Generalizing Trimming Bounds for Endogenously Missing Outcome Data Using Random Forests

In many experimental or quasi-experimental studies, outcomes of interest are only observed for subjects who select (or are selected) to engage in the activity generating the outcome. Outcome data is thus endogenously missing for units who do not engage, in which case random or conditionally random treatment assignment prior to such choices is insufficient to point identify treatment effects. Non-parametric partial identification bounds are a way to address endogenous missingness without having to make disputable parametric assumptions. Basic bounding approaches often yield bounds that are very wide and therefore minimally informative. We present methods for narrowing non-parametric bounds on treatment effects by adjusting for potentially large numbers of covariates, working with generalized random forests. Our approach allows for agnosticism about the data-generating process and honest inference. We use a simulation study and two replication exercises to demonstrate the benefits of our approach

Self-Learning Symmetric Multi-view Probabilistic Clustering

Multi-view Clustering (MVC) has achieved significant progress, with many efforts dedicated to learn knowledge from multiple views. However, most existing methods are either not applicable or require additional steps for incomplete MVC. Such a limitation results in poor-quality clustering performance and poor missing view adaptation. Besides, noise or outliers might significantly degrade the overall clustering performance, which are not handled well by most existing methods. In this paper, we propose a novel unified framework for incomplete and complete MVC named self-learning symmetric multi-view probabilistic clustering (SLS-MPC). SLS-MPC proposes a novel symmetric multi-view probability estimation and equivalently transforms multi-view pairwise posterior matching probability into composition of each view's individual distribution, which tolerates data missing and might extend to any number of views. Then, SLS-MPC proposes a novel self-learning probability function without any prior knowledge and hyper-parameters to learn each view's individual distribution. Next, graph-context-aware refinement with path propagation and co-neighbor propagation is used to refine pairwise probability, which alleviates the impact of noise and outliers. Finally, SLS-MPC proposes a probabilistic clustering algorithm to adjust clustering assignments by maximizing the joint probability iteratively without category information. Extensive experiments on multiple benchmarks show that SLS-MPC outperforms previous state-of-the-art methods

A Lactate Fermentation Mutant of Toxoplasma Stimulates Protective Immunity Against Acute and Chronic Toxoplasmosis

Toxoplasma gondii is an important zoonotic pathogen infecting one-third of the world’s population and numerous animals, causing significant healthcare burden and socioeconomic problems. Vaccination is an efficient way to reduce global sero-prevalence, however, ideal vaccines are not yet available. We recently discovered that the Toxoplasma mutant lacking both lactate dehydrogenases LDH1 and LDH2 (Δldh) grew well in vitro but was unable to propagate in mice, making it a good live vaccine candidate. Here, we tested the protection efficacy of ME49 Δldh using a mouse model. Vaccinated mice were efficiently protected from the lethal challenge of a variety of wild-type strains, including type 1 strain RH, type 2 strain ME49, type 3 strain VEG, and a field isolate of Chinese 1. The protection efficacies of a single vaccination were nearly 100% for most cases and it worked well against the challenges of both tachyzoites and tissue cysts. Re-challenging parasites were unable to propagate in vaccinated mice, nor did they make tissue cysts. High levels of Toxoplasma-specific IgG were produced 30 days after immunization and stayed high during the whole tests (at least 125 days). However, passive immunization of naïve mice with sera from vaccinated mice did reduce parasite propagation, but the overall protection against parasite infections was rather limited. On the other hand, Δldh immunization evoked elevated levels of Th1 cytokines like INF-γ and IL-12, at early time points. In addition, splenocytes extracted from immunized mice were able to induce quick and robust INF-γ and other pro-inflammatory cytokine production upon T. gondii antigen stimulation. Together these results suggest that cellular immune responses are the main contributors to the protective immunity elicited by Δldh vaccination, and humoral immunity also contributes partially. We also generated uracil auxotrophic mutants in ME49 and compared their immune protection efficiencies to the Δldh mutants. The results showed that these two types of mutants have similar properties as live vaccine candidates. Taken together, these results suggest that mutants lacking LDH were severely attenuated in virulence but were able to induce strong anti-toxoplasma immune responses, therefore are good candidates for live vaccines

An Integrated Score and Nomogram Combining Clinical and Immunohistochemistry Factors to Predict High ISUP Grade Clear Cell Renal Cell Carcinoma

Objective: The International Society of Urological Pathology (ISUP) has proposed a grading system to classify renal cell carcinoma (RCC). However, classification using biopsy specimens remains problematic and, consequently, the accuracy of a biopsy-based diagnosis is relatively poor. This study aims to combine clinical and immunohistochemical (IHC) factors for the prediction of high ISUP grade clear cell RCC (ccRCC) in an attempt to complement and improve the accuracy of a biopsy-based diagnosis.Methods: A total of 362 ccRCC patients were enrolled in this study and used for the training set. We performed IHC analysis of 18 protein markers on standard tissue sections using an automated stainer. Multivariate logistic regression models were developed to evaluate independent predictors for high ISUP grade. We evaluated different prediction models using receiver operating characteristic (ROC) curves and area under the ROC curve (AUC) analysis. A nomogram for the derivation of an integrated score for predicting high ISUP grade ccRCC and a calibration curve were also plotted. Finally, an internal validation cohort was examined to evaluate the performance of our integrated scoring system and nomogram.Results: Multivariate logistic analyses revealed seven credible candidates for predicting high grade ISUP. These were age, tumor diameter, surgery, and CK7, Ki-67, PTEN, and MTOR protein expression. The ROC curves for the clinical, IHC and integrated models were compared in the training set, and the AUC for each was 0.731, 0.744, and 0.801, respectively. DeLong's test showed that the integrated model was significantly better at predicting high ISUP grade, when compared with the other models. Internal validation confirmed the good performance of the integrated score in predicting ISUP grade.Conclusion: We have developed a nomogram integrating clinical and immunohistochemical parameters to predict high ISUP grade for M0 ccRCC patients. This nomogram may offer potentially useful information during preoperative individualized patient risk assessment, and consequently may help urologists when planning personalized management regimens

A-Eval: A Benchmark for Cross-Dataset Evaluation of Abdominal Multi-Organ Segmentation

Although deep learning have revolutionized abdominal multi-organ segmentation, models often struggle with generalization due to training on small, specific datasets. With the recent emergence of large-scale datasets, some important questions arise: \textbf{Can models trained on these datasets generalize well on different ones? If yes/no, how to further improve their generalizability?} To address these questions, we introduce A-Eval, a benchmark for the cross-dataset Evaluation ('Eval') of Abdominal ('A') multi-organ segmentation. We employ training sets from four large-scale public datasets: FLARE22, AMOS, WORD, and TotalSegmentator, each providing extensive labels for abdominal multi-organ segmentation. For evaluation, we incorporate the validation sets from these datasets along with the training set from the BTCV dataset, forming a robust benchmark comprising five distinct datasets. We evaluate the generalizability of various models using the A-Eval benchmark, with a focus on diverse data usage scenarios: training on individual datasets independently, utilizing unlabeled data via pseudo-labeling, mixing different modalities, and joint training across all available datasets. Additionally, we explore the impact of model sizes on cross-dataset generalizability. Through these analyses, we underline the importance of effective data usage in enhancing models' generalization capabilities, offering valuable insights for assembling large-scale datasets and improving training strategies. The code and pre-trained models are available at \href{https://github.com/uni-medical/A-Eval}{https://github.com/uni-medical/A-Eval}

SAM-Med3D

Although the Segment Anything Model (SAM) has demonstrated impressive performance in 2D natural image segmentation, its application to 3D volumetric medical images reveals significant shortcomings, namely suboptimal performance and unstable prediction, necessitating an excessive number of prompt points to attain the desired outcomes. These issues can hardly be addressed by fine-tuning SAM on medical data because the original 2D structure of SAM neglects 3D spatial information. In this paper, we introduce SAM-Med3D, the most comprehensive study to modify SAM for 3D medical images. Our approach is characterized by its comprehensiveness in two primary aspects: firstly, by comprehensively reformulating SAM to a thorough 3D architecture trained on a comprehensively processed large-scale volumetric medical dataset; and secondly, by providing a comprehensive evaluation of its performance. Specifically, we train SAM-Med3D with over 131K 3D masks and 247 categories. Our SAM-Med3D excels at capturing 3D spatial information, exhibiting competitive performance with significantly fewer prompt points than the top-performing fine-tuned SAM in the medical domain. We then evaluate its capabilities across 15 datasets and analyze it from multiple perspectives, including anatomical structures, modalities, targets, and generalization abilities. Our approach, compared with SAM, showcases pronouncedly enhanced efficiency and broad segmentation capabilities for 3D volumetric medical images. Our code is released at https://github.com/uni-medical/SAM-Med3D

SA-Med2D-20M Dataset: Segment Anything in 2D Medical Imaging with 20 Million masks

Segment Anything Model (SAM) has achieved impressive results for natural image segmentation with input prompts such as points and bounding boxes. Its success largely owes to massive labeled training data. However, directly applying SAM to medical image segmentation cannot perform well because SAM lacks medical knowledge -- it does not use medical images for training. To incorporate medical knowledge into SAM, we introduce SA-Med2D-20M, a large-scale segmentation dataset of 2D medical images built upon numerous public and private datasets. It consists of 4.6 million 2D medical images and 19.7 million corresponding masks, covering almost the whole body and showing significant diversity. This paper describes all the datasets collected in SA-Med2D-20M and details how to process these datasets. Furthermore, comprehensive statistics of SA-Med2D-20M are presented to facilitate the better use of our dataset, which can help the researchers build medical vision foundation models or apply their models to downstream medical applications. We hope that the large scale and diversity of SA-Med2D-20M can be leveraged to develop medical artificial intelligence for enhancing diagnosis, medical image analysis, knowledge sharing, and education. The data with the redistribution license is publicly available at https://github.com/OpenGVLab/SAM-Med2D

Using aquatic animals as partners to increase yield and maintain soil nitrogen in the paddy ecosystems

Whether species coculture can overcome the shortcomings of crop monoculture requires additional study. Here, we show how aquatic animals (i.e. carp, crabs, and softshell turtles) benefit paddy ecosystems when cocultured with rice. Three separate field experiments and three separate mesocosm experiments were conducted. Each experiment included a rice monoculture (RM) treatment and a rice-aquatic animal (RA) coculture treatment; RA included feed addition for aquatic animals. In the field experiments, rice yield was higher with RA than with RM, and RA also produced aquatic animal yields that averaged 0.52–2.57 t ha-1. Compared to their corresponding RMs, the three RAs had significantly higher apparent nitrogen (N)-use efficiency and lower weed infestation, while soil N contents were stable over time. Dietary reconstruction analysis based on 13C and 15N showed that 16.0–50.2% of aquatic animal foods were from naturally occurring organisms in the rice fields. Stable-isotope-labeling (13C) in the field experiments indicated that the organic matter decomposition rate was greater with RA than with RM. Isotope 15N labeling in the mesocosm experiments indicated that rice used 13.0–35.1% of the aquatic animal feed-N. All these results suggest that rice-aquatic animal coculture increases food production, increases N-use efficiency, and maintains soil N content by reducing weeds and promoting decomposition and complementary N use. Our study supports the view that adding species to monocultures may enhance agroecosystem functions

Temporal dynamics of disgust and morality: an event-related potential study.

Disgust is argued to be an emotion that motivates the avoidance of disease-causing entities in the physical domain and unacceptable behaviors in the social-moral domain. Empirical work from behavioral, physiological and brain imaging studies suggests moral judgments are strongly modulated by disgust feelings. Yet, it remains unclear how they are related in the time course of neural processing. Examining the temporal order of disgust emotion and morality could help to clarify the role of disgust in moral judgments. In the present research, a Go/No-Go paradigm was employed to evoke lateralized readiness potentials (LRPs) to investigate the temporal order of physical disgust and moral information processing. Participants were asked to give a "yes" or "no" response regarding the physical disgust and moral wrongness of a social act. The results showed that the evaluation of moral information was processed prior to that of physical disgust information. This suggests that moral information is available earlier than physical disgust, and provides more data on the biological heterogeneity between disgust and morality in terms of the time course of neural activity. The findings implicate that physical disgust emotion may not be necessary for people to make moral judgments. They also suggest that some of our moral experience may be more fundamental (than physical disgust experience) to our survival and development, as humans spend a considerable amount of time engaging in social interaction

Anti-Tumor Drug Discovery Based on Natural Product β-Elemene: Anti-Tumor Mechanisms and Structural Modification

Natural products are important sources for drug discovery, especially anti-tumor drugs. β-Elemene, the prominent active ingredient extract from the rhizome of Curcuma wenyujin, is a representative natural product with broad anti-tumor activities. The main molecular mechanism of β-elemene is to inhibit tumor growth and proliferation, induce apoptosis, inhibit tumor cell invasion and metastasis, enhance the sensitivity of chemoradiotherapy, regulate the immune system, and reverse multidrug resistance (MDR). Elemene oral emulsion and elemene injection were approved by the China Food and Drug Administration (CFDA) for the treatment of various cancers and bone metastasis in 1994. However, the lipophilicity and low bioavailability limit its application. To discover better β-elemene-derived anti-tumor drugs with satisfying drug-like properties, researchers have modified its structure under the premise of not damaging the basic scaffold structure. In this review, we comprehensively discuss and summarize the potential anti-tumor mechanisms and the progress of structural modifications of β-elemene