SHMC-Net: A Mask-guided Feature Fusion Network for Sperm Head Morphology Classification

Male infertility accounts for about one-third of global infertility cases. Manual assessment of sperm abnormalities through head morphology analysis encounters issues of observer variability and diagnostic discrepancies among experts. Its alternative, Computer-Assisted Semen Analysis (CASA), suffers from low-quality sperm images, small datasets, and noisy class labels. We propose a new approach for sperm head morphology classification, called SHMC-Net, which uses segmentation masks of sperm heads to guide the morphology classification of sperm images. SHMC-Net generates reliable segmentation masks using image priors, refines object boundaries with an efficient graph-based method, and trains an image network with sperm head crops and a mask network with the corresponding masks. In the intermediate stages of the networks, image and mask features are fused with a fusion scheme to better learn morphological features. To handle noisy class labels and regularize training on small datasets, SHMC-Net applies Soft Mixup to combine mixup augmentation and a loss function. We achieve state-of-the-art results on SCIAN and HuSHeM datasets, outperforming methods that use additional pre-training or costly ensembling techniques.Comment: Published on ISBI 202

A cotton leaf nitrogen monitoring model based on spectral-fluorescence data fusion

In the present study, hyperspectral imaging and remote sensing of fluorescence were integrated to monitor the nitrogen content in leaves of drip-irrigated cotton at different growth periods in northern Xinjiang, China. Based on the spectrum and chlorophyll fluorescence parameters of nitrogen content in cotton leaves of different growth periods obtained through the shuffled frog-leaping algorithm (SFLA), the successive projection algorithm (SPA), grey relational analysis (GRA), and competitive adaptive reweighted sampling (CARS), a monitoring model of nitrogen content in cotton leaves was established via on hyperspectral imaging, chlorophyll fluorescence parameters, and spectral-fluorescence data fusion. The results showed that: (1) there were significant positive correlations between the chlorophyll fluorescence parameters Fv'/Fm', Fv/Fm, Yield, Fm, NPQ, and the nitrogen content at each growth period. (2) The effectiveness of chlorophyll fluorescence parameters in inversion of nitrogen content was the highest at the budding period and the blooming period, and the coefficients of determination (R2) of the validation sets were 0.745 and 0.709, respectively. (3) In the monitoring model for cotton leaf nitrogen in the blooming period that was established based on the decision-level algorithm and spectral-fluorescence data fusion, the R2 value of the training set reached 0.961, and that of the validation set was 0.828. In conclusion, the findings of this study suggest that the feature-level fusion and decision-level fusion algorithms of spectral-fluorescence data can effectively improve the accuracy and reliability of cotton leaf nitrogen monitoring

A study on cotton yield prediction based on the chlorophyll fluorescence parameters of upper leaves

The early and accurate monitoring of crop yield is important for field management, storage needs, and cash flow budgeting. Traditional cotton yield measurement methods are time-consuming, labor-intensive, and subjective. Chlorophyll fluorescence signals originate from within the plant and have the advantages of being fast and non-destructive, and the relevant parameters can reflect the intrinsic physiological characteristics of the plant. Therefore, in this study, the top four functional leaves of cotton plants at the beginning of the flocculation stage were used to investigate the pattern of the response of chlorophyll fluorescence parameters (e.g., F0, Fm, Fv/F0, and Fv/Fm) to nitrogen, and the cumulative fluorescence parameters were constructed by combining them with the leaf area index to clarify the correlation between chlorophyll fluorescence parameters and cotton yield. Support vector machine regression (SVM), an artificial neural network (BP), and an XGBoost regression tree were used to establish a cotton yield prediction model. Chlorophyll fluorescence parameters showed the same performance as photosynthetic parameters, which decreased as leaf position decreased. It showed a trend of increasing and then decreasing with increasing N application level, reaching the maximum value at 240 kg·hm-2 of N application. The correlation between fluorescence parameters and yield in the first, second, and third leaves was significantly higher than that in the fourth leaf, and the correlation between fluorescence accumulation and yield in each leaf was significantly higher than that of the fluorescence parameters, with the best performance of Fv/Fm accumulation found in the second leaf. The correlation between Fv/Fm accumulation and yield in the top three leaves combined was significantly higher than that in the top four leaves. The correlation coefficient between Fv/Fm accumulation and yield was the highest, indicating the feasibility of applying chlorophyll fluorescence to estimate yield. Based on the machine learning algorithm used to construct a cotton yield prediction model, the estimation models of Fv/F0 accumulation and yield of the top two leaves combined as well as top three leaves combined were superior. The estimation model coefficient of determination of the top two leaves combined in the BP algorithm was the highest. In general, the Fv/F0 accumulation of the top two leaves combined could more reliably predict cotton yield, which could provide technical support for cotton growth monitoring and precision management

Evaluation of circadian rhythms in depression by using actigraphy: a systematic review and meta-analysis

Objective·To systematically review the effectiveness of actigraphy on the evaluation of circadian rhythm characteristics in patients with depression.Methods·A systematic literature search was conducted in PubMed, Embase, Web of Science, Cochrane Library, PsycINFO, CNKI, WanFang Data, and Chinese biomedical literature database (CBM), from the inception of each database to May 5th, 2023. Case control studies that used actigraphy to evaluate circadian rhythms in patients with depression and compared them with healthy controls were collected. Literature was screened according to the inclusion and exclusion criteria, and the quality of the included literature was evaluated by using the Newcastle-Ottawa Scale. The meta-analysis was performed by using RevMan 5.4 software.Results·A total of 9 articles were included, including 390 patients with depression and 288 healthy controls. The meta-analysis showed that the MESOR (midline statistic of rhythm) (SMD=-0.29, 95% CI -0.51 ‒ -0.07, P=0.009) of the circadian cosine function in patients with depression was lower than that in healthy controls; sleep onset (MD=33.06, 95% CI 14.90 ‒ 51.23, P=0.000) and sleep offset (MD=53.80, 95% CI 22.38 ‒ 85.23, P=0.000) were later in patients with depression than those in healthy controls; no statistical difference was found in the activity level of the most active 10 hours (SMD=-0.26, 95% CI -0.52 ‒ 0.01, P=0.060) between patients with depression and healthy controls, although there was a trend for lower activity in patients with depression; no statistical difference was found in the acrophase (MD=25.33, 95% CI -12.41 ‒ 63.06, P=0.190) of the circadian cosine function between patients with depression and healthy controls; no clear statistical significance of the difference was found in the amplitude (SMD=-0.14, 95% CI -0.42 ‒ 0.14, P=0.340) and the activity level of the least active 5 hours (SMD=0.31, 95% CI -0.10 ‒ 0.71, P=0.140) between patients with depression and healthy controls.Conclusion·Actigraphy can reflect circadian rhythm disruption in patients with depression to some extent, but the limited number of included studies and inconsistencies in the study populations and methodologies have affected the quality and results of the analyses. More high-quality clinical trials are needed to provide evidence

Long-lived quantum memory enabling atom-photon entanglement over 101 km telecom fiber

Long-distance entanglement distribution is the key task for quantum networks, enabling applications such as secure communication and distributed quantum computing. Here we report on novel developments extending the reach for sharing entanglement between a single $^{87}$Rb atom and a single photon over long optical fibers. To maintain a high fidelity during the long flight times through such fibers, the coherence time of the single atom is prolonged to 7 ms by applying a long-lived qubit encoding. In addition, the attenuation in the fibers is minimized by converting the photon's wavelength to the telecom S-Band via polarization-preserving quantum frequency conversion. This enables to observe entanglement between the atomic quantum memory and the emitted photon after passing 101 km of optical fiber with a fidelity better than 70.8$\pm$2.4%. The fidelity, however, is no longer reduced due to loss of coherence of the atom or photon but in the current setup rather due to detector dark counts, showing the suitability of our platform to realize city-to-city scale quantum network links.Comment: 11 pages, 8 figures, comments are welcom

Cardiospecific Overexpression of ATPGD1 (Carnosine Synthase) Increases Histidine Dipeptide Levels and Prevents Myocardial Ischemia Reperfusion Injury

Background: Myocardial ischemia reperfusion (I/R) injury is associated with complex pathophysiological changes characterized by pH imbalance, the accumulation of lipid peroxidation products acrolein and 4-hydroxy trans-2-nonenal, and the depletion of ATP levels. Cardioprotective interventions, designed to address individual mediators of I/R injury, have shown limited efficacy. The recently identified enzyme ATPGD1 (Carnosine Synthase), which synthesizes histidyl dipeptides such as carnosine, has the potential to counteract multiple effectors of I/R injury by buffering intracellular pH and quenching lipid peroxidation products and may protect against I/R injury. Methods and Results: We report here that β-alanine and carnosine feeding enhanced myocardial carnosine levels and protected the heart against I/R injury. Cardiospecific overexpression of ATPGD1 increased myocardial histidyl dipeptides levels and protected the heart from I/R injury. Isolated cardiac myocytes from ATPGD1-transgenic hearts were protected against hypoxia reoxygenation injury. The overexpression of ATPGD1 prevented the accumulation of acrolein and 4-hydroxy trans-2-nonenal-protein adducts in ischemic hearts and delayed acrolein or 4-hydroxy trans-2-nonenal-induced hypercontracture in isolated cardiac myocytes. Changes in the levels of ATP, high-energy phosphates, intracellular pH, and glycolysis during low-flow ischemia in the wild-type mice hearts were attenuated in the ATPGD1-transgenic hearts. Two natural dipeptide analogs (anserine and balenine) that can either quench aldehydes or buffer intracellular pH, but not both, failed to protect against I/R injury. Conclusions: Either exogenous administration or enhanced endogenous formation of histidyl dipeptides prevents I/R injury by attenuating changes in intracellular pH and preventing the accumulation of lipid peroxidation derived aldehydes

Exploring disease interrelationships in older inpatients: a single-centre, retrospective study

BackgroundComorbidity is a common phenomenon in the older population; it causes a heavy burden on societies and individuals. However, the relevant evidence, especially in the southwestern region of China, is insufficient.ObjectivesWe aimed to examine current comorbidity characteristics as well as correlations among diseases in individuals aged >60 years.DesignRetrospective study.MethodsWe included records of 2,995 inpatients treated at the Gerontological Department of Sichuan Geriatric Hospital from January 2018 to February 2022. The patients were divided into groups according to sex and age. Diseases were categorised based on the International Classification of Diseases and their Chinese names. We calculated the age-adjusted Charlson Comorbidity Index (ACCI), categorised diseases using the China Health and Retirement Longitudinal Study questionnaire, and visualised comorbidity using web graphs and the Apriori algorithm.ResultsThe ACCI was generally high, and it increased with age. There were significant differences in the frequency of all diseases across age groups, especially in individuals aged ≥90 years. The most common comorbid diseases were liver diseases, stomach or other digestive diseases, and hypertension. Strong correlations between the most common digestive diseases and hypertension were observed.ConclusionOur findings provide insights into the current situation regarding comorbidity and the correlations among diseases in the older population. We expect our findings to inform future research directions as well as policies regarding general clinical practice and public health, especially for medical consortiums