GAPS: a geometric attention-based network for peptide binding site identification by the transfer learning approach.

Protein-peptide interactions (PPepIs) are vital to understanding cellular functions, which can facilitate the design of novel drugs. As an essential component in forming a PPepI, protein-peptide binding sites are the basis for understanding the mechanisms involved in PPepIs. Therefore, accurately identifying protein-peptide binding sites becomes a critical task. The traditional experimental methods for researching these binding sites are labor-intensive and time-consuming, and some computational tools have been invented to supplement it. However, these computational tools have limitations in generality or accuracy due to the need for ligand information, complex feature construction, or their reliance on modeling based on amino acid residues. To deal with the drawbacks of these computational algorithms, we describe a geometric attention-based network for peptide binding site identification (GAPS) in this work. The proposed model utilizes geometric feature engineering to construct atom representations and incorporates multiple attention mechanisms to update relevant biological features. In addition, the transfer learning strategy is implemented for leveraging the protein-protein binding sites information to enhance the protein-peptide binding sites recognition capability, taking into account the common structure and biological bias between proteins and peptides. Consequently, GAPS demonstrates the state-of-the-art performance and excellent robustness in this task. Moreover, our model exhibits exceptional performance across several extended experiments including predicting the apo protein-peptide, protein-cyclic peptide and the AlphaFold-predicted protein-peptide binding sites. These results confirm that the GAPS model is a powerful, versatile, stable method suitable for diverse binding site predictions.

Risk and related factors of elbow musculoskeletal diseases: A nationwide cross-sectional survey in China.

OBJECTIVE: Although studies have shown that Work-related Musculoskeletal Disorders (WMSDs) are common and continue to be a main source of disability and work time loss, there are few reports on elbow WMSDs. The aim of this study was to explore the prevalence and associated factors of elbow WMSDs. METHODS: The valid questionnaires of 57501 workers from 15 different industries nationwide were collected and the Chi-square test and logistic-regression-analysis were applied to reveal the prevalence and risk factors of elbow. RESULTS: The findings indicated that prevalence of elbow WMSDs among workers was 7.3%. The prevalence of elbow WMSDs in toy manufacturing was 21.3%, which significantly higher than that in other industries (P<0.05). Logistic regression analysis showed that aged 40 and above, married, very poor health, left-handed, lifting weights (more than 20 kg each time) , work requiring upper limb or hand force, work in an uncomfortable position, repetitive operations within one minute, using vibrating tools, work involves cold, cool winds or temperature changes, work being completed in the same workshop, work being done outdoors, frequent deal with customers , two shifts, often work overtime, staff shortage, often work for colleagues were the risk factors of elbow WMSDs.The higer education level and monthly income, and enough rest time were the protective factors of elbow WMSDs. CONCLUSION: The toy manufacturing is a high-risk industry for elbow WMSDs. The publicity and education of ergonomics knowledge should be strengthened, and the workers' ergonomics awareness should be improved to reduce the impact of WMSDs.

PepExplainer: An explainable deep learning model for selection-based macrocyclic peptide bioactivity prediction and optimization.

Macrocyclic peptides possess unique features, making them highly promising as a drug modality. However, evaluating their bioactivity through wet lab experiments is generally resource-intensive and time-consuming. Despite advancements in artificial intelligence (AI) for bioactivity prediction, challenges remain due to limited data availability and the interpretability issues in deep learning models, often leading to less-than-ideal predictions. To address these challenges, we developed PepExplainer, an explainable graph neural network based on substructure mask explanation (SME). This model excels at deciphering amino acid substructures, translating macrocyclic peptides into detailed molecular graphs at the atomic level, and efficiently handling non-canonical amino acids and complex macrocyclic peptide structures. PepExplainer's effectiveness is enhanced by utilizing the correlation between peptide enrichment data from selection-based focused library and bioactivity data, and employing transfer learning to improve bioactivity predictions of macrocyclic peptides against IL-17C/IL-17 RE interaction. Additionally, PepExplainer underwent further validation for bioactivity prediction using an additional set of thirteen newly synthesized macrocyclic peptides. Moreover, it enabled the optimization of the IC50 of a macrocyclic peptide, reducing it from 15 nM to 5.6 nM based on the contribution score provided by PepExplainer. This achievement underscores PepExplainer's skill in deciphering complex molecular patterns, highlighting its potential to accelerate the discovery and optimization of macrocyclic peptides.

HighFold: accurately predicting structures of cyclic peptides and complexes with head-to-tail and disulfide bridge constraints.

In recent years, cyclic peptides have emerged as a promising therapeutic modality due to their diverse biological activities. Understanding the structures of these cyclic peptides and their complexes is crucial for unlocking invaluable insights about protein target-cyclic peptide interaction, which can facilitate the development of novel-related drugs. However, conducting experimental observations is time-consuming and expensive. Computer-aided drug design methods are not practical enough in real-world applications. To tackles this challenge, we introduce HighFold, an AlphaFold-derived model in this study. By integrating specific details about the head-to-tail circle and disulfide bridge structures, the HighFold model can accurately predict the structures of cyclic peptides and their complexes. Our model demonstrates superior predictive performance compared to other existing approaches, representing a significant advancement in structure-activity research. The HighFold model is openly accessible at https://github.com/hongliangduan/HighFold.

Recent advances and mechanism of plasmonic metal-semiconductor photocatalysis.

Benefiting from the unique surface plasmon properties, plasmonic metal nanoparticles can convert light energy into chemical energy, which is considered as a potential technique for enhancing plasmon-induced semiconductor photocatalytic reactions. Due to the shortcomings of large bandgap and high carrier recombination rate of semiconductors, their applications are limited in the field of sustainable and clean energy sources. Different forms of plasmonic nanoparticles have been reported to improve the photocatalytic reactions of adjacent semiconductors, such as water splitting, carbon dioxide reduction, and organic pollutant degradation. Although there are various reports on plasmonic metal-semiconductor photocatalysis, the related mechanism and frontier progress still need to be further explored. This review provides a brief explanation of the four main mechanisms of plasmonic metal-semiconductor photocatalysis, namely, (i) enhanced local electromagnetic field, (ii) light scattering, (iii) plasmon-induced hot carrier injection and (iv) plasmon-induced resonance energy transfer; some related typical frontier applications are also discussed. The study on the mechanism of plasmonic semiconductor complexes will be favourable to develop a new high-performance semiconductor photocatalysis technology.

Anthropogenic noise and habitat structure shaping dominant frequency of bird sounds along urban gradients.

The shifts of bird song frequencies in urbanized areas provide a unique system to understand avian acoustic responses to urbanization. Using passive acoustic monitoring and automatic bird sound recognition technology, we explored the frequency variations of six common urban bird species and their associations with habitat structures. Our results demonstrated that bird song frequencies in urban areas were significantly higher than those in peri-urban and rural areas. Anthropogenic noise and habitat structure were identified as crucial factors shaping the acoustic space for birds. We found that noise, urbanization, and open understory spaces are factors contributing to the increase in the dominant frequency of bird sounds. However, habitat variables such as vegetation density and tree height can potentially slow down this upward trend. These findings offer essential insights into the behavioral response of birds in a variety of urban forest habitats, with implications for urban ecosystem management and habitat restoration.

Multi_CycGT: A Deep Learning-Based Multimodal Model for Predicting the Membrane Permeability of Cyclic Peptides.

Cyclic peptides are gaining attention for their strong binding affinity, low toxicity, and ability to target "undruggable" proteins; however, their therapeutic potential against intracellular targets is constrained by their limited membrane permeability, and researchers need much time and money to test this property in the laboratory. Herein, we propose an innovative multimodal model called Multi_CycGT, which combines a graph convolutional network (GCN) and a transformer to extract one- and two-dimensional features for predicting cyclic peptide permeability. The extensive benchmarking experiments show that our Multi_CycGT model can attain state-of-the-art performance, with an average accuracy of 0.8206 and an area under the curve of 0.8650, and demonstrates satisfactory generalization ability on several external data sets. To the best of our knowledge, it is the first deep learning-based attempt to predict the membrane permeability of cyclic peptides, which is beneficial in accelerating the design of cyclic peptide active drugs in medicinal chemistry and chemical biology applications.

Digitalization, new business Startups, information and Communication Technologies and product innovation: Evidence From China in the lens of sustainability.

The global economies and international organizations are inclined towards sustainable growth, technological advancements and product innovations. China is the leading economy in information and communication technologies and among the major industrially expanded economies covering a substantial share of the global market in exports. The prime objective of this study is to explore the role of digitalization and Information and communication technologies (ICT) for product innovation (PIN). In doing so, the study also attempts to draw some novel implications regarding business, entrepreneurship, and product innovation in the lens of sustainability. This current study use the annual data of China from 1990-2020. The empirical analysis was conducted using the stationarity testing and the Johansen cointegration test. In addition, due to the data's asymmetrical distribution, the non-parametric "quantile regression" is used. For robustness, this study employs the Fully Modified Ordinary Least Square, Canonical Cointegration, and Dynamic Ordinary Least Square methods. The empirical results reveal that economic progress and financial development are substantial factors of product innovation. The robust analysis reveals that medium and high-tech industries and information and communication technology adversely affect product innovation. Further, the presence of financial development transforms the negative influence of information and communication technology into a positive. The current study concludes more investments in the technological industry are required to encourage product innovation in China. The study discusses some policy-related implications in the context of business sustainability and product innovation.

FUNDC1/PFKP-mediated mitophagy induced by KD025 ameliorates cartilage degeneration in osteoarthritis.

Osteoarthritis (OA) is the most common joint disease, but no disease-modifying drugs have been approved for OA treatment. Mitophagy participates in mitochondrial homeostasis regulation by selectively clearing dysfunctional mitochondria, which might contribute to cartilage degeneration in OA. Here, we provide evidence of impaired mitophagy in OA chondrocytes, which exacerbates chondrocyte degeneration. Among the several classic mitophagy-regulating pathways and receptors, we found that FUNDC1 plays a key role in preserving chondrocyte homeostasis by inducing mitophagy. FUNDC1 knockdown in vitro and knockout in vivo decreased mitophagy and exacerbated mitochondrial dysfunction, exacerbating chondrocyte degeneration and OA progression. FUNDC1 overexpression via intra-articular injection of adeno-associated virus alleviated cartilage degeneration in OA. Mechanistically, our study demonstrated that PFKP interacts with and dephosphorylates FUNDC1 to induce mitophagy in chondrocytes. Further analysis identified KD025 as a candidate drug for restoring chondrocyte mitophagy by increasing the FUNDC1-PFKP interaction and thus alleviating cartilage degeneration in mice with DMM-induced OA. Our study highlights the role of the FUNDC1-PFKP interaction in chondrocyte homeostasis via mitophagy induction and identifies KD025 as a promising agent for treating OA by increasing chondrocyte mitophagy.

Over 50 W all-fiber mid-infrared supercontinuum laser.

Broadband supercontinuum laser sources in the mid-infrared region have attracted enormous interest and found significant applications in spectroscopy, imaging, sensing, defense, and security. Despite recent advances in mid-infrared supercontinuum laser sources using infrared fibers, the average power of those laser sources is limited to 10-watt-level, and further power scaling to over 50 W (or hundred-watt-level) remains a significant technological challenge. Here, we report an over 50 W all-fiber mid-infrared supercontinuum laser source with a spectral range from 1220 to 3740 nm, by using low loss (<0.1 dB/m) fluorotellurite fibers we developed as the nonlinear medium and a tilted fusion splicing method for reducing the reflection from the fluorotellurite-silica fiber joint. Furthermore, the scalability of all-fiber mid-infrared supercontinuum laser sources using fluorotellurite fibers is analyzed by considering thermal effects and optical damage, which verifies its potential of power scaling to hundred-watt-level. Our results pave the way for realizing all-fiber hundred-watt-level mid-infrared lasers for real applications.

Laser-Induced Graphene-based Flexible Substrate with Photothermal Conversion and Photoresponse Performance on Polyimide Film.

Graphene-based flexible electronic devices are widely used in photoelectric components and photodetectors. However, it remains a huge challenge to fabricate graphene-based flexible devices efficiently and economically. Compared with the flexible electronic devices made by combining the flexible film with metal and semiconductor materials, the graphene-based flexible substrate (GFS) can be efficiently and conveniently induced by laser direct writing on the flexible film. In this paper, the GFS with a resistance of as low as 15 Ω was successfully induced by CO2 laser on a polyimide (PI) film in one step, and the GFS surface covered with carbon nanoparticles (GFSC) with a resistance of 25 Ω was further induced by femtosecond (fs) laser reprocessing. Benefiting from the laser-induced porous graphene structure, the absorptivity of GFS is up to 90% in the wavelength range of 200-2000 nm. The formation of carbon nanoparticles on the GFSC surface further improves the absorptivity to 97.5% in a wide spectral range. Under white light irradiation of 1 sun, the surface temperature of GFS reaches 65.7 °C and that of GFSC is up to 70.8 °C within 2 min. Under the irradiation of a light-emitting diode (LED) with a central wavelength of 365 nm, the highest photoresponsivity of GFS and GFSC was 8.8 and 1.3 mA/W, respectively. The response time and recovery time of GFS are 8 and 7.3 s, and those of GFSC are 8.3 and 6.7 s, respectively. Importantly, GFSC has a more stable photoresponse performance due to the better electron capture and transfer capability of carbon nanoparticles. It is believed that GFS and GFSC have great application potential in flexible photodetectors and sensors.

Auditory vertical localization in the median plane with conflicting dynamic interaural time difference and other elevation cues.

Both dynamic variation of interaural time difference (ITD) and static spectral cues provide information for front-back discrimination and vertical localization. However, the contributions of the two cues are still unclear. The static spectral cue has conventionally been regarded as the dominant one. In the present work, psychoacoustic experiments were conducted to examine the contribution of dynamic ITD and static spectral cues to vertical localization in the median plane. By modifying the head-related transfer functions used in a dynamic virtual auditory display, binaural signals with conflicting dynamic ITD and spectral cues that were either static or dynamically modified according to instantaneous head position were created. The results indicated that the dynamic ITD and static spectral cues contribute to vertical localization at low and high frequencies, respectively. For full a bandwidth stimulus, conflicting dynamic ITD and static spectral cues usually result in two separated virtual sources at different elevations corresponding to the spatial information conveyed by the low- and high-frequency bands, respectively. In most cases, no fused localization occurs in the high-level cognition system. Therefore, dynamic ITD and static spectral cues contribute to vertical localization at different frequency ranges, and neither of them dominates vertical localization in the case of wideband stimuli.

PepScaf: Harnessing Machine Learning with In Vitro Selection toward De Novo Macrocyclic Peptides against IL-17C/IL-17RE Interaction.

The combination of library-based screening and artificial intelligence (AI) has been accelerating the discovery and optimization of hit ligands. However, the potential of AI to assist in de novo macrocyclic peptide ligand discovery has yet to be fully explored. In this study, an integrated AI framework called PepScaf was developed to extract the critical scaffold relative to bioactivity based on a vast dataset from an initial in vitro selection campaign against a model protein target, interleukin-17C (IL-17C). Taking the generated scaffold, a focused macrocyclic peptide library was rationally constructed to target IL-17C, yielding over 20 potent peptides that effectively inhibited IL-17C/IL-17RE interaction. Notably, the top two peptides displayed exceptional potency with IC50 values of 1.4 nM. This approach presents a viable methodology for more efficient macrocyclic peptide discovery, offering potential time and cost savings. Additionally, this is also the first report regarding the discovery of macrocyclic peptides against IL-17C/IL-17RE interaction.

Surface plasmon assisted preparation of X1-type Y2SiO5:Eu3+-Au luminescent crystals.

Rare-earth doped yttrium orthosilicate (Y2SiO5) crystals have many important applications due to their unique optical and luminescence properties. However, the indispensable high temperature treatment and long period reaction tend to greatly reduce the preparation efficiency. Here, the plasmonic photothermal effect of Au nanoparticles has been properly applied to realize in situ transformation from a composite structure NaYF4:Eu3+@SiO2@Au into a single monoclinic X1-type Y2SiO5:Eu3+-Au particle. It is worth mentioning that the X1-type Y2SiO5-Au particle can be well obtained within about 10 seconds when the thickness of the SiO2 shell is about 15 nm, which is unattainable with conventional approaches. Moreover, the particle turns out to possess good crystallinity, controllable morphology, and significantly improved luminescence performance. This study not only provides a brand-new path for the preparation of yttrium silicate crystals but also further extends the application of surface plasmons in the field of catalytic luminescent materials.

Acetylcysteine and budesonide for the treatment of refractory Mycoplasma pneumoniae pneumonia in children: a clinical observation.

BACKGROUND: To examine the clinical impact of bronchoscope alveolar lavage (BAL) combination with budesonide, ambroxol + budesonide, or acetylcysteine + budesonide in the treatment of refractory Mycoplasma pneumoniae pneumonia (RMPP). METHODS: Eighty-two RMPP patients admitted to Pediatrics at The First People's Hospital of Zhengzhou were retrospectively evaluated between August 2016 and August 2019. All patients were administered BAL in addition to intravenous Azithromycin, expectoration, and nebulizer inhalation. The medications added to the BLA separated the patients into the Budesonide group, Ambroxol + budesonide group, and acetylcysteine + budesonide group. Analyzed were the variations in laboratory examination indices, improvement in lung imaging, overall effective rate, and adverse responses in the three groups. RESULTS: The laboratory test indices of patients in all three groups improved significantly relative to pre-treatment levels, and the results were statistically significant. After therapy, there were no significant differences between the three groups in terms of white blood cell (WBC), C-reactive protein (CRP), or erythrocyte sedimentation rate (ESR). Serum lactate dehydrogenase (LDH) and serum ferritin (SF) varied significantly across the three groups (P < 0.05). In the acetylcysteine + budesonide group, the absorption rate of lung imaging lesions and clinical efficacy were superior to those of the other two groups. There were no significant differences between the three groups in the occurrence of adverse events (P > 0.05). CONCLUSIONS: BLA-coupled acetylcysteine + budesonide was superior to the other two groups in enhancing the effectiveness of RMPP in children, which might increase lung opacity absorption and minimize lung inflammation.

Preparation of a Three-Dimensional Composite Structure Based on a Periodic Au@Ag Core-Shell Nanocube with Ultrasensitive Surface-Enhanced Raman Scattering for Rapid Detection.

The absorption and scattering frequencies of surface plasmon resonance can be selectively adjusted by changing the morphology, size, structure, arrangement, and gap between noble metal nanoparticles so that the local electromagnetic field on the substrate surface can be further enhanced. This change will promote and popularize surface-enhanced Raman spectroscopy. This paper reports the research results and improvement scheme of surface enhanced Raman scattering (SERS) activity of silver-coated gold nanocubed/organism (Au@Ag/CW NCs) prepared by three-phase self-assembly. In the experiment, the uppermost oil phase in the three-phase self-assembly process was optimized as ethanol and n-hexane solution containing a specific concentration of a probe molecule rhodamine 6G or aspartame. The probe molecules were directly self-assembled on the surface of the composite substrate to avoid the possible loss and pollution during immersion and preservation and achieve the purpose of rapid detection. The results show that the Au@Ag/CW NC array substrate is a periodic cubic ring structure. The sensitivity, uniformity, reproducibility, and stability of composite Au@Ag/CW NC array substrates are verified by comparing the Raman activities of various substrates. The feasibility of using the substrate to realize rapid SERS detection, compared with the advantages and disadvantages of the traditional soaking method, proved that the prepared substrate and improvement direction have excellent potential for application and development prospects in the field of rapid food additive detection.

[Efficacy of noninvasive high-frequency oscillatory ventilation versus nasal intermittent positive pressure ventilation as post-extubation respiratory support in preterm infants: a Meta analysis]. / æ— åˆ›é«˜é¢‘æŒ¯è¡é€šæ°”ä¸Žç»é¼»é—´æ­‡æ­£åŽ‹é€šæ°”ä½œä¸ºæ—©äº§å„¿æ‹”ç®¡åŽå‘¼å¸æ”¯æŒç–—æ•ˆæ¯”è¾ƒçš„Meta分析.

OBJECTIVES: To systematically evaluate the efficacy and safety of noninvasive high-frequency oscillatory ventilation (NHFOV) versus nasal intermittent positive pressure ventilation (NIPPV) as post-extubation respiratory support in preterm infants. METHODS: China National Knowledge Infrastructure, Wanfang Data, Chinese Journal Full-text Database, China Biology Medicine disc, PubMed, Web of Science, and the Cochrane Library were searched for articles on NHFOV and NIPPV as post-extubation respiratory support in preterm infants published up to August 31, 2022. RevMan 5.4 software and Stata 17.0 software were used for a Meta analysis to compare related indices between the NHFOV and NIPPV groups, including reintubation rate within 72 hours after extubation, partial pressure of carbon dioxide (PCO2) at 6-24 hours after switch to noninvasive assisted ventilation, and the incidence rates of bronchopulmonary dysplasia (BPD), air leak, nasal damage, periventricular leukomalacia (PVL), intraventricular hemorrhage (IVH), and retinopathy of prematurity (ROP). RESULTS: A total of 9 randomized controlled trials were included. The Meta analysis showed that compared with the NIPPV group, the NHFOV group had significantly lower reintubation rate within 72 hours after extubation (RR=0.67, 95%CI: 0.52-0.88, P=0.003) and PCO2 at 6-24 hours after switch to noninvasive assisted ventilation (MD=-4.12, 95%CI: -6.12 to -2.13, P<0.001). There was no significant difference between the two groups in the incidence rates of complications such as BPD, air leak, nasal damage, PVL, IVH, and ROP (P>0.05). CONCLUSIONS: Compared with NIPPV, NHFOV can effectively remove CO2 and reduce the risk of reintubation, without increasing the incidence of complications such as BPD, air leak, nasal damage, PVL, and IVH, and therefore, it can be used as a sequential respiratory support mode for preterm infants after extubation.

Plasmon Driven Nanocrystal Transformation by Aluminum Nano-Islands with an Alumina Layer.

The plasmonic photothermal effects of metal nanostructures have recently become a new priority of studies in the field of nano-optics. Controllable plasmonic nanostructures with a wide range of responses are crucial for effective photothermal effects and their applications. In this work, self-assembled aluminum nano-islands (Al NIs) with a thin alumina layer are designed as a plasmonic photothermal structure to achieve nanocrystal transformation via multi-wavelength excitation. The plasmonic photothermal effects can be controlled by the thickness of the Al2O3 and the intensity and wavelength of the laser illumination. In addition, Al NIs with an alumina layer have good photothermal conversion efficiency even in low temperature environments, and the efficiency will not decline significantly after storage in air for 3 months. Such an inexpensive Al/Al2O3 structure with a multi-wavelength response provides an efficient platform for rapid nanocrystal transformation and a potential application for the wide-band absorption of solar energy.

Strong Plasmon-Mie Resonance in Si@Pd Core-Ω Shell Nanocavity.

The surface plasmon resonance (SPR) and localized surface plasmon resonance (LSPR) can be used to enhance the generation of the hot electrons in plasmon metal nanocavity. In this paper, Pd nanomembrane (NMB) is sputtered on the surface of Si nanosphere (NS) on glass substrate to form the Si@Pd core-Ω shell nanocavity. A plasmon-Mie resonance is induced in the nanocavity by coupling the plasmon resonance with the Mie resonance to control the optical property of Si NS. When this nanocavity is excited by near-infrared-1 (NIR-1, 650 nm-900 nm) femtosecond (fs) laser, the luminescence intensity of Si NS is dramatically enhanced due to the synergistic interaction of plasmon and Mie resonance. The generation of resonance coupling regulates resonant mode of the nanocavity to realize multi-dimensional nonlinear optical response, which can be utilized in the fields of biological imaging and nanoscale light source.

Light-Triggered Reversible Swelling of Polymeric Spheres via Surfactant-Free RAFT Emulsion Polymerization-Induced Self-Assembly.

Responsive photonic crystals (RPCs) assembled by monodisperse colloidal particles have attracted enormous interest recently due to their tremendous applications in smart devices. Their structural colors can be determined by particle sizes. However, the lack of a reliable way to tune the sizes in situ limits their development. Herein, we present an efficient route to solve this problem through the fabrication of spherical polymeric particles with light-triggered reversible swelling behavior via surfactant-free reversible addition-fragmentation chain transfer (RAFT) emulsion polymerization-induced self-assembly (PISA). Amphiphilic macro-RAFT agents containing azobenzene groups were synthesized and subsequently employed to mediate the polymerization of methyl methacrylate. Uniform submicron spheres were obtained by modulating solid contents and other parameters. Benefiting from the photoisomerization of azobenzene moieties, the particle sizes expanded and contracted upon alternative ultraviolet/visible-light irradiation accordingly. This strategy will be a supplement to the emulsion PISA and especially give aid to the progress of the RPC materials.