Transcriptional response to stressors in arabidopsis thaliana

Stress is defined as any external force that can trigger a defensive response from an organism. In plants, stress is something that has been shown to affect plant reproduction and productivity by activating a defensive response. It can be caused by various things including but not limited to biotic or abiotic conditions such as temperature, drought or salt stress. Exposure to stress leads to the production of various transcriptomes that are governed by signals released as a result of the exposed stress. Arabidopsis thaliana is characterized by its inability to tolerate any form of extreme stress and given its status as a model organism it is an ideal candidate to investigate the various effects of stress on plants. By studying the transcriptomes produced by Arabidopsis thaliana under different stress conditions, a more well-rounded profile of how plant systems react to different stress conditions is produced. Experiments were carried out in KAUST by exposing the stress intolerant plant to Pladienolide B; a drug that is known to affect the slicing mechanism, RNA sequencing was used in order to produce the transcriptome profile of the plant in response to the stress over a series of time points. The classic tuxedo protocol for RNA sequencing analysis was used to assemble the transcripts and following differential gene expression analysis by CuffDiff, the R package CummeRbund was used to visualize the results. Functional analysis of the significant differentially expressed genes was carried out using PANTHER. PANTHER was able to classify 12,646 genes; expressed at after exposure to the treatment for 6 hours, and 10,649 genes; expressed after exposure to the treatment for 24 hours, into functional classes. With around 50% of the differentially expressed gene having catalytic activity and around 25% having binding activity. Further investigation revealed that the alternatively spliced differentially expressed genes were heavily involved in various development and regulatory process that are essential for plant maturation. While a few functionally uncharacterized genes were expressed, some of which may hold valuable information in understanding plant stress response. This research offers a deeper understanding of how plants are effected by stress through the characterization of the differentially expressed genes. Future investigation of the uncharacterized genes expressed is needed as it may provide deeper insights to the plant stress response

Physical Growth and Body Composition of Controlled Versus Uncontrolled Type 1 Egyptian Diabetic Children

BACKGROUND: Type 1 Diabetes Mellitus (T1DM) is one of the most common chronic endocrine disorders of childhood. Data on growth parameters of diabetic children is scarce.AIM: To assess growth and body composition in a group of diabetic children.SUBJECTS AND METHODS: 427 T1DM children (age 2-10 years) were recruited from Diabetic Paediatric Unit, outpatients' clinic of Abou El-Rish Hospital. Anthropometric and body composition parameters were taken and HbA1c was measured for all subjects.RESULTS:  Highly significant difference was detected between controlled and uncontrolled groups as regard to weight/age z-score, height/age z-score, BMI z-score, triceps skin fold thickness, subscapular skin fold thickness, midupper arm circumference, fat mass, fat %, lean mass, and body water (p < 0.001). All values are higher in the controlled group than in the uncontrolled group. Uncontrolled subjects were significantly more at risk of being underweight and short, with odds ratio of 15.131 and 16.877 and 95% confidence interval 1.972-116.130 and 3.973-71.694 respectively. However, controlled subjects were significantly more at risk of being obese than the uncontrolled with an odds ratio 0.116 and 95% confidence interval 0.045-0.302.CONCLUSION: Growth was compromised in uncontrolled T1DM children. This is of utmost importance since most of the clinical features are reversible with better glycemic control and appropriate insulin management

Pre-mRNA splicing repression triggers abiotic stress signaling in plants

[EN] Alternative splicing (AS) of precursor RNAs enhances transcriptome plasticity and proteome diversity in response to diverse growth and stress cues. Recent work has shown that AS is pervasive across plant species, with more than 60% of intron-containing genes producing different isoforms. Mammalian cell-based assays have discovered various inhibitors of AS. Here, we show that the macrolide pladienolide B (PB) inhibits constitutive splicing and AS in plants. Also, our RNA sequencing (RNA-seq) data revealed that PB mimics abiotic stress signals including salt, drought and abscisic acid (ABA). PB activates the abiotic stress-and ABA-responsive reporters RD29A::LUC and MAPKKK18::uidA in Arabidopsis thaliana and mimics the effects of ABA on stomatal aperture. Genome-wide analysis of AS by RNA-seq revealed that PB perturbs the splicing machinery and leads to a striking increase in intron retention and a reduction in other forms of AS. Interestingly, PB treatment activates the ABA signaling pathway by inhibiting the splicing of clade A PP2C phosphatases while still maintaining to some extent the splicing of ABA-activated SnRK2 kinases. Taken together, our data establish PB as an inhibitor and modulator of splicing and a mimic of abiotic stress signals in plants. Thus, PB reveals the molecular underpinnings of the interplay between stress responses, ABA signaling and post-transcriptional regulation in plants.We wish to thank members of the Laboratory for Genome Engineering at King Abdullah University of Science and Technology for helpful discussions and comments on the manuscript. We wish to thank Moussa Benhamed for helpful discussions and suggestions and for providing key materials. We wish to thank Sean Cutler for providing Arabidopsis seeds of MAKPKKK18-uidA. This study was supported by King Abdullah University of Science and Technology. Work in PR's laboratory was funded by grant BIO2014-52537-R from MINECO. Work in PD's laboratory is funded by grant PTDC/BIA-PLA/1084/2014 from FCT. The authors declare no conflicts of interest.Ling, Y.; Alshareef, S.; Butt, H.; Lozano Juste, J.; Li, L.; Galal, AA.; Moustafa, A.... (2017). Pre-mRNA splicing repression triggers abiotic stress signaling in plants. The Plant Journal. 89(2):291-309. https://doi.org/10.1111/tpj.13383S29130989

Pre-mRNA splicing repression triggers abiotic stress signaling in plants

Alternative splicing (AS) of precursor RNAs enhances transcriptome plasticity and proteome diversity in response to diverse growth and stress cues. Recent work showed that AS is pervasive across plant species, with more than 60% of intron-containing genes producing different isoforms. Mammalian cell-based assays have discovered various AS inhibitors. Here, we show that the macrolide Pladienolide B (PB) inhibits constitutive splicing and AS in plants. Also, our RNA-seq data revealed that PB mimics abiotic stress signals including salt, drought, and abscisic acid (ABA). PB activates the abiotic stress- and ABA-responsive reporters RD29A::LUC and MAPKKK18::GUS in Arabidopsis thaliana and mimics the effects of ABA on stomatal aperture. Genome-wide analysis of AS by RNA-seq revealed that PB perturbs the splicing machinery and leads to a striking increase in intron retention and a reduction in other forms of AS. Interestingly, PB treatment activates the ABA signaling pathway by inhibiting the splicing of clade A PP2Cs phosphatases while still maintaining to some extent the splicing of ABA-activated SnRK2 kinases. Taken together, our data establish PB as an inhibitor and modulator of splicing and a mimic of abiotic stress signals in plants. Thus, PB reveals the molecular underpinnings of the interplay between stress responses, ABA signaling, and post-transcriptional regulation in plants.</p

Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P &lt; 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

SHARC : self-healing analog with RRAM and CNFETs

This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 49-50).Next-generation applications require processing on massive amount of data in real-time, exceeding the capabilities of electronic systems today. This has spurred research in a wide-range of areas: from new devices to replace silicon-based field-effect transistors (FETs) to new circuit and system architectures with fine-grained and dense integration of logic and memory. However, isolated improvements in just one area is insufficient. Rather, enabling these next-generation applications will require combining benefits across all levels of the computing stack: leveraging new devices to realize new circuits and architectures. For instance, carbon nanotube (CNT) field-effect transistors (CNFETs) for logic and Resistive Random-Access Memory (RRAM) for memory are two promising emerging nanotechnologies for energy-efficient electronics. However, CNFETs suffer from inherent imperfections (such as of metallic CNTs, m-CNTs), which have prohibited realizing large-scale CNFET circuits in the past. This work proposes a circuit design technique that integrates and combines the benefits of both CNFETs with RRAM to realize three-dimensional (3D) circuits that are immune to m-CNTs. Leveraging this technique, we show the first experimental demonstration of CNFET-based analog mixed-signal circuits.by Aya G. Amer.S.M.S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Scienc

Applications of machine learning in metabolomics: Disease modeling and classification

Metabolomics research has recently gained popularity because it enables the study of biological traits at the biochemical level and, as a result, can directly reveal what occurs in a cell or a tissue based on health or disease status, complementing other omics such as genomics and transcriptomics. Like other high-throughput biological experiments, metabolomics produces vast volumes of complex data. The application of machine learning (ML) to analyze data, recognize patterns, and build models is expanding across multiple fields. In the same way, ML methods are utilized for the classification, regression, or clustering of highly complex metabolomic data. This review discusses how disease modeling and diagnosis can be enhanced via deep and comprehensive metabolomic profiling using ML. We discuss the general layout of a metabolic workflow and the fundamental ML techniques used to analyze metabolomic data, including support vector machines (SVM), decision trees, random forests (RF), neural networks (NN), and deep learning (DL). Finally, we present the advantages and disadvantages of various ML methods and provide suggestions for different metabolic data analysis scenarios

Nonlinear EHD Instability of Two-Superposed Walters’ B Fluids Moving through Porous Media

The current work examines the application of the viscous potential flow to the Kelvin-Helmholtz instability (KHI) of a planar interface between two visco-elastic Walters’ B fluids. The fluids are fully saturated in porous media in the presence of heat and mass transfer across the interface. Additionally, the structure is pervaded via a uniform, normal electrical field in the absence of superficial charges. The nonlinear scheme basically depends on analyzing the linear principal equation of motion, and then applying the appropriate nonlinear boundary-conditions. The current organization creates a nonlinear characteristic equation describing the amplitude performance of the surface waves. The classical Routh–Hrutwitz theory is employed to judge the linear stability criteria. Once more, the implication of the multiple time scale with the aid of Taylor theory yields a Ginzburg–Landau equation, which controls the nonlinear stability criteria. Furthermore, the Poincaré–Lindstedt technique is implemented to achieve an analytic estimated bounded solution for the surface deflection. Many special cases draw upon appropriate data selections. Finally, all theoretical findings are numerically confirmed in such a way that ensures the effectiveness of various physical parameters

Nonlinear EHD Instability of Two-Superposed Walters’ B Fluids Moving through Porous Media

The current work examines the application of the viscous potential flow to the Kelvin-Helmholtz instability (KHI) of a planar interface between two visco-elastic Walters’ B fluids. The fluids are fully saturated in porous media in the presence of heat and mass transfer across the interface. Additionally, the structure is pervaded via a uniform, normal electrical field in the absence of superficial charges. The nonlinear scheme basically depends on analyzing the linear principal equation of motion, and then applying the appropriate nonlinear boundary-conditions. The current organization creates a nonlinear characteristic equation describing the amplitude performance of the surface waves. The classical Routh–Hrutwitz theory is employed to judge the linear stability criteria. Once more, the implication of the multiple time scale with the aid of Taylor theory yields a Ginzburg–Landau equation, which controls the nonlinear stability criteria. Furthermore, the Poincaré–Lindstedt technique is implemented to achieve an analytic estimated bounded solution for the surface deflection. Many special cases draw upon appropriate data selections. Finally, all theoretical findings are numerically confirmed in such a way that ensures the effectiveness of various physical parameters