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One of the fundamental parts of Geometric Function Theory is the study of analytic functions in different domains with critical geometrical interpretations. This article defines a new generalized domain obtained based on the quotient of two analytic functions. We derive various properties of the new class of normalized analytic functions X defined in the new domain, including the sharp estimates for the coefficients a 2 , a 3 , and a 4 , and for three second-order and third-order Hankel determinants, H 2 , 1 X , H 2 , 2 X , and H 3 , 1 X . The optimality of each obtained estimate is given as well. [ABSTRACT FROM AUTHOR]

Many researchers have defined the q-analogous of differential and integral operators for analytic functions using the concept of quantum calculus in the geometric function theory. In this study, we conduct a comprehensive investigation to identify the uses of the Sălăgean q-differential operator for meromorphic multivalent functions. Many features of functions that belong to geometrically defined classes have been extensively studied using differential operators based on q-calculus operator theory. In this research, we extended the idea of the q-analogous of the Sălăgean differential operator for meromorphic multivalent functions using the fundamental ideas of q-calculus. With the help of this operator, we extend the family of Janowski functions by adding two new subclasses of meromorphic q-starlike and meromorphic multivalent q-starlike functions. We discover significant findings for these new classes, including the radius of starlikeness, partial sums, distortion theorems, and coefficient estimates. [ABSTRACT FROM AUTHOR]

This article defines a new operator called the q-Babalola convolution operator by using quantum calculus and the convolution of normalized analytic functions in the open unit disk. We then study a new class of analytic and bi-univalent functions defined in the open unit disk associated with the q-Babalola convolution operator. The main results of the investigation include some upper bounds for the initial Taylor–Maclaurin coefficients and Fekete–Szego inequalities for the functions in the new class. Many applications of the finds are highlighted in the corollaries based on the various unique choices of the parameters, improving the existing results in Geometric Function Theory. [ABSTRACT FROM AUTHOR]

A two-generator Kleinian group f , g can be naturally associated with a discrete group f , ϕ with the generator ϕ of order two and where f , ϕ f ϕ − 1 = f , g f g − 1 ⊂ f , g , f , ϕ : f , g f g − 1 = 2. This is useful in studying the geometry of the Kleinian groups since f , g will be discrete only if f , ϕ is, and the moduli space of groups f , ϕ is one complex dimension less. This gives a necessary condition in a simpler space to determine the discreteness of f , g . The dimension reduction here is realised by a projection of principal characters of the two-generator Kleinian groups. In applications, it is important to know that the image of the moduli space of Kleinian groups under this projection is closed and, among other results, we show how this follows from Jørgensen's results on algebraic convergence. [ABSTRACT FROM AUTHOR]

Closely spaced, multi‐strand ridge transform faults (RTFs) accommodate relative motions along fast spreading mid‐ocean ridges. However, the relations between RTFs and plate spreading dynamics are poorly understood. The Quebrada system is one of the most unique RTF systems at the East Pacific Rise, consisting of four transform faults connected by three short intra‐transform spreading centers (ITSCs). We use seven‐months of ocean bottom seismograph data to study the Quebrada system, and find abundant earthquakes unevenly distributed among three active faults. We identify two deep, diffuse seismicity clouds at the inside corners of the ITSC‐transform fault intersections, and one seismically active fracture zone. The observations suggest a complex regional plate‐motion pattern, including possible heterogeneous rotations within the Quebrada system. Evolution of multi‐strand RTFs may have resulted from a strong three‐dimensional local thermal and fluid effects, while the RTFs may have also regulated regional tectonics, forming an intricate feedback system. Plain Language Summary: Mid‐ocean ridge transform faults (RTFs) are plate boundaries that offset adjacent mid‐ocean ridges. At fast spreading mid‐ocean ridges, such as the East Pacific Rise (EPR), closely spaced, multi‐strand RTFs are often connected by two or more short intra‐transform spreading centers (ITSCs). However, physical processes accommodating plate spreading along such multi‐strand RTF systems and the inter‐relations between the fault system and the tectonic dynamics are not well understood. Quebrada is one of such multi‐strand RTFs at the EPR. We utilize seven‐month seismic data from ocean bottom seismographs of a 2008 experiment to investigate the seismotectonics of the region. We find intriguing, abundant seismicity on one of the fracture zones, contradicting the traditional view that fracture zones are seismically quiescent. Further, we identify two diffuse seismicity clouds penetrating the uppermost mantle at the inside corners of the ITSC‐transform fault intersections, implying complex interactions among ITSCs, transform faults, and their surrounding structure. From these observations, we infer that there are rotational motions within the Quebrada fault system, which have caused slip along the fracture zone and facilitated fluid circulations to produce deep, diffuse seismicity. We speculate that there is a complex feedback system between the multi‐strand RTFs and local three‐dimensional tectonic processes. Key Points: Four segments of Quebrada transform system show significantly different seismic intensitiesOne fracture zone segment appears to be seismogenic indicating local plate rotationDeep clouds of seismicity extend into the uppermost mantle at spreading center‐ transform fault intersections [ABSTRACT FROM AUTHOR]

Overexpression of CD30 has been reported on the surface of some T-cell lymphomas, especially on Hodgkin's lymphoma (HL) and anaplastic large cell lymphoma (ALCL). CD30 targeted immunotherapy has good clinical therapy response. We have produced a novel antibody drug conjugates (ADCs)-anti-CD30-LDM, which shows attractive tumour-targeting capability and extremely potent antitumor efficacy. To further investigate biological characteristics and promote clinical translation of anti-CD30-LDM, we constructed a radiolabeled 123I-anti-CD30-LDM to evaluate the biodistribution characteristics. The anti-CD30-LDM was radioiodinated by the Iodogen method. The radiochemical purity of 123I-anti-CD30-LDM was more over 98%, and the specific activity of 240.5 MBq/mg. The stability and the specificity of 123I-anti-CD30-LDM were evaluated in vitro. Cellular binding assays were used to evaluate the binding capabilities in CD30-positive Karpas299 cells and CD30-negative Raji cells. B-NDG mice bearing Karpas 299 and Raji xenografts were used for in vivo biodistribution studies. Our results demonstrated that anti-CD30-LDM as an ideal ADC targeted to CD30, which was labelled easily with 123I and obtained the sufficient yields. The 123I-anti-CD30-LDM preserved specific binding to CD30 in vitro and uptake in tumour xenografts in B-NDG mice. These results are encouraging for anti-CD30-LDM as a promising clinical translational candidate for various CD30 positive lymphomas and other diseases. [ABSTRACT FROM AUTHOR]

Ambient PM 2.5 pollution has been a major environmental concern in recent years. Beijing, the capital of China, is enduring frequent and severe PM 2.5 pollution. In this study, 186 valid PM 2.5 pollution episodes during the 2014-2017 period were identified and classified into four categories according to the mechanism of pollution formation and evolution. Category I often occurs in autumn, winter and early spring, depending on accumulation during stagnant weather. Category II featured by photochemistry is dominant in summer, and category III caused by dust storms occasionally occurs in spring. Category IV represents a combination during transition periods. Interannual variations show that particulate pollution decreased from 2014 to 2017, and the decline in categories I and II played the most important role. To further understand the PM 2.5 pollution patterns in Beijing, the temporal and spatial characteristics and relationships between PM 2.5 levels and meteorological features were analyzed. Category I is the main pollution type that brings forth heavy or severe pollution and has the longest duration in those cases, while category II often leads to light or moderate pollution. There is a north-south gap in the PM 2.5 levels in Beijing. The high-level pollution in category I tends to evolve northward, while the low-level pollution in category I and category II pollution tend to evolve southward and widen the north-south gap, affected by the regional transport and more local emissions in the south. Additionally, the relationship between the concentrations and meteorology also vary with the pollution categories. High relative humidity, low wind speeds and low boundary layer heights are likely to lead to category I or II pollution, but category III requires high winds. These results provide insights into the annual tendency and characteristics of Beijing's particulate pollution in recent years. Image 1 • The PM 2.5 pollution episodes from 2014 to 2017 were identified and classified. • Particle pollution has been decreasing in recent years due to the decline in accumulation and photochemistry types. • Most of the heavy and severe pollution is due to the type of accumulation. • There is a north-south gap in PM 2.5 levels in Beijing. • The relationships between PM 2.5 levels and meteorological features vary with pollution categories. [ABSTRACT FROM AUTHOR]

This paper presents a geometric approach to the problems in differential subordination theory. The necessary conditions for a function to be in various subfamilies of the class of starlike functions and the class of Carathéodory functions are studied, respectively. Further, several consequences of the findings are derived. [ABSTRACT FROM AUTHOR]

Through cell-based screening models, we have identified a new compound IMB5043, a thiophenylated pyridazinone, which exerted cytotoxicity against cancer cells. In the present study, we evaluated its antitumor efficacy and the possible mechanism. By MTT assay, IMB5043 inhibited the proliferation of various human cancer cells lines, especially hepatocarcinoma SMMC-7721 cells. IMB5043 blocked cell cycle with G2/M arrest, induced cell apoptosis, and inhibited the migration and invasion of SMMC-7721 cells. As verified by comet assay and γ-H2AX foci formation, IMB5043 caused DNA damage and activated ATM, Chk2 and p53 through phosphorylation. As shown by Gene microarray analysis, the differentially expressed genes in SMMC-7721 cells treated with IMB5043 were highly related to cell death and apoptosis. IMB5043 suppressed the growth of hepatocarcinoma SMMC-7721 xenograft in athymic mice. By histopathological examination, no lesions were found in bone marrow and various organs of the treated mice. Our findings reveal that IMB5043 as an active compound consisting of both pyridazinone and thiophene moieties exerts antitumor efficacy through activation of ATM-Chk2 pathway. IMB5043 may serve as a promising leading compound for the development of antitumor drugs. [ABSTRACT FROM AUTHOR]

Virtual geographic environments (VGEs) are extensively used to explore the relationship between humans and environments. Crowd simulation provides a method for VGEs to represent crowd behaviors that are observed in the real world. The social force model (SFM) can simulate interactions among individuals, but it has not sufficiently accounted for inter-group and intra-group behaviors which are important components of crowd dynamics. We present the social group force model (SGFM), based on an extended SFM, to simulate group behaviors in VGEs with focuses on the avoiding behaviors among different social groups and the coordinate behaviors among subgroups that belong to one social group. In our model, psychological repulsions between social groups make them avoid with the whole group and group members can stick together as much as possible; while social groups are separated into several subgroups, the rear subgroups try to catch up and keep the whole group cohesive. We compare the simulation results of the SGFM with the extended SFM and the phenomena in videos. Then we discuss the function of Virtual Reality (VR) in crowd simulation visualization. The results indicate that the SGFM can enhance social group behaviors in crowd dynamics. [ABSTRACT FROM AUTHOR]

Due to their strong immersion and real-time interactivity, helmet-mounted virtual reality (VR) devices are becoming increasingly popular. Based on these devices, an immersive virtual geographic environment (VGE) provides a promising method for research into crowd behavior in an emergency. However, the current cheaper helmet-mounted VR devices are not popular enough, and will continue to coexist with personal computer (PC)-based systems for a long time. Therefore, a heterogeneous distributed virtual geographic environment (HDVGE) could be a feasible solution to the heterogeneous problems caused by various types of clients, and support the implementation of spatiotemporal crowd behavior experiments with large numbers of concurrent participants. In this study, we developed an HDVGE framework, and put forward a set of design principles to define the similarities between the real world and the VGE. We discussed the HDVGE architecture, and proposed an abstract interaction layer, a protocol-based interaction algorithm, and an adjusted dead reckoning algorithm to solve the heterogeneous distributed problems. We then implemented an HDVGE prototype system focusing on subway fire evacuation experiments. Two types of clients are considered in the system: PC, and all-in-one VR. Finally, we evaluated the performances of the prototype system and the key algorithms. The results showed that in a low-latency local area network (LAN) environment, the prototype system can smoothly support 90 concurrent users consisting of PC and all-in-one VR clients. HDVGE provides a feasible solution for studying not only spatiotemporal crowd behaviors in normal conditions, but also evacuation behaviors in emergency conditions such as fires and earthquakes. HDVGE could also serve as a new means of obtaining observational data about individual and group behavior in support of human geography research. [ABSTRACT FROM AUTHOR]

A common approach to derive the sky view factor (SVF) in an urban environment is to combine urban morphological modeling and computational geometry techniques. Computational methods and tools have been developed to derive SVF from digital surface models (DSMs) and small-scale three dimensional (3D) city models, but they are not well suited for accommodating large-scale 3D city models. In this paper, we present a computational framework (SVFEngine) for deriving SVF from a wide variety of 3D city models, including the recently developed oblique airborne photogrammetry-based 3D city model (OAP3D), which is a highly detailed representation of an urban environment. We compared the SVF estimates computed from a large-scale high-resolution OAP3D with those from a set of street view panoramas at 30 locations. The comparison shows that the SVF estimates obtained using both methods closely match each other with a correlation coefficient of 0.99. The result suggests that high-resolution 3D city models have the potential for deriving accurate SVF estimates. Several examples are presented to show how SVFEngine can be applied in urban environmental modeling and analysis. The source code of SVFEngine is freely available ( https://github.com/ljm355/SVFEngine ) so it can be fully integrated into a user-defined workflow. [ABSTRACT FROM AUTHOR]

Introduction: The heterocyclic compound 4‐hydroxy‐(2,2,6,6‐Tetramethylpiperidin‐1‐yl)oxyl (TEMPOL) has a protective effect on neurological function in brain tissues damaged by ischemia and hypoxia. This study explored the effects of TEMPOL pretreatment on postoperative cognitive function in aged rats under sevoflurane anesthesia, focusing on inflammatory response and oxidative stress. Methods: Sixty male rats were divided into normal control (C), sevoflurane anesthesia (S), TEMPOL pretreatment (T), and sevoflurane anesthesia + TEMPOL pretreatment (ST) groups (15 per group). Groups T and ST rats received continuous intraperitoneal TEMPOL (100 mg/kg) for 3 days, while groups C and S rats were injected with 0.9% saline. After pretreatment, groups S and ST received 3% sevoflurane anesthesia. Results: Rats in group S exhibited a longer swimming distance, longer escape latency, lower frequency of platform crossing, and shorter dwell time in the targeted quadrant than those in groups C and T. Rats in group ST exhibited a shorter swimming distance, shorter escape latency, higher frequency of platform crossing, and longer dwell time in the targeted quadrant than those in group S. The expressions of interleukin‐6, tumor necrosis factor‐α, inducible nitric oxide synthase, and Ym1/2 messenger ribonucleic acid were higher in groups S and ST rats than in groups C and T rats and lower in group ST rats than in group S rat (p <.05). Superoxide dismutase (SOD), total antioxidant capacity (T‐AOC), and glutathione peroxidase (GSH‐Px) were lower, while malondialdehyde (MDA) was higher in groups S and ST rats than in groups C and T rats (p <.05). Group ST showed higher SOD, T‐AOC, and GSH‐Px, and lower MDA than group S (p <.05). Conclusions: TEMPOL pretreatment attenuated postoperative cognitive impairment induced by sevoflurane anesthesia in aged rats. This may be attributed to the downregulation of NR2B‐CREB‐BDNF pathway, reducing the inflammatory response and oxidative stress damage in hippocampal tissue. [ABSTRACT FROM AUTHOR]

Previous studies have highlighted the significant role of complement activation in kidney injuries induced by rhabdomyolysis, intravascular hemolysis, sepsis, and ischemia-reperfusion. Nevertheless, the specific role and mechanism of complement activation in acute kidney injury (AKI) caused by wasp venom remain unclear. The aim of this study was to elucidate the specific complement pathway activated and investigate complement activation in AKI induced by wasp venom. In this study, a complement-depleted mouse model was used to investigate the role of complement in wasp venom-induced AKI. Mice were randomly categorized into control, cobra venom factor (CVF), AKI, and CVF + AKI groups. Compared to the AKI group, the CVF + AKI group showed improved pathological changes in kidneys and reduced blood urea nitrogen (BUN) levels. The expression levels of renal complement 3 (C3), complement 5 (C5), complement 1q (C1q), factor B (FB), mannose-binding lectin (MBL), and C5b-9 in AKI group were upregulated compared with the control group. Conversely, the renal tissue expression levels of C3, C5, C1q, FB, MBL, and C5b-9 were decreased in the CVF + AKI group compared to those in the AKI group. Complement activation occurs through all three pathways in AKI induced by wasp venom. Furthermore, complement depletion by CVF attenuates wasp venom-induced nephrotoxicity, suggesting that complement activation plays a primary role in the pathogenesis of wasp venom-induced AKI. [ABSTRACT FROM AUTHOR]

Photovoltaic energy has become a popular renewable energy source for sustainable urban development. As a result, 3D solar radiation models are needed to facilitate the interactive assessment of photovoltaic potential in complex urban environments. SURFSUN3D is a visualization-oriented full 3D solar radiation model that has been shown to achieve efficient computation and visualization for 3D urban models. The present paper introduces a framework to integrate SURFSUN3D into a 3D GIS-based application to interactively assess the photovoltaic potential in urban areas. [ABSTRACT FROM AUTHOR]

Visualization is an important component of the evaluation of meteorological models, forecasting research, and other applications. With advances in computing power, the volume of meteorological data generated by geoscience and climate researchers has been steadily increasing. The emerging technique of virtual globes has been regarded as an ideal platform for visualizing larger geospatial data over the Internet. To visualize and analyze meteorological data with the new virtual globes, this paper proposes a systematic meteorological data visualization (MDV) framework in World Wind, an open-source virtual globe. The key technologies, including a hierarchical octree-based multiresolution data organization, data scheduling, level of detail (LOD) and rendering are described in detail. The framework is then applied to a practical tropical cyclone simulation, including flow vectors, particle tracking, cross-sectional analysis, streamlines, pathway animation, and volume rendering. The results show that virtual globes are effective tools for meteorological data visualization and analysis. [ABSTRACT FROM AUTHOR]

The visual perception of augmented reality (AR) geovisualization is significantly different from traditional controllable 2D and 3D visualization. In this study, we extended the rendering styles of color variables to include natural material color (NMC) and illuminating material color (IMC) and extended the size to include linear size (LS) and angular size (AS). Outdoor AR geovisualization user experiments were conducted, examining the guidance characteristics of five static variables (NMC, IMC, shape, AS, LS) and two dynamic variables (vibration, flicker). The results showed that the two dynamic variables provided the highest guidance, and among all the static variables, the order of guidance was shape, IMC, AS, NMC, and finally LS. This is a new finding that is different from the color, size, and shape guidance order in 2D visualization and the color, shape, and size order in 3D visualization. The results could be utilized to guide the selection of visual variables for symbol design in AR geovisualization. [ABSTRACT FROM AUTHOR]

Effective assessments of air-pollution exposure depend on the ability to accurately predict pollutant concentrations at unmonitored locations, which can be achieved through spatial interpolation. However, most interpolation approaches currently in use are based on the Euclidean distance, which cannot account for the complex nonlinear features displayed by air-pollution distributions in the wind-field. In this study, an interpolation method based on the shortest path distance is developed to characterize the impact of complex urban wind-field on the distribution of the particulate matter concentration. In this method, the wind-field is incorporated by first interpolating the observed wind-field from a meteorological-station network, then using this continuous wind-field to construct a cost surface based on Gaussian dispersion model and calculating the shortest wind-field path distances between locations, and finally replacing the Euclidean distances typically used in Inverse Distance Weighting (IDW) with the shortest wind-field path distances. This proposed methodology is used to generate daily and hourly estimation surfaces for the particulate matter concentration in the urban area of Beijing in May 2013. This study demonstrates that wind-fields can be incorporated into an interpolation framework using the shortest wind-field path distance, which leads to a remarkable improvement in both the prediction accuracy and the visual reproduction of the wind-flow effect, both of which are of great importance for the assessment of the effects of pollutants on human health. [ABSTRACT FROM AUTHOR]

Humane use of preclinical large animal cancer models plays a critical role in understanding cancer biology and developing therapeutic treatments. Among the large animal candidates, goats have great potentials as sustainable sources for large animal cancer model development. Goats are easier to handle and cheaper to raise. The genome of the goats has been sequenced recently. It has been known that goats develop skin, adrenal cortex, breast and other types of cancers. Technically, goats are subject to somatic cell nuclear transfer more efficiently and exhibit better viability through the cloning process. Towards the development of a goat cancer model, we created a transgenic goat fetal fibroblast (GFF) cell as the donor cell for SCNT. Human mutated K-ras (hK-rasG12D) was chosen as the transgene, as it is present in 20% of cancers. Both hK-rasG12D and a herpes simplex viral thymidine kinase (HSV1-tk) reporter genes, flanked by a pair of LoxP sites, were knocked in the GFF endogenous K-ras locus through homologous recombination. Following Cre-mediated activation (with a 95% activation efficiency), hK-rasG12D and HSV1-tk were expressed in the transgenic GFF cells, evidently through the presence of corresponding mRNAs, and confirmed by HSV1-tk protein function assay. The hK-rasG12D expressing GFF cells exhibited enhanced proliferation rates and an anchorage-independent growth behavior. They were able to initiate tumor growth in athymic nude mice. In conclusion, after activating hK-rasG12D gene expression, hK-rasG12D transgenic GFF cells were transformed into tumorgenesis cells. Transgenic goats via SCNT using the above-motioned cells as the donor cells have been established. [ABSTRACT FROM AUTHOR]

The changing spatiotemporal patterns of the individual susceptible-infected-symptomatic-treated-recovered epidemic process and the interactions of information/material flows between regions, along with the 2002-2003 Severe Acute Respiratory Syndrome (SARS) epidemiological investigation data in mainland China, including three typical locations of individuals (working unit/home address, onset location and reporting unit), are used to define the in-out flow of the SARS epidemic spread. Moreover, the input/output transmission networks of the SARS epidemic are built according to the definition of in-out flow. The spatiotemporal distribution of the SARS in-out flow, spatial distribution and temporal change of node characteristic parameters, and the structural characteristics of the SARS transmission networks are comprehensively and systematically explored. The results show that (1) Beijing and Guangdong had the highest risk of self-spread and output cases, and prevention/control measures directed toward self-spread cases in Beijing should have focused on the later period of the SARS epidemic; (2) the SARS transmission networks in mainland China had significant clustering characteristics, with two clustering areas of output cases centered in Beijing and Guangdong; (3) Guangdong was the original source of the SARS epidemic, and while the infected cases of most other provinces occurred mainly during the early period, there was no significant spread to the surrounding provinces; in contrast, although the input/output interactions between Beijing and the other provinces countrywide began during the mid-late epidemic period, SARS in Beijing showed a significant capacity for spatial spreading; (4) Guangdong had a significant range of spatial spreading throughout the entire epidemic period, while Beijing and its surrounding provinces formed a separate, significant range of high-risk spreading during the mid-late period; especially in late period, the influence range of Beijing's neighboring provinces, such as Hebei, was even slightly larger than that of Beijing; and (5) the input network had a low-intensity spread capacity and middle-level influence range, while the output network had an extensive high-intensity spread capacity and influence range that covered almost the entire country, and this spread and influence indicated that significant clustering characteristics increased gradually. This analysis of the epidemic in-out flow and its corresponding transmission network helps reveal the potential spatiotemporal characteristics and evolvement mechanism of the SARS epidemic and provides more effective theoretical support for prevention and control measures. [ABSTRACT FROM AUTHOR]

To avoid crowd evacuation simulations depending on 2D environments and real data, we propose a framework for crowd evacuation modeling and simulation by applying deep reinforcement learning (DRL) and 3D physical environments (3DPEs). In 3DPEs, we construct simulation scenarios from the aspects of geometry, semantics and physics, which include the environment, the agents and their interactions, and provide training samples for DRL. In DRL, we design a double branch feature extraction combined actor and critic network as the DRL policy and value function and use a clipped surrogate objective with polynomial decay to update the policy. With a unified configuration, we conduct evacuation simulations. In scenarios with one exit, we reproduce and verify the bottleneck effect of congested crowds and explore the impact of exit width and agent characteristics (number, mass and height) on evacuation. In scenarios with two exits and a uniform (nonuniform) distribution of agents, we explore the impact of exit characteristics (width and relative position) and agent characteristics (height, initial location and distribution) on agent exit selection and evacuation. Overall, interactive 3DPEs and unified DRL enable agents to adapt to different evacuation scenarios to simulate crowd evacuation and explore the laws of crowd evacuation. [ABSTRACT FROM AUTHOR]

Mucosa-associated microbiota from different regions of the gastrointestinal (GI) tract of adult broilers was studied by analysis of 16S rRNA gene sequences. The microbiota mainly comprised Gram-positive bacteria along the GI tract. Fifty-one operational taxonomic units (OTUs) (from 98 clones) were detected in the ceca, as compared with 13 OTUs (from 49 clones) in the crops, 11 OTUs (from 51 clones) in the gizzard, 14 OTUs (from 52 clones) in the duodenum, 12 OTUs (from 50 clones) in the jejunum and nine OTUs (from 50 clones) in the ileum. Ceca were dominantly occupied by clostridia-related sequences (40%) with other abundant sequences being related to Faecalibacterium prausnitzii (14%), Escherichia coli (11%), lactobacilli (7%) and Ruminococcus (6%). Lactobacilli were predominant in the upper GI tract and had the highest diversity in the crop. Both Lactobacillus aviarius and Lactobacillus salivarius were the predominant species among lactobacilli. Candidatus division Arthromitus was also abundant in the jejunum and ileum. [ABSTRACT FROM AUTHOR]

Bacterial populations in the ileum of broiler chickens were analyzed by molecular analysis of 16S rRNA genes and compared to those in the cecum. Bacteria found in the ileal mucosa were mainly Gram-positive with low G+C content. There were 15 molecular species among 51 cloned sequences. More than 70% of the cloned sequences were related to lactobacilli and Enterococcus cecorum. Two sequences had 95% or less homology to existing database sequences. Terminal restriction fragment length polymorphism (T-RFLP) analysis revealed differences among bacterial populations present in the mucosa and lumen of the ileum. Comparative studies by T-RFLP and sequence analyses of 16S rRNA genes indicated a less diverse bacterial population in the ileum (mucosa and lumen) than in the cecum. Lactobacilli, E. cecorum, and butyrate-producing bacteria related (including both identified and unidentified species) sequences were the three major groups detected in ilea and ceca. Although butyrate-producing bacteria may have good potential in the development of novel probiotics for poultry, verifying the presence of the bacteria in the chicken gut is required to warrant further investigation. [Copyright &y& Elsevier]

The word "symmetry" is a Greek word that originated from "symmetria". It means an agreement in dimensions, due proportion, and arrangement; however, in complex analysis, it means objects remaining invariant under some transformation. This idea has now been recently used in geometric function theory to modify the earlier classical q-derivative introduced by Ismail et al. due to its better convergence properties. Consequently, we introduce a new class of analytic functions by using the notion of q-symmetric derivative. The investigation in this paper obtains a number of the latest important results in q-theory, including coefficient inequalities and convolution characterization of q-symmetric starlike functions related to Janowski mappings. [ABSTRACT FROM AUTHOR]

To overcome the difficulties of quantitatively optimizing and evaluating evacuation guidance systems, we proposed a perception model based on virtual reality (VR) experiments and the social force model (SFM). We used VR and eye tracking devices to carry out experiments. The VR experiment data was mainly used for three purposes: to determine the parameter values of the perception model, to optimize the evacuation guidance system by quantitative analysis, and to validate the perception model. Additionally, we compared the VR experimental and model simulation results before and after the optimization to quantitatively assess the improvement in the optimized evacuation guidance system. The results showed that our model can effectively simulate the perception behaviors of evacuees on the evacuation guidance system and it can quantitatively evaluate different evacuation guidance system schemes. The model simulations showed that the optimized evacuation guidance system improved the evacuation efficiency, with the average escape time and distance of the two starting positions reduced by 37% and 28%, respectively. [ABSTRACT FROM AUTHOR]

Evaluates cytokine production by mycobacterium tuberculosis-stimulated peripheral blood mononuclear cells from children with tuberculosis and children who were healthy tuberculin reactors. Relationship between interferon gamma production in patients and radiographic extent of disease and nutritional status; Influence of changes in messenger RNA expression on interferon gamma production.

Describes the design and test of oligonucleotide probes covering three phylogenetically defined groups of Butyrivibrio fibrisolvens. Probe design and specificity studies; Hybridization to RNA isolated from the rumen; Phylogenetic placement; Use of probes in community-based studies.

Solar3D is an open-source software application designed to interactively calculate solar irradiation on three-dimensional (3D) surfaces in a virtual environment constructed with combinations of 3D-city models, digital elevation models (DEMs), digital surface models (DSMs) and feature layers. The GRASS GIS r.sun solar radiation model computes solar irradiation based on two-dimensional (2D) raster maps for a given day, latitude, surface and atmospheric conditions. With the increasing availability of 3D-city models and demand for solar energy, there is an urgent need for better tools to computes solar radiation directly with 3D-city models. Solar3D extends the GRASS GIS r.sun model from 2D to 3D by feeding the model with input, including surface slope, aspect and time-resolved shading, which is derived directly from the 3D scene using computer graphics techniques. To summarize, Solar3D offers several new features that—as a whole—distinguish this novel approach from existing 3D solar irradiation tools in the following ways. (1) Solar3D can consume massive heterogeneous 3D-city models, including massive 3D-city models such as oblique airborne photogrammetry-based 3D-city models (OAP3Ds or integrated meshes); (2) Solar3D can perform near real-time pointwise calculation for duration from daily to annual; (3) Solar3D can integrate and interactively explore large-scale heterogeneous geospatial data; (4) Solar3D can calculate solar irradiation at arbitrary surface positions including on rooftops, facades and the ground. [ABSTRACT FROM AUTHOR]

Transport emissions and street dust are important sources of summertime air pollution in urban centers. Street greening and buildings have an influence on the diffusion of air pollution from streets. For field measurements, many studies have analyzed the effect of street green space arrangement on the diffusion of air pollution, but these studies have neglected the patterns at the landscape scale. Other studies have analyzed the effects of the large scale of green space on air pollution, but the vertical distribution of street buildings and greening has rarely been considered. In this study, we analyzed the impact of the vertical distribution of urban street green space on summertime air pollution in urban centers on the urban scale for the first time by using a deep-learning method to extract the vertical distribution of street greening and buildings from street view image data. A total of 687,354 street view images were collected. The green index and building index were proposed to quantify the street greening and street buildings. The multilevel regression method was used to analyze the association between the street green index, building index and air pollution indexes. For the cases in this study, including the central urban areas of Beijing, Shanghai and Nanjing, our multilevel regressions results suggested that, in the central area of the city, the vertical distribution of street greening and buildings within a certain range of the monitoring site is association with the summertime air pollution index of the monitoring site. There was a significant negative association between the street greening and air pollution indexes (radius = 1–2 km, NO2, p = 0.042; radius = 3–4 km, AQI, p = 0.034; PM10, p = 0.028). The street length within a certain range of the monitoring site has a positive association with the air pollution indexes (radius = 1–2 km, AQI, p = 0.072; PM10, p = 0.062). With the increase of the distance between streets and the monitoring sites, the association between streets and air pollution indexes decreases. Our findings on the association between the vertical structure of street greening, street buildings and summertime air pollution in urban centers can support urban street planning. [ABSTRACT FROM AUTHOR]

At present, a common drawback of crowd simulation models is that they are mainly simulated in (abstract) 2D environments, which limits the simulation of crowd behaviors observed in real 3D environments. Therefore, we propose a deep reinforcement learning-based model with human-like perceptron and policy for crowd evacuation in 3D environments (HDRLM3D). In HDRLM3D, we propose a vision-like ray perceptron (VLRP) and combine it with a redesigned global (or local) perceptron (GOLP) to form a human-like perception model. We propose a double-branch feature extraction and decision network (DBFED-Net) as the policy, which can extract features and make behavioral decisions. Moreover, we validate our method's ability to reproduce typical phenomena and behaviors through experiments in two different scenarios. In scenario I, we reproduce the bottleneck effect of crowds and verify the effectiveness and advantages of HDRLM3D by comparing it with real crowd experiments and classical methods in terms of density maps, fundamental diagrams, and evacuation times. In scenario II, we reproduce agents' navigation and obstacle avoidance behaviors and demonstrate the advantages of HDRLM3D for crowd simulation in unknown 3D environments by comparing it with other deep reinforcement learning-based models in terms of trajectories and numbers of collisions. [ABSTRACT FROM AUTHOR]

Due to their important role in maintaining temperature and soil moisture, agricultural plastic covers have been widely utilized around the globe for improving crop-growing conditions, which include both plastic-covered greenhouses (PCGs) and plastic-mulched farmlands (PMFs). However, it is a challenging and long-neglected issue to separate PCGs from PMFs due to their spectral similarity. The objective of this study is to propose a deep semantic segmentation model for accurate PCG and PMF mapping based on very high-resolution satellite images and to improve the model's spatial generalization capability using a transfer learning strategy. Specifically, the proposed semantic segmentation model has an encoder-decoder structure, where the encoder is composed of a new convolutional neural network for discriminative spatial feature learning, while the decoder utilizes a multi-task strategy to improve the predictions on the boundaries. Meanwhile, a transfer learning framework is adopted to increase mapping performance and generalization ability under limited samples. Experimental results in several typical regions across the Eurasian continent show that the proposed model could separate PCGs from PMFs accurately with a mean overall accuracy of 94.49% and an average mIoU of 0.8377. Ablation studies verify the role of encoder-decoder and transfer learning strategy in improving classification performance. [ABSTRACT FROM AUTHOR]

The urbanization worldwide leads to the rapid increase of solid waste, posing a threat to environment and people's wellbeing. However, it is challenging to detect solid waste sites with high accuracy due to complex landscape, and very few studies considered solid waste mapping across multi-cities and in large areas. To tackle this issue, this study proposes a novel deep learning model for solid waste mapping from very high resolution remote sensing imagery. By integrating a multi-scale dilated convolutional neural network (CNN) and a Swin-Transformer, both local and global features are aggregated. Experiments in China, India and Mexico indicate that the proposed model achieves high performance with an average accuracy of 90.62%. The novelty lies in the fusion of CNN and Transformer for solid waste mapping in multi-cities without the need for pixel-wise labelled data. Future work would consider more sophisticated methods such as semantic segmentation for fine-grained solid waste classification. [ABSTRACT FROM AUTHOR]

In the current work, by using the familiar q-calculus, first, we study certain generalized conic-type regions. We then introduce and study a subclass of the multivalent q-starlike functions that map the open unit disk into the generalized conic domain. Next, we study potentially effective outcomes such as sufficient restrictions and the Fekete–Szegö type inequalities. We attain lower bounds for the ratio of a good few functions related to this lately established class and sequences of the partial sums. Furthermore, we acquire a number of attributes of the corresponding class of q-starlike functions having negative Taylor–Maclaurin coefficients, including distortion theorems. Moreover, various important corollaries are carried out. The new explorations appear to be in line with a good few prior commissions and the current area of our recent investigation. [ABSTRACT FROM AUTHOR]

Many diverse subclasses of analytic functions, q-starlike functions, and symmetric q-starlike functions have been studied and analyzed by using q-analogous values of integral and derivative operators. In this paper, we define a q-analogous value of differential operators for harmonic functions with the help of basic concepts of quantum (q-) calculus operator theory; and introduce a new subclass of harmonic functions associated with the Janowski and q-Mittag–Leffler functions. We obtain several useful properties of the new class, such as necessary and sufficient conditions, criteria for analyticity, compactness, convexity, extreme points, radii of starlikeness, radii of convexity, distortion bounds, and integral mean inequality. Furthermore, we discuss some applications of this study in the form of some results and examples and highlight some known corollaries for verifying our main results presented in this investigation. Finally, the conclusion section summarizes the fact about the (p , q) -variations. [ABSTRACT FROM AUTHOR]

Background: Paris polyphylla is a herb widely used in traditional Chinese medicine to treat various diseases. Stem rot diseases seriously affected the yield of P. polyphylla in subtropical areas of China. Therefore, cost-effective, chemical-free, eco-friendly strategies to control stem rot on P. polyphylla are valuable and urgently needed. Results: In this paper, we reported the biocontrol efficiency of Paenibacillus peoriae HJ-2 and its complete genome sequence. Strain HJ-2 could serve as a potential biocontrol agent against stem rot on P. polyphylla in the greenhouse and field. The genome of HJ-2 consists of a single 6,001,192 bp chromosome with an average GC content of 45% and 5,237 predicted protein coding genes, 39 rRNAs and 108 tRNAs. The phylogenetic tree indicated that HJ-2 is most closely related to P. peoriae IBSD35. Functional analysis of genome revealed numerous genes/gene clusters involved in plant colonization, biofilm formation, plant growth promotion, antibiotic and resistance inducers synthesis. Moreover, metabolic pathways that potentially contribute to biocontrol mechanisms were identified. Conclusions: This study revealed that P. peoriae HJ-2 could serve as a potential BCA against stem rot on P. polyphylla. Based on genome analysis, the genome of HJ-2 contains more than 70 genes and 12 putative gene clusters related to secondary metabolites, which have previously been described as being involved in chemotaxis motility, biofilm formation, growth promotion, antifungal activity and resistance inducers biosynthesis. Compared with other strains, variation in the genes/gene clusters may lead to different antimicrobial spectra and biocontrol efficacies. [ABSTRACT FROM AUTHOR]

Simultaneous Localization and Mapping (SLAM) has been widely used in emergency response, self-driving and city-scale 3D mapping and navigation. Recent deep-learning based feature point extractors have demonstrated superior performance in dealing with the complex environmental challenges (e.g. extreme lighting) while the traditional extractors are struggling. In this paper, we have successfully improved the robustness and accuracy of a monocular visual SLAM system under various complex scenes by adding a deep learning based visual localization thread as an augmentation to the visual SLAM framework. In this thread, our feature extractor with an efficient lightweight deep neural network is used for absolute pose and scale estimation in real time using the highly accurate georeferenced prior map database at 20cm geometric accuracy created by our in-house and low-cost LiDAR and camera integrated device. The closed-loop error provided by our SLAM system with and without this enhancement is 1.03m and 18.28m respectively. The scale estimation of the monocular visual SLAM is also significantly improved (0.01 versus 0.98). In addition, a novel camera-LiDAR calibration workflow is also provided for large-scale 3D mapping. This paper demonstrates the application and research potential of deep-learning based vision SLAM with image and LiDAR sensors. [ABSTRACT FROM AUTHOR]

Background: Contamination of grains with trichothecene mycotoxins, especially deoxynivalenol (DON), has been an ongoing problem for Canada and many other countries. Mycotoxin contamination creates food safety risks, reduces grain market values, threatens livestock industries, and limits agricultural produce exports. DON is a secondary metabolite produced by some Fusarium species of fungi. To date, there is a lack of effective and economical methods to significantly reduce the levels of trichothecene mycotoxins in food and feed, including the efforts to breed Fusarium pathogen-resistant crops and chemical/physical treatments to remove the mycotoxins. Biological approaches, such as the use of microorganisms to convert the toxins to non- or less toxic compounds, have become a preferred choice recently due to their high specificity, efficacy, and environmental soundness. However, such approaches are often limited by the availability of microbial agents with the ability to detoxify the mycotoxins. In the present study, an approach with PCR-DGGE guided microbial selection was developed and used to isolate DON -transforming bacteria from chicken intestines, which resulted in the successful isolation of several bacterial isolates that demonstrated the function to transform DON to its de-epoxy form, deepoxy-4-deoxynivalenol (DOM-1), a product much less toxic than DON. Results: The use of conventional microbiological selection strategies guided by PCR-DGGE (denaturing gradient gel electrophoresis) bacterial profiles for isolating DON-transforming bacteria has significantly increased the efficiency of the bacterial selection. Ten isolates were identified and isolated from chicken intestines. They were all able to transform DON to DOM-1. Most isolates were potent in transforming DON and the activity was stable during subculturing. Sequence data of partial 16S rRNA genes indicate that the ten isolates belong to four different bacterial groups, Clostridiales, Anaerofilum, Collinsella, and Bacillus. Conclusions: The approach with PCR-DGGE guided microbial selection was effective in isolating DON-transforming bacteria and the obtained bacterial isolates were able to transform DON. [ABSTRACT FROM AUTHOR]

Obtaining water body images quickly and reliably is important to guide human production activities and study urban change. This paper presents a fast and accurate method to identify water bodies in complex environments based on UAV high-resolution images. First, an improved U-Net (SU-Net) model is proposed in this paper. By increasing the number of connections in the middle layer of the neural network, more image features can be retained through S-shaped circular connections. Second, aiming at the interference of mixed ground objects and dark ground objects on water detection, the fusion of a deep learning network and visual features is investigated. We analyse the influence of a wavelet transform and grey level cooccurrence matrix (GLCM) on water extraction. Using a confusion matrix to evaluate accuracy, the following conclusions are drawn: (1) Compared with existing methods, the SU-Net method achieves a significant improvement in accuracy, and the overall accuracy (OA) is 96.25%. The kappa coefficient (KC) is 0.952. (2) SU-Net combined with the GLCM has a higher accuracy (OA is 97.4%) and robustness in distinguishing mixed and dark objects. Based on this method, a distinct water boundary in urban areas, which provides data for urban water vector mapping, can be obtained. [ABSTRACT FROM AUTHOR]

Bovine mastitis is of major concern to the dairy industry worldwide. The bovine teat canal is the primary route through which pathogens enter the mammary gland. The microflora of the teat canals of dairy and beef cattle was investigated by analysis of 16S rRNA gene sequences. The goal was to examine the global difference between dairy cattle, which are sanitized on a regular basis, and beef cattle, which receive little sanitary management. A diverse population of 16S rRNA gene sequences was recovered from both the dairy and the beef herd samples, with diversity higher in the beef sample. Analysis revealed the presence of 90 operational taxonomic units (OTUs) among 156 sequences, with 45 OTUs in the dairy sample and 53 OTUs in the beef sample. Only eight OTUs were common to both samples. Members of the classes Clostridia and Bacilli dominated both samples, followed by Actinobacteria and Proteobacteria. The dairy sample contained a cluster (20/80 clones) of Staphylococcaceae members, seven of which were identifiable as coagulase-negative Staphylococcus species. The beef sample was dominated by members of the genus Clostridia, many of which have not been previously cultured. The results suggest that the microorganisms present in the bovine teat canal are more diverse than previously described. [ABSTRACT FROM AUTHOR]

Attention deficit hyperactivity disorder (ADHD) is one of the most common neurodevelopmental brain disorders in childhood. Despite extensive researches, the neurobiological mechanism underlying ADHD is still left unveiled. Since the deficit functions, such as attention, have been demonstrated in ADHD, in our present study, based on the oddball P3 task, the corresponding electroencephalogram (EEG) of both healthy controls (HCs) and ADHD children was first collected. And we then not only focused on the event-related potential (ERP) evoked during tasks but also investigated related brain networks. Although an insignificant difference in behavior was found between the HCs and ADHD children, significant electrophysiological differences were found in both ERPs and brain networks. In detail, the dysfunctional attention occurred during the early stage of the designed task; as compared to HCs, the reduced P2 and N2 amplitudes in ADHD children were found, and the atypical information interaction might further underpin such a deficit. On the one hand, when investigating the cortical activity, HCs recruited much stronger brain activity mainly in the temporal and frontal regions, compared to ADHD children; on the other hand, the brain network showed atypical enhanced long-range connectivity between the frontal and occipital lobes but attenuated connectivity among frontal, parietal, and temporal lobes in ADHD children. We hope that the findings in this study may be instructive for the understanding of cognitive processing in children with ADHD. [ABSTRACT FROM AUTHOR]

The water-level fluctuation zone (WLFZ) of the Three Gorges Reservoir is a serious landslide-prone area. However, current remote sensing methods for landslide mapping and detection in the WLFZ are insufficient because of difficulties in data acquisition and lack of facade information. We proposed a novel shipborne mobile photogrammetry approach for 3D mapping and landslide detection in the WLFZ for the first time, containing a self-designed shipborne hardware platform and a data acquisition and processing workflow. To evaluate the accuracy and usability of the resultant 3D models in the WLFZ, four bundle block adjustment (BBA) control configurations were developed and adopted. In the four configurations, the raw Global Navigation Satellite System (GNSS) data, the raw GNSS data and fixed camera height, the GCPs extracted from aerial photogrammetric products, and the mobile Light Detection and Ranging (LiDAR) point cloud were used. A comprehensive accuracy assessment of the 3D models was conducted, and the comparative results indicated the BBA with GCPs extracted from the aerial photogrammetric products was the most practical configuration (RMSE 2.00 m in plane, RMSE 0.46 m in height), while the BBA with the mobile LiDAR point cloud as a control provided the highest georeferencing accuracy (RMSE 0.59 m in plane, RMSE 0.40 m in height). Subsequently, the landslide detection ability of the proposed approach was compared with multisource remote sensing images through visual interpretation, which showed that the proposed approach provided the highest landslide detection rate and unique advantages in small landslide detection as well as in steep terrains due to the more detailed features of landslides provided by the shipborne 3D models. The approach is an effective and flexible supplement to traditional remote sensing methods. [ABSTRACT FROM AUTHOR]

With severe corrosion issues afflicting nearly half of all high sulfur gas fields, bimetal composite tubes have emerged as an optimal solution due to their superior corrosion resistance, mechanical properties, and cost-effectiveness. These tubes have gained widespread adoption in gas gathering applications. However, the evaluation of the corrosion performance of Incoloy 825 bimetal composite tubes within high sulfur gas field environments is yet to be adequately addressed. An investigation into the welding performance of both the base metal and weld is instrumental in ascertaining the safety and reliability of bimetal composite pipes. To this end, this paper presents a thermodynamically coupled finite element model of bimetal composite pipes, developed using SYSWELD software, and corroborates the model using the borehole method for empirical verification. The study further delineates the distribution of residual stress encountered during multi-layer and multi-pass welding of bimetal composite pipes. • Welding simulation of bimetal tubes validates residual stress distribution. • Compressive stress dominates base metal; cap-shaped tensile stress is on the weld side. • High-stress near fusion line and transition layer risks cracking in bimetal pipes. • Numerical modeling and verification lead to safe application of bimetal composite pipes. [ABSTRACT FROM AUTHOR]

Summary: Seed aging tolerance and rapid seedling growth are important agronomic traits for crop production; however, how these traits are controlled at the molecular level remains largely unknown. The unaged seeds of two independent maize DEHYDRATION‐RESPONSIVE ELEMENT‐BINDING2A mutant (zmdreb2a) lines, with decreased expression of GRETCHEN HAGEN3.2 (ZmGH3.2, encoding indole‐3‐acetic acid [IAA] deactivating enzyme), and increased IAA in their embryo, produced longer seedling shoots and roots, than the null segregant (NS) controls. However, the zmdreb2a seeds, with decreased expression of RAFFINOSE SYNTHASE (ZmRAFS) and less raffinose in their embryo, exhibit decreased seed aging tolerance, than the NS controls. Overexpression of ZmDREB2A in maize protoplasts increased the expression of ZmGH3.2, ZmRAFS genes and that of a Rennila LUCIFERASE reporter (Rluc) gene, which was controlled by either the ZmGH3.2‐ or ZmRAFS‐promoter. Electrophoretic mobility shift assays and chromatin immunoprecipitation assay quantitative polymerase chain reaction showed that ZmDREB2A directly binds to the DRE motif of the promoters of both ZmGH3.2 and ZmRAFS. Exogenous supplementation of IAA to the unaged, germinating NS seeds increased subsequent seedling growth making them similar to the zmdreb2a seedlings from unaged seeds. These findings provide evidence that ZmDREB2A regulates the longevity of maize seed by stimulating the production of raffinose while simultaneously acting to limit auxin‐mediated cell expansion. [ABSTRACT FROM AUTHOR]

It is challenge to pinpoint the functional variants among numerous genetic variants. Investigating the spatial dynamics of the human brain transcriptome for genes and exploring the expression quantitative trait loci data may provide the potential direction to identify the functional variants among autism spectrum disorders (ASD) patients. In order to explore the association of ITIH3 with ASD, the present study included three components: identifying the spatial-temporal expression of ITIH3 in the developing human brain using the expression data from the Allen Institute for Brain Science; examining the cis-acting regulatory effect of SNPs on the ITIH3 expression using UK Brain Expression Consortium database; validating the effect of identified SNPs using a case-control study with samples of 602 cases and 604 controls. The public expression data showed that ITIH3 may have a role in the development of human brain and suggested a cis-eQTL effect for rs2535629 and rs3617 on ITIH3 in the hippocampus. Genetic analysis of the above two SNPs suggested that the over-dominant model of rs2535629 was significantly associated with decreased risk of ASD. Convergent lines of evidence supported ITIH3 rs25352629 as a susceptibility variant for ASD. [ABSTRACT FROM AUTHOR]

Objective Sclerostin plays a major role in regulating skeletal bone mass, but its effects in articular cartilage are not known. The purpose of this study was to determine whether genetic loss or pharmacologic inhibition of sclerostin has an impact on knee joint articular cartilage. Methods Expression of sclerostin was determined in articular cartilage and bone tissue obtained from mice, rats, and human subjects, including patients with knee osteoarthritis (OA). Mice with genetic knockout (KO) of sclerostin and pharmacologic inhibition of sclerostin with a sclerostin-neutralizing monoclonal antibody (Scl-Ab) in aged male rats and ovariectomized (OVX) female rats were used to study the effects of sclerostin on pathologic processes in the knee joint. The rat medial meniscus tear (MMT) model of OA was used to investigate the pharmacologic efficacy of systemic Scl-Ab or intraarticular (IA) delivery of a sclerostin antibody-Fab (Scl-Fab) fragment. Results Sclerostin expression was detected in rodent and human articular chondrocytes. No difference was observed in the magnitude or distribution of sclerostin expression between normal and OA cartilage or bone. Sclerostin-KO mice showed no difference in histopathologic features of the knee joint compared to age-matched wild-type mice. Pharmacologic treatment of intact aged male rats or OVX female rats with Scl-Ab had no effect on morphologic characteristics of the articular cartilage. In the rat MMT model, pharmacologic treatment of animals with either systemic Scl-Ab or IA injection of Scl-Fab had no effect on lesion development or severity. Conclusion Genetic absence of sclerostin does not alter the normal development of age-dependent OA in mice, and pharmacologic inhibition of sclerostin with Scl-Ab has no impact on articular cartilage remodeling in rats with posttraumatic OA. [ABSTRACT FROM AUTHOR]

Abstract: The degradation of 12 trichothecene mycotoxins by chicken intestinal microbes was monitored by liquid chromatography–ultraviolet-mass spectrometry under positive ion atmospheric pressure chemical ionization. Two pathways were observed: deacylation and deepoxidation. Essentially complete conversions to the deepoxy metabolites were observed for the non-acylated trichothecenes 4-deoxynivalenol, nivalenol, and verrucarol. However, deacetylation was the predominant pathway for the monoacetyl trichothecenes 3-acetyldeoxynivalenol, 15-acetyldeoxynivalenol (15ADON), and fusarenon X. Small amounts of the deepoxy metabolites were observed from 15ADON and large amounts from 15-monoacetoxyscirpenol where steric hindrance protected the C-15 acetyl groups from enzymatic attack. Diacetylated trichothecenes diacetoxyscirpenol and neosolaniol exhibited only deacetylation. The larger isovaleryl functionality was resistant to removal and deepoxidation was the prevalent reaction in HT-2 toxin and T-2 triol, whereas T2 toxin showed only deacetylation. [Copyright &y& Elsevier]