Low-Cost Efficient Magnetic Adsorbent for Phosphorus Removal from Water

Adsorption using magnetic adsorbents makes the phosphorus removal from water simple and efficient. However, most of the reported magnetic adsorbents use chemically synthesized nanoparticles as magnetic cores, which are expensive and environmentally unfriendly. Replacing the nanomagnetic cores by cheap and green magnetic materials is essential for the wide application of this technique. In this paper, coal-fly-ash magnetic spheres (MSs) were processed to produce a cheap and eco-friendly magnetic core. A magnetic adsorbent, ZrO2 coated ball-milled MS (BMS@ZrO2), was prepared through a simple chemical precipitation method. Careful structural investigations indicate that a multipore structural amorphous ZrO2 layer has grown on the MS core. The specific surface area of BMS@ZrO2 is 48 times larger than that of the MS core. The highest phosphorus adsorption is tested as 16.47 mg g-1 at pH = 2. The BMS@ZrO2 adsorbent has a saturation magnetization as high as 33.56 emu g-1, enabling efficient magnetic separation. Zeta potential measurements and X-ray photoelectron spectroscopy analysis reveal that the phosphorus adsorption of BMS@ZrO2 is triggered by the electrostatic attraction and the ligand exchange mechanism. The BMS@ZrO2 adsorbent could be reused several times after proper chemical treatment

Mlsp : A bioinformatics tool for predicting molecular subtypes and prognosis in patients with breast cancer

The molecular landscape in breast cancer is characterized by large biological heterogeneity and variable clinical outcomes. Here, we performed an integrative multi-omics analysis of patients diagnosed with breast cancer. Using transcriptomic analysis, we identified three subtypes (cluster A, cluster B and cluster C) of breast cancer with distinct prognosis, clinical features, and genomic alterations: Cluster A was asso-ciated with higher genomic instability, immune suppression and worst prognosis outcome; cluster B was associated with high activation of immune-pathway, increased mutations and middle prognosis out-come; cluster C was linked to Luminal A subtype patients, moderate immune cell infiltration and best prognosis outcome. Combination of the three newly identified clusters with PAM50 subtypes, we pro-posed potential new precision strategies for 15 subtypes using L1000 database. Then, we developed a robust gene pair (RGP) score for prognosis outcome prediction of patients with breast cancer. The RGP score is based on a novel gene-pairing approach to eliminate batch effects caused by differences in heterogeneous patient cohorts and transcriptomic data distributions, and it was validated in ten cohorts of patients with breast cancer. Finally, we developed a user-friendly web-tool (https://sujiezhulab.shi-nyapps.io/BRCA/) to predict subtype, treatment strategies and prognosis states for patients with breast cancer.(c) 2022 The Authors. Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY-NC-ND license (http://creative-commons.org/licenses/by-nc-nd/4.0/).Peer reviewe

Overexpression of EPHB4 Is associated with poor survival of patients with gastric cancer

Background: Increased expression of erythropoietin-producing human hepatoma (EPHB4) leads to enhanced cell migration, growth and adhesion in tumor cells. However, little is known regarding the effects of EPHB4 in gastric cancer. The present study aimed to examine the clinical relevance of EPHB4 and its association with the prognosis of gastric cancer. Materials and Methods: EPHB4 transcript expression in 324 gastric cancer samples with paired adjacent normal gastric tissues was determined using quantitative polymerase chain reaction and the results were statistically analyzed against patient clinicopathological data. AGS and HGC27 cell lines were transfected with EPHB4 siRNA and the effects examined by functional analysis. Results: EPHB4 mRNA levels in gastric cancer tissues were significantly elevated when compared to non-cancerous tissues (p=0.0110). Tissue samples from male patients exhibited lower expression than those from female patients (p=0.0110). Non-cardiac gastric tumors (fundus, corpus and pylorus) expressed a higher number of EPHB4 transcripts in comparison to cardiac gastric tumors (p<0.001). Increased expression of EPHB4 was significantly associated with poorer overall (p=0.0051) and progression-free (p=0.0262) survival. EPHB4 knockdown appeared to reduce post-wound migration of AGS cells (p=0.0057) and increase migration of HGC27 cells (p=0.0337). EPHB4 knockdown significantly increased adhesive ability in HGC27 (p<0.0001). Conclusion: The expression of EPHB4 was increased in gastric cancer and increased EPHB4 expression was correlated with poor survival. Knockdown of EPHB4 promoted adhesion and exerted diverse effects on migration of gastric cancer cells. Further investigations may highlight its predictive and therapeutic potential in gastric cancer

EphB2 represents an independent prognostic marker in patients with gastric cancer and promotes tumour cell aggressiveness

Dysregulated expression of ephrin type-B receptor 2 (EphB2) has been linked with development and progression of solid tumours. In the current study we attempted to investigate the clinical relevance in GC and the effect of EphB2 expression on gastric cancer (GC) cells. EphB2 protein levels in GC and benign gastric tissues were determined using immunohistochemistry. EphB2 transcript expression in a GC cohort with GC tissue samples (n=171) and paired adjacent normal gastric tissues (n=97) was determined using qPCR. The EphB2 expression was over-activated using a CRISPR activator for the investigation of its cellular function. The expression levels of the EphB2 protein in the tumour tissues of tissue arrays were higher than the benign non-cancerous gastric tissues (P<0.05). EphB2 mRNA expression in GC tissues was also significantly elevated when compared with adjacent non-cancerous tissues (P<0.01). EphB2 activation promoted the migration and invasion abilities of the GC cell lines (P<0.01, respectively). In contrast, EphB2 activation significantly decreased the adhesion in GC cells (P<0.0001, respectively). The enrichment analysis of the correlated genes in a GC cohort indicates that EphB2 may function through mediating the cytokine-cytokine interaction, JAK-STAT and TP53 signaling pathways. In conclusion, EphB2 represents as a novel independent prognostic marker in GC. And activation of the EphB2 gene expression elevated the levels of migration and invasion, but suppressed adhesion of GC cells, indicating that EphB2 may act as a tumour promotor in GC. Our findings thus provide fundamental evidence for the consideration of the therapeutic potential of targeting EphB2 in GC

The Volume of Hippocampal Subfields in Relation to Decline of Memory Recall Across the Adult Lifespan

Background: The hippocampus is an important limbic structure closely related to memory function. However, few studies have focused on the association between hippocampal subfields and age-related memory decline. We investigated the volume alterations of hippocampal subfields at different ages and assessed the correlations with Immediate and Delayed recall abilities.Materials and Methods: A total of 275 participants aged 20–89 years were classified into 4 groups: Young, 20–35 years; Middle-early, 36–50 years; Middle-late, 51–65 years; Old, 66–89 years. All data were acquired from the Dallas Lifespan Brain Study (DLBS). The volumes of hippocampal subfields were obtained using Freesurfer software. Analysis of covariance (ANCOVA) was performed to analyze alterations of subfield volumes among the 4 groups, and multiple comparisons between groups were performed using the Bonferroni method. Spearman correlation with false discovery rate correction was used to investigate the relationship between memory recall scores and hippocampal subfield volumes.Results: Apart from no significant difference in the left parasubiculum (P = 0.269) and a slight difference in the right parasubiculum (P = 0.022), the volumes of other hippocampal subfields were significantly different across the adult lifespan (P &lt; 0.001). The hippocampal fissure volume was increased in the Old group, while volumes for other subfields decreased. In addition, Immediate recall scores were associated with volumes of the bilateral molecular layer, granule cell layer of the dentate gyrus (GC-DG), cornus ammonis (CA) 1, CA2/3, CA4, left fimbria and hippocampal amygdala transition area (HATA), and right fissure (P &lt; 0.05). Delayed recall scores were associated with the bilateral molecular layer, GC-DG, CA2/3 and CA4; left tail, presubiculum, CA1, subiculum, fimbria and HATA (P &lt; 0.05).Conclusion: The parasubiculum volume was not significantly different across the adult lifespan, while atrophy in dementia patients in some studies. Based on these findings, we speculate that volume changes in this region might be considered as a biomarker for dementia disorders. Additionally, several hippocampal subfield volumes were significantly associated with memory scores, further highlighting the key role of the hippocampus in age-related memory decline. These regions could be used to assess the risk of memory decline across the adult lifespan

Ultralight vector dark matter search using data from the KAGRA O3GK run

Among the various candidates for dark matter (DM), ultralight vector DM can be probed by laser interferometric gravitational wave detectors through the measurement of oscillating length changes in the arm cavities. In this context, KAGRA has a unique feature due to differing compositions of its mirrors, enhancing the signal of vector DM in the length change in the auxiliary channels. Here we present the result of a search for U(1)B−L gauge boson DM using the KAGRA data from auxiliary length channels during the first joint observation run together with GEO600. By applying our search pipeline, which takes into account the stochastic nature of ultralight DM, upper bounds on the coupling strength between the U(1)B−L gauge boson and ordinary matter are obtained for a range of DM masses. While our constraints are less stringent than those derived from previous experiments, this study demonstrates the applicability of our method to the lower-mass vector DM search, which is made difficult in this measurement by the short observation time compared to the auto-correlation time scale of DM

Landscape changes and their ecological effects of Miaodao Archipelago with human disturbances and under natural conditions in the past 30 years

The modern landscape patterns of islands usually show obvious spatial heterogeneity and complex ecological effects due to the vulnerability of ecosystems with natural characteristics under increasing human activities. In this work, we studied the variation in landscape pattern of the Miaodao Archipelago in Bohai Sea, North China, from 1990 to 2019, and an evaluation index system was established to explore the impacts of natural conditions and human disturbances on the ecological effects in the pressure-state-response (PSR) framework. Empirical analysis was conducted on the natural conditions, human disturbances, and ecological effects. The results show that forest was the main component of the landscape pattern in the archipelago. Both of the areas of forest and construction land were increasing, and the areas of cropland and grassland were declining. Other landscape types changed slightly, and the landscape fragmentation was increasing. The natural condition exhibited positive effects while human disturbance showed negative effects on the local ecology. Human disturbances come mainly from shoreline use while the natural conditions were mainly from the elevation change. The ecological effects were resulted mainly from the net primary productivity and water yield

Artificial neural network model for ozone concentration estimation and Monte Carlo analysis

Air pollution in urban atmosphere directly affects public-health; therefore, it is very essential to predict air pollutant concentrations. Air quality is a complex function of emissions, meteorology and topography, and artificial neural networks (ANNs) provide a sound framework for relating these variables. In this study, we investigated the feasibility of using ANN model with meteorological parameters as input variables to predict ozone concentration in the urban area of Jinan, a metropolis in Northern China. We firstly found that the architecture of network of neurons had little effect on the predicting capability of ANN model. A parsimonious ANN model with 6 routinely monitored meteorological parameters and one temporal covariate (the category of day, i.e. working day, legal holiday and regular weekend) as input variables was identified, where the 7 input variables were selected following the forward selection procedure. Compared with the benchmarking ANN model with 9 meteorological and photochemical parameters as input variables, the predicting capability of the parsimonious ANN model was acceptable. Its predicting capability was also verified in term of warming success ratio during the pollution episodes. Finally, uncertainty and sensitivity analysis were also performed based on Monte Carlo simulations (MCS). It was concluded that the ANN could properly predict the ambient ozone level. Maximum temperature, atmospheric pressure, sunshine duration and maximum wind speed were identified as the predominate input variables significantly influencing the prediction of ambient ozone concentrations