[Multicenter Survey on Phantom Entrance Surface Air Kerma of Angiography and IVR in Japan].

PURPOSE: In DRLs 2020, the entrance surface air kerma (Ka,e) was set to 17 mGy/min as the reference dose rate in fluoroscopy. But, Ka,e in fluoroscopy for different regions and Ka,e in exposure was not set. A multicenter survey was conducted to evaluate Ka,e by each area. METHODS: Ka,e for each area was analyzed for 79 facilities attending this survey (274 machines and 461 protocols). When the protocols were changed by the difference in disease, angiography, or IVR, the difference rate of Ka,e was evaluated. Ka,e before and after modifying the incident air kerma at the patient entrance reference point (Ka,r) and air kerma area product (PKA) difference rate were calculated when protocols were changed, considering the DRLs 2020. RESULTS: There were dose differences in Ka,e by each area. Compared to DRLs 2020, 36 protocols from 13 facilities modified their protocols, all of which reduced Ka,e. CONCLUSION: Although reducing Ka,e does not necessarily reduce Ka,r, and PKA, comparison of Ka,e by each area is expected to optimize medical exposure protection, including evaluation of quality control.

A Rare Case of Fungal Granuloma Confined to the Nasal Septum.

Granulomatous lesions in the nasal sinuses are associated with a variety of diseases, including immune disorders such as sarcoidosis, vasculitis, immunoglobulin G4 (IgG4)-related diseases, malignant lymphomas, and microbial infections. Here, we report a rare case of fungal granuloma that occurred exclusively within the nasal septum. The patient presented to the Department of Surgery with the chief complaint of nasal obstruction associated with nasal septal deviation. A bulge was found below the right nasal septum. Initially, it was diagnosed as mucosal swelling associated with rhinitis, and surgery was performed. A granulomatous lesion with bone destruction was found under the mucosa of the nasal septum, which led to the diagnosis of fungal granulation based on postoperative pathology. Though bacterial and fungal infections of the nasal septum are occasionally observed, this is the first reported instance of a fungal granuloma confined to the nasal septum. Infection within the nasal septum, although rare, should also be considered as a differential diagnosis for morphological abnormalities of the nasal septum.

Craniospinal irradiation for leptomeningeal metastasis of solid tumors: survival analysis and prognostic factors.

We conducted a study to examine the treatment outcomes and prognostic factors for patients who underwent craniospinal irradiation (CSI) for leptomeningeal metastasis of solid tumors. This retrospective study included patients who received CSI for leptomeningeal metastasis at a single institute between 2010 and 2021. Data from clinical records and the radiation information system were obtained and analyzed. A total of 25 patients were included in the study. Eighteen patients (72%) completed the scheduled CSI. The median overall survival (OS) period was 4.8 months (95% confidence interval (CI): 3.2-10.0 months). Symptom relief was achieved in four out of 23 symptomatic patients (17%). Non-hematological adverse events occurred in 12 patients (48%), with 1 patient (4%) developing Grade 3 bacterial meningitis and the other patients having Grade 1-2 events. Twenty patients (80%) had hematological adverse events of Grade 3 or higher. Grade 4 hematologic toxicities occurred in 3 patients (12%) due to neutropenia and in 11 patients (44%) due to lymphopenia. In multivariate Cox regression analysis, the systemic immune-inflammation index (SII) was identified as the only significant parameter for predicting OS. The median OS periods for patients with SII < 607 and SII ≥ 607 were 6.1 and 2.1 months, respectively (P = 0.003). In conclusion, this study showed the treatment outcomes of CSI for leptomeningeal metastasis of solid tumors. It was shown that a high baseline SII was associated with shorter OS after CSI. The findings will contribute to the evaluation of prognosis after CSI.

Open-Vocabulary Predictive World Models from Sensor Observations.

Cognitive scientists believe that adaptable intelligent agents like humans perform spatial reasoning tasks by learned causal mental simulation. The problem of learning these simulations is called predictive world modeling. We present the first framework for a learning open-vocabulary predictive world model (OV-PWM) from sensor observations. The model is implemented through a hierarchical variational autoencoder (HVAE) capable of predicting diverse and accurate fully observed environments from accumulated partial observations. We show that the OV-PWM can model high-dimensional embedding maps of latent compositional embeddings representing sets of overlapping semantics inferable by sufficient similarity inference. The OV-PWM simplifies the prior two-stage closed-set PWM approach to the single-stage end-to-end learning method. CARLA simulator experiments show that the OV-PWM can learn compact latent representations and generate diverse and accurate worlds with fine details like road markings, achieving 69 mIoU over six query semantics on an urban evaluation sequence. We propose the OV-PWM as a versatile continual learning paradigm for providing spatio-semantic memory and learned internal simulation capabilities to future general-purpose mobile robots.

Changes in rehabilitation students' clinical placement and clinical competence at graduation during the COVID-19 pandemic: A pilot study.

BACKGROUND: The COVID-19 pandemic has affected physical and occupational therapist education in various ways. OBJECTIVE: This study investigated whether the pandemic changed clinical placement, thus influencing the clinical competence of physical and occupational therapists at a hospital in Japan. METHODS: Eleven therapists (seven physical and four occupational) participated in this study in April 2021. Clinical placement teaching methods were surveyed using an online questionnaire. The Objective Structured Clinical Examination (OSCE), comprising attitudinal and technical items, was used to examine clinical competence. The results were calculated as the sum of the attitudinal and technical scores, and the ratios of these scores to the full score were compared using a paired-sample Wilcoxon signed-rank test. RESULTS: During the pandemic, some schools implemented shortened practical clinical placements. The substituted classes included online-only classes and combined online and face-to-face classes. Regarding clinical competence, scores on the OSCE were mostly high. The median proportion of the total attitudinal score to the perfect score was 100% for all three items (range-of-motion test, muscle strength test, Stroke Impairment Assessment Set). The median proportion of the total technical score to the perfect score ranged from 56.7% to 76.7% for the three items. The ratio of the total attitudinal score to the full score was significantly higher than that of the total technical score to the full score (p = 0.001). CONCLUSIONS: Most clinical placements were canceled or partially administered through online learning during the pandemic. This decrease in clinical placements did not affect newly recruited physical and occupational therapists' clinical competence.

Successful Avoidance of Cicatricial Tracheobronchial Stenosis in a Patient With Endobronchial Tuberculosis by Early Administration of Systemic High-Dose Corticosteroids: A Case Report.

A 63-year-old Japanese woman was referred to our hospital due to dry cough, fever, hoarseness, stridor, and difficulty breathing. Chest computed tomography showed circumferential wall thickening in the trachea, carina, right main bronchus, and right upper lobe bronchus, and granular and nodular shadows in right S2. Flexible laryngofiberscopy showed yellowish dry respiratory secretions adhering to the subglottis. Bronchoscopic findings showed that the tracheobronchial mucosa was swollen, hyperemic, and covered with yellowish-white, cheese-like materials, and ulcerative lesions with white coatings were observed from the subglottis to the trachea, carina, right main bronchus, and right upper lobe bronchus. A diagnosis of endobronchial tuberculosis (EBTB) was confirmed by polymerase chain reaction testing, and cultures were positive for Mycobacterium tuberculosis. In addition to anti-tuberculosis chemotherapy, intravenous high-dose methylprednisolone reduced her severe respiratory symptoms and prevented cicatricial tracheobronchial stenosis. Early administration of systemic high-dose corticosteroids may be effective for EBTB patients with severely active tracheobronchial mucosal and submucosal lesions.

A Case of Pott's Puffy Tumor in a Patient With Eosinophilic Chronic Rhinosinusitis.

Eosinophilic chronic rhinosinusitis (ECRS) is a refractory type 2 inflammation-based airway allergic disease that is prone to complications such as bronchial asthma. Pott's puffy tumor (PPT) is a rare clinical entity characterized by osteomyelitis of the frontal bone accompanied by a subperiosteal abscess. A 56-year-old female with a history of cranial surgery and bronchial asthma presented to an otolaryngology clinic with nasal obstruction and loss of smell, later developing swelling and redness on her forehead. She was diagnosed and treated for ECRS and was thought to have developed PPT during her course. Nasal endoscopy reveals bilateral polyp formation originating from the middle meatus. Paranasal computed tomography (CT) indicates substantial swelling extending from the opening of the frontal sinus to the adjacent subcutaneous tissue, accompanied by a defect in the frontal bone and osteolysis at the base of the frontal skull. Her management included conservative antibiotic therapy adjusted due to a drug eruption and, subsequently, endoscopic sinus surgery (ESS). The case was complicated by the patient's medical history and the absence of detailed surgical records, which limited the use of enhanced imaging techniques. This underscores the complexity of diagnosing and managing PPT in adults, particularly those with prior surgeries, emphasizing the need for a tailored diagnostic and therapeutic approach that integrates detailed patient history with current clinical indicators to effectively guide treatment. This case contributes to the limited literature on adult PPT and underscores the critical need for careful patient monitoring and detailed surgical history.

Collaborative hunting in artificial agents with deep reinforcement learning.

Collaborative hunting, in which predators play different and complementary roles to capture prey, has been traditionally believed to be an advanced hunting strategy requiring large brains that involve high-level cognition. However, recent findings that collaborative hunting has also been documented in smaller-brained vertebrates have placed this previous belief under strain. Here, using computational multi-agent simulations based on deep reinforcement learning, we demonstrate that decisions underlying collaborative hunts do not necessarily rely on sophisticated cognitive processes. We found that apparently elaborate coordination can be achieved through a relatively simple decision process of mapping between states and actions related to distance-dependent internal representations formed by prior experience. Furthermore, we confirmed that this decision rule of predators is robust against unknown prey controlled by humans. Our computational ecological results emphasize that collaborative hunting can emerge in various intra- and inter-specific interactions in nature, and provide insights into the evolution of sociality.

From wolves to ants, many animals are known to be able to hunt as a team. This strategy may yield several advantages: going after bigger preys together, for example, can often result in individuals spending less energy and accessing larger food portions than when hunting alone. However, it remains unclear whether this behavior relies on complex cognitive processes, such as the ability for an animal to represent and anticipate the actions of its teammates. It is often thought that 'collaborative hunting' may require such skills, as this form of group hunting involves animals taking on distinct, tightly coordinated roles ­ as opposed to simply engaging in the same actions simultaneously. To better understand whether high-level cognitive skills are required for collaborative hunting, Tsutsui et al. used a type of artificial intelligence known as deep reinforcement learning. This allowed them to develop a computational model in which a small number of 'agents' had the opportunity to 'learn' whether and how to work together to catch a 'prey' under various conditions. To do so, the agents were only equipped with the ability to link distinct stimuli together, such as an event and a reward; this is similar to associative learning, a cognitive process which is widespread amongst animal species. The model showed that the challenge of capturing the prey when hunting alone, and the reward of sharing food after a successful hunt drove the agents to learn how to work together, with previous experiences shaping decisions made during subsequent hunts. Importantly, the predators started to exhibit the ability to take on distinct, complementary roles reminiscent of those observed during collaborative hunting, such as one agent chasing the prey while another ambushes it. Overall, the work by Tsutsui et al. challenges the traditional view that only organisms equipped with high-level cognitive processes can show refined collaborative approaches to hunting, opening the possibility that these behaviors may be more widespread than originally thought ­ including between animals of different species.

R-Cut: Enhancing Explainability in Vision Transformers with Relationship Weighted Out and Cut.

Transformer-based models have gained popularity in the field of natural language processing (NLP) and are extensively utilized in computer vision tasks and multi-modal models such as GPT4. This paper presents a novel method to enhance the explainability of transformer-based image classification models. Our method aims to improve trust in classification results and empower users to gain a deeper understanding of the model for downstream tasks by providing visualizations of class-specific maps. We introduce two modules: the "Relationship Weighted Out" and the "Cut" modules. The "Relationship Weighted Out" module focuses on extracting class-specific information from intermediate layers, enabling us to highlight relevant features. Additionally, the "Cut" module performs fine-grained feature decomposition, taking into account factors such as position, texture, and color. By integrating these modules, we generate dense class-specific visual explainability maps. We validate our method with extensive qualitative and quantitative experiments on the ImageNet dataset. Furthermore, we conduct a large number of experiments on the LRN dataset, which is specifically designed for automatic driving danger alerts, to evaluate the explainability of our method in scenarios with complex backgrounds. The results demonstrate a significant improvement over previous methods. Moreover, we conduct ablation experiments to validate the effectiveness of each module. Through these experiments, we are able to confirm the respective contributions of each module, thus solidifying the overall effectiveness of our proposed approach.

Lymphopenia after palliative radiotherapy for vertebral metastases.

Lymphopenia is a well-known side effect of radiotherapy and has been shown to have a negative impact on patient outcomes. However, the extent of lymphopenia caused by palliative radiotherapy and its effect on patient prognosis has not been clarified. The aim of this study was to determine the incidence and severity of lymphopenia after palliative radiotherapy for vertebral metastases and to determine their effects on patients' survival outcomes. We conducted a retrospective analysis for patients who underwent palliative radiotherapy for vertebral metastases and could be followed up for 12 weeks. Lymphocyte counts were documented at baseline and throughout the 12-week period following the start of radiotherapy and their medians and interquartile ranges (IQRs) were recorded. Exploratory analyses were performed to identify predictive factors for lymphopenia and its impact on overall survival (OS). A total of 282 cases that met the inclusion criteria were analyzed. The median baseline lymphocyte count was 1.26 × 103/µl (IQR: 0.89-1.72 × 103/µl). Peak lymphopenia occurred at a median of 26 days (IQR: 15-45 days) with a median nadir of 0.52 × 103/µl (IQR: 0.31-0.81 × 103/µl). Long-term analysis of patients surviving for 1 year showed that lymphopenia persisted at 1 year after radiotherapy. The main irradiation site, radiation field length and pretreatment lymphocyte count were significantly related to grade 3 or higher lymphopenia. Lymphopenia was identified as a significant predictor of OS by multivariate Cox regression analysis. This study demonstrated the incidence of lymphopenia after palliative radiotherapy for vertebral metastases and its effect on patients' OS.

An evaluation of rehabilitation students' learning goals in their first year: a text mining approach.

Introduction: Qualitative information in the form of written reflection reports is vital for evaluating students' progress in education. As a pilot study, we used text mining, which analyzes qualitative information with quantitative features, to investigate how rehabilitation students' goals change during their first year at university. Methods: We recruited 109 first-year students (66 physical therapy and 43 occupational therapy students) enrolled in a university rehabilitation course. These students completed an open-ended questionnaire about their learning goals at the time of admission and at 6 and 12 months after admission to the university. Text mining was used to objectively interpret the descriptive text data from all three-time points to extract frequently occurring nouns at once. Then, hierarchical cluster analysis was performed to generate clusters. The number of students who mentioned at least one noun in each cluster was counted and the percentages of students in each cluster were compared for the three periods using Cochran's Q test. Results: The 31 nouns that appeared 10 or more times in the 427 sentences were classified into three clusters: "Socializing," "Practical Training," and "Classroom Learning." The percentage of students in all three clusters showed significant differences across the time periods (p < 0.001 for "Socializing"; p < 0.01 for "Practical Training" and "Classroom Learning"). Conclusion: These findings suggest that the students' learning goals changed during their first year of education. This objective analytical method will enable researchers to examine transitional trends in students' reflections and capture their psychological changes, making it a useful tool in educational research.

Anti-MDA5 Antibody-positive Clinically Amyopathic Dermatomyositis with Rapidly Progressing Interstitial Lung Disease Successfully Treated by Initiation of Combined Immunosuppressive Therapy Plus Plasma Exchange and Subsequently Switching Tacrolimus to Tofacitinib.

A 36-year-old man with inverse Gottron's sign was admitted for clinically amyopathic dermatomyositis (CADM) with rapidly progressive interstitial lung disease (RP-ILD). Early addition of plasma exchange (PE) to triple therapy improved severe respiratory failure and transiently decreased serum ferritin levels and anti-melanoma differentiation-associated gene 5 antibody (anti-MDA5 Ab) titers. Furthermore, switching from tacrolimus to tofacitinib resulted in disease remission. Recognition of the inverse Gottron's sign may allow for the earlier diagnosis of anti-MDA5 Ab-positive dermatomyositis, and early addition of PE to triple therapy and administration of tofacitinib in refractory cases may be effective for anti-MDA5 Ab-positive CADM with RP-ILD under life-threatening conditions.

Estimating Counterfactual Treatment Outcomes Over Time in Complex Multiagent Scenarios.

Evaluation of intervention in a multiagent system, for example, when humans should intervene in autonomous driving systems and when a player should pass to teammates for a good shot, is challenging in various engineering and scientific fields. Estimating the individual treatment effect (ITE) using counterfactual long-term prediction is practical to evaluate such interventions. However, most of the conventional frameworks did not consider the time-varying complex structure of multiagent relationships and covariate counterfactual prediction. This may lead to erroneous assessments of ITE and difficulty in interpretation. Here, we propose an interpretable, counterfactual recurrent network in multiagent systems to estimate the effect of the intervention. Our model leverages graph variational recurrent neural networks (GVRNNs) and theory-based computation with domain knowledge for the ITE estimation framework based on long-term prediction of multiagent covariates and outcomes, which can confirm the circumstances under which the intervention is effective. On simulated models of an automated vehicle and biological agents with time-varying confounders, we show that our methods achieved lower estimation errors in counterfactual covariates and the most effective treatment timing than the baselines. Furthermore, using real basketball data, our methods performed realistic counterfactual predictions and evaluated the counterfactual passes in shot scenarios.

Glasgow prognostic score for assessing the efficacy of whole-brain radiation therapy in cases of recursive partitioning analysis class 2 and class 3 multiple brain metastases: a retrospective study.

PURPOSE: Whole-brain radiotherapy (WBRT) may not be beneficial for patients with brain metastases (BMs). The Glasgow Prognostic Score (GPS) is a suggested prognostic factor for malignancies. However, GPS has never been assessed in patients with BMs who have undergone WBRT. The purpose of this study was to determine whether GPS can be used to identify subgroups of patients with BMs who have a poor prognosis, such as recursive partitioning analysis (RPA) Class 2 and Class 3, and who will not receive clinical prognostic benefits from WBRT. MATERIALS AND METHODS: A total of 180 Japanese patients with BMs were treated with WBRT between May 2008 and October 2015. We examined GPS, age, Karnofsky Performance Status (KPS), RPA, graded prognostic assessment (GPA), number of lesions, tumor size, history of brain surgery, presence of clinical symptoms, and radiation doses. RESULTS: The overall median survival time (MST) was 6.1 months. seventeen patients (9.4%) were alive more than 2 years after WBRT. In univariate analysis, KPS ≤ 70 (p = 0.0066), GPA class 0-2 (p = 0.0008), > 3 BMs (p = 0.012), > 4 BMs (p = 0.02), patients who received ≥ 3 Gy per fraction (p = 0.0068), GPS ≥ 1 (p = 0.0003), and GPS ≥ 2 (p = 0.0009) were found to significantly decrease the MST. Patients who had brain surgery before WBRT (p = 0.036) had a longer survival. On multivariate analysis, GPS ≥ 1 (p = 0.008) was found to significantly decrease MST. CONCLUSION: Our results suggest that GPS ≥ 1 indicates a poor prognosis in patients undergoing WBRT for intermediate and poor prognosis BMs.

Decentralized policy learning with partial observation and mechanical constraints for multiperson modeling.

Extracting the rules of real-world multi-agent behaviors is a current challenge in various scientific and engineering fields. Biological agents independently have limited observation and mechanical constraints; however, most of the conventional data-driven models ignore such assumptions, resulting in lack of biological plausibility and model interpretability for behavioral analyses. Here we propose sequential generative models with partial observation and mechanical constraints in a decentralized manner, which can model agents' cognition and body dynamics, and predict biologically plausible behaviors. We formulate this as a decentralized multi-agent imitation-learning problem, leveraging binary partial observation and decentralized policy models based on hierarchical variational recurrent neural networks with physical and biomechanical penalties. Using real-world basketball and soccer datasets, we show the effectiveness of our method in terms of the constraint violations, long-term trajectory prediction, and partial observation. Our approach can be used as a multi-agent simulator to generate realistic trajectories using real-world data.

Understanding hereditary hemorrhagic telangiectasia: From genetic anomalies to systemic manifestations, quality of life, and epistaxis management-Exploring the otolaryngologist's integral role.

Hereditary hemorrhagic telangiectasia (HHT), also known as Osler-Rendu-Weber syndrome, is a rare autosomal dominant disorder characterized by vascular malformations. This comprehensive review aimed to provide an overview and summarize various aspects of HHT, including the genetic abnormalities, complications associated with visceral arteriovenous malformations (AVMs), prognosis of HHT, quality of life (QOL), and treatment of epistaxis. In addition, this review highlights the challenges in diagnosing HHT and emphasizes the critical role of otolaryngologists in the early detection of HHT. Otolaryngologists can refer patients with refractory epistaxis for AVM screening to expedite intervention. Mutation of the genes involved in the transforming growth factor-ß signaling pathway leads to the incidence of HHT, resulting in the formation of abnormal blood vessel formation. These vascular malformations commonly manifest as telangiectasia on the skin and mucous membranes; however, epistaxis remains the hallmark symptom of HHT. The impact of HHT goes beyond the visible symptoms and often includes the formation of life-threatening visceral AVMs in the lungs, liver, and brain. The prognosis of patients with HHT is closely related to the development of these complications, necessitating timely diagnosis and intervention. Refractory epistaxis diminishes the QOL of patients with HHT. The management of epistaxis ranges from conservative measures to advanced interventions such as prevention, conservative treatments, ablation, surgical procedures, and the administration of anti-angiogenic agents. However, effective management requires a multidisciplinary approach. The diagnosis of HHT remains challenging due to its variable presentation and lack of awareness among physicians. This review highlights the importance of reducing the duration between symptom onset and diagnosis. Otolaryngologists who are experienced in the management of refractory epistaxis can aid in identifying potential cases of HHT. They can facilitate the initiation of screening for visceral AVMs via prompt recognition of the signs and symptoms of HHT, contributing to improved patient outcomes. Early detection and intervention through screening can extend the life expectancy of patients with HHT to levels comparable with that of the general population. In conclusion, this review provides insight into various aspects of HHT and emphasizes the importance of timely diagnosis and intervention in the mitigation of the potentially life-threatening complications associated with this disorder. Otolaryngologists play a critical role in this process, serving as gatekeepers to the identification of cases of HHT and implementation of appropriate screening and management pathways, thereby improving the life expectancy and QOL of patients.

The Impact of Pathological Grade Group 3 on Relapse-free Survival After Salvage Radiotherapy for Postoperative Prostate Cancer.

BACKGROUND/AIM: This retrospective study aimed to investigate the outcomes of relapse-free survival (RFS) after salvage radiation therapy (SRT) to the prostate bed for postoperative biochemical recurrence of prostate cancer. PATIENTS AND METHODS: A total of 87 patients were analyzed. There were 27, 32, and 24 patients with pathological grade groups of 1-2, 3, and 4-5, respectively. SRT doses of 64, 66 or 70 Gy were administered to 24, 3 and 60 patients, respectively. The Kaplan-Meier method was used to estimate time-to-event outcomes. The multiple imputations method was used to impute missing values, and Cox proportional-hazards models were applied for multivariate analyses. RESULTS: The median follow-up period for patients overall was 58.6 months. The 5-year RFS rates of the whole cohort was 59.4% and those for pathological grade groups 1-2, 3 and 4-5 were 88.9%, 37.7% and 39.5%, respectively. In multivariate analyses, higher pathological grade group [4-5 vs. 3 vs. 1-2: hazard radio (HR)=8.65, p<0.01], negative surgical resection margin (positive vs. negative: HR=0.41, p=0.02) and higher pre-salvage treatment serum prostate-specific antigen (cutoff value 0.31 ng/ml: HR=3.50, p<0.01) were significantly associated with poorer RFS. The cumulative incidences of grade 2 or more late rectal bleeding and late hematuria were 4.9% and 8.7%, respectively, at 5 years and 4.9% and 15.7%, respectively, at 8 years. These toxicities occurred only in the 70 Gy-treated arm. CONCLUSION: Our study revealed that pathological grade group 3 prostate cancer patients experienced moderately unfavorable RFS after SRT. Higher radiation doses might increase late toxicities without improving RFS.

L-DIG: A GAN-Based Method for LiDAR Point Cloud Processing under Snow Driving Conditions.

LiDAR point clouds are significantly impacted by snow in driving scenarios, introducing scattered noise points and phantom objects, thereby compromising the perception capabilities of autonomous driving systems. Current effective methods for removing snow from point clouds largely rely on outlier filters, which mechanically eliminate isolated points. This research proposes a novel translation model for LiDAR point clouds, the 'L-DIG' (LiDAR depth images GAN), built upon refined generative adversarial networks (GANs). This model not only has the capacity to reduce snow noise from point clouds, but it also can artificially synthesize snow points onto clear data. The model is trained using depth image representations of point clouds derived from unpaired datasets, complemented by customized loss functions for depth images to ensure scale and structure consistencies. To amplify the efficacy of snow capture, particularly in the region surrounding the ego vehicle, we have developed a pixel-attention discriminator that operates without downsampling convolutional layers. Concurrently, the other discriminator equipped with two-step downsampling convolutional layers has been engineered to effectively handle snow clusters. This dual-discriminator approach ensures robust and comprehensive performance in tackling diverse snow conditions. The proposed model displays a superior ability to capture snow and object features within LiDAR point clouds. A 3D clustering algorithm is employed to adaptively evaluate different levels of snow conditions, including scattered snowfall and snow swirls. Experimental findings demonstrate an evident de-snowing effect, and the ability to synthesize snow effects.