other competitive biomolecules in aqueous buffer solution at physiological pH. Complex 2 shows aggregation-induced emission enhancement (AIEE) properties and forms nano-aggregates in the presence of poor solvents. DFT and TD-DFT-based quantum mechanical calculations were performed to substantiate some photophysical features and to establish the intermolecular π-π interactions which detain the vibrational as well as rotational motions to form the aggregates, resulting in enhanced photoluminescence (PL). To corroborate the formation of nano-aggregates and to understand the morphology of the aggregated particles, dynamic light scattering (DLS), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and atomic force microscopy (AFM) measurements were performed. 2[PF6] showed low cytotoxicity and good biocompatibility and was successfully employed in organelle-specific intracellular imaging. The in vivo and in vitro photoluminescence investigations affirmed that the probe stains cell nucleoli and selectively binds rRNA. It is assumed that the supramolecular π-π interactions between the benzimidazole of the BiPT ligand and the secondary structures of rRNA may facilitate aggregation and enable PL enhancement.

A general prediction model for the detection of ADHD and Autism using structural and functional MRI.

This work presents a novel method for learning a model that can diagnose Attention Deficit Hyperactivity Disorder (ADHD), as well as Autism, using structural texture and functional connectivity features obtained from 3-dimensional structural magnetic resonance imaging (MRI) and 4-dimensional resting-state functional magnetic resonance imaging (fMRI) scans of subjects. We explore a series of three learners: (1) The LeFMS learner first extracts features from the structural MRI images using the texture-based filters produced by a sparse autoencoder. These filters are then convolved with the original MRI image using an unsupervised convolutional network. The resulting features are used as input to a linear support vector machine (SVM) classifier. (2) The LeFMF learner produces a diagnostic model by first computing spatial non-stationary independent components of the fMRI scans, which it uses to decompose each subject's fMRI scan into the time courses of these common spatial components. These features can then be used with a learner by themselves or in combination with other features to produce the model. Regardless of which approach is used, the final set of features are input to a linear support vector machine (SVM) classifier. (3) Finally, the overall LeFMSF learner uses the combined features obtained from the two feature extraction processes in (1) and (2) above as input to an SVM classifier, achieving an accuracy of 0.673 on the ADHD-200 holdout data and 0.643 on the ABIDE holdout data. Both of these results, obtained with the same LeFMSF framework, are the best known, over all hold-out accuracies on these datasets when only using imaging data-exceeding previously-published results by 0.012 for ADHD and 0.042 for Autism. Our results show that combining multi-modal features can yield good classification accuracy for diagnosis of ADHD and Autism, which is an important step towards computer-aided diagnosis of these psychiatric diseases and perhaps others as well.

Aggregation-Induced Emission-Active Ruthenium(II) Complex of 4,7-Dichloro Phenanthroline for Selective Luminescent Detection and Ribosomal RNA Imaging.

The development of red emissive aggregation-induced emission (AIE) active probes for organelle-specific imaging is of great importance. Construction of metal complex-based AIE-active materials with metal-to-ligand charge transfer (MLCT), ligand-to-metal charge transfer (LMCT) emission together with the ligand-centered and intraligand (LC/ILCT) emission is a challenging task. We developed a red emissive ruthenium(II) complex, 1[PF6]2, and its perchlorate analogues of the 4,7-dichloro phenanthroline ligand. 1[PF6]2 has been characterized by spectroscopic and single-crystal X-ray diffraction. Complex 1 showed AIE enhancement in water, highly dense polyethylene glycol media, and also in the solid state. The possible reason behind the AIE property may be the weak supramolecular π···π, C-H···π, and C-Cl···H interactions between neighboring phen ligands as well as C-Cl···O halogen bonding (XB). The crystal structures of the two perchlorate analogues revealed C-Cl···O distances shorter than the sum of the van der Waals radii, which confirmed the XB interaction. The AIE property was supported by scanning electron microscopy, transmission electron microscopy, dynamic light scattering, and atomic force microscopy studies. Most importantly, the probe was found to be low cytotoxicity and to efficiently permeate the cell membrane. The cell-imaging experiments revealed rapid staining of the nucleolus in HeLa cells via the interaction with nucleolar ribosomal ribonucleic acid (rRNA). It is expected that the supramolecular interactions as well as C-Cl···O XB interaction with rRNA is the origin of aggregation and possible photoluminescence enhancement. To the best of our knowledge, this is the first report of red emissive ruthenium(II) complex-based probes with AIE characteristics for selective rRNA detection and nucleolar imaging.

Highly Sensitive Bifunctional Probe for Colorimetric Cyanide and Fluorometric H2S Detection and Bioimaging: Spontaneous Resolution, Aggregation, and Multicolor Fluorescence of Bisulfide Adduct.

A 4-methylbenzothiazole linked maleimide-based single molecular bifunctional probe 1 has been synthesized for the colorimetric and fluorometric detection of highly competitive H2S and cyanide ion in aqueous DMSO media. The probe 1 selectively detected CN- under the UV-vis spectroscopy through the rapid appearance of deep pink color. The bright pink color developed due to ICT in the moderately stable cyano substituted enolate intermediate. The absorbance titration of 1 with CN- revealed a new band at 540 nm and the nonlinear curve fitting analysis showed good fit with 1:1 model. In fluorescence channel, 1 was found to be highly selective to H2S in 50% aqueous buffer (pH 7). It exhibited ∼16-fold fluorescence intensity enhancement at 435 nm after reaction with 1 equiv of H2S due to the inhibition of PET. The 1-SH adduct showed TICT phenomenon and behaved like molecular rotor. It further displayed aggregation behavior at higher concentration and excitation wavelength dependent multicolor emission properties. Most interestingly, the spontaneous resolution of chiral S-isomer of the 1-SH adduct occurred during crystallization. The cell imaging study revealed the staining of the cell and multicolor emission in the presence of H2S.