[Clinical and genetic analysis of a child with X-linked intellectual developmental disorder due to a novel variant of NEXMIF gene].

OBJECTIVE: To explore the genetic basis for a child featuring facial dysmorphism and intellectual disabilities. METHODS: A child who was diagnosed at Linyi People's Hospital on January 5 2023 due to "mental retardation" was selected as the study subject. Peripheral blood samples of the child and his parents, in addition with an amniotic fluid sample from the his mother were collected for the extraction of genomic DNA. Whole exome sequencing was carried out for the child, and candidate variant was verified by Sanger sequencing of his family members. RESULTS: The child was found to harbor a hemizygous c.1123dupG (p.E375Gfs*4) variant of the NEXMIF gene, for which both of his parents and the fetus were of the wild type. Based on the guidelines from the American College of Medical Genetics and Genomics (ACMG), the variant was predicted to be pathogenic (PVS1+PS2-P+PM2-P). A healthy infant was subsequently born. CONCLUSION: The hemizygous c.1123dupG (p.E375Gfs*4) variant of the NEXMIF gene probably underlay the disease in this child. Based on his clinical phenotype and genotype, the child was ultimately diagnosed with X-linked intellectual developmental disorder-98. Above finding has also enriched the mutational spectrum of the NEXMIF gene.

Non-local degradation modeling for spatially adaptive single image super-resolution.

Existing methods for single image super-resolution (SISR) model the blur kernel as spatially invariant across the entire image, and are susceptible to the adverse effects of textureless patches. To achieve improved results, adaptive estimation of the degradation kernel is necessary. We explore the synergy of joint global and local degradation modeling for spatially adaptive blind SISR. Our model, named spatially adaptive network for blind super-resolution (SASR), employs a simple encoder to estimate global degradation representations and a decoder to extract local degradation. These two representations are fused with a cross-attention mechanism and applied using spatially adaptive filtering to enhance the local image detail. Specifically, SASR contains two novel features: (1) a non-local degradation modeling with contrastive learning to learn global and local degradation representations, and (2) a non-local spatially adaptive filtering module (SAFM) that incorporates the global degradation and spatial-detail factors to preserve and enhance local details. We demonstrate that SASR can efficiently estimate degradation representations and handle multiple types of degradation. The local representations avoid the detrimental effect of estimating the entire super-resolved image with only one kernel through locally adaptive adjustments. Extensive experiments are performed to quantitatively and qualitatively demonstrate that SASR not only performs favorably for degradation estimation but also leads to state-of-the-art blind SISR performance when compared to alternative approaches.

An integrated feature selection approach to high water stress yield prediction.

The timely and precise prediction of winter wheat yield plays a critical role in understanding food supply dynamics and ensuring global food security. In recent years, the application of unmanned aerial remote sensing has significantly advanced agricultural yield prediction research. This has led to the emergence of numerous vegetation indices that are sensitive to yield variations. However, not all of these vegetation indices are universally suitable for predicting yields across different environments and crop types. Consequently, the process of feature selection for vegetation index sets becomes essential to enhance the performance of yield prediction models. This study aims to develop an integrated feature selection method known as PCRF-RFE, with a focus on vegetation index feature selection. Initially, building upon prior research, we acquired multispectral images during the flowering and grain filling stages and identified 35 yield-sensitive multispectral indices. We then applied the Pearson correlation coefficient (PC) and random forest importance (RF) methods to select relevant features for the vegetation index set. Feature filtering thresholds were set at 0.53 and 1.9 for the respective methods. The union set of features selected by both methods was used for recursive feature elimination (RFE), ultimately yielding the optimal subset of features for constructing Cubist and Recurrent Neural Network (RNN) yield prediction models. The results of this study demonstrate that the Cubist model, constructed using the optimal subset of features obtained through the integrated feature selection method (PCRF-RFE), consistently outperformed the RNN model. It exhibited the highest accuracy during both the flowering and grain filling stages, surpassing models constructed using all features or subsets derived from a single feature selection method. This confirms the efficacy of the PCRF-RFE method and offers valuable insights and references for future research in the realms of feature selection and yield prediction studies.

Scalable Network Coding for Heterogeneous Devices over Embedded Fields.

In complex network environments, there always exist heterogeneous devices with different computational powers. In this work, we propose a novel scalable random linear network coding (RLNC) framework based on embedded fields, so as to endow heterogeneous receivers with different decoding capabilities. In this framework, the source linearly combines the original packets over embedded fields based on a precoding matrix and then encodes the precoded packets over GF(2) before transmission to the network. After justifying the arithmetic compatibility over different finite fields in the encoding process, we derive a sufficient and necessary condition for decodability over different fields. Moreover, we theoretically study the construction of an optimal precoding matrix in terms of decodability. The numerical analysis in classical wireless broadcast networks illustrates that the proposed scalable RLNC not only guarantees a better decoding compatibility over different fields compared with classical RLNC over a single field, but also outperforms Fulcrum RLNC in terms of a better decoding performance over GF(2). Moreover, we take the sparsity of the received binary coding vector into consideration, and demonstrate that for a large enough batch size, this sparsity does not affect the completion delay performance much in a wireless broadcast network.

Aortic pressure waveform reconstruction using a multi-channel Newton blind system identification algorithm.

BACKGROUND: Central aortic pressure (CAP) as the major load on the left heart is of great importance in the diagnosis of cardiovascular disease. Studies have pointed out that CAP has a higher predictive value for cardiovascular disease than peripheral artery pressure (PAP) measured by means of traditional sphygmomanometry. However, direct measurement of the CAP waveform is invasive and expensive, so there remains a need for a reliable and well validated non-invasive approach. METHODS: In this study, a multi-channel Newton (MCN) blind system identification algorithm was employed to noninvasively reconstruct the CAP waveform from two PAP waveforms. In simulation experiments, CAP waveforms were recorded in a previous study, on 25 patients and the PAP waveforms (radial and femoral artery pressure) were generated by FIR models. To analyse the noise-tolerance of the MCN method, variable amounts of noise were added to the peripheral signals, to give a range of signal-to-noise ratios. In animal experiments, central aortic, brachial and femoral pressure waveforms were simultaneously recorded from 2 Sprague-Dawley rats. The performance of the proposed MCN algorithm was compared with the previously reported cross-relation and canonical correlation analysis methods. RESULTS: The results showed that the root mean square error of the measured and reconstructed CAP waveforms and less noise-sensitive using the MCN algorithm was smaller than those of the cross-relation and canonical correlation analysis approaches. CONCLUSION: The MCN method can be exploited to reconstruct the CAP waveform. Reliable estimation of the CAP waveform from non-invasive measurements may aid in early diagnosis of cardiovascular disease.

Entropy Weight Ensemble Framework for Yield Prediction of Winter Wheat Under Different Water Stress Treatments Using Unmanned Aerial Vehicle-Based Multispectral and Thermal Data.

Crop breeding programs generally perform early field assessments of candidate selection based on primary traits such as grain yield (GY). The traditional methods of yield assessment are costly, inefficient, and considered a bottleneck in modern precision agriculture. Recent advances in an unmanned aerial vehicle (UAV) and development of sensors have opened a new avenue for data acquisition cost-effectively and rapidly. We evaluated UAV-based multispectral and thermal images for in-season GY prediction using 30 winter wheat genotypes under 3 water treatments. For this, multispectral vegetation indices (VIs) and normalized relative canopy temperature (NRCT) were calculated and selected by the gray relational analysis (GRA) at each growth stage, i.e., jointing, booting, heading, flowering, grain filling, and maturity to reduce the data dimension. The elastic net regression (ENR) was developed by using selected features as input variables for yield prediction, whereas the entropy weight fusion (EWF) method was used to combine the predicted GY values from multiple growth stages. In our results, the fusion of dual-sensor data showed high yield prediction accuracy [coefficient of determination (R 2) = 0.527-0.667] compared to using a single multispectral sensor (R 2 = 0.130-0.461). Results showed that the grain filling stage was the optimal stage to predict GY with R 2 = 0.667, root mean square error (RMSE) = 0.881 t ha-1, relative root-mean-square error (RRMSE) = 15.2%, and mean absolute error (MAE) = 0.721 t ha-1. The EWF model outperformed at all the individual growth stages with R 2 varying from 0.677 to 0.729. The best prediction result (R 2 = 0.729, RMSE = 0.831 t ha-1, RRMSE = 14.3%, and MAE = 0.684 t ha-1) was achieved through combining the predicted values of all growth stages. This study suggests that the fusion of UAV-based multispectral and thermal IR data within an ENR-EWF framework can provide a precise and robust prediction of wheat yield.

Hematological changes in patients with COVID­19 (Review).

In December 2019, an emergence of pneumonia was detected in patients infected with a novel coronavirus (CoV) in Wuhan (Hubei, China). The International Committee on Taxonomy of Viruses named the virus severe acute respiratory syndrome­CoV­2 and the disease CoV disease­19 (COVID­19). Patients with COVID­19 present with symptoms associated with respiratory system dysfunction and hematological changes, including lymphopenia, thrombocytopenia and coagulation disorders. However, to the best of our knowledge, the pathogenesis of COVID­19 remains unclear. Therefore, understanding the mechanisms underlying the hematological changes that manifest during COVID­19 may aid in the development of treatments and may improve patient prognosis.

[Thrombopoietin promotes megakaryopoiesis via protecting bone marrow endothelial function in patients undergoing chemotherapy for hematological malignancies].

OBJECTIVE: To explore whether thrombopoietin (TPO) can rescue megakaryopoiesis by protecting bone marrowderived endothelial progenitor cells (BM-EPCs) in patients receiving chemotherapy for hematological malignancies. METHODS: Bone marrow samples were collected from 23 patients with hematological malignancies 30 days after chemotherapy and from 10 healthy volunteers. BM-EPCs isolated from the samples were identified by staining for CD34, CD309 and CD133, and their proliferation in response to treatment with TPO was assessed using CCK8 assay. DiL-Ac-LDL uptake and FITC-UEA-I binding assay were performed to evaluate the amount of BM-EPCs from the subjects. Tube-formation and migration experiments were used for functional assessment of the BM-EPCs. The BM-EPCs with or without TPO treatment were co-cultured with human megakaryocytes, and the proliferation of the megakaryocytes was detected with flow cytometry. RESULTS: Flow cytometry indicated that the TPO-treated cells had high expressions of CD34, CD133, and CD309. CCK8 assay demonstrated that TPO treatment enhanced the proliferation of the BM-EPCs, and the optimal concentration of TPO was 100 µg/L. Double immunofluorescence assay indicated that the number of BM-EPC was significantly higher in TPO-treated group than in the control group. The TPO-treated BM-EPCs exhibited stronger tube-formation and migration abilities (P < 0.05) and more significantly enhanced the proliferation of co-cultured human megakaryocytes than the control cells (P < 0.05). CONCLUSIONS: TPO can directly stimulate megakaryopoiesis and reduce hemorrhage via protecting the function of BM-EPCs in patients following chemotherapy for hematological malignancies.

Mechanisms involved in the development of thrombocytopenia in patients with COVID-19.

Corona Virus Disease 2019 (COVID-19) is caused by the novel coronavirus SARS-CoV-2. Emerging genetic and clinical evidence suggests similarities between COVID-19 patients and those with severe acute respiratory syndrome and Middle East respiratory syndrome. Hematological changes such as lymphopenia and thrombocytopenia are not rare in COVID-19 patients, and a smaller population of these patients had leukopenia. Thrombocytopenia was detected in 5-41.7% of the patients with COVID-19. Analyzing the dynamic decrease in platelet counts may be useful in the prognosis of patients with COVID-19. However, the mechanisms underlying the development of thrombocytopenia remain to be elucidated. This review summarizes the hematological changes in patients infected with SARS-CoV-2 and possible underlying mechanisms of thrombocytopenia development.

Exact and heuristic algorithms for Space Information Flow.

Space Information Flow (SIF) is a new promising research area that studies network coding in geometric space, such as Euclidean space. The design of algorithms that compute the optimal SIF solutions remains one of the key open problems in SIF. This work proposes the first exact SIF algorithm and a heuristic SIF algorithm that compute min-cost multicast network coding for N (N ≥ 3) given terminal nodes in 2-D Euclidean space. Furthermore, we find that the Butterfly network in Euclidean space is the second example besides the Pentagram network where SIF is strictly better than Euclidean Steiner minimal tree. The exact algorithm design is based on two key techniques: Delaunay triangulation and linear programming. Delaunay triangulation technique helps to find practically good candidate relay nodes, after which a min-cost multicast linear programming model is solved over the terminal nodes and the candidate relay nodes, to compute the optimal multicast network topology, including the optimal relay nodes selected by linear programming from all the candidate relay nodes and the flow rates on the connection links. The heuristic algorithm design is also based on Delaunay triangulation and linear programming techniques. The exact algorithm can achieve the optimal SIF solution with an exponential computational complexity, while the heuristic algorithm can achieve the sub-optimal SIF solution with a polynomial computational complexity. We prove the correctness of the exact SIF algorithm. The simulation results show the effectiveness of the heuristic SIF algorithm.

Prognostic Significance of TNFR-Associated Factor 1 and 2 (TRAF1 and TRAF2) in Glioblastoma.

BACKGROUND TNFR-associated factor 1 (TRAF1) and TRAF2 have been demonstrated to inhibit apoptosis and promote cell survival in glioblastoma (GBM) cells with experiments in vitro. However, their clinical and prognostic significance have not been elucidated. MATERIAL AND METHODS In our study, we for the first time investigated the expression of TRAF1 and TRAF2 in 105 GBM tissues. Furthermore, we evaluated their clinical significance, including their association with clinicopathologic factors and prognostic value. The association with clinicopathologic factors was assessed by chi-square test. The relation of TRAF1/2 expression to survival rate was assessed by Kaplan-Meier method and Cox-regression model. RESULTS We demonstrated that TRAF1 expression had no significant prognostic value for GBM. On the contrary, high expression of TRAF2 can predict poorer prognosis of GBM and was identified as an independent biomarker in GBM prognosis. CONCLUSIONS High expression of TRAF2 was identified as an independent biomarker in GBM prognosis, indicating TRAF2 as a novel drug target in GBM treatment.

Antagonistic effects of lycopene on cadmium-induced hippocampal dysfunctions in autophagy, calcium homeostatis and redox.

Cadmium (Cd), a widely existed environmental contaminant, was shown to trigger neurotoxicity by regulating autophagy, ion homeostasis and redox. Lycopene (LYC) is a natural substance with potent antioxidant capacity. Nevertheless, little is known about i) the relationship of Cd-induced neurotoxicity and autophagy, ion homeostasis as well as redox in the hippocampus; ii) the role of LYC in the regulation of hippocampal autophagy, ionic balance and antioxidant capacity during Cd exposure. Therefore, this study sought to investigate the Cd exposure-induced hippocampal dysfunctions for neurotoxicity, and the preventive potential of LYC on the hippocampus impairment by reversing the dysfunctions during the exposure. In vivo study with mice model demonstrated that Cd exposure increased gene expression of a wide spectrum of autophagy-related gene (ATG) and gene regulating autophagy in hippocampus. This suggests the activation of hippocampal autophagy mediated by Cd. Cd exposure also decreased Ca2+-ATPase activity, thus increasing intracellular Ca2+ concentration in hippocampus, indicating the possibility that Cd-induced autophagy requires the Ca2+ signaling. Moreover, Cd exposure triggered redox stress in hippocampus cells, as antioxidant enzyme activities were decreased while oxidative productions were promoted. Cd exposure led to severe cytotoxicity in hippocampus cells. Of important note, all the hippocampal dysfunctions upon Cd exposure were reversed by LYC treatment to normal situations, and exposure-induced neurotoxicity was abrogated. The in vivo findings were recapitulated relevantly in the mouse hippocampal neuronal cell line, TH22. In all, the above data imply that LYC could be a potent therapeutic agent in treating Cd-triggered hippocampal dysfunctions and subsequent cell damage.

Expression of Inorganic Pyrophosphatase (PPA1) Correlates with Poor Prognosis of Epithelial Ovarian Cancer.

Ovarian serous carcinoma (OSC) is the most common epithelial ovarian cancer. Inorganic pyrophosphatase (PPA1) catalyzes the hydrolysis of pyrophosphate to inorganic phosphate, thereby providing extra energy for metabolism. The significance of PPA1 in the prognosis of OSC has not been investigated. Our study aimed to explore the expression and predictive role of PPA1 in OSC progression. We screened the expression of PPA1 protein in OSC tissues from 139 patients by immunohistochemistry, and evaluated its correlation with clinicopathological characteristics. PPA1 was categorized as high expression in 58 OSC cases (41.7%), which was correlated with poor differentiation, positive lymph node (LN) metastasis and advanced FIGO (The International Federation of Gynecology and Obstetrics) stages. Univariate and multivariate analyses identified PPA1 as a novel independent prognostic biomarker in OSC patients; meanwhile, conventional factors such as LN status and FIGO stages also showed statistical significance. Moreover, the expression levels of PPA1 protein were higher in A2780 and OVCAR3 human ovarian cancer cell lines than those in normal ovarian surface epithelial cells. Using these ovarian cancer cell lines, we showed that PPA1 overexpression caused the decrease in the expression level of p53, the tumor suppressor, with the increase in ß-catenin level, as determined by Western blot analysis. Conversely, knockdown of PPAI expression was associated with the increase of p53 level and the decreased of ß-catenin level. Consistently, the proliferation and invasion capacities of ovarian cancer cells were enhanced upon PPA1 overexpression. In conclusion, PPA1 serves as a potential prognostic biomarker for patients with OSC.

Cryopreservation of whole bovine ovaries: comparisons of different thawing protocols.

OBJECTIVE: The aim of this study was to perform a comparative investigation of several different thawing protocols and to determine an appropriate protocol for thawing whole bovine frozen ovaries. STUDY DESIGN: Bovine ovaries were slowly frozen and then thawed by applying different protocols. Ultrastructural change, follicle viability, and the hormone levels of culture supernatant were measured. RESULTS: The percentage of morphologically normal primordial follicles and the hormone levels of culture supernatant in group D (two-step, thawing in water at 39°C) were significantly higher than those in any other group. Moreover, the ultrastructural alteration of oocyte in group D (two-step, thawing in water at 39°C) was slighter than those in any other group. CONCLUSIONS: The two-step protocol involving short-term exposure to water at a moderately high temperature (39°C) proved to be a suitable for thawing bovine whole ovaries.

Expression of G Protein-coupled Receptor 56 Is an Unfavorable Prognostic Factor in Osteosarcoma Patients.

G protein-coupled receptor 56 (GPR56) is an atypical G protein-coupled receptor, with the long extracellular N-terminus. GPR56 can trigger various downstream signaling responsible for cell survival, proliferation, adhesion, and migration. Expression of GPR56 is associated with cell malignant transformation and tumor cell metastasis in several carcinomas such as melanoma and glioma. Osteosarcoma is the most common malignant bone tumor in adolescents and young adults with high metastasis tendency. The overall survival of osteosarcoma is unsatisfied, partially due to the lacking of predictive markers for metastasis and overall prognosis. This study aimed at figuring out whether expression of the GPR56 was associated with clinicopathological features of osteosarcoma. Eighty-nine patients who received osteosarcoma operation between March 2004 and February 2011 in Linyi People's Hospital were recruited. Immunohistochemical staining (IHC) was carried out to identify the expression of GPR56 in those osteosarcoma tissues, and our cohort was divided into higher-expression group and lower-expression group according to the cut-off of IHC score. Expression of GPR56 in osteosarcoma tissues was correlated with the TNM stage and overall survival. Univariate and multivariate analysis showed that GPR56 could act as an independent prognosis factor for osteosarcoma. Western blot results demonstrated that GPR56-siRNA down-regulated the expression of GTP-RhoA and Ki67. GTP-RhoA participates in the cell migration process, while Ki67 plays important roles in cell proliferation, indicating GPR56 may function in tumor development. Correspondingly, we show that GPR56 regulates the proliferation and invasion capacity of osteosarcoma cells. Our study has revealed the prognostic value of GPR56 expression in osteosarcoma.

[The Near Infrared Spectral Bands Optimal Selection in the Application of Liquor Fermented Grains Composition Analysis].

In order to improve the technical level of the rapid detection of liquor fermented grains, in this paper, use near infrared spectroscopy technology to quantitative analysis moisture, starch, acidity and alcohol of liquor fermented grains. Using CARS, iPLS and no information variable elimination method (UVE), realize the characteristics of spectral band selection. And use the multiple scattering correction (MSC), derivative and standard normal variable transformation (SNV) pretreatment method to optimize the models. Establish models of quantitative analysis of fermented grains by PLS, and in order to select the best modeling method, using R2, RMSEP and optimal number of main factors to evaluate models. The results showed that the band selection is vital to optimize the model and CARS is the best optimization of the most significant effect. The calculation results showed that R2 of moisture, starch, acidity and alcohol were 0.885, 0.915, 0.951, 0.885 respectively and RMSEP of moisture, starch, acidity and alcohol were 0.630, 0.519, 0.228, 0.234 respectively. After optimization, the model prediction effect is good, the models can satisfy the requirement of the rapid detection of liquor fermented grains, which has certain reference value in the practical.

Microinjection of calcitonin in midbrain periaqueductal gray attenuates hyperalgesia in a chronic constriction injury rat model.

OBJECTIVES: As heat, pain is one of the most common clinical symptoms. Generally, calcitonin (CT) is prescribed as an analgesic agent for the treatment of pain, especially for the pain caused by osteoporosis or primary and metastatic bone tumor. However, the detailed mechanism remains unknown. MATERIALS AND METHODS: In this study, chronic constriction injury (CCI) rat model was created, and hot plate test and von frey filaments test were employed to evaluate thermal withdrawal latency (TWL) and mechanical withdrawal threshold (MWT), respectively. Immunohistochemistry staining and western blot analyses were used to assess the distribution and expression of calcitonin receptor (CT-R) in the midbrain periaqueductal gray (PAG), which was a pivotal site in the modulatory system for the endogenous pain. RESULTS: The TWL and MWT before the surgery (19.6±1.19 sec) were significantly longer than that at day 2 (12.5±1.60 sec), and day 14 (7.75±0.89 sec) (P< 0.01; P< 0.01), respectively. The TWL and MWT at day 14 were significantly increased compared to that at day 7 after microinjection of salmon calcitonin (sCT) with different doses. Interestingly, the expression of CT-R was up-regulated in neuropathic pain. Furthermore, the expression of CT-R was significantly up-regulated and algesia was remarkably relieved when CT was microinjected into PAG. CONCLUSION: These results suggested that an increased CT-R might be associated with hyperalgesia in CCI rat, and CT had a potent antinociceptive effect by the up-regulation of CT-R in the PAG. Thus, it might provide a potential approach for the treatment of bone pain.