CO2, CH3CHO, and C6H6 were the main volatiles detectedduring Mylar thermal decomposition, while Teflon’s thermaldecomposition yielded CO2 and a set of fluorocarbon compoundsincluding CF4, C2F4, C2F6, C3F6, CF2O, and CF3O. PMMA produced CO2 and methyl methacrylate . The mass spectral peak patternsobserved during the thermal decomposition of each material were uniqueto that material and were therefore useful as chemical signatures.It was also observed that the chemical signatures remained consistentand detectable when multiple materials were heated together. Massspectra data sets containing the chemical signatures for each materialand mixtures were collected and analyzed using a random forest panelmachine learning classification. The classification was tested anddemonstrated 100% accuracy for single material spectra and an averageof 92.3% accuracy for mixed material spectra. This investigation presentsa novel technique for the real-time, chemically specific detectionof fire related VOCs through mass spectrometry which shows promiseas a more rapid and accurate method for detecting fires or near-fireevents.Massspectrometry in parallel with real-time machine learning techniqueswere paired in a novel application to detect and identify chemicallyspecific, early indicators of fires and near-fire events involvinga set of selected materials: Mylar, Teflon, and poly(methyl methacrylate)(PMMA). The volatile organic compounds emitted during the thermaldecomposition of each of the three materials were characterized usinga quadrupole mass spectrometer which scanned the 1–200  While mass spectrometry dataof VOCs is typically analyzed through multivariate analysis techniquessuch as principal component analysis, the use of machine learningin VOC analysis has been shown to provide a more streamlined approachto processing VOC profiling data.4Volatile organic compound (VOC) profilingis a highly informativemass spectrometry (MS) technique that has a number of applicationsincluding disease diagnostics, chemical and environmental safety,food quality control, and others.4 For example, Zhu et al. analyzed the volatile metabolites emittedby four species of bacteria and was able to use principal componentanalysis (PCA) to differentiate data from each species.5 In more recent years, machine learning has beenapplied to VOC profiling studies. Franchina et al. used gas chromatography(GC) mass spectrometry (MS) to study VOCs emitted by different speciesof bacteria and then used a random forest to identify chromatographicfeatures distinguishing the volatile profiles of different species.6 A similar methodology was used by Stefanuto etal. which analyzed bronchial washings (BALF) and blind bronchial aspirates(BBA) taken from heart and lung transfer patients for markers of pulmonarygraft dysfunction (PGD).7 VOCs from theBALF and BBA headspaces were analyzed with GCMS, and support vectormachine (SVM) was used to identify discriminatory features in thechromatogram that could help to detect PGD.7 Arora et al. used a combination of PCA and machine learning to distinguishbetween fungal and bacterial pathogens based on the VOCs detectedwith mass spectrometry.4 These studiesdemonstrated the ability of machine learning to identify distinctionsin vastly complex data that can be used to accurately classify analyzedsamples9VOC profiling has been demonstrated as a useful detection and identificationtool most often in the context of microbial and disease detection.10 PTR-MS uses chemical ionization through a proton transfer reactionbetween an analyte and a gas within a drift tube. Both the fragmentationpattern and the reaction time aid in identification of analytes, soit is a useful tool when analyzing multicomponent samples. PTR-MShas been used in a variety of VOC applications such as atmosphericchemistry, food science, medical applications, and others. Anotherpopular technique for MS VOC analysis is secondary electrospray ionizationhigh resolution mass spectrometry (SESI-HRMS). SESI-HRMS has highsensitivity and resolution and is able to function in real time withoutcausing harm to the VOC source.11 Becauseof these advantages, SESI-HRMS has been used in a wide range of applicationsfrom detecting cancers to analyzing cooking emissions for toxic substances13VOC analysiscan be performed with a variety of MS instruments.Proton transfer reaction (PTR) MS is of particular interest as itwas essentially developed for VOC detection.15 Certain MS techniques have beenused to overcome these challenges such as matrix assisted laser desorptionionization (MALDI) or atmospheric pressure solids analysis probe (ASAP)MS. ASAP-MS has been shown to be an especially effective method forpolymer analysis capable of ionizing high molecular weight polymersat atmospheric pressure in the solid state so that dissolving thepolymer is unnecessary.15VOC profiling is commonly used in polymer analysis and identification.However, polymers present a challenge for MS analysis because theyare difficult to ionize and typically have high molecular weight andlow solubility.17 Thermal decomposition refers to the processby which molecules break down and produce smaller molecular fragmentswhen exposed to intense heat.18 Polymer-basedmaterials are especially susceptible to thermal decomposition processes19 and can produce a complicated distribution of VOC. Because thesevolatiles are formed from the original polymer chain, the volatilesmay give some indication of the original structure of the molecule.In fact, many studies have demonstrated that the products of thermaldecomposition reactions are consistent and, often, unique to the polymer.24 Other research has shown that changes in polymer structure as wellas additives can also be detected and studied through thermal decompositionanalyses.14Thermaldecomposition-based techniques such as pyrolysis (Py) MS,Py-GC-MS, or thermal desorption and pyrolysis direct analysis in realtime (TDPy DART) MS are often used in polymer characterization becausethese techniques are time-efficient and demonstrate sufficient analyterecovery.10 To overcome this limitation, this method uses machine learning toprocess data which is capable of analyzing and classifying complexspectra automatically.This study implementsrelatively simple MS instrumentation: electronimpact mass spectrometry (EI-MS) to analyze the VOCs released throughthe thermal decomposition of polymers. Traditionally, EI-MS has notbeen considered appropriate for VOC analysis in part because the EIresults in extensive fragmentation which makes analyte identificationdifficult for complex, multianalyte samples.19 Thus, these VOC products can be useful early indicators of overheatingor prefire conditions. As an example, early detection of fire or near-fireevents during onboard monitoring of a spacecraft is necessary sincespacecraft are confined environments with limited evacuation and resupplyoptions as well as limited ways to extinguish a fire. Fires must bedetected as early as possible to mitigate the harm done to the crewand equipment.26 On board the International SpaceStation (ISS), the current method of fire detection is the Fire Detectionand Suppression system (FDS) which uses an optics-based particle monitorto detect aerosols in smoke.27 However,FDS lacks specificity and has a pattern of producing false alarmsduring housekeeping tasks that increase the amount of dust in cabinair. Lack of specificity is a fundamental problem with the currentsystem and raises concerns for lunar missions where lunar dust hasthe potential to trigger false alarms in the fire detection systemas well.28Volatiles are often emitted at lowertemperatures than smoke, whichis a mixture of volatiles and solid aerosols.29 Early detection of fires can help to lessen the damage caused, whilechemically specific information about the fire can help effectivelyextinguish or prevent the fire.24Chemically specific firedetection is not only applicable to spacecraftbut also to many Earth environments. In 2020, structure fires in theUnited States resulted in 8.7 billion dollars in direct property damage,11,900 injuries, and 2,630 civilian deaths.This study’s approach to improving fire detection specificityis to develop a method for detecting chemically specific indicatorsof fires or near fire events using mass spectrometry and machine learning.The target analytes for this method are the volatiles produced inthe thermal decomposition of common spacecraft materials. The collecteddata was used to develop a machine learning classification capableof determining the material or materials that produced the VOCs detectedwith high accuracy and very limited data preprocessing.30 Each materialwas purchased from McMaster Carr and used as is. Teflon was purchasedin the form of Teflon tape, and Mylar and PMMA were purchased in sheets.Samples used in single material runs had an approximate volume of1.2 cm3. Mixtures containing two materials were preparedto be 50% by volume of each of the two materials, and mixtures ofall three materials were comprised of 33% by volume of each material.Each material’s density was used to calculate the mass of thatmaterial needed for each run. Raw materials were cut into smallerpieces which were weighed out to the necessary mass using a VeriTasAnalytical Balance.Three materials commonly used on spacecraftwere selected for this study: biaxially oriented polyethylene terephthalate(Mylar), poly(methyl methacrylate) (PMMA), and polytetrafluoroethylene(Teflon). Teflon is used in spacesuits, cargo liners, wire insulation,and sampling bags, while PMMA and Mylar make up the windows and thermalinsulation, respectively.The sampling andanalysis instrumentationused in this study is shown in 2 21% O2. These conditions also reflect the environmental conditionson board spacecraft.32Sample heating took place under typical atmosphericconditions:The pressure in the sample chamber was kept at 760 Torr and sampleswere exposed to lab air consisting of 78% Nm/z and a limit of detection (LOD) on theorder of 10–13 Torr inside the MS chamber or approximately400 ppm at atmospheric pressure. This sensitivity was achieved usinga faraday detector. The QMS is also equipped with an SEM detectorwhich is expected to improve sensitivity and achieve an LOD of approximately1 ppm.Volatiles were directedinto the chamber through the differentiallypumped leak valves shown in Prior to analysis of the selected materials, extensivebackgroundmeasurements were performed. Spectra of the background with and withouteach piece of sampling equipment were collected. Additionally, backgroundspectra were collected of the sampling system with no sample at temperaturesranging from room temperature to 700 °C to assess any volatilecontributions from the instrument.As shown in m/z mass range was scanned at an intervalof 0.03 m/z. Samples were heatedfrom room temperature to up to 700 °C, and spectra were collectedthroughout the heating process. Heating ramp rates varied from approximately5 to 10 °C/min.Mass spectra were collectedon each material in full scan mode so that each mass within the 1–200 Detected thermal decomposition productswere identified to help ensure that the chemical signature for eachmaterial was consistent over each run and different from the chemicalsignatures for other materials studied. Product identification alsohelped to ensure that detected products were consistent with the literatureon the studied materials. The products identified in this investigationare the expected products based on the information available. However,for the purpose of machine learning it is not necessary to identifythe molecular formula of individual peaks. As long as the spectralpattern serving as the chemical signature is consistent and uniquefor each material, the machine learning model will be able to performthe classification.36 The NIST spectra were examined to determine if anyreported products for a given material had peaks in common with thecollected spectra for that material. For each material, products wereidentified that had the same peaks as those detected in the spectracollected for this study.To perform the product identification, fullscan data was collectedto determine what peaks were detected during the analysis of eachmaterial. Data was then collected in Multiple Ion Detection (MID)mode by selecting the masses of detected peaks and tracking the growthand decay of certain masses over time. If two masses exhibited thesame pattern of growth and decay, it was concluded that they weremolecular fragments of the same molecule. These analyses made it possibleto identify groups of peaks related to different products. To identifythe products detected, spectra of the products from selected materialswere acquired through the National Institute of Standards and Technologydatabase.Distribution of peak abundance wasalso used to verify if a producthad been detected. The relative abundances of each peak in the NISTspectra were compared to the data collected in this study. Specifically,the distribution of peak abundances for all of the peaks of a singleproduct were examined to determine if the distribution of abundancesin the NIST spectra matched the distribution of abundances in thecollected spectra.The temperatures at which each material beganproducing thermaldecomposition products were also determined by taking measurementsin MID mode. A selected subset of detected masses for a given materialwere monitored. The partial pressure of each mass was tracked overtime at an interval of 100 ms. The temperature of the sample was alsomeasured every second over the course of the run. Time of detectionwas correlated with sample temperature to determine the temperaturerange for which products were first detected. The MID data servedonly to aid in determining which peaks were relevant to the thermaldecomposition and in determining the temperature range at which productswere first detectable. Only full scan data was included in the datasets used to train and test the machine learning model.Data sets of full scan spectra foreach material, Mylar, Teflon, and PMMA, and each mixture of material,Mylar-Teflon, Mylar-PMMA, Teflon-PMMA, and Mylar-PMMA-Teflon, werecompiled. Each spectrum was treated as an individual data point andprocessed as follows to prepare the data for the machine learningclassification.m/z at an intervalof 0.03 m/z. A data reduction methodto condense the number of features in each data set was used. Specifically,the raw output data was reduced so that only integer and half integer m/z values within the mass range of 1–200 m/z were kept in the input data for themachine learning model. Data reduction was performed using a MATLABscript that expressed all peaks as Gaussians centered on the integervalue.The QMS full scan mode recorded the partialpressure at each 37 The linearand binary encoding results for different materials and mixtures areshown in Since an encoded MS data set is more suitable for themachine learningclassification, the original categorical MS data was converted intoboth a linear and binary code. Linear encoding was used to assignvalues 0–6 to each class of material and binary encoding enabledthe classification of mixtures based on the popular One-Hot encodingconcept in machine learning.38 Theentire MS data set including the three single materials  and the four mixtures  was used as the testing data set.The encoded MS data set was split into training andtesting subsetsby randomly selecting 80% of the single material data  as the training subset.39 TheRFP was trained with the training subset. The trained RFP classificationaccuracy was assessed using both single material and mixed materialdata. The accuracy was then compared with other advanced machine learningclassifications, including K-nearest neighbors (KNN),40 decision tree (DT),41 supportvector classification (SVC),42 NaïveBayes (NB),40 and multilayer perceptron(MLP).43After establishing mass spectra datasets consisting of full scan spectra, the machine learning workflowinitially involved the development of a random forest panel (RFP)classification model based on the binary encoded MS data set. A randomforest classifier is characterized by two distinctive aspects: randomsampling and random feature selection, which are used to decorrelatethe different trees in the forest and thus lower the variance of theprediction to avoid overfitting.ŷm. Finally, an overall prediction, ŷ, is achieved through a majority voting from eachindividual prediction.All the steps in the RFPtraining process are shown in ŷM = 1, it indicates Mylar is detectedin the environment. Otherwise, ŷM = 0 means the RFP does not detect Mylar in the environment.Second, the classification results from different sub-RFs were summarizedto achieve binary classification results . Finally, the binary classification result was decoded basedon ŷM = 1, Teflon-RF output ŷT = 0 and PMMA-RF output ŷP = 1, then the binary classificationresult is . The binary results were then decoded as listedin The trained RF with sub-DTs was then used to predict the labelof the spectra in the testing data set. Based on the RF, a RFP containingthree sub-RF models, Mylar-RF, Teflon-RF, and PMMA-RF, is shown in n × n matrix used for evaluating the performance of a classificationmodel, where n is the number of target classes  is calculated by m/z region is shownin The measured composite mass spectra for Mylar, Teflon, and PMMAareshown in Compilations of all full scan spectra collected over single runsof Mylar, Teflon, and PMMA are shown in Multiple runs yielded similarresults with slight variations inabsolute intensities as is typical for mass spectra scans. The massresolution is at least unit mass, and the positions do not change.It is also clear that each species from each sample has a differenttemperature dependence which is critical information to capture forprognostic detection.2, 44 m/z), acetaldehyde, and benzene . CH3CHO and CO2were detectedbetween 300 and 350 °C, whereas C6H6 wasdetected in the range of 325–375 °C. The onset of theseproducts can be seen in the top panel of Based on the spectra shownin 4, 31, 50, and 69 m/z), tetrafluoroethene , hexafluoroethane , and hexafluoropropene , two fluorocarbonswith bound oxygen: carbonic difluoride  and CF3O (85 m/z), and CO2. The Teflon fluorocarbonproducts and carbon dioxide were detected between 440 and 460 °C.The other products, CF2O and CF3O, were detectedat slightly higher temperatures ranging from 450 to 470 °C. Smokeonset was observed at a higher temperature range of 495–500°C. Overall, Teflon was stable at higher temperatures than Mylarand, as discussed below, PMMA.Multiplevolatile molecules were also observedas products of Teflon’s thermal decomposition reactions. Theonset and growth of the detected products can be seen in the middlepanel of 5H8O2, 39, 41, 55, 59, 69, 85,and 100 m/z) and CO2 (44 m/z) were identified as primary thermaldecomposition productsfrom PMMA. All peaks shown in the PMMA spectra in 5H8O2 except for the peak at44 m/z which represents CO2. However, only the most abundant masses that were most useful inidentifying this product are listed in 5H8O2 during MS analysis allowed a distinctchemical signature to be established even though only two productswere detected. Both C5H8O2 and CO2 were detected between 200 and 250 °C while smoke wasnot observed until the material reached 300–330 °C. Thus,PMMA exhibited the greatest difference in the temperature at whichthermal decomposition products were first detected and the temperatureat which smoke was observed.Methyl methacrylate. The minimum number of diagonal elements is N3,3 = 358, which is more than ten times greater than themaximum of the off-diagonal element, N3,6 = 28. This indicates that the trained RFP can achieve perfect predictionaccuracy for data of a single material and a high level of predictionaccuracy for mixed material data.Using the data andmachine-learning approach described above, the results of the RFPdemonstrated strong accuracy in the classification of both singlematerial spectra and mixed material spectra. In 47 The superioraccuracy for the single material data classification is to be expectedbecause the model was trained using only single material data. Therefore,the model performs best on data that most resembles the training set.Mixed data was not included in the training set to enable a more flexibleclassification architecture to which new materials can be added. However,excluding mixed data from the training set also caused a drop in theaccuracy in the mixed data classifications. The overall accuracy ofthe RFP including the mixed classifications is high, so these resultsindicate that the RFP can accurately predict and classify data onmixtures of materials with the training of single material data. Theuse of the RFP allows this method to (1) specifically detect VOCsindicating a fire or near fire event and (2) extract further informationabout the event by identifying a material or materials involved inthat event. The flexible framework of the model allows for the additionof new materials, so the model is adaptable to the monitored environment.The confusion matrixand accuracy distribution show the maximumprediction accuracy for mixed materials is 96.8%. This level of accuracywas achieved for Mylar–PMMA: 4, and the minimum accuracy is90.0% for Mylar–Teflon: 3. The misclassified data includedboth false positives and false negatives, so in some cases, chemicalsignatures that were present in the spectra were not detected. Inother cases, signatures that were not present in the spectra weredetected. The randomness in the misclassifications and the small varianceof prediction accuracy of mixtures indicates the trained RFP predictionis unbiased and free from overfitting.2, have other environmentalsources in spacecraft. Acetaldehyde and benzene are both producedthrough off gassing of materials and human metabolism, and CO2 is produced through human respiration.49 Off gassing of materials contributes approximately 2.5 × 10–5 and 1.1 × 10–4 mg/day perkilogram of material of benzene and acetaldehyde, respectively, whilehuman metabolism produces 2.2 and 0.6 mg/day per person of benzeneand acetaldehyde, respectively.49 Humanrespiration typically generates 1 kg of CO2 per personper day.50 The Spacecraft Maximum AllowableConcentrations (SMAC) for CO2 is 13000 mg/m3; however, the Carbon Dioxide Removal Assembly (CDRA) typically maintainsa concentration of 2–5 mg/m3 51 and proposed systems aim to minimize daily CO2 concentrations further.50 TheSMAC for a 24 h period is 3 mg/m3 for benzene and 6 mg/m3; however, typical concentrations of these contaminants remainvery low, <0.05 mg/m3 for benzene and 0.05–0.33mg/m3 for acetaldehyde.The volatiles detectedduring the analysis of PMMA and Teflon were distinctive and thus provideda highly specific indicator of a fire or near-fire event involvingthese materials. However, the major components of the Mylar chemicalsignature, acetaldehyde, benzene, and CO2 concentrations are controlled using CDRA.50 Because all these contaminants have other sources in spacecraftair it would be necessary to compare chemical signatures to nominalcabin air in order to prevent false alarms. It is expected that afire or near-fire event would produce increased concentrations ofthese volatiles compared to nominal conditions. While faults in theTCC could lead to elevated levels of acetaldehyde and benzene anda fault in CDRA could lead elevated CO2, simultaneous faultsin both systems would be required to mimic the conditions of a fireor near-fire event involving Mylar. As it is unlikely that faultsin these systems would occur at the same time, a false alarm due toother environmental sources of acetaldehyde, benzene, and CO2 is also unlikely.Both benzene and acetaldehydeare scrubbed from spacecraft air using the Trace Contaminant Control(TCC) while COA limitation of this project is that itis expected that TCC andCDRA would remove contaminants produced by a fire or near-fire eventto some extent. Further testing would be required to determine howvolatile scrubbing systems would affect the chemical signature beforethis method could be applied in a spacecraft environment.A mass spectrometry-machine learning method for the identificationof VOCs emitted during the heating and eventual combustion of commonmaterials such as Mylar, Teflon, and PMMA has been designed and tested.To enable chemically specific detection of fire and near-fire eventsinvolving these materials, VOC signatures were measured during controlledheating, and machine learning was used to reliably identify each material.Data sets of the chemical signatures for each material and mixtureof materials were generated to develop an RFP machine learning classificationthat allowed accurate identification of the source materials and mixturesbefore combustion. The model achieved a minimum accuracy of 90% inits classification of mixed material spectra and 100% accuracy inthe classification of single material spectra. With appropriate MSdata sets, the structure of the RFP should allow the classifier toidentify VOC signatures in mixtures with unknown contaminants.The reported results show that this method can provide high accuracyand specificity in the classification of early indicators of firesand near fire events allowing for both the early detection of a fireand the identification of the materials involved in the fire. Theearly detection and automated data processing capabilities demonstratedin this work are key for advancing fire detection techniques and allowingfor more autonomous control of enclosed environments both on Earthand in space.The implementation of machine learning allowedfor the use of simplifiedinstrumentation but still achieved accurate results. The method showspromise as a chemically specific fire detection method and demonstrateshow machine-learning techniques can simplify and streamline MS dataanalysis to facilitate high accuracy VOC profiling and identification.


Solubility is a critical parameter controlling drug absorption after oral administration. For poorly soluble drugs, solubility is influenced by the complex composition of intestinal media and the influence of dosage form excipients, which can cause bioavailability and bioequivalence issues. This study has applied a small scale design of experiment (DoE) equilibrium solubility approach in order to investigate the impact of excipients on fenofibrate solubility in simulated fasted and fed intestinal media. Seven media parameters , phospholipid (PL), fatty acid, monoglyceride, cholesterol, pH and BS/PL ratio) were assessed in the DoE and in excipient-free media, and only pH and sodium oleate in the fasted state had a significant impact on fenofibrate solubility. The impact of excipients were studied at two concentrations, and for polyvinylpyrrolidone  and hydroxypropylmethylcellulose , two grades were studied. Mannitol had no solubility impact in any of the DoE media. PVP significantly increased solubility in a media-, grade- and concentration-dependent manner, with the biggest change in fasted media. HPMC and chitosan significantly reduced solubility in both fasted and fed states in a media-, grade- and concentration-dependent manner. The results indicate that the impact of excipients on fenofibrate solubility is a complex interplay of media composition in combination with their physicochemical properties and concentration. The results indicate that in vitro solubility studies combining the drug of interest, proposed excipients along with suitable simulated intestinal media recipes will provide interesting information with the potential to guide formulation development. When developing oral formulations for poorly water-soluble drugs biopharmaceutics classification system (BCS) (classes II and IV) , it is iPublished studies have sought to advance the understanding of drug solubility in the GIT, using either sampled human intestinal fluids  or simulThe experiments reported in the previous paragraph were performed using solid drug in media to determine an equilibrium solubility value, based on the principle that equilibrium solubility is a fundamental value that can be applied throughout the oral absorption space . HoweverThis study will assess the solubility impact of four excipients , monosodium dihydrogen phosphate (100%), ammonium formate (>99.995%), formic acid (98–100%), sodium chloride (NaCl), potassium hydroxide , hydrochloric acid solution , acetic acid (>99.7%), cholesterol (>99%), chloroform (99.5%), fenofibrate, and chitosan  were purchased from Sigma-Aldrich, Poole, Dorset, UK. Lecithin S PC  was supplied by Lipoid, Ludwigshafen, Germany. Sodium oleate  was obtained from BDH Chemicals Ltd., Poole, England. Monoglyceride  was supplied by CRODA. Polyvinylpyrrolidone  were from Ashland, Singapore. Hydroxypropyl methylcellulose  (Methocel E3 and E50 premium LV) were obtained from Colorcon Ltd., Kent, UK. Mannitol  was obtained from Blackburn Distributions, Nelson, UK. All water used was ultrapure Milli-Q. Methanol and acetonitrile was purchased from VWR Prolabo Chemicals, Poole, UK. ® 17.2.1 and a custom experimental design, a 1/8 factorial DoE with seven factors , lecithin (PL), monoglyceride (MG), cholesterol and BS/PL ratio) and two levels , lower and upper limit concentration values for fasted and fed states were defined . Using M® to assess the normality distribution of each data set, and the Friedman Test and Kruskal–Wallis test (Prism 9.5.1 on Mac OS 10.13.6) used to evaluate the non-parametric difference between data sets. When designing and analysing the DoE, only the main effects have been considered, and higher interactions of factors were not included. For each DoE, the magnitude of each individual factor’s effect on equilibrium solubility was determined by the standardised effect value. This value was used to determine whether these factors raised or lowered drug solubility. Due to the design and the low number of experiments, the standardised effect values calculated for individual factor effects in the fasted and fed state arms indicate a significant increase in drug solubility when it is greater than +2 and a decrease when it is less than −2. The Kolmogorov normality test was used in MinitabThe media preparation and equilibrium solubility measurement method has been applied in previous DoE studies ,12,13.The concentration of each stock solution has been designed to be 15 times greater than the upper limit concentration value required for the DoE, with the exception of oleate, for which only a 5 times concentration was possible see . Preparation of Lipid SuspensionSodium taurocholate, monoglyceride, lecithin and cholesterol were weighed and transferred into a flask, then 2 mL of chloroform was added to dissolve all the solid material. A stream of nitrogen gas was applied in order to remove the chloroform and to ensure the formation of a dried film. Water was added to reconstitute the dried film, and the solution was mixed to obtain a homogenous suspension, transferred to a 5 mL volumetric flask, and brought to volume with water. Preparation of Sodium Oleate SolutionSodium oleate (1.90 g) was weighed and placed into a 50 mL volumetric flask, dissolved in water with the assistance of gentle heating (37 °C) to aid dissolution, and then made up to volume with water and kept under heat to aid solubilisation.Preparation Buffer SolutionA concentration of 0.3 M monosodium dihydrogen phosphate buffer was prepared by adding 20.4 g into a 500 mL volumetric flask and making up to volume with water. This was split into two, and the pH was adjusted to 5 and 7 using aqueous 0.5 M HCl or 0.5 M KOH. Preparation of excipient stock solutionsExcipient solution: The appropriate amount of each excipient was weighed out see , transfePreparation of Individual Design of Experiment Solutions® Centristar™ cap, polypropylene RNase/DNase free, non-pyrogenic) followed by the addition of each component of the simulated intestinal fluid media according to the run order generated by the DoE together with the excipient to be examined  was run for the excipient-free DoE and each excipient concentration, with the lowest linear regression coefficient of 0.9979 for the low chitosan excipient concentration system. The method has been reported previously [Agilent Technologies 1260 Series liquid chromatography system with Clarity chromatography software was used. Gradient method: mobile phase A ammonium formate 10 mM, pH 3 in water; mobile phase B ammonium formate 10 mM, pH 3 in acetonitrile/water 9:1, time 0, 70%A:30%B, 3 min 0%A:100%B, 4 min 0%A:100%B, 4.5 min 70%A:30%B, total run 8 min. Column × Bridge C18 column/186003108/50 mm × 2.1 mm id. 5 μm was used. Fenofibrate (retention time 3.6 min) calibration curve  are presented in The DoE measurements were analysed using Minitab to calculate an individual factor’s standardised effect on measured equilibrium solubility. This provides a value for the magnitude and direction of the factor’s effect and allows for a comparison between different factors . In thisThe impact of the various excipients and grades on fenofibrate equilibrium solubility is presented in Two excipient concentration levels in the final equilibrium solubility media were investigated, with the lower concentration representing systems in which the drug and formulation have been diluted within the tract contents and the higher concentration representing those in which the disintegration and or dissolution is in the initial stages. The concentrations were chosen to be approximately 10× (low) or 100× (high) the mean molar fenofibrate equilibrium solubility (0.8 mM) measured in previous DoE study . In the The results for mannitol  show no The results for PVP K12 and K29/32 are presented in The results for HPMC E3 and E50 are presented in The results for chitosan are presented in The comparison indicates that there is a statistically significant difference between the fasted solubility data set from this study and that from a previously published fasted DoE study, but that there is no difference between the fed systems . The resStatistical analysis calculates that all of the data sets in this study had non-normal distributions see . TherefoThe detection of only two significant standardised effect values  is in maThe differences detected between the excipient-free values in this study and published studies is therefore consistent with the overall behaviour of simulated intestinal media examined using a reduced-experiment-number DoE approach. Despite these differences, since the system without excipients is acting as an internal control for this study, the impact of excipients on solubility can be directly analysed. However, based on the results in this study and other published studies surrounding system variability, the general applicability of excipient affects to all intestinal media systems must be examined cautiously. As far as we can determine, there are no previous published studies assessing the solubility impact of excipients in simulated intestinal media systems using a DoE approach. Therefore, the literature comparisons of results are limited to standard simulated intestinal media recipes. For mannitol, the detection of a significant change in the fed state using the Freidman analysis is potentially due to the experimental protocol with only a single measurement on each media system coupled with the non-parametric analysis (see Both PVP K12 and K29/32 significantly increase fenofibrate equilibrium solubility  in both HPMC E3 and E50 exhibit an obvious solubility impact  that is Chitosan is a naturally occurring mucopolysaccharide of high molecular weight obtained by the alkaline deacetylation of chitin. Its weak solubility in water increases under acidic conditions due to the protonation of the amino groups with a pKa between 6.3 and 7.2 depending on the degree of deacetylation  and willThis is the first study to assess the feasibility of applying a reduced-experimental-number DoE protocol covering both fasted and fed simulated intestinal media to provide data on the impact of excipients on the equilibrium solubility of fenofibrate, a poorly soluble drug. Mannitol had no solubility impact in any of the DoE media. PVP significantly increased solubility in a media-, grade- and concentration-dependent manner, with the biggest change in fasted media. HPMC and chitosan significantly reduced solubility in both fasted and fed states in a media-, grade- and concentration-dependent manner. The results indicate that the impact of excipients on fenofibrate solubility is a complex interplay of media composition in combination with their physicochemical properties and concentration. The finding of media-composition-dependent behaviour indicates that excipient effects will be variable in vivo and complex to asses in vitro based on the inherent intra- and interindividual variability present in intestinal fluid . The res


Seasonal droughts will become more severe and frequent under the context of global climate change, this would result in significant variations in the root distribution and water utilization patterns of plants. However, research on the determining factors of deep fine root and water utilization is limited.We measured the fine root biomass and water utilization of trees, shrubs and herbs, and soil properties, light transmission, and community structure parameters in subtropical pine plantations with seasonal droughts.We found that the proportion of deep fine roots (below 1 m depth) is only 0.2-5.1%, but that of deep soil water utilization can reach 20.9-38.6% during the dry season. Trees improve deep soil water capture capacity by enhancing their dominance in occupying deep soil volume, and enhance their deep resource foraging by increasing their branching capacity of absorptive roots. Shrubs and herbs showed different strategies for deep water competition: shrubs tend to exhibit a “conservative” strategy and tend to increase individual competitiveness, while herbs exhibited an “opportunistic” strategy and tend to increase variety and quantity to adapt to competitions.Our results improve our understanding of different deep fine root distribution and water use strategies between overstory trees and understory vegetations, and emphasize the importance of deep fine root in drought resistance as well as the roles of deep soil water utilization in shaping community assembly. To someRobinia pseudoacacia in the Loess Plateau was found to increase the reliance of shallow and middle soil water but decrease the reliance of deep soil water as thinning intensity increased  in the wet season and that of the deeper soil layer (30-50 cm) in the dry season. However, plants’ water use strategies can be variable due to the micro local changes in soil conditions, i.e. soil depth, stoniness, water holding capacity  can also affect the distribution patterns of fine roots and water use of plants. Generally, the development of root was severely restricted with the increase of soil bulk density since high mechanical could impede the elongation and proliferation of root . HoweverIn our study area, the understory vegetation species are rich and the soil heterogeneity is high, which can induce strong variations in soil conditions at very close distances . In turn22.1Pinus massoniana and P. elliottii plantations which were planted in 1983, and the vegetation picture was shown in This research was conducted at the Qianyanzhou Forest Ecosystem Research Station of the Chinese Academy of Sciences , which is located in the Jiangxi Province of southeastern China  were established in the pine plantations, including 18 plots of The community structure was investigated in August 2015. Each plot was divided into nine quadrats (10 × 10 m) and then taken photos below the canopy. Light transmission was then determined by Side look and Gap Light Analyzer . In each2.3Soil cores of 10 cm diameter were sampled every 20 cm at the up-, mid-, and down slopes from soil surface to 200 cm depth in each plot. Living fine roots were carefully picked out and gently washed with running water. Living fine roots were then sorted to trees, shrubs, and herbs, according to their contrasting traits in terms of morphological structure, color, and mechanics . More details can be seen in -3  were picked up from the soil samples, and then air-dried and weighed. The rock fragment content of each soil sample was determined by using a stone density of 2.65 g cm-3 . Due to -3 . Soil pH2.4Plant samples for water isotope analysis was collected during mid-morning in both the dry season (August 2016) and the wet season (April 2017). For trees, stems from two to three sample trees of middle diameter were collected in each plot, and phloem tissue was peeled off to avoid potential contamination . For shr18O was shown to be more sensitive than δD in evaluating water uptake of plant and therefore δ18O was used to detect the water uptake depths of plants.We extracted water from plant and soil samples through an automatic low-temperature vacuum distilled water extraction system . The extraction time for plant and soil sample was 3 h and 2.5 h, respectively. We analyzed the water isotope ratio of water samples using a liquid water isotope analyzer . The IsoSource model was applied to calculate the relative contribution of soil water at different depths to plant . See Jia2.518O value of the upper 100 cm soil was different from the below 100 cm depth  was conducted in SPSS . To test the differences between trees and shrubs (or between seasons), the independent sample T test was applied in SPSS. To analyse the relationships between the fine root biomass and water utilization in the deep soil layer and the abiotic and biotic factors, the RDA-ordination biplot was carried out in the CANOCO software . Significance was defined at the 0.05 level.33.1In our study, trees and understory vegetations showed contrast deep fine allocation and water use strategies 3. We fo3.2The RDA ordination biplot displayed that the major determining abiotic and biotic factors of deep fine root and water utilization were different 6. For t44.1We found trees and shrubs distributed more fine roots than herbs in deep soil. Though the proportion of total deep fine root biomass in trees and shrubs was not significant, trees had higher proportion of absorptive fine root biomass than shrubs in deep soil. Generally, absorptive roots can make more efficient use of soil resources due to the thinner diameter and thus occupying a dominant position in total length and surface area of root . That isIn our study, we found that trees and understory vegetations had contrast deep water use strategies. For details, trees have a significant deep soil water reliance throughout the year , could further play important roles in determining the deep fine root and water utilization and thus the community assembly. Compared with shallow root, deep fine roots have larger diameters and higher tissue density and thus require more carbon investment  is only 0.2-5.1%, their proportion of deep soil utilization can reach 20.9-38.6% in the dry season, which highlight the roles of deep fine root in drought resistance. Compared with understory vegetations, trees had a significant deep soil water reliance throughout the year and distributed a higher proportion of absorptive roots in deep soil layer. This indicated that trees could improve deep soil water capture capacity by enhancing their dominance in occupying deep soil volume, and enhance their deep resource foraging by increasing their branching capacity of absorptive roots. Moreover, shrubs and herbs showed different strategies for deep water competition: shrubs and herbs exhibited different “strategy” in response to competition: shrubs exhibited a “conservative” strategy and tend to increase individual competitiveness, while herbs exhibited an “opportunistic” strategy and tend to increase variety and quantity to adapt to the competition. Due to the low interpretation rate of RDA ordination biplot, we assumed that traits we didn’t measure might further play important roles in determining the deep fine root and water utilization and thus the community assembly. Future studies on deep fine root and water use patterns should also focus on plant physiological properties  and economic spectrum traits.The original contributions presented in the study are included in the article/PJ: Funding acquisition, Investigation, Methodology, Software, Writing – original draft, Writing – review & editing. JY: Investigation, Writing – review & editing. RL: Investigation, Writing – review & editing. XZ: Methodology, Writing – review & editing. SF: Funding acquisition, Methodology, Writing – review & editing.


In recent years, several studies have consistently reported the beneficial effects of aerobic exercise in alleviating symptoms following concussion. However, exercise modality recommendations by practitioners are often limited to traditional exercise equipment . Advances in digital technologies may help to overcome this limitation, as mobile apps can now provide users with high-quality instructional videos, programs, and monitoring capabilities using alternative modalities such as resistance exercises. Mobile technologies are also rapidly expanding to deliver and complement in-person clinical care. Thus, it is imperative to evaluate this emerging technology on its feasibility, safety, and clinical utility for concussion care.The objective of the study was to determine the feasibility of a mobile app to deliver a resistance exercise protocol with minimal equipment to individuals following concussion. Feasibility was defined by retention, adverse events, and achievement of a target heart rate (HR) of 60%±5% (age-adjusted percentage of max: 220 – age). HR data were collected using an Apple Watch, Series 6. Symptoms were evaluated before and after 3 exercise sessions.A 2-week, prospective, single-arm pilot study was conducted on 21 adults diagnosed with a concussion. Users were provided a continuous aerobic resistance exercise (CARE) protocol through a mobile app.A total of 18 participants  completed a 3-session exercise plan. The median age-adjusted percent of HR max for session 1 was 55.5% (IQR 49%-63%), 58.1% (IQR 50.8%-65.2%) for session 2, and 57.4% (IQR 49.5%-64.7%) for session 3. Individual median HR% across all sessions ranged from 46.9% to 67.4%; furthermore, 10 participants (55.5%) had a total mean HR% within the target HR%, 7 participants had a mean HR% below 55%, and 1 participant had a mean HR% above 65%. In addition, adherence to the plan resulted in a decrease in reported symptom burden with 94% posterior probability.Following concussion, a CARE protocol delivered through a mobile app resulted in no adverse effects with 14% (n=3/21) attrition over 3 sessions. CARE was successful in achieving an aerobic exercise intensity of 55%-65% of age-adjusted maximum HR in the majority of participants and resulted in a decrease in reported symptom burden. The potential for this platform in concussion rehabilitation warrants further investigation. Future studies are needed to assess the use of this technology throughout concussion recovery in both individuals with acute concussion, and those with persistent symptoms. The classic rest approach was well-intended to limit the risk of reinjury or additional injuries, ease discomfort from symptoms, and possibly promote recovery by decreasing energy demands on the brain. However, this approach can lead to feelings of isolation, creating fear-avoidance behaviors in some individuals that contribute to a self-perpetuating cycle of symptoms [rest still has a role in acute management following suspected concussion, it should be limited to 24-48 hours [rest to active rehabilitation.Advising physical and cognitive symptoms ,2. Altho48 hours . Thus, tResearchers have consistently reported the beneficial effects of aerobic exercise to assist in alleviating symptoms following concussion -7. SinceOther forms of exercise, such as resistance training, may provide an alternative solution for exercise reintroduction with minimal equipment and can be completed remotely or to complement in-person therapy. However, resistance training has not been considered as a viable solution for concussion management . This isApprehensions to resistance exercise following a concussion could be alleviated with the appropriate exercise prescriptions. That is, it may be reasonable to reintroduce physical movement to create a physiological demand comparable to aerobic exercise, albeit limiting head acceleration, and bracing to increase intraabdominal pressure demand. Given this, we developed a continuous aerobic resistance exercise (CARE) protocol to reintroduce exercise following concussion. CARE seeks to impose a pseudocontinuous or steady-state response by employing a combination of resistance exercises with cardiovascular and musculoskeletal demands with minimum rest. The exercises incorporated in the CARE protocol require participants to be in a static body orientation or perform head accelerations only in the up or down axis; the goal is to create a demand similar to steady-state cycling or treadmill activity.Resistance exercise programs require greater instruction and cues than those with treadmills and stationary bikes; however, mobile health apps help to overcome this barrier. Mobile tools are ubiquitous, have excellent computational capabilities, and are commonly carried by the person ,21. As aGiven that we are using a novel modality for individuals recovering from a concussion delivered through a mobile app platform, our initial investigation sought to evaluate the feasibility of the protocol. Specifically, we were interested in evaluating (1) adverse events and retention among users across a 3-session plan, and (2) whether users could achieve a target HR during exercise sessions. We also measured symptoms before and after the protocol. We hypothesized that each CARE session would be completed by all participants and would elicit HRs of 55% (±5) of participants’ age-adjusted maximum HR. We also hypothesized that participants’ symptoms would decrease following the 3-session plan.This study was part of a 2-week, community-based, prospective single-arm pilot study in which adults older than 18 years were prescribed an active, home rehabilitation program . A convenience sample of 26 individuals diagnosed with a concussion by a physician were recruited through flyers, word-of-mouth, and social media platforms . Before the study, participants were screened by a research assistant confirming concussion diagnosis, at least 18 years of age, and access to an iPhone. Individuals with co-morbid injuries  or neurological conditions that prevented them from physical activity were excluded from the study. Because of the COVID-19 pandemic and the resultant inability to complete in-person research at our institution, participants were enrolled in the study remotely through Zoom. Study enrollment included documenting relevant medical history, mechanism of injury, initial clinical presentation and current symptoms, a physical activity assessment questionnaire reporting the nature and duration of everyday exercise involvement, and satisfaction with current exercise level. Participants were provided (via mail) with the Rhea Health Inc mobile application on their iPhone and Apple Watch Series 6 (Apple Inc). The exercise prescription on the Rhea Health app was determined by days post concussion and the symptom status of the individual.This study was approved by the Health Sciences Research Ethics Board, University of Toronto (#39143). To ensure that all potential participants were fully aware of the research study, a research team member provided them with an information sheet outlining eligibility and the study's purpose, methods, and possible risks. Upon confirming eligibility, a videoconference through Zoom was completed to address any questions with respect to the study before consenting to participate. Potential participants were reminded that their choice to participate was entirely voluntary and that they could withdraw from the study at any point without any negative repercussions. Informed consent was obtained by reviewing and signing an electronic document outlining the purpose, procedures, and potential risks of participation in this study; the signed form was submitted using REDCap (Research Electronic Data Capture) electronic data capture tools hosted at the University of Toronto. Data captured through the mobile app and Apple Watch was stored on Amazon Web Servers DynamoDB database in Montreal, Quebec, Canada, using Amazon Web Servers proprietary encryption and a restricted access system to keep data secure. Participants’ emails and passwords were required for accessing the mobile app and syncing Apple Watch data to their Apple Health; however, personally identifiable information was stored in a separate database table and not required for data analysis. Upon study completion, participants received CAD $50 (US $37.16) as compensation for participation and were entered into a lottery for an Apple Watch Series 6.We examined the feasibility of providing the CARE protocol among symptomatic individuals. Of the 26 participants, 21 were provided with the CARE protocol as part of their rehabilitation prescription; the 5 participants excluded from analyses were not provided the CARE protocol, either because they were classified as having a symptom burden too high for aerobic activity, or were too early.The CARE protocol was delivered through Rhea Health’s mobile app. The mobile app was created to help people recover from concussion using resistance exercises with minimal equipment. Users are provided with instructional videos of exercises to guide them through their rehabilitation process. The app begins when users create an account and provide basic information about their concussion, such as the date of injury and the severity of their symptoms. From there, the app provides users with a personalized exercise program based on their individual needs and abilities. Each exercise is accompanied by clear instructions and visual demonstrations to ensure that users perform the exercises correctly and safely  experienced while using the mobile app. The worsening of symptoms resulting in the inability to complete the intervention was categorized as a minor adverse effect.The Apple Watch Series 6 is a wrist-worn smart device (weight range 26.7-52.8 g) that includes a retina organic light-emitting diode display (size 38 or 42 mm), triaxial accelerometer (up to 16 g-forces), triaxial gyroscope, barometric altimeter, optical heart sensor, and global positioning system. This device is water-resistant (up to 50 m) and has up to 18 hours of battery life. It is advertised to monitor HR in real-time using the optical heart sensor. The watch was placed on the dominant wrist of the participants following the manufacturer’s recommendations and connected to an accompanied iPhone throughout the study.Participant symptoms were assessed using the symptom checklist included in the mobile application. Because of form factor restrictions of the mobile app, we modified the 22-item postconcussion symptom scale [a priori skepticism of group differences and nonzero coefficient weightings. Priors for regression coefficient parameters were of the form α/β ~ Normal, whereas the exponential distribution was used to model the variance parameter σ ~ Exponential(1). Prior to sampling, continuous variables were z transformed.The objective of the study was to determine the number of participants who achieved the prescribed mean target HR% (±5%) at each session of the 3-session exercise plan. Basic measures of central tendency (mean and median) and spread (SD and IQR) were used to evaluate this objective. Proportions were used to describe the number of participants or sessions that fell within the target HR% range, as well as to evaluate participant session completion. To facilitate group comparisons and compare HR% variance between and within users, and between sessions, a multilevel linear model was employed: mean HR% was modeled with a varying intercept that included an adaptive prior to provide a partially pooled estimate of the mean average HR% for all participants while also providing estimates of the mean HR% at the individual participant level. For differences in symptoms pre- versus postprotocol, an intercept only linear model was used. For between-group differences in HR%, symptoms, and characteristics , linear models with indexed intercepts were employed to facilitate the creation of posterior contrasts. Linear models were also used to evaluate the relationship between HR% and continuous variables . All models employed weakly regularizing priors that had an All models were fitted using the Hamiltonian Monte Carlo engine Stan  through A total of 21 participants were enrolled in the study and prescribed CARE. The participants' median age was 32.3 years (IQR 27.7-38.3) years, they were predominantly female , the majority had a prior history of concussion , 43% (n=9) had a history of depression, and fewer than half of them were following an exercise program at the time of the study . Although only a single participant was a high-performance athlete , the most common mechanism of concussion was sport or physical activity . Only a single participant began using the app in the acute phase of injury (within 1 day); the median time from injury to study enrollment was 85.5 days, with a wide IQR spanning 25.8-618.5 days. See No adverse events were reported during the 3-session CARE protocol. Of the 21 participants who enrolled in the study, 18 completed the 3 sessions (18/21=85.7%). Two participants were uncomfortable using the technology required, whereas one participant reported being too busy to initiate the protocol.Of the 18 participants who completed the protocol, HR data were captured for all sessions on 12 participants, for 2 sessions on 4 participants, and for a single session on 2 participants. The average session length was 22.4 (IQR 21.25-25) minutes. On average, it took participants a median of 6.5 (IQR 5-9) days to complete the CARE protocol. The median HR across all users in each of the 3 sessions was within the target range (55%-65% of age-adjusted HR max). The mean age-adjusted % of HR max for session 1 was 55.5% (IQR 49%-63%), 58.1% (IQR 50.8%-65.2%) for session 2, and 57.4% (IQR 49.5%-64.7%) for session 3; there were no appreciable differences in the average group-level HR% between sessions  and one who is aged 56 years (~highest age in the current sample), the older individual would have a higher average HR% across the 3 sessions with 79% probability, with an estimated difference in HR% of ~12.2% (90% CI of the difference –13.4% to 38.7%). However, caution is warranted in interpreting the point estimate of the difference given the substantial uncertainty on the posterior contrast. See Prior to the beginning of the exercise plan, participants’ median symptom severity score was 32.5 (IQR 25.8-45.2). Participants also reported whether they felt “the same,” “better” or “worse” following each exercise session. Following session 1, 83% (n=15) of participants reported feeling “the same” or “better,” 72% (n=13) of participants felt “the same” or “better” following session 2, and 76% following session 3 . Following the 3 sessions, the median symptom severity score was 22 (IQR 19-26), with all but 1 participant reporting a decrease ; the single participant who did not report a decrease had the same symptom score before and after the plan. The average estimated decrease in symptom severity following the protocol was 10 points, although with large uncertainty on the mean estimate (90% CI of the difference 1.1-21.8 points). Hence, while the degree of symptom alleviation varied substantially, completing the 3 sessions lowered symptom severity with 94% probability. The most prominent reductions were observed in cognitive  and somatic symptoms ; like total symptom severity, there was large uncertainty on the estimates. See There was no relationship between participants’ mean HR% throughout the exercise plan and the amount of symptom alleviation they reported. This held when adjusting for the confound of age (a common cause of symptom reporting and exercise HR%).The findings of our study indicate that it is safe and feasible to provide individuals who are symptomatic from a concussion with an aerobic stimulus delivered through a mobile app. The aerobic stimulus was achieved using primarily bodyweight movements implemented through a CARE protocol, eliciting a relatively consistent HR over 3 sessions. The protocol took approximately 1 week to complete and resulted in meaningful symptom improvement. An important finding from our study was that participants could tolerate the CARE protocol, which was evident by the low number of adverse effects. We wish to highlight that exercise prescriptions for concussion have historically been limited to stationary bikes, treadmills, and other equipment where potential risks to the individual are limited through the careful identification of a target and consistent HR; however, we are encouraged that we found an aerobic stimulus can be delivered through a mobile app using instructional videos that illustrate movement patterns and control work-to-rest time.Heart rates were either at or below the target range of 55% (±5%) of the age-adjusted max in all but one of the 18 participants. However, that 7 individuals were below the target heart range is not a prominent concern, as one of the primary goals of this study was to understand whether participants could complete the 3-session exercise protocol without adverse effects, which they did. Interestingly, even with 39% (n=7/18) of the sample falling below the target HR range, all but 1 participant reported a decrease in symptoms following the program. This, in concert with our observation that HR did not appreciably correlate with symptom reduction, suggests that program completion and minimal elevation of HR are sufficient to provide symptom relief following concussion. That we observed symptom alleviation in individuals who have been symptomatic for months following their concussion, is also encouraging..Interestingly, there was not a consistent response to the CARE protocol across all symptom subtypes. We found that symptom reduction was greatest in cognitive, somatic, and vision symptoms, whereas sleep, fatigue, and balance symptoms were relatively unchanged. The interpretation of these results is speculative but provides evidence for future efforts to examine how specific symptom subtypes respond to aerobic activity. There is an emerging body of evidence noting that persistent postconcussion symptoms are a function of a complex combination of preinjury, personality, and injury characteristics ,36; for Our findings are consistent with prior research reporting that the use of mobile apps for exercise prescription represents a safe, effective, and feasible approach; this has been illustrated in populations with chronic disease ,38 and tResults). Although this is an expected limitation when conducting studies remotely using wearable devices, this further supports the need for more studies with larger sample sizes to account for data loss. Despite these limitations, however, we were able to show that completing 3 sessions of a CARE protocol with relatively minimal HR elevation led to symptom reduction in all but one participant.Although this study's findings are encouraging, they must be evaluated in the context of a few relevant limitations. First, a small, predominantly female sample size may have inflated the uncertainty of our inferences and potentially reduced the generalizability of our findings to males. However, to combat the former, we used a Bayesian analytical approach with regularizing priors, which allows for relatively stable estimates with smaller amounts of data. Yet, validation in a larger sample with more males is needed. Second, we did not directly observe participants’ exercise sessions, as the protocol was performed through a mobile app delivered to their smartphones. Hence, while HR acquisition allowed us to monitor activity, adherence to the specifics of the exercise protocol cannot be confirmed. Furthermore, there were some issues with HR data acquisition causing us to acquire partial data from 6 of 18 participants (see A CARE protocol delivered through a mobile health platform at 55%-65% of age-adjusted maximum HR can be completed by symptomatic individuals following concussion without adverse effects. The completion of a CARE protocol results in a lower reported symptom burden in almost all cases. The potential for this platform in concussion rehabilitation warrants further investigation.


AR) gene (Ensembl number ENSG00000169083) may lead to reduced fertility. Our objective was to determine the association of CAG and GGC repeats with altered sperm parameters in male infertile subjects.Infertility is a global problem that brings about serious sexual and social consequences that strain the health sector and society. The expansion of CAG and GGC repeats in androgen receptor  and group B (N = 168) were comprised of subjects with normal and altered sperm parameters, respectively, from 18 to 60 years. The numbers of CAG and GGC repeats were determined by using PCR amplification and sequence analysis using the Molecular Evolutionary Genetic Analysis (MEGA) software version 6.0. Statistical analysis was performed using the SPSS version 20 and the P-value of <0.05 was considered significant.The mean androgen receptor gene CAG repeats were significantly longer in males with altered sperm parameters as compared to male subjects with normal sperm parameters (P < 0.001). There was no significant difference found for GGC repeats for subjects with altered sperm parameters.Longer CAG length corresponded to greater severity of spermatogenic defect and may lead to subfertility recommendations. The WHO defines infertility as “a disease of the reproductive system defined by the failure to achieve a clinical pregnancy after 12 months or more of regular unprotected sexual intercourse” [The environmental causes of male infertility include prolonged exposure to heat , noise pThe genetic causes underlying male infertility include Y chromosome microdeletions , sex chrAR) gene (Ensembl number ENSG00000169083) is located on chromosome Xq11-12 and has 8 exons and 7 introns, out of which exon 1 has CAG and GGC repeats and is associated with transcription [Androgen is indispensable for the male sex differentiation and spermatogenesis . Testostcription ,16.Polymorphic trinucleotide repeat segment (CAG)n encodes a polyglutamine tract in which n usually ranges from 11 to 31. This CAG repeat tract has been the basis of unprecedented attention in current years because it has been found that the expansion of the CAG segment to more than 40 repeats leads to spinal bulbar muscular atrophy, a mortal neuromuscular disease. Trinucleotide repeat expansions are now associated with many diseases .AR gene is susceptible to one of the largest number of mutations in a steroid receptor, documented at up till 300 mutations [AR gene essentially consists of three main functional domains as the N-terminal transcriptional regulation domain, the DNA-binding domain, and the ligand-binding domain. Androgens bind to the AR gene and exert their actions either through DNA binding-dependent manner, regulating gene transcription, or non-DNA binding-dependent manner, causing phosphorylation of the secondary messenger system [AR gene is expressed on a multitude of tissues in the body such as prostate, bone, adipose tissue, muscle, etc., and plays an important role in the immune, hematopoietic, respiratory, cardiovascular, and neural systems [The utations . The AR r system . The AR  systems ,20.AR gene [The CAG trinucleotide repeat length is normally between 16 and 29 . The varAR gene  since thAR gene .AR resistance is reported to account for 40% of male infertility cases [AR gene has been correlated with male infertility owing to a decreased transcription of the AR gene and diminished spermatogenesis [AR gene [ty cases . Increasogenesis . Therefo[AR gene . Most of[AR gene . Even th[AR gene ,28,29. A[AR gene . An incr[AR gene ,32. Howe[AR gene .AR gene protein levels, suggesting that the GGC repeat length of 13 produced 2.7 times more AR proteins than the GGC repeat length of 17 did. A shorter GGC repeat length has also been linked to prostate cancer [AR gene, while also having an association with the prostate and endometrial cancers [AR gene and altered sperm parameters in our population of male subjects with infertility problems.The transactivation domain of AR also contains a polyglycine tract, encoded by GGT/GGC six-glycine tract proceeded by a polymorphic GGC repeat of a length of 10 to 35 nucleotides. In spite of the limited studies showing the association between GGC repeat polymorphism and male fertility, there have been certain studies  showing  cancers . Hence, This cross-sectional study was conducted from July 2017 to July 2018 at the Department of Biological and Biomedical Science, Aga Khan University, in collaboration with the Sindh Institute of Reproductive Medicine after acquiring the ethical approval of the institutional ethical review board (2019-1226-3970). The sample size was calculated on the Open Epi software version 3.01. A sample size of 398 was estimated to find the CAG and GGC repeat polymorphism in both groups adjusted for 10% nonresponse rate. We assumed a level of significance of 5%, an odds ratio of 2, and power for detecting the true effect of 80%. However, the final recruitment was 376 males on account of a number of refusals to provide semen samples.All the male subjects were recruited from the Sindh Institute of Reproductive Medicine based on nonconception after regular, unprotected intercourse for a period of at least 1 year. Possible genetic causes of male infertility were excluded by Y chromosome microdeletions and Karyotype analysis. The infertile subjects were divided into two groups (A and B) on the basis of normal or altered semen parameters, respectively. All the subjects were collected via convenience sampling after obtaining written and informed consent.6, sperm motility of more than 50%, and normal morphology 34% with age ranging from 18 to 60 years from all ethnic backgrounds [6, a sperm motility less than 50%, and a normal morphology £4% [At the time of recruitment, a data collection form was filled to record clinical data such as age, height, weight, the calculation of body mass index (BMI), the estimation of body fat percentage, blood pressure, smoking habits, hormonal treatment, and clinical history. Group A  comprised of male subjects with a sperm count more than 39 × 10kgrounds . The mallogy £4%  between Blood samples were collected for DNA extraction from antecubital vein in 2 tubes, serum gel tube (3 mL) and EDTA tube (2 mL). Serum was centrifuged at 3500 rpm for 5 min, separated into two aliquots, and stored at −70 °C. DNA was extracted from 2 mL of blood in EDTA tube using the standard protocol of Promega Genomic DNA Extraction Kit Cat# A1125. The steps included taking 6 mL of cell lysis solution with 2 mL of blood (centrifuged at 3500 rpm for 10 min), adding 2 mL of nuclei lysis solution and 660 μL of protein precipitation solution (centrifuged at 3500 rpm for 10 min), transferring the supernatant to a tube of 2 mL cold isopropanol, aspirating and adding 200 μL of rehydration solution, incubating for 24 h at 4 °C, vortex before aliquoting the extracted DNA, and keeping it at −80 °C for further analysis. The extracted DNA was quantified by measuring the ultraviolet absorbance and determining the absorbance ratio (A280/A260) for 2-μL samples using a Nanodrop-ND1000 . The extracted DNA was considered pure at an absorbance ratio of ~1.8.AR gene. Polymerase chain reaction (PCR) for CAG repeats was performed using 2× PCR HotStart Master Mix ) as per the manufacturer’s instructions. The PCR conditions were 1 cycle for 5 min at 95 °C for the initial denaturation followed by 30 cycles at 95 °C for 30 s, 60 °C for 45 s, 72 °C for 45 s followed by a final extension of 10 min at 72 °C. PCR was performed for GGC repeats using GoTaq HotStart Master Mix  as per the manufacturer’s instructions. The PCR conditions were 1 cycle for 5 min at 95 °C for the initial denaturation followed by 30 cycles at 95 °C for 30 s, 50 °C for 45 s, 72 °C for 45 s followed by a final extension of 10 min at 72 °C. The amplified products were electrophoresed on 2% agarose gel. The purification of the PCR products was performed using PCR Clean Up for DNA Sequencing  following the manufacturer’s protocol.The primers were designed using Primer 3 output primer designing tool for CAG  and GGC  repeat region of the AR gene can be detected by PCR amplification and direct sequencing in the published article [AR gene in the samples of Group A and B and the PCR products were sent to the sequencing company Operon (Canada). The samples were sequenced to obtain accuracy in finding the number of repeats in polymorphic trinucleotide repeat segment of the AR gene. The obtained sequences were directly compared to the previously published CAG and GGC repeat region of the AR gene sequence using the MEGABLAST search tool in the National Center for Biotechnology Information (NCBI) database. The sequence files were imported into Chromas Lite, and then assembled using Molecular Evolutionary Genetic Analysis (MEGA) version 6.0.Sanger sequencing is a classical method for sequencing and CAG polymorphism in the  article . This meStatistical analysis was performed with the SPSS version 20 software using the Mann–Whitney U test, and Spearman’s rank correlation tests . Logistic regression analysis with 95% confidence intervals (CIs) was performed to report odds ratios (OR). The sequences were analyzed using Molecular Evolutionary Genetics Analysis (MEGA) software version 6.0.A total of 376 males presenting to infertility centers were enrolled in the study. The subjects in Group A (168) had normal semen parameters, whereas 208 male subjects in Group B had abnormal semen parameters.The mean values of the CAG lengths in infertile men with altered sperm parameters (27) were significantly higher than the ones who had normal sperm parameters (24) as shown in AR gene.However, GGC length alone did not show any significant association with male infertility as demonstrated by our results. The mean values of GGC length in the Group B subjects (17) were comparable with the Group A (16) subjects. The increased prevalence of infertility with psychological and economic burdens of developing countries has motivated researchers to look for the relationship of infertility with genetic factors. The main observation in this study was that infertile males have significantly longer CAG lengths compared with fertile male subjects.Increased fat in the scrotum leads to the production of gonadal heat, which impairs sperm production since sperms need an optimum temperature range of 34–35 degrees. This accounts for the higher BMI among infertile men compared to fertile men in our study. Heat production causes oxidative stress to sperm, the fragmentation of sperm DNA, and diminished sperm motility .AR gene [An increased CAG trinucleotide repeat length has been associated with male infertility . This isAR gene . IncreasAR gene . ShorterAR gene . Thus, oThe total sperm count and sperm motility of the infertile men recruited in our study was reported to be lower than that of the fertile men recruited, which corroborates the negative effect of the increased CAG length (>26) on sperm parameters. Furthermore, there are studies such as Mengual et al., who showed that men with greater than 26 CAG repeats had a risk of being azospermic . Xiao etAR gene is a polymorphism that may be associated  with male infertility, and its clinical relevance is still debated. The limitations of the present study are that the occupational status of each subject was not determined and we did not correlate smoking status with polymorphism. All triplet repeat disorders show anticipation and a significant correlation between age at the onset of the disease and the length of the expanded repeat [AR CAG expansion may transfer infertility to their descendants. This CAG triplet repeat disorder (Group B), however, is anticipated to be expressed in future generations in terms of an alteration in sperm count, motility, and morphology, hence contributing to male infertility. AR CAG polymorphism is not recommended in the routine setting, yet the test may become imperative on the basis of clinical relevance, pharmacogenetic implications, theoretical possibility of transmission to next generation, and tailoring of testosterone replacement therapy on the basis of length of CAG repeat in hypogonadal men.However, GGC length alone did not show any significant association with male infertility as demonstrated by our results. Even though studies such as Gao et al.’s show the deletion of polyglycine tract and the reduction of androgen receptor transcriptional activity, there is little evidence to prove the impact of GGC length and male infertility ,47. Studd repeat ,49. The Longer AR CAG repeat lengths cause decreased sperm motility, corresponding to a greater severity of spermatogenic defect that can lead to disturbance in reproductive functions, thus causing male infertility. However, GGC did not reflect any association with male infertility.


Oryza sativa L.) is the most important food crop worldwide, and its sustainable development is essential to ensure global food security. Panicle morphological and physiological characteristics plays an important role in rice yield formation. However, under different nitrogen (N) fertilization strategies, it is not clear whether the morphological and physiological state of panicles at panicle development stage affects the formation of yield. To understand how the panicle differentiation and development, and grain yield are affected by the N fertilization strategies, and clarify the relationship between related traits and yield in the process of panicle development in different cultivars. In this study consisted of no N fertilizer and four N fertilization strategies, a panicle weight type (PWT) rice cultivar, Dongfu 114 (DF114) and a panicle number type (PNT) rice cultivar, Longdao 11 (LD11) were grown in the field. The results showed that N fertilization strategies could improve the nitrogen use efficiency and yield of rice, but the response of different rice varieties to N fertilizer strategies was different. Different from the DF114, the further increase of panicle N fertilizer ratio could not further improve the yield of LD11, and the highest grain yield of DF114 and LD11 was obtained under N4 and N3 conditions, respectively. In addition, this study found that N fertilizer strategies can affect the content of phytohormones in rice at the panicle differentiation stage, and then regulate the differentiation and development of rice panicles to affect yield. It is of great significance to optimize the application mode of N fertilizer according to the characteristics of varieties to improve rice yield and ensure food security.Rice ( Oryza sativa L.) is one of the most important food crops in the world, about half of the world's people use rice as their staple food. Its sustainable and healthy development is an important support to ensure world's food security. Under the severe situation of increasing population and decreasing land, people have steadily improved the yield of rice by improving rice cultivars, advancing cultivation techniques and increasing production inputs, among them, increasing nitrogen (N) input is one of the effective measures3. N is one of the most important nutrient elements, which can greatly affect the yield of rice. Since the last century, due to the increase of N application, rice yield has also increased significantly. However, rice plants can only use part of the applied N fertilizer, accounting for about 20–30% of the total amount of fertilizer, excessive and unreasonable N fertilizer input has also brought a series of problems such as unstable grain production, decreased nitrogen use efficiency (NUE), increased production costs, exacerbating environmental pollution, which has seriously affected the sustainable development of rice production8.Rice , gibberellins (GAs), cytokinins (CTK), abscisic acid (ABA) and ethylene (ETH), and new phytohormones such as brassinosteroids (BRs), polyamines (PAs), jasmonates (JA), salicylic acid (SA) and strigolactone (SL)23. A large number of studies have shown that phytohormones play an important regulatory role in rice panicle development, grain filling, grain quality and quality, and different phytohormones play different roles and mechanisms. However, the biological mechanism of how phytohormones respond to N fertilization strategies to regulate rice N uptake and utilization efficiency, panicle morphogenesis and yield formation in different rice cultivars is still unclear. In the present study, a panicle weight type (PWT) rice cultivar, Dongfu 114 (DF114) and a panicle number type (PNT) rice cultivar, Longdao 11 (LD11) were used to compare the effects of N fertilization strategies on panicle development characteristics and yield of rice cultivars with contrasting yield types. This study will provide a theoretical basis for the rational and effective application of N fertilizer in rice production.Phytohormones are a class of trace organic matter produced by plants themselves and have obvious physiological effects at very low concentrationsThe field experiments was conducted at the Acheng rice experiment base belonging to Northeast Agricultural University  during the rice growing season in 2021, and repeated in 2022. The region is a cold temperate semi humid continental climate. The meteorological data such as temperature, rainfall, humidity and sunshine hours during the rice growing season in the experimental year were from the National Meteorological Data Center (Table −1. Phosphate fertilizer (P2O5) was applied once as a basal fertilizer at a rate of 90 kg ha−1. Potash fertilizer (K2O) was applied as a basal and panicle fertilizer at a ratio of 5:5, at a rate of 90 kg ha−1. With the exception of the different nitrogen fertilizer strategies, the other cultivation requirements were identical for all plots in both ypanicles. Chemicals were used to control weeds, diseases, and insects to prevent yield loss.The experiments were laid out in a complete randomized block design with three replicates. The N fertilizer strategies was the main plot treatment, and the rice cultivars formed the sub-plot treatment. Each plot was 8-m in length and 6-m in width with 30 cm row spacing and 13.3 cm terrace spacing, and two seedlings per hill. The main plots were separated by a berm. The seeds were sown on 16 April 2021, and 16 April 2022. At the four-leaf hpaniclet stage, seedlings with similar growth were selected and transplanted on May 18, 2021, and May 17, 2022. The experimental treatments included N0 (no N fertilizer), N1 , N2 (base fertilizer : tiller fertilizer : panicle fertilizer of 6:2:2), N3 (base fertilizer:tiller fertilizer:panicle fertilizer of 5:3:2), N4 (base fertilizer:tiller fertilizer:panicle fertilizer of 4:3:3), a total of 5 treatments. The N was applied as urea(46%), at a rates were 150 kg haAt the maturity stage, five representative hills were taken from each plot in 2021 and 2022. Aboveground plants sampled were separated into stems, leaves and grains. Dry weight of each part was determined by oven-drying at 80 °C to constant weight and weighed separately, and recorded the dry weight of each part. Tissue N content was measured with an elemental analyzer to calculate N uptake of each part . The calculation formula of N absorption and utilization related indicators is as follows:2 area for the three replicates in each plot, adjusted to 14% moisture. The yield components were measured for nine replicates in each plot. The panicle number from each samples were recorded to measure the effective panicles number (EP). The filled grains andunfilled grains per panicle were recorded to calculate the number of grains per panicle (GNP) and seed setting rate (SSR). The 1000-grain weight (TGW) was weighed and recorded.In 2021 and 2022, grain yield (GY) and its components were measured at maturity. GY was measured from a harvest area of 1 mIn 2021 and 2022, after 14 days of transplanting, 10 randomly selected plants were investigated every 7 days, and tillers were recorded until the number of tillers did not change. Calculate maximum tiller number (MTN) and productive tiller percentage (PTP) ccording to tillering dynamic survey.At spikelet differentiation stage, pikelets differentiated number (SDN) were measured in 2021 and 2022. The differentiation of florets was observed using a planing microscope, and the SDN was recorded. The grain per panicle was used as the spikelets surviving number to calculate the spikelets degenerated number (SRN) and degraded percentage (RP).According to the heading stage data of the tested cultivars, this study sampled at five time points: 14 days before heading stage, 7 days before heading stage, heading stage, 7 days after heading stage and 14 days after heading stage, which were named S1, S2, S3, S4 and S5, respectively. The dry weight of each panicle was determined by oven-drying at 80 °C to constant weight and weighed separately, and recorded the panicle biomass. The panicle N concentration was measured with an elemental analyzer .25.The panicles and roots per plot were sampled to determine phytohormones content were obtained at S1, S2, S3, S4 and S5 stages. The contents of IAA, ABA, GA and ZR were determined with enzyme-linked immunosorbent assay (ELIAS) method, according to the operation guide of ELIAS kit of China Agricultural Universityhttp://www.r-project.org/) and Origin 2023b software .In this study, for the experimental variables, one-way analysis of variance (ANOVA) of SPSS 22.0  was used to evaluate the differences between different N fertilizer application strategies. According to Fisher's LSD, there were significant differences between different N fertilizer application strategies at the p < 0.05 level. Graphs were drawn using edgeR software  gene encodes a cytokinin-activating enzyme that regulates the biosynthesis of cytokinin in rice. It is an important regulatory factor necessary for maintaining the activity of meristem. The meristem development of the log mutant is terminated in advance, the number of branches and spikelets is significantly reduced, and the floral organ cannot start normally50. Correlation analysis showed that SDN and SSR were the main factors affecting yield of DF114. PCA results showed that ZR content in panicle of DF114 was significantly correlated with N concentration in panicle, and was significantly negatively correlated with SRN and RP in floret differentiation stage. ABA content in panicle was significantly positively correlated with SSR in grain filling stage. The floret differentiation of LD11 was the main factor affecting the yield. PCA results showed that IAA in the panicle of LD11 was significantly positively correlated with SDN in the floret differentiation stage. These results suggest that N fertilizer strategies can affect the content of phytohormones in rice at the panicle differentiation stage, and then regulate the differentiation and development of rice panicles to affect yield. It is of great significance to optimize the application mode of N fertilizer according to the characteristics of varieties to improve rice yield and ensure food security.In order to illustrate the biological basis of the response of panicle traits to N fertilization strategies, this study measured the panicle N and phytohormines content at the main stage of panicle development. Phytohormines play a essential role in regulating the development of plant organs, nutrient absorption and transport, and defensive adaptation to stressCompared with farmer fertilization, N fertilizer application strategy significantly improved the NUE and yield of rice, but the response of rice varieties with different yield types to N fertilizer application strategy was different. Correlation analysis showed that panicle development related indexes SDN, SRN, RP and SSR were the main factors affecting NUE and yield of PNT and PWT rice varieties.The results showed that in the early stage of panicle development, the higher IAA content in the panicle of PNT rice varieties was beneficial to promote the differentiation of spikelets to ensure the yield.. While the indexes of SRN, RP and SSR in the middle and late stages of panicle development of PWT rice varieties were significantly correlated with yield. These results will lay a theoretical foundation for guiding the construction of high-yield and high-efficiency cultivation models for different yield types of rice varieties.