FX Palo Alto Laboratory

On November 13–14, 2023, the National Institute of Allergy and Infectious Diseases (NIAID) in partnership with the Task Force for Global Health, Flu Lab, the Canadian Institutes of Health Research, and the Centers for Disease Control and Prevention convened a meeting on controlled human influenza virus infection model (CHIVIM) studies to review the current research landscape of CHIVIM studies and to generate actionable next steps. Presentations and panel discussions highlighted CHIVIM use cases, regulatory and ethical considerations, innovations, networks and standardization, and the utility of using CHIVIM in vaccine development. This report summarizes the presentations, discussions, key takeaways, and future directions for innovations in CHIVIMs. Experts agreed that CHIVIM studies can be valuable for the study of influenza infection, immune response, and transmission. Furthermore, they may have utility in the development of vaccines and other medical countermeasures; however, the use of CHIVIMs to de‐risk clinical development of investigational vaccines should employ a cautious approach. Endpoints in CHIVIM studies should be tailored to the specific use case. CHIVIM studies can provide useful supporting data for vaccine licensure but are not required and do not obviate the need for the conduct of field efficacy trials. Future directions in this field include the continued expansion of capacity to conduct CHIVIM studies, development of a broad panel of challenge viruses and assay reagents and standards that can be shared, streamlining of manufacturing processes, the exploration of targeted delivery of virus to the lower respiratory tract, efforts to more closely replicate natural influenza disease in CHIVIM, alignment on a definition of breadth to facilitate development of more broadly protective/universal vaccine approaches, and continued collaboration between stakeholders.

Slipping motions of magnetic field lines are a distinct signature of three-dimensional magnetic reconnection, a fundamental process driving solar and stellar flares. While being a key prediction of numerical experiments, the rapid super-Alfvénic field line slippage driven by the ‘slip-running’ reconnection has remained elusive in previous observations. New frontiers into exploring transient flare phenomena were introduced by recently designed high cadence observing programs of the Interface Region Imaging Spectrograph (IRIS). By exploiting high temporal resolution imagery (~2 s) of IRIS, here we reveal slipping motions of flare kernels at speeds reaching thousands of kilometres per second. The fast kernel motions are direct evidence of slip-running reconnection in quasi-separatrix layers, regions where magnetic field strongly changes its connectivity. Our results provide observational proof of theoretical predictions unaddressed for nearly two decades and extend the range of magnetic field configurations where reconnection-related phenomena can occur.

The efficient release of magnetic energy in astrophysical plasmas, such as during solar flares, can in principle be achieved through magnetic diffusion, at a rate determined by the associated electric field. However, attempts at measuring electric fields in the solar atmosphere are scarce, and none exist for sites where the magnetic energy is presumably released. Here, we present observations of an energetic event using the National Science Foundation’s Daniel K. Inouye Solar Telescope, where we detect the polarization signature of electric fields associated with magnetic diffusion. We measure the linear and circular polarization across the hydrogen Hε Balmer line at 397 nm at the site of a brightening event in the solar chromosphere. Our spectro-polarimetric modeling demonstrates that the observed polarization signals can only be explained by the presence of electric fields, providing conclusive evidence of magnetic diffusion, and opening a new window for the quantitative study of this mechanism in space plasmas.

Full-reference point cloud quality assessment (FR-PCQA) aims to infer the quality of distorted point clouds with available references. Most of the existing FR-PCQA metrics ignore the fact that the human visual system (HVS) dynamically tackles visual information according to different distortion levels (i.e., distortion detection for high-quality samples and appearance perception for low-quality samples) and measure point cloud quality using unified features. To bridge the gap, in this paper, we propose a perception-guided hybrid metric (PHM) that adaptively leverages two visual strategies with respect to distortion degree to predict point cloud quality: to measure visible difference in high-quality samples, PHM takes into account the masking effect and employs texture complexity as an effective compensatory factor for absolute difference; on the other hand, PHM leverages spectral graph theory to evaluate appearance degradation in low-quality samples. Variations in geometric signals on graphs and changes in the spectral graph wavelet coefficients are utilized to characterize geometry and texture appearance degradation, respectively. Finally, the results obtained from the two components are combined in a non-linear method to produce an overall quality score of the tested point cloud. The results of the experiment on five independent databases show that PHM achieves state-of-the-art (SOTA) performance and offers significant performance improvement in multiple distortion environments. The code is publicly available at https://github.com/zhangyujie-1998/PHM .

There is an urgent need from various multimedia applications to efficiently compress point clouds. The Moving Picture Experts Group has released a standard platform called geometry-based point cloud compression (G-PCC). However, its k -nearest neighbor (k-NN) based attribute prediction has limited efficiency for point clouds with rich texture and directional information. To overcome this problem, we propose a texture-aware attribute predictive coding framework in a point cloud diffusion model. In our work, attribute intra prediction is solved as a diffusion-based interpolation problem, and a general attribute predictor is developed. It is theoretically proven that G-PCC k -NN based predictor is a degraded case of the proposed diffusion-based solution. First, a point cloud is represented as two levels of details with seeds as the inpainting mask and non-seed points to be predicted. Second, we design point cloud partial difference operators to perform energy-minimizing attribute inpainting from seeds to unknowns. Smooth attribute interpolation can be achieved via an iterative diffusion process, and an adaptive early termination is proposed to reduce complexity. Third, we propose a structure-adaptive attribute predictive coding scheme, where edge-enhancing anisotropic diffusion is employed to perform texture-aware attribute prediction. Finally, attributes of seeds are beforehand encoded and prediction residuals of left points are progressively encoded into bitstream. Experiments show the proposed scheme surpasses the state-of-the-art by an average of 14.14%, 17.52%, and 17.87% BD-BR gains on the coding of Y, U, and V components, respectively. Subjective results on attribute reconstruction quality also verify the advantage of our scheme.

Purpose Patients with dihydropyrimidine dehydrogenase (DPD) deficiency are at high risk for severe and fatal toxicity from fluoropyrimidine (FP) chemotherapy. Pre-treatment DPYD testing is standard of care in many countries, but not the United States (US). This survey assessed pre-treatment DPYD testing approaches in the US to identify best practices for broader adoption. Methods From August to October 2023, a 22-item QualtricsXM survey was sent to institutions and clinicians known to conduct pre-treatment DPYD testing and broadly distributed through relevant organizations and social networks. Responses were analyzed using descriptive analysis. Results Responses from 24 unique US sites that have implemented pre-treatment DPYD testing or have a detailed implementation plan in place were analyzed. Only 33% of sites ordered DPYD testing for all FP-treated patients; at the remaining sites, patients were tested depending on disease characteristics or clinician preference. Almost 50% of sites depend on individual clinicians to remember to order testing without the assistance of electronic alerts or workflow reminders. DPYD testing was most often conducted by commercial laboratories that tested for at least the four or five DPYD variants considered clinically actionable. Approximately 90% of sites reported receiving results within 10 days of ordering. Conclusion Implementing DPYD testing into routine clinical practice is feasible and requires a coordinated effort among the healthcare team. These results will be used to develop best practices for the clinical adoption of DPYD testing to prevent severe and fatal toxicity in cancer patients receiving FP chemotherapy.

The state-of-the-art G-PCC (geometry-based point cloud compression) (Octree) is the fine-grained approach, which uses the octree to partition point clouds into voxels and predicts them based on neighbor occupancy in narrower spaces. However, G-PCC (Octree) is less effective at compressing dense point clouds than multi-grained approaches (such as G-PCC (Trisoup)), which exploit the continuous point distribution in nodes partitioned by the pruned octree over larger spaces. Therefore, we propose a lossy multi-grained compression with extended octree and dual-model prediction. The extended octree, where each partitioned node contains intra-block and extra-block points, is applied to address poor prediction (such as overfitting) at the node edges of the octree partition. For the points of each multi-grained node, dual-model prediction fits surfaces and projects residuals onto the surfaces, reducing projection residuals for efficient 2D compression and fitting complexity. In addition, a hybrid DWT-DCT transform for 2D projection residuals mitigates the resolution degradation of DWT and the blocking effect of DCT during high compression. Experimental results demonstrate the superior performance of our method over advanced G-PCC (Octree), achieving BD-rate gains of 55.9% and 45.3% for point-to-point ( D1 ) and point-to-plane ( D2 ) distortions, respectively. Our approach also outperforms G-PCC (Octree) and G-PCC (Trisoup) in subjective evaluation.

The substantial data volume within dynamic point clouds representing three-dimensional moving entities necessitates advancements in compression techniques. Motion estimation (ME) is crucial for reducing point cloud temporal redundancy. Standard block-based ME schemes, which typically utilize the previously decoded point clouds as inter-reference frames, often yield inaccurate and translation-only estimates for dynamic point clouds. To overcome this limitation, we propose an advanced patch-based affine ME scheme for dynamic point cloud geometry compression. Our approach employs a forward-backward jointing ME strategy, generating affine motion-compensated frames for improved inter-geometry references. Before the forward ME process, point cloud motion analysis is conducted on previous frames to perceive motion characteristics. Then, a point cloud is segmented into deformable patches based on geometry correlation and motion coherence. During the forward ME process, affine motion models are introduced to depict the deformable patch motions from the reference to the current frame. Later, affine motion-compensated frames are exploited in the backward ME process to obtain refined motions for better coding performance. Experimental results demonstrate the superiority of our proposed scheme, achieving an average 6.28% geometry bitrate gain over the inter codec anchor. Additional results also validate the effectiveness of key modules within the proposed ME scheme.

There is a pressing need across various applications for efficiently compressing point clouds. While the Moving Picture Experts Group introduced the geometry-based point cloud compression (G-PCC) standard, its attribute compression scheme falls short of eliminating signal frequency-domain redundancy. This paper proposes a texture-guided graph transform optimization scheme for point cloud attribute compression. We formulate the attribute transform coding task as a graph optimization problem, considering both the decorrelation capability of the graph transform and the sparsity of the optimized graph within a tailored joint optimization framework. First, the point cloud is reorganized and segmented into local clusters using a Hilbert-based scheme, enhancing spatial correlation preservation. Second, the inter-cluster attribute prediction and intra-cluster prediction are conducted on local clusters to remove spatial redundancy and extract texture priors. Third, the underlying graph structure in each cluster is constructed in a joint rate–distortion–sparsity optimization process, guided by geometry structure and texture priors to achieve optimal coding performance. Finally, point cloud attributes are efficiently compressed with the optimized graph transform. Experimental results show the proposed scheme outperforms the state of the art with significant BD-BR gains, surpassing G-PCC by 31.02%, 30.71%, and 32.14% in BD-BR gains for Y, U, and V components, respectively. Subjective evaluation of the attribute reconstruction quality further validates the superiority of our scheme.