Comparing Apples and Oranges: Advances in Disease Resistance Breeding of Woody Perennial Fruit Crops.

Apple and citrus are perennial tree fruit crops that are vital for nutritional security and agricultural economy and to achieve the Sustainable Development Goals of the United Nations. Apple scab and fire blight, along with Huanglongbing, canker, and tristeza virus, stand out as their most notorious diseases and annually destabilize fruit supply. An environmentally sound approach to managing these diseases is improving tree resistance through breeding and biotechnology. Perennial fruit tree germplasm collections are distributed globally and offer untapped potential as sources of resistance. However, long juvenility, specific pollination and flowering habits, and extensive outcrossing hinder apple and citrus breeding. Advances in breeding approaches including trans- and cis-genesis, genome editing, and rapid-cycle breeding, which, in addition to conventional crossbreeding, can all facilitate accelerated integration of resistance into elite germplasm. In addition, the global pool of available sources of resistance can be characterized by the existing genetic mapping and gene expression studies for accurate discovery of associated loci, genes, and markers to efficiently include these sources in breeding efforts. We discuss and propose a multitude of approaches to overcome the challenges of breeding for resistance in woody perennials and outline a technical path to reduce the time required for the ultimate deployment of disease-resistant cultivars.

Fatty Acid Composition, Phenolic Compounds, Phytosterols, and Lipid Oxidation of Single- and Double-Fractionated Olein of Safflower Oil Produced by Low-Temperature Crystallization.

By dry crystallization, concentrations of unsaturated fatty acids and bioactive compounds can be increased in olein and super-olein fractions in vegetable oils. Among all sources of vegetable oils, safflower oil (SO) possesses the maximum linoleic acid content. To boost the industrial applications of SO, two variants were produced by single- and two-stage crystallization. This study aimed to determine the fatty acid compositions, phenolic compounds, phytosterols, and oxidative stability of fractionated olein (OF) and double-fractionated olein (DFO) produced by dry crystallization. For this, SO was cooled to -45 °C and filtered, the filtrate was denoted as single-fractionated olein (OF), and 40% of this section was taken for analytical purposes, while the remaining 60% was again cooled to -70 °C and filtered, and the filtrate was denoted as double-fractionated olein (DFO). Unfractionated safflower (SO) was used as a control, filled in amber glass bottles, and stored at 20-25 °C for 90 days. Fatty acid compositions and phytosterols were determined by gas chromatography-mass spectrometry (GC-MS). Phenolic compounds and induction periods were determined by high-performance liquid chromatography (HPLC) and Rancimat. GC-MS analysis revealed that the C18:2 contents of SO, OF, and DFO were 77.63 ± 0.82, 81.57 ± 0.44, and 89.26 ± 0.48 mg/100 g (p < 0.05), respectively. The C18:1 contents of SO, OF, and DFO were 6.38 ± 0.19, 7.36 ± 0.24, and 9.74 ± 0.32 mg/100 g (p < 0.05), respectively. HPLC analysis showed that phenolic compounds were concentrated in the low-melting-point fractions. In DFO, concentrations of tyrosol, rutin, vanillin, ferulic acid, and sinapic acid were 57.36 ± 0.12, 129.45 ± 0.38, 165.11 ± 0.55, 183.61 ± 0.15, 65.94 ± 0.11, and 221.75 ± 0.29 mg/100 g, respectively. In SO, concentrations of tyrosol, rutin, vanillin, ferulic acid, and sinapic acid were 24.79 ± 0.08, 78.93 ± 0.25, 115.67 ± 0.41, 34.89 ± 0.51, and 137.26 ± 0.08 mg/100 g, respectively. In OF, concentrations of tyrosol, rutin, vanillin, ferulic acid, and sinapic acid were 35.96 ± 0.20, 98.69 ± 0.64, 149.14 ± 0.13, 57.53 ± 0.74, and 188.28 ± 0.82 mg/100 g, respectively. The highest concentrations of brassicasterol, campesterol, stigmasterol, ß-sitosterol, avenasterol, stigmastenol, and avenasterol were noted in DFO followed by OF and SO. The total antioxidant capacities of SO, OF, and DFO were 54.78 ± 0.12, 71.36 ± 0.58, and 86.44 ± 0.28%, respectively. After the end of the storage time, the peroxide values (POVs) of SO, OF, and DFO stored for 3 months were 0.68, 0.85, and 1.16 mequiv O2/kg, respectively, with no difference in the free fatty acid content.

An Analysis of the Performance and Comfort Properties of Fire-Protective Material by Using Inherently Fire-Retardant Fibers and Knitting Structures.

This paper investigates the development of fabric materials using several blends of inherently fire-resistant (FR) fibers and various knitted structures. The samples are evaluated with respect to their performance and comfort-related properties. Inherently fire-resistant fibers, e.g., Nomex, Protex, carbon and FR viscose, were used to develop different structures of knitted fabrics. Cross-miss, cross-relief, and vertical tubular structures were knitted by using optimum fiber blend proportions and combinations of stitches. Several important aspects of the fabric samples were investigated, e.g., their physical, mechanical and serviceability performance. Thermo-physiological and tactile/touch-related comfort properties were evaluated in addition to flame resistance performance. An analysis of mechanical performance indicated that the knitted structure has a significant influence on the tensile strength, bursting strength and pilling resistance. The cross-relief structure proved to be the strongest followed by the cross-miss and vertical tubular structures. The FR station suits made from 70:30 Protex/Nomex exhibited the best combination of tensile and bursting strength; therefore, this material is recommended for making a stable and durable station suit. Interestingly, it was also concluded from the experimental study that knitted samples with a cross-relief structure exhibit the best fire-resistance performance. Fiber blends of 70:30 Protex/Nomex and 70:30 Nomex/carbon were found to be optimum in terms of overall performance. The best flame resistance was achieved with Nomex:carbon fiber blends. These results were confirmed with vertical flammability tests, TGA, DTGA and cone calorimetry analysis. The optimization of blend composition as well as knitting structure/architecture is a crucial finding toward designing the best FR station suit in terms of mechanical, dimensional, thermal, thermo-physiological and flame resistance performance.

Chromosome-level phased genome assembly of "Antonovka" identified candidate apple scab-resistance genes highly homologous to HcrVf2 and HcrVf1 on linkage group 1.

Apple scab, a fungal disease caused by Venturia inaequalis, leads to losses in both yield and fruit quality of apples (Malus domestica Borkh.). Most commercial apple cultivars, including those containing the well-characterized Rvi6-scab-resistance locus on linkage group (LG) 1, are susceptible to scab. HcrVf2 and HcrVf1 are considered the main paralogs of the Rvi6 locus. The major apple scab-resistance loci Vhc1 in "Honeycrisp" and Rvi17 in "Antonovka," were identified in close proximity to HcrVf2. In this study, we used long-read sequencing and in silico gene sequence characterization to identify candidate resistance genes homologous to HcrVf2 and HcrVf1 in Honeycrisp and Antonovka. Previously published chromosome-scale phased assembly of Honeycrisp and a newly assembled phased genome of Antonovka 172670-B were used to identify HcrVf2 and HcrVf1 homologs spanning Vhc1 and Rvi17 loci. In combination with 8 available Malus assemblies, 43 and 46 DNA sequences highly homologous to HcrVf2 and HcrVf1, respectively, were identified on LG 1 and 6, with identity and coverage ranging between 87-95 and 81-95%, respectively. Among these homologs, 2 candidate genes in Antonovka and Honeycrisp haplome A are located in close physical proximity to the scab-resistance marker Ch-Vf1 on LG 1. They showed the highest identity and coverage (95%) of HcrVf2 and only minor changes in the protein motifs. They were identical by state between each other, but not with HcrVf2. This study offers novel genomic resources and insights into the Vhc1 and Rvi17 loci on LG 1 and identifies candidate genes for further resistance characterization.

Identification of genes associated with abiotic stress tolerance in sweetpotato using weighted gene co-expression network analysis.

Sweetpotato, Ipomoea batatas (L.), a key food security crop, is negatively impacted by heat, drought, and salinity stress. The orange-fleshed sweetpotato cultivar "Beauregard" was exposed to heat, salt, and drought treatments for 24 and 48 h to identify genes responding to each stress condition in leaves. Analysis revealed both common (35 up regulated, 259 down regulated genes in the three stress conditions) and unique sets of up regulated (1337 genes by drought, 516 genes by heat, and 97 genes by salt stress) and down regulated (2445 genes by drought, 678 genes by heat, and 204 genes by salt stress) differentially expressed genes (DEGs) suggesting common, yet stress-specific transcriptional responses to these three abiotic stressors. Gene Ontology analysis of down regulated DEGs common to both heat and salt stress revealed enrichment of terms associated with "cell population proliferation" suggestive of an impact on the cell cycle by the two stress conditions. To identify shared and unique gene co-expression networks under multiple abiotic stress conditions, weighted gene co-expression network analysis was performed using gene expression profiles from heat, salt, and drought stress treated 'Beauregard' leaves yielding 18 co-expression modules. One module was enriched for "response to water deprivation," "response to abscisic acid," and "nitrate transport" indicating synergetic crosstalk between nitrogen, water, and phytohormones with genes encoding osmotin, cell expansion, and cell wall modification proteins present as key hub genes in this drought-associated module. This research lays the groundwork for exploring to a further degree, mechanisms for abiotic stress tolerance in sweetpotato.

Effect of Lactiplantibacillus plantarum on the Conversion of Linoleic Acid of Vegetable Oil to Conjugated Linoleic Acid, Lipolysis, and Sensory Properties of Cheddar Cheese.

Conjugated linoleic acid (CLA) is perceived to protect the body from metabolic diseases. This study was conducted to determine the effect of Lactiplantibacillus plantarum (Lp. plantarum) on CLA production and sensory characteristics of cheddar cheese. Lp. plantarum can convert linoleic acid (LA) to CLA. To increase CLA in cheddar cheese and monitor the conversion of LA to CLA by Lp. plantarum, the LA content of cheese milk (3.4% fat) was increased by partially replacing fat with safflower oil (85% LA of oil) at 0, 3, 6, and 9% concentrations (T1, T2, T3, and T4). Furthermore, Lp. plantarum 108 colony-forming units (CFU)/mL (8 log CFU mL-1) was added in all treatments along with traditional cheddar cheese culture (Lactococcus lactis ssp. lactis and L. lactis ssp. cremoris). After 30 days of ripening, Lp. plantarum in T1, T2, T3, and T4 was 6.75, 6.72, 6.65, and 6.55 log CFU g-1. After 60 days of ripening, Lp. plantarum in T1, T2, T3, and T4 was 6.35, 6.27, 6.19, and 6.32 log CFU g-1. After 60 days of ripening, Lp. plantarum in T1, T2, T3, and T4 was 6.41, 6.25, 6.69, and 6.65 log CFU g-1. GC-MS analysis showed that concentrations of CLA in the 90 days' control, T1, T2, T3, and T4 were 1.18, 2.73, 4.44, 6.24, and 9.57 mg/100 g, respectively. HPLC analysis revealed that treatments containing Lp. plantarum and LA presented higher concentrations of organic acids than the control sample. The addition of safflower oil at all concentrations did not affect cheese composition, free fatty acids (FFA), and the peroxide value (POV) of cheddar cheese. Color flavor and texture scores of experimental cheeses were not different from the control cheese. It was concluded that Lp. plantarum and safflower oil can be used to increase CLA production in cheddar cheese.

Smart high-yield tomato cultivation: precision irrigation system using the Internet of Things.

The Internet of Things (IOT)-based smart farming promises ultrafast speeds and near real-time response. Precision farming enabled by the Internet of Things has the potential to boost efficiency and output while reducing water use. Therefore, IoT devices can aid farmers in keeping track crop health and development while also automating a variety of tasks (such as moisture level prediction, irrigation system, crop development, and nutrient levels). The IoT-based autonomous irrigation technique makes exact use of farmers' time, money, and power. High crop yields can be achieved through consistent monitoring and sensing of crops utilizing a variety of IoT sensors to inform farmers of optimal harvest times. In this paper, a smart framework for growing tomatoes is developed, with influence from IoT devices or modules. With the help of IoT modules, we can forecast soil moisture levels and fine-tune the watering schedule. To further aid farmers, a smartphone app is currently in development that will provide them with crucial data on the health of their tomato crops. Large-scale experiments validate the proposed model's ability to intelligently monitor the irrigation system, which contributes to higher tomato yields.

Optical Coherence Tomography Image Classification Using Hybrid Deep Learning and Ant Colony Optimization.

Optical coherence tomography (OCT) is widely used to detect and classify retinal diseases. However, OCT-image-based manual detection by ophthalmologists is prone to errors and subjectivity. Thus, various automation methods have been proposed; however, improvements in detection accuracy are required. Particularly, automated techniques using deep learning on OCT images are being developed to detect various retinal disorders at an early stage. Here, we propose a deep learning-based automatic method for detecting and classifying retinal diseases using OCT images. The diseases include age-related macular degeneration, branch retinal vein occlusion, central retinal vein occlusion, central serous chorioretinopathy, and diabetic macular edema. The proposed method comprises four main steps: three pretrained models, DenseNet-201, InceptionV3, and ResNet-50, are first modified according to the nature of the dataset, after which the features are extracted via transfer learning. The extracted features are improved, and the best features are selected using ant colony optimization. Finally, the best features are passed to the k-nearest neighbors and support vector machine algorithms for final classification. The proposed method, evaluated using OCT retinal images collected from Soonchunhyang University Bucheon Hospital, demonstrates an accuracy of 99.1% with the incorporation of ACO. Without ACO, the accuracy achieved is 97.4%. Furthermore, the proposed method exhibits state-of-the-art performance and outperforms existing techniques in terms of accuracy.

Quantum secure metrology for network sensing-based applications.

Quantum secure metrology protocols harness quantum effects to probe remote systems with enhanced precision and security. Traditional QSM protocols require multi-partite entanglement, which limits its near-term implementation due to technological constraints. This paper proposes a QSM scheme that employs Bell pairs to provide unconditional security while offering precision scaling beyond the standard quantum limit. We provide a detailed comparative performance analysis of our proposal under multiple attacks. We found that the employed controlled encoding strategy is far better than the parallel encoding of multi-partite entangled states with regard to the secrecy of the parameter. We also identify and characterize an intrinsic trade-off relationship between the maximum achievable precision and security under the limited availability of resources. The dynamic scalability of the proposed protocol makes it suitable for large-scale network sensing scenarios.

The Comparative Root System Architecture of Declining and Non-Declining Trees in Two Apple Orchards in New York.

Rapid apple decline is a phenomenon characterized by a weakening of young apple trees in high density orchards, often followed by their quick collapse. The nature of this phenomenon remains unclear. In this work, we investigated the root system architecture (RSA) of declining and non-declining apple trees in two orchards in New York State. High-density orchard A consisted of 4-year-old 'Honeycrisp' on 'Malling 9 Nic29', and conventional orchard B consisted of 8-year-old 'Fuji' on 'Budagovsky 9'. In both orchards, a negative correlation (-0.4--0.6) was observed between RSA traits and decline symptoms, suggesting that declining trees have weaker root systems. Scion trunk diameter at the graft union, total root length, and the length of fine and coarse roots were significantly (p < 0.05) reduced in declining trees in both orchards. Additionally, internal trunk necrosis at, above, and below the graft union was observed in declining trees in orchard A but not in orchard B. Finally, latent viruses were not associated with decline, as their occurrence was documented in declining and non-declining trees in orchard A, but not in orchard B. Together, these results showed weakened root systems of declining trees, suggesting that these trees may experience deficiencies in water and nutrient uptake, although distinct RSA and trunk health traits between the two orchards were noticeable.

Laparoscopy in Low- and Middle-Income Countries: A Survey Study.

INTRODUCTION: An increasing shift towards non-communicable diseases and an existing high surgical burden of disease in low-middle-income countries (LMICs) has impelled the need for implementing laparoscopic surgery, a safe and cost-effective surgical service. However, despite countless benefits, laparoscopic surgery programs remain limited throughout LMICs, and limited understanding is known of healthcare professionals' views regarding the implementation of laparoscopic surgery in their local healthcare environments. Therefore, the purpose of this study is to better understand the perceived challenges and barriers to implementing long-term laparoscopic surgery programs from the perspective of healthcare professionals. METHODS: Upon receiving ethical approval from the McGill University Health Center (MUHC), a nine-question survey (concerning attributes required to establish a successful laparoscopic program in LMICs and to gain insight into what surgeons from LMICs believed were the necessary next steps) was pilot tested amongst faculty members, and subsequently disseminated to healthcare professionals practicing in LMICs. Explicit consent was obtained from the participants before answering the survey.  Results: Thirty-four participants representing a total of 35 countries participated in the survey with the majority having received laparoscopic surgery training. Overall, participant responses were characterized by two major themes. Highlighted in the first theme, Laparoscopic Experience and Training Curriculum, were responses concerning current laparoscopic training and education, improved career opportunities provided by laparoscopic training, and a particular existing potential to incorporate laparoscopic surgery into the current surgical curriculum at various levels of training. Emphasized in the second theme, Challenges and Next Steps, were responses concerning barriers to the implementation of laparoscopic surgery, current institutional capabilities, and the need for improving mentorship through existing surgical societies such as the College of Surgeons of East, Central, and Southern Africa (COSECSA), West African College of Surgeons (WACS), and The Pan-African Academy of Christian Surgeons (PAACS). CONCLUSIONS: A buy-in from the government, hospitals, staff, and industry is crucial for the long-term implementation of laparoscopic surgery in LMICs, which can only be accomplished through increased advocacy and the dissemination of the benefits of minimally invasive surgery both economically and socially.

Examination of Large Chromosomal Inversions in the Genome of Erwinia amylovora Strains Reveals Worldwide Distribution and North America-Specific Types.

Erwinia amylovora is a relatively homogeneous species with low genetic diversity at the nucleotide level. However, phenotypic differences and genomic structural variations among E. amylovora strains have been documented. In this study, we identified 10 large chromosomal inversion (LCI) types in the Spiraeoideae-infecting (SI) E. amylovora strains by combining whole genome sequencing and PCR-based molecular markers. It was found that LCIs were mainly caused by homologous recombination events among seven rRNA operons (rrns) in SI E. amylovora strains. Although ribotyping results identified inter- and intra-variations in the internal transcribed spacer (ITS1 and ITS2) regions among rrns, LCIs tend to occur between rrns transcribed in the opposite directions and with the same tRNA content (tRNA-Glu or tRNA-Ile/Ala) in ITS1. Based on the LCI types, physical/estimated replichore imbalance (PRI/ERI) was examined and calculated. Among the 117 SI strains evaluated, the LCI types of Ea1189, CFBP1430, and Ea273 were the most common, with ERI values at 1.31, 7.87, and 4.47°, respectively. These three LCI types had worldwide distribution, whereas the remaining seven LCI types were restricted to North America (or certain regions of the United States). Our results indicated ongoing chromosomal recombination events in the SI E. amylovora population and showed that LCI events are mostly symmetrical, keeping the ERI less than 15°. These findings provide initial evidence about the prevalence of certain LCI types in E. amylovora strains, how LCI occurs, and its potential evolutionary advantage and history, which might help track the movement of the pathogen.

Multi-robot task assignment for serving people quarantined in multiple hotels during COVID-19 pandemic.

Background: Efficiently combating with the coronavirus disease 2019 (COVID-19) has been challenging for medics, police and other service providers. To reduce human interaction, multi-robot systems are promising for performing various missions such as disinfection, monitoring, temperature measurement and delivering goods to people quarantined in prescribed homes and hotels. This paper studies the task assignment problem for multiple dispersed homogeneous robots to visit a set of prescribed hotels to perform tasks such as body temperature assessment or oropharyngeal swabs for people quarantined in the hotels while trying to minimize the robots' total operation time. Each robot can move to multiple hotels sequentially within its limited maximum operation time to provide the service. Methods: The task assignment problem generalizes the multiple traveling salesman problem, which is an NP-hard problem. The main contributions of the paper are twofold: (I) a lower bound on the robots' total operation time to serve all the people has been found based on graph theory, which can be used to approximately evaluate the optimality of an assignment solution; (II) several efficient marginal-cost-based task assignment algorithms are designed to assign the hotel-serving tasks to the robots. Results: In the Monte Carlo simulations where different numbers of robots need to serve the people quarantined in 30 and 90 hotels, the designed task assignment algorithms can quickly (around 30 ms) calculate near-optimal assignment solutions (within 1.15 times of the optimal value). Conclusions: Numerical simulations show that the algorithms can lead to solutions that are close to the optimal compared with the competitive genetic algorithm and greedy algorithm.

Flammability and comfort properties of blended knit fabrics made from inherently fire-resistant fibers to use for fire fighters.

Achievement of better comfort properties in station suits of fire fighters without compromising flame retardancy is an utmost necessity. Inherently fire-resistant fibers play vital role in this scope. In this work twenty-three plain single jersey knitted fabric samples were developed by using five inherently fire-resistant fibers. The fibers used were meta-aramid Nomex, fire resistant (FR-Viscose, modacrylic (Protex), FR-polyester (Recron) and carbon fibers. All the fibers were blended in different blend ratios. Vertical flammability test was performed to investigate the flammability properties. Thermal characterization of the samples was done using thermogravimetric analysis (TGA). Thermophysiological and wearing comfort properties of all samples were evaluated. Scanning electron microscopy (SEM) was carried out for the evaluation of surface morphology of the fibers after charring. Results of vertical flammability test revealed that sample containing 100% Nomex fibers produced minimum char length. Nomex and carbon fiber blended fabric provided better moisture management along with better flame retardant behavior. Statistical tool named as Principal Component Analysis (PCA) was utilized for the optimization of all responses. All the samples were ranked as per principal component analysis. Sample containing 50/50 FR-polyester and FR-viscose fibers was found to be the top ranked, as this sample provided optimum flammability and comfort related performance.

Evaluation of Malus Germplasm Identifies Genetic Sources of Powdery Mildew and Frogeye Leaf Spot Resistance for Apple Breeding.

Apple is an important fruit crop of temperate regions. The narrow genetic base of commercially cultivated apples has resulted in its vulnerability to a large number of fungal, bacterial, and viral pathogens. Apple breeders are always seeking new sources of resistance within the cross-compatible Malus species that can be deployed into elite genetic backgrounds. We have evaluated resistance to two major fungal diseases of apples: powdery mildew and frogeye leaf spot, using a germplasm collection of 174 Malus accessions to identify novel sources of genetic resistance. In 2020 and 2021, we evaluated these accessions for the incidence and severity of powdery mildew and frogeye leaf spot diseases at Cornell AgriTech, Geneva, New York, in a partially managed orchard. The severity and incidence of powdery mildew and frogeye leaf spot, as well as weather parameters were recorded in June, July, and August. Total incidence of powdery mildew and frogeye leaf spot infections increased from 33 to 38%, and 56 to 97% in 2020 and 2021, respectively. Our analysis showed that relative humidity and precipitation correlate with powdery mildew and frogeye leaf spot susceptibility. The predictor variables with highest impact to the variability of powdery mildew were accessions and relative humidity in May. A total of 65 Malus accessions were found to be resistant to powdery mildew, and only one accession showed moderate resistance to frogeye leaf spot. Several of these accessions belong to Malus hybrid species and domesticated apples and can therefore be potential sources of novel resistance alleles for apple breeding.

Entanglement detection with artificial neural networks.

Quantum entanglement is one of the essential resources involved in quantum information processing tasks. However, its detection for usage remains a challenge. The Bell-type inequality for relative entropy of coherence serves as an entanglement witness for pure entangled states. However, it does not perform reliably for mixed entangled states. This paper constructs a classifier by employing the relationship between coherence and entanglement for supervised machine learning methods. This method encodes multiple Bell-type inequalities for the relative entropy of coherence into an artificial neural network to detect the entangled and separable states in a quantum dataset.

Construction of a high-density bin-map and identification of fruit quality-related quantitative trait loci and functional genes in pear.

Pear (Pyrus spp.) is one of the most common fruit crops grown in temperate regions worldwide. Genetic enhancement of fruit quality is a fundamental goal of pear breeding programs. The genetic control of pear fruit quality traits is highly quantitative, and development of high-density genetic maps can facilitate fine-mapping of quantitative trait loci (QTLs) and gene identification. Bin-mapping is a powerful method of constructing high-resolution genetic maps from large-scale genotyping datasets. We performed whole-genome sequencing of pear cultivars 'Niitaka' and 'Hongxiangsu' and their 176 F 1 progeny to identify genome-wide single-nucleotide polymorphism (SNP) markers for constructing a high-density bin-map of pear. This analysis yielded a total of 1.93 million SNPs and a genetic bin-map of 3190 markers spanning 1358.5 cM, with an average adjacent interval of 0.43 cM. This bin-map, along with other high-density genetic maps in pear, improved the reference genome assembly from 75.5 to 83.7% by re-anchoring the scaffolds. A quantitative genetic analysis identified 148 QTLs for 18 fruit-related traits; among them, QTLs for stone cell content, several key monosaccharides, and fruit pulp acids were identified for the first time in pear. A gene expression analysis of six pear cultivars identified 399 candidates in the identified QTL regions, which showed expression specific to fruit developmental stages in pear. Finally, we confirmed the function of PbrtMT1, a tonoplast monosaccharide transporter-related gene responsible for the enhancement of fructose accumulation in pear fruit on linkage group 16, in a transient transformation experiment. This study provides genomic and genetic resources as well as potential candidate genes for fruit quality improvement in pear.

Path Planning for Wheeled Mobile Robot in Partially Known Uneven Terrain.

Path planning for wheeled mobile robots on partially known uneven terrain is an open challenge since robot motions can be strongly influenced by terrain with incomplete environmental information such as locally detected obstacles and impassable terrain areas. This paper proposes a hierarchical path planning approach for a wheeled robot to move in a partially known uneven terrain. We first model the partially known uneven terrain environment respecting the terrain features, including the slope, step, and unevenness. Second, facilitated by the terrain model, we use A⋆ algorithm to plan a global path for the robot based on the partially known map. Finally, the Q-learning method is employed for local path planning to avoid locally detected obstacles in close range as well as impassable terrain areas when the robot tracks the global path. The simulation and experimental results show that the designed path planning approach provides satisfying paths that avoid locally detected obstacles and impassable areas in a partially known uneven terrain compared with the classical A⋆ algorithm and the artificial potential field method.

Breeding and genetics of disease resistance in temperate fruit trees: challenges and new opportunities.

Climate change, large monocultures of disease-susceptible cultivars, overuse of pesticides, and the emergence of new pathogens or pathogenic strains causing economic losses are all major threats to our environment, health, food, and nutritional supply. Temperate tree fruit crops belonging to the Rosaceae family are the most economically important and widely grown fruit crops. These long-lived crops are under attack from many different pathogens, incurring major economic losses. Multiple chemical sprays to control various diseases annually is a common practice, resulting in significant input costs, as well as environmental and health concerns. Breeding for disease resistance has been undertaken primarily in pome fruit crops (apples and pears) for a few fungal and bacterial diseases, and to a lesser extent in some stone fruit crops. These breeding efforts have taken multiple decades due to the biological constraints and complex genetics of these tree fruit crops. Over the past couple of decades, major advances have been made in genetic and physical mapping, genomics, biotechnology, genome sequencing, and phenomics, along with accumulation of large germplasm collections in repositories. These valuable resources offer opportunities to make significant advances in greatly reducing the time needed to either develop new cultivars or modify existing economic cultivars for enhanced resistance to multiple diseases. This review will cover current knowledge, challenges, and opportunities in breeding for disease resistance in temperate tree fruit crops.