Wavelength-switchable watt-level continuous wave near-infrared Pr3+:LiYF4 lasers pumped by an InGaN laser diode.

We report a high-performance wavelength-switchable near-infrared Pr3+:LiYF4 (Pr:YLF) laser by InGaN laser diode (LD) pumping. The 895, 922, and 924 nm lasers with low emission cross sections in the Pr:YLF crystal have been successfully realized using a birefringent filter Lyot as well as designing and optimizing optical thin films and the laser resonant cavity. The maximum output powers of the 895, 922, and 924 nm lasers are 2.01, 1.92, and 1.95 W, respectively. As far as we know, these are the highest power for Pr:YLF lasers at 895, 922, and 924 nm so far. The beam quality M x2 and M y2 factors are measured to be 1.85 and 1.71 at 895 nm, 1.94 and 1.67 at 922 nm, and 1.76 and 1.60 at 924 nm, respectively. The laser output power fluctuates within ±3%. In addition, the transmittance of the Lyot is theoretically calculated to achieve laser wavelength switching. The successful realization of the wavelength-switchable watt-level continuous wave near-infrared Pr:YLF laser can provide many practical applications in biomedicine and other fields.

Multiparametric MRI subregion radiomics for preoperative assessment of high-risk subregions in microsatellite instability of rectal cancer patients: A multicenter study.

BACKGROUND: Microsatellite instability (MSI) is associated with treatment response and prognosis in patients with rectal cancer (RC). However, intratumoral heterogeneity limits MSI testing in patients with RC. We developed a subregion radiomics model based on multiparametric magnetic resonance imaging (MRI) to preoperatively assess high-risk subregions with MSI and predict the MSI status of patients with RC. METHODS: This retrospective study included 475 patients (training cohort, 382; external test cohort, 93) with RC from two participating hospitals between April 2017 and June 2023. In the training cohort, subregion radiomic features were extracted from multiparametric MRI, which included T2-weighted, T1-weighted, diffusion-weighted, and contrast-enhanced T1-weighted imaging. MSI-related subregion radiomic features, classical radiomic features, and clinicoradiological variables were gathered to build five predictive models using logistic regression. Kaplan-Meier survival analysis was conducted to explore the prognostic information. RESULTS: Among the 475 patients (median age, 64 years [interquartile range, IQR: 55-70 years];304 men and 171 women), the prevalence of MSI was 11.16% (53/475). The subregion radiomics model outperformed the classical radiomics and clinicoradiological models in both training (area under the curve [AUC]=0.86, 0.72, and 0.59, respectively) and external test cohorts (AUC=0.83, 0.73, and 0.62, respectively). The subregion-clinicoradiological model combining clinicoradiological variables and subregion radiomic features performed the optimal, with AUCs of 0.87 and 0.85 in the training and external test cohorts, respectively. The 3-year disease-free survival rate of MSI groups predicted based on the model was higher than that of the predicted microsatellite stability (MSS) groups in both patient cohorts (training, P=0.032; external test, P=0.046). CONCLUSIONS: We developed and validated a model based on subregion radiomic features of multiparametric MRI to evaluate high-risk subregions with MSI and predict the MSI status of RC preoperatively, which may assist in individualized treatment decisions and positioning for biopsy.

Effects of Various Nectar and Pollen Plants on the Survival, Reproduction, and Predation of Neoseiulus bicaudus.

Neoseiulus bicaudus is a predatory mite species that could potentially be used for the biological control of spider mites and thrips. Floral resources can provide excellent habitats and abundant nutrients for natural enemies. The objective of this experiment was to evaluate the effects of eight floral resources on the longevity, fecundity, and predation ability of N. bicaudus. Among the considered plants, Cnidium monnieri led to the highest longevity (24 days) and fecundity (13.8 eggs) of N. bicaudus, while Tagetes erecta resulted in the lowest longevity (7 days) and fecundity (0.1 eggs) observed in the predatory mites. By comparing the effects of three nectar and pollen plants on the predation of predatory mites, it was observed that N. bicaudus still exhibited a type II functional response to Tetranychus turkestani. In the presence of pollen, the predation efficacy (a/Th) of N. bicaudus exhibited a lower value, compared to that in the absence of pollen (Control: a/Th = 24.00). When pollen was supplied, the maximum consumption (1/Th) of predatory mites was higher than in its absence (Control: 1/Th = 9.90 d-1), with the highest value obtained in the presence of B. officinalis pollen (B. officinalis: 1/Th = 17.86 d-1). The influence coefficient of predation of N. bicaudus on T. turkestani in the presence of pollen was compared in the presence of three nectar and pollen plants: Cnidium monnieri, Centaurea cyanus, and Borago officinalis. At low prey densities, the influence coefficient of C. cyanus exceeded that of B. officinalis, and the overall influence coefficient values were negative (i.e., the presence of pollen reduced predatory mite feeding on T. turkestani). They exhibited similar values at high prey densities, and all of the influence coefficient values were close to 0 (i.e., the presence of pollen had no effect on predatory mite feeding on T. turkestani). The findings revealed that diverse plant species exert differential impacts on N. bicaudus, with some influencing its lifespan and others affecting its reproductive capabilities. Furthermore, the presence of nectar and pollen plants had a significant impact on predatory mite feeding on T. turkestani at low prey densities; however, this effect diminished as the prey density increased. Therefore, we recommend planting C. monnieri, C. cyanus, and B. officinalis in the field to ensure an ample population of predatory mites. The obtained results hold significant implications for the utilization of nectar and pollen plants in eco-friendly pest management strategies within agricultural contexts.

Revisiting Initializing Then Refining: An Incomplete and Missing Graph Imputation Network.

With the development of various applications, such as recommendation systems and social network analysis, graph data have been ubiquitous in the real world. However, graphs usually suffer from being absent during data collection due to copyright restrictions or privacy-protecting policies. The graph absence could be roughly grouped into attribute-incomplete and attribute-missing cases. Specifically, attribute-incomplete indicates that a portion of the attribute vectors of all nodes are incomplete, while attribute-missing indicates that all attribute vectors of partial nodes are missing. Although various graph imputation methods have been proposed, none of them is custom-designed for a common situation where both types of graph absence exist simultaneously. To fill this gap, we develop a novel graph imputation network termed revisiting initializing then refining (RITR), where both attribute-incomplete and attribute-missing samples are completed under the guidance of a novel initializing-then-refining imputation criterion. Specifically, to complete attribute-incomplete samples, we first initialize the incomplete attributes using Gaussian noise before network learning, and then introduce a structure-attribute consistency constraint to refine incomplete values by approximating a structure-attribute correlation matrix to a high-order structure matrix. To complete attribute-missing samples, we first adopt structure embeddings of attribute-missing samples as the embedding initialization, and then refine these initial values by adaptively aggregating the reliable information of attribute-incomplete samples according to a dynamic affinity structure. To the best of our knowledge, this newly designed method is the first end-to-end unsupervised framework dedicated to handling hybrid-absent graphs. Extensive experiments on six datasets have verified that our methods consistently outperform the existing state-of-the-art competitors. Our source code is available at https://github.com/WxTu/RITR.

Noninvasive prediction of perineural invasion in intrahepatic cholangiocarcinoma by clinicoradiological features and computed tomography radiomics based on interpretable machine learning: a multicenter cohort study.

BACKGROUND: Perineural invasion (PNI) of intrahepatic cholangiocarcinoma (ICC) is a strong independent risk factor for tumour recurrence and long-term patient survival. However, there is a lack of noninvasive tools for accurately predicting the PNI status. The authors develop and validate a combined model incorporating radiomics signature and clinicoradiological features based on machine learning for predicting PNI in ICC, and used the Shapley Additive explanation (SHAP) to visualize the prediction process for clinical application. METHODS: This retrospective and prospective study included 243 patients with pathologically diagnosed ICC (training, n =136; external validation, n =81; prospective, n =26, respectively) who underwent preoperative contrast-enhanced computed tomography between January 2012 and May 2023 at three institutions (three tertiary referral centres in Guangdong Province, China). The ElasticNet was applied to select radiomics features and construct signature derived from computed tomography images, and univariate and multivariate analyses by logistic regression were used to identify the significant clinical and radiological variables with PNI. A robust combined model incorporating radiomics signature and clinicoradiological features based on machine learning was developed and the SHAP was used to visualize the prediction process. A Kaplan-Meier survival analysis was performed to compare prognostic differences between PNI-positive and PNI-negative groups and was conducted to explore the prognostic information of the combined model. RESULTS: Among 243 patients (mean age, 61.2 years ± 11.0 (SD); 152 men and 91 women), 108 (44.4%) were diagnosed as PNI-positive. The radiomics signature was constructed by seven radiomics features, with areas under the curves of 0.792, 0.748, and 0.729 in the training, external validation, and prospective cohorts, respectively. Three significant clinicoradiological features were selected and combined with radiomics signature to construct a combined model using machine learning. The eXtreme Gradient Boosting exhibited improved accuracy and robustness (areas under the curves of 0.884, 0.831, and 0.831, respectively). Survival analysis showed the construction combined model could be used to stratify relapse-free survival (hazard ratio, 1.933; 95% CI: 1.093-3.418; P =0.021). CONCLUSIONS: We developed and validated a robust combined model incorporating radiomics signature and clinicoradiological features based on machine learning to accurately identify the PNI statuses of ICC, and visualize the prediction process through SHAP for clinical application.

Augmentation of tumour perfusion by ultrasound and microbubbles: A preclinical study.

BACKGROUND: Hypoperfusion and the resulting hypoxia in solid tumours are critical causes of treatment resistance. Ultrasound-stimulated microbubbles (USMB) enhance tumour perfusion in a mechanism named the "sononeoperfusion" effect, which may relieve tumour hypoperfusion and hypoxia. The aim of this study was to determine the optimal mechanical index (MI) and therapeutic ultrasound exposure time for the sononeoperfusion effect and preliminarily explore the mechanism of sononeoperfusion and its effect on tumours. METHODS: A total of 155 mice bearing MC38 tumours were included in this study. A modified diagnostic ultrasound and microbubbles (Zhifuxian) was used for USMB treatment. Tumour perfusion was evaluated by contrast-enhanced ultrasound (CEUS) and Hoechst 33342. The therapeutic pulse was operated with MIs of 0.1 to 0.5. The ultrasound exposure time was set from 150 s to 600 s. Endothelial nitric oxide synthase (eNOS) inhibition and NO, ATP, and phospho-eNOS (p-eNOS) detection were performed to explore the mechanisms of sononeoperfusion. Hypoxia-inducible factor-1α (HIF-1α) and tumour oxygen partial pressure (pO2) represent hypoxic tumour conditions. RESULTS: Tumour perfusion was increased after USMB treatment at MIs of 0.1-0.4 and ultrasound exposure times of 150 s to 600 s, with optimal augmentation achieved at an MI of 0.3 and ultrasound exposure time of 450 s. The mean fluorescence intensity of Hoechst 33342 after USMB treatment was stronger than that of the control group. Biochemical assays showed a significant increase in ATP, p-eNOS and NO after USMB treatment. PO2 in tumour tissue increased significantly after USMB treatment and was maintained for more than 20 min. CONCLUSIONS: The best sononeoperfusion effect was obtained with an MI of 0.3 and an ultrasound exposure time of 450 s. The effect is most likely related to NO and ATP increases. The sononeoperfusion effect might be a novel way to ameliorate tumour hypoperfusion and hypoxia.

AI-algorithm-assisted 895-nm praseodymium laser emitting sub-100-fs pulses.

Praseodymium (Pr) lasers have achieved outstanding pico- and sub-picosecond pulsations covering the near-infrared (NIR) and visible spectral range in recent years. However, it has been a stagnant task for more than two decades to leapfrog into the sub-100 femtosecond (fs) regime as the Pr gain bandwidths are too narrow for their major transition lines. Although the wide tunability at the NIR bands in the Pr:YLF crystals has been explored, the spectral tails in these transitions suffer severely from weak gains for mode locking, combined with the intricate dispersion control to achieve transform-limit formation. In this work, we target the Pr:YLF's 895-nm line with a specially designed edge-pass filter to balance the gain bandwidth and transitional strength. By deploying a symmetric dispersion scheme and tuning with the soft actor-critic artificial intelligence (AI) algorithm, we have achieved the pulse duration down to sub-100-fs in a Pr laser for the first time. This work also enriches the AI-assisted methodology for ultrafast solid-state laser realizations.

Projections from anteromedial thalamus nucleus to the midcingulate cortex mediate pain and anxiety-like behaviors in mice.

Prior research has demonstrated the involvement of the midcingulate cortex (MCC) and its downstream pathway in pain regulation. However, the mechanism via which pain information is conveyed to the MCC remains unclear. The present study utilized immunohistochemistry, chemogenetics, optogenetics, and behavior detection methods to explore the involvement of MCC, anteromedial thalamus nucleus (AM), and AM-MCC pathway in pain and emotional regulation. Chemogenetics or optogenetics methods were employed to activate/inhibit MCCCaMKIIα, AMCaMKIIα, AMCaMKIIα-MCC pathway. This manipulation evokes/relieves mechanical and partial heat hyperalgesia, as well as anxiety-like behaviors. In the complete Freund,s adjuvant (CFA) inflammatory pain model, chemogenetic inhibition of the AMCaMKIIα-MCCCaMKIIα pathway contributed to pain relief. Notably, this study presented the first evidence implicating the AM in the regulation of nociception and negative emotions. Additionally, it was observed that the MCC primarily receives projections from the AM, highlighting the crucial role of this pathway in the transmission of pain and emotional information.

Sononeoperfusion effect by ultrasound and microbubble promotes nitric oxide release to alleviate hypoxia in a mouse MC38 tumor model.

Tumor hypoperfusion not only impedes therapeutic drug delivery and accumulation, but also leads to a hypoxic and acidic tumor microenvironment, resulting in tumor proliferation, invasion, and therapeutic resistance. Sononeoperfusion effect refers to tumor perfusion enhancement using ultrasound and microbubbles. This study aimed to further investigate hypoxia alleviation by sononeoperfusion effect and explore the characteristics and mechanism of sononeoperfusion effect. To stimulate the sononeoperfusion effect, mice bearing MC38 colon cancers were included in this study and diagnostic ultrasound for therapy was set at a mechanical index (MI) of 0.1, 0.3, and 0.5, frequency of 3 MHz, pulse length of 5 cycles, and pulse repetition frequency of 2000 Hz. The results demonstrated that a single ultrasound and microbubble (USMB) treatment resulted in tumor perfusion enhancement at MI = 0.3, and nitric oxide (NO) concentration increased at MI = 0.3/0.5 (P < 0.05). However, there were no significant difference in the hypoxia-inducible factor-1α (HIF-1α) or D-lactate (D-LA) (P > 0.05) levels. Multiple sononeoperfusion effects were observed at MI = 0.3/0.5 (P < 0.05). For each treatment, USMB slightly but steadily improved the tumor tissue oxygen partial pressure (pO2) during and post treatment. It alleviated tumor hypoxia by decreasing HIF-1α, D-LA level and the hypoxic immunofluorescence intensity at MI = 0.3/0.5 (P < 0.05). The sononeoperfusion effect was not stimulated after eNOS inhibition. In conclusion, USMB with appropriate MI could lead to a sononeoperfusion effect via NO release, resulting in hypoxia amelioration. The tumors were not resistant to multiple sononeoperfusion effects. Repeated sononeoperfusion is a promising approach for relieving tumor hypoxia and resistance to therapy.

Hierarchically Contrastive Hard Sample Mining for Graph Self-Supervised Pretraining.

Contrastive learning has recently emerged as a powerful technique for graph self-supervised pretraining (GSP). By maximizing the mutual information (MI) between a positive sample pair, the network is forced to extract discriminative information from graphs to generate high-quality sample representations. However, we observe that, in the process of MI maximization (Infomax), the existing contrastive GSP algorithms suffer from at least one of the following problems: 1) treat all samples equally during optimization and 2) fall into a single contrasting pattern within the graph. Consequently, the vast number of well-categorized samples overwhelms the representation learning process, and limited information is accumulated, thus deteriorating the learning capability of the network. To solve these issues, in this article, by fusing the information from different views and conducting hard sample mining in a hierarchically contrastive manner, we propose a novel GSP algorithm called hierarchically contrastive hard sample mining (HCHSM). The hierarchical property of this algorithm is manifested in two aspects. First, according to the results of multilevel MI estimation in different views, the MI-based hard sample selection (MHSS) module keeps filtering the easy nodes and drives the network to focus more on hard nodes. Second, to collect more comprehensive information for hard sample learning, we introduce a hierarchically contrastive scheme to sequentially force the learned node representations to involve multilevel intrinsic graph features. In this way, as the contrastive granularity goes finer, the complementary information from different levels can be uniformly encoded to boost the discrimination of hard samples and enhance the quality of the learned graph embedding. Extensive experiments on seven benchmark datasets indicate that the HCHSM performs better than other competitors on node classification and node clustering tasks. The source code of HCHSM is available at https://github.com/WxTu/HCHSM.

RayMVSNet++: Learning Ray-Based 1D Implicit Fields for Accurate Multi-View Stereo.

Learning-based multi-view stereo (MVS) has by far centered around 3D convolution on cost volumes. Due to the high computation and memory consumption of 3D CNN, the resolution of output depth is often considerably limited. Different from most existing works dedicated to adaptive refinement of cost volumes, we opt to directly optimize the depth value along each camera ray, mimicking the range (depth) finding of a laser scanner. This reduces the MVS problem to ray-based depth optimization which is much more light-weight than full cost volume optimization. In particular, we propose RayMVSNet which learns sequential prediction of a 1D implicit field along each camera ray with the zero-crossing point indicating scene depth. This sequential modeling, conducted based on transformer features, essentially learns the epipolar line search in traditional multi-view stereo. We devise a multi-task learning for better optimization convergence and depth accuracy. We found the monotonicity property of the SDFs along each ray greatly benefits the depth estimation. Our method ranks top on both the DTU and the Tanks & Temples datasets over all previous learning-based methods, achieving an overall reconstruction score of 0.33 mm on DTU and an F-score of 59.48% on Tanks & Temples. It is able to produce high-quality depth estimation and point cloud reconstruction in challenging scenarios such as objects/scenes with non-textured surface, severe occlusion, and highly varying depth range. Further, we propose RayMVSNet++ to enhance contextual feature aggregation for each ray through designing an attentional gating unit to select semantically relevant neighboring rays within the local frustum around that ray. This improves the performance on datasets with more challenging examples (e.g., low-quality images caused by poor lighting conditions or motion blur). RayMVSNet++ achieves state-of-the-art performance on the ScanNet dataset. In particular, it attains an AbsRel of 0.058m and produces accurate results on the two subsets of textureless regions and large depth variation.

STEdge: Self-Training Edge Detection With Multilayer Teaching and Regularization.

Learning-based edge detection has hereunto been strongly supervised with pixel-wise annotations which are tedious to obtain manually. We study the problem of self-training edge detection, leveraging the untapped wealth of large-scale unlabeled image datasets. We design a self-supervised framework with multilayer regularization and self-teaching. In particular, we impose a consistency regularization which enforces the outputs from each of the multiple layers to be consistent for the input image and its perturbed counterpart. We adopt L0-smoothing as the "perturbation" to encourage edge prediction lying on salient boundaries following the cluster assumption in self-supervised learning. Meanwhile, the network is trained with multilayer supervision by pseudo labels which are initialized with Canny edges and then iteratively refined by the network as the training proceeds. The regularization and self-teaching together attain a good balance of precision and recall, leading to a significant performance boost over supervised methods, with lightweight refinement on the target dataset. Through extensive experiments, our method demonstrates strong cross-dataset generality and can improve the original performance of edge detectors after self-training and fine-tuning.

Delving Into Crispness: Guided Label Refinement for Crisp Edge Detection.

Learning-based edge detection usually suffers from predicting thick edges. Through extensive quantitative study with a new edge crispness measure, we find that noisy human-labeled edges are the main cause of thick predictions. Based on this observation, we advocate that more attention should be paid on label quality than on model design to achieve crisp edge detection. To this end, we propose an effective Canny-guided refinement of human-labeled edges whose result can be used to train crisp edge detectors. Essentially, it seeks for a subset of over-detected Canny edges that best align human labels. We show that several existing edge detectors can be turned into a crisp edge detector through training on our refined edge maps. Experiments demonstrate that deep models trained with refined edges achieve significant performance boost of crispness from 17.4% to 30.6%. With the PiDiNet backbone, our method improves ODS and OIS by 12.2% and 12.6% on the Multicue dataset, respectively, without relying on non-maximal suppression. We further conduct experiments and show the superiority of our crisp edge detection for optical flow estimation and image segmentation.

Enhancing Chinese Character Representation With Lattice-Aligned Attention.

Word-character lattice models have been proved to be effective for some Chinese natural language processing (NLP) tasks, in which word boundary information is fused into character sequences. However, due to the inherently unidirectional sequential nature, prior approaches have only learned sequential interactions of character-word instances but fail to capture fine-grained correlations in word-character spaces. In this article, we propose a lattice-aligned attention network (LAN) that aims to model dense interactions over word-character lattice structure for enhancing character representations. By carefully combining cross-lattice module, gated word-character semantic fusion unit, and self-lattice attention module, the network can explicitly capture fine-grained correlations across different spaces (e.g., word-to-character and character-to-character), thus significantly improving model performance. Experimental results on three Chinese NLP benchmark tasks demonstrate that LAN obtains state-of-the-art results compared to several competitive approaches.

Dynamic Modeling Cross-Modal Interactions in Two-Phase Prediction for Entity-Relation Extraction.

Joint extraction of entities and their relations benefits from the close interaction between named entities and their relation information. Therefore, how to effectively model such cross-modal interactions is critical for the final performance. Previous works have used simple methods, such as label-feature concatenation, to perform coarse-grained semantic fusion among cross-modal instances but fail to capture fine-grained correlations over token and label spaces, resulting in insufficient interactions. In this article, we propose a dynamic cross-modal attention network (CMAN) for joint entity and relation extraction. The network is carefully constructed by stacking multiple attention units in depth to dynamic model dense interactions over token-label spaces, in which two basic attention units and a novel two-phase prediction are proposed to explicitly capture fine-grained correlations across different modalities (e.g., token-to-token and label-to-token). Experiment results on the CoNLL04 dataset show that our model obtains state-of-the-art results by achieving 91.72% F1 on entity recognition and 73.46% F1 on relation classification. In the ADE and DREC datasets, our model surpasses existing approaches by more than 2.1% and 2.54% F1 on relation classification. Extensive analyses further confirm the effectiveness of our approach.

Yellow pumped Pr3+-doped ZBLAN fiber laser at 1015 nm.

To date, lasing in the visible to near-infrared wavelengths has been studied for praseodymium-doped fluoride fibers with the upper energy level of 3P0. In this Letter, a fiber laser operating at 1015 nm has been realized for the first time, to the best of our knowledge, which confirms a new mechanism where 1D2 can be the upper energy level. A maximum output power of 241 mW, with a slope efficiency of 30%, was achieved by using a 150-cm-long active fiber pumped at a maximum pump power of 823 mW. Furthermore, the broad emission spectra of Pr3+-doped fibers in the near-infrared band have been exploited as new, to the best of our knowledge, spectral sources.

Query-Adaptive Late Fusion for Hierarchical Fine-Grained Video-Text Retrieval.

Recently, a hierarchical fine-grained fusion mechanism has been proved effective in cross-modal retrieval between videos and texts. Generally, the hierarchical fine-grained semantic representations (video-text semantic matching is decomposed into three levels including global-event representation matching, action-relation representation matching, and local-entity representation matching) to be fused can work well by themselves for the query. However, in real-world scenarios and applications, existing methods failed to adaptively estimate the effectiveness of multiple levels of the semantic representations for a given query in advance of multilevel fusion, resulting in a worse performance than expected. As a result, it is extremely essential to identify the effectiveness of hierarchical semantic representations in a query-adaptive manner. To this end, this article proposes an effective query-adaptive multilevel fusion (QAMF) model based on manipulating multiple similarity scores between the hierarchical visual and text representations. First, we decompose video-side and text-side representations into hierarchical semantic representations consisting of global-event level, action-relation level, and local-entity level, respectively. Then, the multilevel representation of the video-text pair is aligned to calculate the similarity score for each level. Meanwhile, the sorted similarity score curves of the good semantic representation are different from the inferior ones, which exhibit a "cliff" shape and gradually decline (see Fig. fig1 as an example). Finally, we leverage the Gaussian decay function to fit the tail of the score curve and calculate the area under the normalized sorted similarity curve as the indicator of semantic representation effectiveness, namely, the area of good semantic representation is small, and vice versa. Extensive experiments on three public benchmark video-text datasets have demonstrated that our method consistently outperforms the state-of-the-art (SoTA). A simple demo of QAMF will soon be publicly available on our homepage: https://github.com/Lab-ANT.

Wavelength extension and power improvement of red Pr3+:YLF lasers.

High-power red lasers (mainly at 639 and 670 nm) based on Pr3+:YLF crystals have been presented in many works. However, the spectral resources of Pr3+:YLF in the red region have not been fully developed to obtain lasers due to their relatively low emission cross sections and the irrepressible strong emission at ∼639 nm. In this work, we propose a scheme to further develop the spectral resources of Pr3+:YLF in the red region and improve the red laser powers based on this crystal. The laser wavelengths are obtained from 634.5 to 674.7 nm (continuous tunings are achieved at some wavebands). To the best of our knowledge, the output powers obtained at 638.7, 644.6, 670.1, and 674.7 nm (2.88 W, 1.87 W, 3.55 W, and 1.73 W, respectively) are the highest to date. Furthermore, lasing originating from the 3P2 energy level of Pr3+:YLF (∼653 nm) is realized for the first time.

Efficient UV-visible emission enabled by 532†nm CW excitation in an Ho3+-doped ZBLAN fiber.

Rare-earth-doped ZBLAN (ZrF4-BaF2-LaF3-AlF3-NaF) fibers have evolved to become promising candidates for efficient UV-visible emission because of their low phonon energy and low optical losses, as well as their well-defined absorption bands. We investigate the efficient emission of UV-visible light in a low-concentration (0.1 mol%) Ho3+-doped ZBLAN fiber excited by a 532 nm CW laser. In addition to the direct populating of the thermalized 5F4+5S2 levels by ground-state absorption, the upconversion processes responsible for UV-visible emission from the higher emitting levels, 3P1+3D3, 3K7+5G4, 5G5, and 5F3, of the Ho3+ ions are examined using excited-state absorption. The dependence of UV-visible fluorescence intensity on launched green pump power is experimentally determined, confirming the one-photon and two-photon characters of the observed processes. We theoretically investigate the excitation power dependence of the population density for nine Ho3+ levels based on a rate equation model. This qualitative model has shown a good agreement with the measured power dependence of UV-visible emission. Moreover, the emission cross-sections for blue, green, red, and deep-red light in the visible region are measured using the Füchtbauer-Ladenburg method and corroborated by McCumber theory, and the corresponding gain coefficients are derived. We propose an alternative approach to achieve efficient UV-visible emission in an Ho3+-doped ZBLAN fiber using a cost-effective, high-brightness 532 nm laser.

High-power yellow DSR pulses generated from a mode-locked Dy:ZBLAN fiber laser.

Ultrafast yellow lasers are in high demand in recent biomedical and medical applications; however, direct emission of mode-locked pulses in yellow at the high-power level still presents a huge technical challenge to date. By integrating the nonlinear polarization rotation (NPR) scheme into a Dy:ZBLAN fiber laser, dissipative soliton resonance pulses at ∼575 nm are demonstrated for the first time, to the best of our knowledge. The average output power reaches ∼240 mW at maximum, which is an improvement of almost two orders of magnitude over those reported from the latest mode-locked visible fiber lasers. The laser scheme combines a piece of large-core Dy:ZBLAN gain fiber and free-space NPR components designated at the yellow bandwidth. The maximal pulse energy is 2.4 nJ at the repetition rate of ∼100 MHz and the minimal pulse duration is 83 ps. The achieved wavelength of 575 nm is the shortest ever reached from a fiber-based mode-locked laser to date.