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Urban soils may restrict turfgrass rooting depth with shallow soil layers in high sand content soils, which may influence water conservation. A greenhouse study sought to quantify water usage and determine the physiological response of turfgrasses at four irrigation levels. ‘ATF-1434′ tall fescue (Schedonorus arundinaceus (Schreb.) Dumort. nom. cons.; syn. Festuca arundinacea Schreb.), ‘Jamur' Japanese lawngrass (Zoysia japonica Steud.), and ‘Zeon' Manilagrass [Zoysia matrella (L.) Merr.] were established in 10 cm (4 in) diameter by 17.8 cm (7 in) tall containers. Each species was irrigated with 16.5, 21.9, 27.3, or 32.7 mm.wk−1 (0.65, 0.86, 1.1, or 1.3 in.wk−1). Gravimetric water loss was determined by pre- and post-irrigation pot weights. Turf quality, leaf discoloration, percent green cover, and gross photosynthesis were evaluated weekly and root parameters were measured at the conclusion of each trial. Although root mass was similar among species, water deficit stress and leaf discoloration occurred sooner in tall fescue than the two Zoysia species, reducing turf quality and green cover. Japanese lawngrass and Manilagrass had greater stomatal conductance, resulting in 109 and 89% higher gross photosynthesis relative to tall fescue. Both zoysiagrasses maintained acceptable turf quality with 27.3 mm water.wk−1. However, tall fescue quality was not acceptable at any irrigation level.Index words: Photosynthesis, gravimetric water loss, tall fescue, Japanese lawngrass, Manilagrass.Species used in this study: Tall fescue (Schedonorus arundinaceus (Schreb.) Dumort. nom. cons.; syn. Festuca arundinacea Schreb.); Japanese lawngrass (Zoysia japonica Steud.); Manilagrass [Zoysia matrella (L.) Merr.].

Diagnostic pathology is the foundation and gold standard for identifying carcinomas. However, high inter-observer variability substantially affects productivity in routine pathology and is especially ubiquitous in diagnostician-deficient medical centres. Despite rapid growth in computer-aided diagnosis (CAD), the application of whole-slide pathology diagnosis remains impractical. Here, we present a novel pathology whole-slide diagnosis method, powered by artificial intelligence, to address the lack of interpretable diagnosis. The proposed method masters the ability to automate the human-like diagnostic reasoning process and translate gigapixels directly to a series of interpretable predictions, providing second opinions and thereby encouraging consensus in clinics. Moreover, using 913 collected examples of whole-slide data representing patients with bladder cancer, we show that our method matches the performance of 17 pathologists in the diagnosis of urothelial carcinoma. We believe that our method provides an innovative and reliable means for making diagnostic suggestions and can be deployed at low cost as next-generation, artificial intelligence-enhanced CAD technology for use in diagnostic pathology. Diagnostic pathology currently requires substantial human expertise, often with high inter-observer variability. A whole-slide pathology method automates the prediction process and provides computer-aided diagnosis using artificial intelligence.

Hashing has become a popular tool on histopathology image analysis due to the significant gain in both computation and storage. However, most of current hashing techniques learn features and binary codes individually from whole images, or emphasize the inter-class difference but neglect the relevance order within the same classes. To alleviate these issues, in this paper, we propose a novel pairwise based deep ranking hashing framework. We first define a pairwise matrix to preserve intra-class relevance and inter-class difference. Then we propose an objective function that utilizes two identical continuous matrices generated by the hyperbolic tangent (tanh) function to approximate the pairwise matrix. Finally, we incorporate the objective function into a deep learning architecture to learn features and binary codes simultaneously. The proposed framework is validated on 5356 skeletal muscle and 2176 lung cancer images with four types of diseases, and it can achieve 97.49% classification accuracy, 97.49% mean average precision (MAP) with 100 returned images, and 0.51 NDCG score with 50 retrieved neighbors on 2032 query images.

Idiopathic Inflammatory Myopathy (IIM) is a common skeletal muscle disease that relates to weakness and inflammation of muscle. Early diagnosis and prognosis of different types of IIMs will guide the effective treatment. Interpretation of digitized images of the cross section muscle biopsy, which is currently done manually, provides the most reliable diagnostic information. With the increasing volume of images, the management and manual interpretation of the digitized muscle images suffer from low efficiency and high interobserver variabilities. In order to address these problems, we propose the first complete framework of automatic IIM diagnosis system (AIIMDs) for the management and interpretation of digitized skeletal muscle histopathology images. The proposed framework consists of several key components: (1) Automatic cell segmentation, perimysium annotation, and nuclei detection; (2) Histogram based feature extraction and quantification; (3) Content-based image retrieval (CBIR) to search and retrieve similar cases in the database for comparative study; and (4) Majority voting based classification to provide decision support for computer aided clinical diagnosis. Experiments show that the proposed diagnosis system provides efficient and robust interpretation of the digitized muscle image and computer aided diagnosis of IIM.

Soil microbial communities have been used as indicators of changes in soil health agroecosystems. However, few studies have evaluated soil health under turfgrass systems especially in semiarid climates. Our study determined whether microbial biomass and composition in residential soils were controlled by home age along a turfgrass chronosequence and evaluated effects of turfgrass management in structuring soil microbial communities. Soil samples were obtained from nine locations within each home age category: oldest (1950–1970), middle (1971–1990), newer (1991–2010), and newest (2011–present) in summer 2018 and 2019. Soil microbial biomass and composition were assessed using chloroform fumigation extraction method (CFEM) and ester-linked fatty acid methyl ester (EL-FAME) analysis. Soil microbial biomass carbon (MBC) and nitrogen (MBN) were 46–52% and 65–75% higher in oldest homes when compared to newest homes, respectively. Neither total FAMEs nor fungal FAME abundance differed for home age categories, but bacterial FAME abundance increased with home age. Non-parametric analysis determined no microbial abundance differences with management practices or turfgrass species. Pearson correlations indicated soil organic matter and silt content most consistently altered the microbial community. Soil microbial communities within semiarid, urban environments shifted from high fungal to bacterial dominated as landscapes matured, potentially due to long-term effects of irrigation, fertilization, and pesticide use. Our results indicate time after establishment was more important to development of soil microbial communities in semiarid, perennial turfgrass systems than subtle differences in management, suggesting soil health and resource conservation goals in this setting may be achievable with relatively low levels of management over time.

Soil organic matter (SOM) accumulation and carbon (C) sequestration are ecosystem services (ESs) provided by urban landscapes that are dominated by continuous grass cover, such as residential lawns. Organic matter and C sequestration are expected to increase over time, but few studies have determined potential soil organic carbon (SOC) accumulation from residential lawns in semiarid climates. The objectives were to evaluate physiochemical attributes of urban soils established under turfgrass landscapes of different ages and determine soil factors that differentiate urban soils in semiarid climate of Lubbock, TX. Soil samples (0−10 cm) were obtained from 10 residential lawns of homes built: pre-1970 (oldest), 1971−1990 (middle), 1991−2010 (newer), and after 2011 (newest). Soil texture, bulk density, extractable nutrients [phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), and iron (Fe)], pH, SOM, SOC, and total nitrogen (TN) were determined. Bulk density and pH were highest in newest homes and lowest in oldest homes. Increasing years of lawn establishment increased SOM, SOC, and TN content. Linear regression determined 0.036 % annual increase in SOM for residential lawns, but SOC accumulated at 0.021 kg C m−2 yr-1 for 53.6 yr. In principal component (PC) analysis, SOM, SOC, and TN contributed most to PC1 (36.2 %) reflecting components separating residential lawns by years of establishment, and primarily soil texture components for PC2 (22.5 %). The results of this study show that home age is a strong determinant of soil carbon content in semiarid urban turfgrass systems, with the greatest accumulation threshold in oldest homes over 50 years of age.

Hybrid vesicles, prepared from soy phosphatidylcholine (SPC), ion pair amphiphile (IPA), cholesterol (CHOL) and dihexadecyldimethylammmonium bromide (DHDAB), were investigated to assess their potential as novel drug delivery systems for indomethacin (IMC), a nonsteroidal anti inflammatory drug (NSAID). The size, polydispersity index and zeta potential values of the vesicles were monitored with respect to time using dynamic light scattering (DLS) measurements which confirmed the profound effect of both DHDAB and the drug. Incorporation of DHDAB, although resulting in the size enhancement of the liposome, however, enhanced vesicle stability induced by electrostatic repulsion. Both conventional and freeze-fractured transmission electron microscopic (TEM) studies revealed the spherical morphology of the vesicles. FTIR studies confirmed perceptible interaction among the drug and the lipid–surfactant mixtures. Thermal behavior of the vesicles was assessed by means of differential scanning calorimetric (DSC) studies in order to understand the interaction between the incorporated NSAID and lipids. The state of polarity of IMC and another fluorescent molecular probe (7-hydroxycoumarin) were examined via absorption and emission spectroscopy respectively. Results on the fluorescence anisotropy studies helped in understanding the head group packing of the amphiphiles in the vesicles. An in vitro release study of the NSAID from vesicles revealed that incorporation of DHDAB promoted the release of the drug. Among different proposed models, the Korsmeyer–Peppas model was found to be the best one and the release mechanism was predominantly Fickian diffusion. The formulations, in the absence and presence of IMC showed no cytotoxicity in healthy human blood cell lymphocyte as well as in the human breast adenocarcinoma cell line (MCF 7). The aforementioned studies provided deeper insight into the interaction pattern of DHDAB with hybrid vesicles thereby exploring the usefulness of such systems as effective drug carriers.

Compact binary representations of histopathology images using hashing methods provide efficient approximate nearest neighbor search for direct visual query in large-scale databases. They can be utilized to measure the probability of the abnormality of the query image based on the retrieved similar cases, thereby providing support for medical diagnosis. They also allow for efficient managing of large-scale image databases because of a low storage requirement. However, the effectiveness of binary representations heavily relies on the visual descriptors that represent the semantic information in the histopathological images. Traditional approaches with hand-crafted visual descriptors might fail due to significant variations in image appearance. Recently, deep learning architectures provide promising solutions to address this problem using effective semantic representations. In this paper, we propose a Deep Convolutional Hashing (DCH) method that can be trained “point-wise” to simultaneously learn both semantic and binary representations of histopathological images. Specifically, we propose a convolutional neural network (CNN) that introduces a latent binary encoding (LBE) layer for low dimensional feature embedding to learn binary codes. We design a joint optimization objective function that encourages the network to learn discriminative representations from the label information, and reduce the gap between the real-valued low dimensional embedded features and desired binary values. The binary encoding for new images can be obtained by forward propagating through the network and quantizing the output of the LBE layer. Experimental results on a large-scale histopathological image dataset demonstrate the effectiveness of the proposed method.

Nanostructured lipid carriers (NLCs), with potential drug delivery capabilities, were formulated using soy lecithin (SLC), tristearin (TS) and palmitic acid (PA) in the absence and presence of two anti-inflammatory drugs, diclofenac sodium (DNa) and indomethacin (IMC). Tween 60 was used as a stabilizer separately and in combination with sodium carboxymethyl cellulose (NaCMC, anionic), polyethylene glycol (PEG, nonionic) and an N,N-dimethyl-N-dodecyl derivative of hydroxyethyl cellulose (LM200, cationic). Both DNa and IMC substantially decreased the size and increased the polydispersity index (PDI) of the NLCs. The hydrodynamic parameters, viz., size, zeta potential, and polydispersity index, as well as the thermal behaviour of the NLCs, depended on the type and charge of the added polymers. Weak interactions between the drug and lipid matrices in the bulk mixtures were confirmed through FT-IR studies. The NLC formulations exhibited lower entrapment efficiency and loading content in the case of DNa compared to IMC due to the higher ionic nature of the former drug. The polymers influenced the entrapment efficiency and loading ability of the NLCs in case of both DNa and IMC. 85% of the entrapped DNa was released from the NLC, compared to 54% release in the case of IMC; the drug release rates were higher for the PEG and NaCMC coated systems. LM200 delayed the drug release process with respect to NaCMC and PEG. Both DNa- and IMC-loaded NLCs inhibited the growth of Gram-positive bacteria, Bacillus amyloliquefaciens. It was concluded that the physicochemical properties of NLCs could effectively be modified using polymers; thus, the biomimetic characteristics of lipids and architectural advantage of polymers can be combined to yield a superior drug delivery system.

This study aimed to investigate the effect of hydrocarbon chain length of nonionic surfactants, Tween 40 and Tween 60, on the physicochemical properties of nanostructured lipid carriers (NLCs). Two local anaesthetics, lidocaine (LIDO) and procaine hydrochloride (PRO·HCl), were incorporated in the NLCs. NLC formulations were prepared using sorbitantristearate (Span 65), soy lecithin (SLC) and stearic acid (SA) in a 2 : 2 : 1 mole ratio employing the hot homogenization technique. The systems were characterized by combined dynamic light scattering (DLS), transmission electron microscopy (TEM), differential scanning calorimetry (DSC) and spectroscopic studies. The formulations were found to be stable up to 60 days when kept at 4 °C. NLCs stabilized by Tween 60 were superior to the corresponding Tween 40 based formulations. A spherical morphology with smooth surfaces was evidenced by TEM measurements. DSC and polarity studies indicated that LIDO altered the crystallinity of the lipid matrices as it could insert into the core of the NLC. Entrapment efficiency (EE) and loading content (LC) studies revealed that Tween 60 stabilized NLCs have better drug loading capability than the Tween 40 based formulation. Controlled and prolonged drug release was experienced by Tween 60 stabilized drug loaded NLCs as studied by in vitro release kinetics. The developed NLCs could thus be considered to have prospects as novel drug carriers for controlled/sustained release to improve the time duration of anaesthesia, especially for topical application.

Diseased skeletal muscle expresses mononuclear cell infiltration in the regions of perimysium. Accurate annotation or segmentation of perimysium can help biologists and clinicians to determine individualized patient treatment and allow for reasonable prognostication. However, manual perimysium annotation is time consuming and prone to inter-observer variations. Meanwhile, the presence of ambiguous patterns in muscle images significantly challenge many traditional automatic annotation algorithms. In this paper, we propose an automatic perimysium annotation algorithm based on deep convolutional neural network (CNN). We formulate the automatic annotation of perimysium in muscle images as a pixel-wise classification problem, and the CNN is trained to label each image pixel with raw RGB values of the patch centered at the pixel. The algorithm is applied to 82 diseased skeletal muscle images. We have achieved an average precision of 94% on the test dataset.

In this paper, we introduce the semantic knowledge of medical images from their diagnostic reports to provide an inspirational network training and an interpretable prediction mechanism with our proposed novel multimodal neural network, namely TandemNet. Inside TandemNet, a language model is used to represent report text, which cooperates with the image model in a tandem scheme. We propose a novel dual-attention model that facilitates high-level interactions between visual and semantic information and effectively distills useful features for prediction. In the testing stage, TandemNet can make accurate image prediction with an optional report text input. It also interprets its prediction by producing attention on the image and text informative feature pieces, and further generating diagnostic report paragraphs. Based on a pathological bladder cancer images and their diagnostic reports (BCIDR) dataset, sufficient experiments demonstrate that our method effectively learns and integrates knowledge from multimodalities and obtains significantly improved performance than comparing baselines.

Mutual miscibility of soylecithin, tristearin, fatty acids (FAs), and curcumin was assessed by means of surface pressure-area isotherms at the air-solution interface in order to formulate modified solid lipid nanoparticles (SLN). Appearance of minima in the excess area (Aex) and changes in free energy of mixing (∆G(0)ex) were recorded for systems with 20 mole% FAs. Modified SLNs, promising as topical drug delivery systems, were formulated using the lipids in combination with curcumin, stabilized by an aqueous Tween 60 solution. Optimal formulations were assessed by judiciously varying the FA chain length and composition. Physicochemical properties of SLNs were studied such as the size, zeta potential (by dynamic light scattering), morphology (by freeze fracture transmission electron microscopy), and thermal behavior (by differential scanning calorimetry). The size and zeta potential of the formulations were in the range 300-500 nm and -10 to -20 mV, respectively. Absorption and emission spectroscopic analyses supported the dynamic light scattering and differential scanning calorimetry data and confirmed localization of curcumin to the palisade layer of SLNs. These nanoparticles showed a sustained release of incorporated curcumin. Curcumin-loaded SLNs were effective against a gram-positive bacterial species, Bacillus amyloliquefaciens. Our results on the physicochemical properties of curcumin-loaded SLNs, the sustained release, and on antibacterial activity suggest that SLNs are promising delivery agents for topical drugs.

Accurate segmentation of perimysium plays an important role in early diagnosis of many muscle diseases because many diseases contain different perimysium inflammation. However, it remains as a challenging task due to the complex appearance of the perymisum morphology and its ambiguity to the background area. The muscle perimysium also exhibits strong structure spanned in the entire tissue, which makes it difficult for current local patch-based methods to capture this long-range context information. In this paper, we propose a novel spatial clockwork recurrent neural network (spatial CW-RNN) to address those issues. Specifically, we split the entire image into a set of non-overlapping image patches, and the semantic dependencies among them are modeled by the proposed spatial CW-RNN. Our method directly takes the 2D structure of the image into consideration and is capable of encoding the context information of the entire image into the local representation of each patch. Meanwhile, we leverage on the structured regression to assign one prediction mask rather than a single class label to each local patch, which enables both efficient training and testing. We extensively test our method for perimysium segmentation using digitized muscle microscopy images. Experimental results demonstrate the superiority of the novel spatial CW-RNN over other existing state of the arts.

Ion-pair amphiphiles (IPAs) are neoteric pseudo-double-tailed compounds with potential as a novel substitute of phospholipid. IPA, synthesized by stoichiometric/equimolar mixing of aqueous solution of hexadecyltrimethylammonium bromide (HTMAB) and sodium dodecyl sulfate (SDS), was used as a potential substituent of naturally occurring phospholipid, soylecithin (SLC). Vesicles were prepared using SLC and IPA in different ratios along with cholesterol. The impact of IPA on SLC was examined by way of surface pressure (π)-area (A) measurements. Associated thermodynamic parameters were evaluated; interfacial miscibility between the components was found to depend on SLC/IPA ratio. Solution behavior of the bilayers, in the form of vesicles, was investigated by monitoring the hydrodynamic diameter, zeta potential, and polydispersity index over a period of 100 days. Size and morphology of the vesicles were also investigated by electron microscopic studies. Systems comprising 20 and 40 mol % IPA exhibited anomalous behavior. Thermal behavior of the vesicles, as scrutinized by differential scanning calorimetry, was correlated with the hydrocarbon chain as well as the headgroup packing. Entrapment efficiency (EE) of the vesicles toward the cationic dye methylene blue (MB) was also evaluated. Vesicles were smart enough to entrap the dye, and the efficiency was found to vary with IPA concentration. EE was found to be well above 80% for some stable dispersions. Such formulations thus could be considered to have potential as novel drug delivery systems.