Reduction in precipitation amount, precipitation events, and nitrogen addition change ecosystem carbon fluxes differently in a semi-arid grassland.

The increases in extent and frequency of extreme drought events and increased nitrogen (N) deposition due to global change are expected to have profound impacts on carbon cycling in semi-arid grasslands. However, how ecosystem CO2 exchange processes respond to different drought scenarios individually and interactively with N addition remains uncertain. In this study, we experimentally explored the effects of different drought scenarios (early season extreme drought, 50 % reduction in precipitation amount, and 50 % reduction in precipitation events) and N addition on net ecosystem CO2 exchange (NEE), ecosystem respiration (ER), and gross ecosystem productivity (GEP) over three growing seasons (2019-2021) in a semi-arid grassland in northern China. The growing-season ecosystem carbon fluxes in response to drought and N addition were influenced by inter-annual precipitation changes, with 2019 as a normal precipitation year, and 2020 and 2021 as wet years. Early season extreme drought stimulated NEE by reducing ER. 50 % reduction in precipitation amount decreased ER and GEP consistently in three years, but only significantly suppressed NEE in 2019. 50 % reduction in precipitation events stimulated NEE. Nitrogen addition stimulated NEE, ER, and GEP, but only significantly in wet years. The structural equation models showed that changes in carbon fluxes were regulated by soil moisture, soil temperature, microbial biomass nitrogen (MBN), and the key plant functional traits. Decreased community-weighted means of specific leaf area (CWMSLA) was closely related to the reduced ER and GEP under early season extreme drought and 50 % reduction in precipitation amount. While increased community-weighted means of plant height (CWMPH) largely accounted for the stimulated ER and GEP under 50 % reduction in precipitation events. Our study stresses the distinct effects of different drought scenarios and N enrichment on carbon fluxes, and highlights the importance of soil traits and the key plant traits in determining carbon exchange in this water-limited ecosystem.

Nitrogen addition alleviates the adverse effects of drought on plant productivity in a temperate steppe.

Drought and nitrogen enrichment could profoundly affect the productivity of semiarid ecosystems. However, how ecosystem productivity will respond to different drought scenarios, especially with a concurrent increase in nitrogen availability, is still poorly understood. Using data from a 4-year field experiment conducted in a semiarid temperate steppe, we explored the responses of aboveground net primary productivity (ANPP) to different drought scenarios and nitrogen addition, and the underlying mechanisms linking soil properties, plant species richness, functional diversity (community-weighted means of plant traits, functional dispersion) and phylogenetic diversity (net relatedness index) to ANPP. Our results showed that completely excluding precipitation in June (1-month intense drought) and reducing half the precipitation amount from June to August (season-long chronic drought) both significantly reduced ANPP, with the latter having a more negative impact on ANPP. However, reducing half of the precipitation frequency from June to August (precipitation redistribution) had no significant effect on ANPP. Nitrogen addition increased ANPP irrespective of drought scenarios. ANPP was primarily determined by soil moisture and nitrogen availability by regulating the community-weighted means of plant height, rather than other aspects of plant diversity. Our findings suggest that precipitation amount is more important than precipitation redistribution in influencing the productivity of temperate steppe, and nitrogen supply could alleviate the adverse impacts of drought on grassland productivity. Our study advances the mechanistic understanding of how the temperate grassland responds to drought stress, and implies that management strategies to protect tall species in the community would be beneficial for maintaining the productivity and carbon sequestration of grassland ecosystems under climate drought.

Solid-state high harmonic spectroscopy for all-optical band structure probing of high-pressure quantum states.

High pressure has triggered various novel states/properties in condensed matter, as the most representative and dramatic example being near-room-temperature superconductivity in highly pressured hydrides (~200 GPa). However, the mechanism of superconductivity is not confirmed, due to the lacking of effective approach to probe the electronic band structure under such high pressures. Here, we theoretically propose that the band structure and electron-phonon coupling (EPC) of high-pressure quantum states can be probed by solid-state high harmonic generation (sHHG). This strategy is investigated in high-pressure Im-3m H3S by the state-of-the-art first-principles time-dependent density-functional theory simulations, where the sHHG is revealed to be strongly dependent on the electronic structures and EPC. The dispersion of multiple bands near the Fermi level is effectively retrieved along different momentum directions. Our study provides unique insights into the potential all-optical route for band structure and EPC probing of high-pressure quantum states, which is expected to be helpful for the experimental exploration of high-pressure superconductivity in the future.

Bayesian Estimate of Mean Proper Scores for Diversity-Enhanced Active Learning.

The effectiveness of active learning largely depends on the sampling efficiency of the acquisition function. Expected Loss Reduction (ELR) focuses on a Bayesian estimate of the reduction in classification error, and more general costs fit in the same framework. We propose Bayesian Estimate of Mean Proper Scores (BEMPS) to estimate the increase in strictly proper scores such as log probability or negative mean square error within this framework. We also prove convergence results for this general class of costs. To facilitate better experimentation with the new acquisition functions, we develop a complementary batch AL algorithm that encourages diversity in the vector of expected changes in scores for unlabeled data. To allow high-performance classifiers, we combine deep ensembles, and dynamic validation set construction on pretrained models, and further speed up the ensemble process with the idea of Monte Carlo Dropout. Extensive experiments on both texts and images show that the use of mean square error and log probability with BEMPS yields robust acquisition functions and well-calibrated classifiers, and consistently outperforms the others tested. The advantages of BEMPS over the others are further supported by a set of qualitative analyses, where we visualise their sampling behaviour using data maps and t-SNE plots.

Teacher-student complementary sample contrastive distillation.

Knowledge distillation (KD) is a widely adopted model compression technique for improving the performance of compact student models, by utilizing the "dark knowledge" of a large teacher model. However, previous studies have not adequately investigated the effectiveness of supervision from the teacher model, and overconfident predictions in the student model may degrade its performance. In this work, we propose a novel framework, Teacher-Student Complementary Sample Contrastive Distillation (TSCSCD), that alleviate these challenges. TSCSCD consists of three key components: Contrastive Sample Hardness (CSH), Supervision Signal Correction (SSC), and Student Self-Learning (SSL). Specifically, CSH evaluates the teacher's supervision for each sample by comparing the predictions of two compact models, one distilled from the teacher and the other trained from scratch. SSC corrects weak supervision according to CSH, while SSL employs integrated learning among multi-classifiers to regularize overconfident predictions. Extensive experiments on four real-world datasets demonstrate that TSCSCD outperforms recent state-of-the-art knowledge distillation techniques.

Metronidazole-loaded polydopamine nanomedicine with antioxidant and antibacterial bioactivity for periodontitis.

Aim: This study focused on treating periodontitis with bacterial infection and local over accumulation of reactive oxygen species. Materials & methods: Polydopamine nanoparticles (PDA NPs) were exploited as efficient carriers for encapsulated metronidazole (MNZ). The therapeutic efficacy and biocompatibility of PDA@MNZ NPs were investigated through both in vitro and in vivo studies. Results: The nanodrug PDA@MNZ NPs were successfully fabricated, with well-defined physicochemical characteristics. In vitro, the PDA@MNZ NPs effectively eliminated intracellular reactive oxygen species and inhibited the growth of Porphyromonas gingivalis. Moreover, the PDA@MNZ NPs exhibited synergistic therapy for periodontitisin in vivo. Conclusion: PDA@MNZ NPs were confirmed with exceptional antimicrobial and antioxidant functions, offering a promising avenue for synergistic therapy in periodontitis.

Dosimetric comparison between stereotactic body radiotherapy and carbon-ion radiation therapy for prostate cancer.

Background: Prostate cancer rates have been steadily increasing in recent years. As high-precision radiation therapy methods, stereotactic body radiation therapy (SBRT) and carbon-ion radiation therapy (CIRT) have unique advantages. Analyzing the dosimetric differences between SBRT and CIRT in the treatment of localized prostate cancer can help provide patients with more accurate, individualized treatment plans. Methods: We selected computed tomography positioning images and the contours of target volumes of 16 patients with localized prostate cancer who received radiotherapy. We delineated the organs at risk (OARs) on the CyberKnife (CK) treatment planning system (TPS) MultiPlan4.0, which were imported into the CIRT uniform scanning TPS HIMM-1 ci-Plan. Two treatment plans, SBRT and CIRT, were designed for the same patient, and we used SPSS 22.0 for the statistical analysis of data. Results: Both SBRT and CIRT plans met the prescribed dose requirements. In terms of target volume exposure dose, D2 (P<0.001), D5 (P<0.001), D50 (P<0.001), D90 (P=0.029), D95 (P<0.001), D98 (P<0.001), and Dmean (P<0.001) under SBRT were significantly higher than those under CIRT; the conformity index (CI) under SBRT was significantly better than that under CIRT (P<0.001); the target volume coverage rate (V95%) and dose homogeneity index (HI) under CIRT were significantly better than those under SBRT (P<0.001). In terms of OAR exposure dosage, the Dmax of the bladder and rectum under SBRT was significantly lower than that under CIRT (P<0.001), but Dmean was in the other direction; the exposure dose of the intestinal tract under CIRT was significantly lower than that under SBRT (P<0.05); Dmax of the femoral head under CIRT was significantly lower than that under SBRT (P<0.05), and there was no statistical difference between them at other doses. Conclusions: In this study, we found that when CIRT was used for treating localized prostate cancer, the dose distribution in target volume was more homogeneous and the coverage rate was higher; the average dose of OARs was lower. SBRT had a better CI and higher dose in target volume; the dose hotspot was lower in OARs. It is important to comprehensively consider the dose relationship between local tumor and surrounding tissues when selecting treatment plans.

Ecosystem multifunctionality, maximum height, and biodiversity of shrub communities affected by precipitation fluctuations in Northwest China.

Introduction: Dryland ecosystems face serious threats from climate change. Establishing the spatial pattern of ecosystem multifunctionality, maximum height and the correlation of biodiversity patterns with climate change is important for understanding changes in complex ecosystem processes. However, the understanding of their relationships across large spatial areas remains limited in drylands. Methods: Accordingly, this study examined the spatial patterns of ecosystem multifunctionality, maximum height and considered a set of potential environmental drivers by investigating natural shrub communities in Northwest China. Results: We found that the ecosystem multifunctionality (EMF) and maximum height of shrub communities were both affected by longitude, which was positively correlated with the precipitation gradient. Specifically, the EMF was driven by high precipitation seasonality, and the maximum height was driven by high precipitation stability during the growing season. Among the multiple biodiversity predictors, species beta diversity (SD-beta) is the most common in determining EMF, although this relationship is weak. Discussion: Unlike tree life form, we did not observe biodiversity-maximum height relationships in shrub communities. Based on these results, we suggest that more attention should be paid to the climatical fluctuations mediated biodiversity mechanisms, which are tightly correlated with ecosystem's service capacity and resistance capacity under a rapid climate change scenario in the future.

Multifunctional Hydrogel Eye Drops for Synergistic Treatment of Ocular Inflammatory Disease.

Uveitis is a complex ocular inflammatory disease with a multifactorial etiology that can result in blindness. Although corticosteroid eye drops are the primary treatment for anterior uveitis, their efficacy is limited by low bioavailability, adverse effects, and a narrow focus on inflammation. In this study, the multifunctional hydrogel eye drops (designated as DCFH) were developed by incorporating the anti-inflammatory agent dexamethasone (DSP) and reactive oxygen species (ROS) scavenger cerium-based metal-organic frameworks (Ce-MOFs) into thermosensitive triblock copolymer F127 for the synergistic treatment against uveitis. The resulting F127 eye drops offer a favorable alternative to ophthalmic solution due to its thermosensitivity, thixotropy, light transmittance, improved ocular bioavailability, and unexpected anti-inflammatory efficacy. Notably, the participation of nanoporous Ce-MOFs, functional drug carriers, not only reduces ROS level but also boosts the anti-inflammatory activity of DSP in vitro. Therapeutically, the multifunctional DCFH exhibits superior efficacy in treating endotoxin-induced uveitis by mitigating the ophthalmic inflammatory reaction, suppressing inflammatory cytokines (e.g., TNF-α, IL-6, and IL-17) and downregulating the expression of iNOS and NLPR3. This synergistic treatment provides a valuable and promising approach for the management of uveitis and other ocular inflammatory conditions.

Comparison of machine learning and conventional logistic regression-based prediction models for gestational diabetes in an ethnically diverse population; the Monash GDM Machine learning model.

BACKGROUND: Early identification of pregnant women at high risk of developing gestational diabetes (GDM) is desirable as effective lifestyle interventions are available to prevent GDM and to reduce associated adverse outcomes. Personalised probability of developing GDM during pregnancy can be determined using a risk prediction model. These models extend from traditional statistics to machine learning methods; however, accuracy remains sub-optimal. OBJECTIVE: We aimed to compare multiple machine learning algorithms to develop GDM risk prediction models, then to determine the optimal model for predicting GDM. METHODS: A supervised machine learning predictive analysis was performed on data from routine antenatal care at a large health service network from January 2016 to June 2021. Predictor set 1 were sourced from the existing, internationally validated Monash GDM model: GDM history, body mass index, ethnicity, age, family history of diabetes, and past poor obstetric history. New models with different predictors were developed, considering statistical principles with inclusion of more robust continuous and derivative variables. A randomly selected 80% dataset was used for model development, with 20% for validation. Performance measures, including calibration and discrimination metrics, were assessed. Decision curve analysis was performed. RESULTS: Upon internal validation, the machine learning and logistic regression model's area under the curve (AUC) ranged from 71% to 93% across the different algorithms, with the best being the CatBoost Classifier (CBC). Based on the default cut-off point of 0.32, the performance of CBC on predictor set 4 was: Accuracy (85%), Precision (90%), Recall (78%), F1-score (84%), Sensitivity (81%), Specificity (90%), positive predictive value (92%), negative predictive value (78%), and Brier Score (0.39). CONCLUSIONS: In this study, machine learning approaches achieved the best predictive performance over traditional statistical methods, increasing from 75 to 93%. The CatBoost classifier method achieved the best with the model including continuous variables.

Five blueberry anthocyanins and their antioxidant, hypoglycemic, and hypolipidemic effects in vitro.

The dual epidemic of obesity and diabetes mellitus is becoming an important worldwide public health issue. "Diabesity" is the term used to describe the combined detrimental health effects of both diabetes mellitus and obesity/overweight. Currently, food-derived bioactive compounds are suggested to alleviate diabesity. Blueberries are rich in bioactive anthocyanins, which are associated with contributing to preventing obesity and diabetes mellitus. However, the accurate active compounds and the underlying mechanism are still unclear. The objective of this study was to investigate the beneficial effects of blueberry anthocyanin on diabesity. In total, five anthocyanins (delphinidin-3-O-galactoside, delphinidin-3-O-glucoside, petunidin-3-O-galactoside, petunidin-3-O-glucoside, and malvidin-3-O-galactoside) were isolated from rabbiteye blueberry (Vaccinium virgatum) cultivar "Garden blue." All these anthocyanins exhibited oxygen radical absorbance capacity (ORAC), scavenging power of ABTS+, and DPPH-free radical and inhibitory activity of α-glucosidase in vitro. Moreover, some compounds improved glucose uptake and attenuated lipid accumulation in high glucose and oleic acid-treated HepG2 cells. All these results suggest that blueberry anthocyanins have potential antioxidant, hypoglycemic, and hypolipidemic effects, which may benefit the treatment of diabesity.

An overview of clustering methods with guidelines for application in mental health research.

Cluster analyzes have been widely used in mental health research to decompose inter-individual heterogeneity by identifying more homogeneous subgroups of individuals. However, despite advances in new algorithms and increasing popularity, there is little guidance on model choice, analytical framework and reporting requirements. In this paper, we aimed to address this gap by introducing the philosophy, design, advantages/disadvantages and implementation of major algorithms that are particularly relevant in mental health research. Extensions of basic models, such as kernel methods, deep learning, semi-supervised clustering, and clustering ensembles are subsequently introduced. How to choose algorithms to address common issues as well as methods for pre-clustering data processing, clustering evaluation and validation are then discussed. Importantly, we also provide general guidance on clustering workflow and reporting requirements. To facilitate the implementation of different algorithms, we provide information on R functions and libraries.

Prototype-Guided Memory Replay for Continual Learning.

Continual learning (CL) is a machine learning paradigm that accumulates knowledge while learning sequentially. The main challenge in CL is catastrophic forgetting of previously seen tasks, which occurs due to shifts in the probability distribution. To retain knowledge, existing CL models often save some past examples and revisit them while learning new tasks. As a result, the size of saved samples dramatically increases as more samples are seen. To address this issue, we introduce an efficient CL method by storing only a few samples to achieve good performance. Specifically, we propose a dynamic prototype-guided memory replay (PMR) module, where synthetic prototypes serve as knowledge representations and guide the sample selection for memory replay. This module is integrated into an online meta-learning (OML) model for efficient knowledge transfer. We conduct extensive experiments on the CL benchmark text classification datasets and examine the effect of training set order on the performance of CL models. The experimental results demonstrate the superiority our approach in terms of accuracy and efficiency.

Discriminative and Geometry-Preserving Adaptive Graph Embedding for dimensionality reduction.

Learning graph embeddings for high-dimensional data is an important technology for dimensionality reduction. The learning process is expected to preserve the discriminative and geometric information of high-dimensional data in a new low-dimensional subspace via either manual or automatic graph construction. Although both manual and automatic graph constructions can capture the geometry and discrimination of data to a certain degree, they working alone cannot fully explore the underlying data structure. To learn and preserve more discriminative and geometric information of the high-dimensional data in the low-dimensional subspace as much as possible, we develop a novel Discriminative and Geometry-Preserving Adaptive Graph Embedding (DGPAGE). It systematically integrates manual and adaptive graph constructions in one unified graph embedding framework, which is able to effectively inject the essential information of data involved in predefined graphs into the learning of an adaptive graph, in order to achieve both adaptability and specificity of data. Learning the adaptive graph jointly with the optimized projections, DGPAGE can generate an embedded subspace that has better pattern discrimination for image classification. Results derived from extensive experiments on image data sets have shown that DGPAGE outperforms the state-of-the-art graph-based dimensionality reduction methods. The ablation studies show that it is beneficial to have an integrated framework, like DGPAGE, that brings together the advantages of manual/adaptive graph construction.

Collaborative Knowledge Distillation via Multiknowledge Transfer.

Knowledge distillation (KD), as an efficient and effective model compression technique, has received considerable attention in deep learning. The key to its success is about transferring knowledge from a large teacher network to a small student network. However, most existing KD methods consider only one type of knowledge learned from either instance features or relations via a specific distillation strategy, failing to explore the idea of transferring different types of knowledge with different distillation strategies. Moreover, the widely used offline distillation also suffers from a limited learning capacity due to the fixed large-to-small teacher-student architecture. In this article, we devise a collaborative KD via multiknowledge transfer (CKD-MKT) that prompts both self-learning and collaborative learning in a unified framework. Specifically, CKD-MKT utilizes a multiple knowledge transfer framework that assembles self and online distillation strategies to effectively: 1) fuse different kinds of knowledge, which allows multiple students to learn knowledge from both individual instances and instance relations, and 2) guide each other by learning from themselves using collaborative and self-learning. Experiments and ablation studies on six image datasets demonstrate that the proposed CKD-MKT significantly outperforms recent state-of-the-art methods for KD.

Correlation between the warning symptoms and prognosis of cardiac arrest.

BACKGROUND: A low survival rate in patients with cardiac arrest is associated with failure to recognize the condition in its initial stage. Therefore, recognizing the warning symptoms of cardiac arrest in the early stage may play an important role in survival. AIM: To investigate the warning symptoms of cardiac arrest and to determine the correlation between the symptoms and outcomes. METHODS: We included all adult patients with all-cause cardiac arrest who visited Peking University Third Hospital or Beijing Friendship Hospital between January 2012 and December 2014. Data on population, symptoms, resuscitation parameters, and outcomes were analysed. RESULTS: Of the 1021 patients in the study, 65.9% had symptoms that presented before cardiac arrest, 25.2% achieved restoration of spontaneous circulation (ROSC), and 7.2% survived to discharge. The patients with symptoms had higher rates of an initial shockable rhythm (12.2% vs 7.5%, P = 0.020), ROSC (29.1% vs 17.5%, P = 0.001) and survival (9.2% vs 2.6%, P = 0.001) than patients without symptoms. Compared with the out-of-hospital cardiac arrest (OHCA) without symptoms subgroup, the OHCA with symptoms subgroup had a higher rate of calls before arrest (81.6% vs 0.0%, P < 0.001), health care provider-witnessed arrest (13.0% vs 1.4%, P = 0.001) and bystander cardiopulmonary resuscitation (15.5% vs 4.9%, P = 0.002); a shorter no flow time (11.7% vs 2.8%, P = 0.002); and a higher ROSC rate (23.8% vs 13.2%, P = 0.011). Compared to the in-hospital cardiac arrest (IHCA) without symptoms subgroup, the IHCA with symptoms subgroup had a higher mean age (66.2 ± 15.2 vs 62.5 ± 16.3 years, P = 0.005), ROSC (32.0% vs 20.6%, P = 0.003), and survival rates (10.6% vs 2.5%, P < 0.001). The top five warning symptoms were dyspnea (48.7%), chest pain (18.3%), unconsciousness (15.2%), paralysis (4.3%), and vomiting (4.0%). Chest pain (20.9% vs 12.7%, P = 0.011), cardiac etiology (44.3% vs 1.5%, P < 0.001) and survival (33.9% vs 16.7%, P = 0.001) were more common in males, whereas dyspnea (54.9% vs 45.9%, P = 0.029) and a non-cardiac etiology (53.3% vs 41.7%, P = 0.003) were more common in females. CONCLUSION: Most patients had warning symptoms before cardiac arrest. Dyspnea, chest pain, and unconsciousness were the most common symptoms. Immediately recognizing these symptoms and activating the emergency medical system prevents resuscitation delay and improves the survival rate of OHCA patients in China.

Agreement Between Predicted and Actual Measured Ablation Depth After FS-LASIK Using Different Rotating Scheimpflug Cameras and OCT.

Purpose: To compare the predicted ablation depth (AD) with the postoperatively measured corneal ablation depth (postop-AD) at central, paracentral, and midperipheral locations using two rotating Scheimpflug analyzers and a Fourier-domain optical coherence tomographer in eyes that underwent femtosecond laser-assisted LASIK (FS-LASIK). Methods: The values of corneal thickness were measured preoperatively and postoperatively at one and three months. The difference between preoperative and postoperative was defined as postop-AD. Measurements were performed at the corneal vertex and mid-peripheral area. The mid-peripheral corneal thickness was measured at the superior, inferior, nasal, and temporal locations at a distance of 1.0 or 2.5 mm from the corneal vertex. The predicted AD was calculated by ORK-CAM software (Schwind eye tech-solutions GmbH, Kleinostheim, Germany), and the difference between the predicted AD and postop-AD was defined as Δ-AD. Paired t-test analysis was employed to evaluate the differences, agreement was assessed by the Bland-Altman method. Results: Forty-two eyes of 42 patients were investigated. At one month, the predicted AD in the central and paracentral areas was underestimated by the Pentacam HR (Oculus, Wetzlar, Germany), Sirius (Costruzione Strumenti Oftalmici, Florence, Italy) and RTVue OCT (Optovue Inc., Freemont, CA, United States), whereas Δ-AD was negative as established by all devices and predominantly statistically significant. The Δ-AD values approximated zero at three months. The mean difference of Δ-AD at three months at the corneal vertex was 0.67 ± 9.39 mm, -7.92 ± 9.05 mm and -1.36 ± 8.31 mm, respectively. The mid-peripheral measurements had positive values at one month and even more highly positive at three months (with statistically significant differences in most of the cases). The agreement between the predicted and postop-AD was moderate with all devices, but slightly better with RTVue. Conclusion: The predicted AD seems to be underestimated in the central and paracentral corneal area and overestimated in the mid-periphery. Translational Relevance: The study could help to partly explain and prevent the refractive errors after FS-LASIK.

Comparison of state-of-the-art machine and deep learning algorithms to classify proximal humeral fractures using radiology text.

INTRODUCTION: Proximal humeral fractures account for a significant proportion of all fractures. Detailed accurate classification of the type and severity of the fracture is a key component of clinical decision making, treatment and plays an important role in orthopaedic trauma research. This research aimed to assess the performance of Machine Learning (ML) multiclass classification algorithms to classify proximal humeral fractures using radiology text data. MATERIALS AND METHODS: Data from adult (16 + years) patients admitted to a major trauma centre for management of their proximal humerus fracture from January 2010 to January 2019 were used (1,324). Six input text datasets were used for classification: X-ray and/or CT scan reports (primary) and concatenation of patient age and/or patient sex. One of seven Neer class labels were classified. Models were evaluated using accuracy, recall, precision, F1, and One-versus-rest scores. RESULTS: A number of statistical ML algorithms performed acceptably and one of the BERT models, exhibiting good accuracy of 61% and an excellent one-versus-rest score above 0.8. The highest precision, recall and F1 scores were 50%, 39% and 39% respectively, being considered reasonable scores with the sparse text data used and in the context of machine learning. CONCLUSION: ML and BERT algorithms based on routine unstructured X-ray and CT text reports, combined with the demographics of the patient, show promise in Neer classification of proximal humeral fractures to aid research. Use of these algorithms shows potential to speed up the classification task and assist radiologist, surgeons and researchers.

Whole genome re-sequencing reveals the genetic diversity and evolutionary patterns of Eucommia ulmoides.

Eucommia ulmoides (E. ulmoides) is a deciduous perennial tree belonging to the order Garryales, and is known as "living fossil" plant, along with ginkgo (Ginkgo biloba), metaspaca (Metasequoia glyptostroboides) and dove tree (Davidia involucrata Baill). However, the genetic diversity and population structure of E. ulmoides are still  ambiguous nowdays. In this study, we re-sequenced the genomes of 12 E. ulmoides accessions from different major climatic geography regions in China to elucidate the genetic diversity, population structure and evolutionary pattern. By integration of phylogenetic analysis, principal component analysis and population structure analysis based on a number of high-quality SNPs, a total of 12 E. ulmoides accessions were clustered into four different groups. This result is consistent with their geographical location except for group samples from Shanghai and Hunan province. E. ulmoides accessions from Hunan province exhibited a closer genetic relationship with E. ulmoides accessions from Shanghai in China compared with other regions, which is also supported by the result of population structure analyses. Genetic diversity analysis further revealed that E. ulmoides samples in Shanghai and Hunan province were with higher genetic diversity than those in other regions in this study. In addition, we treated the E. ulmoides materials from Shanghai and Hunan province as group A, and the other materials from other places as group B, and then analyzed the evolutionary pattern of E. ulmoides. The result showed the significant differentiation (Fst = 0.1545) between group A and group B. Some candidate highly divergent genome regions were identified in group A by selective sweep analyses, and the function analysis of candidate genes in these regions showed that biological regulation processes could be correlated with the Eu-rubber biosynthesis. Notably, nine genes were identified from selective sweep regions. They were involved in the Eu-rubber biosynthesis and expressed in rubber containing tissues. The genetic diversity research and evolution model of E. ulmoides were preliminarily explored in this study, which laid the foundation for the protection of germplasm resources and the development and utilization of multipurpose germplasm resources in the future.

Therapeutic effect of two transition metal coordination polymers on ovarian cancer by regulating the expression of estrogen receptor.

In the present study, via using a ligand featuring oxalamide groups N,N'-bis(4-phthalic acid) (H4L), two new Cu(II) and Co(II)-containing coordination polymers with the chemical formulae of [Cu2L(H2O)4]n (1) and [Co(H2L)(H2O)2]n (2) have been successfully prepared via reaction of the corresponding metal salts with the H4L ligand. The as-prepared two coordination polymers have been studied via the single crystal X-ray diffraction, elemental analysis, powder X-ray diffraction and thermogravimetric analysis. Their therapeutic effect and mechanism for ovarian cancer was evaluated and explored. Firstly, the inhibitory activity of the new compounds on the proliferation of the ovarian cancer was measured with CCK-8 assay after compound treatment. Besides, the relative expression of the estrogen receptor on the ovarian cancer cells after compound treatment was also determined with real-time RT-PCR assay.