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Abstract Bitter gourd, being perishable, requires timely harvesting. Delayed harvesting can result in a substantial reduction in fruit quality. while premature harvesting leads to underdeveloped fruit and decreased yields, the continuous flowering pattern in bitter gourd underscores the significance of accurately assessing fruit growth and ensuring timely harvesting for subsequent fruit setting and development. The current reliance on the experience of production personnel represents a substantial inefficiency. We present an improved real-time instance segmentation model based on YOLOv5-seg. The utilization of dynamic snake convolution enables the extraction of morphological features from the curved and elongated structure of bitter gourd. Diverse branch blocks enhance feature space diversity without inflating model size and inference time, contributing to improved recognition of expansion stages during bitter gourd growth. Additionally, the introduction of Focal-EIOU loss accurately locates the boundary box and mask, addressing sample imbalances in the L2 stage. Experimental results showcase remarkable accuracy rates of 99.3%, 93.8%, and 98.3% for L1, L2, and L3 stages using mAP@0.5. In comparison, our model outperforms other case segmentation models, excelling in both detection accuracy and inference speed. The improved YOLOv5-seg model demonstrates strong performance in fine-grained recognition of bitter gourd during the expansion stage. It efficiently segments bitter gourd in real-time under varying lighting and occlusion conditions, providing crucial maturity information. This model offers reliable insights for agricultural workers, facilitating precise harvesting decisions.

Enhancing the accuracy of paddy rice mapping is crucial for bolstering global food security. Prior research incorporating Sentinel imagery with phenological characteristics has identified paddy rice fields effectively. However, challenges such as reliance on a single index, cloud cover interference, and a lack of sufficient training samples continue to complicate the mapping of paddy rice. This study introduces a comprehensive paddy rice mapping framework that incorporates annual phenological features throughout the entire growth phase. This was achieved by expanding the sample size through the extraction of phenological features, and the visually verified samples were then integrated with distinct phenological phases and relevant indices, utilizing hybrid Sentinel-1/2 imagery to map paddy rice distribution. The accuracy of the generated rice map was validated against trusted samples, corroborative agricultural statistics, and another high-resolution 10 m mapping product. Compared with ground-truth samples, the algorithm has achieved an overall accuracy of approximately 92% in most rice production regions with a confusion matrix. Additionally, the estimated rice area in Anhui and several other rice-producing regions shows less than 10% error when compared with governmental statistical records from the yearbook. When compared with another recent paddy rice map at the same spatial resolution (10 m), our approach provided cleaner details and more effectively reduced omission errors. It received values of R2 = 0.991 and slope = 1.08 in a prefecture-level statistical comparison with a counterpart. Our proposed approach is proven to be valid and is expected to offer significant benefits to agricultural sustainability and technological applications in farming.

The hydrological cycle, surface energy balance, and the management of water resources are all significantly impacted by soil moisture. Because it governs the physical processes of evapotranspiration and rainfall penetration, surface soil moisture is a significant climatic variable. In this work, visible-near infrared (VIS-NIR) bands were used to compare and analyze the spectra of loess samples with varying moisture concentrations. The investigation looked at how changes in the soil moisture content impacted the response of the soil spectra. The researchers used a genetic algorithm (GA), interval combination optimization (ICO), and competitive adaptive reweighted sampling (CARS) to filter feature variables from full-band spectral data. To forecast the moisture content of loess on the soil surface, models like partial least squares regression (PLSR), support vector machine (SVM), and random forest (RF) were created. The findings indicate that: (1) the most reliable spectrum preprocessing technique is the first derivative (FD), which can significantly enhance the model’s prediction power and spectral characteristic information. (2) The feature band selection method’s prediction effect of soil moisture content is typically superior to that of full-spectrum data. (3) The random forest (RF) prediction model for soil moisture content with the highest accuracy was built by combining the genetic algorithm (GA) with the FD preprocessed spectra. The results may provide a new understanding on how to use VIS-NIR to measure soil moisture content.

IntroductionThis study aimed to evaluate morphological changes in cortical and subcortical regions and their asymmetrical differences in individuals with subjective cognitive decline (SCD) and mild cognitive impairment (MCI). These morphological changes may provide valuable insights into the early diagnosis and treatment of Alzheimer's disease (AD).MethodsWe conducted structural MRI scans on a cohort comprising 62 SCD patients, 97 MCI patients, and 70 age-, sex-, and years of education-matched healthy controls (HC). Using Freesurfer, we quantified surface area, thickness, the local gyrification index (LGI) of cortical regions, and the volume of subcortical nuclei. Asymmetry measures were also calculated. Additionally, we explored the correlation between morphological changes and clinical variables related to cognitive decline.ResultsCompared to HC, patients with MCI exhibited predominantly left-sided surface morphological changes in various brain regions, including the transverse temporal gyrus, superior temporal gyrus, insula, and pars opercularis. SCD patients showed relatively minor surface morphological changes, primarily in the insula and pars triangularis. Furthermore, MCI patients demonstrated reduced volumes in the anterior-superior region of the right hypothalamus, the fimbria of the bilateral hippocampus, and the anterior region of the left thalamus. These observed morphological changes were significantly associated with clinical ratings of cognitive decline.ConclusionThe findings of this study suggest that cortical and subcortical morphometric changes may contribute to cognitive impairment in MCI, while compensatory mechanisms may be at play in SCD to preserve cognitive function. These insights have the potential to aid in the early diagnosis and treatment of AD.

Abstract Inspired by biological neural networks, the fabrication of artificial neuromorphic systems with multimodal perception capacity shows promises in overcoming the “von Neumann bottleneck” and takes advantage of the efficient perception and computation of diverse types of signals. Here, we combine a triboelectric nanogenerator with an α‐phase indium selenide (α‐In2Se3) optoelectronic synaptic transistor to construct a tribo‐ferro‐optoelectronic artificial neuromorphic device with multimodal plasticity. Based on the excellent ferroelectric and optoelectronic characteristics of the α‐In2Se3 channel, typical synaptic behaviors (e.g., pair‐pulse facilitation and short‐term/long‐term plasticity) are successfully simulated in response to the synergistic effect of mechanical and optical stimuli. The interaction of mechanical displacement and light illumination enables heterosynaptic plasticity and spatiotemporal dynamic logic. Furthermore, multiple Boolean logical functions and associative learning behaviors are successfully implemented using the paired stimuli of displacement pulses and light pulses. The proposed tribo‐ferro‐optoelectronic artificial neuromorphic devices have great potential for application in interactive neural networks and next‐generation artificial intelligence.

With the arrival of the era of artificial intelligence (AI) and big data, the explosive growth of data has raised higher demands on computer hardware and systems. Neuromorphic techniques inspired by biological nervous systems are expected to be one of the approaches to breaking the von Neumann bottleneck. Piezotronic neuromorphic devices modulate electrical transport characteristics by piezopotential and directly associate external mechanical motion with electrical output signals in an active manner, with the capability to sense/store/process information of external stimuli. In this review, we have presented the piezotronic neuromorphic devices (which are classified into strain-gated piezotronic transistors and piezoelectric nanogenerator-gated field effect transistors based on device structure) and discussed their operating mechanisms and related manufacture techniques. Secondly, we summarized the research progress of piezotronic neuromorphic devices in recent years and provided a detailed discussion on multifunctional applications, including bionic sensing, information storage, logic computing, and electrical/optical artificial synapses. Finally, in the context of future development, challenges, and perspectives, we have discussed how to modulate novel neuromorphic devices with piezotronic effects more effectively. It is believed that the piezotronic neuromorphic devices have great potential for the next generation of interactive sensation/memory/computation to facilitate the development of the Internet of Things, AI, biomedical engineering, etc.

The polarization crossfire (PCF) suite carried onboard the Chinese GaoFen-5B satellite is composed of a Particulate Observing Scanning Polarimeter (POSP) and a Directional Polarimetric Camera (DPC), which can provide multi-angle, multi-spectral, and polarization data. In this paper, the influence of polarization and the directionality of reflectance in open oceans on the inversion of chlorophyll a (Chla) concentrations are investigated, from 410 nm to 670 nm. First, we exploit a vector radiative transfer model to simulate the absolute and relative magnitudes of the water-leaving radiance signal (I) and the parallel polarization radiance (PPR) to the top-of-atmosphere (TOA) radiation field. The simulation results show that the PPR can enhance the relative contribution of the water-leaving signal, especially in sunglint observation geometry. The water-leaving signal for PPR exhibits significant directional and spectral variations relative to the observation geometries, and the maximum value of the water-leaving signal for PPR occurs in the backscattering direction. In addition, the sensitivity of the PPR to the Chla concentration is sufficient. The synthetic datasets are utilized to develop retrieval algorithms for the Chla concentrations based on the back-propagation neural network (BPNN). The inversion results show that the PCF strategy improves the accuracy of Chla retrieval, with an RMSE of 0.014 and an RRMSE of 6.57%. Thus, it is an effective method for retrieving the Chla concentration in open oceans, by utilizing both the directionality and polarization of the reflectance.

Nicotinamide phosphoribosyltransferase (NAMPT) is a rate-limiting enzyme in the NAD salvage pathway of mammalian cells and is overexpressed in numerous types of cancers. These include breast cancer, ovarian cancer, prostate cancer, gastric cancer, colorectal cancer, glioma, and b-cell lymphoma. NAMPT is also known to impact the NAD and NADPH pool. Research has demonstrated that NAMPT can be inhibited. NAMPT inhibitors are diverse anticancer medicines with significant anti-tumor efficacy in ex vivo tumor models. A few notable NAMPT specific inhibitors which have been produced include FK866, CHS828, and OT-82. Despite encouraging preclinical evidence of the potential utility of NAMPT inhibitors in cancer models, early clinical trials have yielded only modest results, necessitating the adaptation of additional tactics to boost efficacy. This paper examines a number of cancer treatment methods which target NAMPT, including the usage of individual inhibitors, pharmacological combinations, dual inhibitors, and ADCs, all of which have demonstrated promising experimental or clinical results. We intend to contribute further ideas regarding the usage and development of NAMPT inhibitors in clinical therapy to advance the field of research on this intriguing target.

BackgroundMild cognitive impairment (MCI) is a common neurological disorder. Moxibustion has been shown to be effective in treating MCI, but its therapeutic mechanisms still remain unclear. This study mainly aimed to investigate the modulation effect of moxibustion treatment for patients with MCI by functional magnetic resonance imaging (fMRI).MethodsA total of 47 patients with MCI and 30 healthy controls (HCs) participated in resting-state fMRI imaging (rs-fMRI) scans. Patients with MCI were randomly divided into true moxibustion group (TRUE, n = 30) and sham moxibustion group (SHAM, n = 17). The degree centrality (DC) approach was applied to distinguish altered brain functions. Correlation analysis was then performed to examine the relationships between the neuroimaging findings and clinical symptoms.ResultsCompared with HCs, patients with MCI mainly showed decreased DC in the left middle frontal cortex (MFC) and bilateral middle cingulate cortex (MCC). After moxibustion treatment, the SHAM group had no significant DC findings, while TRUE group mainly showed significant increased DC in the bilateral MFC and MCC, as well as decreased DC in the left middle occipital cortex (MOC). Repeated measures analysis of variance (ANOVA) showed significant interactions between the two groups of patients with MCI. In addition, the higher Mini-Mental State Examination (MMSE) score was significantly positively correlated with increased DC in the right MFC and left MCC after moxibustion treatment.ConclusionOur findings demonstrate that the potential value of moxibustion treatment on MCI, which adds new insights into the popular view that moxibustion treatment may slow cognitive decline in patients with MCI.

Back groundSubjective cognitive decline (SCD) may be the first clinical sign of Alzheimer’s disease (AD). The possible neural mechanisms of SCD are not well known. This study aimed to compare percent amplitude of fluctuation (PerAF) and structural covariance patterns in patients with SCD and healthy controls (HCs).MethodsWe enrolled 53 patients with SCD and 65 HCs. Resting-state functional magnetic resonance imaging (MRI) data and T1-weighted anatomical brain 3.0-T MRI scans were collected. The PerAF approach was applied to distinguish altered brain functions between the two groups. A whole-brain voxel-based morphometry analysis was performed, and all significant regions were selected as regions of interest (ROIs) for the structural covariance analysis. Statistical analysis was performed using two-sample t-tests, and multiple regressions were applied to examine the relationships between neuroimaging findings and clinical symptoms.ResultsFunctional MRI results revealed significantly increased PerAF including the right hippocampus (HIPP) and right thalamus (THA) in patients with SCD relative to HCs. Gray matter volume (GMV) results demonstrated decreased GMV in the bilateral ventrolateral prefrontal cortex (vlPFC) and right insula in patients with SCD relative to HCs. Taking these three areas including the bilateral vlPFC and right insula as ROIs, differences were observed in the structural covariance of the ROIs with several regions between the two groups. Additionally, significant correlations were observed between neuroimaging findings and clinical symptoms.ConclusionOur study investigated the abnormal PerAF and structural covariance patterns in patients with SCD, which might provide new insights into the pathological mechanisms of SCD.

ObjectiveTo explore the effect of moxa cone moxibustion on N-acetyl aspartate/total creatinine (NAA/tCr) and choline/total creatinine (Cho/tCr) in the bilateral hippocampus (HIP) and bilateral posterior cingulate gyrus (PCG) in patients with mild cognitive impairment (MCI) using hydrogen proton magnetic resonance spectroscopy (1H-MRS) and to provide imaging basis for moxa cone moxibustion treatment for MCI.MethodsOne hundred eight patients with MCI were served as the MCI group, and 67 age-matched subjects were enrolled as the normal control group. The MCI group was randomized and allocated into acupoint group, drug group, and sham acupoint group, with 36 cases in each group. Some patients in each group withdrew. Finally, 25 cases were included in the acupoint group, 24 cases in the drug group, and 20 cases in the sham acupoint group. The drug group was treated with oral donepezil hydrochloride. The acupoint group and sham acupoint group received moxa cone moxibustion treatment. Mini-mental state exam (MMSE) and Montreal cognitive assessment (MoCA) scores were recorded before intervention, at the end of the first and the second months of intervention, and in the 5th month of follow-up. The NAA/tCr and Cho/tCr ratios in the HIP and PCG were bilaterally measured by 1H-MRS before and after intervention.ResultsBefore intervention, compared with the normal control group, the MMSE and MoCA scores, the Cho/tCr ratio in the right HIP, the NAA/tCr ratio in the bilateral HIP, and the NAA/tCr ratio in the left PCG in the three treatment groups decreased significantly (both p < 0.01), and the NAA/tCr ratio in the right PCG significantly reduced in the acupoint and drug groups (p < 0.05). After two months of treatment, compared with the normal control group, there were no differences in the MoCA scores, the NAA/tCr, and Cho/tCr ratios in the bilateral PCG and bilateral HIP in the three treatment groups (p > 0.05). However, the MMSE scores in the drug group decreased when compared with the acupoint group and normal control group (p < 0.05, p < 0.01). The scores of MMSE and MoCA in the acupoint group and sham acupoint group at all time points were better than those in the drug group, which were similar to those in the normal control group.ConclusionOur findings suggest that moxibustion could improve the cognitive function of patients with MCI. The mechanism may be related to the improvement of abnormal brain metabolism in HIP and PCG.

BackgroundMild Cognitive Impairment (MCI), as a high risk of Alzheimer’s disease (AD), represents a state of cognitive function between normal aging and dementia. Moxibustion may effectively delay the progression of AD, while there is a lack of studies on the treatments in MCI. This study aimed to evaluate the effect of moxibustion treatment revealed by the amplitude of low-frequency fluctuation (ALFF) in MCI.MethodWe enrolled 30 MCI patients and 30 matched healthy controls (HCs) in this study. We used ALFF to compare the difference between MCI and HCs at baseline and the regulation of spontaneous neural activity in MCI patients by moxibustion. The Mini-Mental State Examination and Montreal Cognitive Assessment scores were used to evaluate cognitive function.ResultsCompared with HCs, the ALFF values significantly decreased in the right temporal poles: middle temporal gyrus (TPOmid), right inferior temporal gyrus, left middle cingulate gyrus, and increased in the left hippocampus, left middle temporal gyrus, right lingual gyrus, and right middle occipital gyrus in MCI patients. After moxibustion treatment, the ALFF values notably increased in the left precuneus, left thalamus, right temporal poles: middle temporal gyrus, right middle frontal gyrus, right inferior temporal gyrus, right putamen, right hippocampus, and right fusiform gyrus, while decreased in the bilateral lingual gyrus in MCI patients. The Mini-Mental State Examination and Montreal Cognitive Assessment scores increased after moxibustion treatment, and the increase in Mini-Mental State Examination score was positively correlated with the increase of ALFF value in the right TPOmid, the right insula, and the left superior temporal gyrus.ConclusionMoxibustion treatment might improve the cognitive function of MCI patients by modulating the brain activities within the default mode network, visual network, and subcortical network with a trend of increased ALFF values and functional asymmetry of the hippocampus. These results indicate that moxibustion holds great potential in the treatment of MCI.

Abstract Background The brain’s dynamic spontaneous neural activity and dynamic functional connectivity (dFC) are both important in supporting cognition, but how these two types of brain dynamics evolve and co-evolve in subjective cognitive decline (SCD) and mild cognitive impairment (MCI) remain unclear. The aim of the present study was to investigate recurrent and concurrent patterns of two types of dynamic brain states correlated with cognitive decline. Methods The present study analyzed resting-state functional magnetic resonance imaging data from 62 SCD patients, 75 MCI patients, and 70 healthy controls (HCs). We used the sliding-window and clustering method to identify two types of recurrent brain states from both dFC and dynamic regional spontaneous activity, as measured by dynamic fractional amplitude of low-frequency fluctuations (dfALFF). Then, the occurrence frequency of a dFC or dfALFF state and the co-occurrence frequency of a pair of dFC and dfALFF states among all time points are extracted for each participant to describe their dynamics brain patterns. Results We identified a few recurrent states of dfALFF and dFC and further ascertained the co-occurrent patterns of these two types of dynamic brain states (i.e., dfALFF and dFC states). Importantly, the occurrence frequency of a default-mode network (DMN)-dominated dFC state was significantly different between HCs and SCD patients, and the co-occurrence frequencies of a DMN-dominated dFC state and a DMN-dominated dfALFF state were also significantly different between SCD and MCI patients. These two dynamic features were both significantly positively correlated with Mini-Mental State Examination scores. Conclusion Our findings revealed novel fMRI-based neural signatures of cognitive decline from recurrent and concurrent patterns of dfALFF and dFC, providing strong evidence supporting SCD as the transition phase between normal aging and MCI. This finding holds potential to differentiate SCD patients from HCs via both dFC and dfALFF as objective neuroimaging biomarkers, which may aid in the early diagnosis and intervention of Alzheimer’s disease.

ObjectiveTo develop and validate a radiomic nomogram for individualized prediction of hepatocellular carcinoma (HCC) in HBV cirrhosis patients based on baseline magnetic resonance imaging examinations and clinical data.Methods364 patients with HBV cirrhosis from five hospitals were assigned to the training, internal validation, external validation-1 or external validation-2 cohort. All patients underwent baseline magnetic resonance image (MRI) scans and clinical follow-up within three-year time. Clinical risk factors and MRI-based features were extracted and analyzed. The radiomic signatures were built using the radiomics-score (Rad-score) that calculated for each patient as a linear weighted combination of selected MRI-based features. Prognostic performances of the clinical and radiomic nomograms were evaluated with Cox modeling in the training and validation cohorts.ResultsEighteen features were selected for inclusion in the Rad-score prognostic model. The radiomic signature from multi-sequence MRI yielded a concordance index (C-index) of 0.710, 0.681, 0.632 and 0.658 in the training, internal validation, external validation-1, external validation-2 cohorts, respectively. Sex and Child-Turcotte-Pugh (CTP) class were the most prognostic clinical risk factors in univariate Cox proportional hazards analyses. The radiomic combined nomogram that integrated the radiomic signature with the clinical factors yielded a C-index of 0.746, 0.710, and 0.641 in the training, internal validation, and external validation-1 cohorts, respectively, which was an improvement over either the clinical nomogram or radiomic signature alone.ConclusionWe developed an MRI-based radiomic combined nomogram with good discrimination ability for the individualized prediction of HCC in HBV cirrhosis patients within three-year time.