G272V and P301L Mutations Induce Isoform Specific Tau Mislocalization to Dendritic Spines and Synaptic Dysfunctions in Cellular Models of 3R and 4R Tau Frontotemporal Dementia.

Tau pathologies are detected in the brains of some of the most common neurodegenerative diseases including Alzheimer's disease (AD), Lewy body dementia (LBD), chronic traumatic encephalopathy (CTE), and frontotemporal dementia (FTD). Tau proteins are expressed in six isoforms with either three or four microtubule-binding repeats (3R tau or 4R tau) due to alternative RNA splicing. AD, LBD, and CTE brains contain pathological deposits of both 3R and 4R tau. FTD patients can exhibit either 4R tau pathologies in most cases or 3R tau pathologies less commonly in Pick's disease, which is a subfamily of FTD. Here, we report the isoform-specific roles of tau in FTD. The P301L mutation, linked to familial 4R tau FTD, induces mislocalization of 4R tau to dendritic spines in primary hippocampal cultures that were prepared from neonatal rat pups of both sexes. Contrastingly, the G272V mutation, linked to familial Pick's disease, induces phosphorylation-dependent mislocalization of 3R tau but not 4R tau proteins to dendritic spines. The overexpression of G272V 3R tau but not 4R tau proteins leads to the reduction of dendritic spine density and suppression of mEPSCs in 5-week-old primary rat hippocampal cultures. The decrease in mEPSC amplitude caused by G272V 3R tau is dynamin-dependent whereas that caused by P301L 4R tau is dynamin-independent, indicating that the two tau isoforms activate different signaling pathways responsible for excitatory synaptic dysfunction. Our 3R and 4R tau studies here will shed new light on diverse mechanisms underlying FTD, AD, LBD, and CTE.

Synergistic Interactions in Sequential Process Doping of Polymer/Single-Walled Carbon Nanotube Nanocomposites for Enhanced n-Type Thermoelectric Performance.

This study focuses on the fabrication of nanocomposite thermoelectric devices by blending either a naphthalene-diimide (NDI)-based conjugated polymer (NDI-T1 or NDI-T2), or an isoindigo (IID)-based conjugated polymer (IID-T2), with single-walled carbon nanotubes (SWCNTs). This is followed by sequential process doping method with the small molecule 4-(2,3-dihydro-1,3-dimethyl-1H-benzimidazol-2-yl)-N,N-dimethylbenzenamine (N-DMBI) to provide the nanocomposite with n-type thermoelectric properties. Experiments in which the concentrations of the N-DMBI dopant are varied demonstrate the successful conversion of all three polymer/SWCNT nanocomposites from p-type to n-type behavior. Comprehensive spectroscopic, microstructural, and morphological analyses of the pristine polymers and the various N-DMBI-doped polymer/SWCNT nanocomposites are performed in order to gain insights into the effects of various interactions between the polymers and SWCNTs on the doping outcomes. Among the obtained nanocomposites, the NDI-T1/SWCNT exhibits the highest n-type Seebeck coefficient and power factor of -57.7 µV K-1 and 240.6 µW m-1 K-2 , respectively. However, because the undoped NDI-T2/SWCNT exhibits a slightly higher p-type performance, an integral p-n thermoelectric generator is fabricated using the doped and undoped NDI-T2/SWCNT nanocomposite. This device is shown to provide an output power of 27.2 nW at a temperature difference of 20 K.

Exploring Pyrazine-Based Organic Redox Couples for Enhanced Thermoelectric Performance in Wearable Energy Harvesters.

Thermoelectric generators (TEGs) based on thermogalvanic cells can convert low-temperature waste heat into electricity. Organic redox couples are well-suited for wearable devices due to their nontoxicity and the potential to enhance the ionic Seebeck coefficient through functional-group modifications.  Pyrazine-based organic redox couples with different functional groups is comparatively analyzed through cyclic voltammetry under varying temperatures. The results reveal substantial differences in entropy changes with temperature and highlight 2,5-pyrazinedicarboxylic acid dihydrate (PDCA) as the optimal candidate. How the functional groups of the pyrazine compounds impact the ionic Seebeck coefficient is examined, by calculating the electrostatic potential based on density functional theory. To evaluate the thermoelectric properties, PDCA is integrated in different concentrations into a double-network hydrogel comprising poly(vinyl alcohol) and polyacrylamide. The resulting champion device exhibits an impressive ionic Seebeck coefficient (Si) of 2.99 mV K-1, with ionic and thermal conductivities of ≈67.6 µS cm-1 and ≈0.49 W m-1 K-1, respectively. Finally, a TEG is constructed by connecting 36 pieces of 20 × 10-3 m PDCA-soaked hydrogel in series. It achieves a maximum power output of ≈0.28 µW under a temperature gradient of 28.3 °C and can power a small light-emitting diode. These findings highlight the significant potential of TEGs for wearable devices.

Intrinsically Stretchable Organic Thermoelectric Polymers Enabled by Incorporating Fused-Ring Conjugated Breakers.

While research on organic thermoelectric polymers is making significant progress in recent years, realization of a single polymer material possessing both thermoelectric properties and stretchability for the next generation of self-powered wearable electronics is a challenging task and remains an area yet to be explored. A new molecular engineering concept of "conjugated breaker" is employed to impart stretchability to a highly crystalline diketopyrrolepyrrole (DPP)-based polymer. A hexacyclic diindenothieno[2,3-b]thiophene (DITT) unit, with two 4-octyloxyphenyl groups substituted at the tetrahedral sp3-carbon bridges, is selected to function as the conjugated breaker that can sterically hinder intermolecular packing to reduce polymers' crystallinity. A series of donor-acceptor random copolymers is thus developed via polymerizing the crystalline DPP units with the DITT conjugated breakers. By controlling the monomeric DPP/DITT ratios, DITT30 reaches the optimal balance of crystalline/amorphous regions, exhibiting an exceptional power factor (PF) value up to 12.5 µW m-1 K-2 after FeCl3-doping; while, simultaneously displaying the capability to withstand strains exceeding 100%. More significantly, the doped DITT30 film possesses excellent mechanical endurance, retaining 80% of its initial PF value after 200 cycles of stretching/releasing at a strain of 50%. This research marks a pioneering achievement in creating intrinsically stretchable polymers with exceptional thermoelectric properties.

A pH-Sensitive Stretchable Zwitterionic Hydrogel with Bipolar Thermoelectricity.

Amid growing interest in using body heat for electricity in wearables, creating stretchable devices poses a major challenge. Herein, a hydrogel composed of two core constituents, namely the negatively-charged 2-acrylamido-2-methylpropanesulfonic acid and the zwitterionic (ZI) sulfobetaine acrylamide, is engineered into a double-network hydrogel. This results in a significant enhancement in mechanical properties, with tensile stress and strain of up to 470.3 kPa and 106.6%, respectively. Moreover, the ZI nature of the polymer enables the fabrication of a device with polar thermoelectric properties by modulating the pH. Thus, the ionic Seebeck coefficient (Si) of the ZI hydrogel ranges from -32.6 to 31.7 mV K-1 as the pH is varied from 1 to 14, giving substantial figure of merit (ZTi) values of 3.8 and 3.6, respectively. Moreover, a prototype stretchable ionic thermoelectric supercapacitor incorporating the ZI hydrogel exhibits notable power densities of 1.8 and 0.9 mW m-2 at pH 1 and 14, respectively. Thus, the present work paves the way for the utilization of pH-sensitive, stretchable ZI hydrogels for thermoelectric applications, with a specific focus on harvesting low-grade waste heat within the temperature range of 25-40 °C.

Computational Prediction of Coiled-Coil Protein Gelation Dynamics and Structure.

Protein hydrogels represent an important and growing biomaterial for a multitude of applications, including diagnostics and drug delivery. We have previously explored the ability to engineer the thermoresponsive supramolecular assembly of coiled-coil proteins into hydrogels with varying gelation properties, where we have defined important parameters in the coiled-coil hydrogel design. Using Rosetta energy scores and Poisson-Boltzmann electrostatic energies, we iterate a computational design strategy to predict the gelation of coiled-coil proteins while simultaneously exploring five new coiled-coil protein hydrogel sequences. Provided this library, we explore the impact of in silico energies on structure and gelation kinetics, where we also reveal a range of blue autofluorescence that enables hydrogel disassembly and recovery. As a result of this library, we identify the new coiled-coil hydrogel sequence, Q5, capable of gelation within 24 h at 4 °C, a more than 2-fold increase over that of our previous iteration Q2. The fast gelation time of Q5 enables the assessment of structural transition in real time using small-angle X-ray scattering (SAXS) that is correlated to coarse-grained and atomistic molecular dynamics simulations revealing the supramolecular assembling behavior of coiled-coils toward nanofiber assembly and gelation. This work represents the first system of hydrogels with predictable self-assembly, autofluorescent capability, and a molecular model of coiled-coil fiber formation.

Visible-Light-Promoted Radical Cascade Cyclization of 2-Vinyl Benzimidazoles: Access to Benzo[4,5]imidazo[1,2-b]isoquinolin- 11(6H)-ones.

A visible-light-enabled photoredox radical cascade cyclization of 2-vinyl benzimidazole derivatives is developed. This chemistry is applicable to a wide range of N-aroyl 2-vinyl benzimidazoles as acceptors, and halo compounds, including alkyl halides, acyl chlorides and sulfonyl chlorides, as radical precursors. The Langlois reagent also serves as an effective partner in this photocatalytic oxidative cascade process. This protocol provides a robust alternative for rendering highly functionalized benzo[4,5]imidazo[1,2-b]isoquinolin-11(6H)-ones.

Functionalized Thienopyrazines on NiOx Film as Self-Assembled Monolayer for Efficient Tin-Perovskite Solar Cells Using a Two-Step Method.

Three functionalized thienopyrazines (TPs), TP-MN (1), TP-CA (2), and TPT-MN (3) were designed and synthesized as self-assembled monolayers (SAMs) deposited on the NiOx film for tin-perovskite solar cells (TPSCs). Thermal, optical, electrochemical, morphological, crystallinity, hole mobility, and charge recombination properties, as well as DFT-derived energy levels with electrostatic surface potential mapping of these SAMs, have been thoroughly investigated and discussed. The structure of the TP-MN (1) single crystal was successfully grown and analyzed to support the uniform SAM produced on the ITO/NiOx substrate. When we used NiOx as HTM in TPSC, the device showed poor performance. To improve the efficiency of TPSC, we utilized a combination of new organic SAMs with NiOx as HTM, the TPSC device exhibited the highest PCE of 7.7 % for TP-MN (1). Hence, the designed NiOx/TP-MN (1) acts as a new model system for the development of efficient SAM-based TPSC. To the best of our knowledge, the combination of organic SAMs with anchoring CN/CN or CN/COOH groups and NiOx as HTM for TPSC has never been reported elsewhere. The TPSC device based on the NiOx/TP-MN bilayer exhibits great enduring stability for performance, retaining ~80 % of its original value for shelf storage over 4000 h.

Robust and Precise Quantitative Real-Time Polymerase Chain Reaction with an Amplification Efficiency-Aware Reaction Kinetics Model.

Quantitative real-time PCR (qPCR) is a method extensively used in nucleic acid testing for plants and animals. During the coronavirus disease 2019 (COVID-19) pandemic, high-precision qPCR analysis was urgently needed since quantitative results obtained from conventional qPCR methods were not accurate and precise, causing misdiagnoses and high rates of false-negative. To achieve more accurate results, we propose a new qPCR data analysis method with an amplification efficiency-aware reaction kinetics model (AERKM). Our reaction kinetics model (RKM) mathematically describes the tendency of the amplification efficiency during the whole qPCR process inferred by biochemical reaction dynamics. Amplification efficiency (AE) was introduced to rectify the fitted data so as to match the real reaction process for individual tests, thus reducing errors. The 5-point 10-fold gradient qPCR tests of 63 genes have been verified. The results of a 0.9% slope bias and an 8.2% ratio bias using AERKM exceed 4.1 and 39.4%, respectively, of the best performance of existing models, which demonstrates higher precision, less fluctuation, and better robustness among different nucleic acids. AERKM also provides a better understanding of the real qPCR process and gives insights into the detection, treatment, and prevention of severe diseases.

Supramolecular Assembly and Small-Molecule Binding by Protein-Engineered Coiled-Coil Fibers.

The ability to engineer a solvent-exposed surface of self-assembling coiled coils allows one to achieve a higher-order hierarchical assembly such as nano- or microfibers. Currently, these materials are being developed for a range of biomedical applications, including drug delivery systems; however, ways to mechanistically optimize the coiled-coil structure for drug binding are yet to be explored. Our laboratory has previously leveraged the functional properties of the naturally occurring cartilage oligomeric matrix protein coiled coil (C), not only for its favorable motif but also for the presence of a hydrophobic pore to allow for small-molecule binding. This includes the development of Q, a rationally designed pentameric coiled coil derived from C. Here, we present a small library of protein microfibers derived from the parent sequences of C and Q bearing various electrostatic potentials with the aim to investigate the influence of higher-order assembly and encapsulation of candidate small molecule, curcumin. The supramolecular fiber size appears to be well-controlled by sequence-imbued electrostatic surface potential, and protein stability upon curcumin binding is well correlated to relative structure loss, which can be predicted by in silico docking.

Efficiency improvement of inverted perovskite solar cells enabled by PTAA/MoS2double hole transporters.

Hole transport layer (HTL) plays a critical role in perovskite solar cells (PSCs). We focus on the improvement of PSCs performance with MoS2nanosheets as the anode buffer layer in the inverted photovoltaic structure. PSC with single MoS2buffer layer shows poor performance in power conversion efficiency (PCE) and the long-term stability. By combination of MoS2and Poly[bis(4-phenyl) (2,4,6-trimethylphenyl) amine] (PTAA) as double-layer HTL, the PCE is improved to 18.47%, while the control device with PTAA alone shows a PCE of 14.48%. The same phenomenon is also found in 2D PSCs. For double-layer HTL devices, the PCE reaches 13.19%, and the corresponding PCE of the control group using PTAA alone is 10.13%. This significant improvement is attributed to the reduced interface resistance and improved hole extraction ability as shown by the electric impedance spectroscopy and fluorescence spectroscopy. In addition, the improved device exhibits better stability because the PCE still maintains 66% of the initial value after 500 h of storage, which is higher than the 47% of the remaining PCE from device based on single PTAA or MoS2. Our results demonstrate the potential of polymer/inorganic nanomaterial as a double-layer buffer material for PSCs.

Motion Similarity Evaluation between Human and a Tri-Co Robot during Real-Time Imitation with a Trajectory Dynamic Time Warping Model.

Precisely imitating human motions in real-time poses a challenge for the robots due to difference in their physical structures. This paper proposes a human-computer interaction method for remotely manipulating life-size humanoid robots with a new metrics for evaluating motion similarity. First, we establish a motion capture system to acquire the operator's motion data and retarget it to the standard bone model. Secondly, we develop a fast mapping algorithm, by mapping the BVH (BioVision Hierarchy) data collected by the motion capture system to each joint motion angle of the robot to realize the imitated motion control of the humanoid robot. Thirdly, a DTW (Dynamic Time Warping)-based trajectory evaluation method is proposed to quantitatively evaluate the difference between robot trajectory and human motion, and meanwhile, visualization terminals render it more convenient to make comparisons between two different but simultaneous motion systems. We design a complex gesture simulation experiment to verify the feasibility and real-time performance of the control method. The proposed human-in-the-loop imitation control method addresses a prominent non-isostructural retargeting problem between human and robot, enhances robot interaction capability in a more natural way, and improves robot adaptability to uncertain and dynamic environments.

Autonomous Identification and Positioning of Trucks during Collaborative Forage Harvesting.

In the process of collaborative operation, the unloading automation of the forage harvester is of great significance to improve harvesting efficiency and reduce labor intensity. However, non-standard transport trucks and unstructured field environments make it extremely difficult to identify and properly position loading containers. In this paper, a global model with three coordinate systems is established to describe a collaborative harvesting system. Then, a method based on depth perception is proposed to dynamically identify and position the truck container, including data preprocessing, point cloud pose transformation based on the singular value decomposition (SVD) algorithm, segmentation and projection of the upper edge, edge lines extraction and corner points positioning based on the Random Sample Consensus (RANSAC) algorithm, and fusion and visualization of results on the depth image. Finally, the effectiveness of the proposed method has been verified by field experiments with different trucks. The results demonstrated that the identification accuracy of the container region is about 90%, and the absolute error of center point positioning is less than 100 mm. The proposed method is robust to containers with different appearances and provided a methodological reference for dynamic identification and positioning of containers in forage harvesting.

[A heart sound classification method based on joint decision of extreme gradient boosting and deep neural network].

Heart sound is one of the common medical signals for diagnosing cardiovascular diseases. This paper studies the binary classification between normal or abnormal heart sounds, and proposes a heart sound classification algorithm based on the joint decision of extreme gradient boosting (XGBoost) and deep neural network, achieving a further improvement in feature extraction and model accuracy. First, the preprocessed heart sound recordings are segmented into four status, and five categories of features are extracted from the signals based on segmentation. The first four categories of features are sieved through recursive feature elimination, which is used as the input of the XGBoost classifier. The last category is the Mel-frequency cepstral coefficient (MFCC), which is used as the input of long short-term memory network (LSTM). Considering the imbalance of the data set, these two classifiers are both improved with weights. Finally, the heterogeneous integrated decision method is adopted to obtain the prediction. The algorithm was applied to the open heart sound database of the PhysioNet Computing in Cardiology(CINC) Challenge in 2016 on the PhysioNet website, to test the sensitivity, specificity, modified accuracy and F score. The results were 93%, 89.4%, 91.2% and 91.3% respectively. Compared with the results of machine learning, convolutional neural networks (CNN) and other methods used by other researchers, the accuracy and sensibility have been obviously improved, which proves that the method in this paper could effectively improve the accuracy of heart sound signal classification, and has great potential in the clinical auxiliary diagnosis application of some cardiovascular diseases.

Nano-Micro Dimensional Structures of Fiber-Shaped Luminous Halide Perovskite Composites for Photonic and Optoelectronic Applications.

Perovskite nanomaterials have been revealed as highly luminescent structures regarding their dimensional confinement. In particular, their promising potential lies behind remarkable luminescent properties, including color tunability, high photoluminescence quantum yield, and the narrow emission band of halide perovskite (HP) nanostructures for optoelectronic and photonic applications such as lightning and displaying operations. However, HP nanomaterials possess such drawbacks, including oxygen, moisture, temperature, or UV lights, which limit their practical applications. Recently, HP-containing polymer composite fibers have gained much attention owing to the spatial distribution and alignment of HPs with high mechanical strength and ambient stability in addition to their remarkable optical properties comparable to that of nanocrystals. In this review, the fabrication methods for preparing nano-microdimensional HP composite fiber structures are described. Various advantages of the luminescent composite nanofibers are also described, followed by their applications for photonic and optoelectronic devices including sensors, polarizers, waveguides, lasers, light-down converters, light-emitting diode operations, etc. Finally, future directions and remaining challenges of HP-based nanofibers are presented.

A SVM and SLIC Based Detection Method for Paddy Field Boundary Line.

Visual based route and boundary detection is a key technology in agricultural automatic navigation systems. The variable illumination and lack of training samples has a bad effect on visual route detection in unstructured farmland environments. In order to improve the robustness of the boundary detection under different illumination conditions, an image segmentation algorithm based on support vector machine was proposed. A superpixel segmentation algorithm was adopted to solve the lack of training samples for a support vector machine. A sufficient number of superpixel samples were selected for extraction of color and texture features, thus a 19-dimensional feature vector was formed. Then, the support vector machine model was trained and used to identify the paddy ridge field in the new picture. The recognition F1 score can reach 90.7%. Finally, Hough transform detection was used to extract the boundary of the ridge field. The total running time of the proposed algorithm is within 0.8 s and can meet the real-time requirements of agricultural machinery.

Intelligent Fault Diagnosis of Diesel Engines via Extreme Gradient Boosting and High-Accuracy Time-Frequency Information of Vibration Signals.

Accurate and timely misfire fault diagnosis is of vital significance for diesel engines. However, existing algorithms are prone to fall into model over-fitting and adopt low energy-concentrated features. This paper presents a novel extreme gradient boosting-based misfire fault diagnosis approach utilizing the high-accuracy time-frequency information of vibration signals. First, diesel engine misfire tests were conducted under different spindle speeds, and the corresponding vibration signals were acquired via a triaxial accelerometer. The time-domain features of signals were extracted by using a time-domain statistics method, while the high-accuracy time-frequency domain features were obtained via the high-resolution multisynchrosqueezing transform. Thereafter, considering the nonlinearity and high dimensionality of the original characteristic data sets, the locally linear embedding method was employed for feature dimensionality reduction. Eventually, to avoid model overfitting, the extreme gradient boosting algorithm was utilized for diesel engine misfire fault diagnosis. Experiments under different spindle speeds and comprehensive comparisons with other evaluation methods were conducted to demonstrate the effectiveness of the proposed extreme gradient boosting-based misfire diagnosis method. The results verify that the highest classification accuracy of the proposed extreme gradient boosting-based algorithm is up to 99.93%. Simultaneously, the classification accuracy of the presented approach is approximately 24.63% higher on average than those of algorithms that use wavelet packet-based features. Moreover, it is shown that it obtains the minimum root mean squared error and can effectively prevent the model from falling into overfitting.

A High Precision Real-time Premature Ventricular Contraction Assessment Method based on the Complex Feature Set.

This paper presents a high precision and low computational complexity premature ventricular contraction (PVC) assessment method for the ECG human-machine interface device. The original signals are preprocessed by integrated filters. Then, R points and surrounding feature points are determined by corresponding detection algorithms. On this basis, a complex feature set and feature matrices are obtained according to the position feature points. Finally, an exponential Minkowski distance method is proposed for PVC recognition. Both public dataset and clinical experiments were utilized to verify the effectiveness and superiority of the proposed method. The results show that our R peak detection algorithm can substantially reduce the error rate, and obtained 98.97% accuracy for QRS complexes. Meanwhile, the accuracy of PVC recognition was 98.69% for the MIT-BIH database and 98.49% for clinical tests. Moreover, benefiting from the lightweight of our model, it can be easily applied to portable healthcare devices for human-computer interaction.

An Improved Convolutional Neural Network Based Approach for Automated Heartbeat Classification.

With age, our blood vessels are prone to aging, which induces cardiovascular disease. As an important basis for diagnosing heart disease and evaluating heart function, the electrocardiogram (ECG) records cardiac physiological electrical activity. Abnormalities in cardiac physiological activity are directly reflected in the ECG. Thus, ECG research is conducive to heart disease diagnosis. Considering the complexity of arrhythmia detection, we present an improved convolutional neural network (CNN) model for accurate classification. Compared with the traditional machine learning methods, CNN requires no additional feature extraction steps due to the automatic feature processing layers. In this paper, an improved CNN is proposed to automatically classify the heartbeat of arrhythmia. Firstly, all the heartbeats are divided from the original signals. After segmentation, the ECG heartbeats can be inputted into the first convolutional layers. In the proposed structure, kernels with different sizes are used in each convolution layer, which takes full advantage of the features in different scales. Then a max-pooling layer followed. The outputs of the last pooling layer are merged and as the input to fully-connected layers. Our experiment is in accordance with the AAMI inter-patient standard, which included normal beats (N), supraventricular ectopic beats (S), ventricular ectopic beats (V), fusion beats (F), and unknown beats (Q). For verification, the MIT arrhythmia database is introduced to confirm the accuracy of the proposed method, then, comparative experiments are conducted. The experiment demonstrates that our proposed method has high performance for arrhythmia detection, the accuracy is 99.06%. When properly trained, the proposed improved CNN model can be employed as a tool to automatically detect different kinds of arrhythmia from ECG.