A digital sensor to measure real-time leaf movements and detect abiotic stress in plants.
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ABSTRACT:
SUBMITTER: Batist G
PROVIDER: S-EPMC8566216 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:IntroductionPeripheral nerve crush injury (PNCI) models are commonly used to study nerve damage and the potential beneficial effects of novel therapeutic strategies. Current models of PNCI rely on inter-device and operator precision to limit the variation with applied pressure. Although the inability to accurately quantify the PNCI pressure may result in reduced reproducibility between animals and studies, there is very limited information on the standardization and quantification of applied pressure with PNCI. To address this deficit, we constructed a novel device comprised of an Arduino UNO microcontroller board and Force Sensitive Resistor capable of reporting the real-time pressure applied to a nerve.MethodsTwo forceps and two needle drivers were used to perform 30-second PNCIs to the sciatic nerves of mice (n = 5/group). Needle drivers were set to the first notch, and a jig was used to hold the forceps pinch at a reproducible pressure. The Force Sensitive Resistor was interposed in-series between the nerve and instrument during PNCI.ResultsData collected from these procedures displayed average needle driver pressures an order of multitude greater than forceps pressures. Additionally, needle driver inter- and intra-procedure pressure remained more consistent than forceps pressure, with needle driver coefficient of variation equal to 14.5% vs. a forceps coefficient of variation equal to 45.4%.ConclusionsThis is the first demonstration of real-time pressure measurements in PNCI models and it reveals that the applied pressures are dependent on the types of device used. The large disparity in pressure represents an inability to apply graded accurate and consistent intermediate pressure gradients in PNCI. These findings indicate a need for documentation of pressure severity as a screening for PNCI in animals, and the real-time pressure sensor could be a useful tool in monitoring and applying consistent pressure, reducing the outcome variability within the same experimental model of PNCI.
| S-EPMC7832820 | biostudies-literature
Project description:Plants detect their neighbors via various cues, including reflected light and touching of leaf tips, which elicit upward leaf movement (hyponasty). It is currently unknown how touch is sensed and how the signal is transferred from the leaf tip to the petiole base that drives hyponasty. Here, we show that touch-induced hyponasty involves a signal transduction pathway that is distinct from light-mediated hyponasty. We found that mechanostimulation of the leaf tip upon touching causes cytosolic calcium ([Ca2+]cyt induction in leaf tip trichomes that spreads towards the petiole. Both perturbation of the calcium response and the absence of trichomes reduce touch-induced hyponasty. Finally, using plant competition assays, we show that touch-induced hyponasty is adaptive in dense stands of Arabidopsis. We thus establish a novel, adaptive mechanism regulating hyponastic leaf movement in response to mechanostimulation by neighbors in dense vegetation.
| S-EPMC10511701 | biostudies-literature
Project description:Species of Candida frequently cause life-threatening infections in neonates, transplant and intensive care unit (ICU) patients, and others with compromised host defenses. The successful management of systemic candidiasis depends upon early, rapid diagnosis. Blood cultures are the standard diagnostic method, but identification requires days and less than half of the patients are positive. These limitations may be eliminated by using real-time polymerase chain reaction (PCR) to detect Candida DNA in the blood specimens of patients at risk. Here, we optimized a PCR protocol to detect 5-10 yeasts in low volumes of simulated and clinical specimens. We also used a mouse model of systemic candidiasis and determined that candidemia is optimally detectable during the first few days after infection. However, PCR tests are often costly, labor-intensive, and inconvenient for routine use. To address these obstacles, we evaluated the innovative microfluidic real-time PCR platform (Advanced Liquid Logic, Inc.), which has the potential for full automation and rapid turnaround. Eleven and nine of 16 specimens from individual patients with culture-proven candidemia tested positive for C. albicans DNA by conventional and microfluidic real-time PCR, respectively, for a combined sensitivity of 94%. The microfluidic platform offers a significant technical advance in the detection of microbial DNA in clinical specimens.
| S-EPMC3939829 | biostudies-literature
Project description:BackgroundThe presence of SARS-CoV-2 RNA in plasma has been linked to disease severity and mortality. We compared RT-qPCR to droplet digital PCR (ddPCR) to detect SARS-CoV-2 RNA in plasma from COVID-19 patients (mild, moderate, and critical disease).MethodsThe presence/concentration of SARS-CoV-2 RNA in plasma was compared in three groups of COVID-19 patients (30 outpatients, 30 ward patients and 30 ICU patients) using both RT-qPCR and ddPCR. Plasma was obtained in the first 24h following admission, and RNA was extracted using eMAG. ddPCR was performed using Bio-Rad SARS-CoV-2 detection kit, and RT-qPCR was performed using GeneFinder™ COVID-19 Plus RealAmp Kit. Statistical analysis was performed using Statistical Package for the Social Science.ResultsSARS-CoV-2 RNA was detected, using ddPCR and RT-qPCR, in 91% and 87% of ICU patients, 27% and 23% of ward patients and 3% and 3% of outpatients. The concordance of the results obtained by both methods was excellent (Cohen's kappa index = 0.953). RT-qPCR was able to detect 34/36 (94.4%) patients positive for viral RNA in plasma by ddPCR. Viral RNA load was higher in ICU patients compared with the other groups (P < .001), by both ddPCR and RT-qPCR. AUC analysis revealed Ct values (RT-qPCR) and viral RNA load values (ddPCR) can similarly differentiate between patients admitted to wards and to the ICU (AUC of 0.90 and 0.89, respectively).ConclusionBoth methods yielded similar prevalence of RNAemia between groups, with ICU patients showing the highest (>85%). RT-qPCR was as useful as ddPCR to detect and quantify SARS-CoV-2 RNAemia in plasma.
| S-EPMC7995030 | biostudies-literature
Project description:It is time to shake up public health surveillance. New technologies for sequencing, aided by friction-free approaches to data sharing, could have an impact on public health efforts.
| S-EPMC4531805 | biostudies-literature
Project description:We developed a highly sensitive and specific assay for the rapid detection of smallpox virus DNA on both the Smart Cycler and LightCycler platforms. The assay is based on TaqMan chemistry with the orthopoxvirus hemagglutinin gene used as the target sequence. With genomic DNA purified from variola virus Bangladesh 1975, the limit of detection was estimated to be approximately 25 copies on both machines. The assay was evaluated in a blinded study with 322 coded samples that included genomic DNA from 48 different isolates of variola virus; 25 different strains and isolates of camelpox, cowpox, ectromelia, gerbilpox, herpes, monkeypox, myxoma, rabbitpox, raccoonpox, skunkpox, vaccinia, and varicella-zoster viruses; and two rickettsial species at concentrations mostly ranging from 100 fg/ microl to 1 ng/ microl. Contained within those 322 samples were variola virus DNA, obtained from purified viral preparations, at concentrations of 1 fg/ microl to 1 ng/ microl. On the Smart Cycler platform, 2 samples with false-positive results were detected among the 116 samples not containing variola virus tested; i.e., the overall specificity of the assay was 98.3%. On the LightCycler platform, five samples with false-positive results were detected (overall specificity, 95.7%). Of the 206 samples that contained variola virus DNA ranging in concentrations from 100 fg/ microl to 1 ng/ microl, 8 samples were considered negative on the Smart Cycler platform and 1 sample was considered negative on the LightCycler platform. Thus, the clinical sensitivities were 96.1% for the Smart Cycler instrument and 99.5% for the LightCycler instrument. The vast majority of these samples were derived from virus-infected cell cultures and variola virus-infected tissues; thus, the DNA material contained both viral DNA and cellular DNA. Of the 43 samples that contained purified variola virus DNA ranging in concentration from 1 fg/ microl to 1 ng/ microl, the assay correctly detected the virus in all 43 samples on both the Smart Cycler and the LightCycler platforms. The assay may be useful for the early detection of smallpox virus infections should such infections occur as a result of a deliberate or an accidental recurrence.
| S-EPMC179856 | biostudies-literature
Project description:Perseverative thinking (PT), or repetitive negative thinking, has historically been measured using global self-report scales. New methods of assessment are needed to advance understanding of this inherently temporal process. We developed an intensive longitudinal method for assessing PT. A mixed sample of 77 individuals ranging widely in trait PT, including persons with PT-related disorders (generalized anxiety disorder, major depression) and persons without psychopathology, used a joystick to provide continuous ratings of thought valence and intensity following exposure to scenarios of differing valence. Joystick responses were robustly predicted by trait PT, clinical status, and stimulus valence. Higher trait perseverators exhibited more extreme joystick values overall, greater stability in values following threatening and ambiguous stimuli, weaker stability in values following positive stimuli, and greater inertia in values following ambiguous stimuli. The joystick method is a promising measure with the potential to shed new light on the dynamics and precipitants of perseverative thinking.
| S-EPMC9365247 | biostudies-literature
Project description:BackgroundTraditional viral illness surveillance relies on in-person clinical or laboratory data, paper-based data collection, and outdated technology for data transfer and aggregation. We aimed to assess whether continuous sensor data can provide an early warning signal for COVID-19 activity as individual physiological and behavioural changes might precede symptom onset, care seeking, and diagnostic testing.MethodsThis multivariable, population-based, modelling study recruited adult (aged ≥18 years) participants living in the USA who had a smartwatch or fitness tracker on any device that connected to Apple HealthKit or Google Fit and had joined the DETECT study by downloading the MyDataHelps app. In the model development cohort, we included people who had participated in DETECT between April 1, 2020, and Jan 14, 2022. In the validation cohort, we included individuals who had participated between Jan 15 and Feb 15, 2022. When a participant joins DETECT, they fill out an intake survey of demographic information, including their ZIP code (postal code), and surveys on symptoms, symptom onset, and viral illness test dates and results, if they become unwell. When a participant connects their device, historical sensor data are collected, if available. Sensor data continue to be collected unless a participant withdraws from the study. Using sensor data, we collected each participant's daily resting heart rate and step count during the entire study period and identified anomalous sensor days, in which resting heart rate was higher than, and step count was lower than, a specified threshold calculated for each individual by use of their baseline data. The proportion of users with anomalous data each day was used to create a 7-day moving average. For the main cohort, a negative binomial model predicting 7-day moving averages for COVID-19 case counts, as reported by the Centers for Disease Control and Prevention (CDC), in real time, 6 days in the future, and 12 days in the future in the USA and California was fitted with CDC-reported data from 3 days before alone (H0) or in combination with anomalous sensor data (H1). We compared the predictions with Pearson correlation. We then validated the model in the validation cohort.FindingsBetween April 1, 2020, and Jan 14, 2022, 35 842 participants enrolled in DETECT, of whom 4006 in California and 28 527 in the USA were included in our main cohort. The H1 model significantly outperformed the H0 model in predicting the 7-day moving average COVID-19 case counts in California and the USA. For example, Pearson correlation coefficients for predictions 12 days in the future increased by 32·9% in California (from 0·70 [95% CI 0·65-0·73] to 0·93 [0·92-0·94]) and by 12·2% (from 0·82 [0·79-0·84] to 0·92 [0·91-0·93]) in the USA from the H0 model to the H1 model. Our validation model also showed significant correlations for predictions in real time, 6 days in the future, and 12 days in the future.InterpretationOur study showed that passively collected sensor data from consenting participants can provide real-time disease tracking and forecasting. With a growing population of wearable technology users, these sensor data could be integrated into viral surveillance programmes.FundingThe National Center for Advancing Translational Sciences of the US National Institutes of Health, The Rockefeller Foundation, and Amazon Web Services.
| S-EPMC9499390 | biostudies-literature
Project description:The CRISPR-associated protein Cas9 is widely used for genome editing because it cleaves target DNA through the assistance of a single-guide RNA (sgRNA). Structural studies have revealed the multi-domain architecture of Cas9 and suggested sequential domain movements of Cas9 upon binding to the sgRNA and the target DNA These studies also hinted at the flexibility between domains; however, it remains unclear whether these flexible movements occur in solution. Here, we directly observed dynamic fluctuations of multiple Cas9 domains, using single-molecule FRET We found that the flexible domain movements allow Cas9 to adopt transient conformations beyond those captured in the crystal structures. Importantly, the HNH nuclease domain only accessed the DNA cleavage position during such flexible movements, suggesting the importance of this flexibility in the DNA cleavage process. Our FRET data also revealed the conformational flexibility of apo-Cas9, which may play a role in the assembly with the sgRNA Collectively, our results highlight the potential role of domain fluctuations in driving Cas9-catalyzed DNA cleavage.
| S-EPMC5978321 | biostudies-literature
Project description:Underwater sensor networks represent an important and promising field of research due to the large diversity of underwater ubiquitous applications that can be supported by these networks, e.g., systems that deliver tsunami and oil spill warnings, or monitor submarine ecosystems. Most of these monitoring and warning systems require real-time communication in wide area networks that have a low density of nodes. The underwater communication medium involved in these networks is very harsh and imposes strong restrictions to the communication process. In this scenario, the real-time transmission of information is done mainly using acoustic signals, since the network nodes are not physically close. The features of the communication scenario and the requirements of the communication process represent major challenges for designers of both, communication protocols and monitoring and warning systems. The lack of models to represent these networks is the main stumbling block for the proliferation of underwater ubiquitous systems. This paper presents a real-time communication model for underwater acoustic sensor networks (UW-ASN) that are designed to cover wide areas with a low density of nodes, using any-to-any communication. This model is analytic, considers two solution approaches for scheduling the real-time messages, and provides a time-constraint analysis for the network performance. Using this model, the designers of protocols and underwater ubiquitous systems can quickly prototype and evaluate their solutions in an evolving way, in order to determine the best solution to the problem being addressed. The suitability of the proposal is illustrated with a case study that shows the performance of a UW-ASN under several initial conditions. This is the first analytic model for representing real-time communication in this type of network, and therefore, it opens the door for the development of underwater ubiquitous systems for several application scenarios.
| S-EPMC5539683 | biostudies-other