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Modeling of Individual Fruit-Bearing Capacity of Trees Is Aimed at Optimizing Fruit Quality of Malus x domestica Borkh. 'Gala'
The capacity of apple trees to produce fruit of a desired diameter, i.e., fruit-bearing capacity (FBC), was investigated by considering the inter-tree variability of leaf area (LA). The LA of 996 trees in a commercial apple orchard was measured by using a terrestrial two-dimensional (2D) light detection and ranging (LiDAR) laser scanner for two consecutive years. The FBC of the trees was simulated in a carbon balance model by utilizing the LiDAR-scanned total LA of the trees, seasonal records of fruit and leaf gas exchanges, fruit growth rates, and weather data. The FBC was compared to the actual fruit size measured in a sorting line on each individual tree. The variance of FBC was similar in both years, whereas each individual tree showed different FBC in both seasons as indicated in the spatially resolved data of FBC. Considering a target mean fruit diameter of 65 mm, FBC ranged from 84 to 168 fruit per tree in 2018 and from 55 to 179 fruit per tree in 2019 depending on the total LA of the trees. The simulated FBC to produce the mean harvest fruit diameter of 65 mm and the actual number of the harvested fruit >65 mm per tree were in good agreement. Fruit quality, indicated by fruit's size and soluble solids content (SSC), showed enhanced percentages of the desired fruit quality according to the seasonally total absorbed photosynthetic energy (TAPE) of the tree per fruit. To achieve a target fruit diameter and reduce the variance in SSC at harvest, the FBC should be considered in crop load management practices. However, achieving this purpose requires annual spatial monitoring of the individual FBC of trees.
Vitronectin-based hydrogels recapitulate neuroblastoma growth conditions
The tumor microenvironment plays an important role in cancer development and the use of 3D in vitro systems that decouple different elements of this microenvironment is critical for the study of cancer progression. In neuroblastoma (NB), vitronectin (VN), an extracellular matrix protein, has been linked to poor prognosis and appears as a promising therapeutic target. Here, we developed hydrogels that incorporate VN into 3D polyethylene glycol (PEG) hydrogel networks to recapitulate the native NB microenvironment. The stiffness of the VN/PEG hydrogels was modulated to be comparable to the in vivo values reported for NB tissue samples. We used SK-N-BE (2) NB cells to demonstrate that PEGylated VN promotes cell adhesion as the native protein does. Furthermore, the PEGylation of VN allows its crosslinking into the hydrogel network, providing VN retention within the hydrogels that support viable cells in 3D. Confocal imaging and ELISA assays indicate that cells secrete VN also in the hydrogels and continue to reorganize their 3D environment. Overall, the 3D VN-based PEG hydrogels recapitulate the complexity of the native tumor extracellular matrix, showing that VN-cell interaction plays a key role in NB aggressiveness, and that VN could potentially be targeted in preclinical drug studies performed on the presented hydrogels.
The Dissemination and Molecular Characterization of Clonal Complex 361 (CC361) Methicillin-Resistant Staphylococcus aureus (MRSA) in Kuwait Hospitals
Methicillin-resistant Staphylococcus aureus (MRSA) belonging to clonal complex 361 (CC361-MRSA) is rare among patients' populations globally. However, CC361-MRSA has been isolated with an increasing trend among patients in Kuwait hospitals since 2010. This study investigated the molecular characteristics of CC361-MRSA isolated from patients in Kuwait hospitals in 2016-2018 to understand their genetic relatedness and virulence determinants. Of 5,223 MRSA isolates investigated by DNA microarray, 182 (3.4%) isolates obtained in 2016 (N = 55), 2017 (N = 56), and 2018 (N = 71) were identified as CC361-MRSA. The CC361-MRSA isolates were analyzed further using antibiogram, spa typing and multi locus sequence typing (MLST). Most of the isolates were resistant to fusidic acid (64.8%), kanamycin (43.4%), erythromycin (36.3%), and clindamycin (14.3%) encoded by fusC, aphA3, and erm(B)/erm(C) respectively. Nine isolates (4.9%) were resistant to linezolid mediated by cfr. The isolates belonged to 22 spa types with t3841 (N = 113), t315 (N = 16), t1309 (N = 14), and t3175 (N = 5) constituting 81.3% of the spa types, four genotypes (strain types), CC361-MRSA-[V/VT + fus] (N = 112), CC361-MRSA-IV, WA MRSA-29 (N = 36), CC361-MRSA-V, WA MRSA-70/110 (N = 33) and CC361-MRSA-[V + fus] variant (N = 1). MLST conducted on 69 representative isolates yielded two sequence types: ST361 (11/69) and ST672 (58/69). All CC361-MRSA isolates were positive for cap8, agr1, and the enterotoxin egc gene cluster (seg, sei, selm, seln, selo, and selu). The tst1 was detected in 19 isolates. The immune evasion cluster (IEC) genes type B (scn, chp, and sak) and type E (scn and sak) were detected in 20 and 152 isolates, respectively. The CC361-MRSA circulating in Kuwait hospitals consisted of two closely related sequence types, ST361 and ST672 with ST672-MRSA [V/VT + fus] as the dominant genotype. The dissemination of these newly emerged clones and the emergence of linezolid resistance limits therapeutic options, as well as present significant challenges for the control of MRSA infections in Kuwait hospitals.
Understanding Beta-Lactam-Induced Lysis at the Single-Cell Level
Mechanical rupture, or lysis, of the cytoplasmic membrane is a common cell death pathway in bacteria occurring in response to β-lactam antibiotics. A better understanding of the cellular design principles governing the susceptibility and response of individual cells to lysis could indicate methods of potentiating β-lactam antibiotics and clarify relevant aspects of cellular physiology. Here, we take a single-cell approach to bacterial cell lysis to examine three cellular features-turgor pressure, mechanosensitive channels, and cell shape changes-that are expected to modulate lysis. We develop a mechanical model of bacterial cell lysis and experimentally analyze the dynamics of lysis in hundreds of single Escherichia coli cells. We find that turgor pressure is the only factor, of these three cellular features, which robustly modulates lysis. We show that mechanosensitive channels do not modulate lysis due to insufficiently fast solute outflow, and that cell shape changes result in more severe cellular lesions but do not influence the dynamics of lysis. These results inform a single-cell view of bacterial cell lysis and underscore approaches of combatting antibiotic tolerance to β-lactams aimed at targeting cellular turgor.
Early Detection of Stripe Rust in Winter Wheat Using Deep Residual Neural Networks
Stripe rust (Pst) is a major disease of wheat crops leading untreated to severe yield losses. The use of fungicides is often essential to control Pst when sudden outbreaks are imminent. Sensors capable of detecting Pst in wheat crops could optimize the use of fungicides and improve disease monitoring in high-throughput field phenotyping. Now, deep learning provides new tools for image recognition and may pave the way for new camera based sensors that can identify symptoms in early stages of a disease outbreak within the field. The aim of this study was to teach an image classifier to detect Pst symptoms in winter wheat canopies based on a deep residual neural network (ResNet). For this purpose, a large annotation database was created from images taken by a standard RGB camera that was mounted on a platform at a height of 2 m. Images were acquired while the platform was moved over a randomized field experiment with Pst-inoculated and Pst-free plots of winter wheat. The image classifier was trained with 224 × 224 px patches tiled from the original, unprocessed camera images. The image classifier was tested on different stages of the disease outbreak. At patch level the image classifier reached a total accuracy of 90%. To test the image classifier on image level, the image classifier was evaluated with a sliding window using a large striding length of 224 px allowing for fast test performance. At image level, the image classifier reached a total accuracy of 77%. Even in a stage with very low disease spreading (0.5%) at the very beginning of the Pst outbreak, a detection accuracy of 57% was obtained. Still in the initial phase of the Pst outbreak with 2 to 4% of Pst disease spreading, detection accuracy with 76% could be attained. With further optimizations, the image classifier could be implemented in embedded systems and deployed on drones, vehicles or scanning systems for fast mapping of Pst outbreaks.