Generative Scene Synthesis via Incremental View Inpainting using RGBD Diffusion Models

We address the challenge of recovering an underlying scene geometry and colors from a sparse set of RGBD view observations. In this work, we present a new solution that sequentially generates novel RGBD views along a camera trajectory, and the scene geometry is simply the fusion result of these views. More specifically, we maintain an intermediate surface mesh used for rendering new RGBD views, which subsequently becomes complete by an inpainting network; each rendered RGBD view is later back-projected as a partial surface and is supplemented into the intermediate mesh. The use of intermediate mesh and camera projection helps solve the refractory problem of multi-view inconsistency. We practically implement the RGBD inpainting network as a versatile RGBD diffusion model, which is previously used for 2D generative modeling; we make a modification to its reverse diffusion process to enable our use. We evaluate our approach on the task of 3D scene synthesis from sparse RGBD inputs; extensive experiments on the ScanNet dataset demonstrate the superiority of our approach over existing ones. Project page: https://jblei.site/project-pages/rgbd-diffusion.htm

A Review of Deep Learning Methods for Photoplethysmography Data

Photoplethysmography (PPG) is a highly promising device due to its advantages in portability, user-friendly operation, and non-invasive capabilities to measure a wide range of physiological information. Recent advancements in deep learning have demonstrated remarkable outcomes by leveraging PPG signals for tasks related to personal health management and other multifaceted applications. In this review, we systematically reviewed papers that applied deep learning models to process PPG data between January 1st of 2017 and July 31st of 2023 from Google Scholar, PubMed and Dimensions. Each paper is analyzed from three key perspectives: tasks, models, and data. We finally extracted 193 papers where different deep learning frameworks were used to process PPG signals. Based on the tasks addressed in these papers, we categorized them into two major groups: medical-related, and non-medical-related. The medical-related tasks were further divided into seven subgroups, including blood pressure analysis, cardiovascular monitoring and diagnosis, sleep health, mental health, respiratory monitoring and analysis, blood glucose analysis, as well as others. The non-medical-related tasks were divided into four subgroups, which encompass signal processing, biometric identification, electrocardiogram reconstruction, and human activity recognition. In conclusion, significant progress has been made in the field of using deep learning methods to process PPG data recently. This allows for a more thorough exploration and utilization of the information contained in PPG signals. However, challenges remain, such as limited quantity and quality of publicly available databases, a lack of effective validation in real-world scenarios, and concerns about the interpretability, scalability, and complexity of deep learning models. Moreover, there are still emerging research areas that require further investigation

The particle surface of spinning test particles

In this work, inspired by the definition of the photon surface given by Claudel, Virbhadra, and Ellis, we give an alternative quasi-local definition to study the circular orbits of single-pole particles. This definition does not only apply to photons but also to massive point particles. For the case of photons in spherically symmetric spacetime, it will give a photon surface equivalent to the result of Claudel, Virbhadra, and Ellis. Meanwhile, in general static and stationary spacetime, this definition can be regarded as a quasi-local form of the effective potential method. However, unlike the effective potential method which can not define the effective potential in dynamical spacetime, this definition can be applied to dynamical spacetime. Further, we generalize this definition directly to the case of pole-dipole particles. In static spherical symmetry spacetime, we verify the correctness of this generalization by comparing the results obtained by the effective potential method.Comment: 12pages, no figures; accepted by The European Physical Journal C; the title has been revies

The promoting effects of soil microplastics on alien plant invasion depend on microplastic shape and concentration

Both alien plant invasions and soil microplastic pollution have become a concerning threat for terrestrial ecosystems, with consequences on the human well-being. However, our current knowledge of microplastic effects on the successful invasion of plants remains limited, despite numerous studies demonstrating the direct and indirect impacts of microplastics on plant performance. To address this knowledge gap, we conducted a greenhouse experiment involving the mixtures of soil and low-density polyethylene (LDPE) microplastic pellets and fragments at the concentrations of 0, 0.5 % and 2.0 %. Additionally, we included Solidago decurrens (native plant) and S. canadensis (alien invasive plant) as the target plants. Each pot contained an individual of either species, after six-month cultivation, plant biomass and antioxidant enzymes, as well as soil properties including soil moisture, pH, available nutrient, and microbial biomass were measured. Our results indicated that microplastic effects on soil properties and plant growth indices depended on the Solidago species, microplastic shapes and concentrations. For example, microplastics exerted positive effects on soil moisture of the soil with native species but negative effects with invasive species, which were impacted by microplastic shapes and concentrations, respectively. Microplastics significantly impacted catalase (P < 0.05) and superoxide dismutase (P < 0.01), aboveground biomass (P < 0.01), and belowground/aboveground biomass (P < 0.01) of the native species depending on microplastic shapes, but no significant effects on those of the invasive species. Furthermore, microplastics effects on soil properties, nutrient, nutrient ratio, and plant antioxidant enzyme activities contributed to plant biomass differently among these two species. These results suggested that the microplastics exerted a more pronounced impact on native Solidago plants than the invasive ones. This implies that the alien invasive species displays greater resistance to microplastic pollution, potentially promoting their invasion. Overall, our study contributes to a better understanding of the promoting effects of microplastic pollution on plant invasion

Transcriptomic analysis reveals transcription factors involved in vascular bundle development and tissue maturation in ginger rhizomes (Zingiber officinale Roscoe)

Ginger (Zingiber officinale Roscoe) is an important vegetable with medicinal value. Rhizome development determines ginger yield and quality. However, little information is available about the molecular features underlying rhizome expansion and maturation. In this study, we investigated anatomy characteristics, lignin accumulation and transcriptome profiles during rhizome development. In young rhizomes, the vascular bundle (VB) was generated with only vessels in it, whereas in matured rhizomes, three to five layers of fibre bundle in the xylem were formed, resulting in VB enlargement. It indicates VB development favouring rhizome swelling. With rhizome matured, the lignin content was remarkably elevated, thus facilitating tissue lignification. To explore the regulators for rhizome development, nine libraries including ginger young rhizomes (GYR), growing rhizomes (GGR), and matured rhizomes (GMR) were established for RNA-Seq, a total of 1264 transcription factors (TFs) were identified. Among them, 35, 116, and 14 differentially expressed TFs were obtained between GYR and GGR, GYR and GMR, and GGR and GMR, respectively. These TFs were further divided into three categories. Among them, three ZobHLHs (homologs of Arabidopsis LHW and AtbHLH096) as well as one DIVARICATA homolog in ginger might play crucial roles in controlling VB development. Four ZoWRKYs and two ZoNACs might be potential regulators associated with rhizome maturation. Three ZoAP2/ERFs and one ZoARF might participate in rhizome development via hormone signalling. This result provides a molecular basis for rhizome expansion and maturation in ginger

Artificial intelligence-aided rapid and accurate identification of clinical fungal infections by single-cell Raman spectroscopy

Integrating artificial intelligence and new diagnostic platforms into routine clinical microbiology laboratory procedures has grown increasingly intriguing, holding promises of reducing turnaround time and cost and maximizing efficiency. At least one billion people are suffering from fungal infections, leading to over 1.6 million mortality every year. Despite the increasing demand for fungal diagnosis, current approaches suffer from manual bias, long cultivation time (from days to months), and low sensitivity (only 50% produce positive fungal cultures). Delayed and inaccurate treatments consequently lead to higher hospital costs, mobility and mortality rates. Here, we developed single-cell Raman spectroscopy and artificial intelligence to achieve rapid identification of infectious fungi. The classification between fungi and bacteria infections was initially achieved with 100% sensitivity and specificity using single-cell Raman spectra (SCRS). Then, we constructed a Raman dataset from clinical fungal isolates obtained from 94 patients, consisting of 115,129 SCRS. By training a classification model with an optimized clinical feedback loop, just 5 cells per patient (acquisition time 2 s per cell) made the most accurate classification. This protocol has achieved 100% accuracies for fungal identification at the species level. This protocol was transformed to assessing clinical samples of urinary tract infection, obtaining the correct diagnosis from raw sample-to-result within 1 h

Molecular cloning and gene expression analysis of Ercc6l in Sika deer (Cervus nippon hortulorum).

BACKGROUND: One important protein family that functions in nucleotide excision repair (NER) factors is the SNF2 family. A newly identified mouse ERCC6-like gene, Ercc6l (excision repair cross-complementing rodent repair deficiency, complementation group 6-like), has been shown to be another developmentally related member of the SNF2 family. METHODOLOGY/PRINCIPAL FINDINGS: In this study, Sika deer Ercc6l cDNA was first cloned and then sequenced. The full-length cDNA of the Sika deer Ercc6l gene is 4197 bp and contains a 3732 bp open reading frame that encodes a putative protein of 1243 amino acids. The similarity of Sika deer Ercc6l to Bos taurus Ercc6l is 94.05% at the amino acid sequence level. The similarity, however, is reduced to 68.42-82.21% when compared to Ercc6l orthologs in other mammals and to less than 50% compared to orthologs in Gallus gallus and Xenopus. Additionally, the expression of Ercc6l mRNA was investigated in the organs of fetal and adult Sika deer (FSD and ASD, respectively) by quantitative RT-PCR. The common expression level of Ercc6l mRNA in the heart, liver, spleen, lung, kidney, and stomach from six different developmental stages of 18 Sika deer were examined, though the expression levels in each organ varied among individual Sika deer. During development, there was a slight trend toward decreased Ercc61 mRNA expression. The highest Ercc6l expression levels were seen at 3 months old in every organ and showed the highest level of detection in the spleen of FSD. The lowest Ercc6l expression levels were seen at 3 years old. CONCLUSIONS/SIGNIFICANCE: We are the first to successfully clone Sika deer Ercc6l mRNA. Ercc6l transcript is present in almost every organ. During Sika deer development, there is a slight trend toward decreased Ercc61 mRNA expression. It is possible that Ercc6l has other roles in embryonic development and in maintaining the growth of animals

Numerical simulation on multi-stage fractured horizontal wells in shale gas reservoirs based on the finite volume method

In order to simulate the flowing of shale gas in multi-scale media, we established a mathematical model for the unsteady seepage of multi-stage fractured horizontal wells in shale gas reservoirs in consideration of the flowing characteristics of shale gas in matrix, natural fractures and large-scale artificial fractures. Grid division in the simulation region was carried out by means of nonstructural tetrahedral grid. Then, a 3D numerical model for the seepage of shale gas was established discretely using finite volume method and solved using sequence solution method. Finally, the production performance of multi-stage fractured horizontal wells in shale gas reservoirs and the reservoir pressure distribution were simulated, and the simulation results were analyzed. And the following research results were obtained. First, the gas production rates of multi-stage fractured horizontal wells calculated by this newly established numerical simulation method are basically consistent with the calculation results by the commercial numerical simulation software Eclipse, which proves that this new model is accurate and feasible. Second, the gas production rates of horizontal wells calculated by the sequential solution method are different from those calculated by the fully implicit solution method in the early production stages, but as the calculation progresses, both of them tend to be consistent, which further verifies the accuracy of this new model. Third, desorbed gas plays a supplementary role to reservoir pressure, but its function is limited, and its effect on gas production is little. As the production goes on, the percentage of desorbed gas increases gradually. Fourth, the key to the stimulation of shale-gas horizontal wells is to determine the number of fractured sections rationally and create longer artificial fractures. In conclusion, the research results are conducive to the design of stimulated reservoir volumes (SRVs) of shale gas reservoirs and the prediction of production performance of multi-stage fractured horizontal wells. Keywords: Shale gas, Horizontal well, Stimulated reservoir volume, Finite volume method, 3D numerical simulation of seepage, Sequential solution, Fully implicit solution, Desorbed gas, Gas production rat

Bacterial-Artificial-Chromosome-Based Genome Editing Methods and the Applications in Herpesvirus Research

Herpesviruses are major pathogens that infect humans and animals. Manipulating the large genome is critical for exploring the function of specific genes and studying the pathogenesis of herpesviruses and developing novel anti-viral vaccines and therapeutics. Bacterial artificial chromosome (BAC) technology significantly advanced the capacity of herpesviruses researchers to manipulate the virus genomes. In the past years, advancements in BAC-based genome manipulating and screening strategies of recombinant BACs have been achieved, which has promoted the study of the herpes virus. This review summarizes the advances in BAC-based gene editing technology and selection strategies. The merits and drawbacks of BAC-based herpesvirus genome editing methods and the application of BAC-based genome manipulation in viral research are also discussed. This review provides references relevant for researchers in selecting gene editing methods in herpes virus research. Despite the achievements in the genome manipulation of the herpes viruses, the efficiency of BAC-based genome manipulation is still not satisfactory. This review also highlights the need for developing more efficient genome-manipulating methods for herpes viruses