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Background: Aging is a complex process that involves all tissues in an organism and shows sex dimorphism. While transcriptional changes in aging have been well characterized, the majority of studies have focused on a single sex and sex differences in gene expression in aging are poorly understood. In this study, we explore sex dimorphism in gene expression in aging mice across three tissues. Methods: We collected gastrocnemius muscle, liver and white adipose tissue from young (6 months, n = 14) and old (24 months, n = 14) female and male C57BL/6NIA mice and performed RNA-seq. To investigate sex dimorphism in aging, we considered two levels of comparisons: (a) differentially expressed genes between females and males in the old age group and (b) comparisons between females and males across the aging process. We utilized differential expression analysis and gene feature selection to investigate candidate genes. Gene set enrichment analysis was performed to identify candidate molecular pathways. Furthermore, we performed a co-expression network analysis and chose the gene module(s) associated with aging independent of sex or tissue-type. Results: We identified both tissue-specific and tissue-independent genes associated with sex dimorphism in aged mice. Unique differentially expressed genes between old males and females across tissues were mainly enriched for pathways related to specific tissue function. We found similar results when exploring sex differences in the aging process, with the exception that in the liver genes enriched for lipid metabolism and digestive system were identified in both females and males. Combining enriched pathways across analyses, we identified amino acid metabolism, digestive system, and lipid metabolism as the core mechanisms of sex dimorphism in aging. Although the vast majority of age-related genes were sex and tissue specific, we identified 127 hub genes contributing to aging independent of sex and tissue that were enriched for the immune system and signal transduction. Conclusions: There are clear sex differences in gene expression in aging across liver, muscle and white adipose. Core pathways, including amino acid metabolism, digestive system and lipid metabolism, contribute to sex differences in aging. Plain Language Summary: Aging is a complex process that occurs differently across tissues, and in men compared to women. However, the mechanisms that cause sex differences are not well understood. Using naturally aging mouse models we compared how specific genes were differently expressed in muscle, liver and fat of old and young female and male mice. We found that the vast majority of genes that were changed with age were only changed in one sex and specific tissues. Overall, sex differences in aging across tissues were related to genes involved in amino acid metabolism, digestive system and lipid metabolism. Notably, lipid metabolism is important in aging females across all tissues. We also identified a set of genes associated with aging independent of sex and tissue-type involved in immune pathways and signaling. These results enhance our understanding of sex differences in aging. Highlights: We performed RNA-seq in three tissues from young (6 months) and old (24 months) mice of both sexes, and applied differential expression analysis and machine learning approaches to identify candidate genes to decipher sex dimorphism in aging. We demonstrated that the vast majority of age-related genes were sex and tissue specific, with liver showing greater changes than muscle or white adipose tissue. Enriched pathways underpinning sex differences for each tissue aligned with the function of the tissues. In addition to tissue-specific findings, we also observed pathways in amino acid metabolism, digestive system and lipid metabolism contributing to tissue-wide sex dimorphism. We applied an unsupervised machine learning approach to select key genes involved in aging regardless of sex and tissue, and these genes were related to the immune system and signaling transduction. [ABSTRACT FROM AUTHOR]

Few prognostic biomarkers exist for lung squamous cell carcinoma (LUSC), which has a poor five-year survival rate. Using bioinformatics, this study evaluated NPLOC4 as a prognostic marker for patients with lung squamous cell carcinoma. Shorter survival periods and tumor growth were linked to high NPLOC4 expression.Disulfiram (DSF) combined with copper (Cu) targets NPLOC4 to achieve antitumor effects in lung squamous cell carcinoma. Thus, we investigated the effects of DSF with Cu in LUSC. Gene-set enrichment analysis identified ubiquitin-mediated proteolysis as the NPLOC4-associated mechanism influencing LUSC prognosis. In SK-MES-1 cell lines, DSF + Cu increased K48-linked ubiquitinated protein expression and apoptosis. This study identified NPLOC4 as a prognostic biomarker and a potential therapeutic target for LUSC. [ABSTRACT FROM AUTHOR]

Missing data in precipitable water vapor derived from global navigation satellite systems (GNSS-PWV) is commonly a large hurdle in climatical applications, since continuous PWV is an important prerequisite. Interpolation using principal component analysis (PCA) is typically used to resolve this problem. However, the popular PCA-based interpolating methods, e.g., rank-deficient least squares PCA (RDPCA) and data interpolating empirical orthogonal function (DINEOF), often lead to unsatisfactory results. This study analyzes the relationship between missing data and PCA-based interpolation results and proposes an improved interpolation-based RDPCA (IRDPCA) that can take into account the PWV derived from ERA5 (ERA-PWV) as an additional aid. Three key steps are involved in the IRDPCA: initially interpolating missing data, estimating principal components through a functional model and optimizing the interpolation through an iterative process. Using a 6-year GNSS-PWV over 26 stations and ERA-PWV in Yunnan, China, the performance of the IRDPCA is compared with the RDPCA and DINEOF using simulation experiments based on both homogeneous data (i.e., interpolating ERA-PWV using available ERA-PWV) and heterogeneous data (i.e., interpolating GNSS-PWV using ERA-PWV). In the case of using homogeneous data, the root mean square (RMS) values of the interpolation errors are 3.45, 1.18 and 1.17 mm for the RDPCA, DINEOF and IRDPCA, respectively; while the values are 3.50, 2.50 and 1.55 mm in the heterogeneous case. These results demonstrate the superior performance of the IRDPCA in both the heterogeneous and homogeneous cases. Moreover, these methods are also applied to the interpolation of the real GNSS-PWV. The RMS, absolute bias and correlation of the GNSS-PWV are calculated by comparison with ERA-PWV. The results reveal that the interpolated GNSS-PWV using the IRDPCA is not impacted by the systematic discrepancies in the ERA-PWV and agrees well with the original data. [ABSTRACT FROM AUTHOR]

Precipitable water vapor (PWV) retrieved from Global Navigation Satellite Systems (GNSS) observations (GNSS-PWV) subjects to non-climatic changepoints (NCCs), mainly due to hardware (e.g., antenna and receiver) changes and geological events, while only the former is documented in station metadata. This study proposed a new strategy to verify the origins of the undocumented NCCs in GNSS-PWV and correct the inhomogeneity based on ERA5 and coordinate time series of GNSS stations. The new strategy initially applies a zero-difference method to detect changepoints in GNSS coordinate time series (coordinate changepoints) and undocumented NCCs in GNSS-PWV with the aid of PWV derived from ERA5 (ERA-PWV) and station metadata. The coordinate changepoints are treated as ancillary to verify the origins of the undocumented NCCs related to geological events in GNSS-PWV since coordinates are also influenced by geological events. After the verification, shifts and directions of all NCCs were robustly estimated and corrected for a final homogenization. The strategy was applied in the GNSS-PWV at 207 stations during 2008–2018 in China. Results showed that among 193 NCCs detected in 110 inhomogeneous GNSS-PWV, only 5 ones (3% of NCCs) were documented in metadata, and 44 ones (23% of NCCs) were related to geological events, indicating the good performance of the new strategy on the identification of the undocumented NCCs. After the homogenization, the RMS and absolute bias between GNSS-PWV and ERA-PWV were decreased by 5% and 22%, and the correlation coefficient of their linear trends was improved to 0.98 from 0.75. [ABSTRACT FROM AUTHOR]

The current coronary artery disease (CAD) risk scores for predicting future cardiovascular events rely on well-recognized traditional cardiovascular risk factors derived from a population level but often fail individuals, with up to 25% of first-time heart attack patients having no risk factors. Non-invasive imaging technology can directly measure coronary artery plaque burden. With an advanced lipidomic measurement methodology, for the first time, we aim to identify lipidomic biomarkers to enable intervention before cardiovascular events. With 994 participants from BioHEART-CT Discovery Cohort, we collected clinical data and performed high-performance liquid chromatography with mass spectrometry to determine concentrations of 683 plasma lipid species. Statin-naive participants were selected based on subclinical CAD (sCAD) categories as the analytical cohort (n = 580), with sCAD+ (n = 243) compared to sCAD− (n = 337). Through a machine learning approach, we built a lipid risk score (LRS) and compared the performance of the existing Framingham Risk Score (FRS) in predicting sCAD+. We obtained individual classifiability scores and determined Body Mass Index (BMI) as the modifying variable. FRS and LRS models achieved similar areas under the receiver operating characteristic curve (AUC) in predicting the validation cohort. LRS enhanced the prediction of sCAD+ in the healthy-weight group (BMI < 25 kg/m2), where FRS performed poorly and identified individuals at risk that FRS missed. Lipid features have strong potential as biomarkers to predict CAD plaque burden and can identify residual risk not captured by traditional risk factors/scores. LRS compliments FRS in prediction and has the most significant benefit in healthy-weight individuals. [ABSTRACT FROM AUTHOR]

Grid-wise Vienna Mapping Functions 1 (VMF1) and Vienna Mapping Functions 3 (VMF3) tropospheric products have been widely used to interpolate the a priori zenith hydrostatic delay (ZHD) and zenith wet delay (ZWD) over the GNSS (Global Navigation Satellite Systems) stations for the mitigation of tropospheric delays inherited in GNSS observations. Since the two products only provide ground surface ZHD and ZWD for global grid points, the ZHD and ZWD of the four grid points nearest to the GNSS site need to be reduced to the same height of the GNSS site before a horizontal interpolation (e.g., bilinear interpolation or inverse-distance weighted interpolation) is implemented. However, the accuracy of the officially recommended simple reduction model may not be as good as desired if the height of a GNSS site largely differs from that of the four ground surface grid points to be used in the interpolation. In this contribution, a new reduction model for each grid point is developed for reducing the grid-wise ZHD and ZWD to the target height to improve the interpolation performance. The sample data for the modelling were the 10-year (2010–2019) ZHD and ZWD profiles over the grid points obtained from ERA5 monthly-mean reanalysis data, while 3-year (2020–2022) ERA5 hourly reanalysis and IGS (International GNSS Service) site-wise ZTD products were used to evaluate the new model. Test results showed that the accuracy of the ZHD, ZWD (as well as the ZTD) interpolated from the VMF1/VMF3 products deduced by the new model was significantly better than the ones deduced by traditional methods, especially for sites with substantial height disparities from adjacent VMF grid points. It is expected that the new model adds good value to related fields such as GNSS positioning and GNSS meteorology for better performance. [ABSTRACT FROM AUTHOR]

In GNSS (Global Navigation Satellite Systems) meteorology, the accuracy of precipitable water vapor (PWV) retrieved from the tropospheric delay of GNSS signals is affected by the conversion factor. Compact VMF1 product (known as GGOS Atmosphere data) provides high‐accuracy global grid‐wise weighted mean temperature (Tm) values, which can be utilized to calculate the conversion factor. However, the Tm provided in the compact VMF1 data are solely ground surface values. To enhance the performance of compact VMF1 product, a new Tm lapse rate model for each grid point was developed for the purpose of reducing its surface Tm to the elevation of the GNSS site. Then the reduced Tm values over the neighboring grid points together with horizontal interpolation were used to obtain the interpolated Tm for the GNSS station. The sample data for the development of the new model were the Tm profiles obtained from ERA5 monthly averaged data spanning 2009–2018. To assess the model's performance, global radiosonde data at 504 radiosonde stations spanning 2019–2021 were employed. Results demonstrated that implementing the Tm lapse rate model significantly enhanced the accuracy of interpolating Tm values for GNSS stations with substantial height disparities from adjacent grid points, thereby improving PWV conversion accuracy. This indicates that employing the new Tm lapse rate model to adjust surface Tm data in the compact VMF1 product holds promise for enhancing its utility in GNSS meteorology. Plain Language Summary: Global Navigation Satellite Systems (GNSS), including GPS, GLONASS, GALILEO and BDS, are powerful tools for retrieving precipitable water vapor (PWV), a crucial parameter in weather and climate research. The process involves estimating the wet delay embedded in GNSS signals caused by water vapor in the atmosphere and then converting the delay into PWV using a conversion factor based on the weighted mean temperature (Tm). Compact VMF1 product provides high‐accuracy ground surface grid‐wise Tm values, however, when the altitude of the GNSS site largely differs from that of the nearby grid points, the accuracy of the Tm interpolated from the surface Tm may be poor. To improve the accuracy of Tm interpolation and better PWV inversion, we developed a grid‐wise Tm lapse rate model that adjusts for altitude differences. Key Points: A Tm ${T}_{m}$ lapse rate model was developed to enhance the performance of the grid‐wise compact VMF1 Tm ${T}_{m}$ dataThe accuracy of the predicted Tm ${T}_{m}$ and PWV was significantly improvedThe new model adds a good value to GNSS meteorology for better performance [ABSTRACT FROM AUTHOR]

The inhomogeneities, the so‐called changepoints, inevitably occur in the precipitable water vapor (PWV) time series derived from Global Navigation Satellite Systems (GNSS‐PWV). Currently, the two predominant types of homogenization methods, that is, absolute and relative methods, are limited in the poor performance and dependency on reference time series. In this study, a new absolute approach named adaptive absolute homogenization test (AAHT) by combining Seasonal‐Trend decomposition based on LOESS (STL) and Penalized Maximal F‐test to account for the red noise (PMFred) is proposed. The performance of the new approach was validated using Monte Carlo simulations. Results showed the success rate and false alarm rate were better than 74.9% and 12.2%; the detection accuracy of the changepoint epochs and the offset magnitudes were 8.0 days and 0.153 mm. AAHT was also applied to homogenizing the real GNSS‐PWV time series over 91 International GNSS Service stations, from which 63 time series were identified as inhomogeneous containing 126 changepoints. Based on a comparison with the PWV time series from the fifth‐generation European center for medium‐range weather forecasts (ECMWF) reanalysis (ERA5), 43 changepoints were connected to climatic drivers, leaving 83 artificial changepoints at 48 stations. The offset magnitudes at these artificial changepoints were estimated and corrected in the inhomogeneous time series. The long‐term trends of those homogenized time series were compared with that of the PWV time series derived from ERA5 data sets (ERA‐PWV) and the correlation coefficient between the two sets of trends was 0.75, which was significantly larger than 0.23 before homogenization. Plain Language Summary: The precipitable water vapor time series derived from Global Navigation Satellite Systems (GNSS‐PWV) can be contaminated by inhomogeneities. This study aims to propose a new reliable method to homogenize the global GNSS‐PWV time series. This method, named adaptive absolute homogenization test (AAHT), combines Seasonal‐Trend decomposition based on LOESS (STL) and Penalized Maximal F‐test to account for the red noise (PMFred) with an overall success rate and a false alarm rate better than 74.9% and 12.2%, respectively. An application on the GNSS‐PWV over 91 GNSS stations showed that homogenization using AAHT significantly improved the trend correlation between GNSS‐PWV and PWV time series retrieved from ERA5 reanalysis. Key Points: A new absolute method is proposed to homogenize a precipitable water vapor (PWV) time series derived from the Global Navigation Satellite Systems (GNSS)The success rate and false alarm rate were better than 74.4% and 12.2%; the accuracy of epochs and magnitudes were 8.3 days and 0.144 mmPWV time series at 91 global GNSS stations were homogenized, and the impact of the homogenization was also investigated [ABSTRACT FROM AUTHOR]

Nitrogen in pond sediments is a major water quality concern and can impact the productivity of aquaculture. Dissolved oxygen is an important factor for improving water quality and boosting fish growth in aquaculture ponds, and plays an important role in the conversion of ammonium-nitrogen (NH 4 +-N) to nitrite-nitrogen (NO 2 −-N) and eventually nitrate-nitrogen (NO 3 −-N). A central goal of the study was to identify the best aeration method and strategy for improving water quality in aquaculture ponds. We conducted an experiment with six tanks, each with a different aeration mode to simulate the behavior of aquaculture ponds. The results show that a 36 hr aeration interval (T c = 36 hr: 36 hr) and no aeration resulted in high concentrations of NH 4 +-N in the water column. Using a 12 hr interval time (T c = 12 hr: 12 hr) resulted in higher NO 2 −-N and NO 3 −-N concentrations than any other aeration mode. Results from an 8 hr interval time (T c = 8 hr: 8 hr) and 24 hr interval time (T c = 24 hr: 24 hr) were comparable with those of continuous aeration, and had the benefit of being in use for only half of the time, consequently reducing energy consumption. Image 1 [ABSTRACT FROM AUTHOR]

Sulfate (SO 4 2−) has received attention as means of monitoring water quality and pollution. However, the SO 4 2− content of rivers, lakes, and reservoirs varies significantly by season, so environmental factors such as temperature can affect it. An experiment was conducted with a series of aerobic and anaerobic tanks containing Pearl River sediments and distilled water to assess the release of SO 4 2− from sediments under controlled conditions. "Black-odor river" refers to near anoxic conditions in the water column and foul odors emanating from affected rivers in southeastern China. These river systems typical have sediments containing ammonia (NH 3), hydrogen sulfide (H 2 S), and organic sulfide compounds in excess, and precipitates of sulfide (S2−), with ferrous (Fe2+) or manganese (Mn2+). SO 4 2− concentration was measured at various depths in pore water and in the water column while controlling temperature and dissolved oxygen (DO) concentrations. Interpolation of study results revealed that SO 4 2− content was highest between temperatures of 20 °C and 25 °C. The relationship between SO 4 2− concentration, which varied with temperature and time, was fit using a linearized Michaelis-Menten function (R2 = 0.69). The release of SO 4 2− to the water column was accelerated during the experiment (for temperatures higher than 20 °C), and led to higher SO 4 2− content in the water column than in pore water. The maximum concentration of SO 4 2− within the sediment occurred at a temperature of 20 °C. Comparing aerated and non-aerated tanks at 20 °C, we found that O 2 restricted SO 4 2− content in the water column; DO could, in turn, also be controlled by temperature. Fe2+ and Mn2+ had a negative correlation with SO 4 2−. Unlabelled Image • Derived a general relationship for sulfate concentration in the water column. • Assessed distribution of sulfate in water column and sediment with temperature. • Estimated sulfate flux at the sediment-water interface at different temperatures. • Assessed the effect of ferrous and manganese ions on sulfate content. • Assessed effects of dissolved oxygen, pH, etc., on sulfate release from sediments. [ABSTRACT FROM AUTHOR]

[Display omitted] • HSPF-WASP integrated modeling system for Rappahannock River Basin is feasible. • Integrated model provides the nitrogen load of each sub-watershed and river segment. • NPS pollution output more correlated with watershed area than mutual reactions. • Nitrogen was more susceptible in winter and stable in summer. • Similar effects on ammonia by temperature of 1.5 ∼ 2℃ and streamflow of 20 ∼ 50% The high nitrogen load in most rivers across the globe causes various environmental issues. Non-point source (NPS) pollution within a watershed has a significant impact on water quality management and prediction accuracy in rivers. Temperature and streamflow, as the most important environmental indicators, significantly affect water pollution management. This study takes the Rappahannock River Basins (RRB) as a case and adopts the method of coupling land and river models to improve the simulation accuracy of river pollution, outputs and analyzes the temporal and spatial distributions of nitrogen pollution in RRB, assesses the impact of temperature and streamflow changes on nitrogen pollution in water bodies. The results show that NPS pollution is mainly affected by topography and land use, and the annual output is more correlated with the watershed area. Temperature is negatively correlated with NH 4 -N and NO 3 -N in the Rappahannock River. Nitrogen is more susceptible to streamflow in winter; but in summer, it was stable and even showed the opposite changing tendency to winter. Changes in temperature of 1.5 ∼ 2℃ and streamflow of 20 ∼ 50% have similar effects on NH 4 -N, while increasing streamflow can significantly increase the concentration of NO 3 -N, and most of these changes are very evident. NH 4 -N and NO 3 -N concentrations will increase in RRB when the summer temperature is below the long-term average. NH 4 -N concentration will increase when upstream streamflow (such as the amount of mountain snowmelt) decreases in winter, while NO 3 -N concentration will decrease. The establishment of the integrated model can provide the nitrogen pollution load of each sub-watershed and each segment, which can be used to develop the TMDL for the Rappahannock River. [ABSTRACT FROM AUTHOR]

GREM1 is a secreted protein that antagonizes bone morphogenetic proteins (BMPs) and participates in critical biological processes, including embryonic development, organogenesis and tissue differentiation. Gremlin 1 (GREM1) is also an inhibitor of TGF-β and a ligand for vascular endothelial growth factor receptor 2. In addition, GREM1 can induce cells, participate in the process of epithelial-mesenchymal transition, and then participate in tumor development. GREM1 has a variety of biological functions and can participate in the malignant progression of a variety of tumors through the BMP signaling pathway. GREM1 also can inhibit TGF-β in some tumors, thereby inhibiting tumors, and its involvement in tumor development varies in different types of cancer. The present review examines the role and function of GREM1 in tumors. GREM1 is expressed in a variety of tumor types. GREM1 expression can affect the epithelial-mesenchymal transformation of tumor cells. GREM1 has been studied in breast and colon cancer, and its potential role is to promote cancer. However, in pancreatic cancer, which was found to act differently from other cancer types, overexpression of GREM1 inhibits tumor metastasis. The present review suggests that GREM1 can be a diagnostic and prognostic indicator. In future studies, the study of GREM1 based on single-cell sequencing technology will further clarify its role and function in tumors. [ABSTRACT FROM AUTHOR]

Talent programmes are treated as a strategic foundation for social–economic development in China. With increasing levels of funding for basic research, talent programmes in basic research are becoming more complex. This increased complexity leads to inefficiencies because it is difficult to coordinate and manage these talent programmes effectively. This paper reports a survey of talent programmes in basic research in China. The results show that talent programmes in basic research have not yet formed an integrated system and that there exist some weaknesses in integration, structure, hierarchy and chronological order. Based on these conclusions, this paper proposes a framework for the talent programme system in basic research using system theory. Furthermore, measures to address weaknesses in the existing talent programme system are provided and analysed according to China's national talent strategy. These measures should be helpful for promoting the operation of talent programmes and optimising their overall performance. [ABSTRACT FROM AUTHOR]

Thalamocortical connections are a neuroanatomical feature shared among vertebrates, although the extent and organization of these connections vary among species. From an evolutionary standpoint, reptiles represent early stages of the pattern of connectivity between the thalamus and cortex, and elucidation of these pathways may help to reveal the biological significance of these projections. The present tract tracing study was performed to examine the organization of thalamocortical projections in the pond turtle, Pseudemys scripta elegans. All experiments were carried out using in vitro brain preparations. Injections of neurobiotin into the medial cortex resulted in labeled neurons in the ipsilateral dorsomedial anterior nucleus of the thalamus, those in the dorsomedial cortex labeled neurons in the dorsolateral anterior nucleus, and injections into the dorsal cortex resulted in labeled neurons in the dorsal lateral geniculate nucleus of the thalamus. Injections of neurobiotin into these thalamic nuclei confirmed the projections to the cortex. Finally, neurobiotin injections primarily into the medial cortex resulted in bilateral label of axons and terminals in the suprapeduncular nucleus of the hypothalamus. The results of the neurobiotin injections revealed a topographic pattern of thalamocortical connections such that medial cortical regions connect with medial thalamic nuclei and lateral cortical regions connect with lateral nuclei. These findings suggest that the presence of functionally segregated thalamocortical projections is a conserved feature of brain organization among amniotes. Moreover, this work describes a descending pathway linking cortical regions with the red nucleus via the hypothalamus thereby providing indirect cortical control of the reptilian rubrospinal system. Copyright © 2005 S. Karger AG, Basel [ABSTRACT FROM AUTHOR]

Abstract: A tract tracing study was performed to examine the localization of the facial nucleus in the brain stem of the pond turtle, Pseudemys scripta elegans. Neurobiotin and the fluorescent tracers alexa fluor 488 and 594 were used to retrogradely label neurons of the abducens or facial nerves. The results showed that the facial nucleus has two subnuclei, a medial group and a lateral group. Measurements of cell size revealed no significant differences between these populations. Double labeling studies showed that the medial cell group of the facial nucleus lies between the principal and accessory abducens nuclei in the pons, whereas the lateral group lies adjacent to the accessory abducens nucleus. The facial nucleus of pond turtles largely overlaps the rostrocaudal extent of the accessory abducens nucleus, but extends well beyond it into the medulla. These data elucidate the position and distribution of the facial nucleus in the brain stem of pond turtles and contribute to the body of comparative neuroanatomical literature on the distribution of the cranial nerve nuclei of reptiles. [Copyright &y& Elsevier]

An in vitro brain stem preparation from turtles exhibits a neural correlate of eyeblink classical conditioning during pairing of auditory (CS) and trigeminal (US) nerve stimulation while recording from the abducens nerve. The premotor neuronal circuits controlling abducens nerve-mediated eyeblinks in turtles have not been previously described, which is a necessary step for understanding cellular mechanisms of conditioning in this preparation. The purpose of the present study was to neuroanatomically define the premotor pathways that underlie the trigeminal and auditory nerve-evoked abducens eyeblink responses. The results show that the principal sensory trigeminal nucleus forms a disynaptic pathway from both the trigeminal and auditory nerves to the principal and accessory abducens motor nuclei. Additionally, the principal abducens nucleus receives vestibular inputs, whereas the accessory nucleus receives input from the cochlear nucleus. The late R2-like component of abducens nerve responses is mediated by the spinal trigeminal nucleus in the medulla. Both the principal sensory trigeminal nucleus and the abducens motor nuclei receive CS-US convergence and therefore both, or either, might be considered potential sites of synapse modification during in vitro abducens conditioning. Further data are required to determine the role of the principal sensory trigeminal nucleus in in vitro conditioning. Copyright © 2004 S. Karger AG, Basel [ABSTRACT FROM AUTHOR]

Water vapor is one of the most important parameters in climatic studies. MODerate-resolution Imaging Spectroradiometer (MODIS) is a key instrument and can provide spatially continuous precipitable water vapor (PWV) products. This study was focused on the performance evaluation of the MODIS near-infrared PWV product (MOD-NIR-PWV) over China. For a comprehensive assessment of the performance of MOD-NIR-PWV, PWV retrieved from the measurements at the global navigation satellite systems (GNSS) stations (i.e., GNSS-PWV) and the ERA5 reanalysis dataset (ERA-PWV) from 2013 to 2018 were used as the reference. To investigate the suitability of using ERA-PWV as the reference for the evaluation, ERA-PWV was compared to the high-accuracy GNSS-PWV at 246 GNSS stations and PWV retrieved from radiosonde observations (RS-PWV) at 78 radiosonde stations over China. The results showed that the mean bias and mean root-mean-square (RMS) of the differences between ERA-PWV and GNSS-PWV across all the stations were 0.5 and 1.7 mm, respectively, and the mean correlation coefficient of the two datasets was above 0.96. The values were 0.4 and 1.9 mm and 0.97, respectively, for the differences between ERA-PWV and RS-PWV. This suggests the suitability of ERA-PWV as the reference for the evaluation of MOD-NIR-PWV. In addition, MOD-NIR-PWV was compared with both GNSS-PWV and ERA-PWV, and their mean bias and mean RMS were 2.9 and 3.8 mm (compared to GNSS-PWV) and 2.1 and 3.0 mm (compared to ERA-PWV), respectively. The positive bias values and the non-normal distribution of the differences between MOD-NIR-PWV and both reference datasets imply that a considerable systematic overestimation of MOD-NIR-PWV over China may exist. To mitigate the systematic bias, ERA-PWV was utilized as the sample data due to its spatial continuities, and a grid-based calibration model was developed based on the annual and semiannual periodicities in the differences between MOD-NIR-PWV and ERA-PWV at each grid point. After applying the calibration model to correct MOD-NIR-PWV, the calibrated MOD-NIR-PWV was compared with ERA-PWV and GNSS-PWV for precision and accuracy analysis, respectively. The comparison showed that the model could significantly improve the precision by 94% and accuracy by 53%, which manifested the effectiveness of the calibration model in improving the performance of MOD-NIR-PWV over China. [ABSTRACT FROM AUTHOR]

[Display omitted] • Nanobubble aeration improved dissolved oxygen levels in water and sediment. • The NIMCSC system exhibited the best aerobic denitrification performance. • The abundance of key indigenous aerobic denitrifiers significantly increased. • Genes related to N metabolism, electron production, and transfer were upregulated. The primary constraints for efficient nitrogen removal in aquaculture ponds by indigenous aerobic denitrifiers are oxygen depletion in sediment and the shortage of electron donors. This study innovatively combined nanobubble aeration with iron-based multi-carbon source composites to stimulate aerobic denitrifiers, significantly improving the sediment's oxygen level and supplementing the electron donors of the system. Compared with the systems that incorporated multi-carbon source composites (MCSC) or iron-based multi-carbon source composites (IMCSC) under diffused air aeration conditions, the system that employed nanobubble aeration and iron-based multi-carbon source composites (NIMCSC) demonstrated an increase in total nitrogen (TN) elimination rate by 14.07% and 7.46%, respectively, reaching 89.75%. Microbial community analysis revealed that the NIMCSC system enriched the mixotrophic aerobic denitrifiers and fostered a symbiotic relationship among the bacterial communities. Moreover, compared with the MCSC and IMCSC systems, the NIMCSC system highlighted a significant increase in the average abundance of key indigenous aerobic denitrifiers. Meanwhile, the production and transfer process of electrons were stimulated, providing more energy and electron donors and assisting various abundant key functional enzymes to achieve efficient mixotrophic aerobic denitrification in the NIMCSC system. This study presented a novel approach to achieve efficient aquaculture wastewater nitrogen removal. [ABSTRACT FROM AUTHOR]

Accurate risk stratification is vital for primary prevention of cardiovascular disease (CVD). However, traditional tools such as the Framingham Risk Score (FRS) may underperform within the diverse intermediate-risk group, which includes individuals requiring distinct management strategies. This study aimed to develop a lipidomic-enhanced risk score (LRS), specifically targeting risk prediction and reclassification within the intermediate group, benchmarked against the FRS. The LRS was developed via a machine learning workflow using ridge regression on the Australian Diabetes, Obesity, and Lifestyle Study (AusDiab; n = 10,339). It was externally validated with the Busselton Health Study (n = 4,492), and its predictive utility for coronary artery calcium scoring (CACS)–based outcomes was independently validated in the BioHEART cohort (n = 994). LRS significantly improved discrimination metrics for the intermediate-risk group in both AusDiab and Busselton Health Study cohorts (all P < 0.001), increasing the area under the curve for CVD events by 0.114 (95% CI: 0.1123-0.1157) and 0.077 (95% CI: 0.0755-0.0785), with a net reclassification improvement of 0.36 (95% CI: 0.21-0.51) and 0.33 (95% CI: 0.15-0.49), respectively. For CACS-based outcomes in BioHEART, LRS achieved a significant area under the curve improvement of 0.02 over the FRS (0.76 vs 0.74; P < 1.0 × 10-5). A simplified, clinically applicable version of LRS was also created that had comparable performance to the original LRS. LRS, augmenting the FRS, presents potential to improve intermediate-risk stratification and to predict atherosclerotic markers using a simple blood test, suitable for clinical application. This could facilitate the triage of individuals for noninvasive imaging such as CACS, fostering precision medicine in CVD prevention and management. [ABSTRACT FROM AUTHOR]

• SWAT has good applicability to the hydrological simulation of the karst area. • Severe storms primarily occur during April to July and November. • The risk of flooding is projected to increase under the five SSP-based future land use scenarios. • The severity of flood events is expected to escalate during the dry season, with runoff coefficients potentially increasing by up to 16%. With economic and social development, the inevitable conflict between growing populations and land use change has become increasingly prominent. In karst areas, such as the Lijiang River Basin (LRB) in South China, land use has undergone rapid and intense changes over the past few decades due to urbanization and agricultural development. Assessing the hydrological effects of future land use change scenarios in karst areas is essential for estimating potential flood risks. In this study, we constructed hydrological models for the LRB based on a widely used hydrological model (SWAT, Soil and Water Assessment Tool), analyzed the runoff coefficient (R c) at event scale, and evaluated the hydrological effects under future land use scenarios in 2100 based on five shared socio-economic pathways (SSPs). The results indicate that the simulated streamflow effectively replicated the hydrographs of the LRB. The model exhibited good performance, as evidenced by daily streamflow simulation with NSE greater than 0.65 and PBIAS within the range of ± 15 %. This demonstrates the applicability of SWAT in karst areas. The analysis of storm events revealed that severe storms in the LRB mainly took place between April and July and in November, with relatively long durations and high R c values. Under all five SSP-based future land use scenarios, it is predicted that flood risk will increase in the LRB. Notably, the severity of flood events during the dry season is expected to escalate, with R c potentially increasing by up to 16 %. To address environmental degradation and achieve sustainable development, it is imperative to adopt appropriate environment-friendly land use practices and implement effective land management strategies. [ABSTRACT FROM AUTHOR]

Fine-bubble-diffusing (FBD) aeration system is widely used in aquaculture ponds. To maximize its re-oxygenation capability, it is needed to have a quantitative understanding of the reoxygenation performance. In practice, two indexes, namely oxygen volume mass transfer coefficient (KLa) and standard oxygen transfer efficiency (E), are commonly used to measure the re-oxygenation performance. However, few mathematical models are available to accurately predict these two indexes. The objective of this regard was to develop such a model driven by commonly available data. In this regard, the results from 54 group laboratory tests were regressed on four independent variables, including air flow rate (Qg), aeration tube length (L), submerged water depth of the diffuser (hd), and plane-view tank area (Acs). The regression revealed that both KLa and E are negatively related to hd and Acs, but they are positively related to L. In addition, KLa was found to be positively related to Qg, whereas E was found to be negatively related to Qg. Two regression models, one for KLa while another for E, are expected to be effective tools for operating FBD aeration system in practice to maximize its re-oxygenation capability though they may need to be further verified using field data. [ABSTRACT FROM AUTHOR]

Hybrid constructed wetlands (HCWs) are a promising solution for water ecology and environmental treatment, not only for conventional types of water pollution but also for antibiotics. Among the critical parameters for wetlands, the hydraulic loading rate (HLR) is especially important given the challenges of antibiotics treatment and frequent extreme rainfall. To investigate the removal performance of different HLRs on nutrients and antibiotics, as well as the response of antibiotics to nutrient removal, and the impact of HLRs on microbial communities, new HCWs with vertical flow constructed wetlands (VFCWs) and floating constructed wetlands (FCWs) in series were built. The results of the study showed that: (1) HCWs are highly effective in removing chemical oxygen demand (COD), N H 4 + − N , N O 2 − − N , and total phosphorus (TP) at low HLR (L_HLR), with removal efficiencies as high as 97.8%, 99.6%, 100%, and 80.5%. However, high HLR (H_HLR) reduced their removal efficiencies; (2) The average removal efficiency of fluoroquinolones (FQs) under different HLRs was consistently high, at 99.9%, while the average removal efficiency of macrolides (MLs) was 96.3% (L_HLR) and 88.4% (H_HLR). The removal efficiency of sulfonamides (SAs) was susceptible to HLRs, and the removal of antibiotics occurred mainly in the rhizosphere zone of wetland; (3) High concentrations of antibiotics in HCWs were found to inhibit and poison plant growth and to reduce the removal efficiency of TP by 12%. However, they had a minor effect on the removal efficiency of carbon and nitrogen nutrients; (4) H_HLR altered the diversity and abundance of microbial communities in different compartments of the wetland and also reduced the relative abundance of Bacillus , Hydrogenophaga , Nakamurella , Denitratisoma and Acidovorax genera, which are involved in denitrification and phosphorus removal processes. This alteration in microbial communities was one of the main reasons for the reduced performance of nitrogen and phosphorus removal. [Display omitted] • Antibiotics and nutrients removal were studied in HCWs with two HLRs. • The wetlands at the front end of HCWs undertake more pollutant removal tasks. • The contribution of HCWs vertical structure to antibiotic removal was quantified. • Antibiotics have little impact on C and N removal, reducing TP removal efficiency. • HLRs profoundly influence microbiota at the genus level and related removal. [ABSTRACT FROM AUTHOR]

The soil moisture from the South-to-North Water Diversion Middle Route Project is assessed in this study. Complex and variable geological conditions complicate the prediction of soil moisture in the study area. To achieve this aim, we carried out research on soil moisture inversion methods for channel slopes in the study area using massive monitoring data from multiple GNSS observatories on channel slopes, incorporating GNSS-R techniques and deep learning algorithms. To address the issue of low accuracy in linear inversion when using a single satellite, this study proposes a multi-satellite and multi-frequency data fusion technique. Furthermore, three soil moisture inversion models, namely, the linear model, BP neural network model, and GA-BP neural network model, are established by incorporating deep learning techniques. In comparison with single-satellite data inversion, with the data fusion technique proposed in this study, the correlation is improved by 12.7%, the root mean square error is reduced by 0.217, the mean square error is decreased by 0.884, and the mean absolute error is decreased by 0.243 with the linear model. With the BP neural network model, the correlation is increased by 15.4%, the root mean square error is decreased by 0.395, the mean square error is decreased by 0.465, and the mean absolute error is reduced by 0.353. Moreover, with the GA-BP neural network model, the correlation is improved by 6.3%, the root mean square error is decreased by 1.207, the mean square error is decreased by 0.196, and the mean absolute error is reduced by 0.155. The results indicate that performing data fusion by using multiple satellites and multi-frequency bands is a feasible approach for improving the accuracy of soil moisture inversion. These research findings provide new technical means for the risk analysis of deformation disasters in the expansive soil channel slopes of the South-to-North Water Diversion Middle Route Project. [ABSTRACT FROM AUTHOR]

Global Navigation Satellite Systems (GNSS) tomography is a well-recognized modeling technique for reconstruction, which can be used to investigate the spatial structure of water vapor with a high spatiotemporal resolution. In this study, a refined near real-time tomographic model is developed based on multi-source data including GNSS observations, Global Forecast System (GFS) products and surface meteorological data. The refined tomographic model is studied using data from Hong Kong from 2 to 11 October 2021. The result is compared with the traditional model with physical constraints and is validated by the radiosonde data. It is shown that the root mean square error (RMSE) values of the proposed model and traditional model are 0.950 and 1.763 g/m3, respectively. The refined model can decrease the RMSE by about 46%, indicating a better performance than the traditional one. In addition, the accuracy of the refined tomographic model is assessed under both rainy and non-rainy conditions. The assessment shows that the RMSE in the rainy period is 0.817 g/m3, which outperforms the non-rainy period with the RMSE of 1.007 g/m3. [ABSTRACT FROM AUTHOR]

The quality of the zenith hydrostatic delay (ZHD) could significantly affect the accuracy of the zenith wet delay (ZWD) of the Global Navigation Satellite System (GNSS) signal, and from the ZWD precipitable water vapor (PWV) can be obtained. The ZHD is usually obtained from a standard model – a function of surface pressure at the GNSS station. When PWV is retrieved from the GNSS stations that are not equipped with dedicated meteorological sensors for surface pressure measurements, blind models, e.g., the global pressure and temperature (GPT) models, are commonly used to determine the pressures for these GNSS stations. Due to the limited accuracies of the GPT models, the ZHD obtained from the model-derived pressure value is also of low accuracy, especially in mid- and high-latitude regions. To address this issue, a new ZHD model, named GZHD, was investigated for real-time retrieval of GNSS-PWV in this study. The ratio of the ZHD to the zenith total delay (ZTD) was first calculated using sounding data from 505 globally distributed radiosonde stations selected from the stations that had over 5000 samples. It was found that the temporal variation in the ratio was dominated by the annual and semiannual components, and the amplitude of the annual variation was dependent upon the geographical location of the station. Based on the relationship between the ZHD and ZTD, the new model, GZHD, was developed using the back propagation artificial neural network (BP-ANN) method which took the ZTD as an input variable. The 20-year (2000–2019) radiosonde data at 558 global stations and the 9-year (2006–2014) COSMIC-1 (Constellation Observing System for Meteorology, Ionosphere, and Climate) data, which were also globally distributed, were used as the training samples of the new model. The GZHD model was evaluated using two sets of references: the integrated ZHD obtained from sounding data and ERA5 reanalysis data. The performance of the new model was also compared with GPT3, the latest version. Results showed the new model outperformed GPT3, especially in mid- and high-latitude regions. When radiosonde-derived ZHD was used as the reference, the accuracy, which was measured by the root mean square error (RMSE) of the samples, of the GZHD-derived ZHD was about 21 % better than the GTP3-derived ones. When ERA5-derived ZHD was used as the reference, the accuracy of the GZHD-derived ZHD was about 30 % better than GPT3-derived ZHD. In addition, the real-time PWV derived from 41 GNSS stations resulting from GZHD-derived ZHD was also evaluated, and the result indicated that the accuracy of the PWV was improved by 21 %. [ABSTRACT FROM AUTHOR]

• The time-variant transit time distribution model (SAS) is coupled with a physically based hydrological model (SWAT). • The coupled model (SWAT-SAS) considers the spatial heterogeneity of aquifer recharge and represents groundwater age release preferences at the subbasin level. • Although both the original and modified models simulated comparable in-stream nitrate concentrations, the nitrate legacy estimations within the aquifers differ. • More knowledge of nutrient legacy information is needed for watershed management and pollution control. The transit time distribution (TTD) is a lumped method to characterize the diverse flow paths of a hydrological system, and the StorAge Selection function (SAS) is one of the time-variant forms, representing the link between storage and outflow. Although it provides age information about the water parcels and nutrient legacy, the spatial heterogeneity cannot be captured by this method. While the distributed physically-based hydrological models (PBHMs) can reflect the spatial heterogeneous in climate, land cover and management, its simplification of subsurface processes prevents it from representing the subsurface nutrient transport and sometimes fails to capture nutrient legacy dynamics of the landscape. We attempted to couple SAS functions into a PBHM (SWAT) for calculating the nitrate dynamics of aquifers. The results show that both SWAT-SAS and SWAT can reproduce the in-stream dynamics of streamflow and nitrate concentration for the Upper Selke catchment; the coupled model allows more flexibility of storage release schemes and provides water age information of the aquifers; even though both the models simulated comparable in-stream nitrate concentrations, the nitrate store remained in aquifers varied, which will have varying implications for pollution goal assessment and nutrient management. [ABSTRACT FROM AUTHOR]

Atmospheric data obtained from the radio occultation (RO) technique are a well-recognized source of information for weather and climate studies. From the Challenging Minisatellite Payload (CHAMP) mission launched in July 2000 to the most recent Constellation Observing System for Meteorology, Ionosphere, and Climate follow-on (COSMIC-2) program, a continuous RO dataset of about 20 years has been collected, and a new opportunity for long-term climate analyses using multi-mission RO observations has subsequently arisen. Therefore, assessments of the long-term homogeneities of multi-mission RO data have become a necessary research task. For this purpose, in this study, we identified systematic discrepancies between the RO temperature profiles from the CHAMP, COSMIC, and Meteorological Operational Polar Satellite (METOP) missions. The results show that the temperature profiles from all three RO missions agree well in the upper troposphere and lower stratosphere (UTLS, 9–20 km altitude) regions, while some systematic discrepancies are found in the lower troposphere (2–8 km) and the high-altitude region (21–30 km). The homogeneities of long-term RO temperature climatologies were assessed by comparing them with radiosonde temperature records. The results of this comparison show obvious temporal inhomogeneities in the lower troposphere. The reasons for these temporal inhomogeneities include the systematic discrepancies between multi-mission RO profiles, the different monthly numbers of RO profiles, and the residual sampling error. The results of this study suggest that the systematic discrepancies between different RO missions should be thoroughly considered in the development of long-term multi-mission RO-based climatologies. [ABSTRACT FROM AUTHOR]

As one of the atmosphere propagation delays, the tropospheric delay is a significant error source that should be properly handled in high-precision global navigation satellite system (GNSS) applications. We propose an improved zenith tropospheric delay (ZTD) modeling method whereby the piecewise model of the atmospheric refractivity is introduced. Compared with using the exponential model to fit ZTD in vertical direction, the ZTD piecewise model has a better performance. Based on ERA5 2.5° × 2.5° reanalysis data produced by the European Centre for Medium-Range Weather Forecasting (ECMWF) from 2013 to 2017, we establish the regional gridded ZTD model (RGZTD) using a trigonometric function for China and the surrounding areas, which ranges from 70° E to 135° E in longitude and from 15° N to 55° N in latitude. To verify the performance of RGZTD model, the ERA5 ZTD data in 2017–2018, the radiosonde ZTD data from 86 radiosonde stations over China in 2017–2018, and the tropospheric delay products on 251 GNSS stations from Crustal Movement Observation Network of China (CMONOC) in 2016–2017 are used as external compliance check data. The results show that the overall accuracy of RGZTD model is better than that of exponential model, UNB3m model, and GPT3 model. Moreover, the accuracy can be improved by about 13.4%, 7.1%, and 6.2% when ERA5 reanalysis data, radiosonde data, and CMONOC data are used as reference values, respectively. High-accuracy ZTD data can be provided because the RGZTD model takes into account the vertical variation of ZTD through the new piecewise model. [ABSTRACT FROM AUTHOR]

Insects feeding on the nutrient-poor diet of xylem plant sap generally bear two microbial symbionts that are localized to different organs (bacteriomes) and provide complementary sets of essential amino acids (EAAs). Here, we investigate the metabolic basis for the apparent paradox that xylem-feeding insects are under intense selection for metabolic efficiency but incur the cost of maintaining two symbionts for functions mediated by one symbiont in other associations. Using stable isotope analysis of central carbon metabolism and metabolic modeling, we provide evidence that the bacteriomes of the spittlebug Clastoptera proteus display high rates of aerobic glycolysis, with syntrophic splitting of glucose oxidation. Specifically, our data suggest that one bacteriome (containing the bacterium Sulcia, which synthesizes seven EAAs) predominantly processes glucose glycolytically, producing pyruvate and lactate, and the exported pyruvate and lactate is assimilated by the second bacteriome (containing the bacterium Zinderia, which synthesizes three energetically costly EAAs) and channeled through the TCA cycle for energy generation by oxidative phosphorylation. We, furthermore, calculate that this metabolic arrangement supports the high ATP demand in Zinderia bacteriomes for Zinderia-mediated synthesis of energy-intensive EAAs. We predict that metabolite cross-feeding among host cells may be widespread in animal–microbe symbioses utilizing low-nutrient diets. [ABSTRACT FROM AUTHOR]

Aerobic denitrification is the key process in the elimination of nitrogen from aquaculture wastewater, especially for wastewater with high dissolved oxygen and low carbon/nitrogen (C/N) ratio. However, a low C/N ratio, especially in low-temperature environments, restricts the activity of aerobic denitrifiers and decreases the nitrogen elimination efficiency. In this study, an iron-based multi-solid carbon source composite that immobilized aerobic denitrifying bacteria ZS1 (IMCSCP) was synthesized to treat aerobic (DO > 5 mg/L), low temperature (<15 °C) and low C/N ratio (C/N = 4) aquaculture wastewater. The results showed that the sequencing batch biofilm reactor (SBBR) packed with IMCSCP exhibited the highest nitrogen removal performance, with removal rates of 95.63% and 85.44% for nitrate nitrogen and total nitrogen, respectively, which were 33.03% and 30.75% higher than those in the reactor filled with multi-solid carbon source composite (MCSC). Microbial community and network analysis showed that Pseudomonas furukawaii ZS1 successfully colonized the SBBR filled with IMCSCP, and Exiguobacterium, Cellulomonas and Pseudomonas were essential for the nitrogen elimination. Metagenomic analysis showed that an increase in gene abundance related to carbon metabolism, nitrogen metabolism, extracellular polymer substance synthesis and electron transfer in the IMCSCP, enabling denitrification in the SBBR to be achieved via multiple pathways. The results of this study provided new insights into the microbial removal mechanism of nitrogen in SBBR packed with IMCSCP at low temperatures. [Display omitted] • The SBBR packed with IMCSCP exhibited the best N removal performance at low temperature. • Acidovorax is the key iron based autotrophic denitrification bacteria. • Genes related to carbon metabolism, EPS synthesis and electron transfer were upregulated. • The narGHI , napAB and nirBD genes were stimulated leading to NO 3 −-N directly removal. [ABSTRACT FROM AUTHOR]

Global Navigation Satellite Systems (GNSS) water vapor tomography has been proved to be an effective method for retrieving three-dimensional (3D) water vapor distribution. Currently, due to the limitation on the availability and the poor distribution of observations, the ill-posed problem of the tomographic system still needs to be solved. In this study, a refined extended tomographic model with combining virtual signals is proposed to address the observational geometry defect and improve the performance of tomographic solutions. The new refined model was generated by adding auxiliary voxels of the same size as the original voxels around the original tomographic model and its tomographic body is with an inverted cone shape. Then, "virtual" signals that pass through the sides of the original tomographic body and the top boundary of the extended region are introduced. The slant wet delays (SWDs) of these virtual rays were obtained from the tropospheric parameters estimated from GNSS data processing and pre-defined elevation and azimuth angles. Three experimental schemes based on GNSS data from the Hong Kong reference network during the 30-day period in July 2019 were implemented to evaluate the proposed tomographic model. Statistical results showed that, compared with the tomographic models of other schemes, the new model shows strong robustness in terms of observational geometry and the accuracy of the 3D water vapor field inferred. The mean value of the root-mean-square errors (RMSEs) of the tomographic solutions during the period studied obtained from the proposed method was improved by 12% and 11% compared with Schemes I and II, when radiosonde data were used as the reference. Furthermore, compared to other schemes, the condition number of the design matrix reduced after the side-crossing virtual signals were added, implying improvements in the ill-condition of a tomographic system for the proposed method. All these results suggest the good performance of the proposed method. • An improved tomographic model is proposed to address the observational geometry defect and improve the performance. • The virtual signals penetrating from the side-faces of the tomographic model are incorporated into the observation eqs. • The new model with an inverted cone shape is built to utilize these virtual signals. [ABSTRACT FROM AUTHOR]

Let $$\mathcal {M}$$ be a finite non-collinear set of points in the Euclidean plane, with the squared distance between each pair of points integral. Considering the points as lying in the complex plane, there is at most one positive square-free integer D, called the 'characteristic' of $$\mathcal {M}$$ , such that a congruent copy of $$\mathcal {M}$$ embeds in $$\mathbb {Q}(\sqrt{-D})$$ . We generalize the work of Yiu and Fricke on embedding point sets in $$\mathbb {Z}^2$$ by providing conditions that characterize when $$\mathcal {M}$$ embeds in the lattice corresponding to $$\mathcal {O}_{-D}$$ , the ring of integers in $$\mathbb {Q}(\sqrt{-D})$$ . In particular, we show that if the square of every ideal in $$\mathcal {O}_{-D}$$ is principal and the distance between at least one pair of points in $$\mathcal {M}$$ is integral, then $$\mathcal {M}$$ embeds in $$\mathcal {O}_{-D}$$ . Moreover, if $$\mathcal {M}$$ is primitive, so that the squared distances between pairs of points are relatively prime, and $$\mathcal {O}_{-D}$$ is a principal ideal domain, then $$\mathcal {M}$$ embeds in $$\mathcal {O}_{-D}$$ . [ABSTRACT FROM AUTHOR]

Aeration plug-flow devices (APFDs) have been used with recirculating aquaculture systems (RASs) to simultaneously improve water circulation and oxygenation. However, it is still unclear how aeration flow rates affect fish growth for better extension and application. Thus, a series of experiments were conducted to explore the water quality and growth performance of tilapia (Oreochromis niloticus , average body length 8.93 ± 0.34 cm, average body weight 23.61 ± 3.50 g) using three self-designed RASs with APFDs with three aeration flow rates for 56 days. Aeration flow rates were applied in triplicate with the values of 0 L/min (control treatment, C), 30 L/min (moderate treatment, M) and 50 L/min (large treatment, L). The results showed that the water velocity and dissolved oxygen (DO) concentration increased with the increase in aeration flow rates, and the non-aerated condition resulted in a low DO and low pH but higher values of N H 4 + (3.22 ± 0.17 mg/L) and P O 4 3 − (5.78 ± 0.13 mg/L) for treatment C on the 56th day. The tilapia's length gain rate, weight gain rate and specific growth rate for treatment M on the 56th day were 49.90 ± 4.07 %, 235.70 ± 19.80 % and 2.81 ± 0.54 %/d, respectively, which were 11.07 %, 20.2 %, and 41.4 % higher than that for treatment C, and increased compared to treatment L by 2.04 %, 19.5 %, and 8.49 %. Additionally, more nutrients (i.e., total protein, total cholesterol, glucose) accumulated in tilapia serum for treatment M compared to those in other treatments. Multiple regression analysis showed that growth rates are correlated with DO, N O 3 − and P O 4 3 − . This study gave a new try by combining water circulation and oxygenation into RASs with APFDs, and it is worth popularizing and applying to reduce the operational costs. [ABSTRACT FROM AUTHOR]