Transferable non-invasive modal fusion-transformer (NIMFT) for end-to-end hand gesture recognition.

Objective.Recent studies have shown that integrating inertial measurement unit (IMU) signals with surface electromyographic (sEMG) can greatly improve hand gesture recognition (HGR) performance in applications such as prosthetic control and rehabilitation training. However, current deep learning models for multimodal HGR encounter difficulties in invasive modal fusion, complex feature extraction from heterogeneous signals, and limited inter-subject model generalization. To address these challenges, this study aims to develop an end-to-end and inter-subject transferable model that utilizes non-invasively fused sEMG and acceleration (ACC) data.Approach.The proposed non-invasive modal fusion-transformer (NIMFT) model utilizes 1D-convolutional neural networks-based patch embedding for local information extraction and employs a multi-head cross-attention (MCA) mechanism to non-invasively integrate sEMG and ACC signals, stabilizing the variability induced by sEMG. The proposed architecture undergoes detailed ablation studies after hyperparameter tuning. Transfer learning is employed by fine-tuning a pre-trained model on new subject and a comparative analysis is performed between the fine-tuning and subject-specific model. Additionally, the performance of NIMFT is compared to state-of-the-art fusion models.Main results.The NIMFT model achieved recognition accuracies of 93.91%, 91.02%, and 95.56% on the three action sets in the Ninapro DB2 dataset. The proposed embedding method and MCA outperformed the traditional invasive modal fusion transformer by 2.01% (embedding) and 1.23% (fusion), respectively. In comparison to subject-specific models, the fine-tuning model exhibited the highest average accuracy improvement of 2.26%, achieving a final accuracy of 96.13%. Moreover, the NIMFT model demonstrated superiority in terms of accuracy, recall, precision, and F1-score compared to the latest modal fusion models with similar model scale.Significance.The NIMFT is a novel end-to-end HGR model, utilizes a non-invasive MCA mechanism to integrate long-range intermodal information effectively. Compared to recent modal fusion models, it demonstrates superior performance in inter-subject experiments and offers higher training efficiency and accuracy levels through transfer learning than subject-specific approaches.

Rediscovering AMD with Swept-Source OCT Imaging: The 2022 Charles L. Schepens, MD, Lecture.

PURPOSE: Swept-source optical coherence tomography angiography (SS-OCTA) scans of eyes with age-related macular degeneration (AMD) were used to replace color, autofluorescence, infrared reflectance, and dye-based fundus angiographic imaging for the diagnosis and staging of AMD. Through the use of different algorithms with the SS-OCTA scans, both structural and angiographic information can be viewed and assessed using both cross-sectional and en face imaging strategies. DESIGN: Presented at the 2022 Charles L. Schepens, MD, Lecture at the American Academy of Ophthalmology Retina Subspecialty Day, Chicago, Illinois, on September 30, 2022 PARTICIPANTS: Patients with AMD METHODS: Review of published literature and ongoing clinical research using SS-OCTA imaging in AMD. MAIN OUTCOME MEASURES: SS-OCTA imaging of AMD at different stages of disease progression. RESULTS: Volumetric SS-OCTA dense raster scans were used to diagnose and stage both exudative and nonexudative AMD. In eyes with nonexudative AMD, a single SS-OCTA scan was used to detect and measure structural features in the macula such as the area and volume of both typical soft drusen and calcified drusen, the presence and location of hyperreflective foci, the presence of reticular pseudodrusen, also known as subretinal drusenoid deposits, the thickness of the outer retinal layer, the presence and thickness of basal laminar deposits, the presence and area of persistent choroidal hypertransmission defects, and the presence of treatment-naïve nonexudative macular neovascularization. In eyes with exudative AMD, the same SS-OCTA scan pattern was used to detect and measure the presence of macular fluid, the presence and type of macular neovascularization, and the response of exudation to treatment with vascular endothelial growth factor inhibitors. In addition, the same scan pattern was used to quantitate choriocapillaris (CC) perfusion, CC thickness, choroidal thickness, and the vascularity of the choroid. CONCLUSIONS: Compared with using several different instruments to perform multimodal imaging, a single SS-OCTA scan provides a convenient, comfortable, and comprehensive approach for obtaining qualitative and quantitative anatomic and angiographic information to monitor the onset, progression, and response to therapies in both nonexudative and exudative AMD.

All-solid highly sensitive fiber-tip magnetic field sensor based on a Fabry-Perot interferometer with a breakpoint structure.

An all-solid fiber-tip Fabry-Perot interferometer (FPI) coated with a nickel film is proposed and experimentally verified for magnetic field sensing with high sensitivity. It is fabricated by splicing a segment of a thin-wall capillary tube to a standard single-mode fiber (SMF), then inserting a tiny segment of fiber with a smaller diameter into the capillary tube, and creating an ultra-narrow air-gap at the SMF end to form an FPI. When the device is exposed to magnetic field, the capillary tube is strained due to the magnetostrictive effect of the nickel film coated on its outer surface. In addition, owing to the unique breakpoint sensitivity-enhancement structure of the air-gap FPI, the elongation of the capillary tube whose length is over 100 times longer than the air-gap width is entirely transferred to the cavity length change of the FPI, and the sensor is extremely sensitive to the magnetic field as proved by our experiments, achieving a high sensitivity of up to 2.236 nm/mT for a linear magnetic field range from 40 to 60 mT, as well as a low-temperature cross-sensitivity of 56 µT/°C. The all-solid stable structure, compact size (total length of ∼3.0 mm), and reflective working mode with high magnetic field sensitivity indicate that this sensor has good application prospects.

Precise molecular engineering for the preparation of pyridinium photosensitizers with efficient ROS generation and photothermal conversion.

Organic photosensitizers (PSs) with aggregation-induced emission properties have great development potential in the integrated application of multi-mode diagnosis and treatment of photodynamic therapy (PDT) and photothermal therapy (PTT). However, preparing high-quality PSs with both optical and biological properties, high reactive oxygen species (ROS) and photothermal conversion ability are undoubtedly a great challenge. In this work, a series of pyridinium AIE PSs modified with benzophenone have been synthesized. A wide wavelength range of fluorescent materials was obtained by changing the conjugation and donor-acceptor strength. TPAPs5 has a significant advantage over similar compounds, and we have also identified the causes of high ROS generation and high photothermal conversion in terms of natural transition orbitals, excited state energy levels, ground-excited state configuration differences and recombination energy. Interestingly, migration of target sites was also found in biological imaging experiments, which also provided ideas for the design of double-targeted fluorescent probes. Therefore, the present work proposed an effective molecular design strategy for synergistic PDT and PTT therapy.

Wearable 12-Lead ECG Acquisition Using a Novel Deep Learning Approach from Frank or EASI Leads with Clinical Validation.

The 12-lead electrocardiogram (ECG) is crucial in assessing patient decisions. However, portable ECG devices capable of acquiring a complete 12-lead ECG are scarce. For the first time, a deep learning-based method is proposed to reconstruct the 12-lead ECG from Frank leads (VX, VY, and VZ) or EASI leads (VES, VAS, and VAI). The innovative ECG reconstruction network called M2Eformer is composed of a 2D-ECGblock and a ProbDecoder module. The 2D-ECGblock module adaptively segments EASI leads into multi-periods based on frequency energy, transforming the 1D time series into a 2D tensor representing within-cycle and between-cycle variations. The ProbDecoder module aims to extract Probsparse self-attention and achieve one-step output for the target leads. Experimental results from comparing recorded and reconstructed 12-lead ECG using Frank leads indicate that M2Eformer outperforms traditional ECG reconstruction methods on a public database. In this study, a self-constructed database (10 healthy individuals + 15 patients) was utilized for the clinical diagnostic validation of ECG reconstructed from EASI leads. Subsequently, both the ECG reconstructed using EASI and the recorded 12-lead ECG were subjected to a double-blind diagnostic experiment conducted by three cardiologists. The overall diagnostic consensus among three cardiology experts, reaching a rate of 96%, indicates the significant utility of EASI-reconstructed 12-lead ECG in facilitating the diagnosis of cardiac conditions.

Development and Validation of an Interpretable Conformal Predictor to Predict Sepsis Mortality Risk: Retrospective Cohort Study.

BACKGROUND: Early and reliable identification of patients with sepsis who are at high risk of mortality is important to improve clinical outcomes. However, 3 major barriers to artificial intelligence (AI) models, including the lack of interpretability, the difficulty in generalizability, and the risk of automation bias, hinder the widespread adoption of AI models for use in clinical practice. OBJECTIVE: This study aimed to develop and validate (internally and externally) a conformal predictor of sepsis mortality risk in patients who are critically ill, leveraging AI-assisted prediction modeling. The proposed approach enables explaining the model output and assessing its confidence level. METHODS: We retrospectively extracted data on adult patients with sepsis from a database collected in a teaching hospital at Beth Israel Deaconess Medical Center for model training and internal validation. A large multicenter critical care database from the Philips eICU Research Institute was used for external validation. A total of 103 clinical features were extracted from the first day after admission. We developed an AI model using gradient-boosting machines to predict the mortality risk of sepsis and used Mondrian conformal prediction to estimate the prediction uncertainty. The Shapley additive explanation method was used to explain the model. RESULTS: A total of 16,746 (80%) patients from Beth Israel Deaconess Medical Center were used to train the model. When tested on the internal validation population of 4187 (20%) patients, the model achieved an area under the receiver operating characteristic curve of 0.858 (95% CI 0.845-0.871), which was reduced to 0.800 (95% CI 0.789-0.811) when externally validated on 10,362 patients from the Philips eICU database. At a specified confidence level of 90% for the internal validation cohort the percentage of error predictions (n=438) out of all predictions (n=4187) was 10.5%, with 1229 (29.4%) predictions requiring clinician review. In contrast, the AI model without conformal prediction made 1449 (34.6%) errors. When externally validated, more predictions (n=4004, 38.6%) were flagged for clinician review due to interdatabase heterogeneity. Nevertheless, the model still produced significantly lower error rates compared to the point predictions by AI (n=1221, 11.8% vs n=4540, 43.8%). The most important predictors identified in this predictive model were Acute Physiology Score III, age, urine output, vasopressors, and pulmonary infection. Clinically relevant risk factors contributing to a single patient were also examined to show how the risk arose. CONCLUSIONS: By combining model explanation and conformal prediction, AI-based systems can be better translated into medical practice for clinical decision-making.

Security enhanced routing and spectrum allocation against crosstalk attacks for confidential lightpath in elastic optical networks.

Elastic optical network (EON) is a critical transmission infrastructure for emerging new applications due to its spectral efficiency and flexibility. Nowadays, numerous confidential lightpaths (CLPs) are carried over EON to support security-sensitive users. However, they are vulnerable to crosstalk attacks at the optical layer, typically aimed at eavesdropping on the carried data or even disrupting connections. Due to the transparent nature of the optical signals, such attacks are difficult to detect and could last for a long time, resulting in data leakage even spreading throughout the network. This paper presents a novel routing and spectrum allocation (RSA) algorithm to protect CLPs from crosstalk attacks. We investigate intra-channel and inter-channel crosstalk attacks and develop a metric to quantify crosstalk leakage risks (CLRs). We first formulate an ILP model to plan CLPs with a minimum CLR. To solve the same problem for large-scale networks, we also propose a heuristic algorithm, i.e., crosstalk-attack-aware RSA. Results indicate that the proposed algorithm is capable of reducing CLR by 23%.

SR-TTS: a rhyme-based end-to-end speech synthesis system.

Deep learning has significantly advanced text-to-speech (TTS) systems. These neural network-based systems have enhanced speech synthesis quality and are increasingly vital in applications like human-computer interaction. However, conventional TTS models still face challenges, as the synthesized speeches often lack naturalness and expressiveness. Additionally, the slow inference speed, reflecting low efficiency, contributes to the reduced voice quality. This paper introduces SynthRhythm-TTS (SR-TTS), an optimized Transformer-based structure designed to enhance synthesized speech. SR-TTS not only improves phonological quality and naturalness but also accelerates the speech generation process, thereby increasing inference efficiency. SR-TTS contains an encoder, a rhythm coordinator, and a decoder. In particular, a pre-duration predictor within the cadence coordinator and a self-attention-based feature predictor work together to enhance the naturalness and articulatory accuracy of speech. In addition, the introduction of causal convolution enhances the consistency of the time series. The cross-linguistic capability of SR-TTS is validated by training it on both English and Chinese corpora. Human evaluation shows that SR-TTS outperforms existing techniques in terms of speech quality and naturalness of expression. This technology is particularly suitable for applications that require high-quality natural speech, such as intelligent assistants, speech synthesized podcasts, and human-computer interaction.

Development and validation of a deep interpretable network for continuous acute kidney injury prediction in critically ill patients.

Early detection of acute kidney injury (AKI) may provide a crucial window of opportunity to prevent further injury, which helps improve clinical outcomes. This study aimed to develop a deep interpretable network for continuously predicting the 24-hour AKI risk in real-time and evaluate its performance internally and externally in critically ill patients. A total of 21,163 patients' electronic health records sourced from Beth Israel Deaconess Medical Center (BIDMC) were first included in building the model. Two external validation populations included 3025 patients from the Philips eICU Research Institute and 2625 patients from Zhongda Hospital Southeast University. A total of 152 intelligently engineered predictors were extracted on an hourly basis. The prediction model referred to as DeepAKI was designed with the basic framework of squeeze-and-excitation networks with dilated causal convolution embedded. The integrated gradients method was utilized to explain the prediction model. When performed on the internal validation set (3175 [15 %] patients from BIDMC) and the two external validation sets, DeepAKI obtained the area under the curve of 0.799 (95 % CI 0.791-0.806), 0.763 (95 % CI 0.755-0.771) and 0.676 (95 % CI 0.668-0.684) for continuousAKI prediction, respectively. For model interpretability, clinically relevant important variables contributing to the model prediction were informed, and individual explanations along the timeline were explored to show how AKI risk arose. The potential threats to generalisability in deep learning-based models when deployed across health systems in real-world settings were analyzed.

Engineering Detrimental Functional Groups in Conductive Additives Toward High-Performance All-Solid-State Batteries.

Conductive additives are of great importance for the adequate utilization of active materials in all-solid-state lithium batteries by establishing conductive networks in the composite cathode. However, it usually causes severe interfacial side reactions with solid electrolytes, especially sulfide electrolytes, leading to sluggish ion transportation and accelerated performance degradation. Herein, a simple hydrogen thermal reduction process is proposed on a commonly used conductive additive Super P, which effectively removes the surface oxygen functional groups and weakens the interfacial side reactions with sulfide. With a small amount of 1 wt % reduced Super P, ASSLBs demonstrates a competitive capacity of 180.2 mAh g-1, which is much higher than the 130.8 mAh g-1 of untreated Super P. Impressively, reduced Super P based ASSLBs also exhibit a higher capacity retention of 81.8 % than 64.6 % of untreated Super P. The cathode interfacial chemical evolutions reveal that reduced Super P could effectively alleviate the side reactions of sulfide. Reduced Super P shows better reversible capacity compared to reduced carbon nanofiber with almost no loss of capacity retention, due to its more complete conductive network. Our results highlight the importance of oxygen-containing functional groups for conductive additives, lightening the prospect of low-cost 0D conductive additives for practical ASSLBs.

Student Loss: Towards the Probability Assumption in Inaccurate Supervision.

Noisy labels are often encountered in datasets, but learning with them is challenging. Although natural discrepancies between clean and mislabeled samples in a noisy category exist, most techniques in this field still gather them indiscriminately, which leads to their performances being partially robust. In this paper, we reveal both empirically and theoretically that the learning robustness can be improved by assuming deep features with the same labels follow a student distribution, resulting in a more intuitive method called student loss. By embedding the student distribution and exploiting the sharpness of its curve, our method is naturally data-selective and can offer extra strength to resist mislabeled samples. This ability makes clean samples aggregate tightly in the center, while mislabeled samples scatter, even if they share the same label. Additionally, we employ the metric learning strategy and develop a large-margin student (LT) loss for better capability. It should be noted that our approach is the first work that adopts the prior probability assumption in feature representation to decrease the contributions of mislabeled samples. This strategy can enhance various losses to join the student loss family, even if they have been robust losses. Experiments demonstrate that our approach is more effective in inaccurate supervision. Enhanced LT losses significantly outperform various state-of-the-art methods in most cases. Even huge improvements of over 50% can be obtained under some conditions. An implementation of the main codes is available at https://github.com/Zhangshuojackpot/Student-Loss.

Galectin from Trichinella spiralis alleviates DSS-induced colitis in mice by regulating the intestinal microbiota.

According to numerous reports, Trichinella spiralis (T. spiralis) and its antigens can reduce intestinal inflammation by modulating regulatory immunological responses in the host to maintain immune homeostasis. Galectin has been identified as a protein that is produced by T. spiralis, and its characterization revealed this protein has possible immune regulatory activity. However, whether recombinant T. spiralis galectin (rTs-gal) can cure dextran sulfate sodium (DSS)-induced colitis remains unknown. Here, the ability of rTs-gal to ameliorate experimental colitis in mice with inflammatory bowel disease (IBD) as well as the potential underlying mechanism were investigated. The disease activity index (DAI), colon shortening, inflammatory cell infiltration, and histological damage were used as indicators to monitor clinical symptoms of colitis. The results revealed that the administration of rTs-gal ameliorated these symptoms. According to Western blotting and ELISA results, rTs-gal may suppress the excessive inflammatory response-mediated induction of TLR4, MyD88, and NF-κB expression in the colon. Mice with colitis exhibit disruptions in the gut flora, including an increase in gram-negative bacteria, which in turn can result in increased lipopolysaccharide (LPS) production. However, injection of rTs-gal may inhibit changes in the gut microbiota, for example, by reducing the prevalence of Helicobacter and Bacteroides, which produce LPS. The findings of the present study revealed that rTs-gal may inhibit signalling pathways that involve enteric bacteria-derived LPS, TLR4, and NF-κB in mice with DSS-induced colitis and attenuate DSS-induced colitis in animals by modulating the gut microbiota. These findings shed additional light on the immunological processes underlying the beneficial effects of helminth-derived proteins in medicine.

Spectral-Domain and Swept-Source OCT Angiographic Scans Yield Similar Drusen Measurements When Processed with the Same Algorithm.

Purpose: An algorithm developed to obtain drusen area and volume measurements using swept-source OCT angiography (SS-OCTA) scans was tested on spectral-domain OCT angiography (SD-OCTA) scans. Design: Retrospective study. Participants: Forty pairs of scans from 27 eyes with intermediate age-related macular degeneration and drusen. Methods: Patients underwent both SD-OCTA and SS-OCTA imaging at the same visit using the 6 mm × 6 mm OCTA scan patterns. Using the same algorithm, we obtained drusen area and volume measurements within both 3 mm and 5 mm fovea-centered circles. Paired 2-sample t-tests were performed along with Pearson's correlation tests. Main Outcome Measures: Mean square root (sqrt) drusen area and cube root (cbrt) drusen volume within the 3 mm and 5 mm fovea-centered circles. Results: Mean sqrt drusen area values from SD-OCTA and SS-OCTA scans were 1.57 (standard deviation [SD] 0.57) mm and 1.49 (SD 0.58) mm in the 3 mm circle and 1.88 (SD 0.59) mm and 1.76 (SD 0.58) mm in the 5 mm circle, respectively. Mean cbrt drusen volume measurements were 0.54 (SD 0.19) mm and 0.51 (SD 0.20) mm in the 3 mm circle, and 0.60 (SD 0.17) mm and 0.57 (SD 0.17) mm in the 5 mm circle. Small differences in area and volume measurements were found (all P < 0.001); however, the correlations between the instruments were strong (all coefficients > 0.97; all P < 0.001). Conclusions: An algorithm originally developed for SS-OCTA scans performs well when used to obtain drusen volume and area measurements from SD-OCTA scans; thus, a separate SD-OCT structural scan is unnecessary to obtain measurements of drusen. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

A Review on Atrial Fibrillation Detection From Ambulatory ECG.

Atrial fibrillation (AF) is a prevalent clinical arrhythmia disease and is an important cause of stroke, heart failure, and sudden death. Due to the insidious onset and no obvious clinical symptoms of AF, the status of AF diagnosis and treatment is not optimal. Early AF screening or detection is essential. Internet of Things (IoT) and artificial intelligence (AI) technologies have driven the development of wearable electrocardiograph (ECG) devices used for health monitoring, which are an effective means of AF detection. The main challenges of AF analysis using ambulatory ECG include ECG signal quality assessment to select available ECG, the robust and accurate detection of QRS complex waves to monitor heart rate, and AF identification under the interference of abnormal ECG rhythm. Through ambulatory ECG measurement and intelligent detection technology, the probability of postoperative recurrence of AF can be reduced, and personalized treatment and management of patients with AF can be realized. This work describes the status of AF monitoring technology in terms of devices, algorithms, clinical applications, and future directions.

Systematic review and meta-analysis of mass spectrometry proteomics applied to ocular fluids to assess potential biomarkers of age-related macular degeneration.

BACKGROUND: Age-related macular degeneration (AMD) is a significant cause of severe vision loss. The main purpose of this study was to identify mass spectrometry proteomics-based potential biomarkers of AMD that contribute to understanding the mechanisms of disease and aiding in early diagnosis. METHODS: This study retrieved studies that aim to detect differences relate to proteomics in AMD patients and healthy control groups by mass spectrometry (MS) proteomics approaches. The search process was accord with PRISMA guidelines (PROSPERO database: CRD42023388093). Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes Pathway Analysis (KEGG) were performed on differentially expressed proteins (DEPs) in the included articles using the DAVID database. DEPs were included in a meta-analysis when their effect size could be computed in at least two research studies. The effect size of measured proteins was transformed to the log2-fold change. Protein‒protein interaction (PPI) analysis was conducted on proteins that were statistically significant in the meta-analysis using the String online database. RESULTS: Eleven studies fulfilled the inclusion criteria, and 161 DEPs were identified. The GO analysis showed that AMD is significantly related to proteolysis, extracellular exosome and protein binding. In KEGG, the most significant pathway was the complement and coagulation cascades. Meta-analysis results suggested that eight proteins were statistically significant, and according to PPI results, the most significant four proteins were serotransferrin (TF), apolipoprotein A1 (APOA1), complement C3 (C3) and lipocalin-1 (LCN1). CONCLUSIONS: Four possible biomarkers, TF, APOA1, C3 and LCN1, were found to be significant in the pathogenesis of AMD and need to be further validated. Further studies should be performed to evaluate diagnostic and therapeutic value of these proteins.

[Artificial intelligence in wearable electrocardiogram monitoring].

Electrocardiogram (ECG) monitoring owns important clinical value in diagnosis, prevention and rehabilitation of cardiovascular disease (CVD). With the rapid development of Internet of Things (IoT), big data, cloud computing, artificial intelligence (AI) and other advanced technologies, wearable ECG is playing an increasingly important role. With the aging process of the population, it is more and more urgent to upgrade the diagnostic mode of CVD. Using AI technology to assist the clinical analysis of long-term ECGs, and thus to improve the ability of early detection and prediction of CVD has become an important direction. Intelligent wearable ECG monitoring needs the collaboration between edge and cloud computing. Meanwhile, the clarity of medical scene is conducive for the precise implementation of wearable ECG monitoring. This paper first summarized the progress of AI-related ECG studies and the current technical orientation. Then three cases were depicted to illustrate how the AI in wearable ECG cooperate with the clinic. Finally, we demonstrated the two core issues-the reliability and worth of AI-related ECG technology and prospected the future opportunities and challenges.

Near-Infrared Conjugated Polymers Containing Thermally Activated Delayed Fluorescence Units Enable Enhanced Photothermal Therapy.

Photothermal therapy (PTT) using near-infrared (NIR) conjugated polymers as photosensitizers has exhibited enormous potential for tumor treatment. However, most NIR conjugated polymers have poor therapeutic efficacy due to their faint absorbance in the NIR region and low photothermal conversion efficiency (PCE). Herein, a valuable strategy for designing NIR polymeric photosensitizer PEKBs with an enhanced PCE accompanied by strong NIR absorbance is proposed by means of inserting TPA-AQ as a thermally activated delayed fluorescence unit into a polymeric backbone. In these PEKBs, PEKB-244 with the appropriate molar content of the TPA-AQ unit displays the strongest NIR absorbance and the highest PCE of 64.5%. Theoretical calculation results demonstrate that the TPA-AQ unit in the polymeric backbone can modulate the intramolecular charge transfer effects and the excited energy decay routes for generating higher heat. The prepared nanoparticles (PEKB-244 NPs) exhibit remarkable photothermal conversion capacities and great biocompatibility in aqueous solutions. Moreover, PEKB-244 NPs also show outstanding photothermal stability, displaying negligible changes in the absorbance within 808 nm irradiation of 1 h (800 mW cm-2). Both in vitro and in vivo experimental results further indicate that PEKB-244 NPs can substantially kill cancer cells under NIR laser irradiation. We anticipate that this novel molecular design strategy can be employed to develop excellent NIR photosensitizers for cancer photothermal therapy.

A gas-permeable, durable, and sensitive wearable strain sensor through thermal-radiation-promoted in situ welding.

A convenient strategy for fabricating a wearable sensor with favorable durability and sensitivity is reported. This approach exploits the reconstructed hydrogen bonds within the thermoplastic polyurethane (TPU) during the heating evaporation of metal to form robust welding of the fibers in the substrate. The sensor can steadily monitor pulse waves and facilitate real-time human-machine interaction.

Free Radical-Mediated Photocyclization of Triphenylphosphindole Oxides for Photoactivated and Self-Reported Lipid Peroxidation.

Photocyclization is demonstrated as a powerful tool for building complicated polycyclic molecules. And efficient photocyclization is competent as an artful strategy to develop photo-responsive smart materials. Herein, an efficient free radical-mediated photocyclization for triphenylphosphindole oxide (TPPIO) derivatives to generate tribenzophosphindole oxide (TBPIO) derivatives at ambient condition is reported. The reaction mechanism and substituent effect on photocyclization efficiency are thoroughly investigated. Additionally, photophysical and photochemical properties of TPPIO and TBPIO derivatives are measured for comparison and deeply deciphered by theoretical calculation. TPPIO derivatives own typical aggregation-induced emission feature but barely generate reactive oxygen species (ROS), while TBPIO derivatives experience aggregation-caused quenching but show efficient Type I ROS generation capacity. Further, in vitro experiments demonstrate that this photo-conversion can efficiently occur in situ in living cells to activate photodynamic therapy (PDT) effect to trigger lipid peroxidation with selective fluorescence "light up" in lipid droplet area under continuous irradiation. This work extends the optoelectronically and biologically interesting phosphindole oxide-containing π-conjugated systems through an efficient synthetic strategy, provides in-depth mechanistic descriptions in the aspects of reaction and property, and further presents their great potentials for photoactivated and self-reported PDT.

Reproduction of mode-locked pulses by spectrotemporal domain-informed deep learning.

The accurate reproduction of unique pulse states in a mode-locked fiber laser is an important scientific issue and has wide applications in the laser industry. We present what we believe to be a novel method for automatically and precisely reproducing targeted soliton states in a mode-locked fiber laser by spectrotemporal domain-informed deep learning. Targeted solitons are experimentally reproduced via a superior matching process with a spectrotemporal mean square error (MSE) of 3.99 × 10-5. The outstanding feature of our reproduction algorithm is that the pulse information in both the spectral and temporal domains is jointly adopted for reconstructing targeted soliton states from white noise, rather than establishing arbitrary mode-locked pulse states, as described in previous studies. Additionally, a single-layer perceptron model is proposed to retrieve the phase distribution of a mode-locked pulse, validating the physical completeness of our reproduction approach. Our approach advances ultrafast laser technology, enabling the precise control of pulse dynamics in applications such as optical communication and nonlinear optics.