Visual and refractive outcomes of opposite clear corneal incision combined with rotationally asymmetric multifocal intraocular lens implantation

PurposeTo evaluate the visual and refractive outcomes of astigmatic cataract patients following opposite clear corneal incision (OCCI) combined with rotationally asymmetric multifocal intraocular lens (IOL) implantation.SettingDepartment of Ophthalmology, Zhongshan Hospital (Xiamen), Fudan University, People’s Republic of China.DesignRetrospective cohort study.MethodsThis study comprised 58 cataract eyes of 54 patients with corneal astigmatism who underwent phacoemulsification and rotationally asymmetric multifocal IOL implantation which received either OCCI (OCCI group) or a single clear corneal incision (SCCI group). The follow-up period was 3 months after surgery. Distance, intermediate and near visual acuity, refractive outcomes, and corneal anterior keratometry were compared between the two groups. Vector analysis was used to evaluate astigmatism correction.ResultsThree months after surgery, the distance, intermediate and near visual acuity, and sphere remained comparable between the two groups, but a significant difference was detected in residual astigmatism and anterior corneal keratometric astigmatism. In the OCCI group, the residual astigmatism and keratometric astigmatism were −0.60 ± 0.29 D and 0.59 ± 0.28 D, respectively, which were lower than those in SCCI groups (−1.18 ± 0.47 D and 1.15 ± 0.45 D, both p < 0.05). In vector analysis, the difference vector (DV), angle of error (AoE), absolute AoE, index of success (IoS) and correction index (CI) were statistically significantly different between the two groups (p < 0.05).ConclusionOCCI combined with rotationally asymmetric multifocal intraocular lens implantation showed predictable and desirable efficacy in treating cataract patients with astigmatism

Read and Reap the Rewards: Learning to Play Atari with the Help of Instruction Manuals

High sample complexity has long been a challenge for RL. On the other hand, humans learn to perform tasks not only from interaction or demonstrations, but also by reading unstructured text documents, e.g., instruction manuals. Instruction manuals and wiki pages are among the most abundant data that could inform agents of valuable features and policies or task-specific environmental dynamics and reward structures. Therefore, we hypothesize that the ability to utilize human-written instruction manuals to assist learning policies for specific tasks should lead to a more efficient and better-performing agent. We propose the Read and Reward framework. Read and Reward speeds up RL algorithms on Atari games by reading manuals released by the Atari game developers. Our framework consists of a QA Extraction module that extracts and summarizes relevant information from the manual and a Reasoning module that evaluates object-agent interactions based on information from the manual. An auxiliary reward is then provided to a standard A2C RL agent, when interaction is detected. Experimentally, various RL algorithms obtain significant improvement in performance and training speed when assisted by our design

Length Generalization in Arithmetic Transformers

We examine how transformers cope with two challenges: learning basic integer arithmetic, and generalizing to longer sequences than seen during training. We find that relative position embeddings enable length generalization for simple tasks, such as addition: models trained on $5$-digit numbers can perform $15$-digit sums. However, this method fails for multiplication, and we propose train set priming: adding a few ($10$ to $50$) long sequences to the training set. We show that priming allows models trained on $5$-digit $\times$ $3$-digit multiplications to generalize to $35\times 3$ examples. We also show that models can be primed for different generalization lengths, and that the priming sample size scales as the logarithm of the training set size. Finally, we discuss potential applications of priming beyond arithmetic

Decoupled Attention Network for Text Recognition

Text recognition has attracted considerable research interests because of its various applications. The cutting-edge text recognition methods are based on attention mechanisms. However, most of attention methods usually suffer from serious alignment problem due to its recurrency alignment operation, where the alignment relies on historical decoding results. To remedy this issue, we propose a decoupled attention network (DAN), which decouples the alignment operation from using historical decoding results. DAN is an effective, flexible and robust end-to-end text recognizer, which consists of three components: 1) a feature encoder that extracts visual features from the input image; 2) a convolutional alignment module that performs the alignment operation based on visual features from the encoder; and 3) a decoupled text decoder that makes final prediction by jointly using the feature map and attention maps. Experimental results show that DAN achieves state-of-the-art performance on multiple text recognition tasks, including offline handwritten text recognition and regular/irregular scene text recognition.Comment: 9 pages, 8 figures, 6 tables, accepted by AAAI-202

SPRING: GPT-4 Out-performs RL Algorithms by Studying Papers and Reasoning

Open-world survival games pose significant challenges for AI algorithms due to their multi-tasking, deep exploration, and goal prioritization requirements. Despite reinforcement learning (RL) being popular for solving games, its high sample complexity limits its effectiveness in complex open-world games like Crafter or Minecraft. We propose a novel approach, SPRING, to read the game's original academic paper and use the knowledge learned to reason and play the game through a large language model (LLM). Prompted with the LaTeX source as game context and a description of the agent's current observation, our SPRING framework employs a directed acyclic graph (DAG) with game-related questions as nodes and dependencies as edges. We identify the optimal action to take in the environment by traversing the DAG and calculating LLM responses for each node in topological order, with the LLM's answer to final node directly translating to environment actions. In our experiments, we study the quality of in-context "reasoning" induced by different forms of prompts under the setting of the Crafter open-world environment. Our experiments suggest that LLMs, when prompted with consistent chain-of-thought, have great potential in completing sophisticated high-level trajectories. Quantitatively, SPRING with GPT-4 outperforms all state-of-the-art RL baselines, trained for 1M steps, without any training. Finally, we show the potential of games as a test bed for LLMs

Integrative physiological, transcriptomic, and metabolomic analysis of Abelmoschus manihot in response to Cd toxicity

Rapid industrialization and urbanization have caused severe soil contamination with cadmium (Cd) necessitating effective remediation strategies. Phytoremediation is a widely adopted technology for remediating Cd-contaminated soil. Previous studies have shown that Abelmoschus manihot has a high Cd accumulation capacity and tolerance indicating its potential for Cd soil remediation. However, the mechanisms underlying its response to Cd stress remain unclear. In this study, physiological, transcriptomic, and metabolomic analyses were conducted to explore the response of A. manihot roots to Cd stress at different time points. The results revealed that Cd stress significantly increased malondialdehyde (MDA) levels in A. manihot, which simultaneously activated its antioxidant defense system, enhancing the activities of superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT) by 19.73%–50%, 22.87%–38.89%, and 32.31%–45.40% at 12 h, 36 h, 72 h, and 7 days, respectively, compared with those in the control (CK). Moreover, transcriptomic and metabolomic analyses revealed 245, 5,708, 9,834, and 2,323 differentially expressed genes (DEGs), along with 66, 62, 156, and 90 differentially expressed metabolites (DEMs) at 12 h, 36 h, 72 h, and 7 days, respectively. Through weighted gene coexpression network analysis (WGCNA) of physiological indicators and transcript expression, eight hub genes involved in phenylpropanoid biosynthesis, signal transduction, and metal transport were identified. In addition, integrative analyses of metabolomic and transcriptomic data highlighted the activation of lipid metabolism and phenylpropanoid biosynthesis pathways under Cd stress suggesting that these pathways play crucial roles in the detoxification process and in enhancing Cd tolerance in A. manihot. This comprehensive study provides detailed insights into the response mechanisms of A. manihot to Cd toxicity

Performance evaluation of an AI-based preoperative planning software application for automatic selection of pedicle screws based on computed tomography images

IntroductionRecent neurosurgical applications based on artificial intelligence (AI) have demonstrated its potential in surgical planning and anatomical measurement. We aimed to evaluate the performance of an AI planning software application on screw length/diameter selection and insertion accuracy in comparison with freehand surgery.MethodsA total of 45 patients with 208 pedicle screw placements on thoracolumbar segments were included in this analysis. The novel AI planning software was developed based on a deep learning model. AI-based pedicle screw placements were selected on the basis of preoperative computed tomography (CT) data, and freehand surgery screw placements were observed based on postoperative CT data. The performance of AI pedicle screw placements was evaluated on the components of screw length, diameter, and Gertzbein grade in comparison with the results achieved by freehand surgery.ResultsAmong 208 pedicle screw placements, the average screw length/diameters selected by the AI model and used in freehand surgery were 48.65 ± 5.99 mm/7.39 ± 0.42 mm and 44.78 ± 2.99 mm/6.1 ± 0.27 mm, respectively. Among AI screw placements, 85.1% were classified as Gertzbein Grade A (no cortical pedicle breach); among free-hand surgery placements, 64.9% were classified as Gertzbein Grade A.ConclusionThe novel AI planning software application could provide an accessible and safe pedicle screw placement strategy in comparison with traditional freehand pedicle screw placement strategies. The choices of pedicle screw dimensional parameters made by the model, including length and diameter, may provide potential inspiration for real clinical discretion

Cellular Internalization-Induced Aggregation of Porous Silicon Nanoparticles for Ultrasound Imaging and Protein-Mediated Protection of Stem Cells

Nanotechnology employs multifunctional engineered materials in the nanoscale range that provides many opportunities for translational stem cell research and therapy. Here, a cell-penetrating peptide (virus-1 transactivator of transcription)-conjugated, porous silicon nanoparticle (TPSi NP) loaded with the Wnt3a protein to increase both the cell survival rate and the delivery precision of stem cell transplantation via a combinational theranostic strategy is presented. The TPSi NP with a pore size of 10.7 nm and inorganic framework enables high-efficiency loading of Wnt3a, prolongs Wnt3a release, and increases antioxidative stress activity in the labeled mesenchymal stem cells (MSCs), which are highly beneficial properties for cell protection in stem cell therapy for myocardial infarction. It is confirmed that the intracellular aggregation of TPSi NPs can highly amplify the acoustic scattering of the labeled MSCs, resulting in a 2.3-fold increase in the ultrasound (US) signal compared with that of unlabeled MSCs. The translational potential of the designed nanoagent for real-time US imaging-guided stem cell transplantation is confirmed via intramyocardial injection of labeled MSCs in a nude mouse model. It is proposed that the intracellular aggregation of protein drug-loaded TPSi NPs could be a simple but robust strategy for improving the therapeutic effect of stem cell therapy.Peer reviewe

Stereospecific access to bridged [ n .2.1] skeletons through gold-catalyzed tandem reaction of indolyl homopropargyl amides

Abstract(#br)An efficient gold-catalyzed anti-Markovnikov cycloisomerization-initiated tandem reaction of Boc-protected indole tethered homopropargyl amides has been achieved. This method delivers a wide range of valuable bridged aza-[ n .2.1] skeletons ( n = 3–7) at room temperature with high diastereoselectivity and enantioselectivity by a chirality-transfer strategy. Moreover, the gold-catalyzed tandem reaction of homopropargyl alcohol is also achieved to produce the bridged oxa-[3.2.1] skeleton

Late-life depression: Epidemiology, phenotype, pathogenesis and treatment before and during the COVID-19 pandemic

Late-life depression (LLD) is one of the most common mental disorders among the older adults. Population aging, social stress, and the COVID-19 pandemic have significantly affected the emotional health of older adults, resulting in a worldwide prevalence of LLD. The clinical phenotypes between LLD and adult depression differ in terms of symptoms, comorbid physical diseases, and coexisting cognitive impairments. Many pathological factors such as the imbalance of neurotransmitters, a decrease in neurotrophic factors, an increase in β-amyloid production, dysregulation of the hypothalamic-pituitary-adrenal axis, and changes in the gut microbiota, are allegedly associated with the onset of LLD. However, the exact pathogenic mechanism underlying LLD remains unclear. Traditional selective serotonin reuptake inhibitor therapy results in poor responsiveness and side effects during LLD treatment. Neuromodulation therapies and complementary and integrative therapies have been proven safe and effective for the treatment of LLD. Importantly, during the COVID-19 pandemic, modern digital health intervention technologies, including socially assistive robots and app-based interventions, have proven to be advantageous in providing personal services to patients with LLD