Genotype-dependent Strategies to "Overcome" Excessive Light: Insights into Non-Photochemical Quenching under High Light Intensity

High light (HL) intensities have a significant impact on energy flux and distribution within photosynthetic apparatus. To understand the effect of high light intensity (HL) on the HL tolerance mechanisms in tomatoes, we examined the response of the photosynthesis apparatus of 12 tomato genotypes to HL. A reduced electron transfer per reaction center (ET0 /RC), an increased energy dissipation (DI0 /RC) and non-photochemical quenching (NPQ), along with a reduced maximum quantum yield of photosystem II (FV /FM ), and performance index per absorbed photon (PIABS ) were common HL-induced responses among genotypes; however, the magnitude of those responses was highly genotype-dependent. Tolerant and sensitive genotypes were distinguished based on chlorophyll fluorescence and energy-quenching responses to HL. Tolerant genotypes alleviated excess light through energy-dependent quenching (qE ), resulting in smaller photoinhibitory quenching (qI ) compared to sensitive genotypes. Quantum yield components also shifted under HL, favoring the quantum yield of NPQ (ՓNPQ ) and the quantum yield of basal energy loss (ՓN0 ), while reducing the efficient quantum yield of PSII (ՓPSII ). The impact of HL on tolerant genotypes was less pronounced. While the energy partitioning ratio did not differ significantly between sensitive and tolerant genotypes, the ratio of NPQ components, especially qI , affected plant resilience against HL. These findings provide insights into different patterns of HL-induced NPQ components in tolerant and sensitive genotypes, aiding the development of resilient crops for heterogeneous light conditions.

Artificial neural network (ANN)-based algorithms for high light stress phenotyping of tomato genotypes using chlorophyll fluorescence features

High light (HL) is a common environmental stress directly imposes photoinhibition on the photosynthesis apparatus. Breeding plants for tolerance against HL is therefore highly demanded. Chlorophyll fluorescence (ChlF) is a sensitive indicator of stress in plants and can be evaluated using OJIP transients. In this study, we compared the ChlF features of plants exposed to HL (1200 μmol m-2 s-1) with that of control plants (300 μmol m-2 s-1). To extract the most reliable ChlF features for discrimination between HL-stressed and non-stressed plants, we applied three artificial neural network (ANN)-based algorithms, namely, Boruta, Support Vector Machine (SVM), and Recursive Feature Elimination (RFE). Feature selection algorithms identified multiple features but only two features, namely the maximal quantum yield of PSII photochemistry (FV/FM) and quantum yield of energy dissipation (ɸD0), remained consistent across all genotypes in control conditions, while exhibited variation in HL. Therefore, considered reliable features for HL stress screening. The selected features were then used for screening 14 tomato genotypes for HL. Genotypes were categorized into three groups, tolerant, semi-tolerant, and sensitive genotypes. Foliar hydrogen peroxide (H2O2) and malondialdehyde (MDA) contents were measured as independent proxies for benchmarking selected features. Tolerant genotypes were attributed with the lowest change in H2O2 and MDA contents, while the sensitive genotypes displayed the highest magnitude of increase in H2O2 and MDA by HL treatment compared to the control. Finally, a FV/FM higher than 0.77 and ɸD0 lower than 0.24 indicates a healthy electron transfer chain (ETC) when tomato plants are exposed to HL.

Robust KALMAN Filter State Estimation for Gene Regulatory Networks

This paper proposes a revised version of the robust generalized maximum likelihood (GM)-type unscented KALMAN filter (GM-UKF) for the state estimation of gene regulatory networks (GRNs) in the presence of different types of deviations from assumptions. As known, the parameters and the power of the assumed noises within the GRN model may change abruptly as a result of jump behavior and bursting process in transcription and translation phases. Moreover, there may be outlying samples among genomic measurement data. Some other outliers may also occur in the model dynamics. The outliers may be misinterpreted by the filtering method if not detected and downweighted. To deal with all such deviations, a robust GM-UKF is designed that includes some modifications to address the challenges in calculating the projection statistics in GRNs such as the nonlinear behavior and the natural distance of the states. The proposed filter is compared to four Bayesian filters, i.e., the conventional UKF, the H _∞-UKF, the downweighting UKF (DW-UKF), and a modified version of the GM-UKF, the so-called maximum-likelihood UKF(M-UKF). The outcome results demonstrate that the GM-UKF outperforms other methods for all outlier types while the H _∞-UKF is appropriate for the changes in noise powers.

Differentially Expressed Genes Enrichment Analysis of Pancreatic Ductal Adenocarcinoma and Pancreatic Intraepithelial Neoplasia; an In Silico Study

Introduction/Objective

Pancreatic ductal adenocarcinoma (PDAC) is a leading cause of cancer-related death in the United States. Determining the genetic alterations involved in the formation of PDAC and its precursor, pancreatic intraepithelial neoplasia (PanIN), may lead to earlier detection and new therapeutic options. We performed an analysis of the genetic alterations responsible for the progression of the normal pancreatic tissue to PanIN and ultimately from PanIN to PDAC.

Methods/Case Report

Initially, we used the continuous bioinformatic analysis in such a way that the RNA-seq datasets were extracted from the Biojupies database. We separately analyzed two datasets that included PDAC and PanIN, where their differentially expressed genes (DEGs) were obtained by comparison with controls. A Venn diagram was drawn to visualize the overlapping and non-overlapping DEGs in both groups. Using the Enrichr and ShinyGO databases, we examined the cell signaling pathways and ontologies of up/down-regulated genes. We mapped the protein network of important genes involved in cancer pathways by the STRING database. Finally, the shared and non-shared candidate proteins in the PDAC and PanIN pathways with the GEPIA database were confirmed in human samples.

Results (if a Case Study enter NA)

We found six shared genes in PDAC and PanIN including RAC1, RAP1A, ITGA5, RHOA, FZD2, and FN1, which appear to take part in the transition of PanIN to PDAC. Our result showed that the aforementioned genes are critical in the cell cycle, angiogenesis, and cell death processes. In the next step, the DEGs analysis in both PDAC and PanIN revealed the role of candidate genes (COX5B, NME2, MGLL, and PAICS for PanIN and PRKCA, PLCG2, NOS3, and PTK2 for PDAC) in cellular aging, MAPK, and PI3K/Akt signaling pathways.

Conclusion

Our findings showed that the overexpression of RAC1, RAP1A, ITGA5, RHOA, FZD2, and FN1 may have an important role in PanIN shifting to PDAC.

Factors affecting hospital costs and revenue: integrating expert opinions and literature review

Purpose

There is not enough comprehensive evidence on factors affecting hospital costs and revenue (HCR). The main objective of the current study is to identify and classify factors affecting HCR integrating experts' opinions and literature review.

Design/methodology/approach

First, a restricted literature review is conducted to identify the factors affecting HCR. In the second step, the targeted semi-structured interviews are conducted with 15 experts to identify, validate and classify the latent factors.

Findings

In addition to the factors identified through the literature review, 22 new important factors were added by the experts as the determinants of HCR, which were not pointed out in previous studies. The final model presented for the factors affecting HCR contains seven main groups, 22 subgroups and 70 variables.

Originality/value

Factors affecting HCR will provide valuable contributions for hospital budgeting, and financial and strategic planning, and they will offer an effective horizon for future research on cost-cutting strategies.

Desulfurization of liquid fuels using aluminum modified mesoporous adsorbent: towards experimental and kinetic investigations

In this study, a modified mesoporous adsorbent (MSU-S) impregnated by aluminum was used to remove the aromatic sulfur compounds from n-decane as the model fuel. Physical and chemical properties of as-synthesized adsorbent were investigated by XRD (X-Ray Diffraction), SEM (Scanning Electron Microscopy), FTIR (Fourier Transform Infrared spectroscopy) and BET (Brunauer–Emmett–Teller) method. Adsorptive desulfurization of model fuel was studied through batch and continues processes under mild temperature and normal atmospheric pressure. The equilibrium adsorption was modeled by Langmuir, Temkin, and Freundlich and the kinetics of adsorption was studied through first, second and intraparticle diffusion models. It was figured out that Temkin and the pseudo-second-order model were best fitting the adsorption equilibrium and describing the kinetics, respectively.

Experimental, kinetic, and thermodynamic studies of adsorptive desulfurization and denitrogenation of model fuels using novel mesoporous materials

The kinetics, equilibrium and thermodynamics studies of adsorptive desulfurization and denitrogenation to eliminate the refractory sulfur and nitrogen compounds like BT, DBT, quinoline and carbazole of model fuel by mesoporous material (MSU-S) and cobalt modified mesoporous material (CoO-MSU-S) adsorbents were carried out. The adsorption performance, capacity and selectivity of the adsorbent toward sulfur and nitrogen compounds were examined. Equilibrium and kinetics experiments confirmed that Co⁺² impregnation would enhance the adsorption capacity of MSU-S. The results demonstrated that CoO-MSU-S led to a considerable improvement in the adsorption performance. The adsorption amounts reached 18.41, 21.20, 39.65 and 24.60 mg.g^-1 for BT, DBT, quinoline and carbazole, respectively. The Langmuir isotherm model showed good fittings with the experimental equilibrium data for benzothiophene, dibenzothiophene and carbazole, and the data for quinoline was expressed well by the Freundlich for CoO-MSU-S adsorbent. Negative Gibbs free energy showed that all sulfur and nitrogen compounds were adsorbed spontaneously. The experimental data revealed that the pseudo-second-order model can describe the kinetics of adsorption of compounds on the adsorbent. The data obtained from the breakthrough curves indicated that DBT < BT < carbazole < quinoline order for the selectivity of modified adsorbent towards the adsorbates.

CFPD simulation of magnetic drug delivery to a human lung using an SAW nebulizer

Targeted drug delivery is an impressive topic that attracted the attention of many scientists in various scientific communities. Magnetic drug targeting is one of the targeted drug delivery techniques, which uses the magnetic field to externally control the magnetic drug particles. In this study, we aim to assess the magnetic drug delivery to the human respiratory system using a new aerosolization technique driven by surface acoustic waves (SAWs) into a realistic lung model geometrically reconstructed using computed tomography scan images. To achieve this aim, a simulation study using computational fluid-particle dynamics considering the Lagrangian approach for particle tracking is carried out. An external magnetic field was applied to govern the Magnetit (Fe₃O₄) particles as the magnetic drug career. The drug particles were assumed to be spherical and inert. The effects of magnetic field intensity, magnetic source position, and SAW injection position were examined for a light breathing condition (Q = 15 L/min). Given the realistic geometry of the respiratory system and its complexity, the airflow patterns vary as it penetrates deeper into the lung and experiences many irregularities, and bending deflections exist in the airways model. High-inertia particles tend to deposit at locations where the geometry experiences a significant reduction in cross section. Our results show that the magnetic field highly affects the particle deposition efficiency for fourfold. However, the magnet and SAW injection positions have a low impact on the deposition efficiency of drug particles.

Relationship between big five personality factors, problem solving and medical errors

Background

Human behavior is recognized as the main factor in the occurrence of accidents (70–90 percent), with human personality and problem solving ability as two related factors in the occurrence of medical errors (annually 42.7 million in the world). The objectives of this study were to investigate the relationship between personality factors, problem solving ability and medical errors.

Material and methods

This study was a questionnaire case control study. Information on 49 members of medical and nursing staff with medical errors (case group) and 46 without medical errors (control group) were analyzed. To collect the data, two Heppner problem solving questionnaires and the NEO-Five Factor Inventory were used, which were completed by the study population.

Results

The results illustrate that individuals without medical errors showed higher scores in contentiousness, extraversion and agreeableness and lower scores in neuroticism than those with medical errors. Individuals without medical errors also showed higher scores in problem solving ability scales than those with medical errors.

Conclusion

Results of this study, suggest that personality factors and problem solving ability are related to medical errors and it may be possible for hospital authorities to use this knowledge when selecting capable medical staff.

Molecular detection of Crimean-Congo haemorrhagic fever virus in ticks collected from infested livestock populations in a New Endemic Area, South of Iran

OBJECTIVE

Crimean-Congo haemorrhagic fever (CCHF) is a viral zoonotic disease with potentially fatal systemic effects on man. We aimed to determine the presence of CCHF virus among collected ticks from domestic livestock from October 2012 to September 2013.

METHODS

A total of 1245 hard and soft ticks were collected from naturally infested ruminants in Marvdasht County, Fars Province, south of Iran. Nine tick species and one unidentified species in four disparate genera were detected. A total of 200 ticks were randomly selected and analysed by reverse transcription-polymerase chain reaction (RT-PCR) for the presence of CCHF virus genome.

RESULTS

The viral genome was detected in 4.5% (9 samples) of the studied tick population. The infected ticks belonged to the species of Hyalomma marginatum' Hyalomma anatolicum and Rhipicephalus sanguineus. The viruses detected in these three tick species were clustered in the same lineage as Matin and SR3 strains in Pakistan and some other Iranian strains. These results indicate that the ticks were wildly infected with a genetically closely related CCHF virus in the region.

CONCLUSION

Regular controls and monitoring of livestock to reduce the dispersion of ticks and providing information to those involved in high-risk occupations are urgently required.

Combined impacts of future climate and land use changes on discharge, nitrogen and phosphorus loads for a Canadian river basin

Both climate and land use changes can influence water quality and quantity in different ways. Thus, for predicting future water quality and quantity trends, simulations should ideally account for both projected climate and land use changes. In this paper, land use projections and climate change scenarios were integrated with a hydrological model to estimate the relative impact of climate and land use projections on a suite of water quality and quantity endpoints for a Canadian watershed. Climatic time series representing SRES change scenario A2 were generated by downscaling the outputs of the Canadian Regional Climate Model (version 4.1.1) using a combination of quantile-quantile transformation and nearest neighbor search. The SWAT (Soil and Water Assessment Tool) model was used to simulate streamflow, nitrogen and phosphorus loading under different climate and land use scenarios. Results showed that a) climate change will drive up maximum monthly streamflow, nitrate loads, and organic phosphorus loads, while decreasing organic nitrogen and nitrite loads; and b) land use changes were found to drive the same water quality/quantity variables in the same direction as climate change, except for organic nitrogen loads, for which the effects of the two stressors had a reverse impact on loading.

Red/blue spectral shifts of laser-induced fluorescence emission due to different nanoparticle suspensions in various dye solutions

Red/blue shifts of laser-induced fluorescence (LIF) are investigated using several guest dielectric nanoscatterers, such as TiO2, ZnO, Al2O3, and SiO2, in the host Rd6G, RdB, Coumarin 4, and Coumarin 7 ethanolic solutions. A couple of inflection points are identified varying nanoparticle (NP) density into dye solutions based on LIF spectroscopy. The inflection of the spectral shift exhibits that the suspension of NPs in dye solutions significantly involves a couple of competitive chemical and optical mechanisms during photon traveling in scattering media regarding ballistic and diffusive transport. It is shown that the low, medium, and high NP additives in fluorescent suspension induce blue, red, and blue spectral shifts, respectively.

Designing Unsupervised Hierarchical Fuzzy Logic Systems

Systems such as robotic systems and systems with large input-output data tend to be difficult to model using mathematical techniques. These systems have typically high dimensionality and have degrees of uncertainty in many parameters. Artificial intelligence techniques such as neural networks, fuzzy logic, genetic algorithms and evolutionary algorithms have created new opportunities to solve complex systems. Application of fuzzy logic [Bai, Y., Zhuang H. and Wang, D. (2006)] in particular, to model and solve industrial problems is now wide spread and has universal acceptance. Fuzzy modelling or fuzzy identification has numerous practical applications in control, prediction and inference. It has been found useful when the system is either difficult to predict and or difficult to model by conventional methods. Fuzzy set theory provides a means for representing uncertainties. The underlying power of fuzzy logic is its ability to represent imprecise values in an understandable form. The majority of fuzzy logic systems to date have been static and based upon knowledge derived from imprecise heuristic knowledge of experienced operators, and where applicable also upon physical laws that governs the dynamics of the process. Although its application to industrial problems has often produced results superior to classical control, the design procedures are limited by the heuristic rules of the system. It is simply assumed that the rules for the system are readily available or can be obtained. This implicit assumption limits the application of fuzzy logic to the cases of the system with a few parameters. The number of parameters of a system could be large. The number of fuzzy rules of a system is directly dependent on these parameters. As the number of parameters increase, the number of fuzzy rules of the system grows exponentially. Genetic Algorithms can be used as a tool for the generation of fuzzy rules for a fuzzy logic system. This automatic generation of fuzzy rules, via genetic algorithms, can be categorised into two learning techniques, supervised and unsupervised. In this paper unsupervised learning of fuzzy rules of hierarchical and multi-layer fuzzy logic control systems are considered. In unsupervised learning there is no external teacher or critic to oversee the learning process. In other words, there are no specific examples of the function to be learned by the system. Rather, provision is made for a task-independent measure of the quality or representation that the system is required to learn. That is the system learns statistical regularities of the input data and it develops the ability to learn the feature of the input data and thereby create new classes automatically [Mohammadian, M., Nainar, I. and Kingham, M. (1997)]. To perform unsupervised learning, a competitive learning strategy may be used. The individual strings of genetic algorithms compete with each other for the “opportunity” to respond to features contained in the input data. In its simplest form, the system operates in accordance with the strategy that ‘the fittest wins and survives’. That is the individual chromosome in a population with greatest fitness ‘wins’ the competition and gets selected for the genetic algorithms operations (cross-over and mutation). The other individuals in the population then have to compete with fit individual to survive. The diversity of the learning tasks shown in this paper indicates genetic algorithm’s universality for concept learning in unsupervised manner. A hybrid integrated architecture incorporating fuzzy logic and genetic algorithm can generate fuzzy rules for problems requiring supervised or unsupervised learning. In this paper only unsupervised learning of fuzzy logic systems is considered. The learning of fuzzy rules and internal parameters in an unsupervised manner is performed using genetic algorithms. Simulations results have shown that the proposed system is capable of learning the control rules for hierarchical and multi-layer fuzzy logic systems. Application areas considered are, hierarchical control of a network of traffic light control and robotic systems. A first step in the construction of a fuzzy logic system is to determine which variables are fundamentally important. Any number of these decision variables may appear, but the more that are used, the larger the rule set that must be found. It is known [Raju, S., Zhou J. and Kisner, R. A. (1990), Raju G. V. S. and Zhou, J. (1993), Kingham, M., Mohammadian, M, and Stonier, R. J. (1998)], that the total number of rules in a system is an exponential function of the number of system variables. In order to design a fuzzy system with the required accuracy, the number of rules increases exponentially with the number of input variables and its associated fuzzy sets for the fuzzy logic system. A way to avoid the explosion of fuzzy rule bases in fuzzy logic systems is to consider Hierarchical Fuzzy Logic Control (HFLC) [Raju G. V. S. and Zhou, J. (1993)]. A learning approach based on genetic algorithms [Goldberg, D. (1989)] is discussed in this paper for the determination of the rule bases of hierarchical fuzzy logic systems.