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The response of tomato plants ( L. cv. Aromata) to continuous light (CL) in relation to photosynthesis, abscisic acid (ABA) and reactive oxygen species (ROS) was investigated to improve the understanding of the development and/or alleviation of CL-induced leaf injury in constant and diurnal temperature fluctuations with similar daily light integral and daily mean temperature. The plants were grown in three photoperiodic treatments for 15 days; One treatment with a 16/8 h light/dark period and a light/dark temperature of 27/17°C (Control), two CL treatments with 24 h photoperiods, one with a constant temperature of 24°C (CLCT) and the other one with variable temperature of 27/17°C for 16/8 ho, respectively (CLVT). A diurnal pattern of stomatal conductance ( ) and [ABA] was observed in the plants grown in the control and CLVT conditions, while the plants in CLCT conditions experienced a significant decrease in stomatal conductance aligned with an increase in ABA. The net photosynthesis ( ) was significantly reduced in CLCT, aligned with a significant decrease in the maximum rate of Rubisco carboxylation ( ), the maximum rate of electron transport ( ) and mesophyll diffusion conductance to CO ( ) in comparison to the control and CLVT. An increased production of H O and O linked with increased activities of antioxidative enzymes was seen in both CL treatments, but despite of this, leaf injuries were only observed in the CLCT treatment. The results suggest that the diurnal temperature fluctuations alleviated the CL injury symptoms, probably because the diurnal cycles of cellular mechanisms were maintained. The ROS were shown not to be directly involved in CL-induced leaf injury, since both ROS production and scavenging was highest in CLVT without leaf chlorotic symptoms.

Cultivated tomato species develop leaf injury while grown in continuous light (CL). Growth, photosynthesis, carbohydrate metabolism and antioxidative enzyme activities of a cultivated (Solanum lycopersicum L. 'Aromata') and a wild tomato species (Solanum pimpinellifolium L.) were compared in this study aiming to analyze the species-specific differences and thermoperiod effects in responses to CL. The species were subjected to three photoperiodic treatments for 12 days in climate chambers: 16-h photoperiod with a light/dark temperature of 26/16°C (P16D10 or control); CL with a constant temperature of 23°C (P24D0); CL with a variable temperature of 26/16°C (P24D10). The results showed that both species grown in CL had higher dry matter production due to the continuous photosynthesis and a subsequent increase in carbon gain. In S. lycopersicum, the rate of photosynthesis and the maximum photochemical efficiency of photosystem II declined in CL with the development of leaf chlorosis, reduction in the leaf chlorophyll content and a higher activity of antioxidative enzymes. The normal diurnal patterns of starch and sugar were only present under control conditions. The results demonstrated that CL conditions mainly affected the photosynthetic apparatus of a cultivated species (S. lycopersicum), and to a less degree to the wild species (S. pimpinellifolium). The negative effects of the CL could be alleviated by diurnal temperature variations, but the physiological mechanisms behind these are less clear. The results also show that the genetic potential for reducing the negative effects of CL does exist in the tomato germplasm.

Iron misregulation promotes oxidative stress, a proposed pathological mechanism in neurodegenerative disease. The aim of this study was to evaluate serum iron metabolism indicators in 60 amyotrophic lateral sclerosis (ALS) patients and 44 age matched controls. Serum ferritin levels were significantly increased in ALS patients compared to controls ( p < 0.001), while no differences in the levels of serum iron, transferrin, iron saturation or total iron binding capacity were found. Likewise no differences in C reactive protein (CRP) or caeruloplasmin were detected, suggesting that the elevated ferritin levels in ALS did not merely indicate an acute phase response. The increased ferritin level may reflect a general increase in stored iron or be a consequence of ongoing muscle degeneration.

Salinity is one of the major abiotic stresses, which adversely affects the crop productivity. Thirty rice genotypes of diverse origin including three salt tolerant check varieties, Binadhan-8, Binadhan-10 and Pokkali, were used to evaluate salt tolerance at seedling stage and to determine the genetic diversity using microsatellite markers. Salinity screening was done at the seedling stage using hydroponic system following IRRI standard protocol. Three salinity levels as 6dSm-1, 8dSm-1, and 10dSm-1 were used along with control. Data were recorded on root length, shoot length and dry weight and the genotypes were scored based on modified standard evaluation score (SES) for visual injury. Sixteen SSR markers were used to study the genetic variation within 30 rice genotypes. A total of 65 alleles with an average of 4.06 allele per locus were detected among 30 rice genotypes. The polymorphism information content (PIC) value ranged from 0.24 to 0.86 with an average of 0.51. The unweighted pair group method with arithmetic mean (UPGMA) dendrogram revealed four clusters. Among them cluster I identified 5 salt tolerant genotypes and cluster IV separated one tolerant and one moderately tolerant genotype. Based on SES evaluation and molecular analysis genotypes Balam, THDB, Q-31, Ab.Hai, BR-5, FR13A ware salt tolerant; Moulota, Super hybrid, Y-1281, Binadhan-16 were moderate salt tolerant. This information could be useful for selection of suitable genotypes for developing salt tolerant rice variety through molecular breeding.

Background: Tolerance is defined as a phenomenon in which exposure to a drug results in a decrease of an effect or the requirement of a higher dose to maintain an effect. The fear of a patient developing opioid tolerance contributes regularly to the stigmatization and withholding of intrathecal opioid therapy for chronic pain of non-cancer origin. Objectives: The aim of this study was to describe the intrathecal opioid dose escalation throughout the years in chronic non-cancer pain patients. A secondary objective was the development of an intrathecal opioid dose predictive model. Study Design: Retrospective assessment of medical records. Setting: Department of Pain Management, Russells Hall Hospital, Dudley, United Kingdom. Methods: Medical records were reviewed and pump refill notes screened from the date of implant through November 2010 for 31 patients undertaking continuous intrathecal opioid therapy. All the patients included had undertaken a minimum of 6 years of intrathecal therapy when the data were collected. Results: Significant increases in the intrathecal morphine dose were verified between follow-up at one year and all subsequent observations, F (2.075, 62.238) = 13.858, 0 < 0.001, but ceased to be significant from year 3 onwards, indicating stability of the morphine dose, F (3, 90) = 2.516, P = 0.63. A model that accounts for 76% of the variability of morphine doses at year 6 based on year 2 assessment combined with duration of pain prior to initiation of intrathecal therapy was developed: year 6 dose = -0.509 + (1.296 x [year 2 dose]) + (0.061 x [duration of pain]). Limitations: Retrospective study. Conclusion: The opioid dose escalation observed throughout the years was modest and not significant following year 3 of therapy. The model developed has the potential to assist the physician in the identification of a need for alternative treatment strategies. Furthermore, since many of the pump replacements are performed prior to year 6, it can also assist in the informed decision of the benefits and risks of the maintenance of this therapy. Key words: Chronic pain, non-cancer pain, intrathecal opioid therapy, opioid dose escalation, predictive mode

Genomic profiling of hematologic malignancies has augmented our understanding of variants that contribute to disease pathogenesis and supported development of prognostic models that inform disease management in the clinic. Tumor only sequencing assays are limited in their ability to identify definitive somatic variants, which can lead to ambiguity in clinical reporting and patient management. Here, we describe the MSK-IMPACT Heme cohort, a comprehensive data set of somatic alterations from paired tumor and normal DNA using a hybridization capture-based next generation sequencing platform. We highlight patterns of mutations, copy number alterations, and mutation signatures in a broad set of myeloid and lymphoid neoplasms. We also demonstrate the power of appropriate matching to make definitive somatic calls, including in patients who have undergone allogeneic stem cell transplant. We expect that this resource will further spur research into the pathobiology and clinical utility of clinical sequencing for patients with hematologic neoplasms. Targeted sequencing panels such as MSK-IMPACT have been successfully used to profile solid tumours in clinical settings. Here, the authors develop and implement the MSK-IMPACT Heme sequencing panel and platform to profile haematologic malignancies using paired tumor and normal tissues.

Status of organic carbon （C）, total nitrogen （N）, available potassium （K）, calcium （Ca） and phosphorus （P） in three different depths （0-5 cm, 5-15 cm and 15-30 cm） on two hill slopes of 35% and 55% in orange orchard cultivated by the Mro tribe of Chittagong Hill. Tracts （CHTs） were evaluated and compared with those in degraded bush forests, through digging three profiles in each land use. The content of all the five nutrients was found to be higher in the soil of orange orchard than in the soil of forest. But the variation was not consistent for both the slopes. The content varied depth wise also, having the highest value in surface soil in case of both the land uses on both the slopes. A mean available K content was significantly higher in orange orchard than in forest on 55% slope, while it was lower on 35% slope. Surface soil contained the nutrients of K and Ca with the amount of 0.2905-mg·g^-1 soil and 3.025-mg·g^-1 soil respectively in the orchard, while 0.1934-mg·g^-1 soil and 1.6083-mg·g^-1 soil were respectively in the forest. Organic carbon and total nitrogen were found more or less similar in surface soil on both the land uses showing a slight difference. Available P was found only in orange orchard, and in forest it was too little in amount to detect by the spectrophotometer. The degraded forests were poor in nutrient content due to high rate of soil erosion, which would be possible to be improved by bringing it under tree cover as proved by the adaptation of orange orchard there.

Disease risk prediction is a rising challenge in the medical domain. Researchers have widely used machine learning algorithms to solve this challenge. The k -nearest neighbour (KNN) algorithm is the most frequently used among the wide range of machine learning algorithms. This paper presents a study on different KNN variants (Classic one, Adaptive, Locally adaptive, k-means clustering, Fuzzy, Mutual, Ensemble, Hassanat and Generalised mean distance) and their performance comparison for disease prediction. This study analysed these variants in-depth through implementations and experimentations using eight machine learning benchmark datasets obtained from Kaggle, UCI Machine learning repository and OpenML. The datasets were related to different disease contexts. We considered the performance measures of accuracy, precision and recall for comparative analysis. The average accuracy values of these variants ranged from 64.22% to 83.62%. The Hassanaat KNN showed the highest average accuracy (83.62%), followed by the ensemble approach KNN (82.34%). A relative performance index is also proposed based on each performance measure to assess each variant and compare the results. This study identified Hassanat KNN as the best performing variant based on the accuracy-based version of this index, followed by the ensemble approach KNN. This study also provided a relative comparison among KNN variants based on precision and recall measures. Finally, this paper summarises which KNN variant is the most promising candidate to follow under the consideration of three performance measures (accuracy, precision and recall) for disease prediction. Healthcare researchers and stakeholders could use the findings of this study to select the appropriate KNN variant for predictive disease risk analytics.

This study synthesized eighteen phenyl and furan rings containing thiazole Schiff base derivatives 2(a–r) in five series, and spectral analyses confirmed their structures. The in vitro antibacterial activities of the synthesized analogs against two gram-positive and two gram-negative bacteria were evaluated by disk diffusion technique. Compounds ( 2d ) and ( 2n ) produced prominently high zone of inhibition with 48.3 ±  0.6 mm and 45.3 ±  0.6 mm against B. subtilis , respectively, compared to standard ceftriaxone (20.0 ±  1.0 mm). However, the antibacterial potency of the compounds with furan ring was more notable than that of phenyl ring-containing derivatives. Molecular docking and dynamic study were performed based on the wet lab outcomes of ( 2d ) and ( 2n ), where both derivatives remained in the binding site of the receptors during the whole simulation time with RMSD and RMSF values below 2 nm. In silico ADMET prediction studies of the synthesized compounds validated their oral bioavailability. A more detailed study of the quantitative structure-activity relationship is required to predict structural modification on bioactivity and MD simulation to understand their therapeutic potential and pharmacokinetics.

Unmanned Aerial Vehicles (UAVs) are widely available in the current market to be used either for recreation as a hobby or to serve specific industrial requirements, such as agriculture and construction. However, illegitimate and criminal usage of UAVs is also on the rise which introduces their effective identification and detection as a research challenge. This paper proposes a novel machine learning-based for efficient identification and detection of UAVs. Specifically, an improved UAV identification and detection approach is presented using an ensemble learning based on the hierarchical concept, along with pre-processing and feature extraction stages for the Radio Frequency (RF) data. Filtering is applied on the RF signals in the detection approach to improve the output. This approach consists of four classifiers and they are working in a hierarchical way. The sample will pass the first classifier to check the availability of the UAV, and then it will specify the type of the detected UAV using the second classifier. The last two classifiers will handle the sample that is related to Bebop and AR to specify their mode. Evaluation of the proposed approach with publicly available dataset demonstrates better efficiency compared to existing detection systems in the literature. It has the ability to investigate whether a UAV is flying within the area or not, and it can directly identify the type of UAV and then the flight mode of the detected UAV with accuracy around 99%.