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Neural radiance fields~(NeRF) have recently been applied to render large-scale scenes. However, their limited model capacity typically results in blurred rendering results. Existing large-scale NeRFs primarily address this limitation by partitioning the scene into blocks, which are subsequently handled by separate sub-NeRFs. These sub-NeRFs, trained from scratch and processed independently, lead to inconsistencies in geometry and appearance across the scene. Consequently, the rendering quality fails to exhibit significant improvement despite the expansion of model capacity. In this work, we present global-guided focal neural radiance field (GF-NeRF) that achieves high-fidelity rendering of large-scale scenes. Our proposed GF-NeRF utilizes a two-stage (Global and Focal) architecture and a global-guided training strategy. The global stage obtains a continuous representation of the entire scene while the focal stage decomposes the scene into multiple blocks and further processes them with distinct sub-encoders. Leveraging this two-stage architecture, sub-encoders only need fine-tuning based on the global encoder, thus reducing training complexity in the focal stage while maintaining scene-wide consistency. Spatial information and error information from the global stage also benefit the sub-encoders to focus on crucial areas and effectively capture more details of large-scale scenes. Notably, our approach does not rely on any prior knowledge about the target scene, attributing GF-NeRF adaptable to various large-scale scene types, including street-view and aerial-view scenes. We demonstrate that our method achieves high-fidelity, natural rendering results on various types of large-scale datasets. Our project page: https://shaomq2187.github.io/GF-NeRF

Generating realistic animated videos from static images is an important area of research in computer vision. Methods based on physical simulation and motion prediction have achieved notable advances, but they are often limited to specific object textures and motion trajectories, failing to exhibit highly complex environments and physical dynamics. In this paper, we introduce an open-domain controllable image animation method using motion priors with video diffusion models. Our method achieves precise control over the direction and speed of motion in the movable region by extracting the motion field information from videos and learning moving trajectories and strengths. Current pretrained video generation models are typically limited to producing very short videos, typically less than 30 frames. In contrast, we propose an efficient long-duration video generation method based on noise reschedule specifically tailored for image animation tasks, facilitating the creation of videos over 100 frames in length while maintaining consistency in content scenery and motion coordination. Specifically, we decompose the denoise process into two distinct phases: the shaping of scene contours and the refining of motion details. Then we reschedule the noise to control the generated frame sequences maintaining long-distance noise correlation. We conducted extensive experiments with 10 baselines, encompassing both commercial tools and academic methodologies, which demonstrate the superiority of our method. Our project page: https://wangqiang9.github.io/Controllable.github.io, Comment: https://wangqiang9.github.io/Controllable.github.io

We present a real-time LiDAR-Inertial-Camera SLAM system with 3D Gaussian Splatting as the mapping backend. Leveraging robust pose estimates from our LiDAR-Inertial-Camera odometry, Coco-LIC, an incremental photo-realistic mapping system is proposed in this paper. We initialize 3D Gaussians from colorized LiDAR points and optimize them using differentiable rendering powered by 3D Gaussian Splatting. Meticulously designed strategies are employed to incrementally expand the Gaussian map and adaptively control its density, ensuring high-quality mapping with real-time capability. Experiments conducted in diverse scenarios demonstrate the superior performance of our method compared to existing radiance-field-based SLAM systems., Comment: Submitted to IROS 2024

Mu-Lan Xu,1,2,* Xiang-Bo Wu,1,* Ying Liang,3,* Ning Li,1 Xu Hu,1 Xiao-Dong Lin,1 Miao-Qiao Sun,1 Chun-Qiu Dai,1 Dan Niu,1 Yan-Rong Zhang,1 Hui Cao,1 Chen-Guang Zhao,1 Xiao-Long Sun,1 Hua Yuan1 1Department of Rehabilitation Medicine, Xi-Jing Hospital, Air Force Medical University (Fourth Military Medical University), Xi’an, Shaanxi, People’s Republic of China; 2Department of Rehabilitation Medicine, Shenshan Medical Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Shanwei, Guangdong, People’s Republic of China; 3Department of Health Statistics, Air Force Medical University (Fourth Military Medical University), Xi’an, Shaanxi, People’s Republic of China*These authors contributed equally to this workCorrespondence: Hua Yuan; Xiao-Long Sun, Department of Rehabilitation Medicine, Xi-Jing Hospital, Air Force Medical University (Fourth Military Medical University), No. 15, Changle West Road, Xi’an, Shaanxi, 710032, People’s Republic of China, Tel/Fax +86-29-84775437 ; +86-29-84775439, Email yuanhua@fmmu.edu.cn; xlsun@fmmu.edu.cnBackground: Neuropathic pain (NP) is a common and severe problem following spinal cord injury (SCI). However, its relationship with functional outcome remains unclear.Methods: A retrospective explorative analysis was performed on SCI patients admitted to a tertiary academic medical center between January 2018 and June 2022. The candidate predictor variables, including demographics, clinical characteristics and complications, were analyzed with logistic and linear regression. Spinal Cord Independence Measure (SCIM) scores at discharge and mean relative functional gain (mRFG) of SCIM were as outcome parameters.Results: A total of 140 SCI patients included for the final analysis. Among them, 44 (31.43%) patients were tetraplegics, and 96 (68.57%) patients were paraplegics; 68 (48.57%) patients developed NP, and 72 (51.43%) patients did not. Logistic and linear regression analyses of SCIM at di

We sought to find clinical subtypes of posttraumatic stress disorder (PTSD) in veterans 6-10 years post-trauma exposure based on current symptom assessments and to examine whether blood biomarkers could differentiate them. Samples were males deployed to Iraq and Afghanistan studied by the PTSD Systems Biology Consortium: a discovery sample of 74 PTSD cases and 71 healthy controls (HC), and a validation sample of 26 PTSD cases and 36 HC. A machine learning method, random forests (RF), in conjunction with a clustering method, partitioning around medoids, were used to identify subtypes derived from 16 self-report and clinician assessment scales, including the clinician-administered PTSD scale for DSM-IV (CAPS). Two subtypes were identified, designated S1 and S2, differing on mean current CAPS total scores: S2 = 75.6 (sd 14.6) and S1 = 54.3 (sd 6.6). S2 had greater symptom severity scores than both S1 and HC on all scale items. The mean first principal component score derived from clinical summary scales was three times higher in S2 than in S1. Distinct RFs were grown to classify S1 and S2 vs. HCs and vs. each other on multi-omic blood markers feature classes of current medical comorbidities, neurocognitive functioning, demographics, pre-military trauma, and psychiatric history. Among these classes, in each RF intergroup comparison of S1, S2, and HC, multi-omic biomarkers yielded the highest AUC-ROCs (0.819-0.922); other classes added little to further discrimination of the subtypes. Among the top five biomarkers in each of these RFs were methylation, micro RNA, and lactate markers, suggesting their biological role in symptom severity.

We sought to find clinical subtypes of posttraumatic stress disorder (PTSD) in veterans 6-10 years post-trauma exposure based on current symptom assessments and to examine whether blood biomarkers could differentiate them. Samples were males deployed to Iraq and Afghanistan studied by the PTSD Systems Biology Consortium: a discovery sample of 74 PTSD cases and 71 healthy controls (HC), and a validation sample of 26 PTSD cases and 36 HC. A machine learning method, random forests (RF), in conjunction with a clustering method, partitioning around medoids, were used to identify subtypes derived from 16 self-report and clinician assessment scales, including the clinician-administered PTSD scale for DSM-IV (CAPS). Two subtypes were identified, designated S1 and S2, differing on mean current CAPS total scores: S2 = 75.6 (sd 14.6) and S1 = 54.3 (sd 6.6). S2 had greater symptom severity scores than both S1 and HC on all scale items. The mean first principal component score derived from clinical summary scales was three times higher in S2 than in S1. Distinct RFs were grown to classify S1 and S2 vs. HCs and vs. each other on multi-omic blood markers feature classes of current medical comorbidities, neurocognitive functioning, demographics, pre-military trauma, and psychiatric history. Among these classes, in each RF intergroup comparison of S1, S2, and HC, multi-omic biomarkers yielded the highest AUC-ROCs (0.819-0.922); other classes added little to further discrimination of the subtypes. Among the top five biomarkers in each of these RFs were methylation, micro RNA, and lactate markers, suggesting their biological role in symptom severity.

We demonstrate the co- and counter-propagation coexistence of distributed acoustic sensing and 100G/200G coherent data communication over a single fiber, proving ubiquitous and practical sensing through pervasive telecom fiber infrastructure.

BackgroundHormone sensitive lipase (HSL) is a neutral lipase that preferentially catalyzes the hydrolysis of diacylglycerol contributing to triacylglycerol breakdown in the adipose tissue. HSL has been implicated to play a role in tumor cachexia, a debilitating syndrome characterized by progressive loss of adipose tissue. Consequently, pharmacological inhibitors of HSL have been proposed for the treatment of cancer-associated cachexia. In the present study we used the conditional KrasG12D (KC) mouse model of pancreatic ductal adenocarcinoma (PDAC) with a deficiency in HSL to determine the impact of HSL suppression on the development of PDAC.MethodsKC;Hsl+/+ and KC;Hsl-/- mice were fed standard rodent chow for 20 weeks. At sacrifice, the incidence of PDAC was determined and inflammation in the mesenteric adipose tissue and pancreas was assessed histologically and by immunofluorescence. To determine statistical significance, ANOVA and two-tailed Student's t-tests were performed. To compare PDAC incidence, a two-sided Fisher's exact test was used.ResultsCompared to KC;Hsl+/+ mice, KC;Hsl-/- mice gained similar weight and displayed adipose tissue and pancreatic inflammation. In addition, KC;Hsl-/- mice had reduced levels of plasma insulin and leptin. Importantly, the increased adipose tissue and pancreatic inflammation was associated with a significant increase in PDAC incidence in KC;Hsl-/- mice.ConclusionsHSL deficiency is associated with adipose tissue and pancreatic inflammation and accelerates PDAC development in the KC mouse model.

BackgroundHormone sensitive lipase (HSL) is a neutral lipase that preferentially catalyzes the hydrolysis of diacylglycerol contributing to triacylglycerol breakdown in the adipose tissue. HSL has been implicated to play a role in tumor cachexia, a debilitating syndrome characterized by progressive loss of adipose tissue. Consequently, pharmacological inhibitors of HSL have been proposed for the treatment of cancer-associated cachexia. In the present study we used the conditional KrasG12D (KC) mouse model of pancreatic ductal adenocarcinoma (PDAC) with a deficiency in HSL to determine the impact of HSL suppression on the development of PDAC.MethodsKC;Hsl+/+ and KC;Hsl-/- mice were fed standard rodent chow for 20 weeks. At sacrifice, the incidence of PDAC was determined and inflammation in the mesenteric adipose tissue and pancreas was assessed histologically and by immunofluorescence. To determine statistical significance, ANOVA and two-tailed Student's t-tests were performed. To compare PDAC incidence, a two-sided Fisher's exact test was used.ResultsCompared to KC;Hsl+/+ mice, KC;Hsl-/- mice gained similar weight and displayed adipose tissue and pancreatic inflammation. In addition, KC;Hsl-/- mice had reduced levels of plasma insulin and leptin. Importantly, the increased adipose tissue and pancreatic inflammation was associated with a significant increase in PDAC incidence in KC;Hsl-/- mice.ConclusionsHSL deficiency is associated with adipose tissue and pancreatic inflammation and accelerates PDAC development in the KC mouse model.

Currently, there are no effective preventive strategies for pancreatic cancer. Obesity has been increasingly recognized as a strong but modifiable risk factor of pancreatic cancer. In this article, we aim to review the literature regarding weight loss on prevention of pancreatic cancer. Epidemiological and laboratory studies have shown that obesity is associated with increased incidence of pancreatic cancer and potentially worse cancer outcome. Whereas the underlying pathomechanisms remain unclear, chronic inflammation, insulin resistance, and altered intestinal microbiota are all implicated in the carcinogenic effect of obesity. Weight loss, especially the durable and significant weight loss after bariatric surgery, has been shown to reduce the risks of multiple cancers and may become a good intervention for pancreatic cancer prevention.

There are still many open questions regarding spatiotemporal chaos although many well developed theories exist for chaos in time. Rayleigh-B'enard convection is a paradigmatic example of spatiotemporal chaos that is also experimentally accessible. Discoveries uncovered using numerics can often be compared with experiments which can provide new physical insights. Lyapunov diagnostics can provide important information about the dynamics of small perturbations for chaotic systems. Covariant Lyapunov vectors reveal the true direction of perturbation growth and decay. The degree of hyperbolicity can also be quantified by the covariant Lyapunov vectors. To know whether a dynamical system is hyperbolic is important for the development of a theoretical understanding. In this thesis, the degree of hyperbolicity is calculated for chaotic Rayleigh-B'enard convection. For the values of the Rayleigh number explored, it is shown that the dynamics are non-hyperbolic. The spatial distribution of the covariant Lyapunov vectors is different for the different Lyapunov vectors. Localization is used to quantify this variation. The spatial localization of the covariant Lyapunov vectors has a decreasing trend as the order of the Lyapunov vector increases. The spatial localization of the covariant Lyapunov vectors are found to be related to the instantaneous Lyapunov exponents. The correlation is stronger as the order of the Lyapunov vector decreases. The covariant Lyapunov vectors are also computed using a spectral element approach. This allows an exploration of the covariant Lyapunov vectors in larger domains and for experimental conditions. The finite conductivity and finite thickness of the lateral boundaries of an experimental convection domain is also studied. Results are presented for the variation of the Nusselt number and fractal dimension for different boundary conditions. The fractal dimension changes dramatically with the variation of the finite conductivity.

ObjectivesObesity increases the incidence of multiple types of cancer. Our previous work has shown that a high-fat, high-calorie diet (HFCD) leads to visceral obesity, pancreatic inflammation, and accelerated pancreatic neoplasia in KrasG12D (KC) mice. In this study, we aimed to investigate the effects of an HFCD on visceral adipose inflammation with emphasis on potential differences between distinct visceral adipose depots.MethodsWe examined the weight and visceral obesity in both wild-type and KC mice on either control diet (CD) or HFCD. After 3 months, mice were killed for histological examination. Multiplex assays were also performed to obtain cytokine profiles between different adipose depots.ResultsBoth wild-type and KC mice on an HFCD exhibited significantly increased inflammation in the visceral adipose tissue, particularly in the peripancreatic fat (PPF), compared with animals on a CD. This was associated with significantly increased inflammation in the pancreas. Cytokine profiles were different between visceral adipose depots and between mice on the HFCD and CD.ConclusionsOur results clearly demonstrate that an HFCD leads to obesity and inflammation in the visceral adipose tissue, particularly the PPF. These data suggest that obesity-associated inflammation in PPF may accelerate pancreatic neoplasia in KC mice.

Hai-Tao He,1,2,* Mu Xu,1,* Ye Kuang,1 Xiao-Yun Han,1 Ming-Qi Wang,1 Qing Yang1 1Department of Pathogenobiology, 2Department of Cell Biology, College of Basic Medical Sciences, Jilin University, Changchun, People’s Republic of China *These authors contributed equally to this work Background: Accumulating evidence suggests long noncoding RNAs (lncRNAs) play important roles in the initiation and progression of cancers. However, their functions in chromophobe renal cell carcinoma (chRCC) are not fully understood.Methods: We analyzed the expression profiles of lncRNA, microRNA, and protein-coding RNA, along with the clinical information of 59 primary chRCC patients collected from The Cancer Genome Atlas database to identify lncRNA biomarkers for prognosis. We also constructed an lncRNA–microRNA–mRNA coexpression network (competitive endogenous RNAs network) by bioinformational approach.Results: One hundred and forty-two lncRNAs were found to be differentially expressed between the cancer and normal tissues (fold change ≥1.5, P<0.001). Among them, 12 lncRNAs were also differentially expressed with the corresponding clinical characteristics (fold change ≥1.5, P<0.01). Besides, 7 lncRNAs (COL18A1-AS, BRE-AS1, SNHG7, TMEM51-AS1, C21orf62-AS1, LINC00336, and LINC00882) were identified to be significantly correlated with overall survival (log-rank P<0.05). A competitive endogenous RNA network in chRCC containing 16 lncRNAs, 18 miRNAs, and 168 protein-coding RNAs was constructed.Conclusion: Our results identified specific lncRNAs associated with chRCC progression and prognosis, and presented competing endogenous RNA potential of lncRNAs in the tumor. Keywords: long noncoding RNA, chromophobe renal cell carcinoma, biomarker, competing endogenous RNA network&nbsp