28 research outputs found
Considering uncertainties expands the lower tail of maize yield projections
Crop yields are sensitive to extreme weather events. Improving the understanding of the mechanisms and the drivers of the projection uncertainties can help to improve decisions. Previous studies have provided important insights, but often sample only a small subset of potentially important uncertainties. Here we expand on a previous statistical modeling approach by refining the analyses of two uncertainty sources. Specifically, we assess the effects of uncertainties surrounding crop-yield model parameters and climate forcings on projected crop yield. We focus on maize yield projections in the eastern U.S.in this century. We quantify how considering more uncertainties expands the lower tail of yield projections. We characterized the relative importance of each uncertainty source and show that the uncertainty surrounding yield model parameters is the main driver of yield projection uncertainty
Spin Coherence and Spin Relaxation in Hybrid Organic-Inorganic Lead and Mixed Lead-Tin Perovskites
Metal halide perovskites make up a promising class of materials for
semiconductor spintronics. Here we report a systematic investigation of
coherent spin precession, spin dephasing and spin relaxation of electrons and
holes in two hybrid organic-inorganic perovskites MA0.3FA0.7PbI3 and
MA0.3FA0.7Pb0.5Sn0.5I3 using time-resolved Faraday rotation spectroscopy. With
applied in-plane magnetic fields, we observe robust Larmor spin precession of
electrons and holes that persists for hundreds of picoseconds. The spin
dephasing and relaxation processes are likely to be sensitive to the defect
levels. Temperature-dependent measurements give further insights into the spin
relaxation channels. The extracted electron Land\'e g-factors (3.75 and 4.36)
are the biggest among the reported values in inorganic or hybrid perovskites.
Both the electron and hole g-factors shift dramatically with temperature, which
we propose to originate from thermal lattice vibration effects on the band
structure. These results lay the foundation for further design and use of lead-
and tin-based perovskites for spintronic applications
Phase-Specific Augmented Reality Guidance for Microscopic Cataract Surgery Using Long-Short Spatiotemporal Aggregation Transformer
Phacoemulsification cataract surgery (PCS) is a routine procedure conducted
using a surgical microscope, heavily reliant on the skill of the
ophthalmologist. While existing PCS guidance systems extract valuable
information from surgical microscopic videos to enhance intraoperative
proficiency, they suffer from non-phasespecific guidance, leading to redundant
visual information. In this study, our major contribution is the development of
a novel phase-specific augmented reality (AR) guidance system, which offers
tailored AR information corresponding to the recognized surgical phase.
Leveraging the inherent quasi-standardized nature of PCS procedures, we propose
a two-stage surgical microscopic video recognition network. In the first stage,
we implement a multi-task learning structure to segment the surgical limbus
region and extract limbus region-focused spatial feature for each frame. In the
second stage, we propose the long-short spatiotemporal aggregation transformer
(LS-SAT) network to model local fine-grained and global temporal relationships,
and combine the extracted spatial features to recognize the current surgical
phase. Additionally, we collaborate closely with ophthalmologists to design AR
visual cues by utilizing techniques such as limbus ellipse fitting and regional
restricted normal cross-correlation rotation computation. We evaluated the
network on publicly available and in-house datasets, with comparison results
demonstrating its superior performance compared to related works. Ablation
results further validated the effectiveness of the limbus region-focused
spatial feature extractor and the combination of temporal features.
Furthermore, the developed system was evaluated in a clinical setup, with
results indicating remarkable accuracy and real-time performance. underscoring
its potential for clinical applications
Learning from Future: A Novel Self-Training Framework for Semantic Segmentation
Self-training has shown great potential in semi-supervised learning. Its core
idea is to use the model learned on labeled data to generate pseudo-labels for
unlabeled samples, and in turn teach itself. To obtain valid supervision,
active attempts typically employ a momentum teacher for pseudo-label prediction
yet observe the confirmation bias issue, where the incorrect predictions may
provide wrong supervision signals and get accumulated in the training process.
The primary cause of such a drawback is that the prevailing self-training
framework acts as guiding the current state with previous knowledge, because
the teacher is updated with the past student only. To alleviate this problem,
we propose a novel self-training strategy, which allows the model to learn from
the future. Concretely, at each training step, we first virtually optimize the
student (i.e., caching the gradients without applying them to the model
weights), then update the teacher with the virtual future student, and finally
ask the teacher to produce pseudo-labels for the current student as the
guidance. In this way, we manage to improve the quality of pseudo-labels and
thus boost the performance. We also develop two variants of our
future-self-training (FST) framework through peeping at the future both deeply
(FST-D) and widely (FST-W). Taking the tasks of unsupervised domain adaptive
semantic segmentation and semi-supervised semantic segmentation as the
instances, we experimentally demonstrate the effectiveness and superiority of
our approach under a wide range of settings. Code will be made publicly
available.Comment: Accepted to NeurIPS 202
Revealing unusual bandgap shifts with temperature and bandgap renormalization effect in phase-stabilized metal halide perovskites
Hybrid organic-inorganic metal halide perovskites are emerging materials in
photovoltaics, whose bandgap is one of the most crucial parameters governing
their light harvesting performance. Here we present temperature and
photocarrier density dependence of the bandgap in two phase-stabilized
perovskite thin films (MA0.3FA0.7PbI3 and MA0.3FA0.7Pb0.5Sn0.5I3) using
photoluminescence and absorption spectroscopy. Contrasting bandgap shifts with
temperature are observed between the two perovskites. By utilizing X-ray
diffraction and in situ high pressure photoluminescence spectroscopy, we show
that the thermal expansion plays only a minor role on the large bandgap
blueshift due to the enhanced structural stability in our samples. Our
first-principles calculations further demonstrate the significant impact of
thermally induced lattice distortions on the bandgap widening and reveal that
the anomalous trends are caused by the competition between the static and
dynamic distortions. Additionally, both the bandgap renormalization and band
filling effects are directly observed for the first time in fluence-dependent
photoluminescence measurements and are employed to estimate the exciton
effective mass. Our results provide new insights into the basic understanding
of thermal and charge-accumulation effects on the band structure of hybrid
perovskites
Multiple influence of immune cells in the bone metastatic cancer microenvironment on tumors
Bone is a common organ for solid tumor metastasis. Malignant bone tumor becomes insensitive to systemic therapy after colonization, followed by poor prognosis and high relapse rate. Immune and bone cells in situ constitute a unique immune microenvironment, which plays a crucial role in the context of bone metastasis. This review firstly focuses on lymphatic cells in bone metastatic cancer, including their function in tumor dissemination, invasion, growth and possible cytotoxicity-induced eradication. Subsequently, we examine myeloid cells, namely macrophages, myeloid-derived suppressor cells, dendritic cells, and megakaryocytes, evaluating their interaction with cytotoxic T lymphocytes and contribution to bone metastasis. As important components of skeletal tissue, osteoclasts and osteoblasts derived from bone marrow stromal cells, engaging in ‘vicious cycle’ accelerate osteolytic bone metastasis. We also explain the concept tumor dormancy and investigate underlying role of immune microenvironment on it. Additionally, a thorough review of emerging treatments for bone metastatic malignancy in clinical research, especially immunotherapy, is presented, indicating current challenges and opportunities in research and development of bone metastasis therapies
Robust estimation of bacterial cell count from optical density
Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data
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