Anchoring Single-Atomic Metal Sites in Metalloporphyrin-Based Covalent Organic Frameworks for Enhanced Photocatalytic Hydrogen Evolution.

A photoactive covalent organic framework (COF) was built from metalloporphyrin and bipyridine monomers and single-atomic Pt sites were subsequently installed. Integrating photosensitizing metalloporphyrin and substrate-activating Pt(bpy) moieties in a single solid facilitates multielectron transfer and accelerates photocatalytic hydrogen evolution with a maximum production rate of 80.4 mmol h-1 gPt-1 and turnover frequency (TOF) of 15.7 h-1 observed. This work demonstrates that incorporation of single-atomic metal sites with photoactive COFs greatly enhances photocatalytic activity and provides an effective strategy for the design and construction of novel photocatalysts.

Efficient screening of pharmacological broad-spectrum anti-cancer peptides utilizing advanced bidirectional Encoder representation from Transformers strategy.

In the vanguard of oncological advancement, this investigation delineates the integration of deep learning paradigms to refine the screening process for Anticancer Peptides (ACPs), epitomizing a new frontier in broad-spectrum oncolytic therapeutics renowned for their targeted antitumor efficacy and specificity. Conventional methodologies for ACP identification are marred by prohibitive time and financial exigencies, representing a formidable impediment to the evolution of precision oncology. In response, our research heralds the development of a groundbreaking screening apparatus that marries Natural Language Processing (NLP) with the Pseudo Amino Acid Composition (PseAAC) technique, thereby inaugurating a comprehensive ACP compendium for the extraction of quintessential primary and secondary structural attributes. This innovative methodological approach is augmented by an optimized BERT model, meticulously calibrated for ACP detection, which conspicuously surpasses existing BERT variants and traditional machine learning algorithms in both accuracy and selectivity. Subjected to rigorous validation via five-fold cross-validation and external assessment, our model exhibited exemplary performance, boasting an average Area Under the Curve (AUC) of 0.9726 and an F1 score of 0.9385, with external validation further affirming its prowess (AUC of 0.9848 and F1 of 0.9371). These findings vividly underscore the method's unparalleled efficacy and prospective utility in the precise identification and prognostication of ACPs, significantly ameliorating the financial and temporal burdens traditionally associated with ACP research and development. Ergo, this pioneering screening paradigm promises to catalyze the discovery and clinical application of ACPs, constituting a seminal stride towards the realization of more efficacious and economically viable precision oncology interventions.

Boosting CO2 Electroreduction over a Covalent Organic Framework in the Presence of Oxygen.

Herein, we propose an oxygen-containing species coordination strategy to boost CO2 electroreduction in the presence of O2. A two-dimensional (2D) conjugated metal-covalent organic framework (MCOF), denoted as NiPc-Salen(Co)2-COF that is composed of the Ni-phthalocyanine (NiPc) unit with well-defined Ni-N4-O sites and the salen(Co)2 moiety with binuclear Co-N2O2 sites, is developed and synthesized for enhancing the CO2RR under aerobic condition. In the presence of O2, one of the Co sites in the NiPc-Salen(Co)2-COF that coordinated with the intermediate of *OOH from ORR could decrease the energy barrier of the activation of CO2 molecules and stabilize the key intermediate *COOH of the CO2RR over the adjacent Co center. Besides, the oxygen species axially coordinated Ni-N4-O sites can favor in reducing the energy barrier of the intermediate *COOH formation for the CO2RR. Thus, NiPc-Salen(Co)2-COF exhibits high oxygen-tolerant CO2RR performance and achieves outstanding CO Faradaic efficiency (FECO) of 97.2 % at -1.0 V vs. the reversible hydrogen electrode (RHE) and a high CO partial current density of 40.3 mA cm-2 at -1.1 V in the presence of 0.5 % O2, which is superior to that in pure CO2 feed gas (FECO=94.8 %, jCO=19.9 mA cm-2). Notably, the NiPc-Salen(Co)2-COF achieves an industrial-level current density of 128.3 mA cm-2 in the flow-cell reactor with 0.5 % O2 at -0.8 V, which is higher than that in pure CO2 atmosphere (jCO=104.8 mA cm-2). It is worth noting that an excellent FECO of 86.8 % is still achieved in the presence of 5 % O2 at -1.0 V. This work provides an effective strategy to enable the CO2RR under O2 atmosphere by utilizing the *OOH intermediates of ORR to boost CO2 electroreduction.

Oxygen-tolerant CO2 electroreduction over covalent organic frameworks via photoswitching control oxygen passivation strategy.

The direct use of flue gas for the electrochemical CO2 reduction reaction is desirable but severely limited by the thermodynamically favorable oxygen reduction reaction. Herein, a photonicswitching unit 1,2-Bis(5'-formyl-2'-methylthien-3'-yl)cyclopentene (DAE) is integrated into a cobalt porphyrin-based covalent organic framework for highly efficient CO2 electrocatalysis under aerobic environment. The DAE moiety in the material can reversibly modulate the O2 activation capacity and electronic conductivity by the framework ring-closing/opening reactions under UV/Vis irradiation. The DAE-based covalent organic framework with ring-closing type shows a high CO Faradaic efficiency of 90.5% with CO partial current density of -20.1 mA cm-2 at -1.0 V vs. reversible hydrogen electrode by co-feeding CO2 and 5% O2. This work presents an oxygen passivation strategy to realize efficient CO2 electroreduction performance by co-feeding of CO2 and O2, which would inspire to design electrocatalysts for the practical CO2 source such as flue gas from power plants or air.

Boosting CO2 Photoreduction via Regulating Charge Transfer Ability in a One-Dimensional Covalent Organic Framework.

Two-dimensional (2D) imine-based covalent organic frameworks (COFs) hold potential for photocatalytic CO2 reduction. However, high energy barrier of imine linkage impede the in-plane photoelectron transfer process, resulting in inadequate efficiency of CO2 photoreduction. Herein, we present a dimensionality induced local electronic modulation strategy through the construction of one-dimensional (1D) pyrene-based covalent organic frameworks (PyTTA-COF). The dual-chain-like edge architectures of 1D PyTTA-COF enable the stabilization of aromatic backbones, thus reducing energy loss during exciton dissociation and thermal relaxation, which provides energetic photoelectron to traverse the energy barrier of imine linkages. As a result, the 1D PyTTA-COF exhibits significantly enhanced CO2 photoreduction activity under visible-light irradiation when coordinated with metal cobalt ion, yielding a remarkable CO evolution of 1003 µmol g-1 over an 8-hour period, which surpasses that of the corresponding 2D counterpart by a factor of 59. These findings present a valuable approach to address in-plane charge transfer limitations in imine-based COFs.

Construction of Thiadiazole-Linked Covalent Organic Frameworks via Facile Linkage Conversion with Superior Photocatalytic Properties.

The establishment of facile synthetic routes to engineer covalent organic frameworks (COFs) with fully conjugated structure and excellent stability is highly desired for practical applications in optoelectronics and photocatalysis. Herein, a novel linkage conversion strategy is reported to prepare crystalline thiadiazole-linked COFs via thionation, cyclization, and oxidation of N-acylhydrazole bonds with Lawesson's reagent (LR). The as-prepared thiadiazole-linked COFs not only remain porosity and crystallinity, but enhance its chemical stability. Furthermore, thiadiazole-linked COFs are more favorable to lower exciton binding energy and promote π-electron delocalization over the whole reticular framework than N-acylhydrazone-linked COFs. Notably, the extended π-conjugation structure and decent crystallinity of the resulting TDA-COF are reflected by its higher photocatalytic H2 evolution rate (61.3 mmol g-1 in 5 h) in comparison with that (7.5 mmol g-1 ) of NAH-COF.

Real-time dense small object detection algorithm based on multi-modal tea shoots.

Introduction: The difficulties in tea shoot recognition are that the recognition is affected by lighting conditions, it is challenging to segment images with similar backgrounds to the shoot color, and the occlusion and overlap between leaves. Methods: To solve the problem of low accuracy of dense small object detection of tea shoots, this paper proposes a real-time dense small object detection algorithm based on multimodal optimization. First, RGB, depth, and infrared images are collected form a multimodal image set, and a complete shoot object labeling is performed. Then, the YOLOv5 model is improved and applied to dense and tiny tea shoot detection. Secondly, based on the improved YOLOv5 model, this paper designs two data layer-based multimodal image fusion methods and a feature layerbased multimodal image fusion method; meanwhile, a cross-modal fusion module (FFA) based on frequency domain and attention mechanisms is designed for the feature layer fusion method to adaptively align and focus critical regions in intra- and inter-modal channel and frequency domain dimensions. Finally, an objective-based scale matching method is developed to further improve the detection performance of small dense objects in natural environments with the assistance of transfer learning techniques. Results and discussion: The experimental results indicate that the improved YOLOv5 model increases the mAP50 value by 1.7% compared to the benchmark model with fewer parameters and less computational effort. Compared with the single modality, the multimodal image fusion method increases the mAP50 value in all cases, with the method introducing the FFA module obtaining the highest mAP50 value of 0.827. After the pre-training strategy is used after scale matching, the mAP values can be improved by 1% and 1.4% on the two datasets. The research idea of multimodal optimization in this paper can provide a basis and technical support for dense small object detection.

Inferring Pyramidal Neuron Morphology using EAP Data.

We report a computational algorithm that uses an inverse modeling scheme to infer neuron position and morphology of cortical pyramidal neurons using spatio-temporal extracellular action potential recordings.. We first develop a generic pyramidal neuron model with stylized morphology and active channels that could mimic the realistic electrophysiological dynamics of pyramidal cells from different cortical layers. The generic stylized single neuron model has adjustable parameters for soma location, and morphology and orientation of the dendrites. The ranges for the parameters were selected to include morphology of the pyramidal neuron types in the rodent primary motor cortex. We then developed a machine learning approach that uses the local field potential simulated from the stylized model for training a convolutional neural network that predicts the parameters of the stylized neuron model. Preliminary results suggest that the proposed methodology can reliably infer the key position and morphology parameters using the simulated spatio-temporal profile of EAP waveforms. We also provide partial support to validate the inference algorithm using in vivo data. Finally, we highlight the issues involved and ongoing work to develop a pipeline to automate the scheme.

A porous metal-organic cage liquid for sustainable CO2 conversion reactions.

Porous liquids are fluids with the permanent porosity, which can overcome the poor gas solubility limitations of conventional porous solid materials for three phase gas-liquid-solid reactions. However, preparation of porous liquids still requires the complicated and tedious use of porous hosts and bulky liquids. Herein, we develop a facile method to produce a porous metal-organic cage (MOC) liquid (Im-PL-Cage) by self-assembly of long polyethylene glycol (PEG)-imidazolium chain functional linkers, calixarene molecules and Zn ions. The Im-PL-Cage in neat liquid has permanent porosity and fluidity, endowing it with a high capacity of CO2 adsorption. Thus, the CO2 stored in an Im-PL-Cage can be efficiently converted to the value-added formylation product in the atmosphere, which far exceeds the porous MOC solid and nonporous PEG-imidazolium counterparts. This work offers a new method to prepare neat porous liquids for catalytic transformation of adsorbed gas molecules.

Approaches to characterizing oscillatory burst detection algorithms for electrophysiological recordings.

BACKGROUND: Cognitive processes are associated with fast oscillations of the local field potential and electroencephalogram. There is a growing interest in targeting them because these are disrupted by aging and disease. This has proven challenging because they often occur as short-lasting bursts. Moreover, they are obscured by broad-band aperiodic activity reflecting other neural processes. These attributes have made it exceedingly difficult to develop analytical tools for estimating the reliability of detection methods. NEW METHOD: To address this challenge, we developed an open-source toolkit with four processing steps, that can be tailored to specific brain states and individuals. First, the power spectrum is decomposed into periodic and aperiodic components, each of whose properties are estimated. Second, the properties of the transient oscillatory bursts that contribute to the periodic component are derived and optimized to account for contamination from the aperiodic component. Third, using the burst properties and aperiodic power spectrum, surrogate neural signals are synthesized that match the observed signal's spectrotemporal properties. Lastly, oscillatory burst detection algorithms run on the surrogate signals are subjected to a receiver operating characteristic analysis, providing insight into their performance. RESULTS: The characterization algorithm extracted features of oscillatory bursts across multiple frequency bands and brain regions, allowing for recording-specific evaluation of detection performance. For our dataset, the optimal detection threshold for gamma bursts was found to be lower than the one commonly used. COMPARISON WITH EXISTING METHODS: Existing methods characterize the power spectrum, while ours evaluates the detection of oscillatory bursts. CONCLUSIONS: This pipeline facilitates the evaluation of thresholds for detection algorithms from individual recordings.

Reticular Synthesis of One-Dimensional Covalent Organic Frameworks with 4-c sql Topology for Enhanced Fluorescence Emission.

Covalent organic frameworks (COFs) have been extensively investigated due to their unique structure, porosity, and functionality. However, at the topological level, COFs remain as two-dimensional (2D) or three-dimensional (3D) structures, while COFs with one-dimensional (1D) topology have not been systematically explored. In this work, we proposed a synthetic strategy for the construction of 1D-COFs based on non-linear edges and suitable high-symmetry vertices. Compared with their 2D-COFs counterparts, the 1D-COFs with AIEgens located at the vertex of the frame exhibited enhanced fluorescence. The density functional theory (DFT) calculations revealed that the dimensional-induced rotation restriction (DIRR) effect could spontaneously introduce additional non-covalent interactions between the strip frames, which could substantially diminish non-radiative transitions. This work also provides protocols for the design of 1D-COFs and a guidance scheme for the synthesis of emitting COFs.

Inferring Morphology of a Neuron from In Vivo LFP Data.

We propose a computational pipeline that uses biophysical modeling and sequential neural posterior estimation algorithm to infer the position and morphology of single neurons using multi-electrode in vivo extracellular voltage recordings. In this inverse modeling scheme, we designed a generic biophysical single neuron model with stylized morphology that had adjustable parameters for the dimensions of the soma, basal and apical dendrites, and their location and orientations relative to the multi-electrode probe. Preliminary results indicate that the proposed methodology can infer up to eight neuronal parameters well. We highlight the issues involved in the development of the novel pipeline and areas for further improvement.

Novel One-Dimensional Covalent Organic Framework as a H+ Fluorescent Sensor in Acidic Aqueous Solution.

Covalent organic frameworks (COFs) represent an emerging class of two- or three-dimensional crystalline porous materials with delicate control over topology, composition, and porosity. Here, we develop a new COF made up of 1,3,6,8-tetrakis(p-formylphenyl)pyrene (TFPPy) and 4,4'-diaminobenzophenone (DABP) that exhibits a rare one-dimensional (1D) structure. The resulting frameworks possess good crystallinity, comparatively high Brunauer-Emmett-Teller (BET) surface area (426 m2/g), and good thermal stability (360 °C). Impressively, this 1D COF shows strong fluorescence and can be used as an excellent H+ sensor in an acidic aqueous solution.

Dye-Modified Metal-Organic Framework as a Recyclable Luminescent Sensor for Nicotine Determination in Urine Solution and Living Cell.

A water-stable and pH-independent sensor for qualitative and quantitative detection of nicotine in urine solution and living cell was successfully developed. This material, named MB@UiO-66-NH2, can be synthesized by encapsulating methylene blue (MB) with a well-known metal-organic framework (MOF) UiO-66-NH2 through a simple impregnation method. The fluorescence intensity of the system was significantly enhanced when a certain amount of nicotine was added. In the meanwhile, MB is reduced by reductive nicotine to form leucomethylene blue (LB). The proposed sensor displayed excellent selectivity and sensitivity toward nicotine with limit of detection (LOD) of 0.98 µM, which is comparable or even better than that of the electrochemistry detecting methods for nicotine. The obvious enhancement and blue shift of the emission arise from the photoinduced electron transfer (PET) from LB to the UiO-66-NH2. The photophysical properties and the sensing applications of MB@UiO-66-NH2 suggest that this composite can be acted as a sensitive, selective, recyclable, and fluorogenic sensor for nicotine determination in urine solution and living cell.

Gamma Oscillations in the Basolateral Amygdala: Biophysical Mechanisms and Computational Consequences.

The basolateral nucleus of the amygdala (BL) is thought to support numerous emotional behaviors through specific microcircuits. These are often thought to be comprised of feedforward networks of principal cells (PNs) and interneurons. Neither well-understood nor often considered are recurrent and feedback connections, which likely engender oscillatory dynamics within BL. Indeed, oscillations in the gamma frequency range (40 - 100 Hz) are known to occur in the BL, and yet their origin and effect on local circuits remains unknown. To address this, we constructed a biophysically and anatomically detailed model of the rat BL and its local field potential (LFP) based on the physiological and anatomical literature, along with in vivo and in vitro data we collected on the activities of neurons within the rat BL. Remarkably, the model produced intermittent gamma oscillations (∼50 - 70 Hz) whose properties matched those recorded in vivo, including their entrainment of spiking. BL gamma-band oscillations were generated by the intrinsic circuitry, depending upon reciprocal interactions between PNs and fast-spiking interneurons (FSIs), while connections within these cell types affected the rhythm's frequency. The model allowed us to conduct experimentally impossible tests to characterize the synaptic and spatial properties of gamma. The entrainment of individual neurons to gamma depended on the number of afferent connections they received, and gamma bursts were spatially restricted in the BL. Importantly, the gamma rhythm synchronized PNs and mediated competition between ensembles. Together, these results indicate that the recurrent connectivity of BL expands its computational and communication repertoire.

Mechanism of microRNA-21 regulating IL-6 inflammatory response and cell autophagy in intervertebral disc degeneration.

This study investigated the mechanism of microRNA-21 in regulating IL-6 inflammatory response and cell autophagy in intervertebral disc degeneration. A total of 10 patients with lumbar disc herniation accompanied by nerve root pain (observation group) and 10 patients with lumbar burst fractures (control group) were selected. The nucleus pulposus tissues of the lesion were obtained during operation for cell culture. Real-time quantitative polymerase chain reaction (PCR) was used to detect the expression of microRNA-21. The ELISA method was used to detect the levels of IL-6, and type II collagen (Col II). Aggrecan and western blotting was used to detect autophagy-related gene 7 (ATG7) and microtubule-associated protein 1 light chain 3 (LC3)-II/-I. As a result, the levels of microRNA-21 and IL-6 in the observation group were significantly higher than those in the control group, but the levels of Col II and aggrecan were significantly lower than those in the control group. The differences were statistically significant (P<0.05). The levels of ATG7 and LC3-II/-I in the observation group were significantly decreased (P<0.05). In conclusion, the expression of microRNA-21 is abnormally high in the nerve root pain of the lumbar intervertebral disc, which can increase the IL-6 inflammatory response and reduce the capacity of cell autophagy.