SCIDA: Self-Correction Integrated Domain Adaptation From Single- to Multi-Label Aerial Images

Most publicly available datasets for image classification are with single labels, while images are inherently multilabeled in our daily life. Such an annotation gap makes many pretrained single-label classification models fail in practical scenarios. For aerial images, this annotation issue is more concerned: Aerial data naturally cover a relatively large land area with multiple labels, while annotated aerial datasets currently publicly available (e.g., UCM and AID) are single-labeled. As manually annotating multilabel aerial images (MAIs) would be time-/ labor-consuming, we propose a novel self-correction integrated domain adaptation (SCIDA) method for automatic multilabel learning. SCIDA is weakly supervised, i.e., automatically learning the multilabel image classification model from using massive, publicly available single-label images. To achieve this goal, we propose a novel labelwise self-correction (LWC) module to better explore underlying label correlations. This module also makes the unsupervised domain adaptation (UDA) from single-label to multilabel data possible. For model training, the proposed method uses single-label information yet requires no prior knowledge of multilabeled data and predicts labels for MAIs. Through extensive evaluations, the proposed model, which is trained with single-labeled MAI-AID-s and MAI-UCM-s datasets, achieves much better performances than comparative methods on our collected multiscene aerial image dataset. The code and data are available on GitHub ( https://github.com/Ryan315/Single2multi-DA )

Soft Actor–Critic-Driven Adaptive Focusing under Obstacles

Electromagnetic (EM) waves that bypass obstacles to achieve focus at arbitrary positions are of immense significance to communication and radar technologies. Small-sized and low-cost metasurfaces enable the accomplishment of this function. However, the magnitude-phase characteristics are challenging to analyze when there are obstacles between the metasurface and the EM wave. In this study, we creatively combined the deep reinforcement learning algorithm soft actor–critic (SAC) with a reconfigurable metasurface to construct an SAC-driven metasurface architecture that realizes focusing at any position under obstacles using real-time simulation data. The agent learns the optimal policy to achieve focus while interacting with a complex environment, and the framework proves to be effective even in complex scenes with multiple objects. Driven by real-time reinforcement learning, the knowledge learned from one environment can be flexibly transferred to another environment to maximize information utilization and save considerable iteration time. In the context of future 6G communications development, the proposed method may significantly reduce the path loss of users in an occluded state, thereby solving the open challenge of poor signal penetration. Our study may also inspire the implementation of other intelligent devices

Moisture chamber goggles for the treatment of postoperative dry eye in patients receiving SMILE and FS-LASIK surgery

Abstract Background The incidence of refractive surgery-related dry eye disease (DED) is rising due to the increasing popularity of corneal refractive surgery. The moisture chamber goggles (MCGs) have been shown to tear evaporation by increasing local humidity and minimizing airflow. The current study aims to evaluate the efficacy of moisture chamber goggles for refractive surgery-related DED. Methods In this nonrandomized open-label controlled study, 78 participants (156 eyes) receiving refractive surgery were enrolled between July 2021 and April 2022, and sequentially allocated to MGC and control groups. 39 participants were allocated to the MGC groups, of which 53.8% received small-incision lenticule extraction (SMILE) and 46.2% received femtosecond laser-assisted in situ keratomileusis (FS-LASIK), and were instructed to wear MCGs for the duration of 1 month postoperatively, in addition to the standard postoperative treatment received by the control groups (56.4% SMILE, 43.6% FS-LASIK). Participants underwent full ophthalmic examinations, including visual acuity, manifest refraction, DED evaluations, and higher-order aberrations (HOAs), both preoperatively and at routine follow-ups 1 day, 1 week, and 1 month after surgery. DED parameters included non-invasive tear film break-up time (NIBUT), tear meniscus height (TMH), conjunctival congestion, lipid layer thickness (LLT), and ocular surface disease index (OSDI) questionnaires. Student’s t-test was used for comparisons between control and MCG groups, and between preoperative and postoperative parameters within groups. Results Postoperative NIBUT decreased in both SMILE and FS-LASIK control groups 1 day after the surgery (SMILE, P = 0.001; FS-LASIK, P = 0.008), but not in the corresponding MCG groups (SMILE, P = 0.097; FS-LASIK, P = 0.331). TMH in the MCG group was significantly higher at 1 week (P = 0.039) and 1 month (P = 0.015) in SMILE, and 1 day (P = 0.003) in FS-LASIK groups. In FS-LASIK participants, significantly lower HOAs and coma levels in the MCG group were observed 1 day (total HOAs, P = 0.023; coma, P = 0.004) and 1 week (total HOAs, P = 0.010, coma, P = 0.004) after surgery. No consistent statistically significant intergroup difference was observed between MCG and control groups in conjunctival congestion, LLT, and OSDI. Conclusions MCGs effectively slowed tear evaporation, increased tear film stability, and improved HOAs in patients receiving SMILE and FS-LASIK surgeries. MCG is an effective adjuvant therapy in the comprehensive management of refractive surgery-related DED

Unifying Top-down Views by Task-Specific Domain Adaptation

In this article, we aim to learn a unified representation of images from satellite/aerial/ground views by exploring their underlying correlations. Inspired by recent advances in domain adaptation (DA), we propose a novel task-specific DA method for this purpose. Different from traditional DA methods, this proposed method not only applies task-specific classifiers1 but also introduces domain-specific tasks for different domains during the adaptation process. The experiments are conducted on two newly proposed ground-/satellite-to-aerial scene adaptation (GSSA) data sets. Since the semantic gap between the ground/satellite scenes and the aerial scenes is much larger than that between ground scenes, the DA task between these scenes is more challenging than traditional DA tasks. On GSSA data sets, we not only demonstrate the proposed unsupervised DA method but also explore the few-shot DA in the discussion section. The proposed method is easy to implement, and our method substantially outperforms the state-of-the-art methods on the studied data sets. We hope that the proposed method for the novel GSSA data sets can be a good baseline for future researchers. The related data sets/codes will be available online

Dynamics and genetic regulation of macronutrient concentrations during grain development in maize

Nitrogen (N), phosphorus (P), and potassium (K) are essential macronutrients that are crucial not only for maize growth and development, but also for crop yield and quality. The genetic basis of macronutrient dynamics and accumulation during grain filling in maize remains largely unknown. In this study, we evaluated grain N, P, and K concentrations in 206 recombinant inbred lines generated from a cross of DH1M and T877 at six time points after pollination. We then calculated conditional phenotypic values at different time intervals to explore the dynamic characteristics of the N, P, and K concentrations. Abundant phenotypic variations were observed in the concentrations and net changes of these nutrients. Unconditional quantitative trait locus (QTL) mapping revealed 41 non-redundant QTLs, including 17, 16, and 14 for the N, P, and K concentrations, respectively. Conditional QTL mapping uncovered 39 non-redundant QTLs related to net changes in the N, P, and K concentrations. By combining QTL, gene expression, co-expression analysis, and comparative genomic data, we identified 44, 36, and 44 candidate genes for the N, P, and K concentrations, respectively, including GRMZM2G371058 encoding a Dof-type zinc finger DNA-binding family protein, which was associated with the N concentration, and GRMZM2G113967 encoding a CBL-interacting protein kinase, which was related to the K concentration. The results deepen our understanding of the genetic factors controlling N, P, and K accumulation during maize grain development and provide valuable genes for the genetic improvement of nutrient concentrations in maize

GWAS and Transcriptome Analysis Reveal Key Genes Affecting Root Growth under Low Nitrogen Supply in Maize

Nitrogen (N) is one of the most important factors affecting crop production. Root morphology exhibits a high degree of plasticity to nitrogen deficiency. However, the mechanisms underlying the root foraging response under low-N conditions remain poorly understood. In this study, we analyzed 213 maize inbred lines using hydroponic systems and regarding their natural variations in 22 root traits and 6 shoot traits under normal (2 mM nitrate) and low-N (0 mM nitrate) conditions. Substantial phenotypic variations were detected for all traits. N deficiency increased the root length and decreased the root diameter and shoot related traits. A total of 297 significant marker-trait associations were identified by a genome-wide association study involving different N levels and the N response value. A total of 51 candidate genes with amino acid variations in coding regions or differentially expressed under low nitrogen conditions were identified. Furthermore, a candidate gene ZmNAC36 was resequenced in all tested lines. A total of 38 single nucleotide polymorphisms and 12 insertions and deletions were significantly associated with lateral root length of primary root, primary root length between 0 and 0.5 mm in diameter, primary root surface area, and total length of primary root under a low-N condition. These findings help us to improve our understanding of the genetic mechanism of root plasticity to N deficiency, and the identified loci and candidate genes will be useful for the genetic improvement of maize tolerance cultivars to N deficiency

Dihydrogen-driven NADPH recycling in imine reduction and P450-catalyzed oxidations mediated by an engineered O2-tolerant hydrogenase

The O2-tolerant NAD+-reducing hydrogenase (SH) from Ralstonia eutropha (Cupriavidus necator) has already been applied in vitro and in vivo for H2-driven NADH recycling in coupled enzymatic reactions with various NADH-dependent oxidoreductases. To expand the scope for application in NADPH-dependent biocatalysis, we introduced changes in the NAD+-binding pocket of the enzyme by rational mutagenesis, and generated a variant with significantly higher affinity for NADP+ than for the natural substrate NAD+, while retaining native O2-tolerance. The applicability of the SH variant in H2-driven NADPH supply was demonstrated by the full conversion of 2-methyl-1-pyrroline into a single enantiomer of 2-methylpyrrolidine catalysed by a stereoselective imine reductase. In an even more challenging reaction, the SH supported a cytochrome P450 monooxygenase for the oxidation of octane under safe H2/O2 mixtures. Thus, the re-designed SH represents a versatile platform for atom-efficient, H2-driven cofactor recycling in biotransformations involving NADPH-dependent oxidoreductases.DFG, 390540038, EXC 2008: Unifying Systems in Catalysis "UniSysCat"TU Berlin, Open-Access-Mittel – 202