bluuebunny/crossref_metadata_2025 huggingface dataset:
PRKCA Promotes Mitophagy through the miR-15a-5p/PDK4 Axis to Relieve Sepsis-Induced Acute Lung Injury
Authors: Zhu Qiu-Jiao , Wang Jian , Li Ying , Bai Zhen-Jiang , Guo Xu-Bei , Pan Tao
Published: November 2022

Acute lung injury (ALI) caused by sepsis is a common respiratory critical illness with high morbidity and mortality. Protein kinase C-alpha (PRKCA) plays a protective role in sepsis-induced ALI.
Redistribution of the Novel Clostridioides difficile Spore Adherence Receptor E-Cadherin by TcdA and TcdB Increases Spore Binding to Adherens Junctions
Authors: Castro-Córdova Pablo , Otto-Medina Macarena , Montes-Bravo Nicolás , Brito-Silva Christian , Lacy D. Borden , Paredes-Sabja Daniel
Published: November 2022

Clostridioides difficile
causes antibiotic-associated diseases in humans, ranging from mild diarrhea to severe pseudomembranous colitis and death. A major clinical challenge is the prevention of disease recurrence, which affects nearly ~20 to 30% of the patients with a primary
C. difficile
infection (CDI).
The Chemoreceptor Sensory Adaptation System Produces Coordinated Reversals of the Flagellar Motors on an Escherichia coli Cell
Authors: Uchida Yumiko , Hamamoto Tatsuki , Che Yong-Suk , Takahashi Hiroto , Parkinson John S. , Ishijima Akihiko , Fukuoka Hajime
Published: November 2022

Under steady-state conditions with no external stimuli, an
Escherichia coli
cell coordinately switches the rotational direction of its flagellar motors. Here, we demonstrate that the CheR and CheB enzymes of the chemoreceptor sensory adaptation system mediate this coordination.
Antibody Recognition of CD4-Induced Open HIV-1 Env Trimers
Authors: Yang Zhi , Dam Kim-Marie A. , Gershoni Jonathan M. , Zolla-Pazner Susan , Bjorkman Pamela J.
Published: November 2022

The RV144 HIV-1 clinical vaccination trial showed modest protection against viral infection. Antibody responses to the V1V2 region of HIV-1 Env gp120 were correlated inversely with the risk of infection, and data from three other clinical vaccine trials suggested a similar signal.
Pregenomic RNA Launch Hepatitis B Virus Replication System Facilitates the Mechanistic Study of Antiviral Agents and Drug-Resistant Variants on Covalently Closed Circular DNA Synthesis
Authors: Zhao Qiong , Chang Jinhong , Rijnbrand Rene , Lam Angela M. , Sofia Michael J. , Cuconati Andrea , Guo Ju-Tao
Published: November 2022

Hepadnaviral pgRNA not only serves as a template for reverse transcriptional replication of viral DNA but also expresses core protein and DNA polymerase to support viral genome replication and cccDNA synthesis. Not surprisingly, cytoplasmic expression of duck hepatitis B virus pgRNA initiated viral replication leading to infectious virion secretion.
New Assay Reveals Vast Excess of Defective over Intact HIV-1 Transcripts in Antiretroviral Therapy-Suppressed Individuals
Authors: Martin Holly Anne , Kadiyala Gayatri Nikhila , Telwatte Sushama , Wedrychowski Adam , Chen Tsui-Hua , Moron-Lopez Sara , Arneson Doug , Hoh Rebecca , Deeks Steven , Wong Joseph , Yukl Steven A.
Published: November 2022

We developed a new assay that can distinguish and quantify intact (potentially infectious) as well as defective HIV RNA. In ART-treated individuals, we found that the vast majority of all HIV RNA is defective at the 3′ end, possibly due to incomplete transcriptional processivity.
A Clinical Study Provides the First Direct Evidence That Interindividual Variations in Fecal β-Lactamase Activity Affect the Gut Mycobiota Dynamics in Response to β-Lactam Antibiotics
Authors: Delavy Margot , Burdet Charles , Sertour Natacha , Devente Savannah , Docquier Jean-Denis , Grall Nathalie , Volant Stevenn , Ghozlane Amine , Duval Xavier , Mentré France , d’Enfert Christophe , Bougnoux Marie-Elisabeth
Published: November 2022

Fungal infections are redoubtable healthcare-associated complications in immunocompromised patients. Particularly, the commensal intestinal yeast
Candida albicans
causes invasive infections in intensive care patients and is, therefore, associated with high mortality.
Draft Genome Sequence of a Xylella fastidiosa Strain Causing Bacterial Leaf Scorch of American Elm in Washington, DC
Authors: Guan Wei , Shao Jonathan , Zhao Tingchang , Huang Qi
Published: November 2022

Here, we report the draft genome sequence of
Xylella fastidiosa
strain ATCC 35873, which was obtained from the American Type Culture Collection and was originally isolated from a symptomatic American elm tree grown in Washington, DC. The ATCC 35873 genome contains 2,454,216 bp and has a GC content of 51.68%.
Experiences and support needs of parents/caregivers of children with cancer through the COVID-19 pandemic in the UK: a longitudinal study
Authors: Collaço Nicole , Gamble Ashley , Morgan Jessica Elizabeth , Phillips Bob , Culliford David , Darlington Anne-Sophie
Published: November 2022

Objective

To explore the experiences, information and support needs of parents/caregivers of children with cancer and how these changed as the COVID-19 pandemic evolved.

Design

Online surveys containing closed and free-text questions on experiences, information and support needs were completed at four time points (between April 2020 and October 2021) during the COVID-19 pandemic. Descriptive statistics of closed items and content analysis of qualitative data were conducted.

Setting

Online.

Participants

Parents/caregivers of children with cancer.

Results

335 parents/caregivers completed the survey over four time points. Findings revealed that parents’/caregivers’ worry about the virus and vigilance about their child’s virus symptoms decreased over time. Parents reporting the need for support on how to reduce their worries and/or family members during the virus outbreak were low, however parents reported a slight increase in need for support at T3 when schools reopened. Qualitative findings reported the following themes: (1) Psychological well-being of parents/caregivers, (2) Changing perceptions of risks/priorities, (3) Adjusting to COVID-19: Living with continued caution, (4) Healthcare and treatment provision, (5) Information seeking and needs during COVID-19.

Conclusions

The COVID-19 pandemic disrupted people’s lives and routines in relation to access to support, finances, education and social lives, leading to psychological distress. Parents highlighted the need for timely, up-to-date and personalised information in relation to COVID-19 and their child with cancer. Further consideration of the development of technology-based health solutions may provide an efficient and safe way to connect with and support parent/caregivers.
Stigma towards leprosy across seven life domains in Indonesia: a qualitative systematic review
Authors: Marpaung Yosi Marin , Ernawati Ernawati , Dwivania Agatha Tunjung
Published: November 2022

Objective

To provide knowledge about the domains of life affected by stigma towards leprosy in Indonesia, including its manifestations, driving factors and consequences.

Design

Qualitative systematic review.

Study selection

PubMed, CINAHL, ProQuest, Taylor&Francis and Google Scholar were used to systematically search studies with qualitative component that were conducted in Indonesia and published from January 2000 to December 2020 in English or Indonesian language. The search was started in November 2020 and reran in April 2021. Quality assessment and thematic synthesis were applied.

Data extraction

Of the 3184 studies, 37 manuscripts were reviewed. Information relating to study characteristics, stigma domains and types following Weiss Extended Scambler’s Hidden Stress Model, stigma consequences and drivers were extracted.

Results

Seven themes were identified. Three themes—community, domestic and intimate relationships—impacted private domains. Four themes—health, economics, education and public entitlements—concerned public domains. Studies mainly discussed enacted stigma rather than anticipated and internalised stigma. Ten stigma-driving factors were found, ranging from negative and positive concepts linked with the condition to aspects not related to the disease process. Five areas of consequences were shown. Impact on public rights, such as education, was very minimally explored, although school absence was often mentioned. Stigma manifestation, drivers and consequences in most public domains were least explored.

Conclusion

Leprosy-affected persons in Indonesia experienced and felt stigma in private and public domains. Disease-related aspects, the culture and history of a particular region are linked with stigma manifestations. Approaches in one domain can affect another domain. More exploratory studies are needed in the endemic areas outside Java, especially considering both the lack of studies there and the unique culture of each Indonesian region.
Swedish Chronic Pain Biobank: protocol for a multicentre registry and biomarker project
Authors: Ghafouri Bijar , Ernberg Malin , Andréll Paulin , Bäckryd Emmanuel , Fisher Marcelo Rivano , Freund-Levi Yvonne , Grelz Henrik , Gräbel Olaf , Karlsten Rolf , Kosek Eva , Löfgren Monika , Ringqvist Åsa , Rudling Karin , Stålnacke Britt-Marie , Sörlén Niklas , Uhlin Karin , Westergren Hans , Gerdle Björn
Published: November 2022

Introduction

About 20% of the adult population have chronic pain, often associated with psychological distress, sick leave and poor health. There are large variations in the clinical picture. A biopsychosocial approach is used in investigation and treatment. The concept of personalised medicine, that is, optimising medication types and dosages for individual patients based on biomarkers and other patient-related factors, has received increasing attention in different diseases but used less in chronic pain. This cooperative project from all Swedish University Hospitals will investigate whether there are changes in inflammation and metabolism patterns in saliva and blood in chronic pain patients and whether the changes correlate with clinical characteristics and rehabilitation outcomes.

Methods and analysis

Patients at multidisciplinary pain centres at University Hospitals in Sweden who have chosen to participate in the Swedish Quality Registry for Pain Rehabilitation and healthy sex-matched and age-matched individuals will be included in the study. Saliva and blood samples will be collected in addition to questionnaire data obtained from the register. From the samples, proteins, lipids, metabolites and micro-RNA will be analysed in relation to, for example, diagnosis, pain characteristics, psychological distress, body weight, pharmacological treatment and clinical rehabilitation results using advanced multivariate data analysis and bioinformatics.

Ethics and dissemination

The study is approved by the Swedish Ethical Review Authority (Dnr 2021–04929) and will be conducted in accordance with the declaration of Helsinki.

The results will be published in open access scientific journals and in popular scientific relevant journals such as those from patient organisations. Data will be also presented in scientific meetings, meeting with healthcare organisations and disseminated in different lecturers at the clinics and universities.
General practitioners’ (GPs) experience, attitudes and needs on clinical genetic services: a systematic review
Authors: Ong Cheryl Siow Bin , Fok Rose Wai‑Yee , Tan Ryo Chee Ann , Fung Si Ming , Sun Shirley , Ngeow Joanne Yuen Yie
Published: November 2022

Objective

The proliferation and growing demands of genetic testing are anticipated to revolutionise medical practice. As gatekeepers of healthcare systems, general practitioners (GPs) are expected to play a critical role in the provision of clinical genetic services. This paper aims to review existing literature on GPs’ experience, attitudes and needs towards clinical genetic services.

Design

A systematic mixed studies review of papers published between 2010 and 2022.

Eligibility criteria

The inclusion criterion was peer-reviewed articles in English and related to GPs’ experience, views and needs on any genetic testing.

Information sources

The PubMed, PsycINFO, Cochrane, EMBASE databases were searched using Mesh terms, Boolean and wildcards combinations to identify peer-reviewed articles published from 2010 to 2022. Study quality was assessed using Mixed Methods Appraisal Tool. Only articles that fulfilled the inclusion criteria were selected. A thematic meta-synthesis was conducted on the final sample of selected articles to identify key themes.

Results

A total of 62 articles were included in the review. Uncertainty over GPs’ role in providing genetic services were attributed by the lack of confidence and time constraints and rarity of cases may further exacerbate their reluctance to shoulder an expanded role in clinical genetics. Although educational interventions were found to increasing GPs’ knowledge and confidence to carry out genetic tasks, varied interest on genetic testing and preference for a shared care model with other genetic health professionals have resulted in minimal translation to clinical adoption.

Conclusion

This review highlights the need for deeper exploration of GPs’ varied experience and attitudes towards clinical genetic services to better facilitate targeted intervention in the adoption of clinical genetics.
Forecasting immune effector cell-associated neurotoxicity syndrome after chimeric antigen receptor t-cell therapy
Authors: Amidi Yalda , Eckhardt Christine A , Quadri Syed A , Malik Preeti , Firme Marcos Santana , Jones Daniel K , Jain Aayushee , Danish Husain H , Rubin Daniel B , Jacobson Caron A , Cash Sydney S , Lee Jong Woo , Dietrich Jorg , Westover M Brandon
Published: November 2022

Background

Immune effector cell-associated neurotoxicity syndrome (ICANS) is a clinical and neuropsychiatric syndrome that can occur days to weeks following administration chimeric antigen receptor (CAR) T-cell therapy. Manifestations of ICANS range from encephalopathy and aphasia to cerebral edema and death. Because the onset and time course of ICANS is currently unpredictable, prolonged hospitalization for close monitoring following CAR T-cell infusion is a frequent standard of care.

Methods

This study was conducted at Brigham and Women’s Hospital from April 2015 to February 2020. A cohort of 199 hospitalized patients treated with CAR T-cell therapy was used to develop a combined hidden Markov model and lasso-penalized logistic regression model to forecast the course of ICANS. Model development was done using leave-one-patient-out cross validation.

Results

Among the 199 patients included in the analysis 133 were male (66.8%), and the mean (SD) age was 59.5 (11.8) years. 97 patients (48.7%) developed ICANS, of which 59 (29.6%) experienced severe grades 3–4 ICANS. Median time of ICANS onset was day 9. Selected clinical predictors included maximum daily temperature, C reactive protein, IL-6, and procalcitonin. The model correctly predicted which patients developed ICANS and severe ICANS, respectively, with area under the curve of 96.7% and 93.2% when predicting 5 days ahead, and area under the curve of 93.2% and 80.6% when predicting the entire future risk trajectory looking forward from day 5. Forecasting performance was also evaluated over time horizons ranging from 1 to 7 days, using metrics of forecast bias, mean absolute deviation, and weighted average percentage error.

Conclusion

The forecasting model accurately predicts risk of ICANS following CAR T-cell infusion and the time course ICANS follows once it has begun.Cite Now
Lost in translation: understanding divergent perspectives on a depleted fish stock
Authors: Dean Micah J. , Hoffman William S. , Buchan Nicholas C. , Scyphers Steven B. , Grabowski Jonathan H.
Published: November 2022

Fishers commonly disagree with stock assessment results, particularly when a stock declines and strict harvest controls become necessary. Such regulations alter fisher perceptions of stock dynamics, contributing to a divergence in perspectives. Some assessments have inconsistent terminal year values (retrospective patterns) which fuel distrust in scientific advice. When assessment and fishery perspectives disagree, independent surveys can help identify biases and interpret discrepancies. We examine fishery trends and assessment results for Atlantic cod in the Gulf of Maine, a stock which has declined for decades. Trends were compared to a scientific industry cooperative trawl survey and a telephone survey of fisher perceptions. Trawl survey results generally corroborate the assessment perspective on population scale and decline, yet suggest a different view of the age structure. Fisher perceptions were at odds with the assessment and trawl survey and likely resulted from regulations that altered fisher behavior, causing catch rates to increase while the stock declined. Divergent perspectives may be an unavoidable consequence of fishery management, yet acknowledging the underlying mechanisms might help avoid future conflict.
99Tc in stars, reactors, and nuclear medicine
Authors: Nagai Yasuki , Igashira Masayuki
Published: November 2022

The roles of 99gTc in stars and in nuclear fission reactors and that of 99mTc in nuclear medicine that were studied using neutrons provided from accelerators are reported. In addition to the critical role of 99gTc in stars, we also describe the roles of related light nuclei such as 16O, and experimental studies on keV-neutron capture cross sections of these light nuclei that were performed using keV neutrons provided from a 3.2-MV Pelletron accelerator are reported. The keV-neutron capture cross section of 99gTc, that is important in studies on the nucleosynthesis of 99Tc in stars and on the nuclear transmutation of long-lived nuclear wastes produced in fission reactors, were measured. The cross sections of both light nuclei and 99gTc were measured using a neutron time-of-flight method with a ns-pulsed neutron source from the 7Li(p,n)7Be reaction and with an anti-Compton γ-ray NaI(Tl) spectrometer. The measurements reported here were compared with the values of recent worldwide evaluated nuclear data libraries.

We proposed a method of producing 99mTc from the 100Mo(n,2n)99Mo reaction, and performed a 99mTc production experiment using a natMo sample and MeV neutrons from the natC(d,n) reaction by 40 MeV deuterons at a cyclotron. Calculations for the 99mTc production with an enriched 100Mo sample were also performed, and calculated results were compared with the experimental results. Moreover, the thermoseparation of 99mTc from 99Mo was discussed. As a result, it was shown that 99mTc prepared by the thermoseparation using 99Mo produced by the 100Mo(n,2n)99Mo reaction can be a promising substitute for the fission product 99Mo.
STUDY ON THE INFLUENCE OF ZINC-COATED WIRE MATERIAL ON 3D PROFILE MACHINING OF INCONEL 825 ALLOY USING WEDM PROCESS
Authors: ARULSELVAN S. , RAVINDRAN D. , SELVAKUMAR G. , MARCEL MOSHI A. ARUL
Published: November 2022

The experimental analysis carried out in this paper aimed at the selection of optimal machining conditions for the 3D profile machining of Inconel 825 alloy using Wire Electrical Discharge Machining (WEDM). It was noticed that the 3D profile machining of Inconel 825 alloy using WEDM process has not been performed and reported so far by the researchers in this domain to the best of our knowledge. As the machining of Inconel 825 alloy is difficult using the conventional machining processes, nontraditional machining is generally used. As the material is conductive, the WEDM process is suitable to machine this material. The effects of input factors such as wire material, pulse on-time, pulse off-time, peak current, wire tension and gap voltage on the material removal rate (MRR), surface roughness (SR) and dimensional shift (DS) have been investigated using [Formula: see text] mixed-level orthogonal array of experiments. The DS value for every machining condition has been predicted and inputted into the CNC system as the wire offset value to enhance the dimensional accuracy of the product. The parametric analysis has been done by Minitab software. Analysis of variance (ANOVA) has been used to find the significant parameters affecting surface quality. The regression equations have also been formulated to study the adequacy of the model. Multi-response optimization based on the Gray Relational Analysis has been employed and the results that optimize the MRR and SR simultaneously have been suggested. SEM images have been taken on the machined surfaces to compare the surface finish on the specimens.
Cohomologies and deformations of Lie triple systems with derivations
Authors: Sun Qinxiu , Chen Shan
Published: November 2022

The aim of this paper is to study cohomological theory of Lie triple systems with derivations. We introduce the notion of a LtsDer pair and its representation. We also introduce the matched pair of LtsDer pairs. Moreover, we characterize the relation between the cohomologies of a LtsDer pair and its associated LeibDer pair. The deformation and [Formula: see text]-operator of LtsDer pairs are also discussed. We show that the representation of the given LtsDer pair becomes a new LtsDer pair and the former LtsDer pair becomes the representation of the new LtsDer pair, naturally to investigate cohomologies of the new LtsDer pair. Finally, we consider abelian extensions of LtsDer pairs.
On Hilbert coefficients and sequentially generalized Cohen–Macaulay modules
Authors: Cuong Nguyen Tu , Long Nguyen Tuan , Truong Hoang Le
Published: November 2022

This paper shows that if [Formula: see text] is a homomorphic image of a Cohen–Macaulay local ring, then [Formula: see text]-module [Formula: see text] is sequentially generalized Cohen–Macaulay if and only if the difference between Hilbert coefficients and arithmetic degrees for all distinguished parameter ideals of [Formula: see text] are bounded.
Development of a Decision Support System for Selection of Reviewers to Evaluate Research and Development Projects
Authors: Koçak Serdar , İç Yusuf Tansel , Sert Mustafa , Atalay Kumru Didem , Dengiz Berna
Published: November 2022

The evaluation of Research and Development (R&D) projects consists of many steps depending on the government funding agencies and the support program. It is observed that the reviewer evaluation reports have a crucial impact on the support decisions of the projects. In this study, a decision support system (DSS), namely R&D Reviewer, is developed to help the decision-makers with the assignment of the appropriate reviewer to R&D project proposals. It is aimed to create an artificial intelligence-based decision support system that enables the classification of Turkish R&D projects with natural language processing (NLP) methods. Furthermore, we examine the reviewer ranking process by using fuzzy multi-criteria decision-making methods. The data in the database is processed primarily to classify the R&D projects and the word embedding model NLP, “Word2Vec”. Also, we designed the Convolutional Neural Network (CNN) model to select the features by using the automatic feature learning approach. Moreover, we incorporate a new integrated hesitant fuzzy VIKOR and TOPSIS methodology into the developed DSS for the reviewer ranking process.
On the Miller basis for the space of cusp forms
Authors: Wang Wei
Published: November 2022

In this paper, we study the existence of the Miller basis for [Formula: see text], the space of cusp forms of weight [Formula: see text] and level [Formula: see text]. We seek an easy criterion to determine whether or not the space [Formula: see text] admits the Miller basis. In particular, we prove that if the level [Formula: see text] is sufficiently composite, then [Formula: see text] does not have the Miller basis. We also prove that there exists a constant [Formula: see text] such that [Formula: see text] admits the Miller basis if and only if [Formula: see text] admits the Miller basis when [Formula: see text].
Automorphic tensor products and cuspidal cohomology of the GL4
Authors: Bhagwat Chandrasheel , Mondal Sudipa
Published: November 2022

In this paper, we establish an asymptotic lower bound estimate on the contribution of cuspidal automorphic representations of [Formula: see text] to cuspidal cohomology of the [Formula: see text] which are obtained from automorphic tensor product of two automorphic representations of [Formula: see text] of given weights and with varying level structure. In the end, we also prove that the symmetric cube of a representation of [Formula: see text] and the automorphic tensor product of two representations of [Formula: see text] cannot be equal (up to a twist by a character of [Formula: see text]) to each other, under the suitable assumptions on the representations being cuspidal and cohomological.
Misiurewicz polynomials and dynamical units, part I
Authors: Benedetto Robert L. , Goksel Vefa
Published: November 2022

We study the dynamics of the unicritical polynomial family [Formula: see text]. The [Formula: see text]-values for which [Formula: see text] has a strictly preperiodic postcritical orbit are called Misiurewicz parameters, and they are the roots of Misiurewicz polynomials. The arithmetic properties of these special parameters have found applications in both arithmetic and complex dynamics. In this paper, we investigate some new such properties. In particular, when [Formula: see text] is a prime power and [Formula: see text] is a Misiurewicz parameter, we prove certain arithmetic relations between the points in the postcritical orbit of [Formula: see text]. We also consider the algebraic integers obtained by evaluating a Misiurewicz polynomial at a different Misiurewicz parameter, and we ask when these algebraic integers are algebraic units. This question naturally arises from some results recently proven by Buff, Epstein, and Koch and by the second author. We propose a conjectural answer to this question, which we prove in many cases.
Lithogeochemistry in exploration for intrusion-hosted magmatic Ni–Cu–Co deposits
Authors: Barnes Stephen J.
Published: November 2022

Magmatic Ni–Cu–Co–platinum group element deposits are notoriously difficult exploration targets owing to a lack of alteration haloes or other extended distal footprints. Success requires prediction of prospective terranes, followed by identification of suitable host intrusions and deposition sites within those intrusions. At the regional scale, potential ore-hosting magmas tend to have lithophile trace element trends falling on mixing lines between primitive or slightly depleted source mantle and typical upper continental crust, with several significant exceptions. Most known deposits have parent magmas that are in the upper range of FeO content for given MgO compared with baseline data sets for continental large igneous province magmas. At the scale of individual intrusions, the presence of cumulate rocks, both mafic and ultramafic, is key. These can be recognized in regional datasets using combinations of magnesium number (molar MgO/(MgO + FeO), Al
2
O
3
, TiO
2
and Zr contents. Combinations of alteration-resistant element ratios between Ni, Cr and Ti are also useful and can also be applied to moderately weathered samples. Concentrations and ratios of Cu and Zr are useful in discriminating chalcophile-enriched and depleted magma suites. In combination, these approaches can be combined to discriminate highly prospective cumulate-dominated magmatic suites and individual intrusions from non-cumulate suites with limited potential.

Supplementary material:
Supplementary Appendix 1, procedure for determination of FeO–MgO contents of magmas from olivine-liquid mixing lines, and Supplementary Appendix 2, procedure and error analysis for correcting major element whole rock analyses volatile and sulfide free, are available at
https://doi.org/10.6084/m9.figshare.c.6267664
Learning Reconstructability for Drone Aerial Path Planning
Authors: Liu Yilin , Lin Liqiang , Hu Yue , Xie Ke , Fu Chi-Wing , Zhang Hao , Huang Hui
Published: November 2022

We introduce the first
learning-based reconstructability predictor
to improve view and path planning for large-scale 3D urban scene acquisition using unmanned drones. In contrast to previous heuristic approaches, our method learns a model that
explicitly
predicts how well a 3D urban scene will be reconstructed from a set of viewpoints. To make such a model trainable and simultaneously applicable to drone path planning, we simulate the proxy-based 3D scene reconstruction during training to set up the prediction. Specifically, the neural network we design is trained to predict the scene reconstructability as a function of the
proxy geometry
, a set of viewpoints, and optionally a series of scene images acquired in flight. To reconstruct a new urban scene, we first build the 3D scene proxy, then rely on the predicted reconstruction quality and uncertainty measures by our network, based off of the proxy geometry, to guide the drone path planning. We demonstrate that our data-driven reconstructability predictions are more closely correlated to the true reconstruction quality than prior heuristic measures. Further, our learned predictor can be easily integrated into existing path planners to yield improvements. Finally, we devise a new iterative view planning framework, based on the learned reconstructability, and show superior performance of the new planner when reconstructing both synthetic and real scenes.
Metappearance
Authors: Fischer Michael , Ritschel Tobias
Published: November 2022

There currently exist two main approaches to reproducing visual appearance using Machine Learning (ML): The first is training models that generalize over different instances of a problem, e.g., different images of a dataset. As one-shot approaches, these offer fast inference, but often fall short in quality. The second approach does not train models that generalize across tasks, but rather over-fit a single instance of a problem, e.g., a flash image of a material. These methods offer high quality, but take long to train. We suggest to combine both techniques end-to-end using meta-learning: We over-fit onto a single problem instance in an inner loop, while also learning how to do so efficiently in an outer-loop across many exemplars. To this end, we derive the required formalism that allows applying meta-learning to a wide range of visual appearance reproduction problems: textures, Bidirectional Reflectance Distribution Functions (BRDFs), spatially-varying BRDFs (svBRDFs), illumination or the entire light transport of a scene. The effects of meta-learning parameters on several different aspects of visual appearance are analyzed in our framework, and specific guidance for different tasks is provided. Metappearance enables visual quality that is similar to over-fit approaches in only a fraction of their runtime while keeping the adaptivity of general models.
Force-Aware Interface via Electromyography for Natural VR/AR Interaction
Authors: Zhang Yunxiang , Liang Benjamin , Chen Boyuan , Torrens Paul M. , Atashzar S. Farokh , Lin Dahua , Sun Qi
Published: November 2022

While tremendous advances in visual and auditory realism have been made for virtual and augmented reality (VR/AR), introducing a plausible sense of physicality into the virtual world remains challenging. Closing the gap between real-world physicality and immersive virtual experience requires a closed interaction loop: applying user-exerted physical forces to the virtual environment and generating haptic sensations back to the users. However, existing VR/AR solutions either completely ignore the force inputs from the users or rely on obtrusive sensing devices that compromise user experience.

By identifying users' muscle activation patterns while engaging in VR/AR, we design a learning-based neural interface for natural and intuitive force inputs. Specifically, we show that lightweight electromyography sensors, resting non-invasively on users' forearm skin, inform and establish a robust understanding of their complex hand activities. Fuelled by a neural-network-based model, our interface can decode finger-wise forces in real-time with 3.3% mean error, and generalize to new users with little calibration. Through an interactive psychophysical study, we show that human perception of virtual objects' physical properties, such as stiffness, can be significantly enhanced by our interface. We further demonstrate that our interface enables ubiquitous control via finger tapping. Ultimately, we envision our findings to push forward research towards more realistic physicality in future VR/AR.
UniColor
Authors: Huang Zhitong , Zhao Nanxuan , Liao Jing
Published: November 2022

We propose the first unified framework
UniColor
to support colorization in multiple modalities, including both unconditional and conditional ones, such as stroke, exemplar, text, and even a mix of them. Rather than learning a separate model for each type of condition, we introduce a two-stage colorization framework for incorporating various conditions into a single model. In the first stage, multi-modal conditions are converted into a common representation of hint points. Particularly, we propose a novel CLIP-based method to convert the text to hint points. In the second stage, we propose a Transformer-based network composed of
Chroma-VQGAN
and
Hybrid-Transformer
to generate diverse and high-quality colorization results conditioned on hint points. Both qualitative and quantitative comparisons demonstrate that our method outperforms state-of-the-art methods in every control modality and further enables multi-modal colorization that was not feasible before. Moreover, we design an interactive interface showing the effectiveness of our unified framework in practical usage, including automatic colorization, hybrid-control colorization, local recolorization, and iterative color editing. Our code and models are available at
https://luckyhzt.github.io/unicolor
.
Neural Brushstroke Engine
Authors: Shugrina Maria , Li Chin-Ying , Fidler Sanja
Published: November 2022

We propose Neural Brushstroke Engine, the first method to apply deep generative models to learn a distribution of interactive drawing tools. Our conditional GAN model learns the latent space of drawing styles from a small set (about 200) of unlabeled images in different media. Once trained, a single model can texturize stroke patches drawn by the artist, emulating a diverse collection of brush styles in the latent space. In order to enable interactive painting on a canvas of arbitrary size, we design a painting engine able to support real-time seamless patch-based generation, while allowing artists direct control of stroke shape, color and thickness. We show that the latent space learned by our model generalizes to unseen drawing and more experimental styles (e.g. beads) by embedding real styles into the latent space. We explore other applications of the continuous latent space, such as optimizing brushes to enable painting in the style of an existing artwork, automatic line drawing stylization, brush interpolation, and even natural language search over a continuous space of drawing tools. Our prototype received positive feedback from a small group of digital artists.
Learning to Generate 3D Shapes from a Single Example
Authors: Wu Rundi , Zheng Changxi
Published: November 2022

Existing generative models for 3D shapes are typically trained on a large 3D dataset, often of a specific object category. In this paper, we investigate the deep generative model that learns from only a
single
reference 3D shape. Specifically, we present a multi-scale GAN-based model designed to capture the input shape's geometric features across a range of spatial scales. To avoid large memory and computational cost induced by operating on the 3D volume, we build our generator atop the tri-plane hybrid representation, which requires only 2D convolutions. We train our generative model on a voxel pyramid of the reference shape, without the need of any external supervision or manual annotation. Once trained, our model can generate diverse and high-quality 3D shapes possibly of different sizes and aspect ratios. The resulting shapes present variations across different scales, and at the same time retain the global structure of the reference shape. Through extensive evaluation, both qualitative and quantitative, we demonstrate that our model can generate 3D shapes of various types.
1
CAD2Sketch
Authors: Hähnlein Felix , Li Changjian , Mitra Niloy J. , Bousseau Adrien
Published: November 2022

Concept sketches are ubiquitous in industrial design, as they allow designers to quickly depict imaginary 3D objects. To construct their sketches with accurate perspective, designers rely on longstanding drawing techniques, including the use of auxiliary construction lines to identify midpoints of perspective planes, to align points vertically and horizontally, and to project planar curves from one perspective plane to another. We present a method to synthesize such construction lines from CAD sequences. Importantly, our method balances the presence of construction lines with overall clutter, such that the resulting sketch is both well-constructed and readable, as professional designers are trained to do. In addition to generating sketches that are visually similar to real ones, we apply our method to synthesize a large quantity of paired sketches and normal maps, and show that the resulting dataset can be used to train a neural network to infer normals from concept sketches.
1
Neural Point Catacaustics for Novel-View Synthesis of Reflections
Authors: Kopanas Georgios , Leimkühler Thomas , Rainer Gilles , Jambon Clément , Drettakis George
Published: November 2022

View-dependent effects such as reflections pose a substantial challenge for image-based and neural rendering algorithms. Above all, curved reflectors are particularly hard, as they lead to highly non-linear reflection flows as the camera moves. We introduce a new point-based representation to compute Neural Point Catacaustics allowing novel-view synthesis of scenes with curved reflectors, from a set of casually-captured input photos. At the core of our method is a neural warp field that models catacaustic trajectories of reflections, so complex specular effects can be rendered using efficient point splatting in conjunction with a neural renderer. One of our key contributions is the explicit representation of reflections with a reflection point cloud which is displaced by the neural warp field, and a primary point cloud which is optimized to represent the rest of the scene. After a short manual annotation step, our approach allows interactive high-quality renderings of novel views with accurate reflection flow. Additionally, the explicit representation of reflection flow supports several forms of scene manipulation in captured scenes, such as reflection editing, cloning of specular objects, reflection tracking across views, and comfortable stereo viewing. We provide the source code and other supplemental material on https://repo-sam.inria.fr/fungraph/neural_catacaustics/
Reconstructing Personalized Semantic Facial NeRF Models from Monocular Video
Authors: Gao Xuan , Zhong Chenglai , Xiang Jun , Hong Yang , Guo Yudong , Zhang Juyong
Published: November 2022

We present a novel semantic model for human head defined with neural radiance field. The 3D-consistent head model consist of a set of disentangled and interpretable bases, and can be driven by low-dimensional expression coefficients. Thanks to the powerful representation ability of neural radiance field, the constructed model can represent complex facial attributes including hair, wearings, which can not be represented by traditional mesh blendshape. To construct the personalized semantic facial model, we propose to define the bases as several multi-level voxel fields. With a short monocular RGB video as input, our method can construct the subject's semantic facial NeRF model with only ten to twenty minutes, and can render a photorealistic human head image in tens of miliseconds with a given expression coefficient and view direction. With this novel representation, we apply it to many tasks like facial retargeting and expression editing. Experimental results demonstrate its strong representation ability and training/inference speed. Demo videos and released code are provided in our project page: https://ustc3dv.github.io/NeRFBlendShape/
IDE-3D
Authors: Sun Jingxiang , Wang Xuan , Shi Yichun , Wang Lizhen , Wang Jue , Liu Yebin
Published: November 2022

Existing 3D-aware facial generation methods face a dilemma in quality versus editability: they either generate editable results in low resolution, or high-quality ones with no editing flexibility. In this work, we propose a new approach that brings the best of both worlds together. Our system consists of three major components: (1) a 3D-semantics-aware generative model that produces view-consistent, disentangled face images and semantic masks; (2) a hybrid GAN inversion approach that initializes the latent codes from the semantic and texture encoder, and further optimizes them for faithful reconstruction; and (3) a canonical editor that enables efficient manipulation of semantic masks in canonical view and produces high-quality editing results. Our approach is competent for many applications, e.g. free-view face drawing, editing and style control. Both quantitative and qualitative results show that our method reaches the state-of-the-art in terms of photorealism, faithfulness and efficiency.
Constant Time Median Filter Using 2D Wavelet Matrix
Authors: Moroto Yuji , Umetani Nobuyuki
Published: November 2022

The median filter is a simple yet powerful noise reduction technique that is extensively applied in image, signal, and speech processing. It can effectively remove impulsive noise while preserving the content of the image by taking the median of neighboring pixels; thus, it has various applications, such as restoration of a damaged image and facial beautification. The median filter is typically implemented in one of two major approaches: the histogram-based method, which requires
O
(1) computation time per pixel when focusing on the kernel radius
r
, and the sorting-based method, which requires approximately
O
(
r
2
) computation time per pixel but has a light constant factor. These are used differently depending on the kernel radius and the number of bits in the image. However, the computation time is still slow, particularly when the kernel radius is in the mid to large range.

This paper introduces novel and efficient median filter with constant complexity
O
(1) for kernel size using the wavelet matrix data structure, which has been applied to query-based searches on one-dimensional data. We extended the original wavelet matrix to two-dimensional data for application to computer graphics problems. The objective of this study was to achieve high-speed median filter computation in parallel computing environment with many threads (i.e., GPUs). Our implementation for the GPU is an order of magnitude faster than the histogram method for 8-bit images. Unlike traditional histogram methods, which suffer from significant computational overhead, the proposed method can handle images with high pixel depth (e.g., 16- and 32-bit high dynamic range images). When the kernel radius is greater than 12 for 8-bit images, the proposed method outperforms the other median filter computation methods.
NeuralRoom
Authors: Wang Yusen , Li Zongcheng , Jiang Yu , Zhou Kaixuan , Cao Tuo , Fu Yanping , Xiao Chunxia
Published: November 2022

We present a novel neural surface reconstruction method called NeuralRoom for reconstructing room-sized indoor scenes directly from a set of 2D images. Recently, implicit neural representations have become a promising way to reconstruct surfaces from multiview images due to their high-quality results and simplicity. However, implicit neural representations usually cannot reconstruct indoor scenes well because they suffer severe shape-radiance ambiguity. We assume that the indoor scene consists of texture-rich and flat texture-less regions. In texture-rich regions, the multiview stereo can obtain accurate results. In the flat area, normal estimation networks usually obtain a good normal estimation. Based on the above observations, we reduce the possible spatial variation range of implicit neural surfaces by reliable geometric priors to alleviate shape-radiance ambiguity. Specifically, we use multiview stereo results to limit the NeuralRoom optimization space and then use reliable geometric priors to guide NeuralRoom training. Then the NeuralRoom would produce a neural scene representation that can render an image consistent with the input training images. In addition, we propose a smoothing method called perturbation-residual restrictions to improve the accuracy and completeness of the flat region, which assumes that the sampling points in a local surface should have the same normal and similar distance to the observation center. Experiments on the ScanNet dataset show that our method can reconstruct the texture-less area of indoor scenes while maintaining the accuracy of detail. We also apply NeuralRoom to more advanced multiview reconstruction algorithms and significantly improve their reconstruction quality.
S
3
-Slicer
Authors: Zhang Tianyu , Fang Guoxin , Huang Yuming , Dutta Neelotpal , Lefebvre Sylvain , Kilic Zekai Murat , Wang Charlie C. L.
Published: November 2022

Multi-axis motion introduces more degrees of freedom into the process of 3D printing to enable different objectives of fabrication by accumulating materials layers upon curved layers. An existing challenge is how to effectively generate the curved layers satisfying multiple objectives simultaneously. This paper presents a general slicing framework for achieving multiple fabrication objectives including support free, strength reinforcement and surface quality. These objectives are formulated as local printing directions varied in the volume of a solid, which are achieved by computing the rotation-driven deformation for the input model. The height field of a deformed model is mapped into a scalar field on its original shape, the isosurfaces of which give the curved layers of multi-axis 3D printing. The deformation can be effectively optimized with the help of quaternion fields to achieve the fabrication objectives. The effectiveness of our method has been verified on a variety of models.
A Weighted Ensemble Classification Algorithm Based on Nearest Neighbors for Multi-Label Data Stream
Authors: Wu Hongxin , Han Meng , Chen Zhiqiang , Li Muhang , Zhang Xilong
Published: November 2022

With the rapid development of data stream, multi-label algorithms for mining dynamic data become more and more important. At the same time, when data distribution changes, concept drift will occur, which will make the existing classification models lose effectiveness. Ensemble methods have been used for multi-label classification, but few methods consider both the accuracy and diversity of base classifiers. To address the above-mentioned problem, a Weighted Ensemble classification algorithm based on Nearest Neighbors for Multi-Label data stream (WENNML) is proposed. WENNML uses data blocks to train Active candidate Ensemble Classifiers (AEC) and Passive candidate Ensemble Classifiers (PEC). The base classifiers of AEC and PEC are dynamically updated using geometric and diversity weighting methods. When the difference value between the number of current instances and the number of warning instances reaches the passive warning value, the algorithm selects the optimal base classifiers from AEC and PEC according to the subset accuracy and hamming score and puts them into the predictive ensemble classifiers. Experiments are carried out on 12 kinds of datasets with 9 comparison algorithms. The results show that WENNML achieves the best average rankings among the four evaluation metrics.