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Deep neural networks (DNNs) are an indispensable machine learning tool despite the difficulty of diagnosing what aspects of a model’s input drive its decisions. In countless real-world domains, from legislation and law enforcement to healthcare, such diagnosis is essential to ensure that DNN decisions are driven by aspects appropriate in the context of its use. The development of methods and studies enabling the explanation of a DNN’s decisions has thus blossomed into an active and broad area of research. The field’s complexity is exacerbated by competing definitions of what it means “to explain” the actions of a DNN and to evaluate an approach’s “ability to explain”. This article offers a field guide to explore the space of explainable deep learning for those in the AI/ML field who are uninitiated. The field guide: i) Introduces three simple dimensions defining the space of foundational methods that contribute to explainable deep learning, ii) discusses the evaluations for model explanations, iii) places explainability in the context of other related deep learning research areas, and iv) discusses user-oriented explanation design and future directions. We hope the guide is seen as a starting point for those embarking on this research field.

Polycystic ovary syndrome (PCOS) is one of the most common metabolic and reproductive endocrinopathies. However, few studies have tried to develop a diagnostic model based on gene biomarkers. In this study, we applied a computational method by combining two machine learning algorithms, including random forest (RF) and artificial neural network (ANN), to identify gene biomarkers and construct diagnostic model. We collected gene expression data from Gene Expression Omnibus (GEO) database containing 76 PCOS samples and 57 normal samples; five datasets were utilized, including one dataset for screening differentially expressed genes (DEGs), two training datasets, and two validation datasets. Firstly, based on RF, 12 key genes in 264 DEGs were identified to be vital for classification of PCOS and normal samples. Moreover, the weights of these key genes were calculated using ANN with microarray and RNA-seq training dataset, respectively. Furthermore, the diagnostic models for two types of datasets were developed and named neuralPCOS. Finally, two validation datasets were used to test and compare the performance of neuralPCOS with other two set of marker genes by area under curve (AUC). Our model achieved an AUC of 0.7273 in microarray dataset, and 0.6488 in RNA-seq dataset. To conclude, we uncovered gene biomarkers and developed a novel diagnostic model of PCOS, which would be helpful for diagnosis.

Digital economy has become an important driving force for green economic growth in China. Based on the province-level data of China from 2003 to 2018, this paper constructed the Total-factor Nonradial Directional Distance Function (TNDDF) model to measure the carbon emission efficiency of industrial sector and discussed the impact of digital economy on carbon emission efficiency. Empirical analysis shows that the carbon emission efficiency of China’s industrial sector is low, and there is obvious regional heterogeneity where the carbon emission efficiency of eastern China is higher than that of central and western China. Areas with high level of digital economy development have higher carbon emission efficiency, and digital economy is conducive to promoting energy conservation and pollution reduction in China’s industrial sector. The optimal threshold interval of digital economy for promoting carbon emission efficiency is explored by means of threshold model. In view of this, the Chinese government should vigorously develop the digital economy, promote industrial enterprises to networking and digital evolution, and improve the efficiency of carbon emission as well.

The axion or axion-like particle motivated from a natural solution of strong CP problem or string theory is a promising dark matter candidate. We study the new observational effects of ultralight axion-like particles by the space-borne gravitational wave detector and the radio telescope. Taking the neutron star-black hole binary as an example, we demonstrate that the gravitational waveform could be obviously modified by the slow depletion of the axion cloud around the black hole formed through the superradiance process. We compare these new effects on the binary with the well-studied effects from dynamical friction with dark matter and dipole radiation in model-independent ways. Finally, we discuss the constraints from LIGO/Virgo and study the detectability of the ultralight axion particles at LISA and TianQin.

Motivation is a key psychological factor influencing athletic performance, especially in high-intensity disciplines such as track and field. However, traditional assessment methods–ranging from self-report questionnaires to static physiological models–often fail to capture the temporal, individualized, and context-dependent nature of the motivation–performance relationship. In this study, we propose a hybrid EEG-based framework for modeling motivational states and forecasting athletic performance. The framework integrates neural indicators of arousal and stress with contextual and biomechanical variables using a dual-attention predictive architecture and a personalized adaptation mechanism. Rather than focusing on static prediction, the model dynamically adjusts to individual athletes’ cognitive and physical states across training scenarios. Experimental validation on four public datasets, including two movement-oriented sets (MoBI and HASC), demonstrates consistent gains over strong baselines, with up to 3.5% improvement in accuracy and 7.6% improvement in early fatigue prediction. These findings suggest that the proposed system can support personalized monitoring and adaptive training strategies in performance-driven environments.

In neonatal rats, MK-801 treatments can produce schizophrenia-like symptoms. Crocin is a water soluble carotenoid in Saffron that exerts potent neuroprotective effects. This work aimed to demonstrate the function of crocin in the alleviation of motor and cognitive impairments elicited by MK-801 in a neonatal rodent schizophrenia model, and to illustrate the underlying molecular mechanisms. Rats were treated with vehicle, MK-801 (1 mg/kg), MK-801 + 25 mg/kg crocin, or MK-801 + 50 mg/kg crocin. Motor learning and coordination, locomotion and exploratory activities, as well as spatial memory were assessed using the rotarod test, pen field test, and the Morris water maze test, respectively. Relative mRNA and protein levels of genes of interest were analyzed using qRT-PCR and Western blot assays, respectively. In the hippocampus of rats with MK-801-elicited schizophrenia, administration of crocin elevated the expression of silent information regulator-1 (SIRT1) and brain derived neurotrophic factor (BDNF), and relieved the oxidative stress. The learning deficits and motor perturbations caused by MK-801 treatments were also alleviated by the crocin administration. Collectively, crocin has exerted neuroprotective effects in the rat model of MK-801-elicited schizophrenia, via regulations of SIRT1 and downstream BDNF expression in the hippocampus. •Crocin restores MK-801-repressed production of SIRT1 and BDNF.•Crocin restores CREB phosphorylation in the MK-801-treated rat hippocampus.•Crocin relieves oxidative stress in the MK-801-treated rat hippocampus.•Crocin attenuates behavioural deficits in MK-801-treated rats.•Crocin improves spatial learning of MK-801-treated rats.

Objective This review aimed to investigate the metabolic profile of women with premature ovarian insufficiency (POI) compared relative to women with normal ovarian functioning. Methods A systematic search of PubMed, EMBASE, and the Web of Science for observational studies published up until the 6 th of July 2021 that compared the metabolic profile of POI women with a healthy control group were assessed. Mean differences (MD) and 95% confidence interval (CI) were pooled using the fixed or random effect models. Results A total of 21 studies involving 1573 women with POI and 1762 control women were included. POI patients presented significantly higher waist circumference, total cholesterol, low-density lipoprotein, high-density lipoprotein, triglycerides, and fasting glucose. Additionally, POI patients had marginally higher insulin level. However, the differences in systolic, and diastolic blood pressure were non-significant relative to the control group. Conclusions POI is associated with alterations in certain metabolic parameters compared to control women. This finding highlights the importance of early screening and the lifelong management of metabolic health for women with POI.

Xanthohumol (Xn) is the most abundant prenylated flavonoid in Hops ( L.), and exhibits a range of pharmacological activities. This study aimed to investigate the effect of Xn on TGF-β1-induced cardiac fibroblasts activation and elucidate the underlying mechanism. The cellTiter 96 AQueous one solution cell proliferation assay kit was adopted to determine the cell viability of cardiac fibroblasts, and the proliferation was detected through 5-ethynyl-2'-deoxyuridine (EdU) incorporation assay. The α-SMA protein expression was measured by using immunofluorescence and Western blotting. Western blotting was conducted to test the protein expressions of collagen I and III, PTEN, p-Akt, Akt, p-mTOR, mTOR, p-Smad3, Smad3 and GAPDH. The mRNA levels of α-SMA, collagen I and III were determined by quantitative real-time polymerase chain reaction (PCR). Xn inhibited the TGF-β1-induced proliferation, differentiation and collagen overproduction of cardiac fibroblasts. TGF-β1 induced the down-regulated PTEN expression, Akt and mTOR phosphorylation. These effects of TGF-β1 were suppressed by Xn, while blocking of PTEN reduced Xn-mediated inhibitory effect on cardiac fibroblasts activation induced by TGF-β1. Xn inhibits TGF-β1-induced cardiac fibroblasts activation via mediating PTEN/Akt/mTOR signaling pathway.

Background Fetal growth restriction (FGR) occurs in up to 10% of pregnancies and is a leading cause of perinatal mortality and neonatal morbidity. Three-dimensional ultrasonography of intracranial structure volume revealed significant differences between fetuses with FGR and appropriate for gestational age (AGA) fetuses. We aimed to compare the frontal lobe development between fetuses with FGR and appropriately grown fetuses and evaluate the impact of fetal circulatory redistribution (FCR) on frontal lobe development in fetuses with FGR. Methods We performed a case–control study at our institution from August 2020 to April 2021. The frontal antero-posterior diameter (FAPD) and occipito-frontal diameter (OFD) were measured on the trans-ventricle view and we calculated the Z-scores for FAPD and OFD standardized for gestational age (GA) and transverse cerebellar diameter (TCD) by performing a standard regression analysis followed by weighted regression of absolute residual values in appropriately grown fetuses. We calculated the FAPD/OFD ratio as 100 × FAPD/OFD and FAPD/HC (head circumference) as 100 × FAPD/HC. To compare intracranial parameters, we randomly selected a control group of appropriately grown fetuses matched with the FGR group at the time of ultrasonography. We performed between-group comparisons of the FAPD Z-score, OFD Z-score, FAPD/OFD ratio and FAPD/HC. Similarly, we compared intracranial parameters between fetuses with FGR with and without FCR. Results FAPD/OFD ratio was curvilinear related to all the independent variables (GA, BPD, FL, and TCD). Compared with appropriately grown fetuses, fetuses with FGR showed a significantly lower FAPD/OFD ratio, FAPD Z-score, and FAPD/HC. There was no significant difference in the FAPD Z-score, FAPD/OFD ratio, and FAPD/HC between FGR fetuses with and without FCR. Conclusions The FAPD/OFD ratio varied during pregnancy, with a mild reduction before and a mild increase after about 33 gestational weeks. Fetuses with FGR showed reduced frontal lobe growth; moreover, fetal frontal lobe development disorders were not significantly different in fetuses with FCR. Trial registration Date: 09–27-2017; Number: [2017]239.

Wild rice ( Zizania spp.), an aquatic grass belonging to the subfamily Gramineae, has a high economic value. Zizania provides food (such as grains and vegetables), a habitat for wild animals, and paper-making pulps, possesses certain medicinal values, and helps control water eutrophication. Zizania is an ideal resource for expanding and enriching a rice breeding gene bank to naturally preserve valuable characteristics lost during domestication. With the Z. latifolia and Z. palustris genomes completely sequenced, fundamental achievements have been made toward understanding the origin and domestication, as well as the genetic basis of important agronomic traits of this genus, substantially accelerating the domestication of this wild plant. The present review summarizes the research results on the edible history, economic value, domestication, breeding, omics research, and important genes of Z. latifolia and Z. palustris over the past decades. These findings broaden the collective understanding of Zizania domestication and breeding, furthering human domestication, improvement, and long-term sustainability of wild plant cultivation.