ActiveNeRF: Learning where to See with Uncertainty Estimation

Recently, Neural Radiance Fields (NeRF) has shown promising performances on reconstructing 3D scenes and synthesizing novel views from a sparse set of 2D images. Albeit effective, the performance of NeRF is highly influenced by the quality of training samples. With limited posed images from the scene, NeRF fails to generalize well to novel views and may collapse to trivial solutions in unobserved regions. This makes NeRF impractical under resource-constrained scenarios. In this paper, we present a novel learning framework, ActiveNeRF, aiming to model a 3D scene with a constrained input budget. Specifically, we first incorporate uncertainty estimation into a NeRF model, which ensures robustness under few observations and provides an interpretation of how NeRF understands the scene. On this basis, we propose to supplement the existing training set with newly captured samples based on an active learning scheme. By evaluating the reduction of uncertainty given new inputs, we select the samples that bring the most information gain. In this way, the quality of novel view synthesis can be improved with minimal additional resources. Extensive experiments validate the performance of our model on both realistic and synthetic scenes, especially with scarcer training data. Code will be released at \url{https://github.com/LeapLabTHU/ActiveNeRF}.Comment: Accepted by ECCV202

Highly sensitive, broadband microwave frequency identification using a chip-based Brillouin optoelectronic oscillator

Detection and frequency estimation of radio frequency (RF) signals are critical in modern RF systems, including wireless communication and radar. Photonic techniques have made huge progress in solving the problem imposed by the fundamental trade-off between detection range and accuracy. However, neither fiber-based nor integrated photonic RF signal detection and frequency estimation systems have achieved wide range and low error with high sensitivity simultaneously in a single system. In this paper, we demonstrate the first Brillouin opto-electronic oscillator (B-OEO) based on on-chip stimulated Brillouin scattering (SBS) to achieve RF signal detection. The broad tunability and narrowband amplification of on-chip SBS allow for the wide-range and high-accuracy detection. Feeding the unknown RF signal into the B-OEO cavity amplifies the signal which is matched with the oscillation mode to detect low-power RF signals. We are able to detect RF signals from 1.5 to 40 GHz with power levels as low as −67 dBm and a frequency accuracy of ± 3.4 MHz. This result paves the way to compact, fully integrated RF detection and channelization.Australian Research Council (ARC) Linkage grant (LP170100112) with Harris Corporation. U.S. Air Force (USAF) through AFOSR/AOARD (FA2386-16-1-4036); U.S. Office of Naval Research Global (ONRG) (N62909-18-1-2013)

Recognition and Treatment of Cognitive Dysfunction in Major Depressive Disorder

Major Depressive Disorder (MDD) is a prevalent, chronic, disabling, and multidimensional mental disorder. Cognitive dysfunction represents a core diagnostic and symptomatic criterion of MDD, and is a principal determinant of functional non-recovery. Cognitive impairment has been observed to persist despite remission of mood symptoms, suggesting dissociability of mood and cognitive symptoms in MDD. Recurrent impairments in several domains including, but not limited to, executive function, learning and memory, processing speed, and attention and concentration, are associated with poor psychosocial and occupational outcomes. Attempts to restore premorbid functioning in individuals with MDD requires regular screenings and assessment of objective and subjective measures of cognition by clinicians. Easily accessible and cost-effective tools such as the THINC-integrated tool (THINC-it) are suitable for use in a busy clinical environment and appear to be promising for routine usage in clinical settings. However, antidepressant treatments targeting specific cognitive domains in MDD have been insufficiently studied. While select antidepressants, e.g., vortioxetine, have been demonstrated to have direct and independent pro-cognitive effects in adults with MDD, research on additional agents remains nascent. A comprehensive clinical approach to cognitive impairments in MDD is required. The current narrative review aims to delineate the importance and relevance of cognitive dysfunction as a symptomatic target for prevention and treatment in the phenomenology of MDD

The dual role of glioma exosomal microRNAs: glioma eliminates tumor suppressor miR-1298-5p via exosomes to promote immunosuppressive effects of MDSCs

Clear evidence shows that tumors could secrete microRNAs (miRNAs) via exosomes to modulate the tumor microenvironment (TME). However, the mechanisms sorting specific miRNAs into exosomes are still unclear. In order to study the biological function and characterization of exosomal miRNAs, we performed whole-transcriptome sequencing in 59 patients’ whole-course cerebrospinal fluid (CSF) small extracellular vesicles (sEV) and matched glioma tissue samples. The results demonstrate that miRNAs could be divided into exosome-enriched miRNAs (ExomiRNAs) and intracellular-retained miRNAs (CLmiRNAs), and exosome-enriched miRNAs generally play a dual role. Among them, miR-1298-5p was enriched in CSF exosomes and suppressed glioma progression in vitro and vivo experiments. Interestingly, exosomal miR-1298-5p could promote the immunosuppressive effects of myeloid-derived suppressor cells (MDSCs) to facilitate glioma. Therefore, we found miR-1298-5p had different effects on glioma cells and MDSCs. Mechanically, downstream signaling pathway analyses showed that miR-1298-5p plays distinct roles in glioma cells and MDSCs via targeting SETD7 and MSH2, respectively. Moreover, reverse verification was performed on the intracellular-retained miRNA miR-9-5p. Thus, we confirmed that tumor-suppressive miRNAs in glioma cells could be eliminated through exosomes and target tumor-associated immune cells to induce tumor-promoting phenotypes. Glioma could get double benefit from it. These findings uncover the mechanisms that glioma selectively sorts miRNAs into exosomes and modulates tumor immunity.publishedVersio

SPI1-induced downregulation of FTO promotes GBM progression by regulating pri-miR-10a processing in an m6A-dependent manner

As one of the most common post-transcriptional modifications of mRNAs and noncoding RNAs, N6-methyladenosine (m6A) modification regulates almost every aspect of RNA metabolism. Evidence indicates that dysregulation of m6A modification and associated proteins contributes to glioblastoma (GBM) progression. However, the function of fat mass and obesity-associated protein (FTO), an m6A demethylase, has not been systematically and comprehensively explored in GBM. Here, we found that decreased FTO expression in clinical specimens correlated with higher glioma grades and poorer clinical outcomes. Functionally, FTO inhibited growth and invasion in GBM cells in vitro and in vivo. Mechanistically, FTO regulated the m6A modification of primary microRNA-10a (pri-miR-10a), which could be recognized by reader HNRNPA2B1, recruiting the microRNA microprocessor complex protein DGCR8 and mediating pri-miR-10a processing. Furthermore, the transcriptional activity of FTO was inhibited by the transcription factor SPI1, which could be specifically disrupted by the SPI1 inhibitor DB2313. Treatment with this inhibitor restored endogenous FTO expression and decreased GBM tumor burden, suggesting that FTO may serve as a novel prognostic indicator and therapeutic molecular target of GBM.publishedVersio

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

A Randomized, Double-Blind, Placebo-Controlled Clinical Trial Evaluating the Effect of Infliximab on General Cognition in a Population of Depressed Individuals with Bipolar Disorder Type I/II

Aim: To evaluate the efficacy of adjunctive infliximab in mitigating cognitive symptoms in depressed bipolar individuals. Methods: A randomized, double-blind, placebo-controlled clinical trial was conducted in outpatients between the ages of 18-65. Participants were randomized to receive adjunctive intravenous infliximab (at 5 mg/kg) or placebo (saline) at week 0, 2, and 6. Digit Symbol Substitution Test (DSST) and Rey Auditory Verbal Learning Test (RAVLT) were used to determine cognitive performance. Generalized estimating equations were used to determine significance of between- and within-group effects. Results: Sixty individuals were enrolled (placebo n=31, infliximab n=29). Despite a significant within-group improvement in cognition from week 0 to week 12, there was no significant difference between infliximab and placebo (p>.05). Conclusions: Within-group improvement in cognition may be due to mechanisms yet uncharacterized. Inflammatory pathoetiology of mood disorders provide an impetus to investigate additional anti-inflammatory agents to mitigate transdiagnostic disturbances such as cognitive dysfunction.M.Sc

Dilemmas in a pregnant woman with myelofibrosis secondary to signet ring adenocarcinoma: a case report

Abstract Background We describe the first reported case of myelofibrosis as an extremely rare complication of gastric cancer during pregnancy; the clinical diagnosis and treatment of which is highly challenging due to nonspecific symptoms coupled with the conflicting needs of immediate disease control and continuation of pregnancy. Case presentation We report a 36-year-old pregnant woman who presented with cytopenia, fatigue, vomiting, and diarrhea for 20 days on the background of newly diagnosed myelofibrosis secondary to gastric signet ring adenocarcinoma. She accepted palliative care and died several months after the delivery of a healthy newborn. Conclusion Signet ring gastric adenocarcinoma is an unusual cause of myelofibrosis during pregnancy. Treatment remains a great challenge as clinicians have to consider the needs of immediate treatment against fetal well-being while taking into account patient preference and fetus rights