AMPActiPred: A three-stage framework for predicting antibacterial peptides and activity levels with deep forest.

The emergence and spread of antibiotic-resistant bacteria pose a significant public health threat, necessitating the exploration of alternative antibacterial strategies. Antibacterial peptide (ABP) is a kind of antimicrobial peptide (AMP) that has the potential ability to fight against bacteria infection, offering a promising avenue for developing novel therapeutic interventions. This study introduces AMPActiPred, a three-stage computational framework designed to identify ABPs, characterize their activity against diverse bacterial species, and predict their activity levels. AMPActiPred employed multiple effective peptide descriptors to effectively capture the compositional features and physicochemical properties of peptides. AMPActiPred utilized deep forest architecture, a cascading architecture similar to deep neural networks, capable of effectively processing and exploring original features to enhance predictive performance. In the first stage, AMPActiPred focuses on ABP identification, achieving an Accuracy of 87.6% and an MCC of 0.742 on an elaborate dataset, demonstrating state-of-the-art performance. In the second stage, AMPActiPred achieved an average GMean at 82.8% in identifying ABPs targeting 10 bacterial species, indicating AMPActiPred can achieve balanced predictions regarding the functional activity of ABP across this set of species. In the third stage, AMPActiPred demonstrates robust predictive capabilities for ABP activity levels with an average PCC of 0.722. Furthermore, AMPActiPred exhibits excellent interpretability, elucidating crucial features associated with antibacterial activity. AMPActiPred is the first computational framework capable of predicting targets and activity levels of ABPs. Finally, to facilitate the utilization of AMPActiPred, we have established a user-friendly web interface deployed at https://awi.cuhk.edu.cn/∼AMPActiPred/.

Gate-Tunable Positive and Negative Photoconductance in Near-Infrared Organic Heterostructures for In-Sensor Computing.

The rapid growth of sensor data in the artificial intelligence often causes significant reductions in processing speed and power efficiency. Addressing this challenge, in-sensor computing is introduced as an advanced sensor architecture that simultaneously senses, memorizes, and processes images at the sensor level. However, this is rarely reported for organic semiconductors that possess inherent flexibility and tunable bandgap. Herein, an organic heterostructure that exhibits a robust photoresponse to near-infrared (NIR) light is introduced, making it ideal for in-sensor computing applications. This heterostructure, consisting of partially overlapping p-type and n-type organic thin films, is compatible with conventional photolithography techniques, allowing for high integration density of up to 520 devices cm-2 with a 5 µm channel length. Importantly, by modulating gate voltage, both positive and negative photoresponses to NIR light (1050 nm) are attained, which establishes a linear correlation between responsivity and gate voltage and consequently enables real-time matrix multiplication within the sensor. As a result, this organic heterostructure facilitates efficient and precise NIR in-sensor computing, including image processing and nondestructive reading and classification, achieving a recognition accuracy of 97.06%. This work serves as a foundation for the development of reconfigurable and multifunctional NIR neuromorphic vision systems.

Identifying Antitubercular Peptides via Deep Forest Architecture with Effective Feature Representation.

Tuberculosis (TB) is a severe disease caused by Mycobacterium tuberculosis that poses a significant threat to human health. The emergence of drug-resistant strains has made the global fight against TB even more challenging. Antituberculosis peptides (ATPs) have shown promising results as a potential treatment for TB. However, conventional wet lab-based approaches to ATP discovery are time-consuming and costly and often fail to discover peptides with desired properties. To address these challenges, we propose a novel machine learning-based framework called ATPfinder that can significantly accelerate the discovery of ATP. Our approach integrates various efficient peptide descriptors and utilizes the deep forest algorithm to construct the model. This neural network-like cascading structure can effectively process and mine features without complex hyperparameter tuning. Our experimental results show that ATPfinder outperforms existing ATP prediction tools, achieving state-of-the-art performance with an accuracy of 89.3% and an MCC of 0.70. Moreover, our framework exhibits better robustness than baseline algorithms commonly used for other sequence analysis tasks. Additionally, the excellent interpretability of our model can assist researchers in understanding the critical features of ATP. Finally, we developed a downloadable desktop application to simplify the use of our framework for researchers. Therefore, ATPfinder can facilitate the discovery of peptide drugs and provide potential solutions for TB treatment. Our framework is freely available at https://github.com/lantianyao/ATPfinder/ (data sets and code) and https://awi.cuhk.edu.cn/dbAMP/ATPfinder.html (software).

Predicting Anti-inflammatory Peptides by Ensemble Machine Learning and Deep Learning.

Inflammation is a biological response to harmful stimuli, aiding in the maintenance of tissue homeostasis. However, excessive or persistent inflammation can precipitate a myriad of pathological conditions. Although current treatments such as NSAIDs, corticosteroids, and immunosuppressants are effective, they can have side effects and resistance issues. In this backdrop, anti-inflammatory peptides (AIPs) have emerged as a promising therapeutic approach against inflammation. Leveraging machine learning methods, we have the opportunity to accelerate the discovery and investigation of these AIPs more effectively. In this study, we proposed an advanced framework by ensemble machine learning and deep learning for AIP prediction. Initially, we constructed three individual models with extremely randomized trees (ET), gated recurrent unit (GRU), and convolutional neural networks (CNNs) with attention mechanism and then used stacking architecture to build the final predictor. By utilizing various sequence encodings and combining the strengths of different algorithms, our predictor demonstrated exemplary performance. On our independent test set, our model achieved an accuracy, MCC, and F1-score of 0.757, 0.500, and 0.707, respectively, clearly outperforming other contemporary AIP prediction methods. Additionally, our model offers profound insights into the feature interpretation of AIPs, establishing a valuable knowledge foundation for the design and development of future anti-inflammatory strategies.

Construction of organic/GaN heterostructures for DUV-to-NIR broadband photodetection.

Herein, a broadband photodetector (BPD) is constructed with consistent and stable detection abilities for deep ultraviolet to near-infrared spectral range. The BPD integrates the GaN template with a hybrid organic semiconductor, PM6:Y6, via the spin-coating process, and is fabricated in the form of asymmetric metal-semiconductor-metal structure. Under an optimal voltage, the device shows consistent photoresponse within 254 to 850 nm, featuring high responsivity (10 to 60 A/W), photo-to-dark-current ratio over 103, and fast response time. These results show the potential of such organic/GaN heterojunctions as a simple and effective strategy to build BPDs for a reliable photo-sensing application in the future.

Anterior cingulate cortex orexin signaling mediates early-life stress-induced social impairment in females.

Early-life stress has long-term impacts on the structure and function of the anterior cingulate cortex (ACC), and raises the risk of adult neuropsychiatric disorders including social dysfunction. The underlying neural mechanisms, however, are still uncertain. Here, we show that, in female mice, maternal separation (MS) during the first three postnatal weeks results in social impairment accompanied with hypoactivity in pyramidal neurons (PNs) of the ACC. Activation of ACC PNs ameliorates MS-induced social impairment. Neuropeptide Hcrt, which encodes hypocretin (orexin), is the top down-regulated gene in the ACC of MS females. Activating ACC orexin terminals enhances the activity of ACC PNs and rescues the diminished sociability observed in MS females via an orexin receptor 2 (OxR2)-dependent mechanism. Our results suggest orexin signaling in the ACC is critical in mediating early-life stress-induced social impairment in females.

Accelerating the Discovery of Anticancer Peptides through Deep Forest Architecture with Deep Graphical Representation.

Cancer is one of the leading diseases threatening human life and health worldwide. Peptide-based therapies have attracted much attention in recent years. Therefore, the precise prediction of anticancer peptides (ACPs) is crucial for discovering and designing novel cancer treatments. In this study, we proposed a novel machine learning framework (GRDF) that incorporates deep graphical representation and deep forest architecture for identifying ACPs. Specifically, GRDF extracts graphical features based on the physicochemical properties of peptides and integrates their evolutionary information along with binary profiles for constructing models. Moreover, we employ the deep forest algorithm, which adopts a layer-by-layer cascade architecture similar to deep neural networks, enabling excellent performance on small datasets but without complicated tuning of hyperparameters. The experiment shows GRDF exhibits state-of-the-art performance on two elaborate datasets (Set 1 and Set 2), achieving 77.12% accuracy and 77.54% F1-score on Set 1, as well as 94.10% accuracy and 94.15% F1-score on Set 2, exceeding existing ACP prediction methods. Our models exhibit greater robustness than the baseline algorithms commonly used for other sequence analysis tasks. In addition, GRDF is well-interpretable, enabling researchers to better understand the features of peptide sequences. The promising results demonstrate that GRDF is remarkably effective in identifying ACPs. Therefore, the framework presented in this study could assist researchers in facilitating the discovery of anticancer peptides and contribute to developing novel cancer treatments.

Tuning the nanostructure and molecular orientation of high molecular weight diketopyrrolopyrrole-based polymers for high-performance field-effect transistors.

As a versatile class of semiconductors, diketopyrrolopyrrole (DPP)-based conjugated polymers are well suited for applications of next-generation plastic electronics because of their excellent and tunable optoelectronic properties via a rational design of chemical structures. However, it remains a challenge to unravel and eventually influence the correlation between their solution-state aggregation and solid-state microstructure. In this contribution, the solution-state aggregation of high molecular weight PDPP3T is effectively enhanced by solvent selectivity, and a fibril-like nanostructure with short-range and long-range order is generated and tuned in thin films. The predominant role of solvent quality on polymer packing orientation is revealed, with an orientational transition from a face-on to an edge-on texture for the same PDPP3T. The resultant edge-on arranged films lead to a significant improvement in charge transport in transistors, and the field-effect hole mobility reaches 2.12 cm2 V-1 s-1 with a drain current on/off ratio of up to 108. Our findings offer a new strategy for enhancing the device performance of polymer electronic devices.

Preparation of multi-dimensional (1D/2D/3D) carbon/g-C3N4 composite photocatalyst with enhanced visible-light catalytic performance.

A multi-dimensional (1D/2D/3D) carbon/g-C3N4 composite photocatalyst (CCN) was successfully prepared by a facile method with carbon from cheap absorbent cotton wool. The activities and stabilities of CCN were evaluated by photo-degrading Rhodamine B (RhB) under visible light irradiation. The effect of carbon content in composite on the catalytic activities was investigated. The results show that a good interfacial contact can be observed between g-C3N4 and carbon materials in CCN. It reveals an enhanced photocatalytic activity in photocatalytic decomposition of RhB compared with g-C3N4. The carbon content has obvious effect on the performance of CCN, and the optimal carbon content in CCN is 1 wt% (CCN1.0). The first-order rate constant (k) of CCN1.0 is approximately 5.5 and 3.4 times those of g-C3N4 and AC1.0/g-C3N4. The CCN1.0 catalyst also shows the excellent photocatalytic stability in the recycling experiments. The enhanced catalytic performance of CCN is mainly due to an increase in electron-hole pair separation efficiency and visible light adsorption after coupling carbon. The hole and •O2- radicals are the main active species, and •O2- plays a more important role than h+. The photocatalytic mechanism over CCN1.0 was proposed. This work will provide a new insight to prepare highly-efficient g-C3N4-based photocatalysts.

Hierarchically porous SiO2/C hollow microspheres: a highly efficient adsorbent for Congo Red removal.

Hierarchically porous SiO2/C hollow microspheres (HPSCHMs) were synthesized by a hydrothermal and NaOH-etching combined route. The adsorption performance of the prepared HPSCHMs was investigated to remove Congo Red (CR) in aqueous solution. The results show that the synthesized composite possesses a hollow microspherical structure with hierarchical pores and a diameter of about 100-200 nm, and its surface area is up to 1154 m2 g-1. This material exhibits a remarkable adsorption performance for CR in solution, and its maximum adsorption amount for CR can reach up to 2512 mg g-1. It shows faster adsorption and much higher adsorption capacity than the commercial AC and γ-Al2O3 samples under the same conditions. The studies of the kinetics and thermodynamics indicate that the adsorption of CR on the PHSCHM sample obeys the pseudo-second order model well and belongs to physisorption. The adsorption activation energy is about 7.72 kJ mol-1. In view of the hierarchically meso-macroporous structure, large surface area and pore volume, the HPSCHM material could be a promising adsorbent for removal of pollutants, and it could also be used as a catalyst support.